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NETWORK OPTIMIZATION BASED ON QUANTUM POSITIONING OF USER DEVICES

Resumen de: US20260301097A1

0000 A method performed by a processing system including at least one processor includes collecting, from a quantum sensor in a communications network, position data for a public safety device, tracking a movement pattern and a usage pattern of the public safety device by providing the position data as input to a quantum machine learning model that generates the movement pattern and the usage pattern as an output, determining, based on an analysis of the movement pattern and the usage pattern, that an emergency service should be contacted, and placing a call to the emergency service.

ARTIFICIAL INTELLIGENCE FOR COGNITIVE INTERFACES

Resumen de: US20260300689A1

In an example embodiment, a machine learning framework is provided that enhances reliability of analysis of non-invasive cognitive input, correlates the cognitive input with additional non-cognitive input, and identifies context-based possible selections from software processes. A graphical user interface may be provided that depicts the possible selections to the user in a manner that makes it clear to the user which thoughts lead to which selections. This graphical user interface may be iteratively updated with each selection by the user, with each selection leading to one or more possible selections displayed in the graphical user interface being replaced with different possible selections. The result is a seamless framework that aids in enhancing cognitive interfaces.

USING MACHINE-LEARNING MODELS TO GENERATE ADAPTIVE USER INTERFACES FOR ATTRIBUTE SELECTION AND ITEM REPLACEMENT SELECTION

Resumen de: US20260299971A1

An online system presented herein generates adaptive and personalized user interfaces for attribute selection and item replacement selection. The online system fulfills an order by picking items at a source and delivering the items to a user. When an item is not found at the source, a picker attempts to find a suitable replacement item. To find acceptable replacements, the online system allows the user, when placing an order, to specify attributes of an item that are important for that order. To provide an improved user interface that helps the user select attributes for a given item, the online system selects a subset of the item's attributes that are likely important and relevant to potential replacements. If the item is not found, the user's attribute selection is communicated to the picker, as well as used to determine and rank replacements for displaying at a user interface of a picker's device.

FAST QUANTIZATION FOR NEURAL NETWORKS

Resumen de: US20260300806A1

0000 A processing unit compares a first graph that represents a machine learning (ML) model and a second graph that represents a previously quantized ML model generated by quantizing a previous version of the ML model. Based on the comparison, a processing unit selectively reuses a first subset of quantization parameters from the previously quantized ML model and re-quantizes a second portion of the version of the ML model to form a second subset of the quantization parameters. The first subset is formed by quantizing a first portion of the previous version of the ML model. The processing unit combines the first subset and the second subset to form an updated quantized ML model. In some cases, the version of the ML model has a floating-point representation, and the previously quantized ML model is generated by quantizing a previous version of the ML model to form a fixed point representation.

FEDERATED LEARNING WITH DISTINCT COMMON COMPONENTS AND SPARSE COMPONENTS

Resumen de: WO2026206327A1

A federated learning system and method for training neural networks addresses the challenge of efficiently merging locally trained models while maintaining data privacy. The system includes a server and multiple clients, where the server dispatches machine learning models to clients for local training. The server decomposes updated model weights into common and sparse components using robust principal component analysis (RPCA), applying distinct scaling factors to enhance model accuracy and convergence speed. This approach can be particularly advantageous in scenarios where data privacy is of significant importance, such as in smart devices and medical applications. The method iteratively refines the global model by leveraging client-specific variations and shared patterns, ensuring that client-specific data is not transferred to a central server.

SELF-LEARNING SYSTEMS AND METHODS USING MOVING EUCLIDEAN DISTANCE MACHINE-LEARNING MODELS FOR ANOMALY AND TREND DETECTION IN TIMESERIES DATA

Resumen de: US20260300807A1

0000 Presented are self-learning systems for anomaly detection in timeseries datasets utilizing Euclidean geometry modeling and methods of operating such systems. A method of analyzing datasets to detect anomalies includes a system controller receiving a timeseries dataset containing a sequence of data values recorded within a select time period, and determining a window length defining a number of the sequential data values assigned to each data window. The controller assigns a respective subsequence of the data values to each data window in a series of data windows, and generates a distance value matrix with matrix cells that contain Euclidean distances between respective pairs of the data windows. The controller generates a shortest distance (SD) matrix containing SD values that each defines a shortest distance between each data window and every other data window. The SD array is analyzed to determine if any SD value is irregular and thereby indicates an anomaly.

