Alerta

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.

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.

METHOD AND SYSTEM FOR OPTIMIZING DEEP LEARNING MODEL USING EDGE-BASED PARTITION AND PARALLEL COMPILATION

Resumen de: US20260178909A1

0000 A method for optimizing a deep learning model includes: providing a computational graph representation of the deep learning model; determining sub-model boundaries of a plurality of sub-models corresponding to the deep learning model, based on at least memory traffic costs respectively associated with a plurality of edges in the computational graph of the deep learning model; respectively generating the plurality of sub-models based on the determined sub-model boundaries; and separately performing compilation operations on the plurality of sub-models to generate a plurality of compilation results.

MACHINE LEARNING EXPLAINABILITY FRAMEWORK FOR MACHINE LEARNING CLASSIFIERS BASED ON LABELED BINARY VECTORS FOR A DATA OBJECT

Resumen de: US20260178945A1

0000 Various embodiments of the present disclosure provide a machine learning framework for machine learning classifiers based on labeled binary vectors for a data object. The techniques comprise generating a data matrix object based on a group of partially masked sets and a group of training predictions respectively generated by a pre-trained classifier using a group of partially masked sets, training a tabular machine learning model using the data matrix object as a training dataset, determining a set of importance scores that respectively correspond to the set of text segments based on one or more parameters of the tabular machine learning model determined during the training, and providing at least one text segment of the set of text segments to associate with the original prediction as a reason the original prediction was generated.

TRAINING MACHINE-LEARNING MODEL TO GENERATE AN EMBEDDING FOR AN ONLINE SESSION

Resumen de: US20260178967A1

An online system trains a machine-learning model to generate an embedding in real time for a current session of a user with the online system. The machine-learning model is trained by applying a masked language modeling algorithm to training data including a training sequence of actions and a masked action to predict a user’s action that follows the training sequence of actions. The online system captures current session data describing a sequence of actions of the user performed during the current session. The online system applies the trained machine-learning model to predict a next user’s action and generate a session embedding that encodes information about the sequence of actions and the next action. Using the session embedding, the online system ranks a list of objects. The online system generates a user interface signal causing a user’s device to display a user interface with the ranked list of objects.

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.

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

Resumen de: WO2026131122A1

Methods, apparatus and computer-readable medium are disclosed for explainability-based AI or ML in a mobile communication network A method performed in a first node operating in a mobile communication network comprises transmitting to a first network node of the mobile communication network, a first indication indicating a capability of the first node to support one or more explainability techniques in artificial intelligence or machine learning within the mobile communication network. The method further comprises receiving a second indication indicating a policy defining how at least one explainability technique of the one or more explainability techniques is to be applied. The method further comprises performing the artificial intelligence or machine learning based on the policy.

SYSTEMS AND METHODS FOR DETERMINING ANOMALIES USING MACHINE LEARNING MODELS

Resumen de: US20260181002A1

The present disclosure relates to systems and methods for determining anomalies using machine learning models. In examples, systems can be configured to receive network operation data representing a plurality of network operations. The system can classify a subset of the network operations as comprising an anomaly using an isolation forest model. The system can then determine, for each network operation of the subset of network operations, that the anomaly is a positive anomaly, a negative anomaly, or noise. In some examples, the systems can be configured to generate a graphical user interface (GUI) comprising a warning message, the warning message identifying at least one network operation of the subset of network operations as being a negative anomaly.

HYBRID MACHINE LEARNING SYSTEM FOR EFFICIENT OPERATION REVIEW WITH ANOMALY DETECTION, ADAPTIVE CONFIDENCE SCORING, AND MULTI-LAYERED CONTEXTUAL RISK SCORING

Resumen de: US20260178934A1

0000 Systems and methods may validate operation request data associated with operations requested or triggered by maker-user inputs. A computing device can execute software routines and/or one or more machine-learning architectures to obtain one or more operation records for an operation from one or more data sources; extract a feature vector for the operation based upon a plurality of operation features extracted using the one or more operation records for the operation; determine an operation type for the operation by applying a classifier of a machine-learning architecture on the feature vector for the operation; generate a risk score for the operation by applying a risk model of the machine-learning architecture on the operation feature vector and the operation type; determine one or more authorization thresholds for the operation based upon the risk score; and transmit the operation record to one or more checker client devices corresponding to the authorization thresholds.

