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90
resultados
Última actualización
14/03/2025 [07:12:00]
Resultados 50 a 75 de 90
Resumen de: US2025069746A1
There is disclosed a method and a system for predicting the efficacy of one or more treatments. A completed questionnaire may be received from a patient requiring treatment. The responses to the questionnaire may be input to a machine learning algorithm (MLA). The MLA may have been trained using labelled patient data. A predicted efficacy of one or more treatments and a prototype corresponding to the patient may be received from the MLA. An interface may be output indicating the predicted efficacy of the one or more treatments and the prototype.
Resumen de: US2025069113A1
A method includes receiving client data of a client that includes at least one of clickstream data and analytic data of the client. For each of a number of trained machine learning (ML) models corresponding, respectively, to a number of campaigns, campaign-specific features are extracted from the client data, and a campaign interest prediction score is generated by inputting the campaign-specific features extracted for the ML model into the ML model. At least one campaign, from among the plurality of campaigns, is assigned to the client based on the generated campaign interest prediction scores. The clickstream data includes a plurality of pages visited by the client, and the analytic data of the client includes at least one of phone call data, chat message data, email data, or survey data of the client.
Resumen de: US2025068981A1
A system for managing and optimizing real world entities with machine learning algorithms are described including a server computer system configured to store and process input data, the server computer system comprising a memory and a processor; and wherein the memory of the server computer system stores a persistent virtual world system comprising virtual replicas of the real world entities, and wherein the server computer system is configured to generate explicit data sets representing functioning and behavior of the real world entities; train the machine learning algorithms with the explicit data sets to generate trained machine learning data sets; and apply the trained machine learning data sets in an artificial intelligence application to manage operation of the real world entities and generate real behavior data of the real world entities during the operation of the real world entities. Corresponding methods are also described.
Resumen de: US2025071801A1
Systems and methods for operating a quantum processor. The methods comprise: training one or more quantum neural networks using modulation class data to make decisions as to a modulation classification for a signal based on one or more feature inputs for the signal; obtaining, by the quantum processor, principle components of real and imaginary components of a signal received by a communication device; and performing first quantum neural network operations by the quantum processor using the principle components as inputs to the trained one or more quantum neural networks to generate a plurality of scores, wherein each said score represents a likelihood that the received signal was modulated using a given modulation type of a plurality of different modulation types.
Resumen de: US2025068980A1
Techniques are disclosed for providing a scalable multi-tenant serve pool for chatbot systems. A query serving system (QSS) receives a request to serve a query for a skillbot. The QSS includes: (i) a plurality of deployments in a serving pool, and (ii) a plurality of deployments in a free pool. The QSS determines whether a first deployment from the plurality of deployments in the serving pool can serve the query based on an identifier of the skillbot. In response to determining that the first deployment cannot serve the query, the QSS selects a second deployment from the plurality of deployments in the free pool to be assigned to the skillbot, and loads a machine-learning model associated with the skillbot into the second deployment, wherein the machine-learning model is trained to serve the query for the skillbot. The query is served using the machine-learning model loaded into the second deployment.
Resumen de: WO2025040769A1
The proposed technical solution relates to the field of information technology, more specifically to the field of machine learning, and can be used to classify the state of an object based on the collected features of the object. A special case of the invention is a method for generating an object state classifier performed using a processor of a computer device.
Resumen de: US2025065909A1
A method for consumption optimization of fully automated or partially automated driving maneuvers of a motor vehicle is provided. The disclosure provides that a self-learning system is operated by a processor circuit, and by way of which driving situation data is received, and the driving situation data is provided to a model of machine learning as input variables and the model is used to generate a specification signal for the at least one driver assistance system from the input variables, and consumption data of a consumption measurement is received and the consumption data is mapped on an evaluation signal by way of an evaluation function, and a training algorithm that trains the model is signaled by way of the evaluation signal, if, for the respective driving situation, the used specification is to be retained or enhanced or avoided.
Resumen de: EP4513392A1
The proposed technical solution relates to the field of information technology, more specifically to the field of machine learning, and can be used to classify the state of an object based on the collected features of the object. A special case of the invention is a method for generating an object state classifier performed using a processor of a computer device.
