Machine learning

CONSTRAINT BASED INFERENCE AND MACHINE LEARNING SYSTEM

Resumen de: US20260220438A1

0000 A method including receiving a pre-determined constraint on user actions. A constraint vector is generated based on the pre-determined constraint. The constraint vector is input into a machine learning model. A first output is generated from the machine learning model by executing the machine learning model using the constraint vector as a first input to the machine learning model. The constraint vector is converted into a legal action mask. A probability vector is generated by executing a masked softmax operator. The masked softmax operator takes, as a second input, the first output. The masked softmax operator takes, as a third input, the legal action mask. The masked softmax operator generates, as a second output, the probabilities vector. Action outputs are generated by applying a sampling system to the probability vector. The action outputs include a subset of the user actions, and wherein the subset includes only allowed user actions.

SYSTEMS AND METHODS FOR TRANSFORMING AND STORING DATA FROM MULTIPLE STUDIES

Resumen de: US20260221293A1

There is disclosed a method and system for combining datasets. Study results may be retrieved. Each study result may include datapoints. Each datapoint may include attributes. Study questions may be extracted from the study results. The questions may be converted into a standardized format. Categories may be assigned to the questions. The questions may be grouped together into groups. A response scale may be determined for each of the groups. The responses may be rescaled using the corresponding response scale. A final dataset may be generated by combining the study results. Features may be selected using the final dataset. A machine learning algorithm may be trained using the features of the final dataset.

METHOD TO IDENTIFY AND MODIFY NWDAF DEPENDENCIES

Resumen de: US20260222304A1

0000 A computer-implemented method performed by a computing device including a NWDAF having a function to identify and modify dependencies between NWDAFs and at least a first NWDAF consumer. The method includes identifying a relationship between at least the first NWDAF consumer from a plurality of NWDAF consumers that respectively subscribe to the NWDAFs that have respective machine learning, ML, models that have an overlap between a first input feature and respectively have different outputs. At least the first NWDAF consumer uses a first output from the NWDAFs to output an action from the first NWDAF consumer. The method further includes determining whether a second input feature to (i) a respective ML model of the NWDAFs or (ii) a new ML model of an unsubscribed NWDAF can make a positive contribution to the prediction of the output of at least the first NWDAF consumer.

CLEANING A MACHINE LEARNING DATASET USING CORESETS

Resumen de: US20260220535A1

A computerized method of cleaning a data set comprises: (a) providing the data set, configured for training of a machine learning model, and comprising data instances. Each instance comprises a label; (b) selecting a number of data instances from the set based on importance-related criteria, indicative of corresponding high importance values associated with the data instances. This generates high importance set(s), giving rise to a set of selected data instances having a high probability of high importance values, as compared to a second probability associated with selecting data instances from the data set. The selected set facilitates determining, for each data instance of, whether it requires an action. This facilitates performing the action, which brings about increased quality data set(s), which facilitate quicker and/or higher-accuracy training of the machine learning model, as compared to a second training of the machine learning model performed utilizing the data set.

BATTERY LIFE ESTIMATION APPARATUS AND METHOD

Resumen de: US20260219323A1

A battery life estimation apparatus includes a charging/discharging unit configured to charge/discharge a battery and a controller configured to calculate a partial accumulative capacity by charging/discharging the battery in a partial voltage period corresponding to a period from a first voltage to a second voltage, and estimate a life corresponding to an entire voltage period of the battery by inputting the first voltage, the second voltage, and the partial accumulative capacity to an estimation model trained based on machine learning.

DETERMINING CONFIDENCE TO START VALUES FOR POWER GENERATION SYSTEMS USING MACHINE LEARNING MODELS

Resumen de: US20260221775A1

0000 Presented herein are systems and methods for applying machine learning (ML) models to determine start confidence values for power generators. The computing system can identify a first plurality of parameters of a first power generator. The plurality of parameters can identify operations of the first power generator. The computing system can apply the first plurality of parameters to a ML model to determine a first confidence value identifying a first likelihood of the first power generator to start upon initiation. The computing system can provide an output based on the first confidence value for the first power generator.

