Alerta

TRAINING A MACHINE LEARNING MODEL

Resumen de: WO2024181895A1

A method performed by a node (100) in a communications network (107). The method comprises: obtaining (202) vulnerability data comprising a plurality of vulnerabilities detected on a host server during a vulnerability scan, matching (204) a first vulnerability in the plurality of vulnerabilities to a first subset of co-occurring vulnerabilities in the plurality of vulnerabilities, the first subset of co-occurring vulnerabilities overlapping in time with the first vulnerability, and training (206) a machine learning model using the first vulnerability as an example input and the first subset of co-occurring vulnerabilities as an example output of the machine learning model.

AUTOMATED METHOD FOR VIRTUAL TECHNICAL ASSISTANCE IN THE CORRECTION OF COMPUTER VULNERABILITIES THROUGH COMBINED USAGE OF SOFTWARE AUTOMATION AND ARTIFICIAL INTELLIGENCE TECHNICS

Resumen de: EP4675478A1

The invention relates to an automatic method for technical assistance in the correction of vulnerabilities of a computer system, where the aforementioned method comprises at least the following steps:a) receiving information relative to computer system vulnerabilities by text, voice, file input or through data exchange;b) launching tools based on artificial intelligence algorithms and machine learning to understand the information and requests entered relative to vulnerabilities;c) performing parsing and text mining of the information received and process it through intelligent data matching with information relative to the computer vulnerabilities present in a database or through interaction with external online vulnerability databases;d) in case of unsatisfactory results, starting an extended online search by web scraping using deep learning algorithms and unsupervised machine learning;e) updating an internal vulnerability database with the additional information found; andf) generating a vulnerability report accompanied by relative remediation.

METHOD FOR ASSESSING THE SEISMIC RISK ON EXISTING BUILDINGS

Resumen de: EP4675519A1

The method for assessing the seismic risk for existing buildings introduces an integrated and codified flow of work steps through a mixed qualitative-quantitative assessment with the combination of the expeditious method with the scientific method, performed on a strategic subset of the population, an operational and targeted use of artificial intelligence with the use of supervised machine learning models, validated on an empirical basis, with APS (accuracy, precision, sensitivity) metrics calculated on a verification sample, with a significant reduction in time with the application of the validated model to a remaining subset S' (67-75% of the building heritage) without the need for a complete engineering assessment, offering a modular structure which may be adapted to different territorial and typological contexts, with the selection of the most relevant variables (P1/P'1, P4/P'4, P6/P'6, P7/P'7) performed through the ANOVA statistical analysis and the Chi-Square analysis, also thanks to a systemic approach with the definition of a clear, replicable operational flow, scalable and provided with objective validation criteria.

ARTIFICIAL INTELLIGENCE-ASSISTED BUILDING AND EXECUTION OF A FEDERATED DATA LAYER FOR ENTERPRISE ENGINEERING

Resumen de: EP4675520A1

Artificial Intelligence-assisted building/execution of federated data layer for enterprise engineering: A system trains at least one machine-learning model to identify information about industrial assets from training data, then map the information to a federated data model. The system retrieves information about data from an application in an industrial asset. The at least one machine-learning model identifies types of the data, relationships between the data, and patterns of the data, from the information and based on data types, data relationships, and data patterns in the federated data model. The at least one machine-learning model maps the types of the data, the relationships between the data, and the patterns of the data to the federated data model. The system identifies knowledge about the types of the data, the relationships between the data, and/or the patterns of the data in the federated data model, in response to a query about data.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT CRITICAL CONSTRAINTS

Resumen de: AU2024229742A1

A computer-implemented method and computer program product for predicting a required committed capacity of an electric utility are provided. The method includes the steps of: (a) performing a stochastic optimization of raw data to produce a total committed capacity from conventional thermal units as a target data, wherein the raw data comprises grid operating conditions; (b) combining the total committed capacity from conventional thermal units with raw features and engineered features to generate training data; (c) training a machine learning model for predicting the required committed capacity of the electric utility using the generated training data; (d) predicting the required committed capacity of the electric utility using the trained machine learning model; and (e) running an augmented version of a deterministic dispatch optimization model based on the predicted required committed capacity of the electric utility. The computer program performs the aforementioned steps.

