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DECISION TREE OF MODELS: USING DECISION TREE MODEL, AND REPLACING THE LEAVES OF THE TREE WITH OTHER MACHINE LEARNING MODELS

Resumen de: US20260187484A1

Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.

SYSTEMS AND METHODS FOR COORDINATING EXECUTION OF AN ENSEMBLE OF MACHINE LEARNING MODELS

Resumen de: US20260187545A1

System for coordinating execution of an ensemble of machine learning models to determine anatomical structures to target during cancer treatment are described herein. In examples, the systems can coordinate execution of multiple machine learning models based on different types of three-dimensional images of a patient. These images can include positron emission tomography (PET) images, computed tomography (CT) images, and/or other similar images. The outputs of the models can be correlated with one another to quantify locations and volumes of tumor lesions within the patient. In some examples, a tumor stage can be determined based on the quantification of the tumor lesions. This information can then be used to determine one or more optimal treatment plans for the patient.

BOOSTING AND MATRIX FACTORIZATION

Resumen de: US20260187197A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a new machine learning model architecture. In some aspects, the methods include obtaining a training dataset with a plurality of training samples that includes feature variables and output variables. A first matrix is generated using the training dataset which is a sparse representation of the training dataset. Generating the first matrix can include generating a categorical representation of numeric features and an encoded representation of the categorical features. The methods further include generating a second, third and a fourth matrix. Each feature of the first matrix is then represented using a vector that includes a multiple adjustable parameters. The machine learning model can learn by adjusting values of the adjustable parameters using a combination of a loss function the fourth matrix, and the first matrix.

OPERATIONALIZING MACHINE LEARNING MODELS AN INFORMATION TECHNOLOGY AND SECURITY OPERATIONS APPLICATION

Resumen de: US20260187518A1

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

USER SEARCH CATEGORY PREDICTOR

Resumen de: US20260187663A1

0000 Described herein are embodiments for improving search engine results of listings of For Sale Objects (FSOs). A search engine may be improved by implementing rules that resolve ambiguity between listings for different (FSOs) that match the same search inputs. An unsupervised machine learning module may evaluate candidate rules and identify improvements that may not be obvious to a human evaluator. An ecommerce site that combines the improved search engine with the unsupervised machine learning module may dynamically evaluate search results using different candidate rules and iteratively improve search results.

VIRTUALLY MONITORING GLUCOSE LEVELS IN A PATIENT USING MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY

Resumen de: US20260182869A1

0000 A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's glucose level. The platform implements a short-term prediction model to generate a daily prediction of the patient's glucose level based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's glucose level during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's glucose level and updates a digital twin of the patient's metabolic profile.

SYSTEM AND METHOD FOR EXTRACTING HIDDEN CUES IN INTERACTIVE COMMUNICATIONS

Resumen de: US20260188340A1

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process interactive communications between at least two participants. Speech and text, within the interactive communications, are analyzed using machine learning classifiers to extract prosodic, semantic and key phrase cues located within the interactive communications to identify changes to emotion, sentiments and key phrases. A summary of the interactive communications between a first participant and a second participant is generated at least, in-part, based on the extracted prosodic, semantic and key phrase cues and the summary is highlighted based on any of the changes to emotion, the sentiments or the key phrases.

TECHNIQUES FOR DETECTING EMERGING PATTERNS IN DATA USED FOR MACHINE LEARNING MODEL-BASED DECISIONS

Resumen de: US20260187524A1

0000 Described are examples for detecting emerging patterns in data. A detection system for detecting patterns outside of supervised machine learning models is provided for determining similarity scores between transactions to detect the emerging patterns. Transactions in the pattern can be reviewed to determine whether to render decisions on the transactions or similar subsequently occurring transactions. A self-correcting detection system is also provided for using machine learning models to correct for emerging patterns in the transaction data.

APPARATUS AND METHOD FOR DESIGNING MULTILAYER FILM

Resumen de: EP4768891A1

An apparatus and method for designing a multilayer film is disclosed. An apparatus for designing a multilayer film may perform: modeling a multilayer film to be designed as a single layer structure having a preset thickness, collecting physical property data with respect to a film corresponding to the single layer 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, performing feature setting based on a plurality of physical indicators selected from the physical property data, depending on the feature setting mode, 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.

QUALITY PREDICTION MODEL GENERATION METHOD, METAL MATERIAL QUALITY PREDICTION METHOD, METAL MATERIAL MANUFACTURING METHOD, METAL MATERIAL MANUFACTURING CONDITION PRESENTATION METHOD, QUALITY PREDICTION MODEL GENERATION DEVICE, METAL MATERIAL QUALITY PREDICTION DEVICE, METAL MATERIAL MANUFACTURING CONDITION PRESENTATION DEVICE, AND METAL MATERIAL MANUFACTURING SYSTEM

Resumen de: EP4769249A1

A method of generating a quality prediction model includes an acquisition step (S1) of acquiring an explanatory variable selected from manufacturing conditions of each process and an objective variable that is a state of quality defects in the manufactured metal material, a storage step (S3) of storing the explanatory variable and the objective variable in association with each other as training data, calculation steps (S4 to S6) of dividing the training data into groups and testing whether a significant difference exists in the state of quality defects, a search step (S7) of searching for a most significant grouping, and a generation step (S8) of generating the quality prediction model by machine learning using a group according to the most significant grouping found.

