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MACHINE-LEARNING FOR CONTENT INTERACTION

Resumen de: US20260212263A1

0000 A system can generate content recommendations and facilitate interactions using machine-learning. The system can receive a request from a provider entity. The system can receive entity data and interaction data associated with a target entity. The system can generate at least a first graph structure and a second graph structure. The system can generate a linked graph structure based on the first graph structure and the second graph structure. The system can determine among a plurality of operations, one or more target operations to perform on data included in the linked graph structure. The system can execute using a trained machine-learning model, the target operations to generate a content recommendation for facilitating an interaction. The system can provide a responsive message based on the content recommendation usable to facilitate the interaction.

METHOD FOR PERFORMING DIRECTORS AND OFFICERS RISK ASSESSMENT USING A DATA-SCIENCE AND RISK PREDICTION MODEL

Resumen de: US20260212298A1

The present invention is directed to a method, performed by a computer system, for the prediction of the directors and officers (D&O) risk of a query company, wherein said method is based on the use of machine learning to construct a quantitative risk-prediction model based on features produced from data that has been acquired from a plurality of data sources, and to train and validate said model. One key aspect of the invention is that the prediction model uses both features related to the query company and features related to the directors and officers of said company.

First Node, Second Node and Methods Performed Thereby, for Handling One or More Machine Learning Models

Resumen de: US20260212223A1

0000 A computer-implemented method performed by a first node (111). The methods is for handling one or more machine learning models. The first node (111) operates in a communications system (100). The first node (111) determines (404), using machine learning, ML, one or more ML models of an indicator of operation of the communications system (100). The determining (404) is based on a respective operation mode of Radio Access Technology (RAT), used by a respective set of nodes (121,122,123) wherefrom respective data has been collected to train, or infer, a respective ML model of the one or more ML models. The first node (111) also provides (405) a respective indication of the determined one or more ML models to a second node (112) operating in the communications system (100).

SYSTEM AND METHOD FOR GENERATING FORMULATIONS USING MACHINE LEARNING ARCHITECTURES

Resumen de: US20260212164A1

0000 There is provided systems and methods for generating formulations with improved performance and lower resource consumption. The population of formulations may be created by a Differential Evolution (DE) process. In each successive generation, lower performing formulations may be replaced with new formulations predicted to have better performance by a modeling pipeline. The modeling pipeline may perform a search for ML architectures and hyperparameter optimization for a suitable ML architecture, and then train one or more machine learning models to minimize error (otherwise maximizing the resulting score for a formulation). The machine learning models may be an ensemble of Random Forest models. These new formulations may form part of the next generation in the DE process. This modified version of the Differential Evolution process using machine learning techniques may result in populations of formulations which are superior in performance, and/or require fewer iterations of generations to achieve formulations which meet performance objectives.

DATA-DRIVEN SIMULATION AND ANALYSIS FRAMEWORK FOR MULTI-MODAL URBAN MOBILITY SYSTEMS

Resumen de: US20260212082A1

0000 An apparatus in an illustrative embodiment comprises a processing platform that includes at least one processing device, the processing device comprising a processor coupled to a memory. The processing platform is configured to implement a machine learning based framework for at least one of simulation and analysis relating to multi-modal mobility, the machine learning based framework comprising a plurality of stages, including at least a population synthesis stage and a trip generation stage. The processing platform is further configured to execute a first machine learning model in the population synthesis stage, and to execute a second machine learning model in the trip generation stage, the second machine learning model being different than the first machine learning model. The first machine learning model illustratively comprises a traveler cluster classifier, and the second machine learning model illustratively comprises a mode-duration choice model.

CONTROLLING POWER TO AN IMPLANTABLE DEVICE

Resumen de: US20260213010A1

0000 Presented herein are techniques for predicting and controlling power for an implantable device. The power prediction for the implantable device may be performed independent of real-time power information from the implantable device, and may utilize artificial intelligence (AI) or machine learning.

