Ikaskuntza automatikoa

Automated analytical content generation using large language models

Resumen de: AU2025271014A1

Aspects of the present disclosure relate to automated analytical content generation. Embodiments include receiving data from one or more data sources. Embodiments further include extracting trends from the data using a heuristic algorithm. Embodiments further include providing an input based on the extracted trends to a generative machine learning model that has been configured to generate content based on extracted trends. Embodiments further include receiving, from the generative machine learning model based on the input, content that represents the extracted trends. Embodiments further include displaying the content via a user interface. ov o v RECEIVE DATA FROM ONE OR MORE DATA SOURCES EXTRACT TRENDS FROM THE DATA USING A HEURISTIC ALGORITHM PROVIDE AN INPUT BASED ON THE EXTRACTED TRENDS TO A GENERATIVE MACHINE LEARNING MODEL THAT HAS BEEN CONFIGURED TO GENERATE CONTENT BASED ON EXTRACTED RECEIVE, FROM THE GENERATIVE MACHINE LEARNING MODEL BASED ON THE INPUT, CONTENT THAT REPRESENTS THE EXTRACTED DISPLAY THE CONTENT VIA A USER INTERFACE RECEIVE DATA FROM ONE OR MORE DATA SOURCES ov o v

MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: WO2026172331A1

Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.

METHOD AND SYSTEM FOR GENERATING A MACHINE LEARNING MEDICAL DIFFERENTIAL DIAGNOSIS

Resumen de: WO2026171902A1

A computer implemented method for generating a machine learning medical differential diagnosis using a differential diagnosis coordination module that is in bidirectional communication with a plurality of agent modules that each perform a specific task. The method is performed by the differential diagnosis coordination module. The method comprises receiving patient profile data and performing a series of iterations that are carried out until a predetermined condition is satisfied. Each iteration comprises selecting an agent module from the plurality of agent modules, generating agent specific instructions that cause the selected agent module to perform its specific task according to the agent specific instructions, logging iteration attribute data that relates to attributes of a current iteration and that includes an output of the selected agent module generated according to the agent specific instructions, and updating the patient profile data based on the logged iteration attribute data. When the predetermined condition is satisfied, the method comprises outputting a medical differential diagnosis based on the logged iteration attribute data. The present disclosure can be used in a variety of applications including, but not limited to, several anticipated use cases in medical diagnostics/applications and in healthcare. The present disclosure can also help in patient/physician decision making and can be used with machine learning.

METHOD AND SYSTEM FOR PREDICTING EIMERIA MAXIMA INFECTION OR PREVALENCE IN ANIMALS

Resumen de: WO2026171696A1

The method (900) for predicting Eimeria maxima infection or prevalence in animals comprises the steps of: - providing (905) a plurality of features and a plurality of empirically measured biomarker data; - training (910), using as input the plurality of features and historical biomarker data, a machine learning model to associate predetermined labels indicating whether the set of animals have Eimeria maxima infection or prevalence to said input; - receiving (915), measured biomarker data corresponding to one or more animals, wherein the measured biomarker data indicates blood concentrations of one or more biomarkers in the one or more animals; providing (920), the biomarker data as input to the trained machine learning model; receiving (925) at least one predetermined label indicating whether the one or more animals are positive for Eimeria maxima infection or prevalence or susceptible to mMX prevalence; and providing (930), upon a computer interface, at least one predetermined label received.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Resumen de: US20260245109A1

Provided are computer-implemented methods and systems for generating a prediction using a cold-start model, including: providing at least one data set from at least one data source, the at least one data set comprising historical transactional data for a first plurality of individuals, and contextual data for a second plurality of individuals, the first plurality of individuals comprising a subset of the second plurality of individuals; determining at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating a cold start prediction comprising at least one candidate identifier; generating at least one attribution value based on the at least one feature of the at least one activity; and generating an explainable prediction. Also provided are computer-implemented methods and systems for generating a cold-start model.

METHOD OF ADVANCED CONFIGURATION SIGNALING FOR MULTIPLE TWO-SIDED MODELS COORDINATION

Resumen de: WO2026171971A1

The present disclosure describes a novel method of using the pre-configured AI/ML (artificial intelligence/machine learning) with cross-RAT model compatibility in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). With AI/ML model applied to radio access network, compatibility of supporting the configured two-sided models is challenging for a single or multiple UEs having different ML operational capabilities and environments. Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring cross-RAT model compatibility.

METHOD OF CELL-BASED MODEL CONFIGURATION SIGNALING

Resumen de: WO2026171953A1

The present invention disclosure provides systems and methods for efficiently managing machine learning (ML) operations in communication networks using cell- independent and cell-dependent identification types. ML IDs, encompassing ML condition IDs, model IDs, and dataset IDs, are mapped to these identifiers to enable dynamic adaptability and optimal performance. A mapping relation table is utilized to associate ML IDs with cell-specific and network-wide configurations, facilitating seamless operation across cell boundaries. The invention also includes techniques for periodic and non-periodic feedback-based performance monitoring and signaling flow for mapping relation updates, ensuring enhanced resource utilization and reduced signaling overhead. These configurations enable flexible ML operation management, supporting UE mobility and adaptive decision-making across varying network conditions.

