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DEVICES, METHODS, APPARATUSES, AND COMPUTER READABLE MEDIA FOR FALLBACK OF MACHINE LEARNING FUNCTIONALITY

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.

MULTIVARIABLE SERVICE TERMINATION RISK CLASSIFICATION USING MACHINE LEARNING

Resumen de: US20260044803A1

A method can include receiving input data comprising a plurality of features for a plurality of users. A method can including providing the input data to a risk prediction model configured to predict a termination likelihood for each user. In some implementations, the risk prediction model can be a random forest model. A method can include identifying, based on the predicted termination likelihood for each user, an at risk population including users with a termination risk above a threshold amount. A method can include determining, for each user of the at risk population, a profile type of a plurality of profile types. The profile type can describe certain attributes of the user. In some implementations, an end user can select a profile type. A method can include outputting members of the at risk population having the selected profile type.

WINDOW CONFIGURATION FOR A REFERENCE SIGNAL RECEIVE RESOURCE-BASED PROCESSING TASK ASSOCIATED WITH AN ARTIFICIAL INTELLIGENCE MACHINE LEARNING MODEL

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.

MEMORY-CONSTRAINED ATTENTION IN MACHINE LEARNING MODELS

Resumen de: US20260044745A1

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.

METHODS AND SYSTEMS FOR DATA COLLECTION FOR ARTIFICIAL INTELLIGENCE BASED POSITIONING MODEL TRAINING IN A WIRELESS NETWORK

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).

CAUSALLY AWARE EDGE-DEPLOYABLE MACHINE LEARNING MODELS AND SYSTEMS

Resumen de: US20260044758A1

A system for generating and deploying savant language models that operate in conjunction with a directed acyclic graph. In some cases, a first stage cloud-based system may utilize large language models and domain specific directed acyclic graphs to generate deployable models. The deployable models may include the savant language models and sub-domain directed acyclic graphs that may operate in computational resource restricted environments.

METHOD AND DEVICE FOR SELECTING BEAM TO BE REPORTED IN MACHINE LEARNING-BASED BEAM MANAGEMENT

Resumen de: WO2026034877A1

The present invention relates to a method by which a terminal selects a beam to be reported in machine learning-based beam management, the method comprising the steps of: receiving, from a base station, configuration information of a measurement resource set and M number of report beams for AI/ML inference; determining, on the basis of measurement values of the measured beams, a beam to be reported; and transmitting the determined beam information to the base station, wherein, when the number of candidate beams to be reported exceeds M due to tie beams having the same or similar measurement values, the final beams to be reported are determined by excluding at least one from among same through a tie beam processing operation.

SHIP RECOURSE OPTIMIZATION

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.

QUANTUM MACHINE LEARNING

Resumen de: WO2026030790A1

The disclosure relates to machine learning, more particular to training a machine learning model comprising a classical sub-model and a quantum sub-model. A classical processor, receives, from a classical device, an intermediate classical output from a classical sub-model of the machine learning model and configures quantum gates of a quantum circuit based on the intermediate classical output. A quantum processor executes the quantum circuit using the quantum gates to determine a quantum circuit output, the quantum circuit being configured to represent a quantum sub-model of the machine learning model. The classical processor adapts the quantum circuit output to determine a further classical output; updates the quantum sub-model based on minimising a loss involving the further classical output; and transmits a loss propagation value to the classical device, to cause the classical device to update the classical sub-model based on the loss propagation value, thereby training the machine learning model.

BIOMASS USE ASSISTANCE DEVICE, METHOD, AND PROGRAM

Resumen de: EP4693123A1

A biomass utilization support device: acquires biomass information relating to a biobased material and product information for each of a plurality of products including information about materials configuring the products; uses a machine learning model, which has been trained to estimate appropriate values for replacement amounts in a case of replacing a portion of the materials configuring the products with the biobased material, and the acquired biomass information and product information to estimate the appropriate values for each of the plurality of products; calculates, for each of the plurality of products, environmental impact indicators in a case in which a portion of the materials configuring the products has been replaced with the biobased material at the replacement amounts represented by the estimated appropriate values; and outputs support information listing the estimated appropriate values and the calculated environmental impact indicators.

LEARNING MODEL GENERATION DEVICE, INSPECTION VALUE PREDICTION DEVICE, LEARNING MODEL GENERATION METHOD, INSPECTION VALUE PREDICTION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

Resumen de: EP4693331A1

This learning model generation device 10 is equipped with a learning model generation unit 11 which, when a function expressing a change in an inspection value obtained by inspecting a person is set, generates a learning model in which the inspection value is the explanatory variable and the parameter is the objective variable, by performing machine learning using inspection values of sample people and parameters of the function for the sample people as training data.

