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76
resultados
Última actualización
07/03/2026 [07:13:00]
Resumen de: US20260067304A1
The present disclosure presents methods and systems for determining cybersecurity risk exposure for entities. In one aspect, a method is provided that includes providing first text data to a trained LLM to identify data associated with a first candidate cybersecurity event for an entity, comparing the entity's identifier to domain information to verify the entity's identifier, determining if the first candidate cybersecurity event represents a new cybersecurity event based on com with previous data, and updating a cybersecurity risk score for the entity based on this determination. Further enhancements include training the LLM with cybersecurity event data, outputting documentation of the event source, and various methods for evaluating the novelty and severity of the cybersecurity event, including similarity measures and manual review triggers. The techniques leverage LLMs, machine learning models, and automated actions to provide a comprehensive approach to cybersecurity risk assessment and response. Other aspects are also provided.
Resumen de: AU2024336136A1
The present invention discloses a method for extracting entities and relationships from technical documents in a mostly automated way which achieves a more complete and accurate result in a reduced timeframe. Various deep learning models are trained using a corpus of the domain of interest annotated by experts and linguists. A vector graph model is also trained. Manual annotations and revisions are the minimum required to obtain automated models capable of automatically extracting entities and relationships from a corpus. Once trained, the models can be used on any corpus within the same knowledge domain.
Resumen de: US20260065166A1
Computer-implemented method and system for deployment of a first machine-learning model, and replacement, without service interruption, of a second machine-learning model in active on-line use, comprising: receiving, at a controller, a replacement request; in response to said replacement request, triggering the deployment of the first model and triggering the calculation of features to be used, collecting output data from the first model, fitting and inserting one or more calibration functions downstream from the first model, and routing inference requests to the first model instead of the second model; wherein the triggered deployment of the first model comprises preloading the first model into CPU or GPU memory, and making available the calculated features and the preloaded first model by CPU or GPU, respectively, before the inference requests are routed to the first model, thus enabling that no additional latency is added when traffic is rerouted to the first model.
Resumen de: US20260065099A1
One or more embodiments of a content system provide machine-learned storage location recommendations for storing content items. Specifically, an online content management system can train a machine-learning model to identify a storage pattern from previously stored content items in a plurality of storage locations corresponding to a user account of a user. Training the machine-learning model includes training a plurality of classifiers for the plurality of storage locations. The online content management system uses the classifiers to determine whether a content item is similar to the content items in any of the storage locations, and based on the output of the classifiers, provides graphical elements indicating recommended storage locations within a graphical user interface. The user can select a graphical element to move the content item to the corresponding storage location.
Resumen de: US20260064302A1
A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.
Resumen de: US20260064726A1
A method and system for providing an intelligent response agent based on a sophisticated reasoning and speculation function can generate and provide response data for queries related to specialized documents using a deep-learning neural network that implements a stepwise process for a sophisticated reasoning and speculation function.
Resumen de: US20260064896A1
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.
Resumen de: US20260065223A1
A system and method for allocation planning comprise a server comprising a processor and memory and configured to calculate a reward for a historical allocation of a product to one or more stores associated with a retailer. Embodiments include simulating what-if scenarios for the historical allocation to identify an allocation having a greater reward than the historical allocation and allocating a quantity of a product for a current allocation to the one or more stores based, at least in part, on a distance calculation of one or more independent variables for the historical allocation and the current allocation and the identified allocation having the greater reward then the historical allocation.
Resumen de: US20260066061A1
A machine learning simulation method of determining a physical state of interaction between atoms from one or more physical properties of the atoms is disclosed. The method including dynamically evolving a first subset of atoms via a first machine learning model within a central high-fidelity region based on the one or more physical properties of the atoms. The method further includes dynamically evolving a second subset of the atoms via a second machine learning model with a remaining low-fidelity region based on the one or more physical properties of the atoms. The method also includes dynamically evolving a third subset of atoms located between the central high-fidelity region and the remaining low-fidelity region based on an interpolation of the first and second machine learning models to determine the physical state between the atoms.
Resumen de: US20260065109A1
An apparatus including a Deep Belief Network is configured to receive, via a processor, input data. The processor is caused to initialize, based on the input data, weights for a learning model of the DBN. The processor is further caused to generate, via the learning model, a representation of the input data. The weights, the input data, and the representation is to be transmitted to a quantum compute device. The processor is caused to receive sampled values from the quantum compute device using an optimization function associated with the quantum compute device. The processor is further caused to update, based on the sampled values, the weights to train the learning model to produce a trained learning model. The trained learning model is configured to generate an updated representation of the input data. The processor is further caused to generate, via a regression layer, output data based on the updated representation.
Resumen de: US20260065188A1
Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to predict one or more second entities that supply materials for the first entity and associated with the good or the service.
Resumen de: US20260063424A1
Techniques for localizing a vehicle in real time using dynamic uncertainty estimates are presented. The techniques include obtaining a terrain image captured by the vehicle; passing the terrain image to a trained evidential deep learning neural network subsystem, from which a dynamic uncertainty value and a first feature vector are obtained in real time; for each of a plurality of candidate terrain locations, comparing the first feature vector to a respective second feature vector representative of a candidate terrain location, from which a respective similarity score is obtained; for at least one of the plurality of candidate terrain locations, updating in real time, by a recursive Bayesian estimator, a respective location weight based on the dynamic uncertainty value and the respective similarity score; estimating, in real time, a location of the vehicle based on the plurality of location weights; and providing the location of the vehicle.
