Alerta

METHODS AND SYSTEMS FOR OPTIMAL CARE SETTING FOR PATIENTS USING MACHINE LEARNING

Resumen de: WO2025106998A1

A method includes: receiving patient information including information about a current hospitalization of a patient; receiving hospital-unit-volume information corresponding to a rolling window of time, indicating a patient number admitted to, transferred into, or discharged from a hospital unit housing the patient; converting the patient information and the hospital-unit- volume information into a machine-interpretable format for at least one binary output machine learning algorithm; executing the at least one algorithm to generate binary models that predict the likelihood of discharging the patient within each of multiple timeframes; combining the binary models into a single combined model, including optimizing weightings of discharge predictions for each binary model; determining, using the single combined model, at least one weighted discharge readiness quantization; transforming the at least one weighted discharge readiness quantization into at least one discharge prediction; and controlling a user interface to provide the at least one discharge prediction to a user.

PREDICTION METHOD AND PREDICTION SYSTEM

Resumen de: EP4811366A1

To design a material required to satisfy a plurality of properties efficiently. A method performed by a prediction system according to an embodiment of the present invention includes acquiring a composition and a manufacturing condition of a material of interest; and predicting a plurality of properties of the material of interest by inputting the acquired composition and manufacturing condition of the material of interest into a machine learning model for predicting properties of a material based on its composition and manufacturing condition. The machine learning model is based on a mixture of continuous probability distributions.

AUTOMATED CONTENT MODALITY TRANSFORMATION

Resumen de: CA3292429A1

Aspects of the present disclosure relate to automated content modality transformation. Embodiments include receiving content that corresponds to a particular modality. Embodiments further include providing the content as input to a generative machine learning model that has been trained to generate versions of provided content items that correspond to a target modality. Embodiments further include receiving, as an output from the generative machine learning model, a new version of the content that corresponds to the target modality. Embodiments further include providing the content and the new version of the content as input to a given machine learning model that is trained to generate a confidence score that indicates a likelihood that the new version is an accurate representation of the content in the target modality. Embodiments further include performing one or more actions involving the new version of the content based on the confidence score.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Resumen de: EP4797167A1

Provided are computer-implemented methods and systems for generating a prediction using a cold-start model, including: providing at least one data set from at least one data source, the at least one data set comprising historical transactional data for a first plurality of individuals, and contextual data for a second plurality of individuals, the first plurality of individuals comprising a subset of the second plurality of individuals; determining at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating a cold start prediction comprising at least one candidate identifier; generating at least one attribution value based on the at least one feature of the at least one activity; and generating an explainable prediction. Also provided are computer-implemented methods and systems for generating a cold-start model.

Method, System, and Computer Program Product for Improving Machine Learning Models

Resumen de: US20260278484A1

Methods, systems, and computer program products are provided for improving machine learning models which include receiving a data set including data records; inputting the data set to a pre-trained first machine learning model to generate first embeddings; inputting the first embeddings to a second machine learning model to generate second embeddings in a user-specific embedding space; inputting the plurality of second embeddings to a third machine learning model to extract feature data associated with a feature; inputting an output from a machine learning system and the feature data to a fourth machine learning model to generate a relevance score for each entity; determining a subset of entities based on the relevance score; communicating a feedback request to a user; receiving feedback data from the user; and training at least one of the models based on the feedback data.

Obstacle Detection Method and Device for Assisting Vehicle in Driving

Resumen de: US20260276813A1

An obstacle detection method for assisting in vehicle driving includes (i) obtaining ultrasonic echo data captured during vehicle movement, (ii) obtaining information associated with echo intersections based on the ultrasonic echo data, (iii) providing at least part of the ultrasonic echo data and the information associated with the echo intersections as feature data to a machine learning model to obtain detection information for an obstacle, wherein the machine learning model employs at least one of a classification algorithm or a regression algorithm, and (iv) assisting in vehicle driving based on the detection information for the obstacle.

MACHINE LEARNING SYSTEMS TO CONDUCT INMATE ASSESSMENT INTERVIEWS

Resumen de: WO2026192915A1

Systems and methods for automatically conducting an assessment interview to predict risks an individual poses to self and others by executing an interview conducting machine-learning model using as input interviewee responses and interview guidance to generate interviewer questions and responses, the interview guidance including guidelines for assessing individuals for risks to themselves and others, and generating an interview transcript including the interviewer questions, the interviewee responses, and the interviewer responses, automatically generating assessment inputs based on the assessment interview by, generating, based on the interviewee responses, assessment inputs, and providing the assessment inputs as input to an analysis engine to generate an assessment of the individual, the assessment reflecting a prediction of risks the individual poses to self and others, automatically generating a report on the assessment by generating, based on the interview transcript and sensor data collected during the assessment interview, a confidence score for the assessment.