Resumen de: EP4513388A1
Disclosed is an engineering system (102) and a method (400) for determining one or more performance indicator values for a plurality of physical components (108A-108N) in a technical installation (106) to be visualized in a multi-layered manner. The method comprises receiving, by a processing unit (202), a request to determine one or more performance indicator values associated with one or more physical components in the technical installation (106), determining one or more physical components associated with the one or more performance indicators based on analysis of an engineering design and corresponding engineering project of the technical installation, determining a first knowledge graph from a second knowledge graph, and determining, the requested one or more performance indicator values using a trained machine learning model on the first knowledge graph.
Resumen de: WO2023212804A1
Producing an augmented dataset to improve performance of a machine learning model. A test series is created for a first type of data transformation, the test series defining a set of test values for at least one parameter characterizing the first type of data transformation. Test datasets are generated based on a source dataset, each of the test datasets corresponding to a respective test value of the set of test values for said at least one parameter characterizing the first type of data transformation. Each of the test datasets is input to the machine learning model to produce a corresponding model output. At least one score is determined for each test dataset based at least in part on the corresponding model output. Robustness metrics of the first type of data transformation are determined based on a function which maps said at least one score of each of the test datasets to said at least one parameter characterizing the first type of data transformation. A set of one or more data augmentations are determined to be applied to the source dataset based at least in part on said one or more robustness metrics of the first type of data transformation. An augmented dataset is generated based on the source dataset using the determined set of one or more data augmentations.
Resumen de: US2025061332A1
A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.
Resumen de: US2025061352A1
A computer-implemented method for artificial intelligence (AI) based risk/value assessment of a geographic area includes performing feature engineering to contextually enrich collected data. Three datasets are generated from the contextually enriched data, where a first dataset is generated by combining positive samples of the contextually enriched collected data with hard negative samples of the contextually enriched data, a second dataset is generated by combining the positive samples with soft negative samples of the contextually enriched data, and a third dataset is generated by combining the positive samples, hard negative samples, and soft negative samples. A machine learning model is trained to generate three different types of predictions for the risk/value assessment of the geographic area based on the three generated datasets.
Resumen de: US2025061171A1
Examples described herein include methods and computing systems which may include examples of calculating risk scores for certain natural disasters perils based on machine learning model outputs. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.
Resumen de: AU2025200759A1
SYSTEMS AND METHODS FOR IMPROVED CORE SAMPLE ANALYSIS Provided herein are methods and systems for improved core sample analysis. At least one image of a core sample may be analyzed to determine structural data associated with the core sample (e.g., attributes of the core sample). A machine learning model may analyze the at least one image and determine one or more attributes associated with the core sample. The machine learning model may generate a segmentation mask. An output image may be generated. A user may interact with the output image and provide one or more user edits. The one or more user edits may be provided to the machine learning model for optimization thereof.
Resumen de: US2025058360A1
Devices, systems and methods for sorting and labelling food products are provided. Respective spectra of food products for a plurality of segments of a line are received at a controller from at least one line-scan dispersive spectrometer configured to acquire respective spectra of the food products for the plurality of segments of the line. The controller applies one or more machine learning algorithms to the respective spectra to classify the plurality of segments according to at least one of one or more food parameters. The controller controls one or more of a sorting device and a labelling device according to classifying the plurality of segments to cause the food products to be one or more of sorted and labelled according to the at least one of the one or more food parameters.
Resumen de: US2025057466A1
Described are platforms, systems, media, and methods for evaluating, monitoring, and/or treating a subject for brain injury based on machine learning analysis of one or more of brain imaging features, clinical features, demographic features, or speech features.
Resumen de: US2025061379A1
A non-transitory computer-readable recording medium stores a pipeline set generation program causing a computer to execute a process including: acquiring, based on a plurality of tasks, a pipeline set in which each pipeline includes a machine learning model; generating a second pipeline by executing a simplification process which includes at least one of a process of deleting a component included in the pipeline and a process of changing a hyper parameter of the component included in the pipeline to a default value on a first pipeline of the pipeline set; acquiring an evaluation value of the second pipeline by executing the second pipeline for the plurality of tasks; and adding the second pipeline to the pipeline set based on the evaluation value.
Resumen de: US2025061349A1
A system and method for inconsistency detection. A method includes semantically analyzing a first set of data to extract features. The features include subjects represented in the first set of data. Semantically analyzing the first set of data includes applying a machine learning model. The first set of data is consolidated into a knowledge base based on the extracted features. The knowledge base includes a graph having nodes and edges. The nodes represent the subjects, and the edges represent connections among the subjects. The knowledge base is queried based on a second set of data in order to obtain knowledge base query results. Querying the knowledge base includes semantically analyzing the second set of data in order to identify more subjects. Semantically analyzing the second set of data includes applying the machine learning model. Data among the second set of data is validated based on the knowledge base query results.
