Abstract

A device according includes at least one memory, and at least one processor. The at least one processor is configured to: generate a score by using a neural network; calculate a derivative value of the score by applying back propagation to the neural network; set a search condition for an optimal solution of the score by using an index indicating an uncertainty of the score, the derivative value of the score, and the score; and determine the optimal solution of the score by a gradient method using the search condition.

IPC Classes  ?

• G06F 16/2453 - Query optimisation

Abstract

This arithmetic device comprises: a plurality of arithmetic units; one or more first memories connected to the plurality of arithmetic units; and one or more second memories connected to the plurality of arithmetic units. The one or more second memories are laminated on the plurality of arithmetic units, and at least a part of data stored in the one or more second memories is used by the arithmetic units without going through the one or more first memories. As a result, it is possible to provide an arithmetic device having a large storage capacity and a large band in which a logic die and a memory die are laminated in a three-dimensional manner.

IPC Classes  ?

• G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
• G11C 5/04 - Supports for storage elementsMounting or fixing of storage elements on such supports
• H01L 25/04 - Assemblies consisting of a plurality of individual semiconductor or other solid-state devices all the devices being of a type provided for in a single subclass of subclasses , , , , or , e.g. assemblies of rectifier diodes the devices not having separate containers

Abstract

An information processing system includes at least one memory, and at least one processor. The at least one processor is configured to obtain information related to an output candidate and a plurality of pieces of target information, calculate first intermediate data by inputting the information related to the output candidate into a machine learning model, and generate output information for each of the plurality of pieces of the target information by executing a single inference process using the machine learning model for each of the plurality of pieces of the target information by using at least a portion of the first intermediate data.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A method by a computing system including one or more processors and a hierarchical memory computer including three or more memory layers, includes calculating, by the one or more processors, data related to a first data transfer time for each of a plurality of parallelization axes combinations, wherein each of the plurality of parallelization axes combinations determines a method of splitting a computational target at each of the three or more memory layers, and the data related to the first data transfer time is calculated based on data related to a plurality of second data transfer times calculated for different pairs of two memory layers among the three or more memory layers; and selecting a parallelization axes combination based on the data related to the first data transfer time calculated for each of the plurality of parallelization axes combinations.

IPC Classes  ?

• G06F 9/54 - Interprogram communication
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 8/30 - Creation or generation of source code
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

Abstract

An information processing apparatus according to an embodiment includes at least one memory, and at least one processor. The at least one processor is configured to: identify target atoms subject to chemical bonding among atoms; acquire information regarding a first action force acting on each of the atoms, the information being generated by inputting an atomic structure of the atoms into a neural network; acquire information regarding a first additional force to be applied to at least one of the target atoms; and execute a molecular dynamics simulation for the atoms using the information regarding the first action force, the information regarding the first additional force, and position information of the atoms.

IPC Classes  ?

• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G16C 20/20 - Identification of molecular entities, parts thereof or of chemical compositions
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

Provided is a system for achieving appropriate work by a worker. This system for puncturing an animal to be punctured comprises at least one memory and at least one processor, wherein the at least one processor sets a parameter on the basis of information relating to the animal to be punctured, said parameter being necessary for performing the puncture, and controls the puncture of the animal on the basis of the set parameter.

IPC Classes  ?

• A61D 1/00 - Surgical instruments for veterinary use
• A61D 7/00 - Devices or methods for introducing solid, liquid, or gaseous remedies or other materials into or onto the bodies of animals

Abstract

A compiler includes at least one memory; and at least one processor configured to: acquire a tensor to be processed in the chip; perform an associating process in which each element of the tensor is associated with a first block among the plurality of first blocks included in the chip, based on at least a number of divisions in the first hierarchy level of the chip, and generate the machine code to be executed in the chip based on the associating process. The first hierarchy level utilized in the association process corresponds to a hardware configuration of the chip.

IPC Classes  ?

• G06F 8/51 - Source to source

Abstract

A system includes at least one memory and at least one processor configured to generate a latent representation of a chemical compound by inputting information regarding the chemical compound into a first machine learning model, the first machine learning model being a neural network, predict a property of the chemical compound by inputting the latent representation of the chemical compound into a prediction model, and train the prediction model with machine learning based on the predicted property.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G16C 20/50 - Molecular design, e.g. of drugs
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

This information processing system repeatedly executes acquisition of information relating to an utterance of a user, generation of information relating to an utterance of a dialog device based on the information relating to the utterance of the user and a machine learning model, and control of the utterance of the dialog device based on the information relating to the utterance of the dialog device, until a predetermined condition is satisfied. The generation of the information relating to the utterance of the dialog device is executed on the basis of information indicating a relationship between the user and the dialog device. The relationship between the user and the dialog device includes at least one of: a relationship in which the user provides a service or a product to the dialog device; a relationship in which the user gives guidance to the dialog device; a relationship in which the dialog device pays a reward to the user; and a relationship in which the user who has information A provides the information A to the dialog device that does not have the information A.

IPC Classes  ?

• G06N 3/0475 - Generative networks
• G06F 3/16 - Sound inputSound output
• G06N 3/09 - Supervised learning
• G06N 20/00 - Machine learning
• G06Q 30/06 - Buying, selling or leasing transactions

Abstract

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 8/30 - Creation or generation of source code
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

Abstract

An information processing system according to an embodiment includes processing circuitry. The processing circuitry determines whether or not processing related to an object disposed in an environment is appropriate based on information related to the object. When determining that the processing is not appropriate, the processing circuitry adds label information designated by a user to data on the object.

IPC Classes  ?

• B25J 9/16 - Programme controls
• G06T 7/60 - Analysis of geometric attributes
• G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
• G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations

Abstract

This information processing system comprises at least one processor. The at least one processor acquires one or more target images indicating one or more target pelvic organs of a subject, and inputs each of the one or more target images to an image analysis model, which is trained so as to identify the pelvic organ and at least one lesion related to the pelvic organ from the input image, to generate one or more analysis result images indicating the result of processing by the image analysis model in a format in which each of the one or more target pelvic organs and a target lesion that is the lesion of the subject are distinguished from each other.

IPC Classes  ?

• A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
• G06T 7/00 - Image analysis

Abstract

Proposed is a new posterior distribution estimation technique. This estimating device comprises at least one memory and at least one processor, wherein, when estimating a posterior distribution of a state at a second time point based on observed values up to the second time point, in a case of a non-linear state space model in which the state distribution at the second time point, evolved over time on the basis of the state at a first time point, is described by a linear Gaussian distribution, and the distribution of observed values at the second time point, based on the state at the second time point, is described by a non-linear Gaussian distribution, the at least one processor uses a model that estimates a linear Gaussian distribution and that has parameters that are optimized to maximize a lower bound on a log-likelihood, to calculate a posterior distribution of the state at the second time point based on the observed values up to the second time point.

IPC Classes  ?

• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

An arithmetic device includes a plurality of multiply-add circuits, each of which includes a plurality of multipliers and a first adder, each of the plurality of multipliers being configured to multiply a data pair of significands of floating-point number data, and the first adder being configured to add results of the multiplication by the plurality of multipliers and a correction value; and an addition circuit configured to add operation results output from the plurality of multiply-add circuits and output a result of the addition as a matrix multiplication result of significand data of any of a plurality of types of floating-point number formats.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation
• G06F 7/487 - MultiplyingDividing

Abstract

A system for managing locations of products includes one or more memories, and one or more processors. The one or more processors are configured to: read an information medium attached to a storage unit loaded with at least one first product, the storage unit being located in a stockroom in a retail store having a sales floor; associate the first product loaded on the storage unit located in the stockroom and a location in the stockroom with each other, using information acquired from reading the information medium attached to the storage unit; and notify a terminal device of the location in the stockroom associated with the first product when the stockroom is searched for the first product with the terminal device.

IPC Classes  ?

• G06Q 10/0875 - Itemisation or classification of parts, supplies or services, e.g. bill of materials

Abstract

This information processing device comprises one or more memories and one or more processors. The one or more processors acquire a moving image of a scene captured by using a rolling shutter method for each viewpoint, and train, on the basis of the moving image captured at each viewpoint and information on distortion due to the rolling shutter method when the moving image is captured, a model for three-dimensionally reconstructing a scene including a temporal change during capturing of the moving image.

IPC Classes  ?

• G06T 19/00 - Manipulating 3D models or images for computer graphics

Abstract

An inference device as an example of the present disclosure comprises at least one memory and at least one processor. The at least one processor repeats outputting information necessary for determining a reaction path from a trained model each time atom information is input to the trained model. The at least one processor determines the reaction path on the basis of the information necessary for determining a reaction path.

IPC Classes  ?

• G06N 3/0475 - Generative networks
• G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like

Abstract

A data processing method includes a first processing which executes a first computation using first data to obtain second data, a second processing which executes a second computation using the second data, and storing, in a memory, the second data having a storing value greater than or equal to a predetermined storing value. The storing value is determined based on a cost of the first computation and a size of the second data.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 7/535 - Dividing only
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks

Abstract

An information processing device includes one or more memories; and one or more processors. The one or more processors are configured to acquire, based on information related to positions of a first atom, a second atom, and one or more other atoms, which are included in a plurality of atoms being analysis targets, interaction information on the first atom and the second atom; generate input information into a model to be used for analysis of the plurality of atoms based on the interaction information; and acquire an analysis result of the plurality of atoms by inputting the input information into the model. The interaction information includes information related to presence or absence of interaction.

