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

Resumen de: US20260270934A1

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.

ESTIMATION MODEL GENERATION METHOD/GENERATION DEVICE FOR ESTIMATING REACTIVE CONDITION, REACTIVE CONDITION PROVIDING METHOD/PROVIDING DEVICE, AND PROGRAM

Resumen de: US20260269023A1

In the present invention, under a condition that acquired information on a plurality of chemical substances as reaction objects and a product is similar to information on a plurality of chemical substances and a product set to a reactive condition, a plurality of the acquired reactive conditions when a plurality of chemical substances as reaction objects are reacted are set from reaction items set to the reactive condition. Furthermore, using an estimation model in which machine learning has been executed with chemical structure information and physical property information of a plurality of chemical substances reacted in the past, reactive conditions in the reactions, chemical structure information and physical property information of produced products, and yields when the reactions are performed under the reactive conditions as training data, the yield is estimated for each of the plurality of reactive conditions, and the reactive condition under which the yield among the estimated yields meets a predetermined condition is displayed (output).

ARTIFICIAL INTELLIGENCE DRIVEN SYSTEMS OF SYSTEMS FOR CONVERGED TECHNOLOGY STACKS

Resumen de: AU2025212532A1

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

FINGERPRINTING AND MACHINE LEARNING FOR PRODUCTION PREDICTIONS

Resumen de: US20260268419A1

A method of hydrocarbon production by obtaining a plurality of samples from a plurality of wells over a period of time, obtaining timelapse production characteristics from each sample, as well as time-lapse fingerprint data. These two datasets are used to train a machine learning model to obtain a predictive model that can be used to optimize and implement a production plan from one or more of the original wells or new wells in the same reservoir.

TRAINING DEVICE, INFORMATION PROCESSING APPARATUS, SUBSTRATE PROCESSING DEVICE, TRAINING METHOD AND PROCESSING CONDITION DETERMINATION METHOD

Resumen de: US20260268050A1

A training device includes a first hardware processor, wherein the first hardware processor acquires a first dataset including a processing condition for a process to be executed by a substrate processing device, and a processing result of the process, generates a pre-processing condition, causes a learning model to execute machine learning using the second dataset, and causes the trained learning model to execute machine learning using the first dataset, with the trained learning model having executed machine learning using the second dataset, and the second dataset includes a pre-processing result that is predicted by a predetermined prediction algorithm based on the pre-processing condition, and the pre-processing condition.

BIOLOGICAL AGENT AEROSOL CLASSIFICATION/IDENTIFICATION USING MACHINE LEARNING ALGORITHMS

Resumen de: US20260269022A1

In accordance with various embodiments, a system and a method for identifying a particle as a bioactive stimulant are provided. The system includes a processor configured to execute machine-readable instructions borne by a non-transitory computer-readable memory device to cause the processor to process one or more steps of the method disclosed herein. The system/method include the steps to: receive a dataset comprising scattered light signals and/or fluorescent light signals of the particle; analyze the dataset using one or more machine learning models, wherein the one or more machine learning models is trained using elastic scattering light intensity data and fluorescent light intensity data of a library of biological molecules; generate a probability score that the particle is bioactive based on the analysis of the dataset; determine, via classification of the probability score, that the particle is bioactive; and/or output a result indicating that the particle is the bioactive stimulant.

SYSTEMS AND METHODS FOR DYNAMICALLY UPDATING MODELS USING MACHINE LEARNING

Resumen de: US20250148482A1

0000 A computing system for detecting patterns in data is provided. The computing system includes a model engine configured to receive an initial dataset, and segment the initial dataset into a plurality of subsets. The model engine is further configured to assign a weight to each subset based at least in part on an age of the subset, train a machine learning model on each subset separately in accordance with the assigned weighting for that subset. The model engine is further configured to receive a candidate dataset, analyze the candidate dataset using the trained machine learning model, and assign a score to the candidate dataset based on the analysis. The computing system further includes a rules engine configured to receive the candidate dataset and the corresponding score from the model engine, and generate and output, based at least in part on the score, a decision regarding the candidate dataset.

