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175
resultados
Última actualización
23/10/2026 [07:08:00]
Resultados 50 a 75 de 175
Resumen de: EP4819625A2
Systems, methods and articles of manufacture for are provided for analyzing user behavior in real time by ingesting telemetry data related to a streaming media application; feeding the telemetry data to a machine learning model (MLM) that produces a User Experience (UX) command based on the telemetry data and prior telemetry data received from the content streaming application; selecting content items to provide to the client device based on the telemetry data; determining, based on the telemetry data, whether the client device has sufficient free resources to receive the UX command and the content items in a current time window while providing a predefined level of service; when client device has sufficient free resources to receive the UX command and the content items, encapsulating the UX command with the content items in a content stream; and transmitting the content stream to the client device.
Resumen de: EP4819147A1
An information processing program causes a computer to execute a process including: determining a weight of an input feature in a regression model, the regression model predicting an amino-acid sequence of a virus after mutation using an amino-acid sequence of the virus as the input feature, the determining being based on a first feature related to a three-dimensional (3D) structure of a protein of the virus and a second feature related to a contribution to prediction of a machine learning model, the contribution being obtained based on the first feature.
Resumen de: EP4818981A2
A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
Resumen de: EP4818902A1
0001 Techniques and apparatuses are described for implementing a machine-learning (ML) based prediction and dynamic core allocation architecture. In example aspects, a plurality of parameters related to one or more operating conditions (e.g., temperature of a core) of one or more processors are generated. The plurality of parameters are used as an input for an ML-based model, which outputs a prediction of future parameters. The prediction is used as input for one or more process modules. The one or more process modules parse the prediction to provide a decision output, which is used to modify one or more facets of the one or more processors (a frequency, a voltage, core assignments for tasks, etc.). In this way, a system-on-chip can dynamically manage multiple operations concems for the one or more processors, such as balancing performance and temperature concerns to realize a tailored user experience.
Resumen de: EP4818906A1
0001 A fault prediction method and apparatus, and a related device are provided, relating to the field of computer technologies. Multiple pieces of error data are obtained, where each piece of error data indicates that a CE occurs in a storage medium. The multiple pieces of error data are classified. Fault prediction is performed, by using a first machine learning model, on first-type error data obtained through classification, and fault prediction is performed, by using a second machine learning model, on second-type error data obtained through classification, to obtain corresponding prediction results indicating whether a UCE occurs in the storage medium. A precision of the first machine learning model in predicting a UCE is greater than a first threshold, and a recall of predicting of the second machine learning model in predicting a UCE is greater than a second threshold. In this way, fault prediction is performed on different types of error data by using the machine learning model with a high prediction precision and the machine learning model with a high recall, so that both a precision and a recall of fault prediction can be considered, thereby effectively improving an overall prediction effect for the multiple pieces of error data.
Resumen de: MX2026010538A
System, methods, apparatuses, and computer program products are disclosed for generating and using a hybrid artificial intelligence classifier for classifying input into one or more nodes of a taxonomy. Training data is received for at least a first portion of the taxonomy and used to train a supervised machine learning (ML) model to classify input into the first portion of the taxonomy having training data. A large language model (LLM) taxonomy is determined for at least a second portion of the taxonomy. The hybrid AI classifier classifies input based on a first classification obtained by providing the input to the supervised ML, and a second classification obtained by providing at least the input and the LLM taxonomy to a pre-trained LLM.
Resumen de: MX2026010422A
An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based thereon. The device may also include a digital camera to capture an image of a face of an attempted user to enable facial recognition to alter a locked state of the device.
Resumen de: MX2026010476A
A method for inspecting a battery, according to an embodiment of the present invention, relates to a method for inspecting the quality of a battery in a manufacturing process, the method comprising the steps of: acquiring an image capturing at least a portion of the exterior of the battery to preprocess the image; detecting one or more defect candidate regions in the preprocessed image by using one or more detection algorithms; extracting position information of the defect candidate regions and shape feature information of the defect candidate regions; and inputting, into a pre-trained machine learning model, information related to the detection algorithms, the position information of the defect candidate regions, and the shape feature information of the defect candidate regions, to determine whether corresponding defect candidate shapes are defective.
Resumen de: US20260300783A1
0000 A failure prediction method including a predicting flow and a model training flow, the predicting flow including receiving a natural language input from a client computer, translating the input into a task by a LLM, selecting a ML model dedicated to the task, receiving first data, converting the first data to second data of a predetermined format, immediately applying, the ML model on the second data for predicting an output and providing a corresponding explanation, storing the second data and the output into historical data in a storage layer, translating the output and the explanation into a prediction in the natural language by the LLM, and transmitting the prediction to the client computer and iterating the predicting flow for a predetermined number of time; and the model training flow including retrieving the historical data from the storage layer, and training the ML model on the historical data.
