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62
resultados
Última actualización
10/01/2026 [07:22:00]
Resumen de: WO2026010990A1
A system and method are described for emitter identification and tracking in an electronic warfare (EW) environment. The system includes an antenna array configured to receive signals from radio frequency (RF) emitters during a dwell. Processing circuitry converts the received signals into digital signals. Pulses are detected and characteristics of the pulses determined to form pulse descriptor words (PDWs). The PDWs obtained during the dwell are deinterleaved using unsupervised machine learning to form clusters. The clusters are categorized using one or more supervised machine learning algorithms to determine whether the PDWs correspond to known or unknown emitters and the results tracked as in or out of library emitters. After merging the in or out of library emitters, an emitter report is generated and used to update a library of emitter profiles used by the supervised machine learning algorithms as well as determine countermeasures to generate.
Resumen de: WO2026009195A1
The present invention discloses a device (100), a system (300), and a method (200) for generating recommendations for predicting malfunctions in a vehicle (500). The invention includes a device (100) for generating recommendations after detecting vehicle (500) malfunctions (500). The device (100) comprises a control unit (101) with a transceiver (103) receiving sensed parameters from vehicle sensors (102) and user inputs. The processors (101-1) within the control unit (101) analyze the combined data to create the datasets to identify faults and predict potential malfunctions. The control unit (101) can further employ a fault identification model (104) like machine learning or artificial intelligence models to aid the analysis. Based on the analysis, the device (100) generates recommendations for users to address potential vehicle (500) malfunctions.
Resumen de: WO2026008166A1
A computer-implemented method for optimizing a prediction of an occurrence of a recurring medical symptom or body behavior of a human being using machine learning, the method comprising obtaining (110) input data comprising features related to the occurrence of the recurring medical symptom or body behavior, dividing (120) the features included in the input data into two or more groups of features, encoding (130), using a trained encoder machine learning model, the features of the two or more groups of features into two or more embeddings, with each embedding representing a group of features, inputting (150) the two or more embeddings into a prediction machine learning model being trained to predict the occurrence of a recurring medical symptom or body behavior based on the two or more embeddings, and providing (160) a prediction of the recurring medical symptom or body behavior based on an output of the prediction machine learning model.
Resumen de: WO2026010723A1
An edge device comprising processing circuitry and memory stores a representation of a trigger condition. The edge device accesses streaming sensor data. The edge device determines, based on the streaming sensor data and using the processing circuitry, that the trigger condition is met. The edge device transmits the streaming sensor data to a computing device in response to determining that the trigger condition is met.
Resumen de: WO2026010799A1
A method includes obtaining (302, 802) information associated with assets and/or personnel to be protected and executing (306-322, 804) a set of weighting functions and a set of algorithms for protecting the assets and/or personnel. The weighting functions and algorithms are arranged in multiple levels of a hierarchy. Each level of the hierarchy includes one or more of the weighting functions and one or more of the algorithms. The one or more weighting functions and the one or more algorithms in at least one level of the hierarchy are applied across a timeline. The method also includes applying (330, 818) an artificial intelligence/machine learning (AI/ML) algorithm (608) across the timeline to update results due to one or more changes during one or more operations involving the assets and/or personnel.
Resumen de: WO2026010874A1
A UE (102) receives (306), from a network entity (104), a configuration configuring a first set of RSs associated with an ML prediction module. The UE receives (310) a second set of RSs different from the first set of RSs. The UE transmits (316) a prediction report based on a measurement of the second set of RSs being used in an input to the ML prediction module. A UE (102) receives (806), from a network entity (104), a configuration configuring a plurality of RSs associated with a plurality of ML prediction modules. The UE receives (808) an indication indicating an RS associated with an ML prediction module. The ML prediction module is being executed at the UE. The UE transmits (816) a prediction report output from the ML prediction module based on a measurement of the RS being used as an input to the ML prediction module.
Resumen de: AU2025271453A1
Examples of the present disclosure describe systems/methods of reducing carbon footprint by generating and tracking a carbon intensity (CI) score assigned to a particular product as the product traverses through a processing plant and discrete steps in a supply chain. In some examples, intermediate CI scores may be assigned to the product as it completes each step in its life cycle. The intermediate CI scores may be aggregated to produce a final CI score. Each intermediate CI score is recorded on a blockchain, such that the CI score is independently verifiable and auditable. In other example aspects, a machine-learning model may be applied to the input data received from each supply chain stakeholder and CI scores, wherein the machine-learning model generates intelligent suggestions to stakeholders for how to tweak their processes to lower CI scores. In other examples, a CI score may be used to derive a value for a CI token. ov o v
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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).
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).
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.
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.
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.
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.
Nº publicación: US2025390745A1 25/12/2025
Solicitante:
MICROSOFT TECH LICENSING LLC [US]
Microsoft Technology Licensing, LLC
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.
BOPI
Sede Electrónica
