Abstract

A method of classifying a dataset based on license risk analysis includes: identifying a context including at least one license clause for each of datasets; determining one of predefined license classification classes for each of the datasets based on the identified context using an artificial intelligence model; associating the determined classification class with an identifier of each of the datasets and storing the datasets in memory; receiving an input signal for inputting condition information including at least one target dataset or target classification class that is a target of license risk analysis; loading at least a part of data stored in the memory through the artificial intelligence model, generating at least one dataset or classification class matching the condition information as recommendation information, and storing the generated recommendation information in the memory; and expressing the generated recommendation information through a user interface.

IPC Classes  ?

• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06F 18/2431 - Multiple classes

Abstract

A system for training an intent classification model using intent descriptions may generate a dataset including intent descriptions of the intent classification model from a language model, perform intent classification by using at least a portion of a dataset as an input prompt for the intent classification model, and determine performance of the intent classification model by using at least a portion of the dataset as an input prompt for the intent classification model. The dataset may include first data including an independent intent description written independently from other intent descriptions and second data including a dependent intent description written dependently on other intent descriptions.

IPC Classes  ?

• G06F 16/903 - Querying
• G06F 16/906 - ClusteringClassification

Abstract

A method executed by a computer includes: obtaining first input information specifying predetermined domain characteristics; obtaining a validity index that quantitatively indicates difficulty in generating output data of the multi-tasking model according to the first input information; generating first guide information specifying the domain characteristics for reducing the generation difficulty based on the obtained validity index; and providing the first guide information. The method further include: obtaining input information specifying predetermined domain characteristics; converting the input information into low-dimensional latent variables represented in a low-dimensional Gaussian space; obtaining sampled latent variables obtained by sampling the converted low-dimensional latent variables; obtaining optimized latent variables obtained by optimizing the obtained sampled latent variables through a genetic algorithm; restoring the optimized latent variables into high-dimensional latent variables represented in a high-dimensional space; and providing the restored high-dimensional latent variables to the multi-tasking model.

IPC Classes  ?

• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/045 - Combinations of networks
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn

Abstract

A system for training an intent classification model using intent descriptions may generate a dataset including intent descriptions of the intent classification model from a language model, perform intent classification by using at least a portion of the dataset as an input prompt for the intent classification model, and determine performance of the intent classification model by using at least a portion of the dataset as an input prompt for the intent classification model. The dataset may include first data including independent intent descriptions in which intent names and intent descriptions for the intent names are mutually indistinguishable and second data including dependent intent descriptions in which intent names and intent descriptions for the intent names are mutually distinguishable.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 40/30 - Semantic analysis
• G06N 5/046 - Forward inferencingProduction systems

Abstract

An asset portfolio prediction method performed by a computing system comprise determining a target prediction task which is data specifying an objective task; collecting raw data to perform the determined target prediction task; aligning the collected raw data in a time-series on a specific time axis; training a predetermined time-series prediction model based on the aligned raw data and the determined target prediction task; predicting daily portfolio asset weight data over a predetermined prediction unit period based on the trained time-series prediction model; generating a single set of portfolio asset weight data by processing the predicted daily portfolio asset weight data at each predetermined rebalancing time; and providing the generated single set of portfolio asset weight data.

IPC Classes  ?

• G06Q 40/06 - Asset managementFinancial planning or analysis
• G06N 20/00 - Machine learning

Abstract

A method executed by a computer, the method includes: loading in-distribution inspection data stored in at least one memory; inputting the loaded in-distribution inspection data into at least one artificial intelligence model, and obtaining process-based noisy inspection data by noising the in-distribution inspection data up to a specific time-step; obtaining a semantic mask which is a mask specifying a semantic element for the in-distribution inspection data, and a nuisance mask which is a mask specifying a nuisance element for the in-distribution inspection data, from the noisy inspection data; generating out-of-distribution inspection data by applying selective data processing guidance configured to preserve the nuisance element and modify the semantic element based on the semantic mask and the nuisance mask; and outputting the out-of-distribution inspection data through at least one interface.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06N 3/0475 - Generative networks
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/08 - Learning methods
• G06T 7/12 - Edge-based segmentation

Abstract

A system for compressing molecular data according to the present invention comprises: a preprocessing unit for converting a plurality of pieces of original molecular data into molecular graphs, respectively; and a molecular compression unit which searches for a maximum common substructure between a first molecular graph and a second molecular graph among the molecular graphs, merges the first and second molecular graphs on the basis of the searched maximum common substructure to generate an integrated molecular graph, verifies whether each of atoms constituting the integrated molecular graph satisfies the valence rule, and tags each node of the integrated molecular graph for which verification has been completed with identifier information for identifying an original molecule from which the node is derived.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/90 - Programming languagesComputing architecturesDatabase systemsData warehousing
• G16C 20/80 - Data visualisation
• G16C 20/40 - Searching chemical structures or physicochemical data
• G16C 20/20 - Identification of molecular entities, parts thereof or of chemical compositions
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/045 - Combinations of networks
• G06N 3/0464 - Convolutional networks [CNN, ConvNet]

Abstract

An artificial intelligence system includes: one memory configured to store instructions; and one processor configured to execute the instructions to encode music data to generate input tokens from input digital music protocol data and store the input tokens in the one memory; access a data structure in the one memory to load the input tokens; generate one data representation by concatenating a token embedding result converted from the loaded input tokens and a structural embedding result comprising one of a part embedding, a type embedding, a time embedding, and a pitch class embedding; ingest the data representation into at least one artificial intelligence model executed by the one processor; infer, by the one artificial intelligence model, a next output token based on the data representation to generate symbolic music data; and manifest the generated symbolic music data through a user interface.

IPC Classes  ?

• G10H 1/00 - Details of electrophonic musical instruments
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G10G 1/00 - Means for the representation of music

Abstract

A method and system for training a language model train a language model without a language degeneration phenomenon of the language model. The method comprises: specifying a first text sequence to be input into the language model to fine-tune the language model; processing the first text sequence as input to the language model; generating, by the language model, a second text sequence including a plurality of tokens based on the first text sequence; storing the second text sequence generated by the language model in a memory; performing masking on the second text sequence stored in the memory; and fine-tuning the language model using the second text sequence on which the masking is performed.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language

Abstract

A method and system for unlearning of a large language model perform operations comprising specifying data to be forgotten and data to be retained for a pre-trained large language model in a training dataset stored in memory; specifying a parameter having a high importance based on a preset reference criterion for the data to be forgotten among a plurality of parameters of the large language model by comparing the data to be forgotten and the data to be retained; initializing a weight of low-rank adaptation based on the parameter having the high importance for the data to be forgotten; and performing unlearning on the large language model based on the weight of the low-rank adaptation.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers

Abstract

A system generates prediction data by processing an input sequence segmented along a time axis based on a predetermined period using neural networks. The neural networks comprise a first neural network configured to apply dilated attention to the input sequence segmented along the time axis based on the predetermined period, and a second neural network configured to apply random partition attention to data arranged along a feature axis.

