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SELECTING A NEURAL NETWORK ARCHITECTURE FOR A SUPERVISED MACHINE LEARNING PROBLEM

Resumen de: US2025390745A1

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

SYSTEM AND METHOD FOR UTILIZING GROUPED PARTIAL DEPENDENCE PLOTS AND GAME-THEORETIC CONCEPTS AND THEIR EXTENSIONS IN THE GENERATION OF ADVERSE ACTION REASON CODES

Resumen de: US2025390794A1

A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on dependencies with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, scores related to each group of input variables for a given instance of the input vector processed by the model are calculated according to one or more algorithms. The algorithms can utilize group Partial Dependence Plot (PDP) values, Shapley Additive Explanations (SHAP) values, and Banzhaf values, and their extensions among others, and a score for each group can be calculated for a given instance of an input vector per group. These scores can then be sorted, ranked, and then combined into one hybrid ranking.

SPECIFIC FILE DETECTION BAKED INTO MACHINE LEARNING PIPELINES

Resumen de: US2025390576A1

A set of features including a first feature and a second feature is received at a server. A subset of the set of features is determined for use in generating a model usable by a device to locally make a malware classification decision. The device has reduced computing resources as compared to computing resources of the server. The subset of the set of features is used to generate the model. The generated model includes the first feature and does not include the second feature. A determination is made, at a time subsequent to the generation of the model, that an updated model should be deployed to the device. An updated model is generated.

ANALYSIS AND CORRECTION OF SUPPLY CHAIN DESIGN THROUGH MACHINE LEARNING

Resumen de: US2025390817A1

A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

COMPUTER-BASED SYSTEMS CONFIGURED FOR ENTITY RESOLUTION AND INDEXING OF ENTITY ACTIVITY

Resumen de: US2025390770A1

In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

DATA PROCESSING METHOD, MODEL TRAINING METHOD, AND RELATED DEVICE

Resumen de: EP4668175A1

A data processing method, a model training method, and a related device are provided, to apply an artificial intelligence technology to the communication field. The method includes: obtaining a value of T, where T represents a quantity of pieces of subdata included in output data of a first machine learning model; and inputting first data into the first machine learning model to obtain second data generated by the first machine learning model, where the second data includes the T pieces of subdata, the first machine learning model includes one or more modules, and one piece of subdata is obtained each time a module in the first machine learning model is invoked at least once. A quantity of times of invoking the module in the first machine learning model may be flexibly adjusted based on the value of T, to generate the T pieces of subdata. Therefore, the first machine learning model can be compatible with a plurality of values of T, and there is no need to store a plurality of machine learning models, so that storage space overheads are reduced.

METHOD AND DEVICE FOR TRANSMITTING AND RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

Resumen de: EP4668825A1

A method performed by a terminal in a wireless communication system according to at least one of the embodiments disclosed herein may include configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmissions and receptions (TRPs), monitoring the at least one AI/ML model, and performing, based on a performance of the monitored at least one AI/ML model, AI/ML model management to maintain or at least partially change the at least one AI/ML model, wherein the performance of the at least one AI/ML model may be determined based on a first multi-TRP data set related to training of the at least one AI/ML model and a second multi-TRP data set related to monitoring of the at least one AI/ML model.

METHOD AND APPARATUS FOR TRANSMITTING AND RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

Resumen de: EP4668838A1

A terminal according to at least one of embodiments disclosed in the present specification may: configure an artificial intelligence/machine learning (AI/ML) model; obtain information about channel state information (CSI) prediction performance of the AI/ML model through monitoring of the AI/ML model; and on the basis of the obtained information about the CSI prediction performance, perform a life cycle management (LCM)-related procedure for the AI/ML model, wherein the LCM-related procedure may include at least one of: (i) transmitting information requesting a data set for updating the AI/ML model; (ii) transmitting information requesting configuration of a time interval in which the update of the AI/ML model is to be performed; (iii) transmitting information requesting switching of the AI/ML model; and (iv) transmitting information requesting a fallback using a non-Al/ML-based operation.

