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78
resultados
Última actualización
06/05/2026 [07:37:00]
Resultados 50 a 75 de 78
Resumen de: US20260105335A1
A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.
Resumen de: US20260105259A1
A computerized method for extracting domain specific insights from a corpus of files containing large documents comprising: breaking down large chunks of text into smaller sentences/short paragraphs in a domain specific way, identifying and removing domain noise; identifying the sentence intents of the non-noise sentences; tagging the sentences with other domain specific attributes; defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes; applying a pre-defined ontology to tag documents with domain specific hashtags; and combining the hashtags using machine learning techniques into insights.
Resumen de: US20260105384A1
In an example embodiment, a model generation component may additionally assign various cloud resources to a machine learned model so that the training or retraining of the model can be performed using these resource. The containers may be weighted to handle model generation work of different weight. Having one single configuration for a container responsible for generating all models leads to overuse of hardware resources because machine learning algorithms are very resource intensive, and thus dynamically selecting the weight improves hardware utilization.
Resumen de: US20260105329A1
Systems and methods for event outcome validation are provided. The system receives a user input indicative of an event and at least one anticipated outcome of the event to be wagered on by the user. The system receives confirmation data associated with an outcome of the event from at least one confirmation data source confirming the outcome of the event and classifies the confirmation data utilizing at least one machine learning algorithm. The system determines a threshold of confirmation data sources to validate the outcome of the event and utilizes the at least one machine learning algorithm to determine a reduced threshold of confirmation data sources to validate the outcome of the event based on at least one of the classified confirmation data and a confirmation rating of the at least one confirmation data source. The system validates the outcome of the event based on the reduced threshold.
Resumen de: US20260105369A1
A method for searching for an output machine learning (ML) algorithm to perform an ML task is described. The method comprising: receiving data specifying an input ML algorithm; receiving data specifying a search algorithm that searches for candidate ML algorithms and an evaluation function that evaluates the performance of candidate ML algorithms; generating data representing a symbolic tree from the input ML algorithm; generating data representing a hyper symbolic tree from the symbolic tree; searching an algorithm search space that defines a set of possible concrete symbolic trees from the hyper symbolic tree for candidate ML algorithms and training the candidate ML algorithms to determine a respective performance metric for each candidate ML algorithm; and selecting one or more trained candidate ML algorithms among the trained candidate ML algorithms based on the determined performance metrics.
Resumen de: US20260106884A1
First event data, indicative of a first activity on a computer network and second event data indicative of a second activity on the computer network, is received. A first machine learning anomaly detection model is applied to the first event data, by a real-time analysis engine operated by the threat indicator detection system in real time, to detect first anomaly data. A second machine learning anomaly detection model is applied to the first anomaly data and the second event data, by a batch analysis engine operated by the threat indicator detection system in a batch mode, to detect second anomaly data. A third anomaly is detected using an anomaly detection rule. The threat indictor system processes the first anomaly data, the second anomaly data, and the third anomaly data using a threat indicator model to identify a threat indicator associated with a potential security threat to the computer network.
Resumen de: US20260105304A1
In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar—and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.
Resumen de: AU2024359770A1
A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.
Resumen de: US20260105371A1
0000 A task prediction model training method includes: when a maintenance task is performed in a power grid, collecting, for the power grid, system running data and operational data of the maintenance task; performing a simulation of the maintenance task for the power grid to expand the operational data under a variety of the system running data; if the expanding is completed, performing pre-processing and feature data extraction on the system running data and the operational data; annotating the feature data with label data based on a maintenance result of the simulation of the maintenance task; and under supervision of the label data, training a preset deep learning model to be a task prediction model based on the feature data. A maintenance task determination method is executed based on the trained task prediction model. A device and a storage medium are also disclosed. Thereby improving the efficiency of power grid maintenance.
Resumen de: US20260105024A1
In an example embodiment, machine learning is leveraged to predict volume importance. A machine learning model is trained to classify volumes based on “importance”, and more specifically how important it is for a backup of a volume to be retained for a longer period. Different volumes may be given different volume importance classifications, and thus may be assigned different backup retention periods. This ensures that the backup strategy remains responsive to changes in data dynamics.
