Abstract

A system and method of manufacturing MXene-based coils on textile substrates. The system and method enable wireless charging of embedded devices in textiles, including energy storage, sensors, and wireless charging coils, removing the need for rigid physical connections. The systems manage print content by deposition of MXene ink compositions with a custom-generated coil design comprising specific geometric parameters that are configured to target required performance targets. Such an approach allows for the augmentation of e-textiles with sufficient energy and/or data transmission capacities so as to support the use of a wide range of smart devices and applications.

IPC Classes  ?

• D03D 1/00 - Woven fabrics designed to make specified articles
• H01F 5/00 - Coils
• H01F 27/28 - CoilsWindingsConductive connections
• H01F 41/04 - Apparatus or processes specially adapted for manufacturing or assembling magnets, inductances or transformersApparatus or processes specially adapted for manufacturing materials characterised by their magnetic properties for manufacturing cores, coils or magnets for manufacturing coils

Abstract

System and method for generating an attack flow graph are disclosed. The method includes, receiving a cyber-attack report from a user device, extracting one or more attack actions from the cyber-attack report, extracting one or more attack assets from the cyber-attack report, determining one or more conditions and one or more operators associated with the one or more attack actions and the one or more attack assets. The method further includes, generating a subgraph using the one or more attack actions, the one or more attack assets, the one or more conditions and the one or more operators, generating an attack flow graph, wherein the attack flow graph is generated based on the subgraph, the cyber-attack report and an attack flow schema, and storing the attack flow graph in an attack flow knowledgebase.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A system and method for electric vehicle operational optimization is disclosed. The system comprises a memory storing processor-executable instructions and a processor, communicably coupled with the memory. The system obtains input data and predict health and performance parameters. The system generates computer simulated instances which emulate a behavior and a performance of the electric vehicle. The system, further, validates the health and the performance parameters by simulating the computer simulated instances in a virtual environment. The system determines a behavior status, a performance status and a health status of the electric vehicle. Thereafter, the system determines abnormality associated with the electric vehicle, followed by determining action for rectifying the abnormality. Consequently, the system controls an operation by performing the determined action at the electric vehicle.

IPC Classes  ?

• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• B60L 15/20 - Methods, circuits or devices for controlling the propulsion of electrically-propelled vehicles, e.g. their traction-motor speed, to achieve a desired performanceAdaptation of control equipment on electrically-propelled vehicles for remote actuation from a stationary place, from alternative parts of the vehicle or from alternative vehicles of the same vehicle train for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
• B60L 58/12 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
• B60L 58/16 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• B60W 30/182 - Selecting between different operative modes, e.g. comfort and performance modes
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G07C 5/04 - Registering or indicating driving, working, idle, or waiting time only using counting means or digital clocks

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enriching or exchanging identifying information using central and edge processing nodes. In some implementations, a method includes generating a hash value for each of one or more components of identifying information of a first entity; discarding the identifying information; storing the hash value for each of the one or more components of the identifying information and not the identifying information; generating a network graph linking the hash value for each of the one or more components of the identifying information with the first entity; providing the hash of the identifying information to a central server; and updating, using response data from the central server, the network graph to additionally link to a respective hash value for each component in a second set of components identifying the first entity.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

System and method for analyzing and tuning an input dataset to a foundation model is disclosed. The method includes, receiving an input dataset from a user, determining a textual representation of the received input dataset on an n-gram data level by processing the received input dataset, and determining a model score for the n-gram level by applying the determined textual representation to a trained artificial intelligence-based regression model. The method further includes, determining a best fit model and a unigram preparation data value for the n-gram level based on the determined model score, an n-gram preparation data value, and a type of n-gram using a trained classification model, wherein the best fit model is one of pretrained existing models and a dynamically trained n-gram model, predicting a bias score and an accuracy score for the input dataset by applying the determined textual representation and the input dataset onto the determined best fit model using a trained clustering model, generating a Prompt Inclusivity Index (PII) value based on the determined best fit model, the predicted bias score and the accuracy score, wherein the PII value indicates presence of bias data in the received input dataset, and outputting the generated PII value to a user via a user interface of a user device.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 18/2411 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
• G06N 20/00 - Machine learning

Abstract

Method, system, and computer program product are disclosed for handling requests specific to operations using a distributed agentic framework. A method includes identifying at least one micro-agent of the plurality of micro-agents to handle a request. The request is specified in a user input. The method includes generating a prompt corresponding to the request specified in the user input and routing the prompt to the at least one micro-agent of the plurality of micro-agents. The method includes performing at least one operation, specific to the request, using the at least one micro-agent of the plurality of micro-agents, to generate a response for the prompt. The method includes evaluating the response by comparing the response with an anticipated response. Upon evaluating that the response meets or exceeds a predetermined threshold criterion, the method includes providing the response as an output to the user input.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations

Abstract

System and method for vehicle toll management are disclosed herein. For example, toll records of toll gates along a roadway are accessed. The toll records include sensor data including data associated with Radio Frequency Identifier (RFID) tags and image data. Further, it is determined, from the toll records of a first toll gate, that a vehicle is passed without paying toll. Furthermore, the vehicle is determined via processing of the image data associated with the first toll gate and/or a subset of the toll gates. Also, a correlation process that automatically correlates the toll records to mobile communication network data of base stations proximate to the first toll gate and/or the subset toll gates is executed. Based on the correlation, identification information of a mobile device corresponding to a user of the vehicle is then determined. A notification is then transmitted to the mobile device regarding payment of the toll.

IPC Classes  ?

• G07B 15/06 - Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
• 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 10/94 - Hardware or software architectures specially adapted for image or video understanding
• G06V 20/54 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
• G06V 20/62 - Text, e.g. of license plates, overlay texts or captions on TV images
• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Systems and methods for generating synthetic data is disclosed. The system includes a convolutional neural network having an encoder, a latent space, and a decoder. The encoder/decoder is trained to generate synthetic datasets having variability with respect to an input dataset. The variability may be introduced via compression and expansion of the data processed via the latent space, as well as via one or more selectively activatable dropout layers of the encoder. The encoder and decoder may be configured to shape data during the encoding/decoding process according to a number of channels in the input data, where the synthetic data produced by the encoder/decoder retains one or more signals of interest present in the input data.

IPC Classes  ?

• G06N 3/048 - Activation functions
• G06N 3/08 - Learning methods

Goods & Services

Technical consulting services, namely, computer software consulting services related to the knowledgeable selection and customization of third-party software and applications to facilitate the creation and modification of artificial intelligence agents; technical computer software consulting services for the evaluation, ranking, and benchmarking of third-party software and applications used in the development of artificial intelligence agents; computer application software development services for the creation and customization of artificial intelligence agents; providing temporary use of non-downloadable computer software to facilitate secure access and access controls to software and applications in the field of artificial intelligence agents; providing temporary use of non-downloadable computer software for comparing third-party artificial intelligence software and applications; Software as a service (SAAS) services featuring software in the field of artificial intelligence data orchestration for coordination and management of AI models, data pipelines, infrastructure, and policies to ensure they work together efficiently and reliably in a unified system

Goods & Services

(1) Technical consulting services, namely, consulting services related to the knowledgeable selection and customization of third-party software and applications to facilitate the creation and modification of artificial intelligence agents; technical consulting services for the evaluation, ranking, and benchmarking of third-party software and applications used in the development of artificial intelligence agents; application development services for the creation and customization of artificial intelligence agents; providing temporary use of non-downloadable computer software to facilitate secure access and access controls to software and applications in the field of artificial intelligence agents; providing temporary use of non-downloadable computer software for comparing third-party artificial intelligence software and applications; software as a service (saas) services featuring software in the field of artificial intelligence data orchestration

Goods & Services

Technical consulting services, namely, consulting services related to the knowledgeable selection and customization of third-party software and applications to facilitate the creation and modification of artificial intelligence agents; technical consulting services for the evaluation, ranking, and benchmarking of third-party software and applications used in the development of artificial intelligence agents; application development services for the creation and customization of artificial intelligence agents; providing temporary use of non-downloadable computer software to facilitate secure access and access controls to software and applications in the field of artificial intelligence agents; providing temporary use of non-downloadable computer software for comparing third-party artificial intelligence software and applications; software as a service (saas) services featuring software in the field of artificial intelligence data orchestration.

