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65
resultados
Última actualización
28/02/2026 [07:26:00]
Resumen de: US20260056009A1
A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.
Resumen de: US20260058966A1
A method for behavior-based threat detection may include obtaining a first set of data corresponding to at least one of an employee or an enterprise associated with the employee. The method may include training a machine learning model for at least one of the employee or the enterprise associated with the employee by providing the first set of data to the machine learning model as training data, the machine learning model configured to identify deviations between behavioral traits of email communications and behavioral traits of the employee or the enterprise. The method may include receiving an email communication addressed to the employee. The method may include determining that the email communication represents a security risk by applying the machine learning model to the email communication. The method may include performing a remediation action on the email communication based on determining that the email communication represents a security risk.
Resumen de: US20260056983A1
A method and system for providing an intelligent response agent based on a sophisticated reasoning and speculation function can generate and provide response data for queries related to specialized documents using a deep-learning neural network that implements a stepwise process for a sophisticated reasoning and speculation function.
Resumen de: US20260057254A1
Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.
Resumen de: US20260059440A1
A method of reducing a power consumption of wireless communication circuitry of an edge device according to one embodiment includes determining a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the edge device and adjusting a wake-up interval of the wireless communication circuitry of the edge device based on the DTIM interval to reduce the power consumption of the wireless communication circuitry of the edge device.
Resumen de: WO2026043591A1
Aspects presented herein may enable a user equipment (UE) to indicate a correlation or a mapping between components of different feature groups (FGs) and differentiation of components in each FG to an artificial intelligence (AI) or machine learning (ML) (AI/ML) model/functionality level. In one aspect, a UE transmits, to a network entity, one or more first indications for a first positioning FG, where a number of the one or more first indications is indicative of a respective AI/ML model or functionality among a plurality of AI/ML models or functionalities associated with the first positioning FG and supported by the UE. The UE receives, from the network entity based on the one or more first indications, a second indication of at least one configuration for AI/ML positioning.
Resumen de: US20260054855A1
The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.
Resumen de: US20260058949A1
A system and method for inferring an operating system version for a device based on communications security data. A method includes identifying a plurality of sequences in communications security data sent by the device; determining an operating system type of an operating system used by the device based on the identified plurality of sequences; applying a version-identifying model to the identified plurality of sequences, wherein the version-identifying model is a machine learning model trained to output a version identifier, wherein the applied version-identifying model is associated with the determined operating system type; and determining the operating system version of the device based on the output of the version-identifying model.
Resumen de: WO2026041226A1
The present invention relates to a method of performing error triaging in an industrial plant (106). The method involves receiving information (502) related to an incident in the industrial plant (106) and generating a knowledge graph (504) by analyzing a plurality of log files (108A-N). The knowledge graph (504) includes data on interdependencies among log events. The method further includes determining one or more nodes (506) of the knowledge graph (504) associated with a set of log events, generating a plurality of templates (508) for these log events using a first machine learning algorithm, and generating a summary report (516) for the incident by utilizing a large language model (514) to process the templates (508). This approach facilitates accurate and efficient identification, categorization, and reporting of errors within the industrial plant (106).
Resumen de: US2024354655A1
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method comprises: generating a set of candidate batches of model inputs; generating, for each candidate batch of model inputs, a respective score for the candidate batch of model inputs that characterizes: (i) an uncertainty of the machine learning model in generating predicted labels for the model inputs in the candidate batch of model inputs, and (ii) a diversity of the model inputs in the candidate batch of model inputs; and selecting the current batch of model inputs from the set of candidate batches of model inputs based on the scores; and training the machine learning model on at least the current batch of model inputs.
Resumen de: EP4700652A1
The present application relates to the technical field of machine learning. Disclosed are a method and system for interpreting a sparse interaction effect modeled by a black-box artificial intelligence model. The method and system can automatically analyze an interactive distribution modeled by a model. The implementation of the method and system comprises the following steps: providing data that needs to be assessed; using a black-box model to perform prediction on the data, so as to obtain a prediction result of the model; on the basis of an output of the black-box model, modeling the interaction effect between input units of samples, calculating the interaction intensity between combinations formed by the input units, and expressing the black-box model as an "AND addition relationships" and an "OR addition relationships" between the combinations of the input units; and performing optimization, such that the "AND addition relationships" and the "OR addition relationships" are sparser. The advantages of the present invention lie in that a quantification method for interpreting the interaction modeled by a black-box artificial intelligence model is provided, and in comparison with previous research, a sparser and concise interactive interpretation can be obtained.
Resumen de: EP4700664A2
A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
Resumen de: EP4700603A1
Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.
Resumen de: WO2024218535A1
The disclosure relates to a ML-based method for determining a CCE aggregation level for a UE in a PDCCH. The method comprises obtaining RBS traces. The method comprises training, using first data obtained from the traces, a machine learning model to predict a probability of discontinuous transmission (DTX) "isDTX probability". The method comprises inputting second data obtained from the traces into the machine learning model, obtaining the isDTX probability and expanding the second data with the isDTX probability. The method comprises, for each of a plurality of probability thresholds (PTs) and for each of a plurality of strategies, selecting a data having an isDTX probability greater or equal to the PT and best satisfying the strategy and using the data to train a classifier. The method comprises selecting one classifier and using the classifier for determining the CCE aggregation level for the UE in the PDCCH.
