Machine learning

APPARATUS, SYSTEMS, AND METHODS FOR STORE-LEVEL ANALYSIS USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260300833A1

Apparatus, systems, and methods for store-level analysis using artificial intelligence are disclosed. An example apparatus includes at least one programmable circuit to generate a feature vector based on an alert report, the feature vector including features corresponding to alert types; execute a supervised machine learning model using the feature vector to generate a supervised output for an alert in the alert report, the supervised output representing a likelihood of the alert warranting an action; execute an unsupervised machine learning model using the feature vector to generate an unsupervised output; classify the alert based on at least one of the supervised output or the unsupervised output; and cause an electronic device to execute the action based on the classification of the alert, the action associated with a status of the alert or a request for additional store-level data.

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.

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 MODEL PARAMETER TRANSFER

Resumen de: US20260304149A1

0000 Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; obtain a set of data samples for inputting to the ML model; determine a maximum change to the set of model parameters that is permitted without performance of the ML model degrading beyond a tolerance threshold value; quantize, based on the determined maximum change, the set of model parameters to generate quantized model parameters; and transmit the quantized model parameters to a further node.

DISTRIBUTED GENERATION OF INFERENCES USING HOSTED MACHINE LEARNING MODELS

Resumen de: US20260300781A1

0000 Systems, methods, and computer program products for distributed generation of inferences using hosted machine learning models are disclosed. A method may comprise fetching, from a remote server, a first checklist comprising one or more pre-processing steps and a second checklist comprising one or more post-processing steps; performing the one or more pre-processing steps at a client on an input dataset to generate a pre-processed input dataset; transmitting, from the client to the remote server, the pre-processed input dataset to be processed by the machine learning model; receiving, at the client from the remote server, an output dataset; performing the one or more post-processing steps at the client on the output dataset to generate a post-processed output dataset; and presenting the post-processed output dataset via a client computing platform at the client..

COORDINATING INTERACTIONS WITH HETEROGENOUS MACHINE LEARNING AGENTS

Resumen de: US20260300001A1

0000 This document relates to facilitating interactions between a user and multiple agents, such as agents that provide different generative machine learning capabilities. In certain disclosed implementations, user input is received and two or more agents are selected to perform a particular task for the user. Then, interactions with the agents are coordinated on behalf of the user based on agent definitions of the selected agents. A result of the particular task is received and provided to the user. For instance, the user input can be received via a chat interface that allows the user to conduct a single chat session that involves the particular task being performed by multiple agents, thus giving the user the appearance of interacting with a single agent.

MACHINE LEARNING MODEL INFERENCE SIMULATION

Resumen de: US20260300818A1

0000 Example embodiments are directed to a system that facilitates machine learning model inference simulation. A model inference simulation request is received. A model specification is identified based on the model inference simulation request. A job workflow is generated based on the model specification and the model inference simulation request. A model inference simulation result is generated using a machine learning model associated with the model specification. The model inference simulation result is then caused to be displayed on a user interface.

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.

MID-CYCLE PREDICTIONS USING MACHINE LEARNING

Resumen de: US20260300780A1

0000 Implementations are disclosed for training and/or applying a single machine learning model to generate mid-cycle inferences based on variable length time series inputs. In some implementations, a time series of satellite imagery samples that depict an agricultural area over all or part of a crop cycle may be obtained. During training, ground truth agricultural classifications may be obtained for geographic units of the agricultural area represented by individual pixels of the satellite imagery samples. During training, sample(s) of the time series may be masked to generate a partially masked satellite imagery samples, which in tum may be used to generate input embedding(s). The input embedding(s) may be applied across a machine learning model to generate output(s) representing in-season agricultural prediction(s). During training, the in-season agricultural prediction(s) may be compared with the ground truth agricultural classifications to train the machine learning model.

SYSTEMS, APPARATUS AND METHODS FOR TREATMENT OF RETINAL AND MACULAR DISEASES USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260294235A1

An optical coherence tomography (OCT) device includes artificial intelligence for recommending a treatment plan for a patient with a retinal or macular disease such as age-related macular degeneration (AMD). The OCT device includes a sensor configured to quantify an initial level of macular edema or retinal exudation. The OCT device receives treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient. The OCT device performs OCT on the patient subsequent to each anti-VEGF injection to determine subsequent levels of edema or retinal exudation. The OCT device collects a set of training data including: the initial and subsequent levels of edema or exudation, patient information, and treatment information. The OCT device applies the training data to a machine-learning model trained on training data for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient.

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.

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.

MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: US20260300068A1

Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.

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

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.

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.

ENSEMBLE MACHINE LEARNING SYSTEM FOR DYNAMIC RESOURCE ALLOCATION USING CROSS-SOURCE DATA AGGREGATION

Resumen de: WO2026198682A1

A computer-implemented method for evaluating resource allocation requests using an ensemble machine learning model is disclosed. A resource request is received, comprising multiple data inputs from different data sources, where each data source provides a specific type of user feature. A first set of machine learning models, each trained on a single data source, generates corresponding first risk scores. A second decision tree-based machine learning model processes the data inputs, leveraging feature dependencies and learned missing feature patterns to generate a second risk score, enabling robust predictions even when certain user features are incomplete or missing. The first and second risk scores are ensembled using an ensemble technique to produce an overall risk score. The ensembled risk score is then transmitted to a resource hosting device, which approves, denies, or adjusts the requested resource based on the assessed risk.