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support generative artificial intelligence (AI)-assisted analytics of structured data sets. For example, a system may receive a prompt that includes information associated with a structured data set which includes at least some numerical data. The system may provide the prompt as input to an agent orchestrator to select one or more generative AI agents to perform analytics tasks corresponding to the information. The agent orchestrator includes a trained AI classifier configured to select the one or more generative AI agents from a plurality of generative AI agents. The system may execute an ensemble model to generate a response to the prompt based on the structured data set. The ensemble model includes the one or more generative AI agents. The system may output a graphical user interface (GUI) that includes one or more elements based on the response.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 40/247 - ThesaurusesSynonyms

Abstract

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

IPC Classes  ?

• G06F 16/2452 - Query translation
• G06F 16/242 - Query formulation
• G06F 40/40 - Processing or translation of natural language

Abstract

An Artificial Intelligence (AI) & Generative AI-driven cross-domain document analysis system enables accurate and consistent narratives across a longitudinal timeline for an entity regarding communications in different operational aspects. The document analysis and insight system includes an Artificial Intelligence (AI) powered Search Interface (AIPS) and an Advanced Intelligent Knowledge Engine (AIKE). The AIPS is configured to pre-process documents from structured and unstructured data sources to generate data taxonomies and custom synonym files. The AIKE generates a preliminary evaluation of the various Large Language Models (LLMs) and uses the data taxonomies and custom synonym files to generate prompts that are configured to address limitations of the various LLMs to obtain accurate replies to user requirements.

IPC Classes  ?

• G06F 40/295 - Named entity recognition
• G06F 40/247 - ThesaurusesSynonyms
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

The present invention discloses an autonomous mobile robotic system and method for store operations and customer engagement. The system comprises an autonomous mobile robotic unit (102) for store area navigation, a robotic manipulator (104) for user-assigned tasks, and a central controller (106) within the autonomous mobile robotic unit (102) for task coordination. The autonomous 10 mobile robotic unit (102) comprises a navigation unit measuring distances and a motion unit executing navigation commands. The robotic manipulator (104) has a gripping unit and an interchangeable gripper's features. The central controller (106) comprises one or more sub-controllers for task execution. The system (100) optimizes store operations, streamlining tasks and reducing human intervention, 15 thus enhancing overall operational efficiency.

IPC Classes  ?

• G05D 1/43 - Control of position or course in two dimensions
• B25J 5/00 - Manipulators mounted on wheels or on carriages
• B25J 9/16 - Programme controls

Goods & Services

Design, development and maintenance of artificial intelligence software for the fields of training and deployment frameworks and architecture; design, development and maintenance of artificial intelligence-powered enterprise software applications; providing cloud-based, non-downloadable software that enables enterprise users to scale adoption of generative artificial intelligence technologies across application and infrastructure management, application and data modernization, and software and platform development; designing of machines, apparatus, instruments including their parts or systems composed of such machines, apparatus and Instruments; computer software design, computer programming, or maintenance of computer software; artificial intelligence consultancy, namely, technology consultation in the field of artificial intelligence; computer technology consultancy; scientific research

Abstract

There is provided methods and systems for generating super resolution images of objects. In particular there is provided a method of generating a super resolution image of an object, the method comprising: receiving a plurality of frames of a video of the object; extracting from the plurality of frames a plurality of images of the object; selecting an image of the plurality of images as a target image; applying a trained model to the plurality of images to generate a super resolution image of the object, wherein the trained model comprises: (a) a correspondence estimation neural network configured to compute a respective optical flow between the target image and each other image of the plurality, and (b) a reconstruction neural network configured to generate a super resolution version of the target image using the plurality of images and the respective optical flows between the target image and each other image of the plurality. There is provided methods and systems for generating super resolution images of objects. In particular there is provided a method of generating a super resolution image of an object, the method comprising: receiving a plurality of frames of a video of the object; extracting from the plurality of frames a plurality of images of the object; selecting an image of the plurality of images as a target image; applying a trained model to the plurality of images to generate a super resolution image of the object, wherein the trained model comprises: (a) a correspondence estimation neural network configured to compute a respective optical flow between the target image and each other image of the plurality, and (b) a reconstruction neural network configured to generate a super resolution version of the target image using the plurality of images and the respective optical flows between the target image and each other image of the plurality. There is also provided a corresponding system, along with systems and methods for training said model.

IPC Classes  ?

• G06T 3/4053 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
• G06T 3/4046 - Scaling of whole images or parts thereof, e.g. expanding or contracting using neural networks
• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
• G06T 15/20 - Perspective computation

Abstract

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for evaluating an energy efficiency of a computer code snippet. A computer-implemented system obtains code data characterizing a computer code snippet and rule data characterizing one or more energy efficiency rules, and processes the code data and rule data using machine-learning models to detect energy efficiency defects in the computer code snippet that negatively impact a dynamic energy efficiency of the computer code snippet upon execution by the computing device.

IPC Classes  ?

• G06F 8/41 - Compilation

Goods & Services

(1) Design, development and maintenance of artificial intelligence training and deployment frameworks and architecture; design, development and maintenance of artificial intelligence-powered enterprise applications; providing cloud-based, non-downloadable software that enables enterprise users to scale adoption of generative artificial intelligence technologies across application and infrastructure management, application and data modernization, and software and platform development; designing of machines, apparatus, instruments [including their parts] or systems composed of such machines, apparatus and Instruments; computer software design, computer programming, or maintenance of computer software; providing computer programs on data networks; artificial intelligence consultancy; computer technology consultancy; scientific research

Goods & Services

Design, development and maintenance of artificial intelligence training and deployment frameworks and architecture; design, development and maintenance of artificial intelligence-powered enterprise applications; providing cloud-based, non-downloadable software that enables enterprise users to scale adoption of generative artificial intelligence technologies across application and infrastructure management, application and data modernization, and software and platform development; designing of machines, apparatus, instruments [including their parts] or systems composed of such machines, apparatus and Instruments; computer software design, computer programming, or maintenance of computer software; providing computer programs on data networks; artificial intelligence consultancy; computer technology consultancy; scientific research.

Abstract

Implementations for receiving, by a GAI integration platform, a request from an application executed by an enterprise system of an enterprise, the application being executed remotely from the GAI integration platform, processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request, the set of modules including one or more of a prompt template module, a prompt quality module, and a personally identifiable information (PII) detection module, transmitting, by the GAI integration platform, the prompt to a GAI system of a plurality of GAI systems, receiving, by the GAI integration platform, a response from the GAI system, the response comprising content generated by the GAI system in response to the prompt, and transmitting, by the GAI integration platform, the response to the application.

IPC Classes  ?

• G06N 3/0475 - Generative networks

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information. Responsive to receiving an augmented user query, query-record mapping operations may be performed based at least in part on a comparison of information associated with the augmented user query to the project name, sentence embedding, word embedding, subgraph information, or a combination thereof.

IPC Classes  ?

• G06Q 10/067 - Enterprise or organisation modelling
• G06F 16/33 - Querying

Abstract

Systems and methods for responsible AI compliance and governance management in AI Products are disclosed. The system receives a request to assess an enterprise product associated with a specific application. Further, the system may determine a plurality of datasets associated with the AI model of the enterprise product. Furthermore, the system generates a training dataset and a test dataset for the determined plurality of datasets associated with the AI model. The system generates a ranked list of recommended metrics for the enterprise product based on the generated training dataset and the test dataset. The system further determines a mitigation strategy for the enterprise product based on the generated ranked list of recommended metrics. Furthermore, the system creates a feedback loop for continuous training and tuning the AI model and the plurality of datasets based on the determined mitigation strategy.

