Alerta

TECHNIQUES FOR INTUITIVE MACHINE LEARNING DEVELOPMENT AND OPTIMIZATION

Resumen de: US20260073308A1

Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.

GENERATION OF DIGITAL STANDARDS USING MACHINE-LEARNING MODEL

Resumen de: US20260073251A1

One embodiment provides a method for generating a digital standard, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein the classifying includes classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; storing the classified extracted conceptual units into a data repository, wherein the storing is performed as defined by the schema; displaying, within a user interface, a digital standard in a format based upon the schema, wherein the displaying includes accessing conceptual units from the data repository corresponding to the digital standard and displaying the conceptual units in a format in accordance with the schema; and providing, within the user interface, search and filter functions allowing for finding information related to the digital standard.

Computer System and Method for Providing a Subject-Related Data Development Platform

Resumen de: US20260072930A1

A method, performed by a computer system connected to a network, comprises processing at least one input data object for standardizing subject-related information. The method further comprises subjecting the subject-related information contained in the processed at least one input data object to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format, and storing the uniform dataset in one or more secured data repositories connected to the network. The method further comprises providing a secured virtual environment accessible to users connected to the network, the secured virtual environment enabling importation of datasets stored in the one or more secured data repositories and a use of imported datasets as part of one or more user-controlled subject-related data development operations for generating at least one workspace-developed data object.

GENERATIVE AI-DRIVEN SYSTEM FOR AGILE EDUCATIONAL CONTENT CREATION AND MANAGEMENT IN RAPIDLY CHANGING AND HIGH-STAKES FIELDS

Resumen de: US20260073246A1

A method for creating and managing knowledge-based content using generative artificial intelligence (AI) includes: storing one or more profiles including a user identifier and a user knowledge-based history for one or more knowledge-based topics; storing one or more knowledge maps for an knowledge-based topic including at least links between concepts of an knowledge-based topic and knowledge-based material items; receiving a content request from a computing device, the content request including a user identifier and an knowledge-based topic; identifying a user profile of the one or more user profiles including the user identifier of the content request; identifying a knowledge map of the one or more knowledge maps matching the knowledge-based topic of the content request; identifying one or more user knowledge gaps; generating one or more new knowledge-based material items for addressing each of the identified one or more user knowledge gaps using a generative machine learning model.

AUTOMATED SOFTWARE APPLICATION PARALLELIZATION, DISTRIBUTION AND EXECUTION ON MULTI-CORE AND MULTI-NODAL DIGITAL SYSTEMS, COMMUNICATION PROTOCOL FOR DISTRIBUTED PROCESSING, AND COORDINATED PARALLELIZATION AND EXECUTION OF SOFTWARE APPLICATIONS

Resumen de: WO2026055302A1

The invention provides for utilization of a machine learning (ML) subsystem to create additional parallel execution on multiple processor cores (CPU, GPU, SPU, etc.) and to further optimize parallel execution within and across nodes of a multi-node system, e.g., through the exchange of objects (instruction, data, task and thread) that define the parallel execution. An abstract object-oriented communication protocol is provided for a multiple node system to enable distribution of objects (thread, task, data, instruction) required for parallel computing of single or multiple applications amongst nodes across any standard network. A method is provided for sharing application parameters (including current and future resource requirements) across a multiple node computing system to enable nodes and aggregates of nodes to further parallelize and further optimize parallel application execution.

ARTIFICIAL-INTELLIGENCE-ENHANCED BIAS RESPONSE PROTOCOLS

Resumen de: US20260073248A1

Bias response methods, systems, and computer program products for detecting and responding to behavioral biases in user plans. A method may include receiving a plan on behalf of a user, calculating an estimated net consequence (ENC) of the plan using machine learning models trained on historical data, and comparing the plan against bias patterns to determine if the plan has recognizable biases. The method may also include generating notifications or tracking user responses to refine response protocols or establish new bias patterns. A system may implement AI enhancement protocols to improve bias detection, analysis, or response capabilities. The system may refine logical bases for plans through user interactions, monitor actual outcomes over time, adjust estimation protocols based on discrepancies between estimated and actual consequences, or improve a bias filter with more or better bias pattern definition.

KNOWLEDGE GRAPH CREATION UTILIZING EMBEDDING AND LARGE LANGUAGE MODELS

Resumen de: US20260073247A1

Certain aspects of the disclosure provide techniques for creating a knowledge graph. A method generally includes for each respective item, of a plurality of items, associated with a respective industry: adding an item node in the knowledge graph for the respective item; adding an industry node in the knowledge graph for the respective industry if no industry node for the respective industry exists in the knowledge graph; generating semantically similar items to the respective item; prompting one or more machine learning models to determine that the respective item and at least one semantically similar item of the set of semantically similar items are associated; and generating an edge between the respective item and the at least one semantically similar item in the knowledge graph based on the association determination.