NON-TRANSITORY COMPUTER-READABLE MEDIUM, MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE

Resumen de: US20260178983A1

0000 There is provided a non-transitory computer-readable medium storing a calculation program for causing a computer to execute a process. The process includes, training a model by using a loss term corresponding to a degree of continuity or discreteness of a variable to be optimized as a cost function in a search process that incorporates continuous relaxation into a discrete optimization problem, and changing the loss term as the search process progresses.

COLLISION DETECTION SYSTEM FOR MOBILE WORKSTATION USING MACHINE LEARNING

Resumen de: WO2026135839A1

A collision detection system is described that integrates machine learning algorithms to enhance the accuracy and reliability of a detection system. In addition, techniques to identify wheel issues, thereby improving the maintenance and operational efficiency of mobile workstations are described. A state machine monitors vibration patterns from the wheels during movement to detect wheel issues, such as a damaged wheel, by analyzing the motion data. Furthermore, techniques to transmit real-time data to an asset management system that may enable proactive maintenance and timely interventions are described.

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

Resumen de: WO2026131125A1

Methods, apparatus and computer-readable medium are disclosed for explainability-based AI or ML in a mobile communication network A method performed in a mobile communication network comprises receiveing training data to perform security analytics with artificial intelligence or machine learning; determining a first set of features relevant to a model training for the security analytics based on an explainability technique to be used for the model training; and performing the security analytics with a subset of the training data for the first set of features.

PROGNOSIS PREDICTION FOR ST-SEGMENT-ELEVATION MYOCARDIAL INFARCTION (STEMI) PATIENTS

Resumen de: WO2026132333A1

Methods of providing a prognosis for a patient who has been treated for ST-segment-elevation myocardial infarction (STEMI) are provided The methods comprise: receiving the values of a plurality of predetermined features associated with the patient, the predetermined features comprising: one or more patient demographic features, one or more hospital admission history features, one or more clinical history features, one or more vital signs features and/or one or more laboratory tests features; and predicting, using the values of said plurality of features, a prognosis for the patient, wherein said predicting comprises using one or more machine learning models to predict a risk of the patient experiencing one or more respective post-treatment complications.

CONTROLLING ACCESS USING MULTIPLE DATA SOURCES WITH VARYING AVAILABILITY

Resumen de: WO2026135669A1

In some aspects, a computing system can train a machine learning (ML) model for risk assessment. Once trained, the ML model can determine a risk indicator for a target entity that indicates a level of risk associated with the target entity. Training the ML model can include: using a foundational model pre-trained to predict multiple outcomes to compute the set of common features; and training the machine learning model using the computed set of common features as training inputs.

MULTI-MODAL LARGE LANGUAGE MODELS TRAINED FOR COMPLIANCE

Resumen de: US20260178897A1

In some implementations, a machine learning host may receive at least one transcript of at least one call performed by an agent. The machine learning host may provide the at least one transcript to a foundational model, included in the suite of large language models, to receive a first score associated with compliance. The machine learning host may provide the at least one transcript to a rapid response model, included in the suite of large language models, to receive a second score associated with compliance. The machine learning host may generate a report based on the first score and the second score. The machine learning host may transmit, to an administrator device, the report.

METHOD, APPARATUS AND COMPUTER PROGRAM FOR DETERMINING A SPECIFICATION FOR AN ELECTRONIC DEVICE

Resumen de: WO2026131127A1

A method for determining a specification for an electronic device is provided. The method includes defining requirements of the electronic device. The method further includes using a trained machine learning model to predict the specification of the electronic device based on the requirements.