IPC Classes  ?

• G06N 3/02 - Neural networks

Abstract

A semiconductor device includes a first chip; and a second chip placed adjacent to the first chip. The first chip transfers data to the second chip via a silicon interposer. The data transferred to the second chip from the first chip via the silicon interposer is used in an arithmetic operation by an internal circuit of the second chip. A layout design of the first chip is the same as a layout design of the second chip.

IPC Classes  ?

• H01L 23/538 - Arrangements for conducting electric current within the device in operation from one component to another the interconnection structure between a plurality of semiconductor chips being formed on, or in, insulating substrates
• G06F 11/10 - Adding special bits or symbols to the coded information, e.g. parity check, casting out nines or elevens
• H01L 23/00 - Details of semiconductor or other solid state devices
• H01L 23/06 - ContainersSeals characterised by the material of the container or its electrical properties
• H01L 23/31 - Encapsulation, e.g. encapsulating layers, coatings characterised by the arrangement
• H01L 23/367 - Cooling facilitated by shape of device
• H01L 23/433 - Auxiliary members characterised by their shape, e.g. pistons
• H01L 25/065 - Assemblies consisting of a plurality of individual semiconductor or other solid-state devices all the devices being of a type provided for in a single subclass of subclasses , , , , or , e.g. assemblies of rectifier diodes the devices not having separate containers the devices being of a type provided for in group

Abstract

A processor includes a plurality of processing elements, wherein the processor is configured to execute, by using one or more first processing elements among the plurality of processing elements, operations of a first layer of a neural network including a plurality of layers, the processor is configured to execute, by using one or more second processing elements among the plurality of processing elements, operations of a second layer of the neural network, the one or more first processing elements and the one or more second processing elements do not entirely overlap, and the second layer is at a later processing stage than the first layer.

IPC Classes  ?

• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
• G06N 3/08 - Learning methods

Abstract

An information processing device includes at least one memory and at least one processor. The at least one processor is configured to generate input information for generating a program to be executed by an experiment device; and acquire the program generated by inputting the input information into a generative model. The input information includes reference information related to the experiment device, and instruction information. The reference information includes definition information related to an interface for executing a function of the experiment device and a sample program using the interface. The instruction information includes information instructing the generative model to generate the program.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

This information processing device comprises at least one memory and at least one processor. The at least one processor acquires an evaluation result obtained by evaluating one or more first templates using a first generation model, generates input information to be input to a second generation model on the basis of the evaluation result, and acquires a second template generated by inputting the input information to the second generation model. The input information includes at least one or more pieces of template information selected on the basis of the evaluation result and information for designating the structure of the templates.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 40/186 - Templates
• G06F 40/56 - Natural language generation
• G06N 3/092 - Reinforcement learning

Abstract

A processor includes a first arithmetic device configured to execute a first instruction, and a second arithmetic device configured to execute a second instruction and a third instruction. The first arithmetic device calculates first data by executing the first instruction. The second arithmetic device stops execution of the third instruction based on the second instruction which is an instruction for waiting issuance of first synchronization information, and thereafter executes the third instruction that uses the first data, based on the issuance of the first synchronization information from the first arithmetic device.

IPC Classes  ?

• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 9/35 - Indirect addressing
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Abstract

This information processing device: acquires data expressing the shape of a three-dimensional model and a plurality of textures obtained on the basis of projection of the shape of each of the plurality of images; receives designation of a viewpoint with respect to the three-dimensional model; acquires one or more textures corresponding to the designated viewpoint on the basis of the plurality of textures; and generates a two-dimensional image of the three-dimensional model corresponding to the designated viewpoint on the basis of the one or more textures.

IPC Classes  ?

• G06T 15/04 - Texture mapping

Abstract

A user assistance device according to the present disclosure comprises: an information acquisition unit that acquires device information including at least one among specification information, operation information, and maintenance information pertaining to a device for inquiry; an inquiry acquisition unit that acquires inquiry information including inquiry content represented in a natural language; a prompt generation unit that generates, on the basis of the device information and the inquiry information, a prompt that can be input to an interactive response device; a transmission unit that transmits the prompt to the interactive response device; a reception unit that receives a response from the interactive response device; and an interaction unit that outputs the received response.

IPC Classes  ?

• G06Q 50/04 - Manufacturing
• G06Q 10/20 - Administration of product repair or maintenance

Abstract

A data generation apparatus includes at least one memory; and at least one processor. The at least one processor is configured to generate data by using latent information and a generative model. The latent information is represented in a blockchain compliant code.

IPC Classes  ?

• G06T 11/60 - Editing figures and textCombining figures or text
• G06F 18/25 - Fusion techniques
• G06N 3/045 - Combinations of networks
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• G06N 3/065 - Analogue means
• G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings

Abstract

An information processing device comprising at least one memory and at least one processor, wherein the at least one processor: generates a first prompt; acquires a first output result generated by inputting the first prompt into a language model; acquires first feedback information on the first output result; generates a second prompt using information acquired by running an optimization algorithm based on the first feedback information; and acquires a second output result generated by inputting the second prompt into the language model.

IPC Classes  ?

• G06F 40/56 - Natural language generation
• G06F 40/44 - Statistical methods, e.g. probability models

Abstract

An inference apparatus according to an embodiment includes at least one memory and at least one processor. The at least one processor calculates a Hamiltonian as an initial value regarding a substance based on a neural network algorithm, and calculates a non-equilibrium Green's function regarding the substance based on the Hamiltonian as the initial value.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations

Abstract

An information processing device includes one or more memories and one or more processors. The one or more processors are configured to set a first state of a structure of a plurality of atoms to search for a reaction path from the first state up to a second state of the structure of the plurality of atoms in a chemical reaction in which the plurality of atoms are involved and search for the reaction path from the first state up to the second state by calculating, from the first state, a change in the structure of the plurality of atoms such that, in the second state, an index of mutual displacement of two or more specific atoms out of the plurality of atoms falls within a defined range.

IPC Classes  ?

• G06F 30/20 - Design optimisation, verification or simulation

Abstract

An estimation apparatus according to an embodiment includes at least one memory and at least one processor. At least one processor described above inputs a feature amount of each of a plurality of atoms to a neural network to update the feature amount, and generates a parameter corresponding to each of the plurality of atoms based on the updated feature amount. At least one processor described above determines each of a plurality of charges corresponding to each of the plurality of atoms by using the parameter.

IPC Classes  ?

• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

A tail vein injection system includes at least one memory; and at least one processor connected to the at least one memory. The at least one processor is configured to: calculate information including a position of a tail vein based on one or more images of a tail; adjust a position and a posture of a syringe needle based on the calculated information; and after the adjusting, perform a puncture of the tail vein using the syringe needle.

IPC Classes  ?

• A61D 1/02 - Trocars or cannulas for teatsVaccination appliances
• A61M 5/32 - NeedlesDetails of needles pertaining to their connection with syringe or hubAccessories for bringing the needle into, or holding the needle on, the bodyDevices for protection of needles
• A61M 5/46 - Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular wayAccessories therefor, e.g. filling or cleaning devices, arm rests having means for controlling depth of insertion

Abstract

[Problem] To improve the performance of an exploration of the structure of a substance. [Solution] This information processing device comprises at least one processor and at least one memory. The at least one processor acquires information on a target of exploration from another information processing device, acquires structures of a plurality of substances on the basis of the information on the target of exploration, updates the structures of the plurality of acquired substances, evaluates the structures of the plurality of updated substances by using a neural network, and transmits, to the other information processing device, information on the structure of a substance that has been explored, on the basis of the result of the evaluation.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/40 - Searching chemical structures or physicochemical data

Abstract

The purpose of the present invention is to construct a mechanism for reproducing a free viewpoint video. Provided is a server device comprising one or multiple memories and one or multiple processors. The one or multiple memories hold one or more restoration models for generating time-series free viewpoint images, said reconstruction model(s) being trained in advance such that a scene from a first time to a second time can be reconstructed by using time-series photographed images of a plurality of viewpoints, these time-series photographed images having been obtained by continuously photographing a scene from each of the plurality of viewpoints over time. The one or multiple processors receive, from a client, a request including viewpoint information and time information pertaining to the scene, generate, by using the one or more reconstruction models, time-series images corresponding to the viewpoint information and the time information included in the request received from the client, and transmit, to the client, the time-series images in a transmission format capable of video reproduction.

IPC Classes  ?

• H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• H04N 5/93 - Regeneration of the television signal or of selected parts thereof
• H04N 5/765 - Interface circuits between an apparatus for recording and another apparatus

Abstract

An information processing device (1) according to an embodiment comprises at least one memory (33, 35) and at least one processor (31). The at least one processor (31) inputs, into a first model, a plurality of feature amounts calculated from a first data group from which at least one value is missing and which is output from a detection device, and outputs at least one interpolation value used for interpolation of the first data group from which at least one value is missing.

IPC Classes  ?

• G06F 17/17 - Function evaluation by approximation methods, e.g. interpolation or extrapolation, smoothing or least mean square method
• G06N 20/00 - Machine learning

Abstract

An information processing device includes at least one memory; and at least one processor. The at least one processor is configured to display a screen for creating a program that causes a character to perform a series of actions by visual programming, a plurality of program components being arranged on the screen, and the plurality of program components indicating a plurality of actions included in the series of actions; and perform, based on a detection of a failure of an action performed by the character, a notification related to a program component indicating the action that the character fails to perform.

