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SYSTEM AND METHOD FOR AUTOMATED DEVELOPMENT OF MEDICAL DIAGNOSTIC SOFTWARE USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Resumen de: US20260178989A1

0000 A method for constructing Artificial Intelligence (AI) medical diagnostic tools via computer-implemented graphical user interfaces, without the need for coding, is described. The method method utilizes automated AI algorithms (autoML) to train an AI model. It implements novel methods for data preparation, training and retraining of AL/ML models. It utilizes Federated Learning technology to make medical datasets in different hospitals available to other hospitals for constructing diagnostic tools. The invention can produce multiple medical diagnostic tools suitable for clinical use. It is intended for use by medical professionals, not requiring coding in computer languages.

CHO That Considers AIML Functionality Applicability

Resumen de: US20260181514A1

0000 A wireless transmit/receive unit (WTRU), which include one or more processors, may be configured to receive a configuration to activate one or more artificial intelligence or machine learning (AI/ML) functionalities and to receive conditional handover (CHO) configurations to one or more candidate cells, and a plurality of signal level thresholds associated with the serving cell. The WTRU may be configured to compare the serving cell signal level with each of the plurality of serving cell signal level thresholds and determine applicabilities of each of the one or more AI/ML functionalities for the CHO configurations based on the comparisons between the serving cell signal level and each of the one or more serving cell signal level thresholds.

NEURAL NETWORK REPRESENTATION OF QUANTUM CIRCUITS

Resumen de: US20260178958A1

A method may include obtaining a configuration of a quantum circuit comprising n qubits and k quantum gates. The k quantum gates include at least a single-qubit gate or a two-qubit control gate. The method may include constructing a neural network representing the quantum circuit, wherein the neural network includes k+1 layers that include k pairs of adjacent layers, with each pair of the adjacent layers corresponding to one of the k quantum gates. The method may include connecting one or more nodes in each pair of the adjacent layers based on a representation of a corresponding quantum gate of the k quantum gates. The method may include training the neural network using machine learning techniques to obtain an output. The method may include applying the output to the quantum circuit.

METHOD, DEVICE AND STORAGE MEDIUM FOR INTERACTION PROCESSING

Resumen de: US20260178889A1

0000 The disclosure provides a method, a device and a storage medium for interaction processing. A method includes: generating, based on context information related to an interaction, task description information for an interaction task with a trained first machine learning model, the task description information at least indicating whether the interaction task is to be performed; generating, in response to the task description information indicating that the interaction task is to be performed, a control instruction for at least one component of a terminal device based on the task description information by using a predetermined association relationship between task description information and control instructions; and controlling, based on the control instruction, the at least one component of the terminal device to perform the interaction task.

METHODS AND SYSTEMS FOR EXPLAINING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Resumen de: US20260178948A1

In some embodiments, a computing system may generate a first set of importance metrics (e.g., scores or values) for a model. The importance metrics may be generated using an explainable artificial intelligence technique, and an individual importance metric may indicate how influential a corresponding feature is for a decision made by a model. The computing system may determine an important feature and create a modified dataset by removing the important feature from the dataset. The computing system may train the model on the modified dataset and evaluate the performance of the model to determine the effect of removing the feature (e.g., which may indicate how important the feature is to output generated by the model). This process may be repeated for additional features and additional performance metrics may be obtained.

System, Method, and Computer Program Product for Generating an Inference Using a Machine Learning Model Framework

Resumen de: US20260178944A1

Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

APPARATUS AND METHOD FOR DESIGNING MULTILAYER FILM

Resumen de: EP4764452A1

0001 An apparatus and method for designing a multilayer film is disclosed. An apparatus for designing a multilayer film may perform: modeling a lamination structure of a multilayer film to be designed, collecting stress-strain data with respect to the single-layer film forming the lamination structure, reading a value pre-stored in a storage space accessible by an apparatus for designing a multilayer film, and obtaining a feature setting mode for designating different feature setting manners depending on the read value, calculating a strain energy from a plurality of physical indicators selected from the stress-strain data, depending on the feature setting mode, and setting the strain energy as the feature, selecting at least one among a plurality of supervised learning models capable of a regression analysis as a machine learning model, predicting the dart impact strength of the multilayer film by using the machine learning model learned by taking the feature as an independent variable, and a dart impact strength of the multilayer film as a target variable, and generating design data for the multilayer film, by combining predicted values for other properties and a predicted value of the dart impact strength, so as to satisfy the design requirements of the multilayer film.