Resumen de: AU2024322317A1
In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.
Resumen de: EP4704091A1
A machine learning simulation method of determining a physical state of interaction between atoms from one or more physical properties of the atoms is disclosed. The method including dynamically evolving a first subset of atoms via a first machine learning model within a central high-fidelity region based on the one or more physical properties of the atoms. The method further includes dynamically evolving a second subset of the atoms via a second machine learning model with a remaining low-fidelity region based on the one or more physical properties of the atoms. The method also includes dynamically evolving a third subset of atoms located between the central high-fidelity region and the remaining low-fidelity region based on an interpolation of the first and second machine learning models to determine the physical state between the atoms.
Resumen de: EP4703968A1
Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to predict one or more second entities that supply materials for the first entity and associated with the good or the service.
Resumen de: EP4703874A1
Computer-implemented method and system for deployment of a first machine-learning model, and replacement, without service interruption, of a second machine-learning model in active on-line use, comprising: receiving, at a controller, a replacement request; in response to said replacement request, triggering the deployment of the first model and triggering the calculation of features to be used, collecting output data from the first model, fitting and inserting one or more calibration functions downstream from the first model, and routing inference requests to the first model instead of the second model; wherein the triggered deployment of the first model comprises preloading the first model into CPU or GPU memory, and making available the calculated features and the preloaded first model by CPU or GPU, respectively, before the inference requests are routed to the first model, thus enabling that no additional latency is added when traffic is rerouted to the first model.
Resumen de: EP4703921A1
There is provided a method for training a machine learning model for identifying flash calls in a set of Call Detail Records, CDRs, the method comprising: receiving a set of CDRs and a set of call network data; creating a first training set, the first training set comprising a first subset of CDRs from the set of CDRs and a second subset of CDRs from the set of CDRs, wherein the first subset of CDRs comprises a plurality of CDRs known to represent flash calls, and the second subset of CDRs comprises a plurality of CDRs known to represent legitimate calls; determining one or more characteristic features in the first training set, the one or more characteristic features comprising a first characteristic feature associated with the first subset of CDRs and a second characteristic feature associated with the second subset of CDRs, wherein the first characteristic feature is different from the second characteristic feature.
Resumen de: US20260058949A1
A system and method for inferring an operating system version for a device based on communications security data. A method includes identifying a plurality of sequences in communications security data sent by the device; determining an operating system type of an operating system used by the device based on the identified plurality of sequences; applying a version-identifying model to the identified plurality of sequences, wherein the version-identifying model is a machine learning model trained to output a version identifier, wherein the applied version-identifying model is associated with the determined operating system type; and determining the operating system version of the device based on the output of the version-identifying model.
Resumen de: US20260054855A1
The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.
Resumen de: US20260056009A1
A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.
Resumen de: US20260058966A1
A method for behavior-based threat detection may include obtaining a first set of data corresponding to at least one of an employee or an enterprise associated with the employee. The method may include training a machine learning model for at least one of the employee or the enterprise associated with the employee by providing the first set of data to the machine learning model as training data, the machine learning model configured to identify deviations between behavioral traits of email communications and behavioral traits of the employee or the enterprise. The method may include receiving an email communication addressed to the employee. The method may include determining that the email communication represents a security risk by applying the machine learning model to the email communication. The method may include performing a remediation action on the email communication based on determining that the email communication represents a security risk.
Resumen de: US20260056983A1
A method and system for providing an intelligent response agent based on a sophisticated reasoning and speculation function can generate and provide response data for queries related to specialized documents using a deep-learning neural network that implements a stepwise process for a sophisticated reasoning and speculation function.
Resumen de: WO2026041226A1
The present invention relates to a method of performing error triaging in an industrial plant (106). The method involves receiving information (502) related to an incident in the industrial plant (106) and generating a knowledge graph (504) by analyzing a plurality of log files (108A-N). The knowledge graph (504) includes data on interdependencies among log events. The method further includes determining one or more nodes (506) of the knowledge graph (504) associated with a set of log events, generating a plurality of templates (508) for these log events using a first machine learning algorithm, and generating a summary report (516) for the incident by utilizing a large language model (514) to process the templates (508). This approach facilitates accurate and efficient identification, categorization, and reporting of errors within the industrial plant (106).
Resumen de: WO2026043591A1
Aspects presented herein may enable a user equipment (UE) to indicate a correlation or a mapping between components of different feature groups (FGs) and differentiation of components in each FG to an artificial intelligence (AI) or machine learning (ML) (AI/ML) model/functionality level. In one aspect, a UE transmits, to a network entity, one or more first indications for a first positioning FG, where a number of the one or more first indications is indicative of a respective AI/ML model or functionality among a plurality of AI/ML models or functionalities associated with the first positioning FG and supported by the UE. The UE receives, from the network entity based on the one or more first indications, a second indication of at least one configuration for AI/ML positioning.
Nº publicación: US20260057254A1 26/02/2026
Solicitante:
EQUINIX INC [US]
Equinix, Inc
Resumen de: US20260057254A1
Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.
BOPI
Sede Electrónica
