Alerta

CLASSIFYING A SOURCE OF A LOGIN ATTEMPT TO A USER ACCOUNT USING MACHINE LEARNING

Resumen de: US20260270271A1

0000 A source of a login attempt to a user account can be classified using machine learning. For example, a computing system can input user activity observations associated with one or more login attempts to one or more user accounts into a trained machine learning model. One or more distinguishing factors for the one or more login attempts can be received from the trained machine learning model. The computing system can determine a source of a current login attempt by applying a clustering algorithm to current values of the one or more distinguishing factors. The current values may be derived from current user activity observations associated with the current login attempt. The computing system can determine an authentication level for the current login attempt to the user account based on the source of the current login attempt.

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.

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.

WEARABLE CARDIOVERTER DEFIBRILLATOR CARE SYSTEM WITH HEALTH AND EMOTIONAL COMPANION ACCESSORY

Resumen de: US20260263821A1

0000 A Wearable Cardioverter Defibrillator system supported with a customizable, goal-oriented, companion device. Functionality can be tailored to the goal for a user type. For a patient, the companion device can improve compliance with wear or prescription. Goals can include emotional support, or a specific health, including activity, support. The goal-oriented companion device can receive and process information using machine learning techniques, and interface with a user and other systems and devices.

SYSTEMS AND METHODS FOR ENHANCED USER SPECIFIC PREDICTIONS USING MACHINE LEARNING TECHNIQUES

Resumen de: US20260268166A1

Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.

METHOD AND SYSTEM FOR AN END-TO-END ARTIFICIAL INTELLIGENCE WORKFLOW

Resumen de: US20260268230A1

0000 In general, certain embodiments of the present disclosure provide methods and systems for enabling a reproducible processing of machine learning models and scalable deployment on a distributed network. The method comprises building a machine learning model; training the machine learning model to produce a plurality of versions of the machine learning model; tracking the plurality of versions of the machine learning model to produce a change facilitator tool; sharing the change facilitator tool to one or more devices such that each device can reproduce the plurality of versions of the machine learning model; and generating a deployable version of the machine learning model through repeated training.

Distributed System for Identifying the Appropriate Heart Valve Implant Using an Ensemble Machine-Learning Algorithm, Synthetic Data, and Edge Computing

Resumen de: US20260269081A1

0000 A system and method for and method for selecting an implantable heart valve for patients using is disclosed. The system includes analyzing population-level environmental exposure data to categorize geographic locations by cardiovascular risk. A synthetic clinical dataset is generated using a generative adversarial network along with predefined machine-learning parameters. The user device calculates an exposomic feature value by weighting pollutant concentrations based on residence durations. The user device is then trained using a machine-learning model, selected from random forest models, gradient-boosted decision tree models, support vector machines, artificial neural networks, and elastic net regression. After training, the model is executed on the user device using an edge computing approach with patient-specific data and exposomic feature values to generate heart valve intervention options that minimize the difference between the predicted remaining lifespan of the patient and the valve's operational lifespan while reducing the risk of failure or complications.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR PROVIDING ACCESS TO A MACHINE-LEARNING MODEL

Resumen de: WO2026187721A1

Provided are systems, methods, and computer program products for providing access to a machine-learning model. A system includes at least one processor configured to display a graphical user interface in a word processing application, the graphical user interface comprising at least a subset of prompts, receive a selection of a prompt from the subset of prompts from a user, execute the prompt, resulting in a model output, modify a textual document being displayed in the word processing application based on the model output, modify the prompt based on input from the user, resulting in a modified prompt, execute the modified prompt, resulting in a second model output, and store the modified prompt in the data storage device.

ARTIFICIAL INTELLIGENCE SENTINEL FOR SURGICAL PLANNING

Resumen de: US20260263157A1

A computer-implemented method of machine learning based surgical planning optimization. Embodiments include receiving, by an artificial intelligence agent, a request related to a surgical procedure that is to be performed on a patient. Embodiments include retrieving medical data that is related to the request from one or more source devices. Embodiments include generating, using a machine learning model, content related to the surgical procedure that is to be performed on the patient based on the request and the medical data, wherein the content comprises a set of relevant data points about the patient with respect to the surgical procedure or an indication of an issue related to a surgical plan. Embodiments include providing the content via an output device.