GAME ENGINE AND ARTIFICIAL INTELLIGENCE ENGINE ON A CHIP

Resumen de: EP4765027A2

An electronic chip, a chip assembly, a computing device, and a method are described. The electronic chip comprises a plurality of processing cores and at least one hardware interface coupled to at least one of the one or more processing cores. At least one processing core implements a game engine and/or a simulation engine and at least one or more processing cores implements an artificial intelligence engine, whereby implementations are on-chip implementations in hardware by dedicated electronic circuitry. The at least one or more game and/or simulation engines performs tasks on sensory, generating data sets that are processed through machine learning algorithms by the hardwired artificial intelligence engine. The data sets processed by the hardwired artificial intelligence engine include at least contextual data and target data, wherein combining both data and processing by dedicated hardware results in enhanced machine learning processing.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY EXTRACTING AND CORRECTING FINANCIAL INFORMATION FROM DOCUMENTS

Resumen de: US12664136B1

A machine learning based (ML-based) method and system for automatically extracting and correcting financial information from documents, is disclosed. Initially, the documents are obtained from data sources and pre-processed to generate the pre-processed data associated with contents within the document. The contents are classified as potential key-value pairs corresponding to the financial information based on the system prompts and extracted using the ML model. The potential key-value pairs are corrected to obtain the corrected key-value pairs based on custom prompts, using the ML model. The corrected key-value pairs corresponding to the financial information are provided as the output to the end users on user interfaces associated with an electronic device. This technique extracts financial information regardless of structure or alignment by learning to recognize any added or removed prefixes or suffixes, enabling the prefixes or suffixes to make corrections and generate accurate key-value pairs.

SYSTEMS, METHODS AND DEVICES FOR INDOOR TRACKING AND NAVIGATION

Resumen de: CA3293754A1

Provided are methods, systems, and devices for indoor tracking and navigation. The method includes receiving a plurality of sensor signals and a navigational repository; integrating, by a fusion algorithm, the plurality of sensor signals and the navigational repository to generate a tempospatial guidance dataset; training a machine learning module on the tempospatial guidance dataset, wherein the machine learning model is configured to learn spatial patterns by analyzing relationships between the plurality of data signals and the navigational repository; and determining, by the machine learning module, a navigation signal for guiding a user, wherein the navigation signal is generated by analyzing a user location and predicted movement patterns.

STRUCTURED PROMPT FRAMEWORK FOR MACHINE LEARNING MODEL OUPUT GENERATION

Resumen de: CA3290647A1

Aspects of the present disclosure relate to structuring prompt frameworks in machine learning models. Embodiments include instructing a machine learning model via a prompt to generate an output according to a series of steps that reference one or more sections of the prompt. Embodiments include providing the machine learning model, via the prompt, with the one or more sections delineated with corresponding tags, each section of the one or more sections being referenced in the prompt via a corresponding tag. Embodiments include providing the machine learning model, via the prompt, with an output template indicating a target structure for the output and instructing the machine learning model to score the generated output according to a set of scoring criteria. Embodiments include instructing the machine learning model via the prompt to provide the output only when a calculated score, based on the scoring of the generated output, exceeds a threshold value.

HALLUCINATION MITIGATION TECHNIQUES FOR TEXT-TO-CODE CONVERSIONS

Resumen de: US20260169714A1

Various embodiments of the present disclosure provide hallucination mitigation techniques for text-to-code conversions that improves the functionality of a computer in various aspects. The techniques comprise receiving a text-based file that defines a set of standards for a prediction domain; generating, using a machine learning model, a decision tree based on (i) the text-based file and (ii) a decisioning prompt for the text-based file; generating, using the machine learning model, computer programmable code for the set of standards based on the decision tree and a code conversion prompt for the decision tree; and providing the computer programmable code to implement an automated task for the prediction domain.