METHODS, SYSTEMS AND COMPUTER READABLE MEDIA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: WO2026197916A1

A method for training a machine learning model, comprising determining a plurality of input data objects from a first unlabelled set of data, and providing the plurality of input data objects to at least one reference model configured to generate a first output for each of the plurality of input data objects. A labelled set of training data is created using the generated first outputs, and a target model is trained using the labelled set of training data. The trained target model is applied to a second unlabelled set of data. A plurality of training data objects is determined from the second unlabelled set of data. The plurality of training data objects is provided as an input to the at least one reference model to generate a second output for each of the plurality of training data objects, and the labelled set of training data is updated.

READER LIFE SPAN PREDICTION MODEL

Resumen de: WO2026195627A1

A system for predicting lifespan of a physical access control (PAC) reader device is described. The system collects data associated with a PAC reader. The system processes the data using one or more machine learning (ML) models to generate a prediction of a remaining lifespan of the PAC reader and one or more configuration changes for extending the remaining lifespan of the PAC reader. The system generates an alert comprising the prediction and the one or more configuration changes.

SYSTEM AND METHOD FOR SMART ELECTRICAL LOAD MANAGEMENT

Resumen de: WO2026198742A1

A system and method of managing an electrical power grid. The method comprises: obtaining measurement data comprising a respective time series for each of a plurality of electrical loads connected to an electrical power grid, each time series comprising electrical power consumption measurements for the corresponding electrical load at each of a plurality of times; processing each of the time series using a machine learning model to determine a respective classification output for each of the electrical loads; processing the measurement data to predict one or more states of the electrical power grid at one or more corresponding times; and adjusting electrical power distribution to one or more of the electrical loads based on the classification outputs for the electrical loads and the predicted one or more states of the electrical power grid.

INFORMATION PROCESSING METHOD AND RELATED DEVICE

Resumen de: US20260285373A1

0000 Embodiments of this disclosure disclose an information processing method and a related device. The method may be applied to the autonomous driving field in artificial intelligence. The method includes: inputting, to a deep learning model, first information corresponding to a traffic scene around an ego vehicle, and obtaining second information corresponding to the first information, where the first information includes first text information, the second information is obtained based on the deep learning model, and the second information corresponds to any one of the following tasks: making a decision on behavior of the ego vehicle, planning a trajectory for the ego vehicle, or controlling the ego vehicle. Input information of the deep learning model provided in this disclosure includes text information that facilitates understanding by a user.

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

Resumen de: US20260288888A1

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.

DYNAMICALLY MONITORING VARIATIONS IN PROCESS EXPLAINABILITY WITHIN A DISTRIBUTED COMPUTING ENVIRONMENT

Resumen de: US20260289340A1

The disclosed embodiments include computer-implemented systems and processes that dynamically monitor variations in process explainability of within a distributed computing environment. For example, an apparatus may determine a first metric value characterizing a variation in an explainability of a machine-learning or artificial-intelligence process between a first temporal interval and a second temporal interval. When the first metric value is inconsistent with an exception criterion, the apparatus may obtain a second metric value characterizing an additional variation in the explainability of the machine-learning or artificial-intelligence process between the first temporal interval and a third temporal interval, and when the second metric value is inconsistent with the exception criterion, the apparatus may perform operations that modify at least one of (i) a value of a process parameter that characterizes the machine-learning or artificial-intelligence process or (ii) a composition of an input dataset associated with the machine-learning or artificial intelligence process.

IDENTIFICATION OF MARKER SEQUENCES FOR RAPID CLASSIFICATION OF MICROBIAL PATHOGENS THROUGH MACHINE LEARNING ANALYSIS OF GENOME ASSEMBLIES

Resumen de: US20260290509A1

0000 The disclosure provides methods to identify marker sequences for rapid classification of microbial strains through machine learning analysis of genome assemblies.

AUTOMATED VALUATION MODEL (AVM) AND TECHNIQUES FOR REDUCING LEAKAGE IN MACHINE LEARNING DATASETS

Nº publicación: WO2026198893A1 24/09/2026

Solicitante:

NEWMARK & CO REAL ESTATE INC [US]
NEWMARK & COMPANY REAL ESTATE, INC.

Resumen de: WO2026198893A1

System, apparatus and method may control receiving time-indexed data comprising values of a plurality of features; generating derived features based on temporal changes of at least one of the plurality of features, the derived features including at least a first derivative of a first value; temporally aligning the plurality of features and the derived features to a reference date such that only data available prior to the reference date is used for training a machine learning model; training a machine learning model on the temporally aligned features and the derived features to predict a forward-looking value; and outputting the forward-looking value.