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66
resultados
Última actualización
29/09/2025 [07:07:00]
Resumen de: US2025301187A1
A method may include determining a combination of values of attributes represented by reference data associated with computing devices by training a machine learning model based on an association between (i) respective values of the attributes and (ii) the computing devices entering a device state. The combination may be correlated with entry into the device state. The method may also include selecting a subset of the computing devices that is associated with the combination of values. The method may additionally include determining a first rate at which computing devices of the subset have entered the device state during a first time period and a second rate at which one or more computing devices associated with the combination have entered the device state during a second time period, and generating an indication that the two rates differ.
Resumen de: US2025299070A1
An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine a feature associated with the base model in making the exception decision. The embodiment may automatically identify a remaining additional feature in making the exception decision, and generating a perforation corresponding to the remaining additional feature. The embodiment may, in response to detecting a subsequent decision including a shared additional feature to the generated perforation, automatically validate a feature boundary within the generated perforation. The embodiment may automatically outputting a decision recommendation for the subsequent decision using both the base model and the generated perforation.
Resumen de: US2025299017A1
A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics includes obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.
Resumen de: AU2024234871A1
A Hellinger decision tree can detect fraudulent transactions in a data set of financial transactions. Applying the Hellinger decision tree uses a Hellinger distance. The Hellinger decision tree can be part of a machine learning algorithm. In an example, the Hellinger decision tree is a positive and unbalanced Hellinger decision tree used with an imbalanced positive and unlabeled data.
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: US2025298920A1
A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.
Resumen de: US2025299802A1
A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.
Resumen de: US2025299780A1
Techniques for predicting performance of biological sequences. The technique may include using a statistical model configured to generate output indicating predictions for an attribute of biological sequences, the biological sequences generated using a machine learning model trained on training data. The statistical model is configured to allow for at least some of the predictions to occur outside a distribution of labels in the training data.
Resumen de: US2025299803A1
A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.
Resumen de: US2025299231A1
Systems and apparatuses for generating object dimension outputs and predicted object outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether it contains one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine an object dimension output. The system may also determine a predicted object output that includes additional objects predicted to be in a room corresponding to the image. Using object dimension outputs and the predicted object output, the system may determine an estimated repair cost.
Resumen de: WO2024104614A1
The present invention describes a self-adaptive system capable of extracting correlations between multiple faults from network topologies, with the innovative component being the data pre-processing phase generating causality matrices to provide as an input to ML models. The proposed fault correlation system is responsible for, without any configuration, identi fying the hierarchical relationships between the multiple alarms, allowing for a better understanding of the causality and impact of each mal function, hence assisting the implementation of RCA rules. This allows, not only for a huge dimensionality reduction of alarms needed to be processed by a TO ' s, but also signi ficantly increases the knowledge about the topology, thus reducing downtime and increasing the quality of service of the network and services.
Resumen de: EP4621643A1
A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics. The method comprises obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.
Resumen de: US2025294033A1
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: AU2025202625A1
Systems and methods for training a machine learning (ML) model to predict outcomes is disclosed. The ML model includes a plurality of ML sub-models and an ensemble model. The method includes: receiving a plurality of data records; creating the ML sub-models based on the data records; assigning at least a subset of the data records to each of the ML sub-models; training each ML sub-model using the assigned subset of data records, each sub-model trained to determine a predicted outcome based on a given user data record; providing the predicted outcomes generated by each of the sub-models to the ensemble model, the ensemble model trained to combine the predicted outcomes from the sub-models to obtain a combined predicted outcome; using the trained ML model to determine a predicted outcome for an individual data record; and reusing the determined predicted outcome for the individual data record to retrain the ML model. Systems and methods for training a machine learning (ML) model to predict outcomes is disclosed. The ML model includes a plurality of ML sub-models and an ensemble model. The method includes: receiving a plurality of data records; creating the ML sub-models based on the data records; assigning at least a subset of the data records to each of the ML sub-models; training each ML sub-model using the assigned subset of data records, each sub-model trained to determine a predicted outcome based on a given user data record; providing the predicted outcomes generated by e
Resumen de: AU2024214090A1
A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (Al) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more Al 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: US2025292309A1
Detecting compatibility mismatch by generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with a compatibility between items (e.g., items and categories of vehicles or an item and another item) and includes a list of recommended compatibilities between the items and a user reported compatibility for at least one item. A machine learning model is generated for detecting a compatibility mismatch between a first item and a second item and/or between an item and a category of vehicle. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate the machine learning model. An update to the list of recommended compatibilities is determined based on the detected compatibility mismatch.