SYSTEM AND METHODS OF OPTIMIZING INFERENCE FOR FOUNDATION MODELS

Resumen de: WO2026123313A1

A computer-implemented method is disclosed. The method includes: receiving a request to process using a pre-trained machine learning model, the request including an input prompt, a first budget, and a task identifier of a first task; pruning the machine learning model, without storing a separate pruned model in memory, wherein the pruning comprises determining, for each layer of the machine learning model, a layer gate value indicating whether to use the layer for inference based on the first budget and the first task; and utilizing the pruned machine learning model in accordance with the layer gate values to generate an output corresponding to the request.

CONNECTED MODEL FRAMEWORK FOR FORECASTING CAUSAL PREDICTIONS

Resumen de: US20260170293A1

Various embodiments of the present disclosure provide a technique for forecasting causal predictions that improves the functionality of a computer in various aspects. The techniques comprise receiving a historical sequence for a time-based prediction, generating, by a connected model framework, a first time-based output for a first time position in a prediction sequence for the time-based prediction, generating, using a directed acyclic graph, an output modification for a second time position in the prediction sequence; generating, using a machine learning model, a second time-based output for the second time position in the prediction sequence based on the output modification and the first time-based output, and initiating performance of a prediction-based action based on the prediction sequence.

WEIGHTED MODEL FUSION FOR MITIGATING CATASTROPHIC FORGETTING IN CUSTOMIZED GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: US20260170346A1

A method for mitigating catastrophic forgetting is provided. The method includes providing an input prompt to a classifier machine learning model configured to classify the input prompt as a general knowledge query for a base generative artificial intelligence model, a specific knowledge query for a customized generative artificial intelligence model, or a mixed knowledge query for a weighted fusion model. The method includes computing, based on an output of the classifier machine learning model, a weighted mean of weights of the base generative artificial intelligence model and corresponding weights of the customized generative artificial intelligence model. The method includes generating the weighted fusion model based on the weighted mean of the weights of base generative artificial intelligence model and the corresponding weights of the customized generative artificial intelligence model. The method includes generating, based on the output, a response to the input prompt using the weighted fusion model.

SEQUENTIAL MODEL PIPELINES FOR SIMULATING DEPENDENT PREDICTION CHAINS

Resumen de: US20260170298A1

Various examples of the present disclosure utilize a sequential model pipeline to predict time-based effect output that improves the functionality of a computer in various aspects. The techniques comprise receiving a labeled output comprising a historical ground truth cause label and a historical ground truth effect label, generating a time-based cause output using a first machine learning model of the sequential model pipeline, generating a time-based effect output using a second machine learning model of the sequential model pipeline, and initiating a performance of a prediction-based action based on the time-based effect output.

CLICK ENGAGEMENT SIGNALS

Resumen de: US20260170513A1

A computer implemented method including determining an expected click-through-rate (CTR) of a query-item pair with a first machine learning model by using content-based features. The computer implemented method can also include, determining a click engagement (CE) feature by determining a Bayesian inference based on the expected CTR and a historical CTR for the query-item pair. The computer implemented method can further include, determining a rerank score of the query-item pair with a second machine learning model by using the content-based features and the CE feature. The computer-implemented method can additionally include reranking the query-item pair based in part on the rerank score and the expected CTR. Other embodiments are described.

SYSTEM AND METHOD FOR PREDICTING A PHYSICAL FIELD

Resumen de: US20260170331A1

0000 The method can include determining a physical problem; determining an initial solution for the physical problem description; determining a final solution of the physical problem description, using the initial solution as a seed; optionally training a machine learning model based on the final solution. The method functions to quickly determine a high-quality solution (e.g., accurate and/or precise solution) to a physical problem by leveraging a predictive machine learning model for determining initial approximate solutions to the physical problem.

CONTEXTUALLY RECOMMENDING LEVELS OF DRIVING AUTOMATION THROUGH MULTI-VEHICLE COLLABORATION

Resumen de: US20260170954A1

A method includes: receiving contextual data from an autonomous vehicle, wherein the contextual data includes data obtained from the autonomous vehicle and data obtained from other vehicles; in response to receiving the contextual data from the autonomous vehicle, determining a level of driving automation that is predicted to provide a highest level of efficiency of the autonomous vehicle based on the contextual data and using a machine learning model; and transmitting, to the autonomous vehicle, a recommendation including the determined level of driving automation.

