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169
resultados
Última actualización
16/10/2026 [07:13:00]
Resultados 25 a 50 de 169
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: 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: 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: 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: 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: 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: 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: 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: US20260300068A1
Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.
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: US20260300833A1
Apparatus, systems, and methods for store-level analysis using artificial intelligence are disclosed. An example apparatus includes at least one programmable circuit to generate a feature vector based on an alert report, the feature vector including features corresponding to alert types; execute a supervised machine learning model using the feature vector to generate a supervised output for an alert in the alert report, the supervised output representing a likelihood of the alert warranting an action; execute an unsupervised machine learning model using the feature vector to generate an unsupervised output; classify the alert based on at least one of the supervised output or the unsupervised output; and cause an electronic device to execute the action based on the classification of the alert, the action associated with a status of the alert or a request for additional store-level data.
Resumen de: WO2026207378A1
Certain aspects of the present disclosure provide techniques for reporting applicability reporting for artificial intelligence (Al) and/or machine learning (ML) inference scenarios. An example method, performed at a user equipment (UE), generally includes obtaining first signaling configuring the UE with a machine learning (ML) model associated with a use case, outputting a reference report with first information indicating a first set of one or more functionalities that are available to be applied by the UE for inference related to the use case, and outputting a subsequent report with updated information that indicates one or more changes relative to the first set.
Resumen de: WO2026206640A1
Some wireless communications systems may perform one or more positioning procedures based on one or more artificial intelligence or machine learning (AI/ML) models. Some positioning applications may be sensitive to position estimate errors. The term "integrity" or variations thereof may refer to a value or measure of confidence (e.g., trust) or probability relative to an accuracy of a position. Some examples of the techniques described herein may provide approaches for determining integrity information for positioning procedures that are based on one or more AI/ML models. For instance, some AI/ML models may not be deterministic or may respond differently when input data includes different ranges of errors. Accordingly, integrity information may be determined differently for AI/ML models than for linear positioning models. In some approaches, a dataset with established error bounds may be utilized to determine integrity information corresponding to a positioning procedure.
Resumen de: WO2026204891A1
A machine learning model (50) causes a computer to function as a first input unit (S10, S120), an inference result generation unit (S20, S130), a first output unit (S30, S140), a second input unit (S120), a weak point identification unit (S150 to S200), a collection condition generation unit (S220), and a second output unit (S230). The first input unit receives input of first input data. The inference result generation unit performs inference on the basis of the first input data and generates an inference result. The first output unit outputs the inference result. The second input unit receives input of second input data including a plurality of pieces of first input data and a plurality of pieces of ground truth data. The weak point identification unit identifies a weak point in the inference. The collection condition generation unit generates a collection condition corresponding to the weak point. The second output unit outputs collection condition data indicating the collection condition.
Resumen de: US20260304149A1
0000 Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; obtain a set of data samples for inputting to the ML model; determine a maximum change to the set of model parameters that is permitted without performance of the ML model degrading beyond a tolerance threshold value; quantize, based on the determined maximum change, the set of model parameters to generate quantized model parameters; and transmit the quantized model parameters to a further node.
Resumen de: US20260300818A1
0000 Example embodiments are directed to a system that facilitates machine learning model inference simulation. A model inference simulation request is received. A model specification is identified based on the model inference simulation request. A job workflow is generated based on the model specification and the model inference simulation request. A model inference simulation result is generated using a machine learning model associated with the model specification. The model inference simulation result is then caused to be displayed on a user interface.
Resumen de: US20260300781A1
0000 Systems, methods, and computer program products for distributed generation of inferences using hosted machine learning models are disclosed. A method may comprise fetching, from a remote server, a first checklist comprising one or more pre-processing steps and a second checklist comprising one or more post-processing steps; performing the one or more pre-processing steps at a client on an input dataset to generate a pre-processed input dataset; transmitting, from the client to the remote server, the pre-processed input dataset to be processed by the machine learning model; receiving, at the client from the remote server, an output dataset; performing the one or more post-processing steps at the client on the output dataset to generate a post-processed output dataset; and presenting the post-processed output dataset via a client computing platform at the client..
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: WO2026206724A1
Some examples of the techniques described herein may relate to data collection for training one or more artificial intelligence or machine learning (AI/ML) models to predict one or more measurements or measurement events. For example, signaling may be utilized for data collection for AI/ML model training. A trained AI/ML model may be utilized for beam management, channel state information (CSI) measurement prediction, CSI feedback prediction for a two-sided model, positioning enhancements, radio resource management (RRM) measurement prediction, or measurement event prediction, among other examples. Some approaches to RRM measurement prediction may include cell-level measurement prediction or beam-level measurement prediction. A user equipment (UE) may be configured by a source gNB with a set of cells to measure, a set of beams on each cell to measure, a set of cells for measurement prediction, or a set of beams on each cell for measurement prediction.