System, Method, and Computer Program Product for Determining Feature Importance

Resumen de: US20260220533A1

Provided is a system that includes a processor to receive a dataset comprising a plurality of feature values of a plurality of features; determine, for each feature of the plurality of features, a plurality of sequence deviation metrics; generate a plurality of sets of features for the plurality of sequence deviation metrics, wherein each set of features comprises a ranked set of features for a sequence deviation metric; train a plurality of machine learning models based on the plurality of sets of features, wherein the plurality of machine learning models comprises a machine learning model for each sequence deviation metric; determine a performance metric of each trained machine learning model for each sequence deviation metric; and select a ranked set of features for a sequence deviation metric that corresponds to a trained machine learning model that has a highest performance metric. Methods and computer program products are also provided.

EXPLAINABLE MACHINE-LEARNING TECHNIQUES FROM MULTIPLE DATA SOURCES

Resumen de: US20260220531A1

0000 In some aspects, a computing system can generate and optimize a hybrid machine learning model for risk assessment based on predictor variables associated with a target entity. The hybrid machine learning model can be trained using training vectors with sets of training predictor variables and training outputs corresponding to the respective sets of training predictor variables. The predictor variables associated with the target entity may include unknown values and the training predictor variables or trainings output may also include unknown values. Additionally, the computing system can generate explanatory data for the target entity to indicate relationships between changes in the risk indicator and changes in the predictor variables associated with the target entity. The risk indicator and the explanatory data can be used in controlling access of the target entity to interactive computing environments.

METHOD FOR IMPROVING THE CLASSIFICATION OF A DIGITAL DOCUMENT USING A PLURALITY OF LEARNING MODELS

Resumen de: US20260220960A1

0000 A method for improving the classification of a document from a plurality of machine learning models, includes receiving a digital document; executing a first learning function generated from a first machine learning model trained from a first training domain processing the first input data sequence and making it possible to classify a document type and generate a prediction of a first automatic action to be performed on the digital document, acquiring a first corrective action from a user relating to the modification of the movement of the first digital document to a second directory; generating a first annotation; modifying the first training domain by adding the first annotation; generating a retraining of the first machine learning model.

HETEROGENEOUS MODEL TREES FOR MACHINE LEARNING WITH DISCONTINUOUS DATASETS

Resumen de: US20260220489A1

A method includes obtaining at least one dataset containing one or more discontinuities, where the one or more discontinuities split data of the at least one dataset into multiple partitions. The method also includes generating feature crosses associated with the at least one dataset. The method further includes generating a decision tree structure based on at least some of the feature crosses, where (i) the decision tree structure includes multiple leaf nodes and (ii) each leaf node corresponds to a different one of the multiple partitions. In addition, the method includes, for each leaf node of the decision tree structure, generating a machine learning model that models data of the corresponding partition.

STATUS INFORMATION FOR IDENTIFIERS RELATED TO ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING

Resumen de: US20260222970A1

0000 Methods, systems, and devices for wireless communications are described. A wireless device may obtain, from a network entity, status information corresponding to one or more identifiers that are associated with one or more network settings. The one or more network settings may relate to communication of reference signaling for an artificial intelligence or machine learning (AI/ML)-based positioning or sensing procedure or measurement of reference signaling for an AI/ML-based positioning or sensing procedure. The wireless device may perform an operation to control the AI/ML-based positioning or sensing procedure based on the status information corresponding to the one or more identifiers. The wireless device and the network entity may communicate based on the status information corresponding to the one or more identifiers and the AI/ML-based positioning or sensing procedure control operation.