OBSTACLE DETECTION METHOD AND DEVICE FOR ASSISTING VEHICLE IN DRIVING

Resumen de: EP4667972A1

The present invention discloses an obstacle detection method for assisting in vehicle driving. The method comprises: obtaining ultrasonic echo data captured during vehicle movement; obtaining information associated with echo intersections based on the ultrasonic echo data; providing at least part of the ultrasonic echo data and the information associated with the echo intersections as feature data to a machine learning model to obtain detection information for an obstacle, wherein the machine learning model employs at least one of a classification algorithm or a regression algorithm; and assisting in vehicle driving based on the detection information for the obstacle.

LANGUAGE MODEL AND ONTOLOGY ASSISTED MACHINE LEARNING SERVICE

Resumen de: EP4668176A1

Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving one or more user inputs identifying a data set and providing a first user request to perform a first task based on at least a portion of the data set, wherein the data set is defined by an ontology; using a large language model ("LLM") to identify a first machine learning ("ML") model type from a plurality of ML model types; using the LLM to identify a first portion of the data set to be used to perform the first task; using the LLM to generate a first ML model training configuration; and executing the first ML model training configuration to train a first custom ML model, of the first ML model type, to perform the first task.

ENSEMBLE MACHINE-LEARNING MODELS TO DETECT RESPIRATORY SYNDROMES

Resumen de: US2025385007A1

Provided is a process including: obtaining, with one or more processors, a set of data comprising a plurality of patient records, selecting a subset of the plurality of parameters for inputs into a machine learning system, generating a classifier using the machine learning system based on the training data and the subset of the plurality of parameters for inputs; receiving, with one or more processors, patient record of a first user; performing an analysis, with one or more processors, to identify acoustic measures from a voice sample of the first user.

TRAINING ENCODER MODEL AND/OR USING TRAINED ENCODER MODEL TO DETERMINE RESPONSIVE ACTION(S) FOR NATURAL LANGUAGE INPUT

Resumen de: US2025384350A1

Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.

METHOD AND SYSTEM TO SUPPORT DATA STREAMING FOR MATRIX OPERATIONS VIA A MACHINE LEARNING HARDWARE

Resumen de: US2025383882A1

A system comprises an on-chip memory (OCM) configured to maintain blocks of data used for a matrix operation and result of the matrix operation, wherein each of the blocks of data is of a certain size. The system further comprises a first OCM streamer configured to stream a first matrix data from the OCM to a first storage unit, and a second OCM streamer configured to stream a second matrix data from the OCM to a second storage unit, wherein the second matrix data is from an unaligned address of the OCM that is a not a multiple of the certain size. The system further comprises a matrix operation block configured to retrieve the first matrix data and the second matrix data from the first storage unit and the second storage unit, respectively, and perform the matrix operation based on the first matrix data and the second matrix data.

Machine Learning Systems and Methods for Many-Hop Fact Extraction and Claim Verification

Resumen de: US2025384223A1

Machine learning (ML) systems and methods for fact extraction and claim verification are provided. The system receives a claim and retrieves a document from a dataset. The document has a first relatedness score higher than a first threshold, which indicates that ML models of the system determine that the document is most likely to be relevant to the claim. The dataset includes supporting documents and claims including a first group of claims supported by facts from more than two supporting documents and a second group of claims not supported by the supporting documents. The system selects a set of sentences from the document. The set of sentences have second relatedness scores higher than a second threshold, which indicate that the ML models determine that the set of sentences are most likely to be relevant to the claim. The system determines whether the claim includes facts from the set of sentences.

SYSTEMS AND METHODS FOR MANAGING OIL AND GAS PRODUCTION

Resumen de: WO2025259798A1

Implementations claimed and described herein provide systems and methods for managing natural resource production. The systems and methods use a machine learning model to generate categorizations associated with communication data. The machine learning model is built from historical data.