PREDICTION OF THE DUCTILE-TO-BRITTLE TRANSITION TEMPERATURE OF POLYMER COMPOSITIONS BASED ON IMPACT CURVES

Resumen de: US20260212964A1

The present invention relates to a computer implemented method for predicting the ductile-to-brittle transition temperature of a test polymer composition based on impact curves. Furthermore, a non-transitory computer readable storage medium is provided for tangibly storing computer program instructions capable of being executed by a processor, the computer program instructions defining the steps the aforementioned computer implemented method. Furthermore, the invention is directed to the use of impact curves and values indicative of the ductile-to-brittle transition temperatures of polymer compositions for training machine learning algorithms.

METHOD FOR PRODUCING AN EVALUATION MODEL FOR DETECTING A SEAT OCCUPANCY STATE OF A SEAT ARRANGEMENT

Resumen de: US20260208743A1

Described is a method for producing a machine learning model for automated detection of a seat occupancy state of a seat arrangement. Parameters are assigned to possible seat occupancy states, and hyperparameters are configured to be adjusted on the basis of a metric. A detection accuracy is determined indicating a discrepancy between the seat occupancy state assigned to the parameters and an evaluation result supplied by the evaluation model with the provided hyperparameters. A metric is evaluated which takes into account a difference between the detection accuracy and a target value to output a value for the determined detection accuracy. Hyperparameters are adjusted appropriately, where the metric is optimized in order to obtain an optimum from the output value and to adjust the hyperparameters in such a way for which the metric reaches the optimum. The evaluation model is produced with the adjusted hyperparameters for further training.

COMPUTER IMPLEMENTED METHOD OF TRAINING A MACHINE LEARNING MODEL CONFIGURED FOR PREDICTING A HYPOGLYCEMIC EVENT FOR A SUBJECT

Resumen de: WO2026154038A1

A computer implemented method of training a machine learning model configured for predicting a hypoglycemic event for a subject is proposed, comprising: i. (126) receiving at least two time series of glucose sensor data, wherein each time series of glucose sensor data comprises a plurality of glucose measurements measured by a glucose monitoring device (112) configured for detecting glucose in a bodily fluid of the subject, wherein a first time series of the two time series of glucose sensor data is measured at least during and/or after being exposed to a first predefined program of glycemic stimuli, and wherein a second time series of the two time series of glucose sensor data is measured at least during and/or after being exposed to a second predefined program of glycemic stimuli, wherein the second predefined program of glycemic stimuli is different from the first predefined program of glycemic stimuli; ii. (128) forming a first training dataset using the first time series and forming a second training dataset using the second time series; iii. (130) training the machine learning model with the first training dataset and the second training dataset.

COMPUTER-IMPLEMENTED METHOD, DATA PROCESSING ARRANGEMENT AND COMPUTER PROGRAM PRODUCT FOR ENABLING AN AUTOMATED ACTION

Resumen de: WO2026153657A1

The present invention relates to a computer-implemented method (50) for enabling an automated action, comprising: providing a rule data set having enabling rules in at least one block of a blockchain (52), and retrieving the rule data set by means of a blockchain read-out device (54), characterized by evaluation of the rule data set by means of an agent device (56), the enabling rules and a machine learning method being taken into account for an enabling decision (58) for the automated action. The present invention further relates to a corresponding data processing arrangement and to a corresponding computer program product.

MANAGING ASSOCIATED IDENTIFIERS IN AI/ML-BASED POSITIONING

Resumen de: US20260213895A1

0000 Example embodiments of the present disclosure are directed to managing associated identifiers (IDs) in Artificial Intelligence Machine Learning (AI/ML) based positioning. A method comprises receiving, from a second apparatus, information comprising a first configuration of a plurality of transmission-reception points (TRPs) and a first association identification, the first association identification associated with at least one TRP of the first configuration of the plurality of TRPs; receiving, from the second apparatus, information comprising a second configuration of a plurality of TRPs, the first association identification associated with at least one TRP of the second configuration of the plurality of TRPs; and determining, for the first association identification, that the at least one TRP of the first configuration of the plurality of TRPs and the at least one TRP of the second configuration of the plurality of TRPs are with consistent physical properties.