Resumen de: WO2025193502A1
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: WO2025193810A1
The present disclosure relates to training a machine learning model based on a dataset comprising sales volume, product distribution logistics records, and product manufacturing data. The present disclosure further relate to extracting from the model a prediction of at least one item selected from a group consisting of future sales volume of an existing product, consumer interest for new products, and failure rates for product distribution equipment.
Resumen de: WO2025193673A1
A system and method for enhancing data feed using a machine learning (ML) model are disclosed. In some embodiments, the method includes receiving multimodal data associated with a plurality of data items and providing, from the received multimodal data, a set of multimodal data samples to the ML model, each multimodal data sample associated with two or more modalities. The method also includes training the ML model using the set of multimodal data samples by optimizing a similarity value computed for each multimodal data sample based on whether the multimodal data sample is associated with a same data item or from different data items. The method further includes receiving new data associated with a new data item, the new data including one or more data components to be enriched, and automatically populating the one or more data components using the trained ML model.
Resumen de: WO2025193266A1
Write protection can be provided in mixed-media datasets. Contextual details may be extracted from a set of media to form a mixed-media dataset. The mixed-media dataset may be used to train a machine-learning model. A request to modify the mixed-media dataset may be received causing the machine-learning model to determine if implementing the request to modify the mixed-media dataset will introduce conflict or a deviation from the current mixed-media dataset. Upon confirming that implementing the request will not introduce a conflict or deviate from the from the current mixed-media, the mixed-media dataset may be modified according to the request and the machine-learning model may be retrained using the modified mixed-media dataset.
Resumen de: WO2025193249A1
Qualification decisioning systems and techniques are described. For instance, a system receives user information that is indicative of a user's income stream and/or asset(s). The system receives product qualification criteria data corresponding to products, with different products corresponding to different product-specific qualification criteria. The product qualification criteria data can change over time. The system dynamically analyzes the user information and the product qualification criteria data using a trained machine learning (ML) model in real-time as the user information and the product qualification criteria data continue to be received. The trained ML model identifies a subset of the plurality of products that the user qualifies for at a specific time. The system outputs recommendations for the subset of the plurality of products. The system dynamically trains the trained ML model further, using the recommendations and the user information as training data, to update the trained ML model for future qualification decisions.
Resumen de: US2025292377A1
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.
Resumen de: US2025292296A1
An asset-exchange feedback system is implemented for performing asset-exchange feedback operations. The asset-exchange feedback system collects historical asset-listing data from an asset-exchange platform. The historical asset-listing data comprises, for each asset listing of a plurality of previous asset listings, a plurality of asset-listing attributes and a result of the asset listing. The asset-exchange feedback system uses a first machine learning model to determine, based on the historical asset-listing data, a first set of attribute-importance scores. Each attribute-importance score in the first set of attribute-importance scores corresponds to a respective asset-listing attribute in the plurality of asset-listing attributes and indicates an importance of the respective asset-listing attribute to one or more offerees participating in the asset-exchange platform. The asset-exchange feedback system performs an asset-exchange feedback operation based on the first set of attribute-importance scores.
Resumen de: US2025292289A1
A home valuation facility is described. The facility accesses information about each of a plurality of homes sold in a geographic area during a distinguished period of time. The accessed information includes, for each home, a selling price for the home and one or more photos depicting the home. The facility uses the accessed information to train a statistical model for predicting the value of a home in the geographic area based on information about the home, including one or more photos depicting the home. The facility receives information about a distinguished home, including one or more photos depicting the distinguished home. The facility subjects the received information about the distinguished home to the trained statistical model to obtain a prediction of the distinguished home's value. The facility causes the obtained prediction of the distinguished home's value to be displayed together with information identifying the distinguished home.
Nº publicación: US2025291921A1 18/09/2025
Solicitante:
SOCKET INC [US]
Socket, Inc
Resumen de: US2025291921A1
A request is received to scan a package integration for a malicious dependency, the package integration to be integrated into an application. Using a known package cache, a subset dependencies of the package integration that have not been previously scanned is determined. Content of each file of the subset is input into a malware detection model, and an identification of an ambiguous pattern is received from the malware detection model. Responsive to receiving the identification of the ambiguous pattern, the ambiguous pattern is input into a severity model, and a level of severity that the ambiguous pattern would impose on an assumption that malware is present is received. Where the level of severity is above a threshold minimum level of severity, a query is transmitted to a generative machine learning model to determine whether malware is present.
BOPI
Sede Electrónica