IPC Classes  ?

• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• A63F 13/56 - Computing the motion of game characters with respect to other game characters, game objects or elements of the game scene, e.g. for simulating the behaviour of a group of virtual soldiers or for path finding
• A63F 13/63 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor by the player, e.g. authoring using a level editor
• G06F 8/34 - Graphical or visual programming

Abstract

An image processing device includes one or more storage devices; and one or more processors configured to create a first image by inputting a first latent variable into a first generative model; store the first latent variable in the one or more storage devices in association with identification information of the first generative model; acquire the first latent variable and the identification information of the first generative model associated with the first latent variable; generate a second latent variable based on the first latent variable; create a second image by inputting the second latent variable into the first generative model; and store the second latent variable in the one or more storage devices in association with the identification information of the first generative model. The second image is different from the first image and includes at least a second object different from a first object included in the first image.

IPC Classes  ?

• G06T 11/60 - Editing figures and textCombining figures or text
• G06T 5/50 - Image enhancement or restoration using two or more images, e.g. averaging or subtraction

Abstract

An information processing device includes at least one memory; and at least one processor. The at least one processor is configured to allow visual programming for creating a program causing a character to perform a series of actions, the program being created by combining program components; and display a screen for selecting an object to be set in a program component indicating an action to be instructed to the character, the object being a target of the action. The screen displays a position of the object on a map of a space in which the character acts.

IPC Classes  ?

• A63F 13/63 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor by the player, e.g. authoring using a level editor
• A63F 13/56 - Computing the motion of game characters with respect to other game characters, game objects or elements of the game scene, e.g. for simulating the behaviour of a group of virtual soldiers or for path finding
• G09B 5/02 - Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip

Abstract

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 8/30 - Creation or generation of source code
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

Abstract

An accelerator includes an interface configured to receive an instruction sequence including a plurality of instructions; a hierarchical memory configured to perform data transfer between a plurality of zeroth memories and a plurality of first memories according to a data transfer instruction specifically for data transfer between the plurality of zeroth memories and the plurality of first memories included in the instruction sequence received by the interface, the hierarchical memory including the plurality of zeroth memories, the plurality of first memories, and one or more second memories, each of the one or more second memories being connected to corresponding first memories among the plurality of first memories, and each of the plurality of first memories being connected to corresponding zeroth memories among the plurality of zeroth memories; and a plurality of arithmetic operators configured to operate in parallel by using the hierarchical memory.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Abstract

[PROBLEM] To execute optimization related to memory access control and/or processing time. [SOLUTION] This information processing device comprises at least one processor. The at least one processor: acquires a first data structure including information about the execution order of a plurality of instructions and information about the amount of memory required for execution of each instruction; executes a predetermined operation on the first data structure to generate a second data structure in which the information about the execution order and the information about the memory amount have been updated; acquires an evaluation value on the basis of the second data structure; and if the evaluation value satisfies a first condition, uses the second data structure to update the first data structure. The first data structure and the second data structure are data structures that support changes and derived values for a prescribed section. The at least one processor executes, multiple times, the process for generating the second data structure, the process for acquiring the evaluation value, and the process for updating the first data structure when the evaluation value satisfies the first condition.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Goods & Services

Computer software utilizing large-scale language models which conducts and supports general-purpose tasks including sending emails, creating minutes, creating documents, creating text summaries, searching, and responding to inquiries; computer software utilizing multimodal foundation models or generative models which conducts and supports operations using multiple types of data including audio, images, 3D models, and videos; computer software utilizing large-scale language models which searches the content of news articles in news distribution apps in interactive mode; computer software utilizing multimodal foundation models or generative models which conducts and supports operations using multiple types of data including text, images, audio, and spatial information; computer software using chatbot; computer software using artificial intelligence; downloadable computer programs and downloadable computer software for artificially generating audio, music, video, images, text, and sensor data; 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downloadable computer software in relation to social networking; downloadable computer software for mobile devices; computer software that enables the transmission, editing, and management of text data, messages, electronic blog type publications, website links, and image data via communication networks, including the Internet; computer software for accessing information on communications networks; personal digital assistants; voice recognition systems; downloadable maps, audio, music, images, videos, text information; recording media that stores maps, audio, music, images, video, and text information; artificial intelligence-equipped computers, including those for robots, as well as parts and accessories thereof; downloadable computer software of artificial intelligence nature that performs text generation and natural language processing, creates content based on themes, summarizes text, answers questions in documents, and simulates natural conversations; downloadable mobile application software of artificial intelligence nature that performs text generation and natural language processing, creates content based on themes, summarizes text, answers questions in documents, and simulates natural conversations; 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programs for mobile phones; programs for smart phones; programs for personal digital assistants; computer programs that utilize machine learning techniques; computer programs that use deep learning technology; computer software that uses machine learning technology and deep learning technology to conduct and support operations. Providing online non-downloadable computer software utilizing large-scale language models which conducts and supports general-purpose tasks including sending emails, creating minutes, creating documents, creating text summaries, searching, and responding to inquiries; developing computer software utilizing large-scale language models which conducts and supports general-purpose tasks including sending emails, creating minutes, creating documents, creating text summaries, searching, and responding to inquiries; providing online non-downloadable computer software utilizing multimodal generative foundation models which conducts and supports operations using multiple types of data including audio, images, 3D models, and videos; developing computer software utilizing multimodal foundation models which conducts and supports operations using multiple types of data including audio, images, 3D models, and videos; providing online non-downloadable computer software utilizing large-scale language models which searches the content of news articles in news distribution apps in interactive mode; 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providing online non-downloadable computer software for converting audio files to text; providing online non-downloadable computer software for recognizing audio, music, images, videos, text, and sensor data; providing online non-downloadable computer software for recognizing various data; providing online non-downloadable computer software for developing, operating, and analyzing algorithms; providing online non-downloadable computer software for developing and implementing neural networks; providing online non-downloadable computer software by application service providers featuring application programming interface (API) software; research and development relating to artificial intelligence; research, design, and development of computer programs and computer software; application service providers with application programming interface (API) software; online software as a service [SaaS] featuring software for processing, generating, recognizing, understanding, and analyzing natural language using artificial intelligence; online software as a service [SaaS] featuring software for using language models; providing online non-downloadable computer software for sharing datasets for building machine learning, foundational, generative, and language models; providing application programming interface (API) software by application service providers; providing online non-downloadable computer software for simulation environments for the purpose of examining and testing artificial intelligence agents, algorithms, and programs; providing online non-downloadable computer software for developing application; providing online application software; providing online non-downloadable computer software; online software as a service [SaaS] featuring software for generating images, videos, audio, and music from text; online software as a service [SaaS] featuring software for generating various data from text; online software as a service [SaaS] featuring software for searching, licensing, purchasing, and downloading digital content; providing online non-downloadable application software using artificial intelligence for generating images, videos, audio, and music from text; providing online non-downloadable application software using artificial intelligence for generating various data from text; design services; computer software design, computer programming, and maintenance of computer software; providing computer programs on data networks; software as a service [SaaS]; creating and maintaining web sites for others; internet security consultancy; website design consultancy; off-site data backup; development of computer platforms; monitoring of computer system operation by remote access; computer system design; computer security consultancy; computer software design; computer software consultancy; maintenance of computer software; conversion of computer programs and data, other than physical conversion; creating and designing website-based indexes of information for others [information technology services]; software development in the framework of software publishing; data security consultancy; rental of web servers; cloud computing; hosting computer websites; rental of computer software; computer rental; server hosting; electronic data storage; providing information relating to computer technology and programming via a website; computer technology consultancy; information technology [IT] support services; computer system analysis; technological research; scientific research; scientific laboratory services; conducting technical project studies; research and development of new products for others; software as a service [SaaS] using artificial intelligence to generate audio, music, video, images, text, and sensor data from requests entered by a user; software as a service [SaaS] using artificial intelligence to generate various data from requests entered by a user; software as a service [SaaS] using artificial intelligence for data compression; providing electronic memory space on Internet servers; providing online non-downloadable computer software, and software as a service [SaaS]; platform as a service [PaaS]; providing search engines for the internet; technological advice relating to computers, automobiles, and industrial machines; rental of computers; providing online non-downloadable computer software enabling to provide electronic media and information via computer and communication networks by means of uploading, downloading, streaming, posting, displaying, blogging, linking, sharing, and other means; providing application software for social networking, building virtual communities, and transmitting music, video, photo information, text, graphics, and data in computer networks; providing search engines for searching information databases containing searchable indexes and text, electronic documents, databases, graphic information, and audiovisual information on computers and communication networks; providing online non-downloadable artificial intelligence computer program with secretarial function; providing online non-downloadable artificial intelligence computer programs; providing online non-downloadable computer software used for scientific and technical analysis of data by artificial intelligence; designing, programming, and maintenance of computer software for document proofreading support, translation support, terminology unification support, translation unification support, and dictionary search support; designing computer programs using artificial intelligence; technical writing; research in the field of artificial intelligence; artificial intelligence consultancy; design of computer-simulated models; providing temporary use of non-downloadable software using artificial intelligence that generates text, processes natural language, creates content, summarizes documents, responds to questions, and simulates natural conversation; providing temporary use of non-downloadable software using artificial intelligence by application service provider that generates text, processes natural language, creates content, summarizes documents, responds to questions, and simulates natural conversation; software as a service [SaaS] using artificial intelligence that generates text, processes natural language, creates content, summarizes documents, responds to questions, and simulates natural conversation; scientific research and development; research and development in the field of artificial intelligence, computer science, and computer software design and development; developing products and engineering in the field of artificial intelligence; technological research in the field of artificial intelligence, computer science, and computer software design and development; consultancy in relation to design and development of technologies in the field of design and development of artificial intelligence, computer science, and computer software; intelligent natural language analysis and reporting service using cloud-based software technology; software as a service [SaaS] for processing, generating, understanding, and analyzing natural language; software as a service [SaaS] for use in machine learning, deep learning, artificial intelligence, natural language processing, data mining, data analytics, predictive analytics, and business intelligence; software as a service [SaaS] for accessing, monitoring, tracking, searching, storing, sharing, analyzing, and evaluating information from natural language text, data, audio, video, and multimedia content; software as a service [SaaS] as services featuring software for digital data intelligence; software as a service [SaaS] for voice recognition; software as a service [SaaS] for speech synthesis; software as a service [SaaS] for image recognition; software as a service [SaaS] for image generation; software as a service [SaaS] for text generation; software as a service [SaaS] for sharing data for machine learning, predictive analytics, and language model building; technological research in the field of analytics, artificial intelligence, natural language understanding, and natural language processing; developing, designing, and creating computer systems; design, programming, and maintenance of computer software in the field of natural language, speech, speaker, language, and speech recognition and voiceprint recognition; research in the field of natural language processing by computers; providing information regarding analysis and storage of technical data; analysis of technical data related to information processing by computers and providing information thereof; providing information relating to analysis and storage of big data; computer-aided scientific research, testing and analysis services; computer-aided industrial research services; scientific and industrial research; creating and maintaining web sites, and consultancy thereof; rental of server memory space for website on the Internet; web site hosting services on the Internet; rental of server memory space on website; design and graphic arts designing for the creation of web sites, and advice, consultancy, and information thereof; planning, design, creation, development, and maintenance of on-line web sites; creation and maintenance of on-line web sites, and consultancy thereof; creation, design, and maintenance of web sites for e-commerce; user authentication services for e-commerce; designing, programming, and maintaining computer programs for use in e-commerce; providing online non-downloadable computer programs for use in systems for processing e-commerce efficiently; research on information processing technology; planning, research, development, design, maintenance, and rental of computer software using large-scale natural language processing technology; creation, design, and maintenance of computer programs using machine learning technology; creation, design, and maintenance of computer programs using deep learning technology; creation, design, and maintenance of computer programs for data mining and data analysis; consultancy relating to creation, design, and maintenance of computer programs; creation, design, and maintenance of programs for application programming interfaces (API); providing online non-downloadable computer programs using machine learning technology; providing online non-downloadable computer programs using deep learning technology; providing online non-downloadable computer programs for data mining and data analysis; consultancy relating to providing computer programs; providing programs for application programming interfaces (API); providing online non-downloadable computer software that uses machine learning technology and deep learning technology to conduct and support operations; developing computer software that uses machine learning technology and deep learning technology to conduct and support operations.

