Alerta

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.

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.

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.

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.

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.

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 OPTIMIZING HYPERPARAMETERS OF MACHINE LEARNING MODELS USING REDUCTION ITERATION TECHNIQUES

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.

Methods and Systems to Validate Physiologic Waveform Reliability and Uses Thereof

Resumen de: US2025269112A1

Methods and systems to validated physiologic waveform reliability and uses thereof are provided. A number of embodiments describe methods to validate waveform reliability, including blood pressure waveforms, electrocardiogram waveforms, and/or any other physiological measurement producing a continuous waveform. Certain embodiments output reliability measurements to closed loop systems that can control infusion rates of cardioactive drugs or other fluids in order to regulate blood pressure, cardiac rate, cardiac contractility, and/or vasomotor tone. Further embodiments allow for waveform evaluators to validate waveform reliability based on at least one waveform feature using data collected from clinical monitors using machine learning algorithms.

HYBRID ARTIFICIAL INTELLIGENCE CLASSIFIER

Resumen de: US2025272608A1

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.

SYSTEM AND METHOD FOR FEEDBACK-DRIVEN AUTOMATED DRUG DISCOVERY

Resumen de: US2025272582A1

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

DETECTING RANSOMWARE IN MONITORED DATA

Resumen de: US2025272394A1

An information management system includes one or more client computing devices in communication with a storage manager and a secondary storage computing device. The storage manager manages the primary data of the one or more client computing devices and the secondary storage computing device manages secondary copies of the primary data of the one or more client computing devices. Each client computing device may be configured with a ransomware protection monitoring application that monitors for changes in their primary data. The ransomware protection monitoring application may input the changes detected in the primary data into a machine-learning classifier, where the classifier generates an output indicative of whether a client computing device has been affected by malware and/or ransomware. Using a virtual machine host, a virtual machine copy of an affected client computing device may be instantiated using a secondary copy of primary data of the affected client computing device.

EXTENSIBLE MACHINE LEARNING POWERED BEHAVIORAL FRAMEWORK FOR RISK COVERAGE

Resumen de: US2025272617A1

Some aspects of the present disclosure relate to systems, methods and computer readable media for outputting alerts based on potential violations of predetermined standards of behavior. In one example implementation, a computer implemented method includes: training a natural language-based machine learning model to detect at least one risk of a violation condition in an electronic communication between persons, wherein the violation condition is a potential violation of a first predetermined standard of behavior; receiving a lexicon, wherein the lexicon comprises topic data; receiving connection data representing a relationship between the trained machine learning model and the lexicon; detecting, using the trained machine learning model, the lexicon, and the connection data, a potential violation of a second predetermined standard of behavior; and outputting for display an alert indicating the potential violation of the second predetermined standard of behavior.

SYSTEMS AND METHODS FOR PROGRAMMATIC LABELING OF TRAINING DATA FOR MACHINE LEARNING MODELS VIA CLUSTERING AND LANGUAGE MODEL PROMPTING

Resumen de: AU2023383086A1

Embodiments introduce an approach to semi-automatically generate labels for data based on implementation of a clustering or language model prompting technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.

SYSTEMS AND METHODS FOR INVENTORY MANAGEMENT AND OPTIMIZATION

Resumen de: US2025265546A1

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

EPISODIC OFF-POLICY EVALUATION OF JOINT ACTIONS FOR A MACHINE-LEARNED POLICY FOR AN ONLINE SYSTEM

Resumen de: US2025265478A1

An off-policy evaluation system performs episodic off-policy evaluations to perform off-policy evaluation (OPE) for multiple, joint episodes. For a single episode, a first machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. For a second episode, a second machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. The second machine learning model is evaluated by determining an importance weight for the first model and the second model to determine the inverse propensity score of the second machine learning model.

Method, System, And Apparatus For Creating Knowledge Graph In Industrial Field

Resumen de: US2025265479A1

Various embodiments of the teachings herein include a method for creating a knowledge graph in the industrial field. An example includes: obtaining unstructured data from a first source in a sub-field of the industrial field, with knowledge annotations; performing machine learning on the unstructured data to generate a first model adapted to extract knowledge; extracting knowledge from second unstructured data provided by the first source based on the first model, without knowledge annotations; obtaining first structured data and first semi-structured data from a second source in a second sub-field; extracting second knowledge from the first structured data; extracting third knowledge from the first semi-structured data; and building a knowledge graph integrating the first and second sub-field based on the first, second, and third knowledge, represented in the form of triples.

AUTOMATED ARCHITECTURAL SPECIFICATION GENERATION AND HARDWARE IDENTIFICATION

Resumen de: US2025265880A1

A method according to one embodiment includes determining, by a server, a location of a door in an architectural drawing and a room function of a room secured by the door based on an analysis of the architectural drawing, determining, by the server, proper access control hardware to be installed on the door based on the room function, a category of access control hardware, and a predictive machine learning model associated with the category of access control hardware, and generating, by the server, a specification based on the determined proper access control hardware.