Resumen de: EP4700611A1
The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.
Resumen de: EP4700604A1
A system for preparing machine learning training data for use in evaluation of term definition quality. The system can include a server having at least one server processor and at least one server memory for storing a plurality of terms with corresponding definitions, and a plurality of client devices each having at least one client memory device and at least one client processor. The client processor programmed to receive at least one of the plurality of terms and its corresponding definition from the server, display the term and its corresponding definition, and receive an indication of whether the definition satisfies one or more definition quality guidelines. The server memory includes instructions for causing the at least one server processor to receive the indications from the plurality of client devices and label each definition as satisfying each of the definition quality guidelines or not based on the received indications.
Resumen de: US20260050503A1
Methods and systems are for generating real-time resolutions of errors arising from user submissions, computer processing tasks, etc. For example, the methods and systems described herein recite improvements for detecting errors in one or more user submissions and providing resolutions in real-time. To provide these improvements, the methods and systems use a machine learning model that is trained to return probability error scores based on a plurality of variables. By using the multivariate approach, the methods and systems may produce a highly accurate detection.
Resumen de: US20260049833A1
An apparatus and method for transport management is presented. The apparatus includes a memory communicatively connected to a processor to output routing data of transport entities as a function of aggregated transport data, wherein the outputting comprises: receive transport data and bound parameters of a transport from a carrier device; iteratively train an aggregation machine-learning model to combine the transport data, wherein the training comprises generating an aggregation training data correlating the transport data as inputs and aggregated transport data as outputs; modify a characteristic of the transport; update the aggregated transport data based on the modification of the characteristic of the transport; retrain the aggregation machine-learning model as a function of the updated aggregated transport data; generate the routing data, wherein the routing data comprises instructions to further modify the characteristic of the transport; and automatically change the characteristic of the transport based on the routing data.
Resumen de: WO2026035326A1
The disclosed concepts relate to training a machine learning model to provide help sessions during a video game. For instance, prior video game data from help sessions provided by human users can be filtered to obtain training data. Then, a machine learning model can be trained using approaches such as imitation learning, reinforcement learning, and/or tuning of a generative model to perform help sessions. Then, the trained machine learning model can be employed at inference time to provide help sessions to video game players.
Resumen de: WO2026035335A1
Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a machine learning model comprising a plurality of layers, and a set of input data for the machine learning model, are accessed. A combination of hyperparameters for the machine learning model is selected based on the set of input data, comprising selecting, for each respective layer of the plurality of layers, a respective cache size based on the input data. The machine learning model is deployed according to the combination of hyperparameters.
Resumen de: WO2026033326A1
An apparatus including at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: transmit, to a network entity, a configuration used when at least one model is trained; wherein the at least one model is an artificial intelligence or machine learning model; and receive, from the network entity, information related to a consistency between the configuration used when the at least one model is trained and a configuration used when the at least one model is to be applied during inference.
Resumen de: WO2026032684A1
Disclosed are devices, methods, apparatuses, and computer readable media for fallback of machine learning functionality An example apparatus for a terminal device may include at least one processor and at least one memory. The at least one memory may store instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive from a network, at least one first configuration for a machine learning functionality of a determined network function, and a second configuration for a non-machine learning functionality of the determined network function, wherein the second configuration is a fallback configuration from the first configuration; receive from the network, a first indication indicating the terminal device to activate fallback from the machine learning functionality; and in response to the first indication, apply modifications to the first configuration for use during fallback, and enable the second configuration in the network function.
Resumen de: WO2026035375A1
Aspects of the disclosure are directed to a (e.g., capability-based window) configuration for a reference signal receive (RS-Rx) resource-based processing task associated with an artificial intelligence machine learning (AIML) model. In an aspect, the RS-Rx resource-based processing task may be related to sensing or positioning or another task type (e.g., beam management, channel state information (CSI) operations, etc.). In an aspect, the RS-Rx task may be associated with any type of RS-Rx resource relative to the UE (e.g., downlink reference signals, sidelink reference signals, etc.). Such aspects may provide various technical advantages, such as AIML processing window configurations that are configured based on AIML model-specific capabilit(ies) of the UE, which may improve functionalities associated with the AIML model (e.g., improved sensing or positioning or beam management, etc.) and/or improved AIML model monitoring.
Resumen de: WO2026035512A1
A network device (PRU, WTRU) may receive a request to collect data for artificial intelligence or machine learning (AI/ML) positioning model training, for example from a network data analytics function (NWDAF) and/or from a model training logical function (MTLF) (450b). The request may include an indication of an area of interest, a time window associated with the data for AI/ML positioning model training, a requested number of data samples of the data for AI/ML positioning model training, and/or a data source type of the data for AI/ML positioning model training. The network device may receive the data for AI/ML positioning model training and/or receive location data associated with the one or more WTRUs. The network device may send the location data and the data for AI/ML positioning model training to the NWDAF or the MTLF (485, 495).
Nº publicación: US20260043656A1 12/02/2026
Solicitante:
TIDALX AI INC [US]
TidalX AI Inc
Resumen de: US20260043656A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
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