Alerta

MODEL-BASED ASSESSMENT OF QUALITY OF EXPERIENCE

Resumen de: US2025278631A1

In one embodiment, a method herein may comprise: establishing a prediction of a quality-of-experience measure from session telemetry regarding execution of an application by one or more users, the prediction established based on inputting the session telemetry into a machine learning model trained to extract attributes that drive user-based quality-of-experience feedback; determining one or more attributes of the session telemetry that significantly contributed to the prediction from the machine learning model; mapping the one or more attributes of the session telemetry that significantly contributed to the prediction to a specific failure pattern from a set of known failure patterns; and mitigating the prediction of the quality-of-experience measure based on the specific failure pattern.

PROGRAMMABLE AND SCALABLE BIT-SLICED VLSI ARCHITECTURE FOR DECISION TREE BASED MACHINE LEARNING EDGE INFERENCE

Resumen de: US2025278545A1

Methods and systems are provided herein for a decision tree layout model. A method for imprinting a decision tree layout model onto a classification chip includes receiving a plurality of target model requirements. One or more decision tree-based inference models are loaded based on the plurality of target model requirements. Training data is obtained. The one or more decision tree-based inference models are trained using a depth layer. Predictions corresponding to the training data are generated using the one or more decision tree-based inference models. Prediction parameters associated with the plurality of predictions is determined. The prediction parameters are compared to the target model requirements. An inference model is selected from the one or more decision tree-based inference models, based on the comparison. A transistor layout is generated based on the selected inference model.

Artificial Intelligence (AI) Assisted Digital Documentation for Digital Engineering

Resumen de: US2025278526A1

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

SYSTEMS AND METHODS FOR AN ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MEDICAL CLAIMS PLATFORM

Resumen de: US2025278457A1

Embodiments of various systems, methods, and devices are disclosed for generating artificial intelligence or machine learning models for predicting denials of medical claims, predicting approvals of resubmitted medical claims, as well as automatic workflow clustering processes for automatically assigning medical claims to workflow queues using predictive segmentation and smart resource allocation.

AUTOMATED METADATA ASSET CREATION USING MACHINE LEARNING MODELS

Resumen de: US2025278435A1

Systems and methods are described that employ machine learning models to optimize database management. Machine learning models may be utilized to decide whether a new database record needs to be created (e.g., to avoid duplicates) and to decide what record to create. For example, candidate database records potentially matching a received database record may be identified in a local database, and a respective probability of each candidate database record matching the received record is output by a match machine learning model. A list of statistical scores is generated based on the respective probabilities and is input to an in-database machine learning model to calculate the probability that the received database record already exists in the local database.

RADIO FREQUENCY SYSTEM INCLUDING RECOMMENDATION TRAINING AGENT FOR MACHINE LEARNING ALGORITHM AND RELATED METHODS

Resumen de: US2025280373A1

A radio frequency (RF) system may include at least one RF sensor in an RF environment and at least one RF actuator. The RF system may also include at least one processor that includes a machine learning agent configured to use a machine learning algorithm to generate an RF model to operate the at least one RF actuator based upon the at least one RF sensor. The processor may also include a recommendation training agent configured to generate performance data from the machine learning agent, and change the RF environment based upon the performance data so that the machine learning agent updates the machine learning algorithm.

MACHINE LEARNING BASED DEPOLARIZATION IDENTIFICATION AND ARRHYTHMIA LOCALIZATION VISUALIZATION

Resumen de: US2025279208A1

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

AUTOMATED MULTIVARIATE SYSTEM PERFORMANCE ANALYSIS

Resumen de: WO2025183999A1

The embodiments described herein generally relate to automated performance analysis of a system. Embodiments include receiving parameter values for a plurality of parameters captured during a time period. Embodiments include providing inputs based on the data set to a supervised machine learning model configured to determine significant parameters with respect to a target variable. Embodiments include receiving, from the supervised machine learning model in response to the inputs, an indication of two or more significant parameters from the plurality of parameters with respect to the target variable. Embodiments include generating a multivariate cluster for the target variable based on the two or more significant parameters and determining an anomalous state of the system with respect to the target variable based on the multivariate cluster for the target variable and data captured after the time period.

HYBRID ARTIFICIAL INTELLIGENCE CLASSIFIER

Resumen de: WO2025181626A1

System, methods, apparatuses, and computer program products are disclosed for generating and using a hybrid artificial intelligence classifier for classifying input into one or more nodes of a taxonomy. Training data is received for at least a first portion of the taxonomy and used to train a supervised machine learning (ML) model to classify input into the first portion of the taxonomy having training data. A large language model (LLM) taxonomy is determined for at least a second portion of the taxonomy. The hybrid AI classifier classifies input based on a first classification obtained by providing the input to the supervised ML, and a second classification obtained by providing at least the input and the LLM taxonomy to a pre-trained LLM.