CONTENT GENERATION ASSISTANT

Resumen de: US20260277957A1

Aspects of the present disclosure involves systems and techniques for content generation system. The systems and techniques include receiving disclosure materials; generating, by a claim generator comprising a machine learning model trained on claim language, one or more claims based on the disclosure materials; receiving one or more changes to the one or more claims; and inputting the one or more changes to the claim generator as training data to further train the claim generator.

System, Method, and Computer Program Product for Feature Similarity-Based Monitoring and Validation of Models

Resumen de: US20260278480A1

Systems, methods, and computer program products calculate a historical feature similarity point distribution associated with a first machine learning model, calculate a real-time feature similarity point distribution associated with a second machine learning model, and automatically provide, based on a comparison of the historical feature similarity point distribution to the real-time feature similarity point distribution, an indication of whether the second machine learning model is aligned with the first machine learning model.

COMMUNICATION SYSTEM USING MACHINE LEARNING FOR ENCODING/DECODING MODELS IN DISTRIBUTED UNIT BASE STATIONS

Resumen de: US20260280764A1

A communication system includes: a communication terminal (UE) capable of encoding data by using an encoding model that encodes and outputs data that has been input; and a base station including a central unit (CU) and one or more distributed units (DU), the distributed units decoding data encoded with the encoding model by using a decoding model that, when encoded data is input, decodes and outputs the data, and the central unit or the distributed units perform machine learning by using learning data including data transmitted by the communication terminal without performing encoding to generate the encoding model and the decoding model, and notify the communication terminal of a learning result of the encoding model.

APPORTIONING DATA CENTER COMPUTE POWER

Resumen de: US20260278689A1

0000 The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.

DIGITAL THERAPEUTIC PLATFORM

Resumen de: US20260279579A1

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient’s health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient’s health.

SYSTEM TO DETECT, ASSESS AND COUNTER DISINFORMATION

Resumen de: US20260278427A1

A computer-readable medium for the identification, measurement, and combatting of the influence of large-scale creation and distribution of disinformation is herein disclosed. An embodiment of this invention is comprised of one or more repositories of data which involve online comments and articles and attributes derived from them, one or more technical targeting systems, a content analysis system, a cost and influence estimation system, a dialog system, a performance management system, a bot design and test system, a security system, a multimedia content generator, one or more machine learning components, a data collection mechanism, separate consumer and human operator applications, and a mechanism for the creation and management of bots across multiple channels.

MACHINE LEARNING FRAMEWORK TO DETECT AND MONITOR COMPLIANCE MATTERS

Resumen de: WO2026193314A1

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.

Smart Micro Fulfillment Centers Powered by Machine Learning Based Clusters

Resumen de: US20260278532A1

A system and method are disclosed for determining inventory to stock at a supply chain site. The method includes determining a set of inventory clusters identifying inventory items that have been purchased together, ranking the set of inventory clusters based on a number of times each cluster of inventory items was purchased together, determining an amount of shelf space needed for each of the set of inventory clusters, ranking any inventory clusters that were purchased an equal number of times based on the amount of shelf space needed to generate a cluster ranking, and determining inventory to stock at the supply chain site based on the cluster ranking. The method further includes generating an inventory stocking plan based on the determined inventory to stock, and implementing the inventory stocking plan at the supply chain site based on the inventory stocking plan using one or more pieces of automated stocking machinery.

SYSTEM AND METHOD OF USING A KNOWLEDGE REPRESENTATION FOR FEATURES IN A MACHINE LEARNING CLASSIFIER

Resumen de: US20260278409A1

Systems and methods are provided for classifying at least one unlabeled content item with a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used as a source of features for which training data is evaluated. The machine learning classifier is trained based on features based on the attributes and the training data.

RADIO ACTIVITY DETECTION IN WIRELESS NETWORKS

Resumen de: US20260281760A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for radio activity detection, e.g., in wireless networks. One of the methods includes detecting one or more signals based on one or more outputs generated by one or more trained machine learning models processing information corresponding to a complex baseband representation of a received RF data.

SELECTING MACHINE LEARNING MODELS FOR TASKS USING A REGRESSION MODEL

Resumen de: US20260278453A1

A computer-implemented method is provided. A processor set receives a benchmark dataset comprising a number of benchmark records for evaluating a number of machine learning models. The processor set creates a number of prompts for the number of machine learning models based on a subset of benchmark records from the number of benchmark records. The processor set retrieves a number of outputs for each machine learning model by inputting the number of prompts to each machine learning model from the number of machine learning models. The processor set determines a performance score for each machine learning model based on correctness for the number of outputs for each machine learning model using a regression model. The processor set selects a subset of machine learning models from the number of machine learning models to perform a task based on performance scores for the number of machine learning models.

