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71
resultados
Última actualización
24/01/2026 [07:05:00]
Resultados 25 a 50 de 71
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: 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: 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: US20260012762A1
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: US20260009880A1
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: US20260010543A1
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: US20260009912A1
Method for assessing the seismic risk on existing buildings, comprising the following steps: a) identifying a set (N) of existing buildings to assess;b) acquiring for all existing buildings belonging to said set (N) qualitative data relating to the formal and construction features of said buildings;c) processing said qualitative data with a rapid analysis method based on qualitative criteria to assess the seismic vulnerability, and the related basic seismic risk, of all existing buildings belonging to the set (N);d) selecting in an organized manner a subset(S) comprising 25% to 33% of buildings of the set (N);e) acquiring for all the buildings of the subset(S) a plurality of analytical parameters;f) processing said plurality of analytical parameters with a scientific analysis method based on quantitative criteria to assess the vulnerability and the basic seismic risk of all the buildings of the subset(S);g) selecting in an organized manner a learning sample (A) comprising 70% to 80% of the buildings of the subset(S), and deriving by subtraction a verification sample (V) comprising 20% to 30% of buildings of the subset(S);h) using an AI-based machine learning model entering into an algorithm, for each building included in said learning sample (A), at least a part of said plurality of analytical parameters and the corresponding seismic vulnerability and basic seismic risk results already obtained with the scientific analysis method referred to in step f), to generate a statisti
Resumen de: US20260011243A1
A system and method for predicting fixed route travel time (e.g., bus speeds along bus routes) is provided. The system and method include a first machine learning model trained to predict speed along the fixed route without turning and dwell times. The speed from the first machine learning model, along with dwell time and turn time can be used with a second machine learning model to determine the overall route time.
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: US20260010624A1
The technology relates to cybersecurity attacks and cloud-based security, and specifically to detecting malicious embeddings in document destined for a networked system. Such embeddings can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology intercepts a document that is compatible with an MS Office file format, finds embedded code, engineers at least five features that characterize the embedded code. The technology inputs the engineered features to a trained machine learning model and determines, as a threat level, a likelihood of malicious embedding from at least the engineered features of the embedded code. Based on the threat level, the technology can block the document with a malicious threat level, accept the document with a non-malicious threat level, and or isolate the document with a suspicious threat level.
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: 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: 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: 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: 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: 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: EP4674678A1
According to one embodiment of the present invention, there is provided a battery life evaluation system including: a data collection unit configured to obtain information about route information, charge/discharge patterns, driving patterns, and cell states from an electric vehicle; and a life evaluation unit configured to predict the life of a battery installed in the electric vehicle by performing machine learning based on the obtained information.
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: 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: 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: 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: 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: US20260004076A1
Systems and methods are described for preparing unstructured data for machine learning analysis. An example method may include: receiving data representing a plurality of processes; analyzing the data to identify, for each process of the plurality of processes, a time-ordered sequence of events that occurred during the process; generating a plurality of emoji sequences by, for each process of the plurality of processes, generating an emoji sequence, each emoji in the emoji sequence representing an event of the events that occurred during the process, and the emoji sequence ordered in accordance with the time-ordered sequence; generating a plurality of feature vectors corresponding to the respective plurality of emoji sequences; and applying a machine learning technique to the plurality of feature vectors.
Nº publicación: US20260004169A1 01/01/2026
Solicitante:
XILINX INC [US]
Xilinx, Inc
Resumen de: US20260004169A1
An inference server is capable of receiving a plurality of inference requests from one or more client systems. Each inference request specifies one of a plurality of different endpoints. The inference server can generate a plurality of batches each including one or more of the plurality of inference requests directed to a same endpoint. The inference server also can process the plurality of batches using a plurality of workers executing in an execution layer therein. Each batch is processed by a worker of the plurality of workers indicated by the endpoint of the batch.
BOPI
Sede Electrónica