MACHINE LEARNING MODEL FEATURE SELECTION IN A COMMUNICATION NETWORK

Resumen de: CN120898407A

Embodiments of the present disclosure provide machine learning model feature selection in a communication network. The method includes, in response to a feature selection trigger of a first machine learning model, determining a target input feature set for an analysis task based on contextual information related to the analysis task, the first machine learning model being currently provisioned for performing the analysis task based on a current input feature set, the current input feature set is different from the target input feature set; and causing a second machine learning model to be provisioned to perform an analysis task based on the determined set of target input features. In this manner, the machine learning model may be supplied with an optimized set of input features that is applicable to the current network context and provides an acceptable level of model performance.

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

Resumen de: EP4694428A1

A method performed by a first device in a wireless communication system, according to at least one embodiment among the embodiments disclosed in the present specification, comprises: receiving, from a second device, one or two or more data sets related to positioning; training an artificial intelligence/machine learning (AI/ML) model on the basis of at least a portion of the one or two or more data sets; and acquiring positioning information outputted from the trained AI/ML model, wherein data label-related information is given to each of the received one or two or more data sets, and the data label-related information may include positioning-related actual measurement information and information related to the quality of the actual measurement information.

SYSTEM FOR RESOURCE ALLOCATION IN A HYBRID DISTRIBUTED COMPUTATIONAL ENVIRONMENT

Resumen de: EP4693046A1

Systems, computer program products, and methods are described for resource allocation in a hybrid distributed computational environment. An example system segments a received task into multiple sub-tasks. Upon partitioning the task, each sub-task is assigned to the appropriate computational resource (e.g., CPU, GPU, or QPU), enabling parallel execution of multiple sub-tasks. Both task partitioning and computational resource determination is determined using a machine learning model. Additionally, the machine learning model may continuously monitor the execution of each sub-task by receiving resource utilization information and performance metrics associated with the execution of each sub-task. The resource utilization information and performance metrics may then be used to update the machine learning model.

MANAGEMENT OF MACHINE LEARNING ENTITY INFERENCE EMULATION IN A CELLULAR NETWORK

Resumen de: WO2024211680A1

A device, a method, a system and one or more computer-readable media. A first example device is to host a management service (MnS) producer for a wireless cellular network. One or more processors of the first device are to receive, from an MnS consumer, a request to perform AI/ML emulation in one or more available machine learning (ML) emulation environments; and send to the MnS consumer one or more instances of an information object class (IOC) associated with the process of the AI/ML emulation. A second example device is to host an MnS consumer. One or more processors of the second device are to send, to an MnS producer, a request to perform AI/ML emulation in one or more available machine learning (ML) emulation environments; and receive, from the MnS producer one or more instances of an information object class (IOC) associated with the process of the AI/ML emulation.

INTERACTIVE INTERFACE TASK AUTOMATION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) ACTION MODELS IMPROVED WITH RETRIEVAL-AUGMENTED GENERATION (RAG)

Resumen de: WO2026029823A1

This disclosure describes a framework for performing user-requested tasks automatically across an interactive interface using various types of machine learning models. Specifically, this disclosure outlines and describes a task execution system that utilizes a generative artificial intelligence (AI) action model and retrieval-augmented generation (RAG) to complete user-requested actions across an interactive interface. The task execution system solves many of the current limitations of LAMs by using a generative AI action model to determine a session plan, which includes a set of actions for accomplishing stages of the actionable task across the interactive interface, obtaining visual context information of each interactive interface segment, integrates RAG results to improve the accuracy of both the session plan and individual actions, and self-corrects when faced with unexpected obstacles.

FRAMEWORK FOR FAILURE DETECTION, FORECASTING AND REMEDIATION COMBINING DOMAIN-SPECIFIC TELEMETRY AND CUSTOMIZED MACHINE LEARNING MODELS

Resumen de: US20260037352A1

Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model. By providing a centralized framework for forecasting IT component failures that combines the use of domain-specific telemetry data with customized ML models, improved fault resilience and reduced system downtime can be achieved.

DATA AGGREGATION AND MODEL TRAINING BASED ON SPARSE DATASETS

Resumen de: WO2026030336A1

A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.

EXECUTION AND SEMANTIC ERROR CORRECTION CAPABILITIES FOR NATURAL LANGUAGE TO LOGICAL FORM MODEL

Resumen de: WO2026030330A1

Techniques are disclosed herein for providing and using a natural language to logical form model having execution and sematic error correction capabilities. In one aspect, a method is disclosed that includes: accessing a set of training examples and generating a set of error correction training examples via an iterative process performed for each training example. The iterative process includes generating an inferred logical form, executing the inferred logical form on a database, when executing the inferred logical form on the database fails, obtaining an execution error message corresponding to the failure, and recording the inferred logical form and the execution error message as part of an execution error example, and populating an error correction prompt template with the execution error example to generate an error correction training example. A machine learning model may then be trained with at least the set of error correction training examples.

ELECTRIC VEHICLE CHARGE TIME PREDICTION

Resumen de: US20260037815A1

Systems and methods for electric vehicle charge time prediction are provided. Embodiments include providing, as inputs to a machine learning model, one or more attributes of a battery of a vehicle and one or more attributes of a vehicle charger. Embodiments include receiving, from the machine learning model in response to the inputs, a set of incremental charge time predictions corresponding to a plurality of increments between a current charge level of the battery of the vehicle and a target charge level of the battery of the vehicle. Embodiments include providing via a user interface screen, based on the set of incremental charge time predictions, a charge time estimate for charging the battery of the vehicle using the vehicle charger.

PERIPHERAL BLOOD SMEAR-BASED DEEP LEARNING TO PREDICT CANCER-ASSOCIATED THROMBOSIS

Resumen de: WO2026030356A1

Presented herein are systems and methods of determining values indicating degrees of risk for venous thromboses (VTEs) in subjects. A computing system can identify a first plurality of images for a first subject at risk of VTE, each of the first plurality of images corresponding to a respective white blood cell (WBC) in a first blood sample obtained from the first subject at a first time. The computing system can provide the first plurality of images to a machine learning (ML) architecture. The computing system can generate a plurality of embedding sets, each embedding set of the plurality of embedding sets corresponding to a respective image of the first plurality of images. The computing system can determine, based on executing the ML architecture, a value indicating a degree of risk of VTE for the first subject at the time interval relative to the first time.

INFORMATION PROCESSING METHOD, INFORMATION PROCESSING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE RECORDING MEDIUM STORING INFORMATION PROCESSING PROGRAM

Resumen de: US20260038241A1

An evaluation device acquires an inference result of an evaluation target image by an image recognition model generated by machine learning and uncertainty information indicating instability degree of the inference result, generates inference index information in which a confidence level indicating reliability of the inference result is assigned to information indicating whether the inference result is correct or incorrect by using a correct answer label, the inference result, and the uncertainty information associated with the evaluation target image, and evaluates inference accuracy of the image recognition model based on the inference index information.

MACHINE LEARNING-BASED MODIFICATION OF IMAGE CONTENT

Resumen de: US20260038163A1

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for modifying a captured image. The program and method provide for displaying, by a messaging application, an image captured by a device camera; providing, by the messaging application, a user interface for selecting from among a plurality of content modifiers to modify the image, the plurality of content modifiers including a first content modifier corresponding to a machine learning model trained with a plurality of image pairs, each image pair including a first image and a second image corresponding to a modified version of the first image; receiving user selection of the first content modifier from among the plurality of content modifiers; determining, in response to receiving the user selection, a modified version of the image based on output from the machine learning model; and displaying the modified version of the image.

SYSTEMS AND METHODS FOR COORDINATING EXECUTION OF AN ENSEMBLE OF MACHINE LEARNING MODELS

Resumen de: WO2026030680A1

System for coordinating execution of an ensemble of machine learning models to determine anatomical structures to target during cancer treatment are described herein. In examples, the systems can coordinate execution of multiple machine learning models based on different types of three-dimensional images of a patient. These images can include positron emission tomography (PET) images, computed tomography (CT) images, and/or other similar images. The outputs of the models can be correlated with one another to quantify locations and volumes of tumor lesions within the patient. In some examples, a tumor stage can be determined based on the quantification of the tumor lesions. This information can then be used to determine one or more optimal treatment plans for the patient.

INTERACTIVE INTERFACE TASK AUTOMATION UTILIZING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) ACTION MODELS IMPROVED WITH RETRIEVAL-AUGMENTED GENERATION (RAG)

Nº publicación: US20260037318A1 05/02/2026

Solicitante:

MICROSOFT TECH LICENSING LLC [US]
Microsoft Technology Licensing, LLC

Resumen de: US20260037318A1

This disclosure describes a framework for performing user-requested tasks automatically across an interactive interface using various types of machine learning models. Specifically, this disclosure outlines and describes a task execution system that utilizes a generative artificial intelligence (AI) action model and retrieval-augmented generation (RAG) to complete user-requested actions across an interactive interface. The task execution system solves many of the current limitations of LAMs by using a generative AI action model to determine a session plan, which includes a set of actions for accomplishing stages of the actionable task across the interactive interface, obtaining visual context information of each interactive interface segment, integrates RAG results to improve the accuracy of both the session plan and individual actions, and self-corrects when faced with unexpected obstacles.