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52
resultados
Última actualización
07/01/2026 [07:19:00]
Resumen de: WO2026006480A1
A method for discontinuing interaction processing using an enumeration detection system may include receiving data associated with a plurality of interaction instances. The plurality of interaction instances may be associated with an entity. The method may further include extracting one or more interaction features from the data. The method may further include providing the one or more interaction features to a determinative machine-learning model. The determinative machine-learning model may be trained to identify enumeration patterns and output an enumeration score based on the identified enumeration patterns. The method may further include determining that the enumeration score exceeds a predetermined threshold. The method may further include discontinuing interaction processing for the entity based on the enumeration score exceeding the predetermined threshold.
Resumen de: WO2026003235A1
Method of decision-support for a vehicle or related vehicle simulation, the method being executed by a system comprising a server (20), a client device (10) and a database (30), the method comprising the following phases: a. acquisition phase (100) in which the server (20) receives an input data from a device onboard of the vehicle, b. selection phase (200) in which the server (20) obtain a plurality of machine learning results R1, …, Rn and computes at least one selection score, then at least one selection score being used to select a preferred machine- learning model MLp, c. transmission phase (300) in which the server (20) sends the result Rp as a recommendation to the client device (10), d. supply phase (400) in which the client device (10) provides the recommendation to a user (60), e. return phase (500) in which the client device returns decision data to a database (30).
Resumen de: WO2026006680A1
The disclosure is directed to systems, methods, and computer-readable media for observability and data audit using implicit data dependency capture. Data dependency information can be intercepted, for example, as a user trains or otherwise interacts with a machine learning (ML) model. Data dependency information can include information regarding files, data sources, inputs, outputs, storage buckets, storage directories, and/or other pertinent information. A log of the data dependency information can be reviewed to determine ML model provenance.
Resumen de: WO2024178006A1
A method may include determining, based at least on a knowledge graph, a plurality of biological interaction profiles associated with a plurality of drugs. The knowledge graph being representative of a plurality of interactions between a variety of drugs, proteins, and a hierarchy of biological functions. Each biological interaction profile may be representative of the effects of a corresponding drug being propagated through protein-protein interactions and biological functions. A liver injury prediction model may be trained, based on a training dataset including the biological interaction profiles, a probability of drug induced liver injury. The liver injury prediction model to may be applied to determine, based on the biological interaction profile of a drug, the probability of liver injury associated with the drug. In some cases, the liver injury prediction model may further determine the probability of liver injury based on the molecular fingerprint and/or the molecular properties of the drug.
Resumen de: EP4671828A1
Systems, apparatuses, methods, and computer program products are provided herein. For example, a method may include access aviation specification data. In some embodiments, the method may include training a generative machine learning model using aviation specification data (504). In some embodiments, the method may include generating synthetic aviation data using the generative machine learning model (506). In some embodiments, the method may include training one or more global positioning system (GPS) spoofing detection machine learning models using the synthetic aviation data and historical aviation operations data (508). In some embodiments, the method may include deploying a first GPS spoofing detection machine learning model of the one or more GPS spoofing detection machine learning models to an edge-based device (510).
Resumen de: EP4672085A1
Method of decision-support for a vehicle or related vehicle simulation, the method being executed by a system comprising a server (20), a client device (10) and a database (30), the method comprising the following phases:a. acquisition phase (100) in which the server (20) receives an input data from a device onboard of the vehicle,b. selection phase (200) in which the server (20) obtain a plurality of machine learning results R1, ..., Rn and computes at least one selection score, then at least one selection score being used to select a preferred machine-learning model MLp,c. transmission phase (300) in which the server (20) sends the result Rp as a recommendation to the client device (10),d. supply phase (400) in which the client device (10) provides the recommendation to a user (60),e. return phase (500) in which the client device returns decision data to a database (30).
Resumen de: EP4672093A1
A training program of a machine learning model outputs a proposal for obtaining a desired result, the training program of a machine learning model causes a computer to execute a process including: acquiring training data including a plurality of attributes; acquiring constraint condition data of the attributes; calculating first information regarding prediction accuracy of the machine learning model based on the training data; calculating second information regarding feasibility of the proposal based on the training data and the constraint condition data; calculating an evaluation index based on the first information and the second information; and training the machine learning model based on the evaluation index.
Resumen de: EP4672040A1
A learning model generation apparatus 10 comprises: a graph generation unit 11 which generates, from a data group including biometric information of persons and information indicating the presence or absence of occurrence of diseases in the persons, a graph composed of nodes representing data points and edges representing relationships between the nodes; a graph supplementation unit 12 which supplements the generated graph for a deficiency therein; and a model generation unit 13 which generates, from the supplemented graph, a data group in which the deficiency is supplemented, performs machine learning using the generated data group as training data, and generates a prediction model for predicting the occurrence of diseases in a person.
Resumen de: WO2025265056A1
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for fine-tuning a target machine learning model to perform a target machine learning task. In one aspect, a method comprises: obtaining initial parameters for a target machine learning model; at each interpolation iteration of a sequence of interpolation iterations: training a plurality of auxiliary machine learning models to perform the target machine learning task using training data for the target machine learning task, interpolating the trained parameters for the plurality of auxiliary machine learning models for the interpolation iteration, and updating the current parameters for the target machine learning model using the interpolated parameters for the interpolation iteration; and, after the final interpolation iteration, determining a trained set of parameters for the target machine learning model based on the current parameters for the target machine learning model.
Resumen de: WO2025261578A1
A first node (111) obtains (302) two or more first sets of data out of a plurality of sets collected by third nodes (113). Each third node has collected a set. A plurality of clusters have been determined. Each of the two or more first sets of data corresponds to a respective set of data in a center of a respective cluster. The plurality of clusters have been determined based on a similarity of respective statistical features of the sets of data. A number of the two or more first sets of data is smaller than a second number of the plurality of sets. The first node (111) determines (303) and tunes a respective hyperparameter for each obtained two or more first sets of data to train a respective machine learning model with a corresponding set of data of the plurality of sets of data and outputs (305) an indication indicating the hyperparameters.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Nº publicación: WO2025259798A1 18/12/2025
Solicitante:
CONOCOPHILLIPS CO [US]
CONOCOPHILLIPS COMPANY
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.
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