IPC Classes  ?

• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

A method and system for unlearning of a large language model perform operations comprising specifying data to be forgotten and data to be retained for a large language model trained with a training dataset; specifying at least one parameter having a high importance based on a preset criterion for the data to be forgotten among a plurality of parameters of the trained large language model by using the data to be forgotten and the data to be retained; initializing a weight of a preset adapter based on an importance of the specified parameter; and performing unlearning on the trained large language model to which the initialized weight of the preset adapter is applied.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods

Abstract

A method and system perform operations comprising: setting a first instruction tuning dataset comprising tasks in a first language; setting a second instruction tuning dataset comprising tasks in a second language; generating a first instruction, written in a same language as the first language, for the first instruction tuning dataset, and storing the first instruction for the first instruction tuning dataset; generating a second instruction, written in a same language as the second language, for the second instruction tuning dataset, and storing the first instruction for the first instruction tuning dataset; generating a cross-language instruction based on the first instruction tuning dataset, the first instruction for the first instruction tuning dataset, the second instruction tuning dataset, and the second instruction for the second instruction tuning dataset; and performing instruction tuning for an artificial intelligence model using the cross-language instruction.

IPC Classes  ?

• G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
• G06N 20/00 - Machine learning

Abstract

A foundation model for performing molecular-level tasks by learning multimodal data in the form of one-dimensional text and two-dimensional graphs, and a system therefor, according to an embodiment of the present invention, enable various molecular-unit tasks such as chemical reaction prediction, molecular attribute prediction, and natural language description generation to be effectively processed through a single foundation model. In addition, it is possible to increase prediction accuracy of the model by maximizing utilization of two-dimensional molecular graph information, and automatically generate, on the basis of statistical sparsity, high-quality descriptive text that emphasizes core and distinctive features of each molecule.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/90 - Programming languagesComputing architecturesDatabase systemsData warehousing
• G16C 20/50 - Molecular design, e.g. of drugs
• G16C 20/40 - Searching chemical structures or physicochemical data
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/096 - Transfer learning
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06F 16/334 - Query execution

Abstract

The objective of one embodiment of the present disclosure is to provide a system comprising: one or more processors; an artificial intelligence prediction model; and one or more memories that collectively store instructions that, when executed by the one or more processors, instruct a computing system to perform operations. The operations may comprise: generating a plurality of observation data by sampling past data with an arbitrary time distribution; generating a propagation signal of each of the plurality of observation data based on mean field theory; determining a predicted value by aggregating propagation signal calculation results for the plurality of observation data; determining a loss value on the basis of the difference between the predicted value and an actual value; and training the artificial intelligence prediction model until the loss value is equal to or less than a preset value.

IPC Classes  ?

• G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06N 3/0499 - Feedforward networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06F 16/242 - Query formulation
• G06F 16/248 - Presentation of query results
• G06F 16/22 - IndexingData structures thereforStorage structures

Abstract

An embodiment of the present disclosure may provide a system comprising: at least one processor; and at least one memory that stores instructions, when executed by the at least one processor, causing the system to perform operations, wherein the operations comprise: an operation of acquiring an observation sequence; an operation of generating, with the observation sequence and time information as inputs, a latent sequence at a first time point by using an encoder module; an operation of generating a latent sequence at a future time point by inputting, into a first linear operation module, a first parameter regarding coefficients of a linear probability differential equation and the latent sequence at the first time point; and an operation of converting the latent sequence at the future time point into a prediction sequence and outputting same, by using a decoder module.

IPC Classes  ?

• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06F 16/242 - Query formulation
• G06F 16/248 - Presentation of query results
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 20/00 - Machine learning
• G06F 123/02 - Data types in the time domain, e.g. time-series data

Abstract

An embodiment of the invention is directed to providing a system including at least one processor, an artificial intelligence prediction model, and at least one memory storing one or more instructions that, when executed by the at least one processor, cause the at least one processor to perform operations. The operations may include generating a plurality of observation data by sampling past data with an arbitrary time distribution, generating a propagation signal associated with each of the plurality of observation data based on mean-field theory, determining a predicted value by aggregating calculation results of the propagation signals associated with the plurality of observation data, determining a loss value based on a difference between the predicted value and a true value, and training the artificial intelligence prediction model until the loss value becomes less than or equal to a predetermined value.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

The present disclosure relates to a method and a system for providing a multi-step inference artificial intelligence agent service. According to an embodiment, the system may dynamically reconfigure an agent persona on the basis of user meta information and decompose a query into a plurality of subtasks to establish an execution plan. In addition, a data analysis task is executed in a sandbox environment, and execution integrity can be ensured through a self-correction loop that corrects a code by itself when an error is detected. Furthermore, it is possible to maximize work automation efficiency by providing reliability-based search results and analysis content as artifacts in the form of editable native office objects.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/09 - Supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/092 - Reinforcement learning

Abstract

The present disclosure relates to a method for training a generative artificial intelligence model having a hybrid attention architecture, and a system for providing content on the basis of inference control by using same. A method for providing content, according to one embodiment of the present disclosure, comprises the steps of: analyzing a query received from a user terminal, so as to determine an execution mode to be a non-inference mode or an inference mode; and, according to the determined mode, generating a response by using an artificial intelligence model having a hybrid attention mechanism. The artificial intelligence model has a hybrid structure in which a global attention layer and local attention layers are mixed and arranged in a preset ratio, and includes a rearrangement layer normalization structure, thereby ensuring both long context processing efficiency and learning stability. In addition, the model can be in a state of having been trained through integrated mode fine-tuning in which inference data and non-inference data are mixed and reinforcement learning based on global gain policy optimization.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/09 - Supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/092 - Reinforcement learning

Abstract

The present disclosure relates to an artificial intelligence model having a hybrid attention-based mixture-of-experts architecture and an operating method thereof. The operating method of an artificial intelligence model according to an embodiment of the present disclosure includes a step of converting input data into an embedding vector, and calculating the embedding vector through the artificial intelligence model to generate a final output. The artificial intelligence model includes a mixture-of-experts block group that selectively utilizes some of a plurality of experts, and a hybrid attention layer that combines and performs local attention and global attention. According to the present disclosure, computational efficiency and learning stability of a large-scale parameter model can be simultaneously secured through a mixture-of-experts network architecture of a sandwich structure, and computational complexity during long context processing can be significantly reduced through sliding window-based hybrid attention.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/09 - Supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/092 - Reinforcement learning

Abstract

A method and system for large language model alignment may search for high-quality responses among various responses of a language model using a self-reflection mechanism and improve the performance of the language model based on the high-quality responses.

IPC Classes  ?