IPC Classes  ?

• G06Q 30/018 - Certifying business or products
• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06Q 30/0203 - Market surveysMarket polls

Abstract

In some implementations, a device may receive information identifying a computing system for energy management, the computing system having a set of hardware components, a set of virtual machines, and a set of software entities. The device may generate a digital twin of the computing system for simulation of the set of hardware components, the set of virtual machines, and the set of software entities. The device may determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters. The device may generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters. The device may transmit information associated with identifying the one or more recommendations.

IPC Classes  ?

• G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
• G06N 20/00 - Machine learning
• G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations

Abstract

Method, data processing system, and computer-readable storage media for responding to a user query. Receiving query from user, query pertaining to request for information. Based on query, generate prompts by masking sensitive information in query. Receive responses from foundation models in response to inputting prompts. Based on responses, generate common result set. By validating common result set with sensitive information, generate response. By supplementing response with sensitive information, generate user response. Providing user response in response to query to the user.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

The invention provides a platform for evaluating and distributing packaged software capabilities. The packaged software capabilities described herein may be discrete pieces of software designed to perform a specific task and multiple packaged software capabilities may be combined to create larger functionality, such as an application. Packaged software capabilities may be analyzed using a variety of techniques to evaluate interoperability based on standard(s) to determine whether the packaged software capabilities support desired hardware, authentication, authorization, and the like. A catalog of packaged software capabilities satisfying the standard(s) may be created and the platform may provide a variety of techniques for searching the catalog based on search parameters to identify best fit packaged software capabilities meeting a user's needs.

IPC Classes  ?

• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06F 8/60 - Software deployment
• G06F 16/248 - Presentation of query results
• G06F 21/60 - Protecting data

Abstract

An Artificial Intelligence (AI) based filter apparatus includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.

IPC Classes  ?

• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing a presentation based on stored user experience data. In one aspect, a method includes receiving a data stream including a plurality of biometric data points that each correspond to a particular timestamp in a plurality of timestamps; determining, for a particular timestamp, that a particular biometric data point in the data stream satisfies a predetermined biometric response threshold; in response to determining that the particular biometric data point satisfies the predetermined biometric response threshold, identifying a time period spanning an interval before the particular timestamp through an interval after the particular timestamp; storing user experience data for the identified time period in a storage device; and providing a presentation based on the stored user experience data.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
• G06T 17/00 - 3D modelling for computer graphics
• G06V 20/40 - ScenesScene-specific elements in video content
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for obtaining inventory data associated with a product or service; generating, using the inventory data, a monotonically increasing function indicative of cumulative demand; predicting future inventory events using the monotonically increasing function and a machine learning model that includes survival curve analysis; in response to predicting future inventory events using the monotonically increasing function of data and survival curve analysis, generating an instruction configured to procure one or more items of inventory; and transmitting the instruction to an inventory system.

IPC Classes  ?

• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 30/0202 - Market predictions or forecasting for commercial activities

Abstract

Methods, systems, and computer-readable storage media for improving the accuracy of embedding vector generation for domain-specific text for purposes of semantic search and generative AI. A dictionary of domain-specific terms can be built to include embedding vectors generated for respective domain-specific terms using a pre-trained large language model. A list of domain-specific terms pertaining to a particular domain can be identified and textual content comprising a description or a definition of a respective domain-specific term can be obtained. A domain-adapted embedding vector for each domain-specific term can be generated based on the textual content for that term. The domain-specific dictionary can be built to include a combination of a domain-specific term, a corresponding domain-adapted embedding vector and the domain-specific term definition or description.

IPC Classes  ?

• G06F 16/33 - Querying
• G06F 40/242 - Dictionaries
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent digital content generation using first-party data. For example, a system may extract features from items of first-party data to generate a vector table of vectorized embeddings. A user prompt may be vectorized and input to a machine learning (ML) model to generate a result vector that is a similar to the prompt vector in a latent space representation of the vector database. The system may compare the result vector to the prompt vector, and based on a result of the comparison, output either a sourced digital content item or an unsourced digital content item. The sourced digital content item may be generated by generative ML model(s) based on the result vector, and the unsourced digital content item may be generated by generative ML model(s) based on a custom prompt derived from the prompt vector.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/0475 - Generative networks

Abstract

Implementations are directed to receiving a set of time series image frames within a time period including a plurality of time points; identifying a first entity, wherein the first entity is coupled with a plurality of first positions corresponding to the plurality of time points; identifying a second entity, wherein the second entity is coupled with a plurality of second positions corresponding to the plurality of time points; determining a position difference of the first entity between any two consecutive time points; determining a position difference of the second entity between any two consecutive time points; determining an interaction between the first entity and the second entity based on i) the position difference of the first entity over the time period, and ii) the position difference of the second entity over the time period; determining whether metadata of the interaction satisfies a threshold; and providing feedback on the interaction.

IPC Classes  ?

• G06V 40/20 - Movements or behaviour, e.g. gesture recognition
• G06T 7/20 - Analysis of motion
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces

Abstract

Methods, systems, and apparatus for enhancing cryptographic hash digests. In one aspect, a method includes obtaining a hash digest that represents a data input. Symbols in the hash digest are divided into a number of groups of equal size. Each group is mapped to a respective point on a Bloch sphere. A quantum system comprising multiple qubits is initialized by initializing each qubit of the multiple qubits in a quantum state specified by a respective point on the Bloch sphere. An expectation value of the initialized quantum system is computed to obtain a quantum fingerprint for the data input.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/08 - Key distribution

Abstract

Methods, systems, and apparatus for generating a cryptographic hash digest. In one aspect, a method includes obtaining data representing a quantum circuit. An initial quantum state vector is generated, where the initial quantum state vector includes an input quantum state vector for the quantum circuit. The initial quantum state vector is used to generate one or more evolved quantum state vectors, where each evolved quantum state vector is generated by applying a respective portion of the quantum circuit to the initial quantum state vector. Logical operations are iteratively applied to floating point binary representations of the initial and evolved quantum state vectors to obtain a binary string, and a hash function is applied to the binary string to obtain a cryptographic hash digest of the quantum circuit.

IPC Classes  ?

• H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
• H04L 9/08 - Key distribution

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects. In one aspect, a method includes obtaining data indicative of initial project plan parameters associated with a software development project; identifying multiple program modules associated with the software development project; obtaining, for a first set of modules of the multiple modules, corresponding source code segments; executing a quality assessment process to compute, for each corresponding source code segment, a corresponding quality score; identifying, based on the quality scores, that at least a subset of the corresponding source code segments are integrable into a corresponding first subset of the first set of modules; and generating a revised project plan by generating updates to the initial project plan parameters by accounting for the corresponding source code segments being integrated in to corresponding ones of the first set of modules.

IPC Classes  ?

• G06F 8/77 - Software metrics
• G06F 11/36 - Prevention of errors by analysis, debugging or testing of software

Abstract

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

IPC Classes  ?

• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 40/40 - Processing or translation of natural language

Abstract

Systems and methods for democratizing compliance in an enterprise. A system receives a selection of a project in the enterprise and a compliance type, causes a list of controls associated with the compliance type to be displayed on a user interface, receives a set of configurations for each of the list of controls from the user, dynamically generates a schema based on the compliance type, the list of controls, and the set of configurations, automatically triggers execution of validation of each of the list of controls for the compliance type based on the generated schema, generates results of the validation, the results including a list of non-compliant controls and a list of compliant controls. The execution of validation of the list of controls is retriggered until each of the list of controls corresponding to the compliance type is compliant.

IPC Classes  ?

