Alerta

CONFIGURING AN ELECTRONIC DEVICE USING ARTIFICIAL INTELLIGENCE

Absstract of: EP4542395A2

The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Absstract of: 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.

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

Absstract of: 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.

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

Absstract of: 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).

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

Absstract of: 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.

MACHINE LEARNING MODEL FOR PREDICTING DRIVING EVENTS

Absstract of: 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.

CLASSIFYING AN ENTITY FOR FOLFOX TREATMENT

Absstract of: US2025125034A1

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).

HIERARCHICAL MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MOLECULAR CATEGORIES FROM EXPRESSION DATA

Absstract of: US2025125013A1

Described herein in some embodiments is a method comprising: obtaining expression data previously obtained by processing a biological sample obtained from a subject; processing the expression data using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of machine learning classifiers comprising first and second machine learning classifiers corresponding to the first and second molecular categories; and identifying, using at least some of the machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

MACHINE EVALUATION OF CONTRACT TERMS

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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.

METHOD AND SYSTEM FOR SUPERVISED GENERATIVE OPTIMIZATION FOR SYNTHETIC DATA GENERATION

Absstract of: US2025124332A1

A method for facilitating supervised generative optimization for synthetic data generation is disclosed. The method includes receiving, via an application programming interface, inputs that include input data and parameters; partitioning the input data to generate data sets, the data sets including training data sets, validation data sets, and test data sets; tuning hyperparameters of synthesizers by using the data sets and supervised optimization that is based on downstream performance metrics; determining a mixture distribution from among the tuned synthesizers; training machine learning models based on the mixture distribution; and generating, by using the trained machine learning models, sets of synthetic data based on the input data.

DESIGNING BACTERIAL COMMUNITIES USING MACHINE LEARNING

Absstract of: US2025124305A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model that is configured to process a model input that defines a bacterial community to generate a predicted score that predicts a performance of the bacterial community in performing a bacterial task. According to one aspect, a method comprises: generating data identifying a set of bacterial communities; obtaining, for each bacterial community, a target score for the bacterial community; generating a set of training examples, wherein each training example corresponds to a respective bacterial community and comprises: (i) a training input that identifies the bacterial strains included in the bacterial community, and (ii) the target score for the bacterial community; training the machine learning model on the set of training examples; and identifying one or more candidate bacterial communities for performing the bacterial task using the trained machine learning model.

AGENTIC ARTIFICIAL INTELLIGENCE FOR A SYSTEM OF AGENTS

Absstract of: 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.

METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR USING A MACHINE LEARNING (ML) MODEL IN BATTERY MANAGEMENT

Absstract of: US2025124336A1

One example method for using a machine learning (ML) model in battery management comprises: receiving one or more selection inputs for selecting an ML model for providing battery management information, wherein the one or more selection inputs include a state of health (SOH) value associated with a battery system; selecting, using the selection inputs, the ML model from a plurality of ML models; obtaining, using model inputs and the ML model, the battery management information associated with the battery system; and performing, using the battery management information, a battery management decision for managing the battery system.

MANAGING INFORMATION FOR MODEL TRAINING USING DISTRIBUTED BLOCKCHAIN LEDGER

Absstract of: 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 BASED DEFECT EXAMINATION AND RANKING FOR SEMICONDUCTOR SPECIMENS

Absstract of: US2025124307A1

There is provided a system and method of defect examination on a semiconductor specimen. The method comprises: obtaining an inspection dataset informative of a group of defect candidates and attributes thereof resulting from examining the specimen by an inspection tool; classifying, by a classifier, the group of defect candidates into a plurality of defect classes such that each defect candidate is associated with a respective defect class; and ranking, by a decision model, the group of defect candidates into a total order using a sorting rule. Each defect candidate is associated with a distinct ranking in the total order representative of the likelihood of the defect candidate being a defect of interest (DOI). The decision model is previously trained to learn the sorting rule pertaining to the plurality of defect classes associated with the group of defect candidates and a series of attributes in the inspection data.

SYSTEMS AND METHODS FOR DYNAMICALLY GENERATING NEW DATA RULES

Absstract of: US2025124042A1

Systems, apparatuses, methods, and computer program products are disclosed for dynamically generating a new data rule. An example method includes receiving a data rule including an operation to be applied to a data entry and determining regarding whether the data rule belongs to a set of previously-reviewed data rules. The example method further includes, when the data rule does not belong to the set of previously-reviewed data rules, providing the data rule to a user for human validation and receiving human validation. The example method further includes, causing an update, by rule generation circuitry, of an element of a machine learning model, where the element corresponds to the data rule where the update changes a weight value based on the validation and generating the new data rule based on the updated machine learning model. The example method further includes replacing the data rule with the new data rule.

MACHINE LEARNING SYSTEMS ARCHITECTURES FOR RANKING

Absstract of: 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

Absstract of: 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.

MACHINE LEARNING ON OVERLAY MANAGEMENT

Absstract of: 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.

ANALYZING AN INFERENCE OF A MACHINE LEARNING PREDICTOR

Absstract of: 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.

TECHNIQUES FOR ENERGY USAGE ESTIMATION IN COMPUTING SYSTEMS

Absstract of: US2025117686A1

Tracing data including a plurality of traces for a plurality of operations performed by a distributed computing system on behalf of a plurality of users of a distributed computing system during a period of time is identified. Each trace having latencies for a plurality of segments of a corresponding operation. A set of overall latencies comprising an overall latency for each segment is determined. A set of user latencies including a latency for each segment is determined for each of the plurality of users. A set of energy usage estimates including an energy usage estimate for one or more of the plurality of users is generated, by a processing device, based on the set of overall latencies and the set of user latencies using a machine learning (ML) model.

MACHINE LEARNING GUIDED SIGNAL ENRICHMENT FOR ULTRASENSITIVE PLASMA TUMOR BURDEN MONITORING

Nº publicación: US2025118439A1 10/04/2025

Applicant:

CORNELL UNIV [US]
NEW YORK GENOME CENTER INC [US]
MEMORIAL SLOAN KETTERING CANCER CENTER [US]
Cornell University,
New York Genome Center, Inc,
Memorial Sloan-Kettering Cancer Center

Absstract of: 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).