METHOD FOR DETERMINING A CONTRIBUTION OF A FEATURE COMBINATION TO AN OUTPUT OF A MACHINE LEARNING MODEL

Resumen de: EP4804085A1

A computer-implemented method, wherein the method determining a contribution of, optionally pairwise or higher-order, combinations of features between at least two application feature sets to an application output of a trained machine learning model, each combination of features comprising at least one feature of a first application feature set and at least one feature of a second application feature set.

METHOD FOR IMPROVING ACCURACY OF MACHINE LEARNING MODELS

Resumen de: GB2642421A

Method for training a neuro-symbolic machine learning model, comprising: for each image depicting at least two objects of a training dataset: inputting the image into a neural module 102, 200, 202 to obtain bounding boxes and features therein (digit); inputting each bounding box and object feature into a symbolic module (106, Fig.1; rest of Fig.2) to obtain a plurality of possible labels i.e. partial labels 212 and possible relationships 210 as a new partially-labelled training dataset; and training the neuro-symbolic model (neural module and the symbolic module) by calculating a loss from a ground truth label for the image. The symbolic module may use a set of logical rules to constrain the labels and explanations (R1-R5, Fig.7). The trained neuro-symbolic model may generate a scene graph, perform action recognition, perform visual question answering (Fig.4) or control an autonomous or semi-autonomous electronic device. The electronic device may be a moveable robot or a wearable augmented reality device.

RISK ASSESSMENT OF ROTOR ANGLE INSTABILITY IN A POWER NETWORK

Resumen de: EP4804364A1

Rotor angle instability is a key criterion of dynamic stability in power networks. State-of-the-art machine-learning approaches are difficult to scale and have limited inputs with which to make predictions as to rotor angle instability. Accordingly, disclosed embodiments utilize a machine-learning model that is applied to bus voltage angles, which are local quantities available at every bus in the power network, to derive a prediction of the risk of rotor angle stability in the power network. These predictions may be biased in order to avoid false negatives. The machine-learning model may be a message-passing neural network. The resulting predictor is capable of quickly and reliably flagging potential rotor instability within a power network.

DATA COLLECTION ACROSS MULTIPLE USER EQUIPMENT CAPABILITY TYPES

Resumen de: WO2026180870A1

Example embodiments of the present disclosure are directed to data collection across multiple user equipment (UE) capability types. A method comprises providing, to a second apparatus, results of measurements associated with data collection for a machine learning functionality and ground truth information for the machine learning functionality; receiving, from the second apparatus, an indication for an adjustment of measurement performance at the first apparatus; and adjusting the measurement performance based on the indication.

INTELLIGENT PARKING SPACE ALLOCATION SYSTEM AND METHOD WITH DYNAMIC MATCHING, MACHINE LEARNING AND BLOCKCHAIN INTEGRATION

Resumen de: WO2026180016A1

The invention relates to a computer-implemented method and system for dynamically allocating and coordinating parking spaces by means of real-time synchronisation between a departing driver and a driver searching for a parking space. In order to determine a precise departure time, the system uses sensor fusion comprising GNSS data, inertial sensor technology, and OBD-II vehicle data, as well as activity recognition. A hybrid machine-learning model comprising LSTM networks and random-forest regressors predicts the handover probability and generates an optimised assignment of the participants. Transaction security is ensured by a blockchain-based smart contract (preferably a layer-2 solution) which validates arrival, departure and payment by means of zero-knowledge proofs or a cryptographic handshake. By integrating predictive analytics and decentralised validation, traffic caused by drivers searching for parking is proactively reduced and utilisation of parking areas is optimised.

USING MACHINE LEARNING TECHNIQUES TO GENERATE SPATIO-TEMPORAL PREDICTIONS RELEVANT TO NOVEL GEOGRAPHIC AREAS

Resumen de: WO2026178648A1

In some embodiments, a computer-implemented method is provided. A computing system ingests historical data for a first geographic area and causal priors from a Bayesian model to create a knowledge graph. The computing system trains a spatial-temporal model to represent relationships in the knowledge graph over time and space. The computing system trains, using the knowledge graph and the spatial-temporal model, an imputation model to generate latent representations usable to infer missing data and to represent causal relationships. The computing system trains a generative model to generate counterfactual scenarios based on latent representations generated by the imputation model.