• G06Q 30/018 - Certifying business or products
• G06F 8/30 - Creation or generation of source code

Abstract

This disclosure relates generally to the technical field of knowledge graphs, and in particular to automatic and intelligent link prediction. The proposed circuitry and system operate semantic analytics on an input knowledge graph to extract its ontology and derive a set of semantic rules. The extracted ontology and rules are then used for an automatic candidate generation strategy based on an input query for link prediction by filtering down from possible candidate triples to a reduced set of semantically plausible candidates. The semantically plausible candidate triples are then evaluated by a link prediction circuitry trained based on machine learning techniques. As such, the various disclosed implementations provide a refinement of the hypothesis triple set returned by the link prediction circuitry towards semantical plausibility, thereby reducing if not eliminating hallucinations (false hypotheses) in link prediction and at the same time improving practicality of link inference and testing.

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation

Abstract

The present disclosure provides a framework for enhancing artificial intelligence models while reducing its memory requirements, for example, via a multi-field embedding approach. In one exemplary aspect, this framework may be applied to knowledge graphs to mitigate the effects of node degree extremity and to reduce embedding memory demand. For example, this framework may provide multiple graph fields in knowledge graphs that embed nodes in different dimensions to correct for skewness in the distribution of node degrees and decrease memory requirements when training KGE models. In some aspects, the framework may implement a user-specified strategy to split a knowledge graph into a number of distinct fields with similar connectivity patterns. KGEs may then be computed in a model-agnostic manner to allow each field to embed into a different dimensionality, and mathematical transformations may be used to convert embedding dimensions between fields when needed during algorithmic processing.

IPC Classes  ?

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

Abstract

A testing apparatus for testing a battery's responses to charge and discharge cycles includes components that enable greater control over the compressive force applied to the battery being tested than typical testing apparatuses. A force applicator of the testing apparatus is able to effect an adjustable, non-linear force (e.g., according to a parabolic curve) on a battery undergoing testing, which typical techniques using mechanical springs are unable to do. A controller may adjust a force that the force applicator applies, according to a test procedure, based on information captured by at least one sensor. The testing apparatus may include sensors for measuring a thickness of the battery, and thereby an amplitude of battery swelling, over time.

IPC Classes  ?

• H01M 10/42 - Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
• H01M 10/04 - Construction or manufacture in general
• H01M 10/48 - Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support workload management. A computing device may generate recommendation information for processing of the workload, the recommendation information associated with a first execution environment selected from the set of candidate execution environments based on execution information that indicates, for each candidate execution environment of the set of candidate execution environments, a carbon intensity value associated with the candidate execution environment. The computing device may output a first indicator that indicates the recommendation information.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

A code translation apparatus receives a source code including one or more code vulnerabilities and automatically generates remediated code. The source code provided to the code translation apparatus is converted to a source directional graph. The edges of the source directional graph are augmented with additional edge attributes. The source directional graph thus augmented is further converted into a source graph vector representation. The source graph vector representation is provided to an encoder of a trained code transformer. The remediated code is obtained from the decoder of the trained code transformer.

IPC Classes  ?

• G06F 8/41 - Compilation

Abstract

Implementations are directed to methods, systems, and apparatus for automated prioritization of cyber risk to digital identities. Actions include obtaining graph data defining a knowledge graph including nodes and edges, the nodes representing respective objects of the enterprise network including digital identities and resources, each node being associated with an explicit risk score and properties of the represented object, each edge representing a relation between objects; determining priority scores for the objects, including, for a first object represented by a first node: determining an implicit risk score for the first node; determining a total risk score for the first node; and determining a priority score for the first node based on the total risk score and properties associated with the first node; generating a ranking of the objects according to the priority scores; and providing, for presentation on a display, cyber security risk data indicating the ranking of the objects.

IPC Classes  ?

• H04L 9/40 - Network security protocols

Abstract

A data processing and analysis system that optimizes the resources to be used for data storage and refresh events. A partitioner module for a data analysis system can receive a first client criteria and a first client dataset that includes tabular data and calculate scores that are used to generate partitioning strategies. The selected partitioning strategy can be implemented to produce aggregated data that can be stored in an intelligent data mart. The partitions can then be accessed by a data visualization platform for intelligent, dynamic responses to user requests for data analyses and generation of visualizations. By providing synchronous partitioning of data (especially big data) and intelligent refresh, the data can move from the back-end to the front-end with minimal user clicks and minimal latency in performance.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/26 - Visual data miningBrowsing structured data

Abstract

A device may receive a three-dimensional (3D) computer-aided design (CAD) model, and may generate an assembly graph with nodes that represent components and edges that represent contact between the components. The device may generate component graphs for the components, and may generate an assembly descriptor based on the assembly graph and the component graphs. The device may process the assembly descriptor, with a graph convolution network model, to generate node embeddings, and may apply pooling to the node embeddings to generate graph embeddings. The device may calculate a cross attention between the components to generate component interrelations, and may utilize the graph embeddings and the component interrelations to predict links between the components. The device may predict poses and joint axes for the components, and may generate assembly instructions based on the graph embeddings, the component interrelations, the links, the poses, and the joint axes.

IPC Classes  ?

• B25J 9/16 - Programme controls

Abstract

Implementations include actions of receiving EHR data for a set of patients, defining a set of patient groups from the EHR data that is representative of a subset of patients, the set of patient groups being defined using a set of criteria, generating, for each patient group, a set of demographics triples and a set of medical triples, demographics triples including one or more links between patient groups and one or more demographics entities, and medical triples in the set of medical triples including one or more links between patient groups and one or more medical entities, providing a patients graph using the set of demographics triples and the set of medical triples, training a KGE model using a KG and the patients graph to provide a trained KGE model, and providing the trained KGE model for inference to predict likelihood that a link between entities is factually correct.

IPC Classes  ?

• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

Methods, systems, and computer-readable storage media for forecasting an engagement index score. A trained machine learning model is executed to forecast a set of engagement index score for a respective set of entities based on provided data for the set of entities. The data includes performance properties of each entity of the set. In response to the executing the trained machine learning model, a set of influencing factors is determined based on the engagement index scores of the set of entities. Actions are identified to be performed in association with the set of entities based on the identified influencing factors. The actions are provided for display at a display of a device.

IPC Classes  ?

• G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
• G06N 20/00 - Machine learning

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a cloud-computing system architecture. In some implementations, target performance criteria associated with an application executable at least in part on a cloud-computing system is obtained. Target process parameters related to processes associated with the execution of the application are generating using machine-learning models from the target performance criteria. Simulations of the execution of the application on respective ones of candidate computing systems are performed. Each candidate computing system includes a corresponding sequence of multiple architecture components at least a portion of which represents components of the clouding computing system. At least one of the candidate computing systems is selected that satisfies the target process parameters. Instructions are provided for deploying the selected computing system for execution of the application.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines

Abstract

Methods, systems, and computer-readable storage media for improving the accuracy of embedding vector generation for domain-specific text for purposes of semantic search and generative AI. A dictionary of domain-specific terms can be built to include embedding vectors generated for respective domain-specific terms using a pre-trained large language model. A list of domain-specific terms pertaining to a particular domain can be identified and textual content comprising a description or a definition of a respective domain-specific term can be obtained. A domain-adapted embedding vector for each domain-specific term can be generated based on the textual content for that term. The domain-specific dictionary can be built to include a combination of a domain-specific term, a corresponding domain-adapted embedding vector and the domain-specific term definition or description.

IPC Classes  ?

• G06F 16/33 - Querying
• G06F 40/242 - Dictionaries
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

The present disclosure tunes a generative AI model using synthetic data. A system implementing a generative AI model may receive first input data. The first input data may include one or more rules. Based on the first input data, the system may generate an item of first synthetic data that complies with the one or more rules. Additionally, the system may receive second input data. The second input data may include a negation of the one or more rules received in the first input data, referred to as a modified set of rules. Moreover, the second input data may include the item of the first synthetic data. Based on the second input data, the system may generate an item of second synthetic data that violates the one or more rules. Subsequently, the system may tune the generative AI model based on the first synthetic data and the second synthetic data.

IPC Classes  ?

• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning

Abstract

An artificial intelligence (AI) technique to process and query data pertaining to an enterprise. A user raises a request which is processed to predict a knowledge context area based on a predetermined structure of the enterprise. The knowledge context area is predicted from multiple knowledge context areas, on the basis of the received user request and a conversation history of the user in past. Further, a knowledge database is selected from multiple knowledge databases based on the user request and the predicted knowledge context. The knowledge databases include preprocessed data from multiple data sources. The knowledge database is queried on the basis of the user request related to the knowledge context to obtain a result and the result is then displayed as an output.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 5/02 - Knowledge representationSymbolic representation

Abstract

An automated system and method of printing designs. The system and method can manage print content by application of programmable inks to selected areas in the packaging where frequently modified designs are printed. Such an approach allows the area to be precisely activated to correspond to the target pattern of the desired design and permanently change color. In some embodiments, any change in the text on the packaging/label would then only need to be translated into a corresponding UV or heat pattern, thereby avoiding the production of a new print cylinder to print the changed design. The proposed embodiments are effective in reducing downtime during print operations as well as expanding the capacity of the print apparatus to dynamically respond to changes in print designs.

IPC Classes  ?

• B41J 29/393 - Devices for controlling or analysing the entire machine
• B41J 2/32 - Typewriters or selective printing mechanisms characterised by the printing or marking process for which they are designed characterised by selective application of heat to a heat sensitive printing or impression-transfer material using thermal heads
• B41J 2/47 - Typewriters or selective printing mechanisms characterised by the printing or marking process for which they are designed characterised by selective application of radiation to a printing material or impression-transfer material using the combination of scanning and modulation of light

Abstract

Systems and methods for defending an artificial intelligence model against an adversarial input are disclosed. The system may include an artificial intelligence model, such as a machine learning model. The system may include a transformation engine executable by one or more processors. The transformation engine may be configured to receive an input to the artificial intelligence model, and apply a pre-determined transformation set to the input to produce a transformed input. The transformation engine may be configured to generate a first output based on the input using the artificial intelligence model and may also apply the artificial intelligence model to the transformed input to produce a second output. The transformation engine may be configured to determine whether the input is associated with an adversarial attack based on a comparison of the first output and the second output. The system also facilitates generating transformation sets for defending against adversarial attacks.

IPC Classes  ?

• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A process circularity optimization system improves the circularity of industrial processes via a non-linear analysis of process attributes. A process input provides one or more target attributes to be optimized along with an objective function for improving the circularity of a manufacturing process. The input variables to be set to optimize the target attributes along with any constraints are accessed. Multiple input variable value combinations are generated via non-linear processing of the input variables and a combination of input variable values with the highest probability of success is implemented in the manufacturing process to optimize circularity.

IPC Classes  ?

• G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme

Abstract

Improved denoising and gene filtering systems and methods for single cell sequencing datasets using graph mining. The system encodes the dataset into a graph data structure including cell nodes, gene nodes, and edges representing the gene expression levels measured for each cell node. The graph data structure is then processed using a node embedding algorithm and a link prediction model to identify edges that should have been captured in the dataset. In addition, the graph data structure can be processed using a community detection algorithm to identify nodes of highly associated communities. Specificity gene scores can then be computed based on the communities to filter genes with greater accuracy.

IPC Classes  ?

• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G06F 30/20 - Design optimisation, verification or simulation
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

The present disclosure relates to a system, computer readable medium, and method for identifying and quantifying relationships amongst cells and genes. Aspects of Random Forest are leveraged in a way that is unconventional. Traditionally, Random Forest, a machine learning algorithm, is used to combine the output of multiple decision trees to reach a single result. However, in the disclosed system and computer implemented method using the structure of decision trees created by Random Forest to determine relationships between genes and cells. The relationships may include similarity between pairs of cells and/or interactions of genes within individual cells.

IPC Classes  ?

• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G06N 20/20 - Ensemble learning
• G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
• G16B 40/20 - Supervised data analysis
• G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated optimization of product management systems. In embodiments, automated optimization of component tools of the product management system is provided by automated evaluation and optimization of associated rules and metadata. In embodiments, metadata and rules may be associated to each other by an assessment engine. A recommendation engine may then identify non-compliant metadata, may determine a condition of the rule, and/or may generate recommendations for the rules based on the non-compliant metadata. Automated optimization of the product management system may include automated creation and mapping of decomposition relationships between commercial products and technical products. In embodiments, input data may be parsed into a set of object with unique attribute fields, which may then be validated. Validated data objects are then processed by an optimization engine that automatically creates and maps decomposition relationships from the validated data.

IPC Classes  ?

• G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06Q 30/0201 - Market modellingMarket analysisCollecting market data

Abstract

Methods, systems, and apparatus are provided for detecting and extracting data from a nested table. A contour image of a first table disposed within a cell of a second table from an input image is identified. The first table on the input image is masked to generate a masked image of the second table that omits the first table. Cells of the first table and the second table are detected by identifying a plurality of regions of interest (ROIs), determining a row of a plurality of cells based on a common y-coordinate among of the plurality of ROIs, and generating cell parameter information for each of the plurality of cells that identifies the y-coordinate, a size, and a relative position of each determined cell. An output file is generated based on the cell parameter information of the plurality of cells.

IPC Classes  ?

• G06V 30/146 - Aligning or centering of the image pick-up or image-field
• G06F 40/232 - Orthographic correction, e.g. spell checking or vowelisation
• G06V 30/148 - Segmentation of character regions
• G06V 30/18 - Extraction of features or characteristics of the image

Abstract

Systems and methods for providing an optimized customer fallout framework are disclosed. A system receives an input from a user via a digital platform, processes the input and historical data to extract a set of quantifiable features, determines an engagement stage from a plurality of engagement stages for the user based on the set of quantifiable features and an n-helix multi-dimensional model, and determines, via a deep learning model corresponding to a combination of the engagement stage and an advanced engagement stage, a set of positive drivers for the user to move from the engagement stage to the advanced engagement stage. Further, the system generate a multi-nodal network comprising a plurality of grids and aggregates the plurality of grids to generate a global grid for the determined set of positive drivers. Based on the global grid, the system dynamically generates personalized recommendations for the user.

IPC Classes  ?

• G06Q 30/0202 - Market predictions or forecasting for commercial activities
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06Q 30/0204 - Market segmentation

Abstract

A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

IPC Classes  ?

• G06F 8/73 - Program documentation
• H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference

Abstract

In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

IPC Classes  ?

• G06F 1/26 - Power supply means, e.g. regulation thereof
• G06Q 30/0601 - Electronic shopping [e-shopping]

Abstract

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a generative machine-learning model. The system obtains a plurality of training images, groups the training images into a plurality of image clusters, and for each respective image cluster, generates a respective set of instances of the generative machine-learning model based on the training images in the image cluster.

IPC Classes  ?

• G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
• G06T 11/00 - 2D [Two Dimensional] image generation
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features

Abstract

The disclosure below is directed to optical/photonic devices and systems configured to control polarization state of light for selective privacy protection in optical imaging. In particular, the various embodiments disclosed below provide an application of polarization manipulation techniques with multiple optical polarization filters to enable partial or full privacy while maintaining functioning of image-based spatial mapping and localization algorithms used in video devices such as virtual reality (VR), augmented reality (AR), and robot/drone cameras. In the disclosed example approaches, the various polarization filters are configured to allow no or negligibly small amount of light from the privacy protected regions of a scene into the cameras in a privacy mode and to reject 100% of all light and enable full privacy in a block mode. The disclosed approaches also dynamically account for orientation changes during operation of the camera devices such that privacy is dynamically protected in real-time.

IPC Classes  ?

• G02B 27/28 - Optical systems or apparatus not provided for by any of the groups , for polarising

Abstract

In some examples, digital twin-based floor layout generation may include receiving, for a floor plan that is to be generated, an activity map that includes movement of at least one user within a digital twin of a specified area. Based on the activity map, embedding vectors may be generated for each room type of a plurality of room types in the specified area. An input boundary feature map may be received. The floor plan may be generated based on an analysis of the embedding vectors for each room type of the plurality of room types and based on an analysis of the input boundary feature map.