METHODS AND SYSTEMS FOR ENCODER PARAMETER SETTING OPTIMIZATION

Resumen de: US20260214134A1

0000 Methods and systems are described for encoder parameter setting optimization. A media item to be provided to users of a platform is identified. A request for content is received from a client device, and a media item associated with the content is identified. An indication of the media item is provided as input to a machine learning model. Outputs are obtained from the model identifying one or more sets of encoder parameter settings and, for each set, a confidence level that the settings satisfy a performance criterion based on the media item's media class. Based on the model outputs, at least one set of encoder parameter settings having a confidence level satisfying a confidence criterion is identified. The media item is encoded using the identified encoder parameter settings and provided for presentation via the client device.

METHOD AND APPARATUS FOR ADAPTING MACHINE LEARNING TO CHANGES IN USER INTEREST

Resumen de: US20260212225A1

0000 Aspects of the subject disclosure may include, for example, assigning a first interest measure associated with a first input to a learning machine at a first cycle, determining a first intelligence level according to a first product of the first interest measure and a first performance level based on the first input, and responsive to receiving a subsequent input at a subsequent cycle, reducing the first interest measure associated with the first input at the first cycle of the learning machine according to a total number of cycles that have occurred since the first cycle, assigning a new interest measure to the subsequent input at the subsequent cycle, generating a subsequent performance level according to the subsequent input, and determining a subsequent intelligence level according to a second product of the subsequent interest level and a subsequent performance level. Other embodiments are disclosed.

METHOD AND APPARATUS FOR PREDICTING DISCOLORATION OF ARTIFICIAL LEATHER

Resumen de: EP4779534A1

0001 A method of generating a post-exposure discoloration estimation model may include obtaining color information of a substrate to be dyed and dye combination information corresponding to the color information, obtaining process information corresponding to the dye combination, obtaining post-exposure discoloration information of artificial leather that has been dyed based on the process information, and generating a machine learning model by using, as a data set, the dye combination information, the process information, and the post-exposure discoloration information, the machine learning model being configured to estimate the post-exposure discoloration information from the dye combination information and the process information.

DETECTING UNWANTED SOFTWARE USING A MACHINE LEARNING OPERATION

Resumen de: US2025086093A1

0000 Systems, methods, and software can be used to determine whether a software code is unwanted. In some aspects, a method includes: obtaining, by an electronic device, a set of software features of a software code; obtaining, by the electronic device, a set of user features of a user of the electronic device; and determining, by the electronic device, a classification score of the software code based on the set of software features and the set of the user features, wherein the classification score indicates whether the software code is potentially unwanted for the user.

MECHANISMS FOR MACHINE LEARNING ENABLEMENT

Resumen de: WO2025059438A1

A method may include receiving by a Rule Engine (RE) service one or more requests to define an RE definition for a Digital Twin (DT) system; communicating with a DT service, a storage service, a Machine Learning (ML) service, or a combination thereof, according to rules specified in the DT system, for processing the one or more requests; and sending, by the RE service, one or more responses with a status to the one or more requests, wherein the one or more response comprise one or more of an overall status of processing the RE definition, an individual status for each event condition, action, state, and/or state transition that was specified in the RE definition, and the status for an interconnection of services between the RE and ML services.

DETERMINING EFFECT OF A DRUG BASED ON BRAIN ACTIVITY AND A TRAINED MACHINE LEARNING MODEL

Resumen de: WO2025059464A1

A computer-implemented method for determining an effect of a drug on a subject, including: obtaining brain activity data of the subject; determining, using a trained machine learning model and the brain activity data of the subject, an effect of the drug administered to the subject, the trained machine learning model trained with brain activity data of subjects treated with at least one of the drug or a second drug and with brain activity data of subjects treated with a placebo; and providing an output indicative of the effect of the drug administered to the subject determined by the trained machine learning model.