USING MACHINE LEARNING ALGORITHMS TO PREDICT TRANSACTIONS THAT MATCH EACH OTHER USING PATTERNS FROM MATCHING FEEDBACK

Resumen de: WO2026054830A1

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and/or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and/or

AUTOMATIC RADIATION THERAPY TREATMENT PLANNING WITH DEEP REINFORCEMENT LEARNING GUIDED BY DOSE DISTRIBUTION-BASED REWARD FUNCTION

Resumen de: WO2026055619A1

In some embodiments, there is provided a method of automating radiation therapy treatment planning. The method may include receiving at least an objective for a control structure associated with a plurality of organs-at-risk; optimizing, using a first machine learning model configured as an optimization engine, a treatment plan including the control structure and the objective; selecting, based on one or more scores, at least a first organ‐at‐risk to enable objective adjustment; adjusting, using a second machine learning model using a continuous action space, one or more objective parameters for the selected first organ‐at‐risk; and re-optimizing, using the first machine learning model configured as the optimization engine, the treatment plan including the adjusted one or more objective parameters for the selected first organ‐at‐risk; and outputting the re-optimized treatment plan including one or more treatment parameters. Related systems, methods, and articles of manufacture are also disclosed.

SYSTEM AND METHOD FOR USE WITH A DATA ANALYTICS ENVIRONMENT TO ENABLE USE OF AI IN PROVIDING CUSTOMER SUPPORT

Resumen de: WO2026055146A1

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to enable use of AI in providing customer support. Machine learning AI models are trained based on one or more previous service request lifecycles of service requests of a customer to determine latent emotions of the customer based on determined customer problem data. A customer service prioritization signal related to a current service request of the customer is generated by a predictive analytics application that includes the models. The customer service prioritization signal is indicative of a need to prioritize a current service request of the customer based on the determined latent emotions of the customer and is generated during and prior to the end of the lifecycle of the current service request whereby escalation of the current service request may be deferred or prevented.

AUTOMATED SOURCE ROCK CHARACTERISTICS AND CLASS PREDICTION

Resumen de: WO2026054765A1

Disclosed is a method comprising: determining a computing platform for modeling source rocks, the computing platform including a database system, a data processing system, and a machine learning engine; generating, using the database system, analyzed graph data; filtering, using the data processing system, the analyzed graph data based on vitrinite reflectance data and thereby generate trainable data; resolving, using the data processing system, data discrepancies within the trainable data and thereby generate resolved data; holistically enhancing, using the data processing system, the resolved data to be compatible with a plurality of subterranean structures and thereby generate training data; applying, using the machine learning engine, the training data to train a subterranean model and thereby generate a trained subterranean model; and testing, using the machine learning engine, the trained subterranean model and thereby generate a prediction report indicating rock characteristics and classification of a source rocks.

ANOMALY DETECTION BASED ON ENSEMBLE MACHINE LEARNING MODEL

Resumen de: US20260073310A1

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

DIGITAL PATHOLOGY MACHINE LEARNING INFRASTRUCTURE

Resumen de: WO2026055331A1

Techniques for using a digital pathology machine learning model implementation without requiring transmission of digital pathology images to a location of the digital pathology machine learning model implementation are presented. The techniques may include: providing, on a server computer, a digital pathology image embeddings API; receiving, from a client computer, an embeddings job request including an identification of at least one digital pathology image, an image resolution instruction, and an identification of an embeddings network; passing, to an embeddings server, metadata characterizing the digital pathology image(s) resolved according to the resolution instruction; obtaining, from the embedding server, at least one embeddings vector after the embeddings server transforms a resolved set of digital pathology image(s) into at least one embeddings vector; and transmitting, by the server computer, the embeddings vector(s) to a storage location, without the client computer transmitting or receiving the digital pathology image(s).

VISUAL LOCATION OF AERIAL VEHICLES USING DYNAMIC ALEATORIC UNCERTAINTY

Nº publicación: EP4707735A1 11/03/2026

Solicitante:

BOEING CO [US]
The Boeing Company

Resumen de: EP4707735A1

Techniques for localizing a vehicle in real time using dynamic uncertainty estimates are presented. The techniques include obtaining a terrain image captured by the vehicle; passing the terrain image to a trained evidential deep learning neural network subsystem, from which a dynamic uncertainty value and a first feature vector are obtained in real time; for each of a plurality of candidate terrain locations, comparing the first feature vector to a respective second feature vector representative of a candidate terrain location, from which a respective similarity score is obtained; for at least one of the plurality of candidate terrain locations, updating in real time, by a recursive Bayesian estimator, a respective location weight based on the dynamic uncertainty value and the respective similarity score; estimating, in real time, a location of the vehicle based on the plurality of location weights; and providing the location of the vehicle.