Resumen de: US20260301015A1
0000 A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.
Resumen de: US20260294235A1
An optical coherence tomography (OCT) device includes artificial intelligence for recommending a treatment plan for a patient with a retinal or macular disease such as age-related macular degeneration (AMD). The OCT device includes a sensor configured to quantify an initial level of macular edema or retinal exudation. The OCT device receives treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient. The OCT device performs OCT on the patient subsequent to each anti-VEGF injection to determine subsequent levels of edema or retinal exudation. The OCT device collects a set of training data including: the initial and subsequent levels of edema or exudation, patient information, and treatment information. The OCT device applies the training data to a machine-learning model trained on training data for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient.
Resumen de: US20260303475A1
The disclosure relates to a ML-based method for determining a CCE aggregation level for a UE in a PDCCH. The method comprises obtaining RBS traces. The method comprises training, using first data obtained from the traces, a machine learning model to predict a probability of discontinuous transmission (DTX) “isDTX probability”. The method comprises inputting second data obtained from the traces into the machine learning model, obtaining the isDTX probability and expanding the second data with the isDTX probability. The method comprises, for each of a plurality of probability thresholds (PTs) and for each of a plurality of strategies, selecting a data having an isDTX probability greater or equal to the PT and best satisfying the strategy and using the data to train a classifier. The method comprises selecting one classifier and using the classifier for determining the CCE aggregation level for the UE in the PDCCH.
Resumen de: US20260300780A1
0000 Implementations are disclosed for training and/or applying a single machine learning model to generate mid-cycle inferences based on variable length time series inputs. In some implementations, a time series of satellite imagery samples that depict an agricultural area over all or part of a crop cycle may be obtained. During training, ground truth agricultural classifications may be obtained for geographic units of the agricultural area represented by individual pixels of the satellite imagery samples. During training, sample(s) of the time series may be masked to generate a partially masked satellite imagery samples, which in tum may be used to generate input embedding(s). The input embedding(s) may be applied across a machine learning model to generate output(s) representing in-season agricultural prediction(s). During training, the in-season agricultural prediction(s) may be compared with the ground truth agricultural classifications to train the machine learning model.
Resumen de: US20260301385A1
0000 A method of improving a main output of a main AI model includes processing first video data using a machine learning model includes continuously analyzing incoming video data via a first processing pipeline and concurrently via a second processing pipeline. The first processing pipeline can include preprocessing incoming video data according to first preprocessing parameters that format the incoming video data to create the first video data and processing the first video data by the main AI model to determine the main output that is indicative of a first inference dependent upon the first video data. The second processing pipeline can include identifying first test preprocessing parameters that are predictive to produce a first test output that satisfies a baseline criterion and preprocessing the incoming video data according to the first test preprocessing parameters that format the incoming video data to create first test video data.
Resumen de: US20260301451A1
0000 System apparatus, article of manufacture, method and/or computer program embodiments are provided for detecting and monitoring compliance matters. An example method may include obtaining a first set of image data corresponding to an environment from one or more image sensors; processing the first set of image data using a first machine learning algorithm to yield annotated image data that identifies at least one object within the environment; processing the annotated image data using a second machine learning algorithm to determine one or more attributes associated with the at least one object within the environment; and generating, based on the one or more attributes, an evidence package corresponding to the at least one object within the environment, the evidence package being configured to facilitate a determination of one or more action items associated with the at least one object.
Resumen de: US20260300771A1
0000 A system enables agile model development to speed up innovation by data scientists. Model training and deployment are coordinated and standardized to reduce redundancy. Data is obtained for feature generation and reformatted and de-sensitized for storage. The features are stored in locations available to all models and training modules of a system so data does not need to be adjusted for new models. To generate a machine learning model, the system establishes a cohort for evaluation by the model. A model template and features for use by the model are identified. The selected template and features are used for experimentation and evaluation. Model training artifacts, such as model weights are subsequently recorded in a model store and the model scripts and settings can then be registered in a centralized database where it can be accessed for execution.
Resumen de: AU2025374609A1
The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.
Resumen de: WO2026206375A1
An interactive multimedia with multiple levels and a method for designing the interactive multimedia are provided. The interactive multimedia includes: a first level program, providing at least one first level interactive activity to determine one of multiple lines for the user to proceed from a first level to a next level; and a second level program, providing at least one second level interactive activity to determine one of multiple lines for the user to proceed from the second level to a next level. The levels and the lines of the interactive multimedia are designed at least partially based on a decision tree generated by a machine learning technique with a source data including predictor variables indicating attributes of respondents and a target variable indicating related outcomes of the respondents.