MACHINE LEARNING MODEL REGISTRY

Resumen de: US2025384356A1

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.

PARAMETER ADJUSTMENT VISUALIZATION DEVICE, PARAMETER OPTIMIZATION DEVICE, PARAMETER ADJUSTMENT VISUALIZATION PROGRAM, AND PARAMETER ADJUSTMENT VISUALIZATION METHOD

Resumen de: WO2025258091A1

The present invention comprises: a parameter selection unit (101) that selects a parameter; a partial dependency calculation unit (102) that calculates a partial dependency value of an evaluation index value relating to the parameter selected by the parameter selection unit (101), on the basis of a parameter adjustment data set including a plurality of sets of parameter values and evaluation index values, and a trained machine learning model that can output information indicating a predicted value of the evaluation index value; an uncertainty calculation unit (103) that calculates, on the basis of the trained machine learning model and the parameter adjustment data set, an uncertainty value of the evaluation index value relating to the parameter selected by the parameter selection unit (101); and an uncertainty-attached partial dependency plot output unit (104) that outputs information indicating the partial dependency value calculated by the partial dependency calculation unit (102) and the uncertainty value calculated by the uncertainty calculation unit 103.

PREDICTING NANOBODY STABILITY AND GENERATING STABILITY-MODULATED SEQUENCES

Resumen de: WO2025259965A1

In some embodiments, a computer-implemented method of generating one or more artificial amino acid sequences representing artificial proteins predicted to have a different level of stability than a protein represented by an input amino acid sequence is provided. A computing system receives the input amino acid sequence and uses a tuned base machine learning model to generate an encoding of the input amino acid sequence. The tuned base machine learning model was fine-tuned to encode characteristics of proteins having the different level of stability. The computing system generates the one or more artificial amino acid sequences by decoding the encoding of the input amino acid sequence.

DISTRIBUTED INFERENCE ENGINE

Resumen de: US2025384312A1

A distributed inference engine system that includes multiple inference engines is disclosed. A particular inference engine of the multiple inference engines may receive a prompt and its associated data, and divide the data into multiple data portions that are distributed to the multiple inference engines. Operating in parallel, and using a machine-learning model and respective data portions, the multiple inference engines generate an initial token. The multiple inference engines also generate, in parallel and using corresponding portions of the machine-learning model and the initial token, a subsequent token.

LANGUAGE MODEL AND ONTOLOGY ASSISTED MACHINE LEARNING SERVICE

Resumen de: US2025384290A1

Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving one or more user inputs identifying a data set and providing a first user request to perform a first task based on at least a portion of the data set, wherein the data set is defined by an ontology; using a large language model (“LLM”) to identify a first machine learning (“ML”) model type from a plurality of ML model types; using the LLM to identify a first portion of the data set to be used to perform the first task; using the LLM to generate a first ML model training configuration; and executing the first ML model training configuration to train a first custom ML model, of the first ML model type, to perform the first task.

COMPUTER SYSTEM, METHOD, AND DEVICE FOR ACTIVE LEARNING

Nº publicación: US2025384668A1 18/12/2025

Solicitante:

MUSASHI AI NORTH AMERICA INC [CA]
Musashi AI North America Inc

Resumen de: US2025384668A1

Systems, and methods, and devices for active learning are provided. The system includes a data storage device, dataset analysis tool, synthetic data generation module, anomaly module, image search engine module, explainable AI module, automated training platform, and optionally, a federated learning module. The system may be configured to operate on a general purpose or purpose-built computer, and may further include a processor, memory, and network interface. The system, through interaction of its constituent components, analyzes a provided dataset and generates synthetic data to augment data within the provided dataset. This provided data and generated data is used to train a machine learning model. The system may be operated iteratively to continuously improve the machine learning model trained by the system by applying explainable artificial intelligence techniques with little to no human intervention.