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149
resultados
Última actualización
09/10/2025 [13:08:00]
Resultados 25 a 50 de 149
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: 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: US2025291864A1
A system for determining data requirements to generate machine-learning models. The system may include one or more processors and one or more storage devices storing instructions. When executed, the instructions may configure the one or more processors to perform operations including: receiving a sample dataset, generating a plurality of data categories based on the sample dataset; generating a plurality of primary models of different model types using data from the corresponding one of the data categories as training data; generating a sequence of secondary models by training the corresponding one of the primary models with progressively less training data; identifying minimum viable models in the sequences of secondary models; determining a number of samples required for the minimum viable models; and generating entries in the database associating: model types; corresponding data categories; and corresponding numbers of samples in the training data used for the minimum viable models.
Resumen de: US2025292211A1
A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.
Resumen de: US2025292148A1
Systems and methods are disclosed for analyzing real-time data utilizing machine learning for determining the condition of an entity. The method includes receiving a control dataset and a system dataset or a non-system dataset for a first entity; determining, via input of a first subset of the control dataset into a first machine learning model, classification of the first entity; determining, via input of the system dataset into a second machine learning model or the non-system dataset into a third machine learning model, system score or non-system score, respectively; determining, via input of the system score, the non-system score, or a second subset of the control dataset into a fourth machine learning model, composite score; determining lateral score or longitudinal score based on the classification of the first entity or the composite score; and comparing the lateral score or the longitudinal score with a pre-determined threshold for initiating mitigation action(s).
Resumen de: US2025292096A1
An approach is provided for partnership optimization. Using a supervised machine learning model, a categorized profile of partners is generated based on feedback from clients and past performances of the partners. The categorized profile indicates strengths of the partners. A network graph is generated based on data about interactions between the partners and clients and the categorized profile. The network graph has nodes representing the partners and the clients and edges representing connections between the partners and the clients. Using the categorized profile and the network graph, a three-dimensional model is generated and represented by a three-dimensional matrix of cells. A given cell represents a part of a project and includes constraint(s) of the project. Using a machine learning algorithm based on Thistlethwaite's algorithm, the model is solved to optimally match the constraint(s) with strength(s) of partner(s) and strength(s) of connection(s) between the partner(s) and client(s).
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: 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: 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: WO2025189301A1
The invention encompasses innovative AI-powered platforms that predict various parameters (e.g. compostability, sustainability, material mechanical properties, formulate solutions properties, economic factors, etc.) and generates formulations for sustainable materials, while accounting for the various physical and non-physical properties as wells the applications they serve. The invention encompasses systematic search algorithms and/or machine learning techniques for identification of biomaterials that are predicted to have applicable properties. This invention encompasses a powerful method and tool to streamline the design and development of materials that are sustainable. In various embodiments, by employing advanced data processing, polymer chemistry knowledge and predictive modeling, the embodiments of the present invention provides precise and efficient recommendations, enhancing the research and development of sustainable biomaterials. Leveraging evolutionary and machine learning methodologies, the embodiments of the present invention identify polymer materials that adhere to real-world renewable constraints and other parameters, emphasizing a multitude of pertinent physical and non-physical properties.
Resumen de: US2025292903A1
According to certain aspects of the present disclosure, systems and methods are disclosed for tracking and predicting the optimal wake windows of individual infants at scale and thus, the optimal sleep times of a user on a screen customized to each child prioritizing the use of highly personalized values based on machine learning to augment existing expert opinions of wake windows by age that will be automatically adaptive to the changes in child sleep and to generate predictions personalized for each child.
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.
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.
Resumen de: EP4617989A1
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: US2024152869A1
A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the system to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information. A method includes receiving content migration project parameters, resource migration projecting parameters and one or more services parameters of a user; scanning a tenant computing environment; processing the parameters by applying a multiplier displaying the costs, profits and pricing information. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information.
Resumen de: US2024152798A1
Some embodiments select a machine learning model training duration based at least in part on a fractal dimension calculated for a training data dataset. Model training durations are based on one or more characteristics of the data, such as a fractal dimension, a data distribution, or a spike count. Default long training durations are sometimes replaced by shorter durations without any loss of model accuracy. For instance, the time-to-detect for a model-based intrusion detection system is shortened by days in some circumstances. Model training is performed per a profile which specifies particular resources or particular entities, or both. Realistic test data is generated on demand. Test data generation allows the trained model to be exercised for demonstrations, or for scheduled confirmations of effective monitoring by a model-based security tool, without thereby altering the model's training.
Nº publicación: EP4617903A1 17/09/2025
Solicitante:
AMADEUS SAS [FR]
Amadeus S.A.S
Resumen de: EP4617903A1
Method, systems and computer programs for handling search requests at a search platform are provided. The search platform determines, using a cache with a number of incomplete search results, one or more of the incomplete search results with first data fields that correspond to the least one search parameter. For each determined incomplete search result, the search platform generates at least one second data field using a machine learning model. The at least one second data field corresponds to at least one search parameter and the at least one first data field of each determined incomplete search result. The search platform assembles a number of completed search results on the basis of the determined incomplete search results and the generated at least one second data field and returns at least one of the completed search results.
BOPI
Sede Electrónica