DEEP-LEARNING BASED PERSONA SPECIFIC INSIGHT GENERATOR

Resumen de: WO2024181975A1

A method for generating persona specific insights. The method may include receiving sensor data associated with a device; extracting features from the received sensor data; processing the features using a machine learning model to generate machine learning metrics; ingesting the machine learning metrics and the features to generate insights data associated with the device; generating personas data using the insights data and the features, and mapping the insights to the personas data; generating custom insights using the insights data, the personas data, and the features, wherein the custom insights are text-based summaries; and disseminating each of the custom insights to respective persona of the personas data to place service orders associated with the device.

FORMULATION GRAPH FOR MACHINE LEARNING OF CHEMICAL PRODUCTS

Resumen de: CN120693653A

A chemical recipe for a chemical product may be represented by a digital recipe diagram for a machine learning model. The digital recipe graph may be input to a graph-based algorithm, such as a graph neural network, to produce feature vectors that are denser descriptions of the chemical product than the digital recipe graph. The feature vectors may be input to a supervised machine learning model to predict one or more attribute values of the chemical product to be produced by the recipe without actually having to pass through the production process. The feature vectors may be input to an unsupervised machine learning model that is trained to compare the chemical products based on the feature vectors of the chemical products. The unsupervised machine learning model may recommend alternative chemical products based on the comparison.

METHOD OF DECISION-SUPPORT FOR A VEHICLE OR RELATED VEHICLE SIMULATION AND ASSOCIATED SYSTEM

Resumen de: WO2026003235A1

Method of decision-support for a vehicle or related vehicle simulation, the method being executed by a system comprising a server (20), a client device (10) and a database (30), the method comprising the following phases: a. acquisition phase (100) in which the server (20) receives an input data from a device onboard of the vehicle, b. selection phase (200) in which the server (20) obtain a plurality of machine learning results R1, …, Rn and computes at least one selection score, then at least one selection score being used to select a preferred machine- learning model MLp, c. transmission phase (300) in which the server (20) sends the result Rp as a recommendation to the client device (10), d. supply phase (400) in which the client device (10) provides the recommendation to a user (60), e. return phase (500) in which the client device returns decision data to a database (30).

SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE FOR FRAUD DETECTION USING AN ENUMERATION DETECTION SYSTEM

Resumen de: WO2026006480A1

A method for discontinuing interaction processing using an enumeration detection system may include receiving data associated with a plurality of interaction instances. The plurality of interaction instances may be associated with an entity. The method may further include extracting one or more interaction features from the data. The method may further include providing the one or more interaction features to a determinative machine-learning model. The determinative machine-learning model may be trained to identify enumeration patterns and output an enumeration score based on the identified enumeration patterns. The method may further include determining that the enumeration score exceeds a predetermined threshold. The method may further include discontinuing interaction processing for the entity based on the enumeration score exceeding the predetermined threshold.

SYSTEM AND METHOD FOR OBSERVABILITY AND DATA AUDIT USING IMPLICIT DATA DEPENDENCY CAPTURE

Resumen de: WO2026006680A1

The disclosure is directed to systems, methods, and computer-readable media for observability and data audit using implicit data dependency capture. Data dependency information can be intercepted, for example, as a user trains or otherwise interacts with a machine learning (ML) model. Data dependency information can include information regarding files, data sources, inputs, outputs, storage buckets, storage directories, and/or other pertinent information. A log of the data dependency information can be reviewed to determine ML model provenance.

DISEASE PREDICTION DEVICE, LEARNING MODEL GENERATION DEVICE, DISEASE PREDICTION METHOD, LEARNING MODEL GENERATION METHOD, AND COMPUTER-READABLE RECORDING MEDIUM

Resumen de: EP4672040A1

A learning model generation apparatus 10 comprises: a graph generation unit 11 which generates, from a data group including biometric information of persons and information indicating the presence or absence of occurrence of diseases in the persons, a graph composed of nodes representing data points and edges representing relationships between the nodes; a graph supplementation unit 12 which supplements the generated graph for a deficiency therein; and a model generation unit 13 which generates, from the supplemented graph, a data group in which the deficiency is supplemented, performs machine learning using the generated data group as training data, and generates a prediction model for predicting the occurrence of diseases in a person.