FLEXIBILE ENTITY RESOLUTION NETWORKS

Resumen de: WO2025175313A1

In various embodiments, a computing system is configured to provide a multi-stage cascade of large language models and stage N neural networks that identifies matching data records within a set of data records and then merges the matching data records. More specifically, the computing system can use a combination of domain-agnostic large language models and downstream neural network classifiers to identify matching data records that would otherwise not be possible with other machine learning or rules-based entity resolution systems. In one example, a computing system receives an entity resolution request. The entity resolution request can indicate a first entity and a second entity. For example, a data steward may provide the entity resolution request to help determine whether the entities are the same or different.

TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY

Resumen de: CN119895449A

Methods, systems, and devices for wireless communication are described. A machine learning server may generate a set of low-dimensional parameters representing training data for the machine learning server, the training data associated with one or more communication environments or one or more channel environments, or a combination thereof. The machine learning server may receive, from one or more devices within a communication environment or a channel environment or both, a set of low-dimensional parameters representing test data associated with the communication environment or the channel environment or both. The machine learning server may generate a reproducibility metric according to a correlation between the set of parameters representing the training data and the set of parameters representing the test data. The machine learning server may send a message indicating the reproducibility metric to the one or more devices, and the one or more devices may perform a communication procedure based on the reproducibility metric.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR GENERATING A MACHINE LEARNING MODEL BASED ON ANOMALY NODES OF A GRAPH

Resumen de: WO2024081350A1

Provided are systems that include at least one processor to receive a dataset comprising a set of labeled anomaly nodes, a set of unlabeled anomaly nodes, and a set of normal nodes, randomly sample a node to provide a set of randomly sampled nodes, generate a plurality of new nodes based on the set of labeled anomaly nodes and the set of randomly sampled nodes, combine the plurality of new nodes with the set of labeled anomaly nodes to provide a combined set of labeled anomaly nodes, and train a machine learning model based on an embedding of each labeled anomaly node in the combined set of labeled anomaly nodes, a center of the combined set of labeled anomaly nodes in an embedding space, and a center of the set of normal nodes in the embedding space. Methods and computer program products are also disclosed.

METHOD AND APPARATUS FOR MONITORING MODEL IN BEAM MANAGEMENT BY USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Resumen de: EP4604410A1

Provided are a method and apparatus for monitoring a model in beam management by using artificial intelligence and machine learning. The method may include: in relation to a reference signal configured for a terminal, receiving second reference signal resource set configuration information of the reference signal for monitoring an AI/ML model; on the basis of the second reference signal resource set configuration information, measuring signal strength or signal quality for the reference signal; and reporting the performance result of the AI/ML model by comparing a measured value of the reference signal with a predicted value of the reference signal inferred via the AI/ML model.

SPECTRAL ANALYSIS, MACHINE LEARNING, AND FRAC SCORE ASSIGNMENT TO ACOUSTIC SIGNATURES OF FRACKING EVENTS

Resumen de: US2025258311A1

System, method, and apparatus for classifying fracture quantity and quality of fracturing operation activities during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fracking fluid in the fracking wellhead into an electrical signal; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the electrical signal in a window of time into a current frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum, the machine-learning system having been trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine-learning system; and a user interface configured to return a classification of the current frequency domain spectrum to an operator of the fracking wellhead.

OBTAINING INFERENCES TO PERFORM ACCESS REQUESTS AT A NON-RELATIONAL DATABASE SYSTEM

Resumen de: US2025258821A1

Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

SYSTEMS, METHODS, AND GRAPHICAL USER INTERFACES FOR MITIGATING BIAS IN A MACHINE LEARNING-BASED DECISIONING MODEL

Resumen de: US2025259070A1

A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

STABLE CLASSIFICATION BY COMPONENTS FOR INTERPRETABLE MACHINE LEARNING

Nº publicación: WO2025168228A1 14/08/2025

Solicitante:

NEC LABORATORIES EUROPE GMBH [DE]
NEC LABORATORIES EUROPE GMBH

Resumen de: WO2025168228A1

The present disclosure relates to a stable classification by components (SCBC) data processing architecture, configured to classify input data into one or more classes, comprising: a component detection module configured to compare the input data to a set of detection components, representing data patterns relevant for the classification, and determine a detection probability for each detection component based on the comparison. The SCBC data processing architecture further comprises a probabilistic reasoning module configured to compute one or more class prediction probabilities for the one or more classes based on the determined detection probabilities, a set of class-specific prior probabilities for the determined detection probabilities, and a set of class-specific reasoning probabilities for the determined detection probabilities. Application scenarios include medical and pharmaceutical applications, as well as healthcare in general such as interpretable and secure diagnosis and treatment recommendation systems. Related SCBC data processing system, methods and computer programs are also disclosed, as well as corresponding model training methods and systems.