SYSTEMS AND METHODS FOR TRAINING MACHINE LEARNING MODELS USING FORWARD VELOCITIES

Resumen de: WO2026173714A1

A computer system for labeling anomalous data for re-training a scoring machine-learning model is provided. The computer system includes a processor programmed to: receive transaction data associated with a plurality of declined transactions; apply a scoring model to the transaction data for the plurality of declined transactions; rank the plurality of declined transactions from low probability to high probability of fraud; apply a labeling model to the transaction data of a set of the plurality of declined transactions, the set including a batch of the declined transactions having higher probability scores assigned thereto; generate, using the labeling model, a precision percentage for the set of the plurality of declined transactions representing a ratio of the declined transactions labeled as fraud by the labeling model relative to the total number of declined transactions included in the set of declined transactions; and refine the precision percentage by examining subsets of the set.

APPARATUS AND METHODS FOR GENERATING STRUCTURED DATA OUTPUTS

Resumen de: US20260244949A1

0000 Apparatus for generating structured data outputs and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive entity data associated with an entity, the entity data including projection data and location-based data, determine at least a selection criterion as a function of the entity data, receive from a data repository a plurality of metrics as a function of the at least a selection criterion, select at least an output parameter by applying the at least a selection criterion to a plurality of output parameters, as a function of the plurality of metrics, and synthesize, using an output generation machine-learning model trained on output generation training data, a structured data output as a function of the at least an output parameter, wherein the structured data output includes a plurality of event handler graphics.

AUGMENTED-INTELLIGENCE BASED PERSONALIZED VIRTUAL PATIENT SIMULATOR

Resumen de: US20260245740A1

A system, comprising at least one cardiac sensor adapted to measure a hemodynamic profile of a patient; and a computing node operatively coupled to the cardiac sensor and configured to perform the steps of reading a hemodynamic profile of a patient; based on the hemodynamic profile, tuning a plurality of parameters of a cardiovascular model to create a digital twin of the patient; augmenting the hemodynamic profile of the patient with at least one parameter generated from the digital twin; providing the augmented hemodynamic profile to a pretrained machine learning model and receiving therefrom a patient profile; and outputting the patient profile for clinical decision support.

STATEFUL TRAINING OF MACHINE LEARNING MODELS IN WIRELESS NETWORKING

Resumen de: US20260244985A1

There is provided a user equipment apparatus that includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the user equipment apparatus at least to: access a usable machine learning (ML) model; receive, from a network apparatus, a freeze-to-adaptive ratio value; determine, based on the freeze-to-adaptive ratio, a frozen portion of the current ML model to not train and an adaptive portion of the usable ML model to train; access a performance measure for the usable ML model; retrain the adaptive portion of the usable ML model to provide a retrained ML model; determine a performance measure for the retrained ML model; and select one of the retrained ML model or the usable ML model based on the performance measure of the usable ML model and the performance measure of the retrained ML model.WO

RANKING AND MARKING MESSAGES IN A GROUP-BASED COMMUNICATION SYSTEM USING MACHINE LEARNING TECHNIQUES

Resumen de: US20260244950A1

Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.

Configuring Applications Based On A User'S Wakefulness State

Resumen de: US20260240490A1

0000 Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.

PREDICTING SUBJECTIVE RECOVERY FROM ACUTE EVENTS USING CONSUMER WEARABLES

Resumen de: US20260245733A1

0000 In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.

SYSTEMS AND METHODS FOR IDENTITY GRAPH BASED FRAUD DETECTION

Resumen de: US20260245097A1

A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud. The method also includes performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.

MODEL FILE LOADING METHOD AND SYSTEM, COMPUTER DEVICE, AND STORAGE MEDIUM

Resumen de: WO2026170718A1

The present application relates to the technical field of machine learning, and discloses a model file loading method and system, a computer device, and a storage medium. The method comprises: when an inference service starts, acquiring an update request; if it is detected that the update request carries a local storage path of a model file, using the local storage path as a target storage volume of a target scheduling unit; mounting the target storage volume in an init container of the target scheduling unit, and generating a target mount item of the init container; generating response information on the basis of the target storage volume and the target mount item, wherein the response information is used for updating the target scheduling unit to establish a communication link between the updated target scheduling unit and a local directory; and sending the response information to a first slave node, so that the first slave node updates the target scheduling unit, and loads the model file on the basis of the communication link. The present application can solve problems such as high transmission delay, redundant occupation of hard disk resources, and namespace limitations during model file loading.