MACHINE LEARNING MODEL ANALYSIS

Resumen de: US20260268227A1

Disclosed are various embodiments for analyzing machine learning models. A selection is obtained of a first tuple comprising a first feature vector and a first result generated by a machine learning model and a second tuple comprising a second feature vector and a second result generated by the machine learning model. Then, a plurality of emulated feature vectors are generated. Next, a plurality of emulated results are generated. Subsequently, a plurality of emulated decision instances are generated. Next, a decision tree is built based at least in part on the first tuple, the second tuple, and the plurality of emulated decision instances. Finally, an importance of each feature on the decision tree is computed.

SYSTEMS AND METHODS FOR GENERATING ROLE-PLAYING AI PERSONAS CONSTRUCTED FROM VARIOUS MARKETING SEGMENTS

Resumen de: US20260268178A1

0000 Methods and systems are described for an AI persona generation system. A system can provide groups of AI personas with specific facets reflecting a desired consumer group. AI/ML tools can be used to optimize the generation of AI personas. Systems and methods include receiving, via a user interface, a request for one or more artificial-intelligence (AI) personas; determining, by the persona generation server, a plurality of key persona facets for the request; generating an AI persona mold based on the plurality of key persona facets; generating, by an artificial intelligence/machine learning (AI/ML) model, a plurality of AI personas consistent with the persona mold; and validating the plurality of AI personas based on one or more validation criteria.

METHODS AND SYSTEMS OF PROGNOSTIC MODELS THAT CAN PERFORM STOCHASTIC TIME-SERIES FORECASTING

Resumen de: WO2026188052A1

A method to predict covariates related to progression of a disease includes receiving, for a study participant, baseline sample data corresponding to a baseline set of prognostic covariates, and predicting, via a first machine learning model based on the baseline sample data, first predicted sample data for each of a first set of prognostic covariates for a first time period for the study participant. At least one covariate of the first set of prognostic covariates is non-overlapping with any prognostic covariate of the baseline set of prognostic covariates. The method further includes predicting, via a second machine learning model based on the baseline sample data, second predicted sample data for each of a second set of prognostic covariates for a second time period. At least one covariate of the second set of prognostic covariates is non-overlapping with any covariate of the first set of prognostic covariates.

RECONFIGURABLE LOGIC GATE ARRAY, IMAGE SENSOR ASSEMBLY, AND METHOD OF OPERATING

Resumen de: WO2026185211A1

An image sensor assembly includes an image sensor and a logic gate array. The image sensor generates digital image data. The logic gate array includes a plurality of interconnected, reconfigurable logic blocks. Each logic block is arranged to output a binary output signal having a value dependent on at least one input signal of the logic block and on a Boolean function implementable by the logic block. The logic gate array is configurable to generate and output machine learning data based on the digital image data.

SYSTEMS AND METHODS FOR AUTOMATED PSYCHOLOGICAL ASSESSMENT AND MENTAL HEALTH EVALUATION

Resumen de: WO2026187714A1

An Al-driven psychological assessment system automates mental health evaluations using structured assessments, machine learning diagnostics, and adaptive referrals. The system collects user inputs, analyzes responses through an Al module, and compares results against a diagnostic database incorporating DSM-V criteria and historical case data. A severity engine assigns risk scores, prioritizes findings, and generates provisional diagnoses validated against prior records. Based on assessed severity, the system recommends treatment pathways, delivers AI-guided support for non-severe conditions, and limits automated counseling for high-risk users. When elevated risk is detected, the platform initiates structured referrals and emergency interventions, connecting users with licensed mental health professionals. Through real-time data processing, continuous diagnostic refinement, and risk-sensitive safeguards, the system improves the accuracy, scalability, and accessibility of psychological assessments while maintaining patient safety via controlled escalation protocols and professional oversight.

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).

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.

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.

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.

Automated routine platform

Resumen de: US20260267688A1

0000 Embodiments include systems and methods for automated routine generation and execution. In some embodiments, the method includes receiving a request to automate a target workflow; retrieving at least one document relevant to the target workflow from a knowledge base; generating a plurality of candidate routines based on the at least one document using a first machine learning model; generating an aggregate routine based on the plurality of candidate routines, the aggregate routine comprising instructions for automatically implementing the target workflow; mapping at least one step of the aggregate routine to at least one function configured to provide external functionality to a second machine learning model; and executing the aggregate routine using the second machine learning model, the step of executing the aggregate routine including calling the at least one function

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.

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.

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.

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.

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.

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

Nº publicación: US20260259270A1 03/09/2026

Solicitante:

SES HOLDINGS PTE LTD [SG]
SES Holdings Pte. Ltd.

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.