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48
resultados
Última actualización
29/12/2025 [07:04:00]
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: 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: 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.
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.
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.
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.
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.
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.
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: 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: 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.
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: 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.
Resumen de: AU2024274930A1
A method and system for training machine learning models using natural language interactions as well as techniques utilizing machine learning models trained using natural language interactions. A method includes applying a language model to text of a set of natural language interactions in order to output a set of domain-specific language (DSL) data, wherein the set of natural language interactions is between a user and at least one other entity, wherein the set of natural language interactions indicates at least one user-defined concept; querying a knowledge base based on the set of DSL data in order to obtain at least one DSL query result; integrating the at least one DSL query result with a structured representation of the natural language interactions in order to create at least one contextualized DSL query result; and training the language model using the at least one contextualized DSL query result.
Resumen de: WO2025252383A1
The present subject matter relates to a method as follows. A current proof state may be encoded into a vector of real numbers of a fixed length. The current proof state defines a task for proving at least part of the statement. The vector may be encoded into a quantum state of a quantum system of the quantum computer. The quantum state may be used as an input quantum state by a quantum machine learning model for providing by the quantum machine learning model an output quantum state, wherein the measurement of the output quantum state represents a proof step for the defined task. The proof step may be provided to the proof assistant. In response to providing the proof step, a next proof state may be received from the proof assistant. It may be determined whether the received proof state indicates that the proof is completed. In response to determining that the proof is not completed, the received proof state may be used as the current proof state for repeating the method.
Resumen de: WO2025252418A1
The present subject matter relates to a method for proving a statement descriptive of a functionality of an electronic circuit using a quantum computer and a proof assistant. The method comprises: encoding a current proof state into a vector of real numbers of a fixed length, the current proof state defining a task for proving at least part of the statement, encoding the vector into a quantum state of a quantum system of the quantum computer, using the quantum state as an input quantum state by a quantum machine learning model for providing an output quantum state whose measurement represents a proof step for the defined task, measuring the output quantum state, thereby obtaining the proof step for the defined task, providing the proof step to the proof assistant, in response to providing the proof step, receiving a next proof state from the proof assistant.
Resumen de: WO2025254902A1
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: WO2025254972A1
In some aspects, a network node obtains one or more first quantities corresponding to positioning, sensing, or both machine learning model inputs, wherein the one or more first quantities comprise one or more channel measurements, quality indicators of the one or more channel measurements, timestamps of the one or more channel measurements, or any combination thereof, obtains one or more second quantities corresponding to positioning, sensing, or both machine learning model outputs, wherein the one or more second quantities comprise one or more ground truth labels, quality indicators of the one or more ground truth labels, timestamps of the one or more ground truth labels, or any combination thereof, and determines whether the one or more first quantities are associated with the one or more second quantities based on one or more association rules.
Resumen de: US2025379798A1
The disclosure provides a device capability discovery method and a wireless communication device. The wireless communication device transmits a capability message of the wireless communication device to a source device having a pool of machine learning (ML) models. The capability message shows whether the wireless communication device is capable of executing multiple ML models. The wireless communication device downloads if needed, and activates one or more ML models from a subset in the pool of ML models. The subset in the pool of ML models matches the capability message of the wireless communication device.
Resumen de: US2025377647A1
Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.
Resumen de: US2025378307A1
Aspects of the disclosure include methods for leveraging a universal embedding based entity retrieval deep learning model for candidate recommendations. A method can include receiving a request for a candidate pair having a first entity and a second entity and generating a filtered candidate pool including a first number of candidates. The filtered candidate pool can include a subset of an initial candidate pool having a second number of candidates larger than the first number of candidates. A learned distance function is selected from a plurality of distance functions. At least one distance function was predetermined prior to receiving the request and at least one distance function is generated in response to receiving the request. A distance measure is determined for each candidate in the filtered candidate pool using the learned distance function and a response is returned including top K candidates according to the determined distance measures.
Nº publicación: US2025378508A1 11/12/2025
Solicitante:
CONOCOPHILLIPS CO [US]
ConocoPhillips Company
Resumen de: US2025378508A1
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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