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MACHINE LEARNING MODEL FOR PREDICTING DRIVING EVENTS

Resumen de: US2025121818A1

A processor retrieves data associated with a set of driving sessions and generates a training dataset by labeling a first subset of data that corresponds to driving sessions that included a first event and labeling a second subset of the data that corresponds to driving sessions that included an indication of an airbag activation. The processor then trains an artificial intelligence model using the training dataset, such that trained artificial intelligence model predicts a score indicative of a likelihood of a new driving session associated with a new driver being associated with at least the first event or an airbag activation. Once trained, the processor can augment the score using data retrieved after each driving session. The processor can also notify the driver if the driver's actions has caused their score to increase/decrease and provide an underlying reason.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING MODELS TO EFFECT VIRTUAL TRY-ON AND STYLING ON ACTUAL USERS

Resumen de: WO2025080826A1

Disclosed are example embodiments of systems and methods for virtual try-on of articles of clothing. An example method of virtual try-on of articles of clothing includes selecting a garment from a pre-existing database. The method also includes loading a photo of a source model wearing the selected garment. Additionally, the method includes generating a semantic segmentation of the model image. The method also includes extracting the selected garment from the photo of the model. Additionally, the method includes determining a correspondence between a target model and the source model by performing a feature point detection and description of the target model and the source model, and performing feature matching and correspondence validation. The method also includes performing garment warping and alignment of the extracted garment. Additionally, the method includes overlaying and rendering the garment.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Resumen de: WO2025080315A1

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

SYSTEMS AND METHODS FOR THE DETECTION OF EATING DISORDER BEHAVIORS USING WEARABLE SENSORS

Resumen de: WO2025081156A1

An exemplary system (180) and method (300) are disclosed for detecting and preventing a future eating disorder behavior for a user. A user is provided a wearable device (105) that includes multiple physiological sensors (106) (305). At regular intervals, the wearable device collects physiological data(102) of the user including heart rate, electrodermal activity, and skin temperature (310). Features (108) are generated from the physiological data and used as input to a machine learning model (112) that is trained to predict a future eating disorder behavior based on features of physiological data (315; 320). If the model predicts a future eating disorder behavior, one or more actions (116) to prevent the eating disorder behavior are performed including alerting the user through the wearable device or a smart phone associated with the user, or alerting a caregiver, parent, or medical practitioner associated with the user (325).

MACHINE LEARNING ON OVERLAY MANAGEMENT

Resumen de: US2025123572A1

The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and machine learning are used to train a classification that correlates the overlay error source factors with overlay metrology categories. The overlay error source factors include tool signals. The trained classification includes a base classification and a Meta classification.

METHOD FOR EVALUATING A USER OF A MARKETPLACE SYSTEM BY A GRAPH MACHINE LEARNING MODEL

Resumen de: WO2025080201A1

Aspects concern a method for evaluating a user of a marketplace system, comprising generating training data elements for a graph machine learning model, wherein each training data element comprises a graph comprising a plurality of user nodes, each user node being associated with a user and a user node feature generated from historical transaction values of the user until a predetermined training date in historical data and the training data element comprises, for the at least one user node, a label generated according to a historical transaction values of the user after the predetermined training date in the historical data, training the graph machine learning model to predict the labels of the training data elements from the respective graphs of the training data elements, generate, for a user of the marketplace system to be evaluated, a graph comprising a node for the user, wherein the node for the user comprises a user node feature comprising historical transaction values of the user; and predicting a value of the user by processing the graph generated for the user by means of the trained graph machine learning model.

MACHINE EVALUATION OF CONTRACT TERMS

Resumen de: US2025124530A1

Embodiments of the present disclosure provide a method that may include defining an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end. The method may include accessing a machine learning classifier comprising a plurality of rule sets. The method may include applying the plurality of rule sets to one or more words of each corresponding contract document. The method may include linking identified one or more core attributes and one or more words of each corresponding contract document to an applicable object of the object model, determining prevailing terms of each corresponding contract document, and evaluating contract data variables and assigning a contract data risk value to one or more of contract data values. The method may include communicating an alert via email or text message when a contract risk exceeds a threshold value.