Resumen de: WO2026202381A2
An apparatus for validating at least one AI/ML model or functionality according to an embodiment is provided. The apparatus comprises a validation module (120) configured to conduct an evaluation by evaluating a performance and/or an applicability of the at least one AI/ML model or functionality depending on classes and/or attributes and/or performance criteria. The validation module (120) is configured to generate a validation output indicating whether the at least one AI/ML model or functionality meet one or more predefined criteria depending on the evaluation.
Resumen de: US20260300716A1
0000 A method includes generating a first set of coarse-grained labels, a first set of fine-grained labels, a second set of coarse-grained labels, and a second set of fine-grained labels. The first set of coarse-grained labels are generated by a first machine learning (ML) model and based on textual content and contextual information of each data point. The first set of fine-grained labels are generated by a second ML model and based on textual content and contextual information of each labeled data point. The second set of coarse-grained labels and the second set of fine-grained labels are generated by a third ML model and based on textual content and contextual information of each data point. The method also includes validating the subset of the input dataset based on comparisons of the first set of coarse-grained labels with the second set of coarse-grained labels and comparisons of the first set of fine-grained labels with the second set of fine-grained labels.
Resumen de: US20260301007A1
0000 The present disclosure relates to a method for explainable task-based optimization. The method includes receiving a target decision variable that is of interest to the user and an auxiliary dataset associated with a decision-making problem. Further, the method includes generating a prediction of the target decision variable using a forecasting machine learning (ML) model. Furthermore, the method includes determining an optimal decision variable for the decision-making problem with uncertain parameters and variables. Furthermore, the method includes executing the optimal decision variable and determining an actual cost outcome. Furthermore, the method includes updating the forecasting ML model using a rolling window technique based on the actual cost outcomes. Furthermore, the method includes evaluating the actual cost outcome of an executed decision based on comparing with the optimal performance and updating optimization parameters based on the actual cost outcome to refine the forecasting ML model.
Resumen de: US20260300634A1
0000 Techniques to extract narratives from unstructured data using generative machine-learning models are provided. A computer-implemented method can include accessing a plurality of unstructured data items from one or more digital communication platforms. The computer-implemented method can also include applying a narrative-discovery machine-learning model to the plurality of unstructured data items to extract a set of narratives. In some instances, a narrative of the set of narratives includes semantic content. The computer-implemented method can also include generating a set of narrative mappings. A narrative mapping can include a corresponding narrative of the set of narratives and one or more assigned unstructured data items. In some instances, the assigned unstructured data items include content that substantially matches the semantic content of the corresponding narrative. The computer-implemented method can also include generating a narrative output that includes the set of narrative mappings.
Resumen de: US20260301097A1
0000 A method performed by a processing system including at least one processor includes collecting, from a quantum sensor in a communications network, position data for a public safety device, tracking a movement pattern and a usage pattern of the public safety device by providing the position data as input to a quantum machine learning model that generates the movement pattern and the usage pattern as an output, determining, based on an analysis of the movement pattern and the usage pattern, that an emergency service should be contacted, and placing a call to the emergency service.
Resumen de: US20260300689A1
In an example embodiment, a machine learning framework is provided that enhances reliability of analysis of non-invasive cognitive input, correlates the cognitive input with additional non-cognitive input, and identifies context-based possible selections from software processes. A graphical user interface may be provided that depicts the possible selections to the user in a manner that makes it clear to the user which thoughts lead to which selections. This graphical user interface may be iteratively updated with each selection by the user, with each selection leading to one or more possible selections displayed in the graphical user interface being replaced with different possible selections. The result is a seamless framework that aids in enhancing cognitive interfaces.
Nº publicación: US20260299971A1 01/10/2026
Solicitante:
MAPLEBEAR INC [US]
Maplebear Inc.
Resumen de: US20260299971A1
An online system presented herein generates adaptive and personalized user interfaces for attribute selection and item replacement selection. The online system fulfills an order by picking items at a source and delivering the items to a user. When an item is not found at the source, a picker attempts to find a suitable replacement item. To find acceptable replacements, the online system allows the user, when placing an order, to specify attributes of an item that are important for that order. To provide an improved user interface that helps the user select attributes for a given item, the online system selects a subset of the item's attributes that are likely important and relevant to potential replacements. If the item is not found, the user's attribute selection is communicated to the picker, as well as used to determine and rank replacements for displaying at a user interface of a picker's device.
BOPI
Sede Electrónica