Optimization Method for Distributed Execution of Deep Learning Task and Distributed System

Resumen de: US20260203135A1

Disclosed are an optimization method for distributed execution of a deep learning task and a distributed system. The method includes that: a computation graph is generated based on a deep learning task and hardware resources are allocated for the distributed execution of the deep learning task; the allocated hardware resources are grouped to obtain at least one grouping scheme; for each grouping scheme, tensor information related to multiple operators contained in the computation graph is split based on the value of at least one factor under this grouping scheme to obtain multiple candidate splitting solutions; and an optimal efficiency solution for executing the deep learning task of the hardware resources is selected by using a cost model. Through operator splitting based on device grouping combined with optimization solving based on the cost model, automatic optimization of distributed execution for various deep learning tasks is realized.

APPARATUS AND METHODS FOR CHARACTERIZATION OF MULTIPLE ELECTRIC MOTORS

Resumen de: US20260203472A1

The techniques described herein relate to systems and methods for characterization of multiple electric motors. An example method for processing multiple electric motor designs into outputs of respective performance evaluations across different operating conditions using machine learning includes mapping input geometric parameters to at least one of a plurality of electric motor designs, and inputting the plurality of electric motor designs to at least one machine learning model and outputting, from the at least one machine learning model, performance evaluations for the plurality of electric motor designs under a variety of operating conditions, the at least one machine learning model trained to generate the performance evaluations in accordance with control waveforms for the plurality of electric motor designs.

MODEL CASCADE FOR EFFICIENT RULE EXTRACTION

Resumen de: US20260203056A1

0000 A method including determining whether to use a functional processing model or a machine-learning processing model and executing the functional processing model or the machine-learning processing model using as input the first input document to transform data in a first input document into the target data format, executing an extraction model using as input the data in the first input document in the target data format to generate first structured data, executing a compilation model using as input a plurality of structured data generated based on data in a set of input documents including the first input document to generate an aggregate structured data, and executing a synthesis model using as input the aggregate structured data and the plurality of structured data to generate an output data structure.

METHOD FOR REPORTING INDICATION OF INVALID INFERENCE/PREDICTION REPORT

Resumen de: WO2026151371A1

A method, system and apparatus are disclosed A method implemented in a user equipment configured to communicate with a network node includes: generating (S204), based on a machine learning, ML, model/functionality, a report for one or both of an inference report and a prediction report, the report comprising an indication that the one or both of the inference report and the prediction report is one or more of invalid, out of range, and inaccurate; and transmitting (S206) the report to the network node.

VARIABLE-FOCUS DYNAMIC VISION FOR ROBOTIC SYSTEM

Resumen de: US20260200090A1

0000 A dynamic vision system for a robotic system includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. A robotic system is configured to simulate human or animal species capabilities having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to the robotic system. A processing system is configured to train a machine learning model to recognize an object relating to the robotic system from training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic system.

DEVICES, METHODS, APPARATUSES AND COMPUTER READABLE MEDIUM FOR COMMUNICATIONS

Resumen de: US20260205212A1

0000 Embodiments of the present disclosure disclose devices, methods and apparatuses for communications. In the embodiments, a network device receives at least one of reference signal received power (RSRP) and reference signal received quality (RSRQ) associated with a serving cell of a terminal device from the terminal device. Then, the network device determines, based on the at least one of the RSRP and the RSRQ associated with the serving cell, a signal quality level associated the neighboring cell of the serving cell using a machine learning (ML) or artificial intelligence (AI) model. In this way, the throughput of the communication system can be improved.

MACHINE-LEARNING TECHNIQUES FOR RISK ASSESSMENT BASED ON CLUSTERING

Resumen de: US20260203611A1

Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.

USER INTERFACE FOR MATERIALS AND MATERIAL PROPERTIES PREDICTION USING A MACHINE LEARNING MODEL

Nº publicación: US20260204366A1 16/07/2026

Solicitante:

DOW GLOBAL TECH LLC [US]
Dow Global Technologies LLC

Resumen de: US20260204366A1

0000 Machine learning can be used to predict formulations for an output formulation. The machine learning can be implemented by a machine learning model, which employs a forward model and an inverse model. A user interface can be used to gather raw materials selections and output formulation property selections. The selections can be used to generate formulations that comply with selections using the ML model.