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70
resultados
Última actualización
17/01/2026 [07:09:00]
Resumen de: WO2026015586A1
Systems and methods are described for determining and assigning tasks for performing medical procedures. The system may be configured to receive a plurality of data streams related to a medical procedure, wherein the plurality of data streams includes one or more of system data, medical environment data, and indications of personnel performing the medical procedure; analyze, using a task generation machine learning model, the plurality of data streams to generate natural language output relating to one or more tasks to be performed in furtherance of the medical procedure, wherein one or more inputs into the task generation machine learning model includes inputting embeddings of the plurality of data streams; analyze, via a task assignment machine learning model, the one or more tasks to assign the tasks to respective personnel; and provide indications to the respective personnel for performing the respective tasks assigned to the respective personnel.
Resumen de: US20260016310A1
A computing device comprising: obtaining telematics data generated by an autonomous vehicle; building, using a machine learning algorithm, a transit model based at least in part upon the telematics data; generating, based at least in part upon the transit model, a dynamic transit route; calculating a potential benefit comprising at least one of an amount of fuel cost savings, reduced travel time, insurance savings, or environmental pollution reduction when the dynamic route is used compared to a different route; transmitting a notification comprising the dynamic route and the potential benefit to a display or touchscreen of the autonomous vehicle; receiving, via the display screen or touchscreen, a selection input indicating acceptance or declination of the dynamic route; when the selection input indicates declination, modifying the route; and when the selection input indicates acceptance, instructing the autonomous vehicle to autonomously drive along the dynamic route.
Resumen de: US20260017284A1
Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.
Resumen de: US20260017517A1
A computer-implemented method and apparatus for feature selection using a distributed machine learning (ML) model in a network comprising a plurality of local computing devices and a central computing device is provided. The method includes training, at each local computing device, the ML model during one or more initial training rounds using a group of input features representing a input features layer of the ML model. The method further includes generating, at each local computing device, based on the one or more initial training rounds, feature group values. The method further includes transmitting, from each local computing device, to the central computing device, the generated feature group values. The method further includes receiving, at each local computing device, from the central computing device, central computing device gradients. The method further includes computing, at each local computing device, local computing device gradients, using the received central computing device gradients. The method further includes generating, at each local computing device, a gradient trajectory for each input feature in the group of input features based on the computed local computing device gradients. The method further includes identifying, at each local computing device, based on the generated gradient trajectory, whether each input feature in the group of input features is non-contributing. The method further includes removing, at each local computing device, from the group
Resumen de: US20260017544A1
Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.
Resumen de: US20260019655A1
Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.
Resumen de: WO2026015162A1
Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.
Resumen de: WO2026015208A1
Disclosed are techniques for wireless communication. In an aspect, a processing device may receive, from a server device, a request for an output based on application of a plurality of artificial intelligence machine learning (AIML) models associated with a same functionality. The processing device may apply the plurality of AIML models to obtain a plurality of respective candidate outputs, the plurality of candidate outputs being associated with the functionality. The processing device may transmit the output to the server device in response to the request, the output indicating at least one of the plurality of candidate outputs.
Resumen de: GB2642421A
Method for training a neuro-symbolic machine learning model, comprising: for each image depicting at least two objects of a training dataset: inputting the image into a neural module 102, 200, 202 to obtain bounding boxes and features therein (digit); inputting each bounding box and object feature into a symbolic module (106, Fig.1; rest of Fig.2) to obtain a plurality of possible labels i.e. partial labels 212 and possible relationships 210 as a new partially-labelled training dataset; and training the neuro-symbolic model (neural module and the symbolic module) by calculating a loss from a ground truth label for the image. The symbolic module may use a set of logical rules to constrain the labels and explanations (R1-R5, Fig.7). The trained neuro-symbolic model may generate a scene graph, perform action recognition, perform visual question answering (Fig.4) or control an autonomous or semi-autonomous electronic device. The electronic device may be a moveable robot or a wearable augmented reality device.
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: 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: 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: 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: 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: 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: 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: 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: US20260010807A1
A method for predicting risk exposure can include receiving data from a sensor. The method for predicting risk exposure also can include analyzing the data via a machine learning (ML) model. The analyzing can include determining that the data represents a light exposure pattern correlated with a risk pattern. The ML model can be trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure further can include predicting a risk exposure for a user based on the analyzing the data. The method for predicting risk exposure further can include providing a notice indicating the risk exposure, as predicted. Other embodiments are disclosed herein.
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: 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.
Nº publicación: EP4673793A1 07/01/2026
Solicitante:
FLUENCE ENERGY LLC [US]
Fluence Energy, LLC
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.
BOPI
Sede Electrónica