METHOD OF DECISION-SUPPORT FOR A VEHICLE OR RELATED VEHICLE SIMULATION AND ASSOCIATED SYSTEM

Resumen de: EP4672085A1

Method of decision-support for a vehicle or related vehicle simulation, the method being executed by a system comprising a server (20), a client device (10) and a database (30), the method comprising the following phases:a. acquisition phase (100) in which the server (20) receives an input data from a device onboard of the vehicle,b. selection phase (200) in which the server (20) obtain a plurality of machine learning results R1, ..., Rn and computes at least one selection score, then at least one selection score being used to select a preferred machine-learning model MLp,c. transmission phase (300) in which the server (20) sends the result Rp as a recommendation to the client device (10),d. supply phase (400) in which the client device (10) provides the recommendation to a user (60),e. return phase (500) in which the client device returns decision data to a database (30).

TRAINING PROGRAM OF MACHINE LEARNING MODEL, TRAINING METHOD OF MACHINE LEARNING MODEL, AND TRAINING APPARATUS OF MACHINE LEARNING MODEL

Resumen de: EP4672093A1

A training program of a machine learning model outputs a proposal for obtaining a desired result, the training program of a machine learning model causes a computer to execute a process including: acquiring training data including a plurality of attributes; acquiring constraint condition data of the attributes; calculating first information regarding prediction accuracy of the machine learning model based on the training data; calculating second information regarding feasibility of the proposal based on the training data and the constraint condition data; calculating an evaluation index based on the first information and the second information; and training the machine learning model based on the evaluation index.

DEEP LEARNING ENABLED PREDICTION OF DRUG-INDUCED LIVER INJURY

Resumen de: WO2024178006A1

A method may include determining, based at least on a knowledge graph, a plurality of biological interaction profiles associated with a plurality of drugs. The knowledge graph being representative of a plurality of interactions between a variety of drugs, proteins, and a hierarchy of biological functions. Each biological interaction profile may be representative of the effects of a corresponding drug being propagated through protein-protein interactions and biological functions. A liver injury prediction model may be trained, based on a training dataset including the biological interaction profiles, a probability of drug induced liver injury. The liver injury prediction model to may be applied to determine, based on the biological interaction profile of a drug, the probability of liver injury associated with the drug. In some cases, the liver injury prediction model may further determine the probability of liver injury based on the molecular fingerprint and/or the molecular properties of the drug.

SYSTEMS, APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR GPS SPOOFING DETECTION

Resumen de: EP4671828A1

Systems, apparatuses, methods, and computer program products are provided herein. For example, a method may include access aviation specification data. In some embodiments, the method may include training a generative machine learning model using aviation specification data (504). In some embodiments, the method may include generating synthetic aviation data using the generative machine learning model (506). In some embodiments, the method may include training one or more global positioning system (GPS) spoofing detection machine learning models using the synthetic aviation data and historical aviation operations data (508). In some embodiments, the method may include deploying a first GPS spoofing detection machine learning model of the one or more GPS spoofing detection machine learning models to an edge-based device (510).

WEIGHT AVERAGED REWARDED POLICY TRAINING FOR MACHINE LEARNING MODELS

Resumen de: WO2025265056A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for fine-tuning a target machine learning model to perform a target machine learning task. In one aspect, a method comprises: obtaining initial parameters for a target machine learning model; at each interpolation iteration of a sequence of interpolation iterations: training a plurality of auxiliary machine learning models to perform the target machine learning task using training data for the target machine learning task, interpolating the trained parameters for the plurality of auxiliary machine learning models for the interpolation iteration, and updating the current parameters for the target machine learning model using the interpolated parameters for the interpolation iteration; and, after the final interpolation iteration, determining a trained set of parameters for the target machine learning model based on the current parameters for the target machine learning model.

FIRST NODE, SECOND NODE, COMPUTER SYSTEM AND METHODS PERFORMED THEREBY, FOR HANDLING HYPERPARAMETERS CORRESPONDING TO A PLURALITY OF SETS OF DATA

Resumen de: WO2025261578A1

A first node (111) obtains (302) two or more first sets of data out of a plurality of sets collected by third nodes (113). Each third node has collected a set. A plurality of clusters have been determined. Each of the two or more first sets of data corresponds to a respective set of data in a center of a respective cluster. The plurality of clusters have been determined based on a similarity of respective statistical features of the sets of data. A number of the two or more first sets of data is smaller than a second number of the plurality of sets. The first node (111) determines (303) and tunes a respective hyperparameter for each obtained two or more first sets of data to train a respective machine learning model with a corresponding set of data of the plurality of sets of data and outputs (305) an indication indicating the hyperparameters.