Abstract

A data processing device includes an instruction issue circuit configured to issue instructions; a plurality of execution circuits configured to execute, in parallel, the instructions issued from the instruction issue circuit; and a plurality of delay circuits configured to delay arrival timings of when the instructions issued from the instruction issue circuit arrive at the plurality of execution circuits, the plurality of delay circuits being arranged between the instruction issue circuit and the plurality of execution circuits. The arrival timings of the instructions arriving at at least two execution circuits included in the plurality of execution circuits are different from each other.

IPC Classes  ?

• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Abstract

An inference device according to an embodiment includes at least one memory and at least one processor. The at least one processor executes channel masking processing with respect to first polarized complex synthetic aperture radar (SAR) images relating to a plurality of directions, and outputs real SAR images relating to a plurality of directions in which speckle noise is reduced due to relationships between polarizations, using first polarized complex SAR relating to the plurality of directions for which the channel masking processing has been performed.

IPC Classes  ?

• G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
• G06N 3/0475 - Generative networks
• G06T 5/70 - DenoisingSmoothing

Abstract

A processor includes an instruction execution circuit configured to execute an instruction; an instruction supply circuit configured to output the instruction to be executed by the instruction execution circuit; and an instruction selection circuit configured to output the instruction received from the instruction supply circuit to the instruction execution circuit, and output another instruction to the instruction execution circuit in response to detecting that instruction reception from the instruction supply circuit is stopped.

IPC Classes  ?

• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead
• G06F 1/28 - Supervision thereof, e.g. detecting power-supply failure by out of limits supervision

Abstract

With respect to a method performed by at least one processor, the method includes obtaining, by the at least one processor, data related to a first process for a first object, obtaining, by the at least one processor, non-processed object data of the first object, generating, by the at least one processor, first data including the data related to the first process for the first object and the non-processed object data of the first object, and adjusting a second process for a second object based on the first data.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06T 7/00 - Image analysis
• G06V 10/32 - Normalisation of the pattern dimensions
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

A processor includes an instruction decoder configured to decode an instruction including bypass information and generate a bypass control signal based on the bypass information; a data holding circuit configured to hold data to be used to execute the instruction; an arithmetic circuit configured to execute the instruction and output operation result data; and a first selector configured to select the data held in the data holding circuit or the operation result data based on the bypass control signal and output the selected data or the selected operation result data to the arithmetic circuit.

IPC Classes  ?

• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Abstract

A processor includes an arithmetic circuit configured to execute an arithmetic instruction; and a register configured to hold data used by the arithmetic circuit. The processor receives a data movement instruction and the arithmetic instruction corresponding to the data movement instruction, and moves the data from a first memory to the register based on a data movement instruction. The arithmetic circuit executes the arithmetic instruction after a data movement of the data movement instruction is completed.

IPC Classes  ?

• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode

Abstract

An operation circuit includes a plurality of multipliers each configured to multiply each of respective first mantissas of a plurality of first data to which a first common exponent is set as a common exponent, by each of respective second mantissas of a plurality of second data to which a second common exponent is set as a common exponent; and a first adder configured to add up a plurality of products calculated by the plurality of multipliers.

IPC Classes  ?

• G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 7/485 - AddingSubtracting
• G06F 7/487 - MultiplyingDividing

Abstract

Provided is a 3D reconstruction method capable of increasing the accuracy of reconstruction of an object. According to the 3D reconstruction method, respective viewpoint images that are obtained by capturing an actual object, on which structured light is being projected, from at least a first viewpoint and a second viewpoint are acquired; on the basis of a projection pattern of the structured light, a pixel in the image of the first viewpoint and a pixel in the image of the second viewpoint that correspond to the same point on the surface of the actual object onto which the structured light is being projected are identified as a first pixel and a second pixel, respectively; on the basis of current parameters in an object shape neural network for reconstructing a 3D shape of the object, a first coordinate corresponding to the first pixel and a second coordinate corresponding to the second pixel on the surface of the object reconstructed in the object shape neural network are computed; and the parameters of the object shape neural network are updated using at least the first coordinates and the second coordinates.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 17/30 - Surface description, e.g. polynomial surface description

Abstract

An information processing system comprises at least one memory and at least one processor. The at least one processor is configured to receive a request of a user; determine whether or not to permit processing of the request based on a term of use of the user; and execute the processing using a neural network based on the request whose processing is determined as being permitted. The term of use includes a condition related to a structure of a processing target of the neural network.

IPC Classes  ?

• G06F 21/51 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems at application loading time, e.g. accepting, rejecting, starting or inhibiting executable software based on integrity or source reliability

Abstract

A computer system includes one or more memories and one or more processors configured to cause a generative model to generate structural information regarding a chemical compound by inputting a latent representation into the generative model, wherein the generative model has been trained such that differences between structural information regarding other chemical compounds and reconstructions of the structural information regarding the other chemical compounds generated by the generative model are reduced, the reconstructions being generated by inputting latent representations into the generative model, the latent representations being generated based on the structural information regarding the other chemical compounds.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G16C 20/50 - Molecular design, e.g. of drugs
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

A determination device as an example according to the present disclosure comprises at least one memory and processor. The at least one processor inputs a first atomic structure to a trained model to generate at least one of a first energy or a first force corresponding to the first atomic structure, calculates at least one of a second energy or a second force corresponding to the first atomic structure on the basis of the first atomic structure, a predetermined parameter set, and a potential model, and updates the predetermined parameter set, on the basis of at least one of the difference between the first energy and the second energy or the difference between the first force and the second force to determine a parameter set.

IPC Classes  ?

• G06N 3/096 - Transfer learning
• G16C 10/00 - Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like

Abstract

An information processing device includes one or more memories and one or more processors. The one or more processors are configured to receive information on a plurality of graphs from one or more second information processing devices; select a plurality of graphs which are simultaneously processable using a graph neural network model among the plurality of graphs; input information on the plurality of graphs which are simultaneously processable into the graph neural network model and simultaneously process the information on the plurality of graphs which are simultaneously processable to acquire a processing result for each of the plurality of graphs which are simultaneously processable; and transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

IPC Classes  ?

• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G06N 3/02 - Neural networks
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• H04L 67/08 - Protocols specially adapted for terminal emulation, e.g. Telnet
• H04W 12/06 - Authentication
• H04W 88/06 - Terminal devices adapted for operation in multiple networks, e.g. multi-mode terminals

Abstract

A training device includes processor. The processor inputs a first atomic structure including a surface and an adsorbed molecule close to the surface into a model to obtain an energy outputted from the model in response to the input, and obtains a first error based on the outputted energy of the first atomic structure and a ground truth value of the energy of the first atomic structure, input a fourth atomic structure including a cluster and an adsorbed molecule close to the cluster into the model to obtain an energy outputted from the model in response to the input, and obtains a fourth error based on the outputted energy of the fourth atomic structure and a ground truth value of the energy of the fourth atomic structure, and update a parameter of the model by the first and the fourth error. The surface and the cluster include the same atom.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 5/04 - Inference or reasoning models

Abstract

An inferring device includes one or more memories; and one or more processors. The one or more processors are configured to input information on each atom in an atomic system into a second model to infer a difference between energy based on a first-principles calculation corresponding to the atomic system and energy of an interatomic potential function corresponding to the atomic system.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

The present invention easily searches for a product in the backroom of a store. An information processing device according to an embodiment of the present disclosure, which manages products in a store, is provided with: a salesroom in which the products are sold; and a backroom in which the products, which have been transported in a state of being stacked in a carriage, are preserved at the transportation destination of the carriage with the products stacked in the carriage, wherein, in order for a user of a terminal to find, from the backroom, the carriage with a product searched for by the user and stacked therein, the information processing device notifies the terminal of a user of information which is to be used by the user and specifies an area in which the carriage with the product searched for by the user and stacked therein is placed among one or more carriages with products stacked therein in the backroom.

IPC Classes  ?

• G06Q 30/06 - Buying, selling or leasing transactions
• B65G 1/137 - Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

An inferring device includes one or more processors. The one or more processors are configured to acquire an output from a neural network model based on information related to an atomic structure and label information related to an atomic simulation, wherein the neural network model is trained to infer a simulation result with respect to the atomic structure generated by the atomic simulation corresponding to the label information.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G06N 3/045 - Combinations of networks

Goods & Services

Downloadable computer software for utilizing large-scale language models which conducts and supports general-purpose tasks including sending emails, creating minutes, creating documents, creating text summaries, searching, and responding to inquiries; downloadable computer software for utilizing multimodal foundation models or generative models which conducts and supports operations using multiple types of data including audio, images, and videos; downloadable computer software for utilizing multimodal foundation models or generative models which conducts and supports operations using multiple types of data including text, images, and audio; downloadable computer software for using chatbot for processing audio, video, images, text, and sensor data; downloadable computer software using artificial intelligence for processing audio, video, images, text, and sensor data; downloadable computer programs and downloadable computer software for processing, generating, recognizing, understanding and analyzing audio, video, images, text, and sensor data; downloadable machine learning-based computer programs and downloadable computer software for processing audio, video, images, text, and sensor data; downloadable computer chatbot software for conversation simulation; downloadable computer programs and downloadable computer software for using machine learning models, foundation models, generative models, and language models; downloadable computer programs and downloadable computer software for translating text and audio from one language to another; downloadable computer programs and downloadable computer software for converting audio files to text; downloadable computer programs and downloadable computer software for recognizing audio, images, videos, text, and sensor data; downloadable computer programs and downloadable computer software for artificial intelligence for processing audio, video, images, text, and sensor data; downloadable computer programs and computer software for processing audio, video, images, text, and sensor data; downloadable computer software and application software for generating images, videos, and audio from text; computer programs, recorded for processing audio, video, images, text, and sensor data; computer programs, downloadable for processing audio, video, images, text, and sensor data; downloadable computer software that uses artificial intelligence to generate audio, video, images, text, and sensor data from requests entered by a user; downloadable computer software that uses artificial intelligence to generate various data from requests entered by a user; downloadable computer software for processing audio, video, images, text, and sensor data; downloadable computer software via the Internet and wireless devices for processing audio, video, images, text, and sensor data; downloadable computer software used as an application programming interface (API) for processing audio, video, images, text, and sensor data; downloadable audios featuring musical entertainment; downloadable images featuring artwork, art pictures, animations, and digital art; downloadable videos in the field of arts, entertainment, business, technology, and science; downloadable text information featuring articles in the field of a general interest online encyclopedia and dictionary, and business news, analysis, and information; downloadable computer software of artificial intelligence nature that performs text generation and natural language processing, creates content based on themes, summarizes text, answers questions in documents, and simulates natural conversations; downloadable software and mobile application software for text generation; downloadable computer programs that utilize machine learning techniques for processing audio, video, images, text, and sensor data; downloadable computer programs that use deep learning technology for processing audio, video, images, text, and sensor data; downloadable computer software that uses machine learning technology and deep learning technology to conduct and support operations for processing audio, video, images, text, and sensor data Providing online non-downloadable computer software for utilizing large-scale language models which conducts and supports general-purpose tasks including sending emails, creating minutes, creating documents, creating text summaries, searching, and responding to inquiries; providing online non-downloadable computer software for utilizing multimodal generative models which conducts and supports operations using multiple types of data including audio, images, and videos; providing online non-downloadable computer software for utilizing multimodal foundation models, which conducts and supports operations using multiple types of data including text, audio, images, and audio; providing temporary use of online application programming interface (API) for using large-scale language models; providing temporary use of online application programming interface (API) for using chatbot; software as a service (SaaS) featuring software for using chatbot; software as a service (SaaS) featuring software for using artificial intelligence for processing audio, video, images, text, and sensor data; providing online non-downloadable software for processing, generating, understanding and analyzing audio, video, images, text, and sensor data; providing online non-downloadable machine learning-based software for processing audio, video, images, text, and sensor data; providing online non-downloadable chatbot software for conversation simulation; software as a service (SaaS) featuring software for using machine learning models, foundation models, generative models, and language models; providing online non-downloadable computer software for processing, generating, recognizing, understanding, and analyzing natural language; providing online non-downloadable computer software for translating text and audio from one language to another; providing online non-downloadable computer software for recognizing audio, video, images, text, and sensor data; online software as a service (SaaS) featuring software for processing, generating, recognizing, understanding, and analyzing natural language using artificial intelligence; online software as a service (SaaS) featuring software for using language models; providing online application software (PaaS) featuring software for processing audio, video, images, text, and sensor data; providing online non-downloadable computer software for processing audio, video, images, text, and sensor data; online software as a service (SaaS) featuring software for generating images, videos, and audio, from text; online software as a service (SaaS) featuring software for generating various data from text; providing online non-downloadable application software using artificial intelligence for generating various data from text; software as a service (SaaS) featuring software for processing audio, video, images, text, and sensor data; software as a service (SaaS) using artificial intelligence to generate audio, video, images, text, and sensor data from requests entered by a user; software as a service (SaaS) using artificial intelligence to generate various data from requests entered by a user; providing online non-downloadable computer software, and software as a service (SaaS) featuring software for processing audio, video, images, text, and sensor data; providing online non-downloadable artificial intelligence computer programs featuring software for processing audio, video, images, text, and sensor data; providing online non-downloadable computer software used for scientific and technical analysis of data by artificial intelligence; processing information using artificial intelligence, namely, processing audio, video, images, text, and sensor data; software as a service (SaaS) using featuring software that uses artificial intelligence that generates text, processes natural language, creates content, summarizes documents, responds to questions, and simulates natural conversation; software as a service (SaaS) featuring software for processing, generating, understanding, and analyzing natural language; software as a service (SaaS) featuring software for use in machine learning, deep learning, artificial intelligence, natural language processing, data mining, data analytics, predictive analytics, and business intelligence for processing audio, video, images, text, and sensor data; providing online non-downloadable computer programs using machine learning technology for processing audio, video, images, text, and sensor data; providing online non-downloadable computer programs using deep learning technology for processing audio, video, images, text, and sensor data; providing programs for application programming interfaces (API) for processing audio, video, images, text, and sensor data; providing online non-downloadable computer software that uses machine learning technology and deep learning technology to conduct and support operations for processing audio, video, images, text, and sensor data

Abstract

An inferring device comprises one or more memories and one or more processors. The one or more processors execute decision of an action based on a tree representation including a node and an edge of a molecular graph, and a trained model trained through reinforcement learning, and execute generation of a state including information on the molecular graph based on the action, wherein the edge has connection information on the nodes.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/50 - Molecular design, e.g. of drugs

Abstract

The purpose of the present invention is to improve the accuracy of the evaluation of a pathological condition. A pathological condition evaluation device that is one embodiment of the present disclosure is provided with at least one processor and at least one memory, in which the at least one processor extracts feature amounts associated with the shapes of at least two organs from an image including a plurality of organs, and inputs the feature amounts associated with the shapes of the at least two organs into a model, thereby generating pathological condition evaluation information.

IPC Classes  ?

• A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• G06T 1/00 - General purpose image data processing
• G06T 7/00 - Image analysis

Abstract

[Problem] To search for a highly accurate reaction route. [Solution] This information processing device comprises: one or more memories; and one or more processors. The one or more processors acquire a route for connection from a start state to an end state, divide the route on the basis of local minimum points in the route, and optimize the divided routes, to acquire a reaction path from the start state to the end state.

IPC Classes  ?

• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes

Abstract

A learning device (1) according to an embodiment of the present invention comprises: at least one memory (33, 35); and at least one processor (31). The at least one processor (31): acquires teacher data of a physical parameter; inputs the teacher data into a first model to generate a potential function; uses the potential function to output a first value of the physical parameter; and on the basis of the first value of the physical parameter and the teacher data, adjusts a network parameter of the first model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons

Abstract

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 8/30 - Creation or generation of source code
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

Abstract

A semiconductor device includes a first chip, a second chip, a third chip, and a fourth chip. The first chip is placed adjacent to the second chip and the fourth chip. The third chip is placed adjacent to the second chip and the fourth chip at a position different from a position of the first chip. Data of the first chip is transferred from the first chip to the third chip via the second chip. Data of the third chip is transferred from the third chip to the first chip via the fourth chip. The data transferred from the first chip to the second chip is transferred via a wiring layer formed over a silicon and placed at a position different from positions of the first chip, the second chip, the third chip, and the fourth chip.

IPC Classes  ?

• H01L 23/538 - Arrangements for conducting electric current within the device in operation from one component to another the interconnection structure between a plurality of semiconductor chips being formed on, or in, insulating substrates
• G06F 11/10 - Adding special bits or symbols to the coded information, e.g. parity check, casting out nines or elevens
• H01L 23/00 - Details of semiconductor or other solid state devices
• H01L 23/06 - ContainersSeals characterised by the material of the container or its electrical properties
• H01L 23/31 - Encapsulation, e.g. encapsulating layers, coatings characterised by the arrangement
• H01L 23/367 - Cooling facilitated by shape of device
• H01L 23/433 - Auxiliary members characterised by their shape, e.g. pistons
• H01L 25/065 - Assemblies consisting of a plurality of individual semiconductor or other solid-state devices all the devices being of a type provided for in a single subclass of subclasses , , , , or , e.g. assemblies of rectifier diodes the devices not having separate containers the devices being of a type provided for in group

Abstract

One aspect of the present disclosure relates to an analysis device including one or more memories and one or more processors. The one or more processors are configured to estimate an arrangement region of a group of products of a same type based on a sales floor image, and notify information on a display state of the group of products in the arrangement region estimated.

IPC Classes  ?

• G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
• G06T 5/50 - Image enhancement or restoration using two or more images, e.g. averaging or subtraction
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

Provided is a service or a device capable of reusing information which has been used during image style changes. This image processing device comprises at least one memory and at least one processor. The at least one processor performs generation of a second image by changing the style of a first image on the basis of a style vector and a change model, wherein the style vector is a style vector which is selected from among one or more style vectors in accordance with a user instruction, and the selected style vector is a style vector which has been used to generate a fourth image by changing the style of a third image based on the change model.

IPC Classes  ?

• G06T 5/00 - Image enhancement or restoration

Goods & Services

Downloadable image files of digital virtual humans, creatures, and fictional characters; data processors; integrated circuits; computer hardware for deep learning, machine learning, artificial intelligence, and neural networks; computer hardware and recorded software system for deep learning, machine learning, artificial intelligence development, and developing and managing neural networks; accelerator for deep learning, in the nature of computer hardware expansion cards, being computer expansion boards, for use in deep learning, machine learning, artificial intelligence, and neural networks; deep learning accelerator cards in the nature of deep learning computer hardware; downloadable computer programs for playing video games; downloadable computer programs featuring craft games and action games; downloadable game software; downloadable image files of digital art and illustrations featuring virtual humans, creatures, and fictional characters with line drawing which are automatically colored by AI powered computers (Based on Use in Commerce) Entertainment services, namely, providing image files featuring digital virtual humans, creatures, and fictional characters online and in mobile wireless form; entertainment services, namely, providing online non-downloadable images of digital virtual humans, creatures, and fictional characters via the Internet; providing online non-downloadable images featuring digital virtual humans, creatures, and fictional characters; (Based on Intent To Use) Providing online computer craft games and action games; providing computer games via the Internet, computer network, communication network on demand and interactive communication; providing online electronic game services, not downloadable; gaming services for entertainment via online computer network, namely, providing online non-downloadable computer games; game services provided online from a computer network, namely, providing online non-downloadable computer games, and providing information relating thereto; entertainment services, namely, providing online multiplayer video games Providing a website featuring online non-downloadable software tools for image editing; software as a service (SaaS), featuring computer software platform for designing and generating digital virtual humans, creatures, and fictional characters; providing online non-downloadable computer programs for creating digital virtual humans, creatures, and fictional character images; providing online non-downloadable computer programs for creating images; providing a website featuring on-line non-downloadable software that enables users to design and generate digital virtual humans, creatures, and fictional characters; computer time-sharing services; providing virtual computer systems and virtual computer environments through cloud computing; cloud hosting provider services; providing temporary use of online non-downloadable game software; providing a website featuring online non-downloadable software tools for digital automatic coloring on line drawing arts and illustrations; software as a service (SaaS), featuring computer software platform for automatic coloring on digital line drawing arts and illustrations; providing online non-downloadable computer programs for automatic coloring on digital line drawing arts and illustrations; providing a website featuring on-line non-downloadable software that helps users colorize digital illustration, images, and artwork

Goods & Services

Downloadable image files of digital virtual humans, creatures, and fictional characters; data processors; integrated circuits; computer hardware for deep learning, machine learning, artificial intelligence, and neural networks; computer hardware and recorded software system for deep learning, machine learning, artificial intelligence development, and developing and managing neural networks; accelerator for deep learning, in the nature of computer hardware expansion cards, being computer expansion boards, for use in deep learning, machine learning, artificial intelligence, and neural networks; deep learning accelerator cards in the nature of deep learning computer hardware; downloadable computer programs for playing video games; downloadable computer programs featuring craft games and action games; downloadable game software; downloadable image files of digital art and illustrations featuring virtual humans, creatures, and fictional characters with line drawing which are automatically colored by AI powered computers Entertainment services, namely, providing image files featuring digital virtual humans, creatures, and fictional characters online and in mobile wireless form; entertainment services, namely, providing online non-downloadable images of digital virtual humans, creatures, and fictional characters via the Internet; providing online non-downloadable images featuring digital virtual humans, creatures, and fictional characters; Providing online computer craft games and action games; providing computer games via the Internet, computer network, communication network on demand and interactive communication; providing online electronic game services, not downloadable; gaming services for entertainment via online computer network, namely, providing online non-downloadable computer games; game services provided online from a computer network, namely, providing online non-downloadable computer games, and providing information relating thereto; entertainment services, namely, providing online multiplayer video games Providing a website featuring online non-downloadable software tools for image editing; software as a service (SaaS), featuring computer software platform for designing and generating digital virtual humans, creatures, and fictional characters; providing online non-downloadable computer programs for creating digital virtual humans, creatures, and fictional character images; providing online non-downloadable computer programs for creating images; providing a website featuring on-line non-downloadable software that enables users to design and generate digital virtual humans, creatures, and fictional characters; computer time-sharing services; providing virtual computer systems and virtual computer environments through cloud computing; cloud hosting provider services; providing temporary use of online non-downloadable game software; providing a website featuring online non-downloadable software tools for digital automatic coloring on line drawing arts and illustrations; software as a service (SaaS), featuring computer software platform for automatic coloring on digital line drawing arts and illustrations; providing online non-downloadable computer programs for automatic coloring on digital line drawing arts and illustrations; providing a website featuring on-line non-downloadable software that helps users colorize digital illustration, images, and artwork

Abstract

According to one embodiment, an inference device includes at least one memory and at least one processor. The at least one processor performs a computation for geometry optimization of a substance by a first algorithm. After a predetermined condition is satisfied, the at least one processor performs, based on a result of the computation by the first algorithm, a geometry optimization of the substance by a second algorithm different from the first algorithm.

IPC Classes  ?

• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model

Abstract

Provided is a three-dimensional reconfiguration system capable of easily photographing a marker irrespective of a photographing direction. This three-dimensional reconfiguration system comprises: one or more cameras that photograph an object; and a control device configured to output reconfiguration information on the object on the basis of a stereoscopic marker, a plurality of photographed images of the object photographed from various directions by the cameras, and attitude information of the cameras based on an image of the stereoscopic marker included in the photographed images. The stereoscopic marker include a marker portion that is disposed at an angle with respect to a mount surface for the stereoscopic marker.

IPC Classes  ?

• G06T 7/55 - Depth or shape recovery from multiple images

Abstract

Provided is a three-dimensional reconstruction system that can reconstruct a variety of objects. A three-dimensional reconstruction method is a method of three-dimensionally reconstructing an object based on a plurality of images obtained by photographing the object from different directions. The background image area of the object included in the plurality of images has a pattern.

IPC Classes  ?

• G06T 7/593 - Depth or shape recovery from multiple images from stereo images
• G06T 15/20 - Perspective computation
• G06T 19/00 - Manipulating 3D models or images for computer graphics

Abstract

A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

IPC Classes  ?

• B25J 9/16 - Programme controls

Abstract

[Problem] To speed up or the like the calculation of reaction path search. [Solution] This information processing device 1 comprises: one or a plurality of memories and one or a plurality of processors. The one or plurality of processors searches a reaction path by using one or a plurality of trained models that output physical quantities pertaining to molecules when a three-dimensional arrangement for two or more atoms constituting the molecules is input.

IPC Classes  ?

• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G06N 99/00 - Subject matter not provided for in other groups of this subclass
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

An estimation device according to an embodiment of the present invention is provided with at least one memory and at least one processor. The at least one processor: inputs, to a neural network, feature amounts of a plurality of atoms; calculates the feature amounts that have been updated; outputs parameters respectively corresponding to the plurality of atoms on the basis of the calculated feature amounts; and determines, by using the parameters, a plurality of electric charges respectively corresponding to the plurality of atoms.