METHOD FOR OPERATING A BEAM DEVICE, COMPUTER PROGRAM AND BEAM DEVICE FOR CARRYING OUT THE METHOD AS WELL AS A METHOD FOR GENERATING A TRAINING DATA SET AND A METHOD FOR TRAINING A MACHINE LEARNING MODEL

Resumen de: US20260246526A1

The system described herein relates to operating a beam device for obtaining information about an object. Moreover, the invention relates to a computer program product having a program code, which, when executed, controls the beam device in such a way that the method for operating the beam device is carried out. Additionally, the invention relates to a method for generating a training data set for a processing unit and/or for a machine learning model. Furthermore, the invention relates to a method for training a machine learning model of a beam device. The processing unit determines which machine learning model of a plurality of machine learning models is to be used for determining control values of control parameters. The control values of the control parameters are used to operate the control unit for generating the information about the object.

DYNAMIC VISUALIZATION FOR THE PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS AND ANY OTHER TYPE OF PREDICTIVE MODEL

Resumen de: WO2025080778A1

A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Resumen de: US11966704B1

The techniques described herein relate to techniques for verifying a veracity of machine learning outputs. An example method includes receiving a first output generated by a first model responsive to a first input, the first output comprising one or more verifiable statements in text, verifying, using a second model and first reference data stored in at least one first datastore, the one or more verifiable statements to produce first verification results indicating which of them has been verified, when it is determined that at least one of them remains unverified based on the first verification results, identifying, using at least one of the first or second models, at least one second datastore having second reference data attesting to veracity of the first output; and verifying, using the second model and the second reference data, the at least one unverified statement to produce second verification results to be provided as output.

Using Machine Learning to Power a Brand Integrity Platform

Resumen de: US20260236965A1

0000 Transcribed text associated with a predetermined source is accessed by a brand integrity platform. Features of the transcribed text are input into a classifier that is configured to detect presence of sensitive content of a predetermined category in the input features. The features are input into a tonal model that is trained to detect a neutral emotion score. A plurality of neutral score thresholds associated with the predetermined category of sensitive content whose presence is detected in the input features are accessed. The accessed plurality of neutral score thresholds are applied to the detected neutral emotion score to determine, for the predetermined source, a risk level associated with the predetermined category of sensitive content. An action is performed when the risk level for the predetermined category of sensitive content for the predetermined source meets a tolerance threshold associated with a user.

Patient Ventilator Asynchrony Detection

Resumen de: US20260237511A1

A decision support tool is provided for identifying and assisting clinicians with patient ventilator asynchrony. The information used to make the identification may include data from a patient's ventilator including the volume, flow, and pressure associated with that ventilator. At least some of this information may be used to compute one or more features for a time series of the data received for the patient. These features may be used in connection with heuristic rules and machine learning algorithms to identify instances of patient ventilator asynchrony. Based on the identification, one or more intervening actions may be initiated to reduce the impact of patient ventilator asynchrony.

MACHINE LEARNING FEATURE RECOMMENDATION

Resumen de: US20260236829A1

0000 A pre-trained model trained to predict a measure of expected model performance based at least in part on a feature relevance score associated with a text field data type is generated. A specification of a desired target field for machine learning prediction and one or more text fields storing input content is received. A corresponding feature relevance score for each of the one or more text fields storing the input content is calculated. Based on the corresponding calculated feature relevance scores, a corresponding measure of expected model performance for each of the one or more text fields storing the input content is predicted using the pre-trained model. The predicted measures of expected model performance are provided for use in feature selection among the one or more text fields storing the input content for generating a machine learning model to predict the desired target field.

BIOMASS UTILIZATION SUPPORT DEVICE, METHOD, AND PROGRAM

Resumen de: US20260237002A1

A biomass utilization support device: acquires biomass information relating to a biobased material and product information for each of a plurality of products including information about materials configuring the products; uses a machine learning model, which has been trained to estimate appropriate values for replacement amounts in a case of replacing a portion of the materials configuring the products with the biobased material, and the acquired biomass information and product information to estimate the appropriate values for each of the plurality of products; calculates, for each of the plurality of products, environmental impact indicators in a case in which a portion of the materials configuring the products has been replaced with the biobased material at the replacement amounts represented by the estimated appropriate values; and outputs support information listing the estimated appropriate values and the calculated environmental impact indicators.

INTERACTIVE MACHINE LEARNING OPTIMIZATION

Resumen de: US20260236847A1

Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: examining an enterprise dataset, the enterprise dataset defined by enterprise collected data; selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise; training a set of predictive models using data of the one or more synthetic dataset to provide a set of trained predictive models; testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models.

COMPUTING SYSTEM AND METHOD FOR CREATING A DATA SCIENCE MODEL HAVING REDUCED BIAS

Nº publicación: US20260236986A1 13/08/2026

Solicitante:

CAPITAL ONE FINANCIAL CORP [US]
Capital One Financial Corporation

Resumen de: US20260236986A1

A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.