Resumen de: US2025061492A1
Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.
Resumen de: WO2025036871A1
Disclosed is a system, apparatus, method for efficiently rendering one or more scenes in a computer simulated environment. The method comprises receiving visual data from one or more data acquisition devices configured to acquire data pertaining to one or more entities interacting in an industrial environment. The method comprises identifying real-world scene from the industrial environment in real-time, wherein the real-world scene is identified using one or more machine learning models on the received visual data; fetching a preconfigured animated scene from a knowledge base based on a comparison with the identified real-world scene; rendering the preconfigured animated scene fetched from the knowledge base in the computer simulated environment; detecting a deviation in the real-world scene when compared with the preconfigured animated scene being rendered in the computer simulated environment; and rendering the real-world scene in the computer simulated environment in response to the detected deviation.
Resumen de: EP4510056A1
Disclosed is a system, apparatus, method for efficiently rendering one or more scenes in a computer simulated environment. The method comprises receiving visual data from one or more data acquisition devices configured to acquire data pertaining to one or more entities interacting in an industrial environment. The method comprises identifying real-world scene from the industrial environment in real-time, wherein the real-world scene is identified using one or more machine learning models on the recevied visual data; fetching a preconfigured animated scene from a knowledge base based on a comparison with the identified real-world scene; rendering the preconfigured animated scene fetched from the knowledge base in the computer simulated environment; detecting a deviation in the real-world scene when compared with the preconfigured animated scene being rendered in the computer simulated environment; and rendering the real-world scene in the computer simulated environment in response to the detected deviation.
Resumen de: EP4510036A1
A circuit wiring determining method relates to the field of circuit layouts. The method includes: obtaining information used to describe a port and a pin; determining a plurality of candidate connection paths based on the information about the port and the pin; obtaining a cost value of each candidate connection path by using a machine learning model, where the cost value indicates impact of the candidate connection path on a total quantity of connection paths between a plurality of ports and a plurality of pins; and determining at least one target connection path from the plurality of candidate connection paths based on the cost value. In this application, the cost value of the candidate connection path is effectively evaluated by using the machine learning model, and a preferred target connection path is determined based on the cost value, to increase a quantity of connections between the ports and the pins of an entire circuit.
Resumen de: US2025053832A1
Systems, methods and apparatus of machine-learning-predictive-analytics, the method performed by a predictive analytic control computer and including receiving from a second computer a training-profile data that describes one or more contributions of resources that are associated and identified with particular entities, receiving from a third computer a training-profile data that are associated and identified with the particular entities, that does not describe one or more contributions of resources the training-profile data, and that includes data that is that is received from additional computers that host websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration that describe a first set of features and representations of issues of interest of the particular entities, generating a machine-learning-predictive-analytic model by a machine learning-predictive-analytic trainer in reference to the training-profile data, generating predictions from the machine-learning-predictive-analytic model and from a second set of features and representations of issues of interest.
Resumen de: US2025053879A1
A method for enabling user feedback and summarizing return of investment for machine learning systems includes providing a training data set and an initial machine learning model; providing a result of the initial machine learning model; receiving feedback on the result of the initial machine learning model from a user enriching the training dataset based on the feedback to an enriched data set; and retraining the initial machine learning model to a retrained machine learning model based on an enriched data set.
Nº publicación: US2025053643A1 13/02/2025
Solicitante:
AMERICAN EXPRESS TRAVEL RELATED SERVICES COMPANY INC [US]
American Express Travel Related Services Company, Inc
Resumen de: US2025053643A1
Disclosed are various embodiments for using machine learning models to identify appropriate security patterns to follow during the application development process. A computing device can receive a request to identify a collection of security patterns to apply to an application and then identify a plurality of features associated with the application. Next, the computing device can submit the plurality of features to a random forest machine learning model and receive a first set of security patterns in response. Then, the computing device can submit the plurality of features to a k-nearest neighbor (KNN) machine learning model and receive a second set of security patterns in response. Then, the computing device can identify a subset of the security patterns that is included in both the first set of security patterns and the second set of security patterns and return the subset of the security patterns.
BOPI
Sede Electrónica