COMPUTER-IMPLEMENTED METHOD OF AUTOMATED TRAVEL ITINERARY PLANNING

Resumen de: US20260170074A1

0000 The present invention relates to an automated travel planning data processing system and method that leverages advanced artificial intelligence (Al) and machine learning (ML) algorithms to generate personalized travel itineraries in real-time. The system comprises a central server with one or more processors, memory, and a machine learning module, as well as a travel database that stores aggregated data from multiple sources. Users interact with the system through a natural language processing-based user interface, which receives inputs comprising travel dates, destinations, and preferences. An Al-powered itinerary generation engine processes user inputs and aggregated data to create personalized travel plans, utilizing a multithreading module for simultaneous data retrieval and processing. The machine learning module continuously optimizes the itinerary generation process by analyzing user preferences and travel data patterns. The invention also provides a method for automated travel itinerary planning using the data processing system.

METHOD FOR GENERATING TRAINED PREDICTION MODEL THAT PREDICTS ENERGY EFFICIENCY OF MELTING FURNACE, METHOD FOR PREDICTING ENERGY EFFICIENCY OF MELTING FURNACE, AND COMPUTER PROGRAM

Resumen de: US20260168731A1

0000 A method of generating a trained model includes: a step of acquiring a process state parameter for every single charge (S110); a step of performing preprocessing by applying machine learning to a data set of one or more process state parameters acquired through m charges (where m is an integer of 2 or greater) (S130); a step of generating a learning data set (S140); and a step of generating a trained model (S150). The learning data set is generated based on n-dimensional features (where n is an integer of 1 or greater) that have been extracted through the preprocessing, and at least contains one or more process target parameters representing process fundamental information that is set for every single charge.

CLUSTER AND LANGUAGE-BASED FORECASTING FOR NUMERIC TIME SERIES

Resumen de: US20260170292A1

In an example embodiment, a large language model (LLM) is utilized to generate a semantic vector for each given time series. These semantic vectors represent additional information generated based on descriptions of the type of the time series (e.g., a description of the material, whose demand over time comprises the time series). The semantic vectors can then be used to stabilize the assignment of clusters in a cluster-based machine learning model, especially for short time series, to improve reliability of predictions.

SOFTWARE SOLUTION AND PLATFORM THAT INTEGRATES AND COMBINES MULTIPLE INTERCHANGEABLE SOFTWARE COMPONENTS USING CUSTOM DATA PIPES

Resumen de: US20260169703A1

Disclosed herein is a software solution and platform that integrates and combines multiple interchangeable software components using custom data pipes. The software solution and platform, as well as the multiple interchangeable software components, can include machine learning (ML), artificial intelligence (AI), and generative AI. The software solution and platform is a processor executable code or software that is necessarily rooted in process operations by, and in processing hardware of, computing equipment. For ease of explanation, the software solution and platform is described herein with respect to an integration engine.

OUTCOME-MAPPED DECISION MODEL FOR NETWORK ROUTING

Resumen de: US20260172341A1

A method and related systems may generate and use a machine learning model that determines multiple outcome values for multiple message routes. Some embodiments may then compare the multiple outcome values to select a target route for the message. Moreover, some embodiments may facilitate routing through one or more networks by mapping aggregated performance metrics through controlled nodes.

POLICY-CENTRIC DATA COLLECTION FOR DISTRIBUTED MACHINE-LEARNING

Nº publicación: US20260170400A1 18/06/2026

Solicitante:

RED HAT INC [US]
Red Hat, Inc.

Resumen de: US20260170400A1

A computer system can: identify a training target associated with training a machine-learned model; communicate, to a remote computing device, a data capture policy that is implementable by the remote computing device to cause the remote computing device to capture data responsive to the training target; obtain a component training dataset from the remote computing device, the component training dataset comprising data captured according to the data capture policy; train the machine-learned model using an aggregate training dataset comprising the component training dataset aggregated with a plurality of additional component training datasets from a plurality of additional remote computing devices to generate an update for the machine-learned model; and communicate an update for the machine-learned model to the remote computing device for updating a local instance of the machine-learned model at the remote computing device.