APPARATUS AND METHOD FOR INSPECTING BATTERY

Resumen de: EP4768897A1

0001 A method for inspecting a battery, according to an embodiment of the present invention, relates to a method for inspecting the quality of a battery in a manufacturing process, the method comprising the steps of: acquiring an image capturing at least a portion of the exterior of the battery to preprocess the image; detecting one or more defect candidate regions in the preprocessed image by using one or more detection algorithms; extracting position information of the defect candidate regions and shape feature information of the defect candidate regions; and inputting, into a pre-trained machine learning model, information related to the detection algorithms, the position information of the defect candidate regions, and the shape feature information of the defect candidate regions, to determine whether corresponding defect candidate shapes are defective.

TECHNIQUES FOR IMPROVING DECISIONS RENDERED BASED ON MACHINE LEARNING MODEL OUTPUT

Resumen de: EP4769235A1

Described are examples for rendering decisions based on machine learning (ML) model output. A set of segments for a historical set of data for a division of interest, and associated budgets for the decision of interest, can be obtained. For each segment in the set, a budget for incorrect decisions rendered based on output from the ML model can be computed. For each data entry in a current set of data, a current decision can be rendered based on a configured cutoff value and also a shadow decision based on the candidate cutoff value for a segment of the set of segments associated with the data entry. The candidate cutoff value can be promoted to replace the configured cutoff value, for rendering subsequent decisions for a subsequent set of data, based on comparing the shadow decisions for the current set of data based on the budget for incorrect decisions.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY REDISTRIBUTING DATA

Resumen de: EP4769259A1

A machine learning based (ML-based) method and system for redistributing data, is disclosed. Initially, an input data associated actual bank cash is obtained from data sources. The input data is pre-processed to generate pre-processed data. A month level data associated with the actual bank cash is predicted for a pre-determined horizon based on at least one of: historical cash flow data and seasonality, using machine learning (ML) models on the pre-processed data. At least one of: the month level data to week of month (WOM) level data and the WOM level data to day level data, is redistributed based on a pro-rata configuration using hyperparameters. At least one of: the WOM level data and the day level data, redistributed from the month level data, is provided as an output, to the users on user interfaces associated with electronic devices associated with the users.

MACHINE-LEARNING-BASED SYSTEM AND METHOD FOR AUTOMATICALLY EXTRACTING FIELDS FROM DOCUMENTS

Resumen de: EP4769285A1

0001 A machine-learning based (ML-based) system and method for automatically extracting one or more data fields from one or more documents, are disclosed. The ML-based system includes a document obtaining subsystem to obtain documents, a document pre-processing subsystem to generate pre-processed data, a field identifying subsystem to identify data fields using a trained ML model, and a field extracting subsystem to extract financial information. The ML-based system also comprises an output subsystem to deliver the extracted data to end users via user interfaces. The ML model is trained using historical documents, labelled data fields, and features such as distance-based features, direction-based features, dimension-based features, positional features, and value-based features. The M-based system employs hyperparameter optimization, noise removal, and accuracy assessment mechanisms to enhance performance. This ML-based system provides a scalable, accurate, and automated solution for financial information extraction, ensuring efficiency, adaptability, and seamless integration with enterprise systems.

METHOD FOR INTELLIGENT ANALYSIS OF IMPORT AND EXPORT HAZARDOUS CHEMICALS BASED ON MACHINE LEARNING

Resumen de: NL4001738A

0001 The present disclosure relates to the technical field of data processing, in particular to a method for intelligent analysis of import and export hazardous chemicals based on machine learning. The method constructs a hazardous chemical knowledge graph and performs path detection on the hazardous chemical knowledge graph to obtain at least one valid path and at least one invalid path; acquires, for any invalid path, a value index of the any invalid path according to a degree of information coverage between intermediate entities of the any invalid path, a similarity feature between adjacent intermediate entities of the any invalid path, and a degree of reliability of each connected segment in the any invalid path; utilizes a value index of each invalid path to screen at least one high-value path among all invalid paths.

MACHINE LEARNING MODEL AND NARRATIVE GENERATOR FOR PROHIBITED TRANSACTION DETECTION AND COMPLIANCE

Resumen de: US20260179097A1

There are provided systems and methods for a machine learning model and narrative generator for prohibited transaction detection and compliance. A service provider server, such as an electronic transaction processor, may generate a machine learning model using a supervised training technique, which may detect transactions that may be money laundering. The model may be iteratively trained by detecting flagged transactions and outputting those transactions to an agent for identification of false positives, which may be used to retrain the model. When outputting the flagged transactions, a narrative may be generated using an explainer graph and a machine learning prediction explainer that identifies the features of the transaction data that caused the transactions to be flagged. Further, once the model is trained additional transactions may be processed to determine whether the features of those transactions indicate prohibited behavior.