Resumen de: WO2026207543A1
A self-driving laboratory system and associated methods for autonomous scientific experimentation are disclosed. The system integrates automated laboratory instrumentation, multi-modal sensors, and artificial intelligence models to design, execute, and iteratively optimize biological or chemical experiments. A computing platform receives research objectives, generates experimental protocols, and controls laboratory instruments to perform experimental procedures. Experimental data are collected from imaging systems, analytical instruments, and environmental sensors and are integrated using machine learning and sensor fusion techniques to estimate experimental state. Optimization algorithms, including probabilistic and multi-objective models, analyze the data to generate modified experimental protocols that improve experimental outcomes. The system may dynamically adjust environmental parameters and measurement strategies during experimentation and may share learning across distributed laboratory sites. Digital twin models may simulate laboratory workflows to guide experimental planning. The disclosed platform enables efficient exploration of complex experimental parameter spaces while improving reproducibility, resource utilization, and discovery in automated laboratory environments.
Resumen de: US20260300001A1
0000 This document relates to facilitating interactions between a user and multiple agents, such as agents that provide different generative machine learning capabilities. In certain disclosed implementations, user input is received and two or more agents are selected to perform a particular task for the user. Then, interactions with the agents are coordinated on behalf of the user based on agent definitions of the selected agents. A result of the particular task is received and provided to the user. For instance, the user input can be received via a chat interface that allows the user to conduct a single chat session that involves the particular task being performed by multiple agents, thus giving the user the appearance of interacting with a single agent.
Resumen de: GB2704984A
A method for calibration-based pruning of machine learning model comprises: training 110 the machine learning model; generating 120 a reliability diagram of the trained machine learning model for a plurality of samples; identifying 130 bins where the predicted confidence differs from the observed accuracy; for each of said identified bins: for each sample in the identified bin 140: deciding whether increasing or decreasing the predicted confidence for that sample will reduce a bin’s confidence-accuracy difference; computing using a feature importance method respective relevance scores of elements of the machine learning model; negating the relevance scores for those samples where a decrease instead of an increase in their predicted confidence is decided to reduce the bin’s confidence-accuracy-difference; identifying 160 elements with a lowest aggregated relevance score; and pruning 170 the elements with that lowest aggregated relevance score to obtain a pruned model. The feature importance method may comprise layer-wise relevance propagation, while the elements of the machine learning model may be neurons, filters, weights. figure 1
Resumen de: EP4814814A1
A system and a method are disclosed for fine-tuning an artificial intelligence/machine learning, Al/ML, model online in a wireless communication system. A user equipment, UE, (502) transmits to a base station (504) capability information of fine-tuning online one or more Al/ML models of the UE (502); receives, from the base station (504), channels and/or signals for the UE (502) to determine whether a first Al/ML model of the one or more Al/ML models of the UE (502) needs to be fine-tuned; determines to fine-tune the first Al/ML model online based on the channels and/or signals received from the base station (504) and model-generated output from the first Al/ML model; and fine-tunes the first Al/ML model to generate an output that is used for a downlink transmission.
Resumen de: EP4815542A1
0001 A method for determining a policy for a SON is provided. The method may comprise the steps of: receiving PM data of a communication network; generating feature data representing the PM data by using a deep learning-based encoder; determining an outlier score of the feature data by using a deep learning-based outlier detection model; and selecting one of an artificial intelligence (AI)-based policy and a non-AI-based policy for performing SON in the communication network according to the outlier score.
Nº publicación: EP4814811A1 30/09/2026
Solicitante:
CHUGAI PHARMACEUTICAL CO LTD [JP]
CHUGAI SEIYAKU KABUSHIKI KAISHA
Resumen de: EP4814811A1
An information processing system of the present disclosure comprises at least one processor. The at least one processor acquires, for each of a plurality of first molecules, training data representing a plurality of feature values related to the first molecule, acquires, for each of a plurality of second molecules, test data representing a plurality of feature values related to the second molecule, calculates, for each of a plurality of feature values, an index based on a first probability distribution which is a probability distribution in the training data and a second probability distribution which is a probability distribution in the test data, and selects, on the basis of the index of each of a plurality of feature values, one or more of the plurality of feature values as one or more input feature values to be used as input parameters of a prediction model for predicting a characteristic value of a molecule on the basis of machine learning.
BOPI
Sede Electrónica