COMBINING MACHINE LEARNING MODELS USING SELF-DISTILLATION

Resumen de: WO2026193038A1

A computer-implemented method, and a corresponding system and code, for combining a plurality of (pre-trained) model implementations of a machine learning model, i.e. of the same machine learning model, into a combined, more particularly merged, model. The method involves obtaining a combined model training dataset comprising a set of training model inputs, and training the combined model on the set of training model inputs using a self-distillation process. Some implementations of the techniques can be used to facilitate distributed, parallelized training of a machine learning model.

MODEL UNLEARNING FOR MACHINE LEARNING MODELS BY DELINEATING UNDERLYING DATA

Resumen de: US20260278455A1

0000 A computer-implemented method is provided. A processor set identifies a dataset for model learning for a number of machine learning models. The processor set delineates a subset of data in the dataset. Data entries from the subset of data are relevant to context of subject matter to be removed from the model learning for the number of machine learning models. The processor set labels a portion of data from the subset of data. Data entries from the portion of data are associated with metric scores that indicate removal of the data will not prevent the number of machine learning models from performing tasks. The processor set modifies the portion of data from the dataset to form an updated dataset. The portion of data from the dataset is not used for the model learning for the number of machine learning models.

MACHINE LEARNING-BASED AUTOMATIC EVALUATION THRESHOLD CALIBRATION

Resumen de: US20260278475A1

A method and apparatus provide machine learning automatic evaluation threshold calibration. Human-generated labels are accessed. Each human-generated label represents a decision of acceptability of a respective output of a plurality of outputs of a first ML model. A plurality of automatic evaluation metric scores are accessed. Each automatic evaluation metric score represents a determination by a second ML model of a quality of a respective output of the plurality of outputs. An automatic evaluation metric score threshold is determined using a third ML model based on the human-generated labels and the plurality of automatic evaluation metric scores.

MODEL INSTRUCTION-QUALITY EVALUATION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM

Resumen de: US20260278402A1

A model instruction—quality evaluation method includes obtaining a model instruction and a plurality of sample sets, and for each sample set of the plurality of sample sets, fusing the model instruction with each quality evaluation sample in the sample set to obtain a model input set corresponding to the sample set, triggering, based on the model input set corresponding to the sample set, a machine learning model corresponding to the model instruction to perform a specified task specified by the model instruction, to obtain a task result set, and determining, based on accuracy of each task execution result in the task result set, a quality evaluation score of the model instruction. The method further includes determining a comprehensive quality evaluation score of the model instruction based on the quality evaluation scores obtained by evaluation based on the plurality of sample sets.

AI DISCOVERY

Resumen de: AU2025219950A1

This disclosure relates generally to detecting artificial intelligence (AI) implementation in a software application comprising one or more application packages (APs). One or more processors extract one or more AP strings from the software application, which each represent an AP; and create a prompt for a machine learning model, trained to generate output text, comprising the one or more AP strings, the prompt representing instructions to provide a classification and provide functionality information of each of the one or more APs, the classification being AI relevant or non-AI relevant and the functionality information describing a functionality of the respective AP. The one or more processors then evaluate the machine learning model on the prompt to generate output text corresponding to the classification and the functionality information of each of the one or more APs; and generate a report of the AI implementation based on the output text.

METHODS AND SYSTEMS FOR THE ESTIMATION OF THE COMPUTATIONAL COST OF SIMULATION

Resumen de: US20260278215A1

Estimating the computational cost of simulation using a machine learning model. An example method includes inputting a feature data set into a machine learning model. The feature data set includes model geometry metadata and simulation metadata. The method further includes predicting, using the machine learning model, a computational cost characteristic for a simulation process.

MACHINE LEARNING BASED MEDICAL DATA CHECKER

Nº publicación: US20260279525A1 17/09/2026

Solicitante:

ROCHE MOLECULAR SYSTEMS INC [US]
Roche Molecular Systems, Inc.

Resumen de: US20260279525A1

A method of verifying multi-modal medical data is proposed. The method comprises: accessing multi-modal medical data of a subject, the multi-modal medical data comprising a medical image of a specimen slide, wherein a specimen in the specimen slide was collected from the subject; generating a prediction pertaining to a biological attribute of the medical image based on the medical image; determining a degree of consistency between the biological attribute of the medical image and other modalities of the multi-modal medical data; and outputting, based on the degree of consistency, an indication of whether the multi-modal medical data contain inconsistency.