MACHINE LEARNING TECHNIQUES FOR AUTOMATIC EVALUATION OF CLINICAL TRIAL DATA

Resumen de: US2025124529A1

Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

CLOUD BASED MACHINE LEARNING

Resumen de: US2025124353A1

Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.

TEAM MEMBER BEHAVIOR IDENTIFICATION IN CUSTOMER COMMUNICATIONS USING COMPUTER-BASED MODELS

Resumen de: US2025124330A1

Techniques are described for performing team member behavior identification and classification using a machine learning model and one or more rule-based models for customer communications. A computing system receives a message from a user device. The computing system uses output of a machine learning model to determine whether the message includes an indication of team member behavior including at least one behavior term and at least one team member reference. The computing system also uses output of one or more rule-based models to determine whether the message includes an indication of a type of team member behavior including a type of behavior term and a type of team member reference substantially proximate to each other within the message. Based on the message including the indication of team member behavior, the computing system sends the message to another system corresponding to the type of team member behavior included in the message.

AGENTIC ARTIFICIAL INTELLIGENCE FOR A SYSTEM OF AGENTS

Resumen de: US2025124069A1

An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.

MANAGING INFORMATION FOR MODEL TRAINING USING DISTRIBUTED BLOCKCHAIN LEDGER

Resumen de: US2025124311A1

Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.

MACHINE LEARNING SYSTEMS ARCHITECTURES FOR RANKING

Resumen de: US2025124038A1

Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning ranking. An example computing method includes receiving a search query and determining a plurality of machine learning model execution engines based on the search query and a plurality of search result types. The example computing method further includes generating a plurality of subsets of search results based on the search query and the plurality of machine learning model execution engines. The example computing method further includes generating a set of search results comprising at least one search result from each of the plurality of subsets of search results.

SYSTEMS WITH SOFTWARE ENGINES CONFIGURED FOR DETECTION OF HIGH IMPACT SCENARIOS WITH MACHINE LEARNING-BASED SIMULATION AND METHODS OF USE THEREOF

Resumen de: US2025123953A1

Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enable improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation. Moreover, the disclosed technology may provide and/or realize a minimized variation to input data that correspond to a point of transition from one state to another state in an outcome that results from the input data, where the transition to another state is termed a “significant” variation to the output data.

ANALYZING AN INFERENCE OF A MACHINE LEARNING PREDICTOR

Resumen de: US2025094811A1

A relevance score for a predictor portion of a machine learning predictor is determined by performing a reverse propagation of an initial relevance score, which is attributed to a first predetermined predictor portion, along propagation paths of the machine learning predictor, and by filtering the reverse propagation with respect to a second predetermined predictor portion. Furthermore, respective affiliation scores for a set of data structures with respect to a predictor portion of a machine learning predictor are determined by performing reverse propagations of an initial relevance score from a first predetermined predictor portion to the predictor portion.

FRAUDULENT TRANSACTION MANAGEMENT

Resumen de: US2025117797A1

A method includes receiving, by a processor of a transaction processing entity, a transaction attempt. The method includes receiving a risk score from a risk strategy decision model, the risk score being determined from a machine learning model. The method includes in response to receiving the risk score, determining, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent. In response to determining the risk score exceeds the threshold: the method includes determining, whether to approve or decline the transaction attempt; and determining a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score. The method includes outputting an indication to approve or decline the transaction attempt in response to determining whether to approve or decline the transaction attempt and the reason for approving or declining the transaction attempt.

SYSTEMS, METHODS, AND GRAPHICAL USER INTERFACES FOR MITIGATING BIAS IN A MACHINE LEARNING-BASED DECISIONING MODEL

Resumen de: US2025117664A1

A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

METHODS AND SYSTEMS FOR MACHINE-LEARNING BASED DOCUMENT PROCESSING

Resumen de: US2025117575A1

The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.