Resumen de: EP4725391A1
0001 A method and device for diagnosing renal disease are disclosed. A control method of a diagnostic device according to one embodiment comprises: obtaining a retinal image of a subject; and obtaining renal disease diagnostic information regarding the subject using a machine learning model based on the retinal image, wherein the machine learning model includes a first model and a second model, wherein the first model is a neural network model, and wherein the second model is a regression-based machine learning model.
Resumen de: WO2026073374A1
Systems and techniques for machine learning model operation and/or optimization are described. In some examples, a system identifies a difference between an input size of an input and a context window size associated with a trained machine learning model. The trained machine learning model is associated with a computational graph having a static shape. The computational graph includes a plurality of operations. The system processes the input using the trained machine learning model to generate an output. To process the input using the trained machine learning model, the system executes a first subset of the plurality of operations (e.g., valid operations and/or semi-valid operations) and skips a second subset of the plurality of operations (e.g., invalid operations). The first subset of the plurality of operations is associated with the input. The second subset of the plurality of operations is associated with the difference.
Resumen de: WO2026075809A1
Aspects presented herein may enable a user equipment (UE) to determine a collaboration level to be applied to at least one artificial intelligence (AI) or machine learning (ML) (AI/ML) model/functionality based on a set of defined conditions, thereby improving and promoting collaborations between the UE and a network entity related to AI/ML positioning and/or sensing. In one aspect, a UE communicates, with a network entity, a request for a performance of at least one AI/ML-based functionality. The UE selects, based on a set of conditions, a collaboration level from a set of collaboration levels to be applied to the performance of at least one AI/ML-based functionality. The UE performs the at least one AI/ML-based functionality based on the selected collaboration level.
Resumen de: WO2026075975A1
Systems and methods disclosed herein comprise providing operational history and an electrolyte of a used Li-ion battery to a machine-learning model; receiving, from the machine-learning model, an estimate of a state of health (SoH) of the used Li-ion battery; reading parameters of the used Li-ion battery; providing the parameters and the estimate of the SoH of the used Li-ion battery to a machine-learning model trained to output a rate of degradation of the SoH of the used Li-ion battery in response to receiving parameters and a SoH; receiving, from the machine-learning model, a rate of degradation of the SoH of the used Li-ion battery; generating, based on the estimate of the SoH and the rate of degradation, a recommendation for an application of the used Li-ion battery, the application being a second-life application, recycling, or end-of-life; and providing the used Li-ion battery and a recommendation to a facility.
Resumen de: US20260099758A1
0000 An application server may receive a request to train the machine learning model on a dataset, and may generate a first set of randomized solutions based on inputting one or more of a set of model parameters into the machine learning model, where the first set of randomized solutions correspond to a set of outputs generated by the machine learning model and spans at least a subset of a set of local minimums. The application server may then select a first solution from the first set of randomized solutions and generate a second set of randomized solutions based on the first solution and inputting one or more of the set of model parameters into the machine learning model. The application server may then determine that the second set of randomized solutions includes a global minimum of the dataset based on the second set of randomized solutions satisfying a threshold.
Resumen de: US20260099694A1
Techniques for improved user experience prediction are disclosed herein. An example computer-implemented method includes receiving a sequence of web pages visited by a user and applying a machine learning model to (i) the sequence of web pages and (ii) a set of metrics data corresponding to the sequence of web pages. Applying the machine learning model includes generating embeddings of web page identifiers associated with the sequence of web pages, determining, by a first hidden layer, a first modified embedding based on respective cross-effects associated with one or more other embeddings, determining, by a second hidden layer, a second modified embedding based on the set of metrics data associated with a respective first modified embedding, and outputting a user experience value for each second modified embedding. The example computer-implemented method further includes generating one or more data objects indicating one or more of the user experience values.
Resumen de: US20260099756A1
0000 Various methods and processes, apparatuses/systems, and media for generating recourse data with data-driven actionability constraints for a negatively classified individual are disclosed. A processor trains a machine learning model by using a first set of training data and a second set of training data which outputs risk classification data associated with a negative decision; identifies, based on the risk classification data, a negatively classified individual who received the negative decision; applies a feature attribution algorithm to the trained model; ranks, in response to applying the feature attribution algorithm, a list of features that explain a negative classification for the negatively classified individual; filters the list of features that explain the negative classification for each negatively classified individual by utilizing computed actionability labels (i.e., “likely to improve,” unlikely to improve”) for all features; and outputs advice statements based on significant and actionable (likely to improve) features.