IPC Classes  ?

• G16Z 99/00 - Subject matter not provided for in other main groups of this subclass

Abstract

[Problem] To search for a desired reaction pathway using few parameters. [Solution] This information processing device is provided with one or more memories and one or more processors. The one or more processors, with regard to a pre- and a post-reaction state of two or more atoms, fixes the displacement magnitude of the atoms to perform optimization on the basis of the energy with which the atoms transition from the pre-reaction state to the post-reaction state, and searches for a stable structure on the basis of the optimization result.

IPC Classes  ?

• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G06N 99/00 - Subject matter not provided for in other groups of this subclass

Abstract

A processing system includes a first chip including one or more first processors, and a second chip including one or more second processors. For a weight of a neural network to be updated, the one or more second processors execute a backward operation of the neural network and calculate a gradient of the weight, the calculated gradient is transferred to the one or more first processors, and the one or more first processors update the weight based on the calculated gradient. The one or more first processors transfer the updated weight to the second chip. The one or more first processors update the weight of the neural network in accordance with a method using multiple types of parameters.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers
• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead
• G06F 13/16 - Handling requests for interconnection or transfer for access to memory bus
• G06F 13/40 - Bus structure
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

[Problem] To propose an application field using a neural network model that forms an NNP. [Solution] This information processing device comprises one or more memories and one or more processors. The one or more processors input information concerning an atom of a substance to a first model, and acquire information concerning the substance from an output layer of the first model. The first model is a model that: is provided with layers ranging from an input layer to a predetermined layer in a second model for receiving input of information concerning atoms and outputting a value of at least one of energy or force; and is trained to output information concerning the substance.

IPC Classes  ?

• G06N 3/096 - Transfer learning
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

To provide a tail vein injection system that facilitates tail vein puncture, this tail vein injection system comprises: a calculation unit that calculates the position of the tail vein on the basis of an image of the tail; and an adjustment unit that adjusts the position and the orientation of an injection needle using a position adjustment mechanism.

IPC Classes  ?

• A61D 1/00 - Surgical instruments for veterinary use
• A61M 5/42 - Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular wayAccessories therefor, e.g. filling or cleaning devices, arm rests having means for desensitising skin, for protruding skin to facilitate piercing, or for locating point where body is to be pierced
• G01B 11/00 - Measuring arrangements characterised by the use of optical techniques

Abstract

The present disclosure relates to estimating a missing value of body information. An information processing device according to one embodiment of the present disclosure comprises at least one memory and at least one processor. The at least one processor executes inputting of body information in which the value of a first attribute is missing to at least one trained model to thereby acquire an estimated value of the first attribute, and inputting of body information in which the value of a second attribute different from the first attribute is missing to the at least one trained model to thereby acquire an estimated value of the second attribute, the first and second attributes representing body information other than basic information.

IPC Classes  ?

• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 50/10 - Services
• G16H 10/00 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data

Abstract

Provided is a service or device with which it is possible to implement a variety of image processing. This image processing device comprises one or a plurality of storage devices, and one or a plurality of processors. The one or plurality of processors each implement: inputting of a first latent variable to a first generation model to thereby generate a first image; association of the first latent variable with identification information pertaining to the first generation model and storing of the associated items of information in the one or plurality of storage devices; acquisition of the first latent variable, and identification information pertaining to the first generation model associated with the first latent variable, from the one or plurality of storage devices; generation of a second latent variable on the basis of the first latent variable; inputting of the second latent variable to the first generation model to thereby generate a second image; and association of the second latent variable with the identification information pertaining to the first generation model and storing of the associated items of information in the one or plurality of storage devices.

IPC Classes  ?

• G06T 11/80 - Creating or modifying a manually drawn or painted image using a manual input device, e.g. mouse, light pen, direction keys on keyboard
• G06T 7/00 - Image analysis
• G06T 1/00 - General purpose image data processing
• G06T 1/40 - Neural networks

Abstract

The present invention comprises increasing the recognition accuracy in object recognition processing. This image processing device comprises one or a plurality of memories and one or a plurality of processors. The one or a plurality of processors execute, regarding a predetermined space including at least one object: acquisition of a multiple-tone image and three-dimensional information of the predetermined space; masking of a part of the multiple-tone image on the basis of the three-dimensional information; and predetermined processing using the masked multiple-tone image.

IPC Classes  ?

• G06T 7/00 - Image analysis

Abstract

According to the present invention, a protease inhibitor contains, as an active ingredient, a compound represented by general formula (I). In general formula (I), R11and R12each independently represent a hydrogen atom or a group represented by general formula (II), and when either one of R11and R12is a hydrogen atom, the other is a group represented by general formula (II). R13represents a 1H-imidazol-4-yl group or a pyrrolidin-2-on-3-yl group, R1422R', -C(=O)-R", or a cyano group, R' represents a substituted or unsubstituted alkyl group having 1-4 carbon atoms, or a benzyl group, and R" represents a substituted or unsubstituted alkyl group having 1-4 carbon atoms.

IPC Classes  ?

• A61P 31/14 - Antivirals for RNA viruses
• C07D 207/263 - 2-Pyrrolidones with only hydrogen atoms or radicals containing only hydrogen and carbon atoms directly attached to other ring carbon atoms
• C07D 233/64 - Heterocyclic compounds containing 1,3-diazole or hydrogenated 1,3-diazole rings, not condensed with other rings having two double bonds between ring members or between ring members and non-ring members with substituted hydrocarbon radicals attached to ring carbon atoms, e.g. histidine
• C07D 403/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links
• C07D 403/14 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing three or more hetero rings
• A61K 31/4015 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil having oxo groups directly attached to the heterocyclic ring, e.g. piracetam, ethosuximide
• A61K 31/4174 - Arylalkylimidazoles, e.g. oxymetazolin, naphazoline, miconazole
• A61K 31/4178 - 1,3-Diazoles not condensed and containing further heterocyclic rings, e.g. pilocarpine, nitrofurantoin
• A61K 31/496 - Non-condensed piperazines containing further heterocyclic rings, e.g. rifampin, thiothixene or sparfloxacin

Abstract

[Problem] To provide a novel graph generation model. [Solution] An information processing device according to the present invention is provided with one or more memories and one or more processors. The one or more processors input a latent representation into a second model to acquire node information, input the latent representation into a third model to acquire edge information, and generate a second graph on the basis of the node information and the edge information.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 5/02 - Knowledge representationSymbolic representation

Abstract

An inferring device includes one or more memories and one or more processors. The one or more processors are configured to input input data including at least information regarding a first state in a differentiable physical model to calculate an inferred second state; and infer, based on a second state and the inferred second state, a parameter that transits from the first state to the second state.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models

Abstract

An inferring device includes one or more memories and one or more processors. The one or more processors are configured to generate information on a tree including information on a node and information on an edge from a latent representation by using a trained inference model; and generate a graph from the information on the tree. The information on the tree includes connection information on the nodes.

IPC Classes  ?

• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn

Goods & Services

Downloadable interactive entertainment software for playing video games; downloadable video game software and recorded video game software; downloadable computer game software featuring downloadable virtual goods, namely, gems, accessories, finger rings, sunglasses, swords, weapons, clothing, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports equipment, toys, dolls, recreational equipment, watches, eyewear, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and virtual parts and accessories therefor, for use in online virtual worlds; downloadable computer game software for creating environments in a virtual world, namely, for building, constructing and landscaping nature features including mountains, forests, woods, seas, lakes, rivers, hills, plains, valleys and volcanoes, for manually and automatically programming functions of collecting and processing materials of the abovementioned items, for creating avatars, player characters and non-player characters, and for enabling additional equipment of behavior patterns thereof; downloadable computer game software for craft games, role playing games and action games, and recorded computer game software for craft games, role playing games and action games; downloadable computer game software featuring virtual goods used in online virtual world, namely, gems, accessories, finger rings, sunglasses, swords, weapons, clothing, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports equipment, toys, dolls, recreational equipment, watches, eyewear, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and parts and accessories therefor; downloadable electronic game programs, and recorded electronic game programs; downloadable game software, and recorded game software; downloadable virtual goods, in the nature of image files of gems, accessories, finger rings, sunglasses, swords, weapons, clothing, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports equipment, toys, dolls, recreational equipment, watches, eyewear, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and parts and accessories therefor, all for online use and online virtual world use; downloadable virtual goods, namely, computer programs featuring characters, items, and structures in games for use in online virtual worlds; downloadable computer game programs, and recorded computer game programs; computer programs, recorded, for use data management; downloadable software for virtual reality and augmented reality for use in mobile devices for integrating electronic data and physical world environment for creating virtual environment and augmented environment for users to enable interactive communication for recreational, leisure and entertainment purposes; downloadable computer software for operating metaverse contents; downloadable game software for metaverse environments; downloadable virtual reality computer game software; downloadable virtual reality game software; recorded and downloadable media in the nature of recorded and downloadable computer game programs; downloadable interactive entertainment software for playing computer games; downloadable and recorded consumer video game programs; electronic circuits and CD-ROMs featuring recorded electronic game programs for hand-held games with liquid crystal displays; downloadable music files; pre-recorded DVDs, DVD-ROMs, CD-ROMs, video tapes, video discs, compact discs, machine-readable magnetic computer tapes, optical discs and optical data digital media devices, all featuring interactive audio computer game programs including craft games, action games and role playing games Entertainment services, namely, providing online non downloadable images featuring virtual goods, namely, footwear, special footwear for sports, clothing, headwear, eyewear, bags, sport bags, backpacks, sports equipment, works of art, toys, personal ornaments, dolls, houses, buildings, land, campgrounds, campground facilities, gems, accessories, finger rings, sunglasses, swords, weapons, garments, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports goods, recreational equipment, watches, spectacles, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and parts and accessories therefor, all for use in virtual environments created for entertainment purposes; entertainment services, namely, providing online non-downloadable electronic games and online non-downloadable videos featuring animated cartoons, not downloadable phones; providing online non-downloadable files featuring image data of characters in books, animated cartoons, toys and games; providing images, not downloadable, in the field of providing images of characters, items, and structures in games, via communication networks; organizing, arranging and organizing, arranging and conducting of online computer game events; organizing, arranging and conducting of game events in the field of computer games; organizing, arranging and conducting of entertainment events in the nature of computer games; organization of entertainment exhibition events excluding movies, shows, plays, musical performances, sports, horse races, bicycle races, boat races and auto races; virtual reality game services provided online from a computer network; online game services, namely, providing online computer games in virtual reality space on websites; virtual reality game services provided online from a computer network and providing information and advice relating thereto; providing non-downloadable network personal computer games, online Internet games, online computer games, and non-downloadable computer games via a games-on-demand transmission service on a global computer network and providing information relating thereto; providing online electronic games and computer games via the Internet and via communications by mobile phones, and providing information relating thereto; providing on-line computer games via the Internet, computer network, cable network, communication network on demand and interactive communication by mobile phones; providing on-line computer games in the nature of craft games, role playing games and action games in online virtual world enabling players to collect and process virtual items, namely, gems, accessories, finger rings, sunglasses, swords, weapons, clothing, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports equipment, toys, dolls, recreational equipment, watches, eyewear, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and parts and accessories therefor, and to create environment in virtual world, namely, building, constructing and landscaping virtual great nature including mountains, forests, woods, seas, lakes, rivers, hills, plains, valleys and volcanoes, and having manually and automatically programming functions collecting and processing materials of the above-mentioned items and creating avatars, player characters and non-player characters and enabling additional equipment of behavior patterns thereof; providing online non-downloadable data files featuring the data of the images of virtual goods used in virtual reality games, namely, footwear, special footwear for sports, clothing, headwear, eyewear, bags, sport bags, backpacks, sports equipment, works of art, toys, personal ornaments, dolls, houses, buildings, land, campgrounds, campground facilities, gems, accessories, finger rings, sunglasses, swords, weapons, garments, foods and beverages, living ware, cars, furniture, joinery fittings, electrical machinery and apparatuses, bladed or pointed hand tools, hand tools, hand operated, metal hardware, kitchen utensils, cleaning tools and washing utensils, agricultural machines, implements and supplies, flowers, trees, sports goods, recreational equipment, watches, spectacles, building materials, semi-wrought precious stones and their imitations, animals, cattle, pets, and parts and accessories therefor; interactive audio game services featuring the provision of on-line computer games in the nature of craft games, role playing games and action games; online game services, namely, providing online interactive computer games, online computer games, online video games and online electronic games; organization, arranging and conducting interactive immersive virtual reality events and social entertainment services related thereto thereof; providing online virtual reality games; providing online computer games for playing in virtual reality space on websites; providing online non-downloadable computer game software for operating contents of a virtual reality space in which users can interact with a computer-generated environment and other users; gaming services, namely, providing online computer games for a virtual-reality space in which users can interact with a computer-generated environment and other users; gaming services, namely, providing online computer games for entertainment and continuing education via online computer network Providing on-line non-downloadable computer software for playing computer games; providing on-line non-downloadable computer programs in the nature of computer game software on data networks; Software as a service (SAAS) services featuring software for us in data management; application service provider (ASP), namely, hosting computer software applications for others; hosting computer software applications for others; Platform as a service (PAAS) featuring computer software platforms for use in data management; application service provider, namely, developing, hosting and managing social networking software of others and software platforms for virtual reality based virtual worlds; design, development, repair and upgrading of computer software, applications, computer games game software, video game software, and websites and audiovisual content

Abstract

A scheduling apparatus includes at least one memory and at least one processor, and the at least one processor is configured to generate a schedule from a state specified based on received information. The generating includes causing the state to transition such that a process of transferring data from a memory is replaced with a recomputation process that obtains the data.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06N 20/00 - Machine learning

Abstract

A model generation method includes updating, by at least one processor, a weight matrix of a first neural network model at least based on a first inference result obtained by inputting, to the first neural network model which discriminates between first data and second data generated by using a second neural network model, the first data, a second inference result obtained by inputting the second data to the first neural network model, and a singular value based on the weight matrix of the first neural network model. The model generation method also includes at least based on the second inference result, updating a parameter of the second neural network model.

IPC Classes  ?

• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06N 3/045 - Combinations of networks

Abstract

A control system includes at least one processor and at least one memory. The at least one processor is configured to determine operation data by repeating a process of calculating control target data indicating a predicted value of a control target in a plant and the operation data indicating an operation value of a control device of the plant by a given calculation model based on observation data indicating an actual value of the plant.

IPC Classes  ?

• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

This computer system has a plurality of first nodes connected in a tree structure, and second nodes connected to leaves of the first nodes. A second participating node participating in collective communication transmits target data of the collective communication and identification information on nodes participating in the collective communication to the first node. A first participating node participating in collective communication calculates target data received from the second participating node, and transmits the calculation result to a higher-level node together with identification information received from the second participating node or identification information, identifying nodes participating in the collective communication, generated on the basis of the identification information received from the second participating node. As a result, low-latency collective communication can be achieved, on the basis of identification information received from a lower node, without providing a management device for managing collective communication.

IPC Classes  ?

• G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

With respect to a method of generating an instruction to be executed by an arithmetic processing device including first blocks, each of the first blocks including execution sections, the method includes generating, by at least one processor, at least one data transfer instruction that causes the arithmetic processing device to perform at least one of first data transfers, second data transfers, third data transfers, or fourth data transfers. Transfer sources of the first data transfers are execution sections, transfer destinations of the first data transfers are execution sections, transfer sources of the second data transfers are first blocks, transfer destinations of the second data transfers are first blocks, transfer sources of the third data transfers are first blocks, transfer destinations of the third data transfers are execution sections, transfer sources of the fourth data transfers are execution sections, and transfer destinations of the fourth data transfers are first blocks.

IPC Classes  ?

• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 8/41 - Compilation

Abstract

A compiler device, for generating an instruction sequence to be executed by an arithmetic processing device, includes at least one memory and at least one processor. The at least one processor is configured to receive a first instruction sequence for a first process and a second instruction sequence for a second process to be executed after the first process; generate third instructions, each third instruction being generated by merging a first instruction included in the first instruction sequence and a second instruction included in the second instruction sequence; and generate a third instruction sequence by concatenating the third instructions, instructions included in the first instruction sequence that are not merged into the third instructions, and instructions other than the second instruction among the plurality of instructions included in the second instruction sequence that are not merged into the one or more third instructions.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead

Abstract

According to the present invention, the effect of a target drug is predicted. This prediction method comprises a prediction step for predicting the effect of a drug on a disease on the basis of a value pertaining to predetermined miRNA derived from a target sample.

IPC Classes  ?

• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• C12M 1/00 - Apparatus for enzymology or microbiology
• C12M 1/34 - Measuring or testing with condition measuring or sensing means, e.g. colony counters

Abstract

A compiler, for generating machine code to be executed in a chip including a plurality of distributed memories connected by a tree structure topology, includes at least one memory and at least one processor. The at least one processor is configured to associate each element of a tensor to be processed with an address in the plurality of memories included in the chip, based on a stride and a number of divisions in a predetermined hierarchy of the tree structure with respect to the tensor to be processed.

IPC Classes  ?

• G06F 8/51 - Source to source

Abstract

A distributed reinforcement learning system includes one or more actor devices configured to acquire experience data, the experience data being used for reinforcement learning and corresponding to an action determined based on a model to be trained, a plurality of replay buffers configured to store the experience data acquired from the one or more actor devices, and one or more learner devices configured to train the model in the reinforcement learning, the reinforcement learning using the experience data stored in the plurality of replay buffers. The plurality of replay buffers are distributed and arranged in a plurality of nodes.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

This semiconductor device has a plurality of function blocks, and has an operation information storage unit for storing operation information indicating whether each of the plurality of function blocks is a normally operating function block or a defective function block, and an enablement setting unit for enabling the plurality of function blocks on the basis of enablement information indicating whether a function block is to be enabled or disabled. Thus, a plurality of function blocks installed in a semiconductor device can be used without waste.

IPC Classes  ?

• G06F 11/18 - Error detection or correction of the data by redundancy in hardware using passive fault-masking of the redundant circuits, e.g. by quadding or by majority decision circuits
• H01L 21/822 - Manufacture or treatment of devices consisting of a plurality of solid state components or integrated circuits formed in, or on, a common substrate with subsequent division of the substrate into plural individual devices to produce devices, e.g. integrated circuits, each consisting of a plurality of components the substrate being a semiconductor, using silicon technology
• H01L 27/04 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier the substrate being a semiconductor body

Abstract

There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

IPC Classes  ?

• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06F 8/30 - Creation or generation of source code