Method and System for Interactive Explanations in Industrial Artificial Intelligence Systems

Resumen de: US2025117537A1

A method for interactive explanations in industrial artificial intelligence systems includes providing a machine learning model and a set of test data, a set of training data and a set of historical data simulating a piping and process equipment; predicting a result for the piping and process equipment based on the machine learning model using the set of test data and the set of training data, wherein the set of historical data is used by the machine learning model to predict at least one parameter of the piping and process equipment; and presenting the predicted at least one parameter on a piping and instrumentation diagram of the piping and process equipment.

UNSUPERVISED TECHNIQUES FOR IDENTIFYING UNIVARIATE AND MULTIVARIATE ANOMALIES IN SCIENTIFIC INSTRUMENT RESULTS WITHIN A LABORATORY INFORMATION MANAGEMENT SYSTEM

Resumen de: US2025116678A1

A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.

Methods and Systems for Analyzing Cybersecurity Threats

Resumen de: US2025119448A1

To analyze cybersecurity threats, an analysis module of a processor may receive log data from at least one network node. The analysis module may identify at least one statistical outlier within the log data. The analysis module may determine that the at least one statistical outlier represents a cybersecurity threat by applying at least one machine learning algorithm to the at least one statistical outlier.

METHODS AND APPARATUS FOR VISUALIZATION OF MACHINE LEARNING MALWARE DETECTION MODELS

Resumen de: US2025119451A1

Embodiments disclosed include methods and apparatus for visualization of data and models (e.g., machine learning models) used to monitor and/or detect malware to ensure data integrity and/or to prevent or detect potential attacks. Embodiments disclosed include receiving information associated with artifacts scored by one or more sources of classification (e.g., models, databases, repositories). The method includes receiving inputs indicating threshold values or criteria associated with a classification of maliciousness of an artifact and for selecting sample artifacts. The method further includes classifying and selecting the artifacts, based on the criteria, to define a sample set, and based on the sample set, generating a ground truth indication of classification of maliciousness for each sample artifact in the sample set. The method further includes using the ground truth indications to evaluate and display, via an interface, a representation of a performance of sources of classification and/or quality of data.

Counterfactual and Recourse Method for Recommending Network Configurations towards Favorable Outcome

Resumen de: US2025119360A1

A method for improving communication network performance comprises identifying a favorability status of individual predictions and/or decisions of a plurality of decisions of a machine-learning algorithm acting on the communication network. The favorability statuses are stored with corresponding values of network parameters used as features in the algorithm. A counterfactual algorithm is generated, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and network parameter values, to derive rules for producing a favorable status based on one or more of the network parameters. A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s). Whether to implement the proposed recourse action is determined, based on the confidence level.

MACHINE LEARNING GUIDED SIGNAL ENRICHMENT FOR ULTRASENSITIVE PLASMA TUMOR BURDEN MONITORING

Resumen de: US2025118439A1

Systems, methods, and computer program products are provided for diagnosing, prognosing, or monitoring cancer in a subject, particularly the assessment of minimal residual disease (MRD).

UNSUPERVISED TECHNIQUES FOR IDENTIFYING UNIVARIATE AND MULTIVARIATE ANOMALIES IN SCIENTIFIC INSTRUMENT RESULTS WITHIN A LABORATORY INFORMATION MANAGEMENT SYSTEM

Nº publicación: WO2025074193A1 10/04/2025

Solicitante:

THERMO ELECTRON LTD [GB]
FISHER SCIENTIFIC COSTA RICA SRL [CR]
THERMO ELECTRON LIMITED,
FISHER SCIENTIFIC COSTA RICA SOCIEDAD DE RESPONSABILIDAD LIMITADA

Resumen de: WO2025074193A1

A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.