CELL CHARACTERISATION

Resumen de: WO2025180826A1

A method of determining one or more intrinsic mechanical properties and/or size of one or more subcellular components of a cell, the method comprising: determining cell boundary profile data associated with a cell subject to steady or transient deformation; inputting the cell boundary profile data into a machine learning model; and using the machine learning model to determine one or more intrinsic mechanical properties and/or size of one or more subcellular components of the cell based on the cell boundary profile data.

COUNTERFACTUALS WITH FEATURE PREFERENCES FOR CONSISTENT AND DIVERSE EXPLANATIONS

Resumen de: US2025278669A1

An embodiment includes detecting by an artificial intelligence system a feature of a machine learning model. The embodiment includes responsive to detecting the feature of the machine learning model, computing by a Counterfactual Engine of the artificial intelligence system a counterfactual objective based on modifying a product of a weight and a perturbation of the feature. The embodiment also includes transforming by the Counterfactual Engine a counterfactual based on the counterfactual objective.

PREDICTING DOCUMENT IMPACT USING A MACHINE LEARNING MODEL

Resumen de: US2025278667A1

This document relates to predicting the impact of documents using a trained machine learning model. For instance, the disclosed implementations can train a gradient-boosted decision tree or neural network to predict impact scores of previously-published documents using features such as author features, journal features, document metadata features, and/or text embeddings representing text from the previously-published documents. Once trained, the machine learning model can be employed to predict impact scores of newly-published documents. The impact scores can be employed for operations such as ranking the newly-published documents in response to a received query.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR STATE CORRECTION OF ARTIFICIAL INTELLIGENCE MODELS

Resumen de: WO2025183683A1

Systems, methods, and computer program products are provided for state correction of stateful machine learning (ML) models. A system may include a processor configured to receive an input for stateful ML model, generate an output of the stateful ML model based on the input, determine whether the output of the stateful ML model corresponds to a ground truth value associated with the input, assign an initial state of the stateful ML model as an active state of the stateful ML model based on determining that the output of the stateful ML model corresponds to the ground truth value associated with the input, and assign a correction state of the stateful ML model as the active state of the stateful ML model based on determining that the output of the stateful ML model does not correspond to the ground truth value associated with the input.

MACHING LEARNING SYSTEMS

Resumen de: US2025278675A1

Systems and methods for training a machine learning model to assess risk as disclosed. The machine learning model includes a plurality of machine learning sub-models and an ensemble model. The method includes: receiving a plurality of user data records, each user data record comprising data collected for an individual user from multiple data sources; creating the plurality of machine learning sub-models based on the plurality of user data records; assigning at least a subset of the plurality of user data records to each of the plurality of machine learning sub-models; training each machine learning sub-model using the assigned subset of the plurality of user data records, each sub-model trained to accurately determine a risk score based on a given user data record; providing the risk scores generated by each of the plurality of machine learning sub-models to an ensemble machine learning model, the ensemble machine learning model being trained to combine the risk scores from the sub-models to obtain a combined risk score; using the trained machine learning model to determine a risk score for an individual user data record; and reusing the determined risk score for the individual user record to retrain the machine learning model.

SYSTEMS AND METHODS FOR OPTIMIZING HYPERPARAMETERS OF MACHINE LEARNING MODELS USING REDUCTION ITERATION TECHNIQUES

Resumen de: US2025278672A1

The most fundamental task in ML models is to automate the setting of hyperparameters to optimize performance. Traditionally, in machine learning (ML) models hyperparameter optimization problem has been solved using brute-force techniques such as grid search, and the like. This strategy exponentially increases computation costs and memory overhead. Considering the complexity and variety of the ML models there still remains practical difficulties of selecting right combinations of hyperparameters to maximize performance of the ML models. Embodiments of the present disclosure provide systems and methods for hyperparameters optimization in machine learning models and to effectively reduce the hyperparameter search dimensions and identify the important hyperparameter dimensions that are high variable to identify the best hyperparameter thereby saving the computing energy of machine learning process and eliminate categorical dimensions by using a combination of reduction-iteration techniques.

SYSTEMS AND METHODS FOR OPTIMIZING HYPERPARAMETERS OF MACHINE LEARNING MODELS USING REDUCTION ITERATION TECHNIQUES

Nº publicación: EP4610891A1 03/09/2025

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]
Tata Consultancy Services Limited

Resumen de: EP4610891A1

The most fundamental task in ML models is to automate the setting of hyperparameters to optimize performance. Traditionally, in machine learning (ML) models hyperparameter optimization problem has been solved using brute-force techniques such as grid search, and the like. This strategy exponentially increases computation costs and memory overhead. Considering the complexity and variety of the ML models there still remains practical difficulties of selecting right combinations of hyperparameters to maximize performance of the ML models. Embodiments of the present disclosure provide systems and methods for hyperparameters optimization in machine learning models and to effectively reduce the hyperparameter search dimensions and identify the important hyperparameter dimensions that are high variable to identify the best hyperparameter thereby saving the computing energy of machine learning process and eliminate categorical dimensions by using a combination of reduction-iteration techniques.