SCALABLE DETECTION OF DECENTRALIZED GENETIC BIOMARKERS IN HUMAN MICROBIAL DATA TO PERSONALIZE MEDICAL TREATMENT

Resumen de: WO2026181071A1

Detecting biomarkers in human microbiome DNA to predict a biological condition and administer its treatment. A microbiome network may be generated comprising nodes representing microbiome DNA sequences and edges representing a co-occurrence of each pair of microbiome DNA sequences in a same DNA sample or sub-length. Nodes may be bundled into distinct groups based on the node's degree quantifying a number of its edges indicating a number of unique microbiome DNA sequences that co¬ occur with the microbiome DNA sequence represented by the node in the same DNA sample or sub-length. Groups of bundled microbiome DNA sequences may be validated having an internal connectivity that satisfies an anomaly condition indicating the group's sequences co-occur with a probability that is unlikely randomly statistical, e.g., deviating from a power law distribution. A machine learning model may be trained with the validated groups to predict a biological condition correlated therewith to administer its treatment.

MITIGATING CYBER-THREATS IN A NETWORK

Resumen de: WO2026180318A1

A method of mitigating cyber-threats in a target network comprising a plurality of nodes, the method comprising: deploying a decentralised multi-agent model to the target network, wherein the decentralised multi-agent model comprises a plurality of local models; and at each of the plurality of nodes: monitoring a local region of the network to obtain local network information; and using the local model to predict threat mitigation actions, based on the local network information The decentralised multi-agent model may be a machine learning model trained using reinforcement learning wherein, for each of one or more training networks: the local models are deployed in respective nodes of the training network; and a trainer system iteratively evaluates a performance of the multi-agent model and adjusts the local models.

PREDICTING PERFORMANCE OF CLINICAL TRIAL FACILITATORS USING PATIENT CLAIMS AND HISTORICAL DATA

Resumen de: US20260260719A1

0000 A clinical trial site evaluation system applies a machine learning technique to predict recruitment performance of a candidate clinical trial facilitator (such as a clinical trial site or a clinical trial investigator) for a clinical trial based on patient claims data or other data associated with the candidate clinical trial facilitator. In a training phase, a training system trains the machine learning model based on historical recruitment data associated with historical clinical trials and patient claims data (or other data) associated with the clinical trial facilitators associated with those trials. In a prediction phase, the machine learning model is applied to claims data (or other data) associated with candidate clinical trial facilitators to predict recruitment performance.

CROSS-DEVICE DATA SYNCHRONIZATION BASED ON SIMULTANEOUS HOTWORD TRIGGERS

Resumen de: US20260260649A1

Techniques are described herein for cross-device data synchronization based on simultaneous hotword triggers. A method includes: executing a first instance of an automated assistant in an inactive state at least in part on a first computing device operated by a user; while in the inactive state, receiving, via one or more microphones of the first computing device, audio data that captures a spoken utterance of the user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a threshold that is indicative of the one or more hotwords being present in the audio data; in response to determining that the predicted output satisfies the threshold, performing arbitration with at least one other computing device that is executing at least in part at least one other instance of the automated assistant; and in response to performing arbitration with the at least one other computing device, initiating synchronization of user data or configuration data between the first instance of the automated assistant on the first computing device and the at least one other instance of the automated assistant on the at least one other computing device, the user data comprising data that is based on one or more interactions with the user at the first computing device, the one or more interactions occurring prior to the receiving of the au

DYNAMICALLY DIVIDING ACTIVATIONS AND KERNELS FOR IMPROVING MEMORY EFFICIENCY

Resumen de: US20260260115A1

Embodiments are generally directed to dynamically dividing activations and kernels for improving memory efficiency. An embodiment of a method in a compute engine performing machine learning comprises: receiving, by a convolutional layer of a convolutional neural network (CNN) implemented on the compute engine, a plurality of activation groups contained in an input data, wherein the convolutional layer includes one or more kernel groups and the one or more kernel groups each include a plurality of kernels; determining a plurality of memory efficiency metrics based on the number of activation groups of the plurality of activation groups and the number of kernels of the plurality of kernels; selecting a first optimal number of activation groups and a second optimal number of kernels that are associated with an optimal memory efficiency metric in the plurality of memory efficiency metrics; and performing a convolutional operation on the input data based on the first optimal number and the second optimal number.