• G06F 40/279 - Recognition of textual entities

Abstract

A prediction system may predict valid customer companies or valid customers in business-to-business (B2B) and/or business-to-consumer (B2C) sales situations. A computerized learning method of a prediction system may comprise specifying a train dataset including a plurality of records having values for a plurality of different categories; classifying the plurality of records included in the train dataset based on at least one value corresponding to a target category among the plurality of different categories; configuring a plurality of different sub-datasets based on indexes corresponding to the plurality of the classified records; and training at least one target prediction model using each of the plurality of different sub-datasets.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06Q 30/02 - MarketingPrice estimation or determinationFundraising

Abstract

An embodiment provides a method comprising the steps of: receiving an activation signal for accessing data in at least one memory; accessing a data structure in the at least one memory according to the reception of the activation signal, wherein the data structure includes a diagnostic image; loading the diagnostic image from the at least one memory; generating, by at least one processor, at least one response with respect to the diagnostic image by using at least one artificial intelligence model using the diagnostic image as an input, wherein the at least one artificial intelligence model is pre-trained to perform image analysis on the basis of a feature representation for a causal dependency relationship between a low-magnification feature and a high-magnification feature of the diagnostic image; and inputting (ingesting) the at least one response to at least one subsequent processing component.

IPC Classes  ?

• G06T 7/00 - Image analysis
• 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
• G06N 20/00 - Machine learning

Abstract

An embodiment provides a method comprising the steps of: receiving an activation signal for accessing data in at least one memory; accessing a data structure in the at least one memory according to the reception of the activation signal; loading a diagnostic image from the at least one memory; generating, by at least one processor, at least one response with respect to the diagnostic image by using at least one artificial intelligence model using the diagnostic image as an input; and inputting the at least one response to at least one subsequent processing component.

IPC Classes  ?

• G06T 7/00 - Image analysis
• 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
• G06N 20/00 - Machine learning

Abstract

The present invention relates to a method and a system for providing an agent based on multi-language model inference. More specifically, the present invention relates to a method and a system for providing an agent based on multi-language model inference, and provides a method and a system for providing an agent based on multi-language model inference, in which a language model generates a plurality of inference paths and can select, from among same, a path having the highest probability of achieving a goal.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

The present invention relates to a data generation system and a control method thereof, and provides: a data generation system for automatically generating data specialized for at least one domain using an artificial intelligence model; and a control method thereof.

IPC Classes  ?

• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06N 3/0475 - Generative networks
• G06N 3/096 - Transfer learning
• G06Q 10/10 - Office automationTime management
• G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation

Abstract

The present invention relates to a data generation system and a control method thereof, and provides a data generation system and a control method thereof, in which the data generation system automatically generates, using an artificial intelligence model, domain-specific data and data reflecting user preference.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/096 - Transfer learning
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06F 16/3329 - Natural language query formulation
• G06Q 10/10 - Office automationTime management
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

Abstract

The present invention relates to a method and system for image detection, and provides a method and system for image detection, which can distinguish between a real image and an artificially generated image by using an artificial intelligence model.

IPC Classes  ?

• G06V 20/00 - ScenesScene-specific elements
• G06V 40/40 - Spoof detection, e.g. liveness detection
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

The present invention relates to an image recognition performance optimization method and system, and provides an image recognition performance optimization method and system, in which a visual token is re-tokenized in units of semantics in order to improve image recognition efficiency.

IPC Classes  ?

• G06T 5/00 - Image enhancement or restoration
• G06T 7/10 - SegmentationEdge detection
• G06F 40/279 - Recognition of textual entities

Abstract

An embodiment of the present disclosure relates to a method and system for generating a training dataset through data consistency analysis and providing an artificial intelligence service by using same, wherein, in a training dataset environment that may include noise labels, feature consistency, structure consistency, and gradient consistency of data are analyzed in an integrated manner so as to detect and correct noise labels and suppress the influence thereof.

IPC Classes  ?

• G06N 3/09 - Supervised learning
• G06N 3/047 - Probabilistic or stochastic networks
• G01N 21/88 - Investigating the presence of flaws, defects or contamination
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/096 - Transfer learning

Abstract

A Bayesian integrated vision inspection method and a system thereof according to one embodiment of the present disclosure implement a vision inspection framework which integrates and processes various types of data and labels by modeling class-specific distributions of patch embeddings extracted from vision data and classifying the patch embedding through inference using Bayes' theorem.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/10 - SegmentationEdge detection
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• G01N 21/88 - Investigating the presence of flaws, defects or contamination
• G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration

Abstract

A vision inspection method based on coarse-to-fine patch level classification and a system thereof according to one embodiment of the present disclosure relate to a vision inspection method based on coarse-to-fine patch level classification and a system thereof which simultaneously improve the accuracy and processing speed of vision inspection by coarsely detecting a region suspected of being defective from given vision data and finely classifying the detected region on the basis of a pre-built data pool.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/11 - Region-based segmentation
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G01N 21/88 - Investigating the presence of flaws, defects or contamination
• G06V 10/40 - Extraction of image or video features
• G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• G06N 3/08 - Learning methods

Abstract

A vision inspection camera parameter setting automation method and system according to an embodiment of the present disclosure relate to a vision inspection camera parameter setting automation method and system which quantitatively measure a distance between features extracted from vision data of good (OK) and defective (NG) samples, and automatically optimize parameters of a vision inspection camera by searching for a point that maximizes the measured distance.

IPC Classes  ?

• H04N 23/61 - Control of cameras or camera modules based on recognised objects
• H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/096 - Transfer learning
• G06N 3/09 - Supervised learning
• G06N 3/088 - Non-supervised learning, e.g. competitive learning

Abstract

The present invention relates to a user-customized data compliance management method and system for: analyzing dataset license compliance by using an AI model; allowing a plurality of real or virtual agents to collaborate (discuss) and assess the analyzed result; and further refining data so as to satisfy a risk class not smaller than a preset threshold value.

IPC Classes  ?

• G06F 21/16 - Program or content traceability, e.g. by watermarking
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06Q 10/10 - Office automationTime management
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 50/10 - Services

Abstract

The present invention relates to a response generation method and system, and provides a response generation method and system using generative artificial intelligence (AI) or a large language model (LLM).

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/3332 - Query translation
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 16/334 - Query execution
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06F 40/258 - Heading extractionAutomatic titlingNumbering
• G06F 40/205 - Parsing
• G06F 40/279 - Recognition of textual entities
• G06F 40/247 - ThesaurusesSynonyms

Abstract

A spatial information processing system according to an embodiment of the present invention may comprise the operations of: receiving an image and a query as inputs; extracting, from the image, a multi-dimensional visual feature representation including captions, object coordinates, and OCR text; converting the multi-dimensional visual feature representation into a linguistic representation understandable by a large-scale language model by using at least one lightweight artificial intelligence model; and receiving the converted linguistic representation and the query as inputs and generating a final response.

IPC Classes  ?