COMMUNICATIONS FOR DATA COLLECTION CONFIGURATIONS

Resumen de: WO2026206724A1

Some examples of the techniques described herein may relate to data collection for training one or more artificial intelligence or machine learning (AI/ML) models to predict one or more measurements or measurement events. For example, signaling may be utilized for data collection for AI/ML model training. A trained AI/ML model may be utilized for beam management, channel state information (CSI) measurement prediction, CSI feedback prediction for a two-sided model, positioning enhancements, radio resource management (RRM) measurement prediction, or measurement event prediction, among other examples. Some approaches to RRM measurement prediction may include cell-level measurement prediction or beam-level measurement prediction. A user equipment (UE) may be configured by a source gNB with a set of cells to measure, a set of beams on each cell to measure, a set of cells for measurement prediction, or a set of beams on each cell for measurement prediction.

INTERACTIVE MULTIMEDIA AND METHODS FOR DESIGNING INTERACTIVE MULTIMEDIA BASED ON DECISION TREES GENERATED BY MACHINE LEARNING

Resumen de: WO2026206375A1

An interactive multimedia with multiple levels and a method for designing the interactive multimedia are provided. The interactive multimedia includes: a first level program, providing at least one first level interactive activity to determine one of multiple lines for the user to proceed from a first level to a next level; and a second level program, providing at least one second level interactive activity to determine one of multiple lines for the user to proceed from the second level to a next level. The levels and the lines of the interactive multimedia are designed at least partially based on a decision tree generated by a machine learning technique with a source data including predictor variables indicating attributes of respondents and a target variable indicating related outcomes of the respondents.

METHOD AND SYSTEM FOR PREPROCESSING OPTIMIZATION OF STREAMING VIDEO DATA USING MACHINE LEARNING

Resumen de: US20260301385A1

0000 A method of improving a main output of a main AI model includes processing first video data using a machine learning model includes continuously analyzing incoming video data via a first processing pipeline and concurrently via a second processing pipeline. The first processing pipeline can include preprocessing incoming video data according to first preprocessing parameters that format the incoming video data to create the first video data and processing the first video data by the main AI model to determine the main output that is indicative of a first inference dependent upon the first video data. The second processing pipeline can include identifying first test preprocessing parameters that are predictive to produce a first test output that satisfies a baseline criterion and preprocessing the incoming video data according to the first test preprocessing parameters that format the incoming video data to create first test video data.

MACHINE LEARNING FRAMEWORK TO DETECT AND MONITOR COMPLIANCE MATTERS

Resumen de: US20260301451A1

0000 System apparatus, article of manufacture, method and/or computer program embodiments are provided for detecting and monitoring compliance matters. An example method may include obtaining a first set of image data corresponding to an environment from one or more image sensors; processing the first set of image data using a first machine learning algorithm to yield annotated image data that identifies at least one object within the environment; processing the annotated image data using a second machine learning algorithm to determine one or more attributes associated with the at least one object within the environment; and generating, based on the one or more attributes, an evidence package corresponding to the at least one object within the environment, the evidence package being configured to facilitate a determination of one or more action items associated with the at least one object.

MACHINE LEARNING PLATFORM AND FEATURE GENERATION FOR EFFICIENT DATA PROCESSING

Resumen de: US20260300771A1

0000 A system enables agile model development to speed up innovation by data scientists. Model training and deployment are coordinated and standardized to reduce redundancy. Data is obtained for feature generation and reformatted and de-sensitized for storage. The features are stored in locations available to all models and training modules of a system so data does not need to be adjusted for new models. To generate a machine learning model, the system establishes a cohort for evaluation by the model. A model template and features for use by the model are identified. The selected template and features are used for experimentation and evaluation. Model training artifacts, such as model weights are subsequently recorded in a model store and the model scripts and settings can then be registered in a centralized database where it can be accessed for execution.

IDENTIFYING SEPARATION-RELATED PROBLEMS IN PETS

Resumen de: AU2025374609A1

The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.

COMPUTER-BASED SYSTEMS AND METHODS FOR MACHINE LEARNING BASED EXCEPTION PREDICTIONS

Resumen de: US20260300783A1

0000 A failure prediction method including a predicting flow and a model training flow, the predicting flow including receiving a natural language input from a client computer, translating the input into a task by a LLM, selecting a ML model dedicated to the task, receiving first data, converting the first data to second data of a predetermined format, immediately applying, the ML model on the second data for predicting an output and providing a corresponding explanation, storing the second data and the output into historical data in a storage layer, translating the output and the explanation into a prediction in the natural language by the LLM, and transmitting the prediction to the client computer and iterating the predicting flow for a predetermined number of time; and the model training flow including retrieving the historical data from the storage layer, and training the ML model on the historical data.