Methods of Operating Electrochemical Storage Devices Based on Anomaly Clustering, and Software and Systems Including Same

Resumen de: US20260259270A1

Methods of operating electrochemical storage devices, such as secondary batteries, battery modules, and battery cells, using machine-learning models for detecting operating conditions that indicate that one or more electrochemical storages device is/are experiencing an anomaly that may affect its operation. In some embodiments such a method may include deploying an anomaly handler that implements a trained clustering model to identify anomalous operating data and using output of the clustering model to take an operation-control action to control an operation of one or more electrochemical storage devices and/or provide an indication that attention may be needed. In some embodiments a trained detector model is deployed to filter out “normal” operating data so that the trained clustering model handles only “anomalous” operating data, which can drive improvements to the anomaly handler. Methods of training machine-learning models and apparatuses and systems implementing anomaly handlers are also disclosed.

COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE-LEARNING CONTEXT-AWARE CURATION AND METHODS OF USE THEREOF

Resumen de: US20260260134A1

0000 Systems and methods of context-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: obtaining a trained activity completion time estimation machine learning model that determines activity completion time prediction data for an activity of an entity; receiving, from a first computing device of a user, current entity-specific device-executed user activity data of a current entity-specific user activity associated with a user and an entity; receiving from a second computing device associated with the particular entity, current user-specific entity activity data associated with a current user-specific entity activity, related to the current entity-specific user activity; utilizing the trained activity completion time prediction machine learning model to determine current user-specific entity activity completion time prediction data for the current user-specific entity activity; and determining a current displaying context to notify the user of the current user-specific entity activity completion time prediction.

Hellinger Decision Trees for Fraud Detection

Resumen de: US20260260174A1

A Hellinger decision tree can detect fraudulent transactions in a data set of financial transactions. Applying the Hellinger decision tree uses a Hellinger distance. The Hellinger decision tree can be part of a machine learning algorithm. In an example, the Hellinger decision tree is a positive and unbalanced Hellinger decision tree used with an imbalanced positive and unlabeled data.

INFERENCE DATA SIMILARITY FEEDBACK FOR MACHINE LEARNING MODEL PERFORMANCE MONITORING IN BEAM PREDICTION

Resumen de: US20260260136A1

0000 In an aspect, a UE may obtain a first indication of a first set of characteristics associated with a plurality of reference datasets. The UE may calculate a respective level of similarity between an inference dataset and each of the plurality of reference datasets based on the first set of characteristics and a second set of characteristics associated with the inference dataset. The UE may output at least one second indication of the respective level of similarity between the inference dataset and each of the plurality of reference datasets.

ADVERSARIAL TRAINING OF A MODEL FOR QUESTION ANSWERING

Resumen de: US20260260122A1

Embodiments of the present disclosure provide a solution for adversarial model training. A method includes: generating a prompt input using an adversarial machine learning model; providing the prompt input to a target machine learning model, to generate a response to the prompt input; determining a first reward score for the response with respect to the prompt input; and fine-tuning the target machine learning model according to a first optimization objective, the first optimization objective being configured to increase or maximize the first reward score for the target machine learning model.

SYSTEM AND METHOD FOR MANAGING AN ON-SENSOR MACHINE LEARNING (ML) MODEL

Resumen de: US20260260170A1

0000 A system and method for managing an on-sensor machine learning (ML) model includes monitoring performance parameters of plurality of on-sensor ML models present in an industrial plant; detecting a degradation of at least one on-sensor ML model based on the monitored ML model performance parameters of the plurality of on-sensor ML models, wherein degradation of the at least one on-sensor ML model comprises at least one of: data distribution change, training serving skew, model drift, occurrence of outlier event, and data quality issue; and updating the at least one on-sensor ML model based on the ML model upgradation parameters retrieved from one of the plurality of sources.

DATA COLLECTION ACROSS MULTIPLE USER EQUIPMENT CAPABILITY TYPES

Nº publicación: WO2026180869A1 03/09/2026

Solicitante:

NOKIA TECH OY [FI]
NOKIA TECHNOLOGIES OY

Resumen de: WO2026180869A1

Example embodiments of the present disclosure are directed to data collection across multiple user equipment (UE) capability types. A method comprises providing, to a second apparatus, results of measurements associated with data collection for a machine learning functionality and ground truth information for the machine learning functionality; receiving, from the second apparatus, further results of the measurements obtained by a third apparatus; and adjusting a measurement performance based on a comparison between the results and the further results.