• G06F 8/33 - Intelligent editors
• G06F 8/30 - Creation or generation of source code
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 16/532 - Query formulation, e.g. graphical querying
• G06F 16/538 - Presentation of query results
• G06F 16/3329 - Natural language query formulation

Abstract

An intelligent data analysis system according to an embodiment of the present invention may comprise the operations of: acquiring data to be analyzed and a user query in a natural language form; analyzing the user query so as to derive an analysis objective, and establishing an analysis plan defining a logical procedure to be performed in order to achieve the analysis objective; generating, on the basis of the established analysis plan and structural attribute information of the data to be analyzed, a program code mechanically executable in a computing environment; and driving the program code in an execution environment so as to calculate and output an analysis result corresponding to the user query.

IPC Classes  ?

• G06F 8/33 - Intelligent editors
• G06F 8/30 - Creation or generation of source code
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 16/532 - Query formulation, e.g. graphical querying
• G06F 16/538 - Presentation of query results
• G06F 16/3329 - Natural language query formulation

Abstract

An embodiment provides a method comprising the steps of: receiving an image for analysis and storing the image in at least one memory; loading the image for analysis from the at least one memory; generating, by at least one processor, an analysis result for the image for analysis by using at least one artificial intelligence model using the image for analysis as an input thereto, wherein the at least one artificial intelligence model is pre-trained to output the analysis result for the image by performing a predetermined operation using a disentangling mask for controlling the interaction between a spatial token including spatial information on the image and a non-spatial token including temporal information or language information; and ingesting the analysis result into at least one subsequent processing component.

IPC Classes  ?

• G06F 8/33 - Intelligent editors
• G06F 8/30 - Creation or generation of source code
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 16/532 - Query formulation, e.g. graphical querying
• G06F 16/538 - Presentation of query results
• G06F 16/3329 - Natural language query formulation

Abstract

The method and system of the present invention, for predicting and profiling impurities in a chemical reaction, match reactants using reaction templates and thereby arrange impurity candidates in a priority queue. Provided is an efficient and accurate method for predicting impurities by combination of text conditions and reaction characteristics through text embedding and RXN embedding.

IPC Classes  ?

• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/90 - Programming languagesComputing architecturesDatabase systemsData warehousing
• G16C 20/50 - Molecular design, e.g. of drugs
• G16C 20/40 - Searching chemical structures or physicochemical data

Abstract

According to an embodiment of the present invention, a prompt recommendation and judgment system may comprise: an operation of analyzing context of user input data and in which at least one artificial intelligence model generates a plurality of candidate prompts corresponding to the context; an operation in which the at least one artificial intelligence model selects a reference judgment index matching the context and creates a dynamic judgment rubric by extending the reference evaluation index so as to correspond to the characteristics of the context; and an operation of calculating suitability for the plurality of candidate prompts by applying the dynamic judgment rubric and determining a recommendation prompt on the basis of the calculated result.

IPC Classes  ?

• G06F 8/33 - Intelligent editors
• G06F 8/30 - Creation or generation of source code
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 16/532 - Query formulation, e.g. graphical querying
• G06F 16/538 - Presentation of query results
• G06F 16/3329 - Natural language query formulation

Abstract

Provided, in one embodiment of the present disclosure, is a method performed by a computer. The method may comprise the operations of: when first observation data of a first agent is received at a first time point, updating history information of the first agent by inputting the first observation data of the first agent and time information corresponding to the first observation data into a history encoder of the first agent; when observation data of a second agent is not received at the first time point, maintaining history information of the second agent; and calculating a value function by inputting the history information of the first agent and the history information of the second agent into an aggregation module.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning
• G06N 3/098 - Distributed learning, e.g. federated learning
• G06N 3/045 - Combinations of networks
• 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]
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 10/10 - Office automationTime management
• G06Q 50/04 - Manufacturing
• G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
• G06N 20/00 - Machine learning

Abstract

An expert-agent interaction-based anomaly detection platform, and an operating method thereof, according to one embodiment of the present disclosure, which are capable of: continuously reflecting feedback of a domain expert in a system in a real-world industrial environment that dynamically changes, such as a smart factory; and interacting with a large-scale language model (LLM)-based agent so as to autonomously construct and update an anomaly detection model.

IPC Classes  ?

• G06Q 50/10 - Services
• G06N 3/0475 - Generative networks
• G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/40 - Processing or translation of natural language
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/047 - Probabilistic or stochastic networks

Abstract

A method for generating a dynamic anomaly detection workflow on the basis of agent orchestration and a modular protocol, and a system therefor according to an embodiment of the present disclosure relate to a method and a system therefor, the method autonomously generating and executing a customized anomaly detection workflow by allowing a large language model (LLM)-based agent to analyze characteristics of a user's natural language request or data in real time, and dynamically selecting and combining a plurality of functional tools defined by means of a model context protocol (MCP).

IPC Classes  ?

• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/047 - Probabilistic or stochastic networks

Abstract

The present disclosure relates to an artificial intelligence agent system having multi-stage inference and self-correction functions and an integrated service platform hosting same. The agent system analyzes the complexity of a user query and deconstructs the user query into subtasks, establishes an execution plan based on data dependencies, and performs closed loop control for analyzing an error occurring during code execution in a sandbox environment and self-correcting the error. In addition, the platform system determines whether to approve distribution by preventing contamination of training data and performing automated red teaming using an adversarial prompt during registration of a tuned model. Furthermore, the platform performs precise rate limit control in units of tokens in response to an API request and generates differential charging data for assigning a weight to an output token, thereby ensuring efficient resource management and service stability.

IPC Classes  ?

• G06N 3/0475 - Generative networks
• G06N 3/045 - Combinations of networks
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/09 - Supervised learning
• G06N 3/096 - Transfer learning
• G06N 3/092 - Reinforcement learning
• G06Q 10/10 - Office automationTime management
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

Abstract

The present invention relates to an artificial intelligence-based graph structure learning method and system, and a graph-based data processing method and system using same, and provides an artificial intelligence-based graph structure learning method and system, and a graph-based data processing method and system using same, which are capable of more effectively analyzing and learning graph data.

IPC Classes  ?

• G06T 5/00 - Image enhancement or restoration
• G06T 7/10 - SegmentationEdge detection
• G06F 40/279 - Recognition of textual entities

Abstract

The present invention relates to a method and a system for training a demand forecasting model. The method for training a demand forecasting model, according to the present invention, which is performed by a computer, comprises the steps of: specifying time series data of different attributes related to a future time point as training data for a demand forecasting target item; specifying static data related to a unique attribute of the demand forecasting target item as training data; processing the time series data to obtain at least one basis pattern data from the time series data; processing at least one of the static data or the time series data to generate at least one adjustment information to be applied to the basis pattern data; and training the demand forecasting model to calculate a demand forecasting value related to the demand forecasting target item by using a result of combining the basis pattern data and the adjustment information.

IPC Classes  ?

• G06Q 30/02 - MarketingPrice estimation or determinationFundraising
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/045 - Combinations of networks

Abstract

The present invention relates to a document understanding method and system, and provides a document understanding method and system using deep document understanding (DDU) technology.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/40 - Searching chemical structures or physicochemical data
• G16C 20/80 - Data visualisation
• G06V 30/19 - Recognition using electronic means
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06N 3/08 - Learning methods
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G16C 20/90 - Programming languagesComputing architecturesDatabase systemsData warehousing

Abstract

One embodiment provides a method comprising the steps in which: an image to be analyzed is received and stored in at least one memory; the image to be analyzed is loaded from the at least one memory; at least one processor generates the result of the analysis of the image being analyzed using at least one artificial intelligence model having the image being analyzed as an input, the at least one artificial intelligence model being pre-trained to infer the geometric relationships between a plurality of objects in an image; and the result of the analysis is ingested into at least one post-processing component.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06V 20/00 - ScenesScene-specific elements

Abstract

A combinatorial optimization system, its control method, and a learning method of a combinatorial optimization system. The learning method includes specifying a training dataset including at least one combinatorial optimization problem instance and at least one reference solution corresponding to the instance; performing supervised learning on a combinatorial optimization model using the training dataset; acquiring a supervised-learned combinatorial optimization model based on the training data; and performing reinforcement learning on the supervised-learned combinatorial optimization model.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A prediction system predict valid customer companies or valid customers in business-to-business (B2B) and/or business-to-consumer (B2C) sales situations. A computerized method comprise: specifying a train dataset including values for a plurality of different categories; configuring a plurality of different sub-datasets based on a specific value of a specific category using the train dataset such that a ratio of different values corresponding to the specific category among the plurality of different categories satisfy a preset ratio criterion; training a plurality of target prediction models on each of the plurality of different sub-datasets; inputting input data to be predicted to each of the plurality of the trained target prediction models; acquiring a plurality of prediction values for the input data from the plurality of trained target prediction models; and specifying a final prediction value for the input data using the plurality of prediction values.

IPC Classes  ?

• G06Q 30/0202 - Market predictions or forecasting for commercial activities
• G06N 5/01 - Dynamic search techniquesHeuristicsDynamic treesBranch-and-bound

Abstract

The present invention relates to a method and a system for generating training data, and provides a method and a system for generating training data, for training a model so as to be capable of self-debugging and correcting an execution error occurring in text-to-SQL generation.

IPC Classes  ?

• G06F 11/3698 - Environments for analysis, debugging or testing of software
• G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06N 3/096 - Transfer learning

Abstract

The present invention relates to a training data generation method and system, and provides a training data generation method and system in which a large-scale training data set for training an artificial intelligence model is generated.

IPC Classes  ?

• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06N 3/0475 - Generative networks
• G06Q 50/20 - Education

Abstract

The present invention relates to a method and a system utilizing a large language model for topic allocation based on quotations. According to the present invention, a computer-implemented method utilizing a large language model for topic allocation based on quotations may comprise the steps of: specifying content to be analyzed, which includes at least one sentence; specifying at least one topic related to the at least one sentence included in the content to be analyzed; extracting, by using a large language model, at least one quotation corresponding to each of the at least one topic from the content to be analyzed; and providing, by using the topic and the quotation, a topic analysis result for the content to be analyzed.

IPC Classes  ?

• G06F 40/258 - Heading extractionAutomatic titlingNumbering
• G06F 40/205 - Parsing
• G06F 40/30 - Semantic analysis
• G06Q 10/10 - Office automationTime management
• G06N 3/0475 - Generative networks
• G06N 3/045 - Combinations of networks
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

The present invention relates to a red teaming assessment method and system for detecting vulnerabilities of an AI model on the basis of risk items and attack scenarios derived through ethics impact assessment, and provides the effect of enhancing the safety of artificial intelligence by retraining the model by utilizing, as feedback data, mitigation measures for the detected vulnerabilities.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation

Abstract

One embodiment of the present disclosure may provide a system comprising one or more memories collectively storing instructions which, when executed by one or more processors, instruct the system to perform operations, the operations comprising: an operation of generating a plurality of groups including a first group and a second group; an operation of replicating a pivot schedule of each of the plurality of groups into a plurality of branch schedules for each of the plurality of groups; an operation of, for each of the plurality of branch schedules, expanding a schedule by performing a macro operation in which an agent trained on the basis of an artificial intelligence model reflects an operation scenario corresponding to each of the plurality of groups; an operation of evaluating a branch schedule of the first group and another group branch schedule at the same or higher level of constraint as the first group at a first synchronization time point with a suitability estimator network, and updating a pivot schedule of the first group according to the evaluation result; and an operation of determining a final schedule on the basis of the updated pivot schedule of the first group.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 10/10 - Office automationTime management
• G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
• G06N 3/092 - Reinforcement learning
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Abstract

The present invention relates to a method and a system for generating a coding guide on the basis of a large language model. According to the present invention, the method for generating a coding guide on the basis of a large language model, which is performed by a computer, may comprise the steps of: receiving a user query related to at least one coding-related task among a plurality of different coding-related tasks from a user terminal; processing the user query as an input for a pre-trained large language model with respect to the plurality of coding-related tasks; obtaining, from the large language model, coding guide information which is related to at least one programming language related to the user query and is for the at least one coding task; generating a response to the user query for the at least one coding-related task by using the obtained coding guide information; and providing the response to the user query to the user terminal.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

A vision inspection method using in-context learning, and a system therefor, according to one embodiment of the present disclosure, relate to a method and a system for performing, by using sequence model-based in-context learning, vision inspection on a query image by controlling an operation of a model through a prompt including a small number of images and label examples without updating model parameters.

IPC Classes  ?

• G01N 21/88 - Investigating the presence of flaws, defects or contamination
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/096 - Transfer learning

Abstract

The present invention relates to an artificial intelligence-based patent examination support method and system, which generate a search keyword by extracting components of a patent specification on the basis of various artificial intelligence models and algorithms, and automatically generate a report including the basis for determining patentability on the basis of the similarity with a prior art document that has been searched and filtered by using the search keyword.

IPC Classes  ?

• G06Q 50/18 - Legal services
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 16/334 - Query execution
• G06F 16/35 - ClusteringClassification
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/205 - Parsing
• G06F 40/166 - Editing, e.g. inserting or deleting
• G06F 18/22 - Matching criteria, e.g. proximity measures

Abstract

One embodiment provides a system implemented by a computer, the system comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the system to perform operations, wherein the operations include: collecting history data including information on past states and actions of each of a plurality of agents; encoding the history data with temporal information by using a history encoding module; integrating the encoded history data by applying a self-attention mechanism; estimating a joint value function on the basis of the integrated history data; and updating the policy of each of the plurality of agents on the basis of the joint value function.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06N 3/092 - Reinforcement learning
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06Q 50/04 - Manufacturing
• G06Q 10/10 - Office automationTime management
• G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
• G06N 20/00 - Machine learning

Abstract

The present disclosure provides an agentic AI driving method for dynamically determining an optimal toolchain on the basis of an execution mode according to a user input and an expected latency of a toolchain DB. Accordingly, an external tool and an internal module are controlled to execute a main task and output a final response including the execution result.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

A system, a method, and a program for acquiring data by using a language model are disclosed. The method comprises the steps of: receiving a query generated on the basis of a user query or a system event; analyzing the type of the query through a first language model; selecting, through the first language model, at least one API and a search range for acquiring real-time information according to the analyzed type of the query; requesting real-time data from a server by using the selected API and acquiring the real-time data as an API response; and generating and outputting a natural language response through a second language model by using the API response.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 40/30 - Semantic analysis
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06Q 10/10 - Office automationTime management
• G06N 3/096 - Transfer learning

Abstract

A table-type data analysis system, method, and program using a language model are disclosed. The method comprises the steps of: receiving a query and at least one table-type data, and converting the table-type data into at least one text-type data; inputting the query and the text-type data into a first language model to identify the text-type data corresponding to an analysis request included in the query and extracting the identified text-type data as analysis data; inputting the query and the analysis data into a second language model to generate an execution command for performing the analysis request; inputting the execution command into a third language model to convert the execution command into a code configured to perform the analysis request; and executing the code to output a data analysis result.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 40/30 - Semantic analysis
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06Q 10/10 - Office automationTime management
• G06N 3/096 - Transfer learning

Abstract

The present invention relates to a method and system for document understanding, and provides a method and system for document understanding using deep document understanding (DDU) technology.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G16C 20/40 - Searching chemical structures or physicochemical data
• G16C 20/80 - Data visualisation
• G06V 30/19 - Recognition using electronic means
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06N 3/08 - Learning methods
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G16C 20/10 - Analysis or design of chemical reactions, syntheses or processes
• G16C 20/90 - Programming languagesComputing architecturesDatabase systemsData warehousing

Abstract

One embodiment provides a method comprising the steps of: receiving a user query and storing same in at least one memory; loading the user query from the at least one memory; preprocessing the user query by at least one processor; generating, by the at least one processor, at least one response to the user query by using at least one artificial intelligence model taking, as an input, a prompt based on the preprocessed user query, wherein the prompt is generated on the basis of data obtained through external knowledge retrieval and is dynamically changed through user feedback; and ingesting the at least one response to at least one post-processing component.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 16/338 - Presentation of query results
• G06F 16/38 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/332 - Query formulation
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence
• G06F 8/33 - Intelligent editors
• G06F 8/30 - Creation or generation of source code

Abstract

An answer generation method is performed by cooperation of a memory and at least one processor. The answer generation method and system perform operations including specifying an analysis target document, extracting a plurality of content from the document, storing the plurality of content extracted from the document in the memory, receiving a user query from a user terminal, specifying specific content related to the user query among the plurality of content stored in the memory, processing the specific content as input to a pre-trained chemical reaction prediction model, and generating an answer to the user query using output data of the chemical reaction prediction model.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models

Abstract

The present invention relates to a method and a system for data normalization for training using tabular data, and provides a B-spline-based method and system for data normalization for efficient deep neural network (DNN) training using tabular data.

IPC Classes  ?

• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 3/04 - Architecture, e.g. interconnection topology
• G05B 23/02 - Electric testing or monitoring
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks

Abstract

The present invention relates to a method and a system for combinatorial optimization. More particularly, the present disclosure relates to a method and a system for combinatorial optimization, in which parameter optimization is performed through a combination of a hybrid evolutionary algorithm (EA) and Bayesian optimization (BO).

IPC Classes  ?

• G06N 20/20 - Ensemble learning
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

The present invention relates to a method and system for data augmentation for learning tabular data. More specifically, the present invention relates to a method and system for data augmentation based on a self-attention mechanism for contrastive learning of tabular data.

IPC Classes  ?

• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/045 - Combinations of networks
• G06N 3/096 - Transfer learning
• 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

The present invention relates to a method comprising: verifying a license of conversation data; using the conversation data as seed data to perform fine-tuning and additional training on a dual language model; constructing a license-free database based on the trained model; and providing the dual language model based on the constructed license-free database and services related thereto.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/31 - IndexingData structures thereforStorage structures
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06N 3/096 - Transfer learning

Abstract

A printed circuit board (PCB) wiring automatic design method in a PCB wiring automatic design system, the system including at least one processor and at least one memory including an instruction, and the PCB wiring automatic design method performed in cooperation with the instruction, the memory, and the processor, includes: receiving PCB data including a net list and information on a plurality of terminals; updating a cost of a wiring exploration region so that a cost of at least some of the wiring exploration region is increased based on a preset constraint condition; exploring a shortest path for wiring the plurality of terminals according to the net list in a cost-updated wiring exploration region; and performing wiring of the plurality of terminals based on wiring probability according to the shortest path.

IPC Classes  ?

• G06F 30/398 - Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM]
• G06F 30/392 - Floor-planning or layout, e.g. partitioning or placement
• G06F 30/394 - Routing
• G06F 111/04 - Constraint-based CAD
• G06F 111/08 - Probabilistic or stochastic CAD
• G06F 111/10 - Numerical modelling
• G06F 115/12 - Printed circuit boards [PCB] or multi-chip modules [MCM]

Abstract

A system for reinforcement learning includes at least one processor, and at least one memory storing at least one instruction that, when executed by the at least one processor, is configured to: perform offline reinforcement learning; and perform online reinforcement learning. The performing of the offline reinforcement learning includes identifying a data-retained region and a data-unretained region, and reducing a Q-value estimated in the data-unretained region.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning

Abstract

A system for predicting a property of a material of the present invention may extract a graph embedding by inputting material information into a first artificial intelligence (AI) model, and extract a text embedding by inputting a text description of a crystal structure of the material into a second AI model, classify the text embedding into a plurality of structure information embeddings, and concatenate the graph embedding with at least one of the plurality of structure information embeddings, wherein the structure information embeddings may be classified to include global information, semi-global information, and local information of the crystal structure. The provided system and method may be employed to predict aspects of a crystal structure, a molecular structure, a protein structure, a catalyst structure, or a metal-organic framework of a target material, or to predict physical properties of a target material.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/096 - Transfer learning

Abstract

The present invention relates to a method and system for Bayesian optimization. More specifically, the present invention relates to a method and system for Bayesian optimization using individual parameter kernel density estimation (KDE) in a mixed variable environment.

IPC Classes  ?

• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G01N 21/88 - Investigating the presence of flaws, defects or contamination
• 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
• G06F 18/2321 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06N 20/20 - Ensemble learning

Abstract

The present invention relates to a method and system for providing a web navigation service on the basis of an action agent , in which an AI model is trained with demonstration data in which actual web page manipulation is recorded, an action plan to be automatically performed by the trained AI is generated, and a web page is actually manipulated according to the generated plan to complete a requested task.

IPC Classes  ?

• G06F 16/954 - Navigation, e.g. using categorised browsing
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
• G06F 16/9532 - Query formulation
• G06F 16/9538 - Presentation of query results
• G06F 16/9032 - Query formulation
• G06F 16/9038 - Presentation of query results
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text
• G06N 20/00 - Machine learning

Abstract

The present invention relates to a technology for identifying a plurality of interactive elements on a web page when a user sets a goal, automatically generating an execution plan including at least one action to achieve the goal by identifying attributes of the identified interactive elements, and performing web navigation accordingly.

IPC Classes  ?

• G06F 16/954 - Navigation, e.g. using categorised browsing
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
• G06F 16/9532 - Query formulation
• G06F 16/9538 - Presentation of query results
• G06F 16/9032 - Query formulation
• G06F 16/9038 - Presentation of query results
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text
• G06N 20/00 - Machine learning

Abstract

The present invention relates to a method and a system for predicting process defects. More specifically, the present invention relates to a method and a system for predicting process defects capable of determining whether a product is defective by using time series data extraction.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring
• G06N 3/08 - Learning methods

Abstract

The present invention relates to a method and system for predicting process defects. Provided are a method and system for predicting process defects capable of determining whether a product is abnormal by constructing an artificial intelligence model applicable to various process environments.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/045 - Combinations of networks
• G06N 3/096 - Transfer learning

Abstract

A visual inspection method, computing system and computing device are provided. An exemplary method includes providing a uniform group of manufactured products, performing a first process for detecting noisy label data within a training data set, acquiring a noisy data set and a clean data set according to the noisy label data detected based on the first process, performing a second process for training a vision inspection model based on the acquired noisy data set and clean data set, visually inspecting the products using the vision inspection model to identify defective products within the group, and removing the defective products from the group. The two stage vision inspection model training method and system prevents performance degradation due to mislabeled training data, maintains maximum recall on defective data, and improves precision for good product data.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06N 20/00 - Machine learning
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects

Abstract

A computerized method and system for providing and implementing an artificial intelligence (AI) model that includes a plurality of models are provided. The method employs, for example, a computer having at least one processor and at least one memory and comprises receiving a query, identifying a user's context based on the query, and receiving the identified context through at least one artificial intelligence model and generating a response to the query, wherein the generating of a response may comprise controlling at least one router to determine at least one of a plurality of artificial intelligence models as a task execution model for the query, ingesting input data based on the context into the at least one determined task execution model, and sequentially or concurrently operating the at least one task execution model into which the input data has been ingested to generate a response to the query.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06N 20/00 - Machine learning

Abstract

A method for a computing system including at least one memory and at least one processor configured to predict characteristics regarding a plurality of material properties includes: obtaining experimental data including characteristics data on the plurality of material properties regarding materials; pre-training an integrated prediction model for a plurality of tasks of predicting characteristics regarding the plurality of material properties from the obtained experimental data; inputting, to the pre-trained integrated prediction model, material information to be predicted; outputting, by the integrated prediction model, a characteristic value for each of the plurality of material properties in regard to the material information; and providing the output characteristic value for each of the plurality of material properties.

IPC Classes  ?

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

Abstract

A method of performing action-based automation tasks includes: receiving an input signal representing a target task; identifying a context from the input signal; determining the target task and an application or web for performing the target task through the context; acquiring user interface information of a user interface of the application or web; identifying interaction elements included in the user interface information using an artificial intelligence model, and determining and storing attribute information of each of the interaction elements; generating and storing an execution plan including an action for at least one of the interaction elements based on the target task and the attribute information; and performing the target task according to the execution plan through at least one artificial intelligence model, wherein the performing of the target task comprises controlling the action to be executed through the interaction elements.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

A multi-tasking model training method and a multi-tasking performing method using a machine learning model trained on the basis thereof, which mutually transfer and learn knowledge data of a latent space for each task through geometric alignment in one integrated latent space in order to process a multi-task for output according to a plurality of domains.

IPC Classes  ?

• G06N 3/096 - Transfer learning

Abstract

A method for providing an artificial intelligence (AI) agent based on a large language model (LLM) applying an artificial intelligence model including a plurality of models includes: executing an on-device AI agent service; acquiring predetermined input data based on the executed on-device AI agent service; determining a domain according to the acquired input data; deciding an application model, which is an AI model that will process a task according to the determined domain; generating output data for the input data based on the decided application model; and providing the generated output data based on the on-device AI agent service.

IPC Classes  ?

• G06N 5/043 - Distributed expert systemsBlackboards

Abstract

A system and method for generating protein amino acid sequences having a user-desired property are provided. Using a noise-based diffusion model, the system and method can generate amino acid sequences of proteins that have excellent disease treatment effects and are safe for use as therapeutic agents in a human body. The system can function by obtaining reference protein sequence information, generating noise-added protein sequence information, iteratively generating noise-removed protein sequence information and partially noise-added protein sequence information, and generating noise-removed output protein sequence information. Noise may be added to protein sequence information using a Gaussian or other known noise model. Noise may be removed from protein sequence information using an artificial neural network model trained by a method of minimizing a loss function. By incorporating sequence guidance and structure guidance derived from known proteins, users can generate improved candidate protein drugs for testing.

IPC Classes  ?

• G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
• 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

Abstract

One embodiment provides a method executed by a computer, the method comprising steps in which: at least one processor performs limited-range augmentation on numerical variable values included in original tabular data, so as to generate augmented tabular data; at least one artificial intelligence model is trained to recognize patterns of the original tabular data and the augmented tabular data; labeled tabular data stored in at least one memory is loaded; and the at least one artificial intelligence model is fine-tuned to output a result value for the tabular data by using the labeled tabular data.

IPC Classes  ?

• G06N 3/096 - Transfer learning
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/045 - Combinations of networks

Abstract

A suspected defect region detection method according to one embodiment of the present disclosure is a method by which a computing system comprising a memory and a processor detects a suspected defect region, the method comprising the steps of: acquiring N (N>1) non-defective product images; acquiring an inspection target image, which is an image obtained by photographing an object for which the presence or absence of a defect is to be determined; aligning the inspection target image on the basis of the non-defective product images; dividing the aligned inspection target image into patch units; using a predetermined pre-training model so as to acquire a multi-scale feature based on the divided patches; estimating a non-defective product probability for each patch on the basis of the acquired multi-scale feature; determining, on the basis of the estimated non-defective product probability for each patch, whether a defect is suspected for the inspection target image; and providing a result of the suspected defect determination.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/11 - Region-based segmentation
• G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06T 3/18 - Image warping, e.g. rearranging pixels individually
• G01N 21/88 - Investigating the presence of flaws, defects or contamination

Abstract

The present invention relates to a method and a system for data augmentation for learning from tabular data. More specifically, the present invention relates to a method and a system for expression level data augmentation for improving the performance of self-supervised learning (SSL) in tabular data.

IPC Classes  ?

• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/045 - Combinations of networks
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks
• G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

Abstract

A knowledge distillation-based anomaly detection learning method according to an embodiment of the present invention comprises the steps of: acquiring a plurality of test sample images; calculating an anomaly score that is data obtained by digitizing a probability of a defect per patch feature for each of the plurality of test sample images; determining, as a defective candidate image, a test sample image in which the anomaly score satisfies a predetermined first condition; detecting, as a defective candidate patch feature, a patch feature in which the anomaly score satisfies a predetermined second condition, from among patch features of the defective candidate image; training a first adapter that is in charge of training in the current period, on the basis of the defective candidate patch feature; and updating a memory bank storing training information about a predetermined outlier detection model in a past period, on the basis of the training, wherein the first adapter is a network that performs the training in the current period, which reflects new information according to the plurality of test sample images while maintaining the training information in the past period at a certain level.

IPC Classes  ?

• G06N 3/096 - Transfer learning
• G06N 3/045 - Combinations of networks
• G06T 7/00 - Image analysis

Abstract

A deep learning-based environmentally adaptive outlier detection method according to an embodiment of the present invention is a method by which a computing system comprising a memory and a processor performs deep learning-based environmentally adaptive outlier detection and comprises the steps of: acquiring a corresponding fair quality image dataset for each predetermined material; acquiring corresponding Gaussian application information for each material, on the basis of Gaussian density estimation based on the fair quality image dataset; constructing a database comprising the Gaussian application information; determining whether a predetermined inspection target image is suspected of being defective, on the basis of the database; and providing a result of whether the predetermined inspection target image is suspected of being defective, wherein the Gaussian application information is information comprising mean (μ) and covariance (Σ) data of feature vectors extracted from N (N > 1) fair quality images included in the fair quality image dataset.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/11 - Region-based segmentation
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 5/20 - Image enhancement or restoration using local operators
• G06N 3/096 - Transfer learning

Abstract

A system for predicting physical properties of a material which: extracts a “graph embedding” by inputting material information into a first artificial intelligence (AI) model; extracts a “text embedding” by inputting a textual description of a crystal structure of the material into a second AI model; divides the “text embedding” into a plurality of “structural information embeddings”; and combines the “graph embedding” with at least one of the plurality of “structural information embeddings”. The “structural information embeddings” may be categorized into global information, semi-global information, and local information of the crystal structure.

IPC Classes  ?

• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/045 - Combinations of networks
• G16C 20/70 - Machine learning, data mining or chemometrics

Abstract

A method of enhancing language model performance through structured knowledge injection performed by a computing system including a memory and a processor including obtaining knowledge base data including a predetermined knowledge graph, generating linearly structured data by structuring the obtained knowledge base data into a text format, training a first language model based on the generated linearly structured data, and providing a predetermined application service based on the trained first language model. The generating linearly structured data includes generating the first linearly structured data by structuring the knowledge graph in the text format based on multi-hop linearization.

IPC Classes  ?

• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/096 - Transfer learning

Abstract

A method for learning a 3D geometric structure of a molecule and a target property prediction method including the same concern a target property prediction method in which a computing system including a memory and a processor learns a 3D geometric structure of a molecule and predicts a target property. The method includes: performing denoising-based, first pre-training based on a 3D conformer encoder which takes, as input, 3D molecular data specifying a 3D-level molecular structure; performing distillation-based, second pre-training based on the first pre-trained 3D conformer encoder and a 2D graph encoder which takes, as input, 2D molecular data specifying a 2D-level molecular structure; performing fine-tuning-based third pre-training based on the second pre-trained 2D graph encoder; and providing the third pre-trained 2D graph encoder.

IPC Classes  ?

• G16C 20/70 - Machine learning, data mining or chemometrics
• G06N 3/045 - Combinations of networks
• G06N 3/096 - Transfer learning
• G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures

Abstract

An artificial intelligence system performing operations including: an operation of calculating an importance score for data points of a dataset, an operation of calculating an oversampling rate for each of the data points, an operation of calculating a sample weight based on the oversampling rate for each of the data points, and an operation of oversampling the data points in correspondence to the calculated sample weight.

IPC Classes  ?

• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 20/00 - Machine learning

Abstract

The artificial intelligence system of the present invention may comprise the steps of: calculating importance scores for data points of a dataset; calculating an oversampling rate for each of the data points; calculating sample weights on the basis of the oversampling rate for each of the data points; and oversampling the data points in response to the calculated sample weights.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 18/10 - Pre-processingData cleansing
• G06N 3/096 - Transfer learning
• G06N 3/09 - Supervised learning

Abstract

A time series prediction method using external information-based dynamic model control according to the present disclosure is a prediction method performed by a computer, and comprises the steps of: collecting time series data for at least one of a target to be predicted and a target influence variable associated with the target; obtaining unstructured data for at least one of the target and the target influence variable; extracting content information related to the target or the target influence variable from the obtained unstructured data; converting the extracted content information into an external control signal; and performing control to input the collected at least one time series data into a prediction model and to output a prediction result for the target on the basis of the converted external control signal.

IPC Classes  ?

• G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• G06N 3/045 - Combinations of networks

Abstract

One embodiment of the present disclosure provides a reinforcement learning system comprising one or more processors, and one or more memories collectively storing instructions that, when executed by the one or more processors, cause the system to perform operations for performing offline reinforcement learning, and performing online reinforcement learning, wherein the operation for performing the offline reinforcement learning includes the operations for identifying a data retention area and a data non-retention area, and reducing a Q-value estimated in the data non-retention area.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning
• G06N 3/096 - Transfer learning

Abstract

An embodiment of the present disclosure may provide a system comprising: one or more processors; and one or more memories that collectively store instructions that, when executed by the one or more processors, cause the system to perform operations. In an embodiment, the operations may comprise the operations of: learning a first value function by performing offline reinforcement learning on the basis of offline data; learning a second value function by performing online pre-training; and performing online reinforcement learning by using a third value function generated on the basis of the first value function and the second value function.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning
• G06N 3/096 - Transfer learning

Abstract

A target prediction method for predicting a future outlook of a target performed by a computing device or a processor may collect related structured and unstructured data when a user requests target prediction, analyze the relationship between the target and a variable affecting the target at a semantic level, and compute a target outlook of a future.

IPC Classes  ?

• G06N 5/048 - Fuzzy inferencing

Abstract

A target prediction method for predicting a future outlook of a target performed by a computing device or a processor may collect related structured and unstructured data when a user requests predictive generation, analyze the relationship between the target and a variable affecting the target at a semantic level, and compute a target outlook of a future.

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 16/3329 - Natural language query formulation
• G06F 16/3332 - Query translation
• G06F 16/338 - Presentation of query results
• G06F 40/295 - Named entity recognition
• G06N 5/022 - Knowledge engineeringKnowledge acquisition

Abstract

A predictive generation method for predicting a future outlook of a target performed by a computing device or a processor may collect related structured and unstructured data when a user requests predictive generation, analyze the relationship between the target and a variable affecting the target at a semantic level, and compute a target outlook of a future.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/242 - Query formulation
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models