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.

AUTOMATED VEHICLE CONTROL REQUIREMENTS PROCESSING USING MACHINE LEARNING MODELS

Resumen de: US20260175854A1

An example system for automated vehicle control requirements processing includes at least one processor configured to preprocess multiple data artifacts associated with vehicle control system requirements and including at least two different modalities, including reducing redundancy and resolving conflicts between the multiple data artifacts, determine a modality of each data artifact, create an embedding for each data artifact according to the modality of the data artifact, generate, as an output of at least one machine learning model, at least one of a message sequence chart, a finite state machine and a Gherkin use case, according to the embeddings of the multiple data artifacts, and build a unified requirements model according to the at least one of the message sequence chart, the finite state machine and the Gherkin use case, wherein the unified requirements model defines control requirements for at least one vehicle control feature.

PLATFORMS, SYSTEMS, AND METHODS FOR OPTIMIZATION USING MACHINE LEARNING MODELS

Resumen de: US20260179727A1

A system may include data integration facilities for integrating content of publication data sets relating to strains and proprietary data sets including parameters of a process in which the strain produces functional outputs, wherein integrated data is input to machine learning models. The machine learning models generate recommendations relating to modifications of the strain.

TRANSFORMER-BASED ASSISTANT FOR IDENTIFYING, ORGANIZING, AND RESPONDING TO CUSTOMER CONCERNS

Resumen de: AU2024376764A1

Transformer-based agent assistant systems as machine learning-based customer service tools that analyze past customer-agent conversations to build a knowledge base of problem-resolution steps are disclosed. The system may include a natural language processing (NLP) model and a transfomer-based model to extract and generate customer concerns and resolutions. One embodiment also includes a head-topic and subtopic detection module for identifying trends in customer concerns. Another embodiment uses a question-answering model and a zero-shot-NLI (natural language inference) classifier for entity extraction and detection. The system is designed to be flexible, incorporating new data over time, and can retrieve company documentation or FAQs for the agent based on cosine similarity.

DYNAMIC TISSUE TYPING

Resumen de: AU2024408349A1

Systems, apparatuses, and methods as described herein can provide in part a validated AI model integrated with tumor profiling that enhances diagnostic accuracy, including resolution of CUP cases, and prompts clinically relevant therapeutic recommendation changes without requiring additional specimen. Machine learning models in a hierarchal sample type tree can be used, e.g., to determine a tumor type of a cancer.

COMPUTER-BASED SYSTEMS AND METHODS FOR MACHINE LEARNING BASED EXCEPTION PREDICTIONS

Resumen de: AU2024393160A1

A failure prediction method including a predicting flow and a model training flow, the predicting flow including receiving a natural language input from a client computer, translating the input into a task by a LLM, selecting a ML model dedicated to the task, receiving first data, converting the first data to second data of a predetermined format, immediately applying, the ML model on the second data for predicting an output and providing a corresponding explanation, storing the second data and the output into historical data in a storage layer, translating the output and the explanation into a prediction in the natural language by the LLM, and transmitting the prediction to the client computer and iterating the predicting flow for a predetermined number of time; and the model training flow including retrieving the historical data from the storage layer, and training the ML model on the historical data.

MACHINE-LEARNING TECHNIQUES FOR AUTOMATED CLOUD SERVICE MANAGEMENT

Resumen de: WO2026135664A1

In some aspects, a computing system can train a machine-learning model to analyze a graph database for risk assessment. The computing system can use the machine-learning model to identify a risk indicator for a target component of one or more interactive computing environments. The graph database can include a set of nodes where each node represents a respective infrastructure service of one or more infrastructure services and a set of edges connecting individual nodes of the set of nodes. The computing system can generate the risk indicator for the target component based on an output of the machine-learning model The computing system additionally can output a graphical user interface including at least the risk indicator for use in controlling access to the one or more infrastructure services.

MACHINE LEARNING (ML) SYSTEM AND METHOD FOR OPTIMIZING MEETINGS

Resumen de: WO2026136314A1

A machine-learning system and method for engineering and optimizing meetings. The method includes defining a meeting agenda comprising agenda items, generating prompts to solicit structured participant inputs, and enforcing contribution thresholds to ensure sufficient input. Participant inputs are integrated with internal organizational information and external information sources and analyzed using machine-learning techniques to generate results for each agenda item, including predictions, recommendations, and other optimized meeting products. Each completed meeting is stored as a structured dataset, and machine learning is applied across multiple completed meeting datasets over time to generate emergent knowledge and build an organization foundation model. The organization foundation model supports evaluation of participant and information source contributions, informs subsequent meetings, and enables time-based analysis. The system enables asynchronous, data-driven meetings that improve efficiency, accountability, and knowledge generation within and across organizations.

METHODS AND APPARATUS FOR EXPLAINABILITY-BASED ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING IN A MOBILE COMMUNICATION SYSTEM

Nº publicación: WO2026131122A1 25/06/2026

Solicitante:

NOKIA TECHNOLOGIES OY [FI]
NOKIA TECHNOLOGIES OY