IPC Classes  ?

• G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads

Abstract

A remote location monitoring system identifies a place of interest and a subset of artificial satellites that can capture images of the place of interest within a threshold period. The next available artificial satellite with image sensors that can capture images of the place of interest earliest is selected and the cache heating signal is transmitted to be stored in a cache associated with the next available image sensor. The cache heating signal activates particular image sensors for image capture and enables the transmission of meaningful images that enable monitoring the place of interest.

IPC Classes  ?

• G06V 20/13 - Satellite images
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

Methods, systems, and apparatus for multi-cloud network verification using quantum machine learning. In one aspect, a method includes obtaining, by a classical computer, network data from the network, wherein the network data comprises network monitoring data and network configuration data; processing, by the classical computer, the network data to generate data that represents invariant properties of the network; processing, by the classical computer, the network data to generate a multi-layer graph model of the network; processing, by a quantum computer, the data that represents invariant properties of the network and the multi-layer graph model of the network using a quantum machine learning decision engine to select one or more network verification mechanisms for the network; and initiating a live check of the network using the verification mechanisms to validate the network.

IPC Classes  ?

• G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
• G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent model selection for style-specific digital content generation. For example, a system that provides a digital content generation service may include a trained style detection model may receive reference digital content items from a user and extract a user style embedding that represents a style preference of the user. In some implementations, the reference digital content items may include text documents or images provided or selected by the user. The system may compare the user style embedding to a plurality of model style embeddings that each correspond to a respective generative artificial intelligence (AI) model to generate a ranked list of generative AI models. The system may access one or more highest ranked generative AI models from the ranked list to generate novel digital content based on a prompt from the user.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language
• G06T 11/00 - 2D [Two Dimensional] image generation
• G06V 30/22 - Character recognition characterised by the type of writing

Abstract

In some examples, source code differential pruning-based dataset creation may include receiving source code that includes at least one vulnerability and at least one remediation that remediates the at least one vulnerability, extracting at least one remediated section, and identifying each sentence of the remediated section. A plurality of clusters may be generated based on an analysis of each identified sentence of the remediated section to determine a score with respect to a specified cluster that includes the identified sentence. Further, a determination may be made as to whether the score is greater than a specified threshold. Each identified sentence for which the score is greater than the specified threshold may be designated as a relevant sentence. An auxiliary dataset may be generated based on a plurality of relevant sentences and include at least one relevant vulnerability and at least one relevant remediation that remediates the relevant vulnerability.

IPC Classes  ?

• G06F 8/72 - Code refactoring
• G06F 8/77 - Software metrics

Abstract

Systems and methods for generating digital content and selecting targets for presentation of digital content using artificial intelligence are disclosed. In one example, a nanosegment of customers can be selected as targets for digital content based on their propensity to accept offers for a given campaign. By incorporating a nanosegment-based classification of customers, there can be significantly reduced memory usage by the system, since the customer base will be reduced to a fewer number of groups, and each group more specifically targets traits for one cluster of customers. Attributes of the selected customers can be used to automatically design and generate digital content, such as personalized offers for distribution to the customers, including AI-generated taglines, content, and images. Feedback from each cycle of the campaign can be fed back into subsequent cycles to continuously improve performance and offer outcomes.

IPC Classes  ?

• G06Q 30/0242 - Determining effectiveness of advertisements
• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06Q 30/0204 - Market segmentation
• G06Q 30/0251 - Targeted advertisements

Abstract

Methods, systems, and apparatus are provided for generating an image. A personalized text prompt is generated by processing an input embedding using a transformer model followed by a first fully connected neural network. The input embedding comprises a multi-dimensional embedding vector associated with a user profile and a plurality of user items. A scored label set is generated identifying a user's preferences by processing a set of attributes for the plurality of user items using a second fully connected neural network. The image is generated by processing the personalized text prompt and the scored label set using a diffusion model.

IPC Classes  ?

• G06F 16/535 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/538 - Presentation of query results
• G06F 16/56 - Information retrievalDatabase structures thereforFile system structures therefor of still image data having vectorial format
• G06N 3/08 - Learning methods

Abstract

Artificial intelligence (AI)-based systems and methods for AI application development using codeless creation of AI workflows is disclosed. The system receives request for creating an artificial intelligence (AI)-based workflow from the user device. Further, the system obtains input data from data sources and pre-process the obtained data using AI based pre-processing model. Further, the system identifies plurality of AI and Generative AI service nodes to be executed on the pre-processed data. The system further generates an AI-based workflow by connecting AI and Generative AI service nodes. Further, the system generates a metadata for AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes. The system validates the metadata based on AI-based rules. Furthermore, the system determines actions to be performed on the metadata based on results of validation and performs the set of actions on the AI-based workflow. Additionally, the system deploys the AI-based workflow onto external system based on configuration parameters.

IPC Classes  ?

• G06N 3/0464 - Convolutional networks [CNN, ConvNet]

Abstract

A method and system for generating recommended data insights are disclosed. The method may include obtaining available Key Performance Indicators (KPIs) associated with an operation process from a KPI repository. The available KPIs may represent metrics that can be calculated based on data for the operation process stored in a data repository. The method may include identifying in-use KPIs comprising a subset of the available KPIs and calculating a data enthalpy metric for the operation process based on a number of the available KPIs and a number of the in-use KPIs. The method may further include obtaining an insight recommendation model trained to predict a significance of an available KPI not in use, executing the insight recommendation model to generate a KPI recommendation for the operation process based on the data enthalpy metric, and outputting the KPI recommendation via the user interface.

IPC Classes  ?

• G06Q 10/10 - Office automationTime management

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for dynamically allocating computing resources for processing data. In some implementations, a method includes obtaining, over a network, data from one or more units associated with a manufacturing plant indicating addition of a new process or asset to the manufacturing plant; determining an amount of processing resources for performing one or more calculations; generating one or more signals configured to commission the amount of processing resources as a combination of resources from (i) processing resources associated with a cloud-computing system and (ii) processing resources located at a site of the manufacturing plant; processing, by the combination of resources, the data obtained from the one or more units associated with the manufacturing plant to generate one or more performance indicators associated with the manufacturing plant; and providing, to a user device, the one or more performance indicators.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06Q 50/04 - Manufacturing

Abstract

The present disclosure describes methods and systems for quantifying certainty for a prediction based on a knowledge graph. The method includes receiving a target triple and a knowledge graph comprising a set of structured data and a set of certainty scores for the structured data; converting the target triple to an embeddings space according to neighborhood sampling by a neural network, wherein the embeddings space includes a set of point coordinates; generating a plausibility prediction for the target triple using a scoring function; repeating converting the target triple to the embedding space and generating another plausibility prediction for the target triple N times with dropouts to obtain N plausibility scores for the target triple, wherein N is an integer larger than one; generating a predicted plausibility score and a certainty score for the target triple; and outputting the predicted plausibility score and the certainty score.

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation
• G06N 3/047 - Probabilistic or stochastic networks

Abstract

A computer-implemented method, system, and non-transitory, computer-readable medium that performs operations including obtaining alerts representing signals in a computing system and corresponding feedback indicators, indicating an association of the alert for the represented signal. The computing system can connect to a computing platform that includes a data mining engine. The operations include identifying a first subset of negative alerts, determining a first set of alert attributes, determining a type of model to analyze the alert attributes for signals represented by the alerts and analyzing, by the model, the first set of alert attributes to identify a subset of alert attributes with likelihoods representing alert attributes that caused the negative association of the alert. The operations include filtering the alerts to exclude a second subset of the alerts based on the likelihood of negative association, and providing for output, a set of alerts that exclude the second subset of the alerts.

IPC Classes  ?