SPECTRAL CLUSTERING FOR SPECTRAL TREE GUIDANCE IN HETEROGENEOUS MODEL TREE GENERATION OR OTHER FUNCTIONS FOR MACHINE LEARNING WITH DISCONTINUOUS DATASETS

Resumen de: US20260220490A1

0000 A method includes obtaining at least one dataset containing one or more discontinuities, where the one or more discontinuities split data of the at least one dataset into multiple partitions. The method also includes performing spectral clustering of the at least one dataset to identify multiple initial clusters of data in the at least one dataset. The method further includes trimming the initial clusters of data in order to identify an estimated number of partitions in the at least one dataset. The method also includes performing spectral clustering of the at least one dataset based on the estimated number of partitions to identify multiple updated clusters of data in the at least one dataset, where each updated cluster of data corresponds to one of the multiple partitions. In addition, the method includes providing the updated clusters of data as input to a machine learning algorithm.

MULTI-FACTOR ANTI-PHISHING SYSTEMS AND METHODS

Resumen de: US20260222445A1

The present application describes a multi-factor anti-phishing system and method, featuring real-time local analysis of webpages on user devices. The system comprises a feature extraction system integrated with a web browser to extract a comprehensive set of features from visited webpages. These features include URL and host characteristics, content and structure indicators, resource files and scripts, form and action elements, and embedded media analysis. A machine learning (ML) model, trained on these features, analyzes the extracted data to predict phishing risks. The system uses a cloud secure enclave to manage allow lists and block lists, process encrypted feedback, and retrain the ML model, such that sensitive information remains confidential. The retrained ML model is periodically distributed to user devices to enhance phishing detection capabilities.

SYSTEMS AND METHODS FOR PREDICTION OF ANTIBIOTIC RESISTANCE FROM BACTERIAL GENOMES

Resumen de: US20260220336A1

Systems, methods, and devices disclosed herein provide antibiotic resistance predictions using a pan-antibiotic resistance prediction (PARP) model. The PARP model includes a machine learning system trained with a training data set of genetic information associated with a plurality of bacterial species, and/or antibiotic feature information associated with a plurality of antibiotics. The PARP model is deployed to a cloud-based service for scalability which provides access to the PARP model for clinic devices, hospital devices, and/or laboratory devices. For instance, a web-based portal of the cloud-based service receives a genomic sequence associated with a particular bacterial isolate, uploaded via a remote device. The PARP model outputs a predictive indication of an antibiotic resistance, for the particular bacterial isolate. The predictive indication can include a bar graph (e.g., presented at a graphical user interface) showing, for the particular bacterial isolate, susceptibility/resistance predictions for a plurality of antibiotics.

SYSTEM AND METHOD FOR DYNAMICALLY PREDICTING FRAUD USING MACHINE LEARNING

Resumen de: US20260220715A1

0000 A computing device configured to communicate with a central server in order to predict likelihood of fraud in current transactions for a target claim. The computing device then extracts from information stored in the central server (relating to the target claim and past transactions for past claims including those marked as fraud), a plurality of distinct sets of features: text-based features derived from the descriptions of communications between the requesting device and the endpoint device, graph-based features derived from information relating to a network of claims and policies connected through shared information, and tabular features derived from the details related to claim information and exposure details. The features are input into a machine learning model for generating a likelihood of fraud in the current transactions and triggering an action based on the likelihood of fraud (e.g. stopping subsequent related transactions to the target claim).

EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S)

Resumen de: EP4783078A2

Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: EP4783079A1

An information processing device includes: a machine learning unit (12) to learn a relationship between an evaluation value and a parameter on a basis of a search point of the parameter and an evaluation value of the search point, and predict the evaluation value for a search candidate point of the parameter; and a search progress acquiring unit (13) to acquire progress information indicating progress of a search on a basis of the search point, the evaluation value of the search point, the search candidate point, and the evaluation value of the search candidate point predicted by the machine learning unit (12).

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.

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.

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.

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.

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.

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.

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

Nº publicación: US20260212156A1 23/07/2026

Solicitante:

VISA INT SERVICE ASSOCIATION [US]
Visa International Service Association

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.