Resumen de: US20260099711A1
Various techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status.
Resumen de: US20260099429A1
A computer-implemented method and system can be used to test a code modification for a microservice application. The code modification is analyzed using a machine learning model trained on historical test run results and code change data, Based on the analysis, a subset of test cases relevant to the code modification are predicted and selected from a test case repository. The selected subset of test cases can be executed to test the code modification. If the test cases are stored in natural language, natural language processing can be used to determine actionable words and assign weightages from the test case information. Test scripts can be developed based on the determined actionable words and assigned weightages.
Resumen de: WO2026075381A1
The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a first user equipment (UE) in a wireless communication system, according to various embodiments of the present disclosure, may comprise the steps of: receiving, from a second base station, an artificial intelligence (AI) model on the basis of first network-side training information associated with a first base station; receiving, from the second base station, second network-side training information associated with the second base station; when the first network-side training information corresponds to the second network-side training information, transmitting, to the second base station, information indicating that the AI model is applicable; and receiving, from the second base station, information for configuring inference using the AI model.
Resumen de: WO2026076047A1
A computer-implemented method for generating a response to a query. The method comprises receiving one or more query tokens, the one or more query tokens indicative of the query, providing the one or more query tokens as input to a machine learning model, outputting, from a first head of the machine learning model, a first embedding based upon the one or more query tokens, generating an intermediate input embedding based upon the one or more query tokens and the first embedding, outputting, from a second head of the machine learning model, output data based upon the intermediate input embedding, and generating the response to the query based upon the output data.
Resumen de: WO2026075835A1
In some examples of the techniques described herein, one or more network settings may be associated with an identifier. In some approaches, a network entity may indicate one or more identifiers to a wireless device for checking an artificial intelligence or machine learning (AI/ML) model on the wireless device. For instance, a network entity may help to maintain a correspondence or alignment between identifiers and corresponding network settings during training data collection or inference. Network settings may change over time, and a network entity may control AI/ML positioning running at the wireless device. For instance, the network entity may indicate the wireless device to activate, deactivate, select, or switch an AI/ML model, or to fall back to a non-AI/ML-based positioning procedure. One or more operations may be utilized to enable life cycle management (LCM) based on the associated identifiers.
Resumen de: US20260101217A1
0000 Provided are a method and apparatus for monitoring a model in beam management by using artificial intelligence and machine learning. The method may include: in relation to a reference signal configured for a terminal, receiving second reference signal resource set configuration information of the reference signal for monitoring an AI/ML model; on the basis of the second reference signal resource set configuration information, measuring signal strength or signal quality for the reference signal; and reporting the performance result of the AI/ML model by comparing a measured value of the reference signal with a predicted value of the reference signal inferred via the AI/ML model.
Resumen de: US20260099769A1
In an aspect, an apparatus for machine operator feedback correlation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive, through a sensing device, performance data of at least a machine operator. At least a processor is configured to classify performance data to a performance category through a performance classifier. At least a processor is configured to calculate a performance determination. At least a processor is configured to generate a feedback correlation through a machine operator feedback correlation machine learning model. At least a processor is configured to provide a feedback correlation to a user through a display device.
Nº publicación: WO2026075831A1 09/04/2026
Solicitante:
QUALCOMM INCORPORATED [US]
Resumen de: WO2026075831A1
A method comprises determining, by a network entity, a device group of at least one selected wireless device, wherein the one or more selected wireless devices are a subset of available wireless devices in an environment; and configuring the one or more selected wireless devices to send measurement data generated by the one or more selected wireless devices to a consumer entity configured to use training examples to train a machine learning (ML) system to generate output data, the training examples being based on the measurement data, the measurement data comprising measurements of wireless signals received by the one or more selected wireless devices, the output data indicating physical positions of one or more User Equipment (UE) devices in the environment or the output data being input data to a process that determines the physical positions of the one or more UE devices.
BOPI
Sede Electrónica