• G06F 16/26 - Visual data miningBrowsing structured data
• G08B 29/18 - Prevention or correction of operating errors
• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor

Abstract

An automated machine learning-based system and method of managing content for data migrations. The system can process bulk tabular datasets and real-time legacy application parameters. The data can then be segmented into segments based on an estimated time for execution of the data migration for the given dataset generated by a machine learning model. In some embodiments, the system can automatically generate a code that can perform the data migration based on the proposed segment segmentation. The proposed embodiments are effective in reducing downtime during migrations as well as limiting the impact of failure events on the process.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

Intelligent AI-based systems and methods of generating architecture diagrams for cloud computing-based infrastructures. The system can ingest and process requirement data and identify intents associated with the software. Based on the classifications of the requirement data, the system can automatically extract dependencies between software layers and microservices in order to identify the most appropriate components for the application. In some embodiments, validation can be performed in which a digital twin model is implemented. Implementation of such as system can eliminate manual errors and variability based on human skill sets, as well as enable risk-free testing of the architecture based on the application goals.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation

Abstract

An application development data processing system identifies one or more of a plurality of criteria associated with the development of a software application that do not meet standards and provides recommendations for improvement of the lagging criteria Responses to questions about the plurality of criteria are clustered and queries are generated from a set of response clusters. The queries are executed to extract information from external data sources and a set of result clusters are generated from the extracted information. The maturity levels of the plurality of criteria for the software application development are determined based on a comparison of the maturity levels of the set of response clusters and the set of result clusters. Recommendations are output to improve the maturity levels of the lagging criterion

IPC Classes  ?

• G06F 8/77 - Software metrics
• G06F 16/33 - Querying
• G06F 16/332 - Query formulation

Abstract

A vulnerability detection and management system that identifies differences between a current version and updated version of code to determine potential impacts on a software application. The system continuously monitors a third-party library to detect whether new versions of a particular code are released. When such an event occurs, the system can automatically identify the changes and determine whether the differences are substantive. A vulnerability impact assessment can then be generated detailing the likely effects of any identified differences that can serve as guide to end-users when making decisions regarding updates and upgrades or migrations of their software.

IPC Classes  ?

• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 8/65 - Updates

Abstract

The systems and method described provide a dynamic and automated process for producing test data for cloud migrations. Metadata for transformation designs with attribute classification and relationship data can be used to generate an enriched design-driven data set. The test data can then be used to validate infrastructure provisioned in multi-cloud environments and accommodate most cloud providers. The system can avoid tedious testing cycles that use non-relevant test data (i.e., unrelated to the specific design requirements).

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns

Abstract

Intelligent machine learning-based systems and methods of generating recommendations for individualized skill development. The system and method offer an automated framework by which organizations can easily pre-screen bulk profiles, hire based on region and projects, and provide employees with a skill upgrade program to enhance their capabilities. The system can use natural language processing techniques to ingest and process candidate and job data and machine learning techniques to identify the degree to which the skills of each candidate match the organization's available roles. In some embodiments, the framework facilitates a bench reduction program that helps optimize the workforce by identifying underutilized employees and help organizations build a strong, capable workforce that can achieve their current and future goals.

IPC Classes  ?

• G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/1053 - Employment or hiring

Goods & Services

(1) Downloadable software for use in deepfake and cybersecurity fraud detection and remediation; downloadable cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; downloadable cybersecurity software used for identification verification and multi-factor identification authentication (1) Education and training services, namely, provision of materials and associated training on the topics of deepfake technology, synthetic media created via the use of artificial intelligence technology, and threats to organizations and individuals created by the malicious use of deepfakes and synthetic media (2) Providing temporary use of online, non-downloadable software for use in deepfake and cybersecurity fraud detection and remediation; software as a service (saas) services featuring cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; technical consulting services provided in the fields of deepfake technology, synthetic media, and cybersecurity; technical services provided in the field of cybersecurity, namely, services to identify security threats to organizations and individuals resulting from the use of fraudulent content and communications employing deepfake and synthetic media technology; providing temporary use of online, non-downloadable cybersecurity software used for identification verification and multi-factor identification authentication

Goods & Services

Downloadable software for use in deepfake and cybersecurity fraud detection and remediation; downloadable cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; downloadable cybersecurity software used for identification verification and multi-factor identification authentication. Education and training services, namely, provision of materials and associated training on the topics of deepfake technology, synthetic media created via the use of artificial intelligence technology, and threats to organizations and individuals created by the malicious use of deepfakes and synthetic media. Providing temporary use of online, non-downloadable software for use in deepfake and cybersecurity fraud detection and remediation; software as a service (saas) services featuring cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; technical consulting services provided in the fields of deepfake technology, synthetic media, and cybersecurity; technical services provided in the field of cybersecurity, namely, services to identify security threats to organizations and individuals resulting from the use of fraudulent content and communications employing deepfake and synthetic media technology; providing temporary use of online, non-downloadable cybersecurity software used for identification verification and multi-factor identification authentication.

Goods & Services

Downloadable software for use in deepfake and cybersecurity fraud detection and remediation; downloadable cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; downloadable cybersecurity software used for identification verification and multi-factor identification authentication Providing temporary use of online, non-downloadable software for use in deepfake and cybersecurity fraud detection and remediation; software as a service (saas) services featuring cybersecurity software, namely, software employing artificial intelligence technology to detect and remediate the risks caused by use of deepfakes, synthetic media, and other images, video, and audio generated by artificial intelligence; technology consultation in the fields of deep fake computer technology, synthetic media, and cybersecurity; technology consultation in the field of cybersecurity, namely, security threat analysis for protecting data in order to identify security threats to organizations and individuals resulting from the use of fraudulent content and communications employing deepfake and synthetic media technology; providing temporary use of online, non-downloadable cybersecurity software used for identification verification and multi-factor identification authentication

Abstract

Systems and methods for providing a digital human in a virtual space are disclosed herein. A system receives, from a user via a digital platform, a selection of a persona, for a digital human, from a plurality of personas. Further, the system receives, in real-time, an input from the user via the digital platform, and identifies a set of parameters associated with the input. Furthermore, the system determines, from a knowledge database, a response information based on the context of the input, and generates, using a repository, a set of attributes for the response information based at least on the set of parameters associated with the input and the persona selected by the user. Additionally, the system aggregates the set of attributes and the response information to generate a personalized response to the input, and renders the personalized response by the digital human on the digital platform.

IPC Classes  ?

• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06F 16/2457 - Query processing with adaptation to user needs

Abstract

Implementations for receiving an integrated digital twin including multiple digital twins, each digital twin including a computer-executable model of a real-world system used to execute a portion of a process, within the integrated digital twin, a first digital twin providing output to generate input to a second digital twin, receiving enterprise data, the enterprise data being provided from a set of real-world systems used to execute the process, executing, by the integrated digital twin module, simulations of the process using the integrated digital twin and one or more agent-based models based on the enterprise data, at least one agent-based model providing input to the first digital twin, determining simulation results from the simulations, the simulation results including a value of at least one objective function, and adjusting one or more parameters of at least one real-world system used to execute the process based on the simulation results.

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

A device may receive source code and a database to be migrated to a cloud computing environment, and may perform a first stage analysis of the source code to generate a first report. The device may cause a second stage analysis of the source code to be performed based on the first report and to generate refactored and rewritten code, and may perform a third stage analysis of the database to generate a second report. The device may cause a fourth stage analysis of the database to be performed and to generate a refactored and rewritten database, and may perform the first stage analysis of the refactored and rewritten code and the third stage analysis of the refactored and rewritten database to generate a final report. The device may generate a migration strategy based on the final report and may perform actions based on the migration strategy.

IPC Classes  ?