PERSONALIZED RECOMMENDATION SYSTEMS TO REMEDIATE INEFFICIENCIES IN USER BEHAVIOR

Resumen de: US20260301015A1

0000 A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.

A method, apparatus, and computer-readable medium for calibration-driven pruning of machine learning models

Resumen de: GB2704984A

A method for calibration-based pruning of machine learning model comprises: training 110 the machine learning model; generating 120 a reliability diagram of the trained machine learning model for a plurality of samples; identifying 130 bins where the predicted confidence differs from the observed accuracy; for each of said identified bins: for each sample in the identified bin 140: deciding whether increasing or decreasing the predicted confidence for that sample will reduce a bin’s confidence-accuracy difference; computing using a feature importance method respective relevance scores of elements of the machine learning model; negating the relevance scores for those samples where a decrease instead of an increase in their predicted confidence is decided to reduce the bin’s confidence-accuracy-difference; identifying 160 elements with a lowest aggregated relevance score; and pruning 170 the elements with that lowest aggregated relevance score to obtain a pruned model. The feature importance method may comprise layer-wise relevance propagation, while the elements of the machine learning model may be neurons, filters, weights. figure 1

METHOD AND APPARATUS FOR TRIGGERING FINE-TUNING OF AI/ML WIRELESS MODELS

Resumen de: EP4814814A1

A system and a method are disclosed for fine-tuning an artificial intelligence/machine learning, Al/ML, model online in a wireless communication system. A user equipment, UE, (502) transmits to a base station (504) capability information of fine-tuning online one or more Al/ML models of the UE (502); receives, from the base station (504), channels and/or signals for the UE (502) to determine whether a first Al/ML model of the one or more Al/ML models of the UE (502) needs to be fine-tuned; determines to fine-tune the first Al/ML model online based on the channels and/or signals received from the base station (504) and model-generated output from the first Al/ML model; and fine-tunes the first Al/ML model to generate an output that is used for a downlink transmission.

GENERATING SPATIALLY DISTRIBUTED RESPONSE TO QUERY BY INTELLIGENT SELECTION OF TIME-SERIES REMOTE SENSING DATA

Resumen de: EP4814745A1

0001 This disclosure relates generally to a method and system for generating spatially distributed response to query by intelligent selection of time-series remote sensing data. The current available methods for processing and analyzing geospatial data have limitations such as manual satellite data selection, limited natural language query support and the like. The method disclosed receives multi-contextual user query, intelligently selects the relevant time-series satellite data and climate information from repository using intents and entities generated utilizing pre-defined knowledge graph. The method intelligently selects time-series satellite data by creating and utilizing relationship strength-based node ranking graph. This time-series satellite data is combined with the climate data selected from climate repository to generate the spatially distributed response for the multi-contextual user query using machine learning model and large language model. The method enables stakeholders to access actionable insights without needing expertise in satellite data processing and using it for informed agricultural decision-making.

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, INFORMATION PROCESSING PROGRAM, AND MOLECULAR COMPOUND PRODUCTION METHOD

Resumen de: EP4814811A1

An information processing system of the present disclosure comprises at least one processor. The at least one processor acquires, for each of a plurality of first molecules, training data representing a plurality of feature values related to the first molecule, acquires, for each of a plurality of second molecules, test data representing a plurality of feature values related to the second molecule, calculates, for each of a plurality of feature values, an index based on a first probability distribution which is a probability distribution in the training data and a second probability distribution which is a probability distribution in the test data, and selects, on the basis of the index of each of a plurality of feature values, one or more of the plurality of feature values as one or more input feature values to be used as input parameters of a prediction model for predicting a characteristic value of a molecule on the basis of machine learning.

METHOD FOR DETERMINING POLICY FOR SON AND ELECTRONIC DEVICE USING SAME

Nº publicación: EP4815542A1 30/09/2026

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]
Samsung Electronics Co., Ltd.

Resumen de: EP4815542A1

0001 A method for determining a policy for a SON is provided. The method may comprise the steps of: receiving PM data of a communication network; generating feature data representing the PM data by using a deep learning-based encoder; determining an outlier score of the feature data by using a deep learning-based outlier detection model; and selecting one of an artificial intelligence (AI)-based policy and a non-AI-based policy for performing SON in the communication network according to the outlier score.