Alerta

SYSTEMS AND METHODS FOR IDENTIFICATION AND MANAGEMENT OF PERSONALLY IDENTIFIABLE INFORMATION ASSOCIATED WITH ENTERPRISE IT NETWORKS

Resumen de: US20260212226A1

Various examples are provided related to identification of protected information elements associated with unique entities in data files present in data file collections associated with enterprise IT networks. The unique entities can be associated with one or more entity identifications in one or more data files. Computer-generated identification of entity identifications and protected information elements can be conducted, in part, by at least some human review. Information generated accordingly to the disclosed methodology can be used to generate plans for a time and number of human reviewers needed to review data files. Information generated from the processes herein can be configured as user notifications, reports, dashboards, machine learning for subsequent data file analyses, and notifications of unique entities having protected information elements present in one or more data files.

INFORMATION PLATFORM SYSTEMS AND METHODS

Resumen de: US20260211963A1

Various aspects describe an information platform for consistently integrating and/or quantifying the underlying principles of ESG into financial analyses, analytical tools, metrics, and/or available information on reviewed companies, business entities, etc. . . . , and further provide integration of analysis with community-based insight, contextual information and tools for readily understanding both. Various embodiments implement machine learning tools for curating data sources and incorporating the data sources into the knowledge platform. The incorporation of AI moderated information sources enables succinct views of often massive information pools, and further provides for transitions between types of information (e.g., qualitative, quantitative, and interactive data source (e.g., engagements, collaborative information, etc.)). The platform facilitates user understanding and can eliminate the need to design and execute complicate queries by allowing users to transition between data types and view to develop better understanding and context of various information sources.

MODULAR SYSTEMS AND METHODS FOR SELECTIVELY ENABLING CLOUD-BASED ASSISTIVE TECHNOLOGIES

Resumen de: US20260212108A1

0000 Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

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.

System, Method, and Computer Program Product for Efficient Node Embeddings for Use in Predictive Models

Resumen de: US20260212156A1

0000 Described are a system, method, and computer program product for efficient node embeddings for use in predictive models. The method includes receiving graph data associated with a graph comprising a plurality of nodes associated with a plurality of entities and a plurality of edges associated with interactions between entities. The method also includes generating a plurality of node embeddings for the plurality of nodes, and generating a matrix based on each positive pair of nodes and the plurality of node embeddings. The method further includes decomposing the matrix to provide a left unitary matrix, a diagonal matrix, and a right unitary matrix. The method further includes determining a plurality of updated node embeddings for the plurality of nodes based on the left unitary matrix and the diagonal matrix. The method further includes communicating the plurality of updated node embeddings for inputting into a machine learning model to generate a prediction.

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.

MEHTODS, ARCHITECTURES AND SYSTEMS FOR GENERATING AUDIBLE CONTENT

Resumen de: US20260212201A1

Systems and methods are provided for training an artificial intelligence system and generating audible content for output. The method utilizing a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit, a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface to receive and store data input content from one or more data sources other than the control plane layer, the data input content being subject to transformation into audible content for output. Data input content information is used in synthesizing audible output content at least in part by transforming the data input content into the audible output content.

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.

ARTIFICIAL INTELLIGENCE BASED HEALTH COACHING BASED ON KETONE LEVELS OF PARTICIPANTS

Resumen de: US20260212781A1

A system is disclosed that uses profiles of users, including monitored ketone levels of the users, to assess effectiveness levels of health programs (such as weight loss programs) assigned to the users, and to select health program modifications for the users. The system may use a machine learning (artificial intelligence) algorithm to adaptively learn how to classify users and to select messaging and behavioral modifications for the users. For example, in some embodiments the system classifies the users and provides associated health program recommendations using a computer model trained with expert-classified user data records. As another example, a set of rules may be used to generate the health program recommendations and related messaging, and the set of rules may automatically be modified over time based on feedback data reflective of health program effectiveness levels produced by such rules. In some embodiments the system includes a mobile application that runs on mobile devices of users and communicates wirelessly with breath analysis devices of the users. The mobile application may also communicate with a server-based system that generates the health program recommendations.

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.

GENERATING VIEWS FOR BIAS METRICS AND FEATURE ATTRIBUTION CAPTURED IN MACHINE LEARNING PIPELINES

Resumen de: US20260211980A1

0000 Views may be generated for bias metrics or feature attribution captured in machine learning pipelines. A request to create a view of bias metrics or feature attribution may be received. The bias metrics or feature attribution may have been determined in a machine learning pipeline as part of executing a training job that specified the bias metrics or the feature attribution. A development application may access a data store that stores the bias metrics or the feature attribution determined in the machine learning pipeline. A view based on the bias metrics or feature attribution may be generated and provided.

REAL-TIME MESSAGING OVER A NETWORK USING ELECTRONIC COMMUNICATIONS

Resumen de: US20260212980A1

0000 Techniques are presented for delivering point of care message content, including defining criteria for message content delivered to a user via a graphical user interface during an encounter with a third party client, receiving input data in real-time from the user, and determining, by machine learning, an aspect of therapy or indicator thereof for the third party client. This aspect of therapy or indicator may be absent in the input data. Techniques may further include determining particular message content for the user using the aspect of therapy or indicator and the criteria, and delivering the particular message content to the user during the encounter.

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.

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.

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.

METHOD AND APPARATUS FOR PREDICTING DISCOLORATION OF ARTIFICIAL LEATHER

Nº publicación: EP4779534A1 22/07/2026

Solicitante:

KOLON INC [KR]
KYUNGPOOK NAT UNIV IND ACADEMIC COOP FOUND [KR]
Kolon Industries, Inc.
Kyungpook National University Industry-Academic Cooperation Foundation

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.