• G06F 8/72 - Code refactoring
• G06F 8/73 - Program documentation
• G06F 8/76 - Adapting program code to run in a different environmentPorting

Abstract

The present disclosure relates to a system, computer readable medium, and method for applying hyperdimensional computing and dimension reduction to extract properties from a sparse data set. Applying hyperdimensional computing can solve issues of dimensionality and dropout causing sparse data by expanding the dimension of the data. The result of hyperdimensional computing can involve too much data to be reasonably suitable for downstream computing processes (e.g., clustering for classification). Transforming the hyperdimensional embeddings provided by hyperdimensional computing into simplified/reduced embeddings can solve the problems of processing extremely large data. This improvement in accuracy and usefulness/useability of the sparse data helps reduce the need for extensive time, computing resources, and expensive equipment to extract expression data from deeper from cells.

IPC Classes  ?

• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 40/20 - Supervised data analysis
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Abstract

Systems and methods for exchanging data between a set of microservices are disclosed herein. A system may establish an event queue-based persistent connection between a set of microservices, where the set of microservices may include a requesting microservice and a providing microservice. Further, the system may receive a request in a queue of a plurality of requests from the requesting microservice, and retrieve, from the queue, a pre-determined number of requests based on a weighting score associated with each of the pre-determined number of requests. Furthermore, the system may process the pre-determined number of requests in parallel, generate a response to the received request based on the processing, and provide the generated response to the requesting microservice.

IPC Classes  ?

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

Abstract

In some implementations, a user device may receive input indicating metaverse content. Accordingly, the user device may transmit, to a remote service, a request for the metaverse content. The user device may additionally transmit, to the remote service, at least one capability indication associated with the user device and at least one preference associated with a user of the user device. The user device may receive, from the remote service and in response to the request, one or more packets encoding the metaverse content. A format of the metaverse content is based on the at least one capability indication and the at least one preference.

IPC Classes  ?

• H04L 67/131 - Protocols for games, networked simulations or virtual reality
• H04L 67/565 - Conversion or adaptation of application format or content

Abstract

A method and system for evaluating and enhancing a prompt for use by a generative artificial intelligence processing model are disclosed. The method may include obtaining a prompt representing a natural language text for use by a generative artificial intelligence processing model, obtaining a prompt classifier trained to evaluate a classification of the prompt, and inputting the prompt to the prompt classifier to generate a classification of the prompt. The method may further include identifying an intent underlying the prompt and detecting an implicit constraint for the prompt based on the intent. The method may further include transforming the intent in the prompt into a constraint-enhanced intent based on the implicit constraint, generating an enhanced prompt based on the constraint-enhanced intent, and outputting the enhanced prompt for the generative artificial intelligence processing model.

IPC Classes  ?

• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks

Abstract

A device may receive, from a user device, a machine learning model, training data, and user input for the machine learning model, and may process the training data and the user input, with the machine learning model, to generate a prediction and an explanation of the prediction. The device may provide the prediction and the explanation to the user device and may receive, from the user device, prediction feedback for the prediction and explanation feedback for the explanation. The device may determine whether an agreement is achieved between the prediction feedback and the explanation feedback based on a threshold and may update the machine learning model based on the agreement being achieved. The device may cryptographically protect the updated machine learning model to generate an updated and cryptographically protected machine learning model and may perform actions based on the updated and cryptographically protected machine learning model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence

Abstract

Implementations for training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set, providing an artificial neural network (ANN) including multiple hidden layers, at least one hidden layer including at least a portion of an encoder of the DAE, the at least a portion of the encoder comprising parameters determined during training of the DAE, training the ANN using a training dataset, and providing a version of the ANN for inference.

IPC Classes  ?

• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06N 3/09 - Supervised learning

Abstract

The disclosed system and method provide an artificial intelligence (AI) model trained using clustering and reinforcement learning. Data in a dataset can be loaded for de-identification, along with data scope answers. The data can be audited, and once audited, the audited data and the data scope answers can be provided to a strategy recommendation engine including the trained AI model. The engine can determine a cluster corresponding to the dataset and assesses strategies for data de-identification based on the determined cluster. The strategies can be ranked and provided as output, providing the ability to better de-identify the dataset by indicating which techniques will be the most effective. Additionally, the system and method can automatically implement a top-ranked strategy satisfying certain criteria as a determined optimal approach for data de-identification. Clustering and reinforcement learning may efficiently and automatically glean information from unlabeled data. Feedback-based retraining may improve performance further.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support machine learning-based application management for enterprise systems. The aspects described herein enable resource and time-efficient scheduling of training anomaly detection models (e.g., machine learning (ML) models) corresponding to the applications based on log data generated by the applications. Aspects also provide integration of the trained anomaly detection models with an application dependency graph to enable prediction of application failures based on detected anomalies and relationships between applications determined from the application dependency graph. Further aspects leverage this integration to output reasons associated with predicted application failures and to provide recommended recovery actions to be performed to recover from the predicted application failures. Other aspects and features are also described.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

Implementations include methods, systems, computer-readable storage medium for compiling ontologies. A method for providing a composite ontology from a plurality of base ontologies, each ontology being provided as a computer-readable data structure, includes: identifying a plurality of base ontologies; and combining the plurality of base ontologies to generate a composite ontology by, automatically: comparing entity names of classes of a first base ontology to classes of a second base ontology, and determining that an entity name of a first class of the first base ontology matches an entity name of a second class of the second base ontology, and in response, providing a class within the composite ontology that represents the first class and the second class at least partially by determining a union of data properties, object properties, and cardinality restrictions of the first class and the second class for the class.

IPC Classes  ?

• G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
• G06F 40/279 - Recognition of textual entities

Abstract

Implementations for receiving, by DSF system, data representative of a set of constants, determining, by the DSF system, data representative of a set of predictions, at least a portion of predictions being determined from a set of ML models, optimizing, by the DSF system, a value of an objective function subject to a set of constraints, the value of the object function being optimized for a time interval based on a set of constants, the set of predictions, and a set of variables, providing, by the DSF system, the set of variables as output of optimizing the value of the objective function, and transmitting, by the DSF system, instructions to a set of assets of the power grid to provision power based on values of at least a sub-set of variables in the set of variables, the set of assets at least partially comprising a set of EVs.

IPC Classes  ?

• B60L 55/00 - Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• G06Q 50/06 - Energy or water supply

Abstract

Systems and methods that support functionality for performing disassembly sequence planning are disclosed. The systems and methods may obtain 3D model data for a product to be disassembled and may convert the 3D model to one or more graph-based representations. An encoder is provided to generate a set of embeddings based on the one or more graph-based representations. A decoder is provided to generate disassembly data based on the set of embeddings. In an aspect, the disassembly data may indicate an order in which each component is to be removed during disassembly of the product and a direction in which each component should be removed.

IPC Classes  ?

• G06F 30/17 - Mechanical parametric or variational design
• 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
• G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks

Abstract

Systems and methods for interpreting a user's brain wave data from EEGs using machine learning algorithms are described. In one example, a brain activity interpretation system classifies the EEG recording into five separate component signals, representing the five categories of brain waves (alpha, beta, theta, delta, gamma). In one example, the most dominant component signal is analyzed to determine whether the amplitude is higher or lower than an optimal range within the bandwidth for that brain wave. The system can then provide intelligent recommendations to the user for beneficial action(s) to help improve their everyday functioning and promote better regulation of their brain states. The system can help the user become more aware of their mental state and how to improve their mental state. EEG data is highly complex and unsuitable for immediate human comprehension, thus, the disclosed systems and methods improve the speed and accuracy of brain wave analysis.

IPC Classes  ?

• A61B 5/369 - Electroencephalography [EEG]
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons

Abstract

Systems and methods for predicting a person's likelihood of a situational mental health condition (such as post-partum depression (PPD)) diagnosis using machine learning models are described. In one example, a mental health risk assessment system receives data at recurring intervals for a person during their pregnancy and in the year after their pregnancy. Based on both the recurring data for the person as well as the specific time period at which the data is obtained, a machine learning model can generate highly accurate mental health predictions. The mental health risk assessment system can further implement a software application that manages the flow of information from patients as well as presentation of information to the patients. Furthermore, the application can present targeted information for a patient to the patient's doctor or other medical personnel/facility.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems