Abrégé

A system, a method, and a program using a language model perform operations comprising receiving a query generated based on a user query or a system event; analyzing a type of the query by a first language model; selecting, by the first language model, a search scope and at least one application programming interface (API) for obtaining real-time information according to the analyzed type of the query; requesting real-time data to the server using the selected API and obtaining the real-time data as an API response from the server; and generating and outputting a natural language response through a second language model using the API response.

Classes IPC  ?

• G06F 16/2453 - Optimisation des requêtes
• G06F 9/54 - Communication interprogramme
• G06F 16/242 - Formulation des requêtes

Abrégé

The present invention relates to a method and system for training an artificial intelligence model and provides a method and system for training an artificial intelligence model, which improve the prediction performance of an artificial intelligence model in various target tasks, the method comprising the steps of: acquiring input data comprising at least one constraint condition related to a target task; generating at least one candidate prediction value satisfying the at least one constraint condition, by processing the input data as an input into a pre-trained artificial intelligence model; calculating cost information corresponding to the at least one candidate prediction value; and training the artificial intelligence model by using the cost information as a reward, such that the artificial intelligence model outputs a prediction value that minimizes the cost information.

Classes IPC  ?

• G06N 3/092 - Apprentissage par renforcement
• G06N 3/09 - Apprentissage supervisé
• G06N 3/0475 - Réseaux génératifs
• G06N 3/096 - Apprentissage par transfert

Abrégé

The present disclosure relates to a method and a system for automatically generating a portfolio analysis report. The method comprises: collecting structured data regarding configuration changes and operation performance of a target portfolio during a predetermined analysis target period; generating summary data by summarizing and classifying unstructured text data across a plurality of temporal intervals and subject areas by using a language model; generating a structured summary for each asset from public documents related to assets within the portfolio; constructing reference data from an official source; and then generating an integrated prompt in which a numerical signal section including the structured data and an event summary section including the summary data and the like are structurally divided, so that the language model generates a narrative that causally links a change in the portfolio and a cause thereof.

Classes IPC  ?

• G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/9032 - Formulation de requêtes
• G06F 16/9038 - Présentation des résultats des requêtes
• G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/21 - Conception, administration ou maintenance des bases de données
• G06F 40/279 - Reconnaissance d’entités textuelles
• G06F 40/30 - Analyse sémantique
• G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
• G06F 9/52 - Synchronisation de programmesExclusion mutuelle, p. ex. au moyen de sémaphores
• H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]

Abrégé

The present disclosure relates to a method and a system for promptly training a simplified vision-language transformer, in which large uncurated datasets are augmented (e.g., through image enlargement and/or masking, etc.) and vision-language transformers are pre-trained by reflecting, through a knowledge distillation framework, misaligned information between an augmented image and text upon the augmentation, thereby reducing both the necessary size of the utilized data set and data processing overhead.

Classes IPC  ?

• G06N 3/096 - Apprentissage par transfert
• G06N 3/045 - Combinaisons de réseaux
• G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte

Abrégé

A method and system for data augmentation for learning of tabular data and, more specifically, a method and system for representation-level data augmentation for improving performance of self-supervised learning (SSL) in tabular data.

Classes IPC  ?

• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs

Abrégé

The present disclosure relates to a patent review automation system using multi-agent discussion and dynamic search/re-ranking. The system breaks down a patent claim into atomic elements and ensures breakdown accuracy through original text substring verification. A plurality of search queries are generated for each broken down element to search for prior art, and a correspondence relationship is generated through multi-level re-ranking and an N×M coverage matrix. A first agent of a rejection perspective and a second agent of a permission perspective perform structured discussion, and a verification agent supervises the structured discussion and generates a component-specific decision. Dynamic evidence reinforcement and identification detection are performed during a discussion process. On the basis of a discussion result, a claim chart having a two-way analysis structure of novelty and non-obviousness is automatically generated, and a gap analysis-based improvement proposal is provided when rejected. The accuracy of analysis continuously improves through a human feedback loop.

Classes IPC  ?

• G06Q 50/10 - Services
• G06N 3/045 - Combinaisons de réseaux
• G06F 16/334 - Exécution de requêtes
• G06F 16/3332 - Traduction de requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06Q 50/18 - Services juridiques
• G06F 16/35 - PartitionnementClassement

Abrégé

The present disclosure relates to an adaptive artificial intelligence system and a method therefor for dynamic causal structure learning of time series data and counterfactual outcome prediction, wherein the system automatically detects structural changes in data from non-stationary time series data in which underlying causal relationships change over time and segments the data into a plurality of regimes, dynamically learns a causal structure specialized for each regime through a bi-level optimization technique, and precisely predicts a probability distribution of potential outcomes according to a virtual intervention (i.e., a counterfactual query) of a user on the basis of the learned causal structure, and provides results accordingly.

Classes IPC  ?

• G06N 3/049 - Réseaux neuronaux temporels, p. ex. éléments à retard, neurones oscillants ou entrées impulsionnelles
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/047 - Réseaux probabilistes ou stochastiques
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06Q 40/06 - Gestion de biensPlanification ou analyse financières
• G06Q 50/10 - Services

Abrégé

The present disclosure relates to a method and a system for analyzing a business process and deriving an automation opportunities by using an AI agent. The method structuralizes business data collected from a plurality of stakeholders and synthesizes same into a multi-dimensional integrated map (including time axis alignment, conflict and gap detection). Thereafter, pain points on the map are clustered on the basis of composite similarity so as to automatically generate AI solution questions, and an input idea is converted into a visual sketch. Finally, a process is included in which multi-axis feasibility is evaluated on the basis of data availability, AI technology reliability and business influence, and priority categories of solutions are classified.

Classes IPC  ?

• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
• G06F 40/205 - Analyse syntaxique
• G06F 40/186 - Gabarits
• G06F 16/334 - Exécution de requêtes
• G06F 16/35 - PartitionnementClassement
• G06N 3/0475 - Réseaux génératifs

Abrégé

One embodiment provides a method comprising the steps of: receiving an image for analysis and storing same in at least one memory; loading the image for analysis from the at least one memory; extracting, by at least one processor, a vision feature map from the image for analysis; generating, by the at least one processor, a global token, a spatial token, and a temporal token for an image for training by using the vision feature map; receiving a user query for the image for analysis; generating, by the at least one processor, a text token by encoding the user query; encoding location information by applying harmonic rotation location embedding to an input sequence comprising the spatial token, the temporal token, and a text token; performing, on the input sequence comprising the tokens with the location information encoded, a predetermined attention operation having applied thereto a separation mask controlling interaction between the spatial token and the temporal token; generating a result of analysis of the image for analysis on the basis of a result of the attention operation; and inputting the result of the analysis into at least one subsequent processing component.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/08 - Méthodes d'apprentissage
• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images

Abrégé

The present invention relates to a simulation method and system, and relates to a technology for providing a simulation method and system for implementing physical AI. The present invention provides a user interface for generating an environmental layout related to a robot operation, and causes the environmental layout generated by the user interface and a control strategy model to interlock with each other. The present invention is intended to execute robot simulation on the basis of the environmental layout and the control strategy model interlocked with each other. The executed result is output to the user interface.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/065 - Moyens analogiques
• G06T 7/00 - Analyse d'image
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs

Abrégé

The present disclosure relates to a method and a system for providing a follow-up question in real time through distributed computation between a server and a client in a conversational artificial intelligence service. The server derives a conversation purpose and related knowledge through chain-of-thought (CoT) inference based on a conversation history, so as to generate and rank follow-up question candidates, stores metadata including a KV cache pointer, and then transmits a candidate pack to the client. The client constructs a prefix trie index by using the received data, searches the index locally without server communication when a user inputs a keystroke, and provides a matching candidate in real time. Through this, computational load on the server is reduced and response latency is minimized.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/3349 - Réutilisation des résultats stockés de requêtes précédentes
• G06F 16/36 - Création d’outils sémantiques, p. ex. ontologie ou thésaurus
• G06F 40/35 - Représentation du discours ou du dialogue
• G06N 3/0475 - Réseaux génératifs

Abrégé

The present disclosure relates to a phenotype prediction system comprising: a memory storing one or more instructions; and at least one processor that executes the one or more instructions stored in the memory, wherein an operation performed by the one or more instructions comprises a step of learning phenotype prediction from omics data, and the step of learning the phenotype prediction comprises the steps of: acquiring at least one data including the omics data; generating an omics feature on the basis of the omics data; generating, by a reconstruction model, a latent vector on the basis of the omics feature; and predicting, by a phenotype prediction model, a phenotype on the basis of the latent vector.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée
• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G16B 50/00 - TIC pour la programmation d’outils ou de systèmes de bases de données spécialement adaptées à la bio-informatique
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 3/0442 - Réseaux récurrents, p. ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p. ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06N 3/0475 - Réseaux génératifs
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux

Abrégé

The present invention relates to a system and method for predicting a protein structure, wherein protein data including amino acid sequence information of one or more proteins is converted into embeddings, a first model and a second model each generate logits from the embeddings, a softmax function using a temperature parameter is applied to the logits respectively generated by the first model and the second model, to generate prediction distributions, a loss function is calculated by using the prediction distributions, and learning is carried out such that the loss function decreases.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 50/00 - TIC pour la programmation d’outils ou de systèmes de bases de données spécialement adaptées à la bio-informatique
• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/048 - Fonctions d’activation
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/0475 - Réseaux génératifs

Abrégé

The present disclosure relates to a system and method for phenotype prediction, and provides a system and method for phenotype prediction, the method comprising a step of training phenotype prediction from omics data, wherein the step of training phenotype prediction comprises the steps of: acquiring omics data about a plurality of modalities from one or more omics datasets; converting the omics data about the plurality of modalities into modality-specific embeddings via an encoder; generating, by an integration model, an integrated embedding from the modality-specific embeddings; generating, by a prediction model, a phenotype prediction value from the integrated embedding; and improving phenotype prediction performance by calculating a loss function by using the phenotype prediction value and training to reduce the loss function.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée
• G16B 25/10 - Profilage de l’expression de gènes ou de protéinesEstimation ou normalisation de ratio d’expression
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
• G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 20/00 - Apprentissage automatique

Abrégé

An embodiment provides a method performed by a computer, the method comprising the steps of: receiving tabular data for analysis; generating, by at least one processor, analysis result information about the tabular data for analysis by using at least one artificial intelligence model, wherein the at least one artificial intelligence model is pre-trained on the basis of a training dataset comprising tabular augmented data generated on the basis of recognition of a user labeling pattern; and inputting the analysis result information into at least one subsequent processing component.

Classes IPC  ?

• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

The present disclosure relates to artificial intelligence and bioinformatics technology, and more specifically, to an artificial intelligence system and method for designing a protein binder capable of binding to a specific protein with high affinity and specificity by dynamically considering changes in protein structure.

Classes IPC  ?

• G16B 40/20 - Analyse de données supervisée
• G16B 15/30 - Ciblage de médicament à l’aide de données structurellesPrévision d’amarrage ou de liaison moléculaire
• G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
• G16B 50/00 - TIC pour la programmation d’outils ou de systèmes de bases de données spécialement adaptées à la bio-informatique
• G16B 35/00 - TIC spécialement adaptées aux bibliothèques combinatoires in silico d’acides nucléiques, de protéines ou de peptides
• G06N 3/045 - Combinaisons de réseaux
• G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/0475 - Réseaux génératifs

Abrégé

The present invention relates to a method and a system for artificial intelligence inference, and provides a method and a system for artificial intelligence inference, which are for solving various problems through inference by using artificial intelligence.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/065 - Moyens analogiques
• G06T 7/00 - Analyse d'image
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs

Abrégé

The present disclosure relates to a method for automatically generating a robot control interface by means of a computer processor. Specifically, the present invention receives a request including a task description from a user, and generates structured metadata by loading hardware specifications and available variable information about a robot and a simulation environment. On the basis of this, a system prompt including an observation and behavior design area is configured, and then the system prompt is input to a pre-trained artificial intelligence model to automatically generate a candidate code set including observation and behavior model codes. A final execution environment wrapper for robot control learning is constructed by verifying the finally generated candidate code, and the corresponding state and result are implemented on a user interface.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel
• G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
• G06F 11/32 - Surveillance du fonctionnement avec indication visuelle du fonctionnement de la machine
• G06F 11/3698 - Environnements pour l’analyse, le débogage ou le test de logiciel
• G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06N 3/092 - Apprentissage par renforcement

Abrégé

The present disclosure relates to a method for high-speed prototyping and control of an industrial automation environment. When a user places infrastructure components through a visual editor and configures agent tasks, a system serializes corresponding placement information and operating parameters into a structured text format. Subsequently, a hardware specification file is parsed to normalize state variables and actuator spaces into metadata, the normalized metadata and task inputs are fed into a pre-trained artificial intelligence model to automatically generate interface code that mediates data exchange between agents and a simulation environment, and thus a behavior model is linked to the simulation environment on the basis of the generated code, and an interaction result of an agent cluster is visually displayed on a user interface. Through this, it is possible to rapidly establish an environment and intuitively pre-verify a control algorithm without complex hard-coding.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel
• G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
• G06F 11/32 - Surveillance du fonctionnement avec indication visuelle du fonctionnement de la machine
• G06F 11/3698 - Environnements pour l’analyse, le débogage ou le test de logiciel
• G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06N 3/092 - Apprentissage par renforcement

Abrégé

The present invention relates to a method for controlling an artificial intelligence model based on multi-phase learning. An experiment setup is received from a user to load a benchmark dataset, and raw data is preprocessed according to a second-phase standard through an observation model function. First-phase learning is performed with the preprocessed data so as to generate a checkpoint, a specific parameter is transferred to a second-phase model and initialized, and then second-phase learning is performed through interacting with the environment. Finally, the learning lineage and connection relationship between the two phases are visualized and provided to a user interface. The present invention fundamentally prevents data standard mismatch between online and offline learning, and supports parameter transfer between heterogeneous algorithms, thereby enabling intuitive multi-phase learning monitoring and optimal phase switching.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel
• G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
• G06F 11/32 - Surveillance du fonctionnement avec indication visuelle du fonctionnement de la machine
• G06F 11/3698 - Environnements pour l’analyse, le débogage ou le test de logiciel
• G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06N 3/092 - Apprentissage par renforcement

Abrégé

The present invention relates to a method and system for document understanding, and the method for document understanding according to the present invention may comprise the steps of: receiving a user request including at least one document; identifying, through analysis of the document, at least one layout region each including at least one content having different data characteristics; specifying one or more models configured to perform processing on each of the at least one content, on the basis of the data characteristics of the content included in each of the at least one layout region; extracting the at least one content included in each of the at least one layout region by using each of the one or more models; converting the document into a form corresponding to the user request by using the at least one content extracted using the one or more models; and as a response to the user request, providing a user terminal with the document converted into the form corresponding to the user request.

Classes IPC  ?

• G06V 30/41 - Analyse du contenu de documents
• G06V 30/412 - Analyse de mise en page de documents structurés avec des lignes imprimées ou des zones de saisie, p. ex. de formulaires ou de tableaux d’entreprise
• G06F 40/166 - Édition, p. ex. insertion ou suppression
• G06N 20/00 - Apprentissage automatique
• G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
• G06F 40/106 - Affichage de la mise en page des documentsPrévisualisation
• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques

Abrégé

The present invention relates to a method and a system for document understanding. The document understanding method according to the present invention may comprise the steps of: specifying at least one document to be analyzed; inputting the at least one document into a document analysis model trained on the basis of a training document data set including at least one augmented document data; by analyzing the at least one document using the document analysis model, identifying at least one layout area having an attribute different from that of another layout area in the at least one document; extracting at least one content included in each of the identified at least one layout area; generating output data for the at least one document by using the extracted at least one content; and providing the output data to a user terminal.

Classes IPC  ?

• G06V 30/412 - Analyse de mise en page de documents structurés avec des lignes imprimées ou des zones de saisie, p. ex. de formulaires ou de tableaux d’entreprise
• G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques
• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/80 - Visualisation de données
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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

Classes IPC  ?

• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/80 - Visualisation de données
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G16C 20/10 - Analyse ou conception des réactions, des synthèses ou des procédés chimiques
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données

Abrégé

The present invention relates to a method and a system for document understanding. The document understanding method according to the present invention may comprise the steps of: specifying at least one document to be analyzed; processing the document to be analyzed, as an input of at least one information processing model configured to process information related to a chemical domain; generating, by using the at least one information processing model, a first processing result for molecular structure information included in the document to be analyzed and a second processing result for chemical reaction information included in the document to be analyzed; and generating output data for chemical information reflecting a correlation between the molecular structure information and the chemical reaction information by using the first processing result and the second processing result.

Classes IPC  ?

• G06V 30/42 - Reconnaissance des formes à partir d’images axée sur les documents basées sur le type de document
• G06V 30/41 - Analyse du contenu de documents
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques
• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/80 - Visualisation de données
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

One embodiment of the present disclosure relates to a method performed by a computer, the method comprising the operations of: generating, on the basis of an initial state of a process, a pivot schedule including an initial macro operation sequence; duplicating the pivot schedule so as to generate a plurality of branch schedules; using an artificial intelligence agent so as to reflect different operation scenarios or probabilistic behavior policies for the respective branch schedules and sequentially add macro operations, thereby expanding the schedule; evaluating fitness for the respective expanded branch schedules when a predefined synchronization point is reached; and, on the basis of the evaluation result, selecting at least one from among the plurality of branch schedules so as to repeatedly perform a process of updating the pivot schedule, thereby determining final scheduling information.

Classes IPC  ?

• G06N 3/10 - Interfaces, langages de programmation ou boîtes à outils de développement logiciel, p. ex. pour la simulation de réseaux neuronaux
• G06N 3/045 - Combinaisons de réseaux
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
• G06N 3/092 - Apprentissage par renforcement

Abrégé

An artificial intelligence system includes at least one memory storing instructions and at least one processor configured to: receive protein amino acid sequence information; generate an embedding of the sequence using at least one AI model; input the embedding into a first AI model and a second AI model to produce respective logit vectors; generate prediction distributions by applying temperature-scaled softmax functions to the logits; compute a soft loss by using the first model's prediction distribution as a soft target and comparing it to the second model's prediction distribution; compute a loss function including the soft loss; and train the second AI model to predict a protein structure from amino acid sequence information by reducing the loss function.

Classes IPC  ?

• G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
• G06N 20/00 - Apprentissage automatique
• G16B 15/20 - Repliement de protéines ou de domaines

Abrégé

The present invention relates to a method and system for document understanding, and the method for document understanding according to the present invention may comprise the steps of: receiving at least one document from a user terminal; identifying, through analysis of the document, at least one layout region each including at least one content having different data characteristics; specifying one or more models configured to perform processing on each of the at least one content, on the basis of the data characteristics of the content included in each of the at least one layout region; extracting the at least one content included in each of the at least one layout region by using each of the one or more models; generating a structured document understanding result related to the at least one content by performing multimodal data processing on the at least one content extracted using the one or more models and having the different data characteristics; and on the basis of information included in the structured document understanding result, providing an editing function for the at least one document to the user terminal.

Classes IPC  ?

• G06V 30/41 - Analyse du contenu de documents
• G06V 30/412 - Analyse de mise en page de documents structurés avec des lignes imprimées ou des zones de saisie, p. ex. de formulaires ou de tableaux d’entreprise
• G06F 40/166 - Édition, p. ex. insertion ou suppression
• G06N 20/00 - Apprentissage automatique
• G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
• G06F 40/106 - Affichage de la mise en page des documentsPrévisualisation
• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques

Abrégé

A schedule providing method according to an embodiment of the present disclosure comprises the steps of: generating a first GUI for acquiring user input information; acquiring the user input information on the basis of the first GUI; specifying a target period; on the basis of the user input information and the target period, determining whether a schedule can be generated; generating the schedule during the target period when it is identified that the schedule can be generated; and controlling a display to output a second GUI for providing the schedule during the target period, wherein the schedule during the target period includes a first schedule pertaining to the operation of a warehousing tank during the target period, a second schedule pertaining to the operation of a blending tank during the target period, and a third schedule pertaining to the operation of a cracking furnace during the target period, and at least one among the first schedule, the second schedule, and the third schedule may be determined by one or more AI agents for performing reinforcement learning.

Classes IPC  ?

• G06Q 50/04 - Fabrication
• G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G16C 20/10 - Analyse ou conception des réactions, des synthèses ou des procédés chimiques
• G06N 3/10 - Interfaces, langages de programmation ou boîtes à outils de développement logiciel, p. ex. pour la simulation de réseaux neuronaux
• G06N 3/045 - Combinaisons de réseaux
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 10/10 - BureautiqueGestion du temps

Abrégé

A method for answering a query using a text-to-SQL (Structured Query Language) model for an electronic health record performed by a computing device comprises: receiving a natural language query from a user; generating an output value including one of an SQL query and a null value for the natural language query through the text-to-SQL model configured to process the natural language query; and providing the user with a response result for the natural language query based on the output value.

Classes IPC  ?

• G06F 16/2452 - Traduction des requêtes
• G06F 16/242 - Formulation des requêtes
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients

Abrégé

A method for adding tasks to a multi-tasking model, which corresponds to a method for expanding tasks of a multi-tasking model by a computing system including a memory and a processor, including: performing primary training of the multi-tasking model based on first experimental data so as to learn first multi-tasks; obtaining second experimental data for training the multi-tasking model on new tasks other than the first multi-tasks; performing secondary training of the primarily-trained multi-tasking model based on the obtained second experimental data; and providing the secondarily-trained multi-tasking model. The performing of the secondary training of the multi-tasking model includes freezing at least some parameters of the primarily-trained multi-tasking model and performing additional training of the multi-tasking model on the new tasks.

Classes IPC  ?

• G06F 9/48 - Lancement de programmes Commutation de programmes, p. ex. par interruption
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
• G06F 18/2431 - Classes multiples

Abrégé

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.

Classes IPC  ?

• G06F 16/903 - Requêtes
• G06F 16/906 - GroupementClassement

Abrégé

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.

Classes IPC  ?

• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre

Abrégé

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.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 40/30 - Analyse sémantique
• G06N 5/046 - Inférence en avantSystèmes de production

Abrégé

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.

Classes IPC  ?

• G06Q 40/06 - Gestion de biensPlanification ou analyse financières
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06N 3/0475 - Réseaux génératifs
• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G06N 3/08 - Méthodes d'apprentissage
• G06T 7/12 - Découpage basé sur les bords

Abrégé

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.

Classes IPC  ?

• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données
• G16C 20/80 - Visualisation de données
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/20 - Identification d’entités moléculaires, de leurs parties ou de compositions chimiques
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]

Abrégé

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.

Classes IPC  ?

• G10H 1/00 - Éléments d'instruments de musique électrophoniques
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G10G 1/00 - Moyens de représentation de la musique

Abrégé

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.

Classes IPC  ?

• G06F 40/40 - Traitement ou traduction du langage naturel

Abrégé

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.

Classes IPC  ?

• G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement

Abrégé

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.

Classes IPC  ?

• G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données
• G16C 20/50 - Conception moléculaire, p. ex. de médicaments
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/096 - Apprentissage par transfert
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06F 16/334 - Exécution de requêtes

Abrégé

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.

Classes IPC  ?

• G06N 3/049 - Réseaux neuronaux temporels, p. ex. éléments à retard, neurones oscillants ou entrées impulsionnelles
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 3/0499 - Réseaux à propagation avant
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
• G06F 16/242 - Formulation des requêtes
• G06F 16/248 - Présentation des résultats de requêtes
• G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage

Abrégé

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.

Classes IPC  ?

• G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
• G06F 16/242 - Formulation des requêtes
• G06F 16/248 - Présentation des résultats de requêtes
• G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
• G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
• G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
• G06N 20/00 - Apprentissage automatique
• G06F 123/02 - Types de données dans le domaine temporel, p. ex. des données de séries temporelles

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0475 - Réseaux génératifs
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/09 - Apprentissage supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement

Abrégé

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.

Classes IPC  ?

• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0475 - Réseaux génératifs
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/09 - Apprentissage supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement

Abrégé

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.

Classes IPC  ?

• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0475 - Réseaux génératifs
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/09 - Apprentissage supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement

Abrégé

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.

Classes IPC  ?

• G06F 40/279 - Reconnaissance d’entités textuelles

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 3/4053 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur la super-résolution, c.-à-d. où la résolution de l’image obtenue est plus élevée que la résolution du capteur
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 3/4053 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur la super-résolution, c.-à-d. où la résolution de l’image obtenue est plus élevée que la résolution du capteur
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06N 3/0475 - Réseaux génératifs
• G06N 3/096 - Apprentissage par transfert
• G06Q 10/10 - BureautiqueGestion du temps
• G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel

Abrégé

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.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0475 - Réseaux génératifs
• G06N 3/096 - Apprentissage par transfert
• G06F 40/20 - Analyse du langage naturel
• G06F 40/40 - Traitement ou traduction du langage naturel
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G06V 20/00 - ScènesÉléments spécifiques à la scène
• G06V 40/40 - Détection d’usurpation, p. ex. détection d’activité
• G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• G06T 5/00 - Amélioration ou restauration d'image
• G06T 7/10 - DécoupageDétection de bords
• G06F 40/279 - Reconnaissance d’entités textuelles

Abrégé

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.

Classes IPC  ?

• G06N 3/09 - Apprentissage supervisé
• G06N 3/047 - Réseaux probabilistes ou stochastiques
• G01N 21/88 - Recherche de la présence de criques, de défauts ou de souillures
• G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 7/10 - DécoupageDétection de bords
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
• G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
• G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
• G01N 21/88 - Recherche de la présence de criques, de défauts ou de souillures
• G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 7/11 - Découpage basé sur les zones
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G01N 21/88 - Recherche de la présence de criques, de défauts ou de souillures
• G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
• G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• H04N 23/61 - Commande des caméras ou des modules de caméras en fonction des objets reconnus
• H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
• G06N 20/00 - Apprentissage automatique
• G06T 7/00 - Analyse d'image
• G06N 3/047 - Réseaux probabilistes ou stochastiques
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs
• G06N 3/096 - Apprentissage par transfert
• G06N 3/09 - Apprentissage supervisé
• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif

Abrégé

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.

Classes IPC  ?

• G06F 21/16 - Traçabilité de programme ou de contenu, p. ex. par filigranage
• G06N 20/00 - Apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 50/10 - Services

Abrégé

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).

Classes IPC  ?

• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 16/3332 - Traduction de requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/334 - Exécution de requêtes
• G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
• G06F 40/258 - Extraction des en-têtesInsertion automatique des titresNumérotation
• G06F 40/205 - Analyse syntaxique
• G06F 40/279 - Reconnaissance d’entités textuelles
• G06F 40/247 - ThésaurusSynonymes

Abrégé

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.

Classes IPC  ?

• G06F 8/33 - Éditeurs intelligents
• G06F 8/30 - Création ou génération de code source
• G06F 8/36 - Réutilisation de logiciel
• G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
• G06F 16/538 - Présentation des résultats des requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel

Abrégé

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.

Classes IPC  ?

• G06F 8/33 - Éditeurs intelligents
• G06F 8/30 - Création ou génération de code source
• G06F 8/36 - Réutilisation de logiciel
• G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
• G06F 16/538 - Présentation des résultats des requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel

Abrégé

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.

Classes IPC  ?

• G06F 8/33 - Éditeurs intelligents
• G06F 8/30 - Création ou génération de code source
• G06F 8/36 - Réutilisation de logiciel
• G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
• G06F 16/538 - Présentation des résultats des requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel

Abrégé

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.

Classes IPC  ?

• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données
• G16C 20/50 - Conception moléculaire, p. ex. de médicaments
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques

Abrégé

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.

Classes IPC  ?

• G06F 8/33 - Éditeurs intelligents
• G06F 8/30 - Création ou génération de code source
• G06F 8/36 - Réutilisation de logiciel
• G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
• G06F 16/538 - Présentation des résultats des requêtes
• G06F 16/3329 - Formulation de requêtes en langage naturel

Abrégé

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.

Classes IPC  ?

• G06N 3/092 - Apprentissage par renforcement
• G06N 3/098 - Apprentissage distribué, p. ex. apprentissage fédéré
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0442 - Réseaux récurrents, p. ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p. ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 50/04 - Fabrication
• G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06Q 50/10 - Services
• G06N 3/0475 - Réseaux génératifs
• G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/40 - Traitement ou traduction du langage naturel
• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/047 - Réseaux probabilistes ou stochastiques

Abrégé

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).

Classes IPC  ?

• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/047 - Réseaux probabilistes ou stochastiques

Abrégé

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.

Classes IPC  ?

• G06N 3/0475 - Réseaux génératifs
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/09 - Apprentissage supervisé
• G06N 3/096 - Apprentissage par transfert
• G06N 3/092 - Apprentissage par renforcement
• G06Q 10/10 - BureautiqueGestion du temps
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G06T 5/00 - Amélioration ou restauration d'image
• G06T 7/10 - DécoupageDétection de bords
• G06F 40/279 - Reconnaissance d’entités textuelles

Abrégé

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.

Classes IPC  ?

• G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06N 3/045 - Combinaisons de réseaux

Abrégé

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.

Classes IPC  ?

• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/80 - Visualisation de données
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G16C 20/10 - Analyse ou conception des réactions, des synthèses ou des procédés chimiques
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06V 20/00 - ScènesÉléments spécifiques à la scène

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
• G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation

Abrégé

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.

Classes IPC  ?

• G06F 11/3698 - Environnements pour l’analyse, le débogage ou le test de logiciel
• G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G06N 3/0475 - Réseaux génératifs
• G06Q 50/20 - Éducation

Abrégé

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.

Classes IPC  ?

• G06F 40/258 - Extraction des en-têtesInsertion automatique des titresNumérotation
• G06F 40/205 - Analyse syntaxique
• G06F 40/30 - Analyse sémantique
• G06Q 10/10 - BureautiqueGestion du temps
• G06N 3/0475 - Réseaux génératifs
• G06N 3/045 - Combinaisons de réseaux
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06Q 10/10 - BureautiqueGestion du temps
• G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
• G06N 3/092 - Apprentissage par renforcement
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• G01N 21/88 - Recherche de la présence de criques, de défauts ou de souillures
• G06N 20/00 - Apprentissage automatique
• G06T 7/00 - Analyse d'image
• G06N 3/047 - Réseaux probabilistes ou stochastiques
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G06Q 50/18 - Services juridiques
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/334 - Exécution de requêtes
• G06F 16/35 - PartitionnementClassement
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 40/205 - Analyse syntaxique
• G06F 40/166 - Édition, p. ex. insertion ou suppression
• G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
• G06N 3/092 - Apprentissage par renforcement
• G06N 3/0455 - Réseaux auto-encodeursRéseaux encodeurs-décodeurs
• G06Q 50/04 - Fabrication
• G06Q 10/10 - BureautiqueGestion du temps
• G05B 19/418 - Commande totale d'usine, c.-à-d. commande centralisée de plusieurs machines, p. ex. commande numérique directe ou distribuée [DNC], systèmes d'ateliers flexibles [FMS], systèmes de fabrication intégrés [IMS], productique [CIM]
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine

Abrégé

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.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 40/30 - Analyse sémantique
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G06Q 10/10 - BureautiqueGestion du temps
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 40/30 - Analyse sémantique
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G06Q 10/10 - BureautiqueGestion du temps
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
• G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
• G16C 20/80 - Visualisation de données
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
• G16C 20/10 - Analyse ou conception des réactions, des synthèses ou des procédés chimiques
• G16C 20/90 - Langages de programmationArchitectures informatiquesSystèmes de bases de donnéesStockage de données

Abrégé

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.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 16/332 - Formulation de requêtes
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G06N 5/045 - Explication d’inférenceIntelligence artificielle explicable [XAI]Intelligence artificielle interprétable
• G06F 8/33 - Éditeurs intelligents
• G06F 8/30 - Création ou génération de code source

Abrégé

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.

Classes IPC  ?

• G06N 5/04 - Modèles d’inférence ou de raisonnement

Abrégé

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.

Classes IPC  ?

• G06N 3/0985 - Optimisation d’hyperparamètresMeta-apprentissageApprendre à apprendre
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G05B 23/02 - Test ou contrôle électrique
• G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux

Abrégé

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).

Classes IPC  ?

• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
• G06N 3/126 - Algorithmes évolutionnaires, p. ex. algorithmes génétiques ou programmation génétique

Abrégé

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.

Classes IPC  ?

• G06N 3/0895 - Apprentissage faiblement supervisé, p. ex. apprentissage semi-supervisé ou auto-supervisé
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/096 - Apprentissage par transfert
• G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques

Abrégé

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.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
• G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
• G06N 3/096 - Apprentissage par transfert

Abrégé

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.

Classes IPC  ?

• G06F 30/398 - Vérification ou optimisation de la conception, p. ex. par vérification des règles de conception [DRC], vérification de correspondance entre géométrie et schéma [LVS] ou par les méthodes à éléments finis [MEF]
• G06F 30/392 - Conception de plans ou d’agencements, p. ex. partitionnement ou positionnement
• G06F 30/394 - Routage
• G06F 111/04 - CAO basée sur les contraintes
• G06F 111/08 - CAO probabiliste ou stochastique
• G06F 111/10 - Modélisation numérique
• G06F 115/12 - Cartes de circuits imprimés [PCB] ou modules multi-puces [MCM]