COMPUTER-BASED SYSTEMS CONFIGURED FOR ENTITY RESOLUTION AND INDEXING OF ENTITY ACTIVITY

Resumen de: US2025390770A1

In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

SPECIFIC FILE DETECTION BAKED INTO MACHINE LEARNING PIPELINES

Resumen de: US2025390576A1

A set of features including a first feature and a second feature is received at a server. A subset of the set of features is determined for use in generating a model usable by a device to locally make a malware classification decision. The device has reduced computing resources as compared to computing resources of the server. The subset of the set of features is used to generate the model. The generated model includes the first feature and does not include the second feature. A determination is made, at a time subsequent to the generation of the model, that an updated model should be deployed to the device. An updated model is generated.

ANALYSIS AND CORRECTION OF SUPPLY CHAIN DESIGN THROUGH MACHINE LEARNING

Resumen de: US2025390817A1

A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

SYSTEM AND METHOD FOR UTILIZING GROUPED PARTIAL DEPENDENCE PLOTS AND GAME-THEORETIC CONCEPTS AND THEIR EXTENSIONS IN THE GENERATION OF ADVERSE ACTION REASON CODES

Resumen de: US2025390794A1

A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on dependencies with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, scores related to each group of input variables for a given instance of the input vector processed by the model are calculated according to one or more algorithms. The algorithms can utilize group Partial Dependence Plot (PDP) values, Shapley Additive Explanations (SHAP) values, and Banzhaf values, and their extensions among others, and a score for each group can be calculated for a given instance of an input vector per group. These scores can then be sorted, ranked, and then combined into one hybrid ranking.

SELECTING A NEURAL NETWORK ARCHITECTURE FOR A SUPERVISED MACHINE LEARNING PROBLEM

Resumen de: US2025390745A1

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

DATA PROCESSING METHOD, MODEL TRAINING METHOD, AND RELATED DEVICE

Resumen de: EP4668175A1

A data processing method, a model training method, and a related device are provided, to apply an artificial intelligence technology to the communication field. The method includes: obtaining a value of T, where T represents a quantity of pieces of subdata included in output data of a first machine learning model; and inputting first data into the first machine learning model to obtain second data generated by the first machine learning model, where the second data includes the T pieces of subdata, the first machine learning model includes one or more modules, and one piece of subdata is obtained each time a module in the first machine learning model is invoked at least once. A quantity of times of invoking the module in the first machine learning model may be flexibly adjusted based on the value of T, to generate the T pieces of subdata. Therefore, the first machine learning model can be compatible with a plurality of values of T, and there is no need to store a plurality of machine learning models, so that storage space overheads are reduced.

OBSTACLE DETECTION METHOD AND DEVICE FOR ASSISTING VEHICLE IN DRIVING

Resumen de: EP4667972A1

The present invention discloses an obstacle detection method for assisting in vehicle driving. The method comprises: obtaining ultrasonic echo data captured during vehicle movement; obtaining information associated with echo intersections based on the ultrasonic echo data; providing at least part of the ultrasonic echo data and the information associated with the echo intersections as feature data to a machine learning model to obtain detection information for an obstacle, wherein the machine learning model employs at least one of a classification algorithm or a regression algorithm; and assisting in vehicle driving based on the detection information for the obstacle.

METHOD AND DEVICE FOR TRANSMITTING AND RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

Nº publicación: EP4668825A1 24/12/2025

Solicitante:

LG ELECTRONICS INC [KR]
LG Electronics Inc

Resumen de: EP4668825A1

A method performed by a terminal in a wireless communication system according to at least one of the embodiments disclosed herein may include configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmissions and receptions (TRPs), monitoring the at least one AI/ML model, and performing, based on a performance of the monitored at least one AI/ML model, AI/ML model management to maintain or at least partially change the at least one AI/ML model, wherein the performance of the at least one AI/ML model may be determined based on a first multi-TRP data set related to training of the at least one AI/ML model and a second multi-TRP data set related to monitoring of the at least one AI/ML model.