SYSTEMS AND METHODS FOR FORECASTING VARIABLE IRRIGATION ELECTRICITY NEEDS AND CURTAILING AGRICULTURAL IRRIGATION

Resumen de: US20260178003A1

0000 In some embodiments, apparatuses and methods are provided herein useful for use in forecasting electrical load needed for a region including a computer and a trained machine learning model. The computer including a control circuit. In some embodiments, the trained machine learning model is configured to: receive forecast environmental data corresponding to the region; determine a day-ahead forecast electrical load needed for the irrigation (such as agricultural irrigation) in the region; transmit a communication configured to cause the day-ahead forecast electrical load needed to be displayed to a user; determine a difference between the day-ahead forecast electrical load needed and an actual electrical load used for the irrigation in the region on a forecast day; obtain actual environmental data corresponding to the region for the forecast day; and apply the difference and the actual environmental data to the random forest algorithm to adjust the trained machine learning model.

SYSTEMS AND METHODS FOR FAIR PREDICTION OF UNDIAGNOSED DISEASE

Resumen de: WO2026136828A1

Systems and methods are disclosed for developing models for predicting Alzheimer's Disease (AD) and other disease states with improved fairness and bias mitigation. An example method includes receiving an EMR dataset comprising a first EMR subset for a first population of patients having confirmed positive disease indications, and an unlabeled EMR subset for a remainder population of the patients. The patients are categorized into various demographic groups. The method further includes using the EMR dataset to train a set of machine learning models via positive unlabeled learning (PUL) to first determine a second EMR subset for a second population of patients having reliable negative indications for the disease state, and then determine additional positive and additional negative indications. Biases specific to each group are mitigated by applying probabilistic criteria specific to each demographic group to subsets of the EMR data during the training of these machine learning models.

AUTOMATED ALERT RECOMMENDER FOR REAL-TIME RISK ASSESSMENT AND ADVISORY

Resumen de: WO2026131899A1

A computer-implemented method including training an artificial intelligence/machine learning (AI/ML) algorithm to generate at least one of alerts and alert rules using a historical data set of an industrial system in an alert generation system. The historical data set includes operational data of the industrial system, at least one alert rule, a set of alerts correlated with operational data, and a set of responses to each alert in the set of alerts. The computer- implemented method receives a set of operational data from an industrial system at the alert generation system and applies the AI/ML algorithm to the set of operational data. The computer-implemented method generates an alert based on the application of the AI/ML algorithm to the set of operational data and provides the generated alert to a user.

DEVICE AND METHOD FOR PROCESSING A QUERY FOR INFORMATION FOR A CONTROL TASK

Resumen de: WO2026135558A1

Aspects concern a method for processing a query for information for a control task, comprising receiving a query, retrieving a plurality of data elements from one or more data sources, wherein the data elements containing information related to the query, determining a ranking score for each of the plurality of data elements according to each of a plurality of ranking methods, determining a combined ranking score for each data element of the plurality of data elements by combining the ranking scores determined for the data element according to the plurality of ranking methods, selecting a subset of the plurality of data elements based on the combined ranking score, formulating a prompt with a request to respond to the query for a generative machine learning model using information from the selected subset, supplying the prompt to the generative machine learning model and generating a response to the query using an output provided by the generative machine learning model in response to the prompt.

MULTI-MODAL LARGE LANGUAGE MODELS COUPLED WITH PROBABILITY ENGINES

Nº publicación: US20260178887A1 25/06/2026

Solicitante:

CAPITAL ONE SERVICES LLC [US]
Capital One Services, LLC

Resumen de: US20260178887A1

0000 In some implementations, a machine learning (ML) host may receive, from a client device, a request indicating an institution. The ML host may provide an indication of the institution to a foundational model, included in the suite of large language models, to receive a summary associated with the institution. The ML host may output the summary to the client device. The ML host may receive, from the client device, an audio stream associated with the institution and may generate a transcript of the audio stream. The ML host may provide the transcript to a rapid response model, included in the suite of large language models, to receive a conversation suggestion. The rapid response model may communicate with the probability engine to generate the conversation suggestion, and the conversation suggestion may increase a probability output by the probability engine. The ML host may output the conversation suggestion to the client device.