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Machine learning for modeling preference data to optimize interactions with candidate partners

Resumen de: GB2635074A

Technologies are provided for optimizing candidate partners for a user interaction. The technologies can facilitate a trust in facts and identify mutual interest. The technologies can identify the location of users, share personalized information, provide tools for matching users to candidates, exchange data, advertise to users with tracking algorithms, create avatars, host digital interactions between users, and provide user assessments of other users. Nodes of users, and information on user behavior patterns can help identify matches. Users can share their current moods to communicate with other users. Machine learning is included for modeling a user's own feedback from actual interactions in a pool of candidates. The input to the model includes sets of interaction data on users, and the output from the model is an improved, modeled set of user preferences to improve the user's candidate pool. Images of virtual candidates can be created at each iteration of the model.

Method and system for diagnosing machine learning model underperformance or failure

Resumen de: GB2634926A

Diagnosing machine learning model underperformance or failures, comprising: embedding validation data samples and text string phrase candidates in a joint embedding space to generate data embeddings and text embeddings S2. A machine learning model trained to perform a number of tasks is received S4 and inference is conducted with the validation dataset using the trained machine learning model to obtain numerical metrics per validation data sample S6. A spatio-temporal relationship representation is generated for the validation data samples S8. A plurality of data slices are generated S10, and a semantic description for each data slice is generated based on the text embeddings S12. The number of tasks may be selected from a group comprising object recognition, object identification, object detection, pose estimation, and semantic segmentation. The spatio-temporal relationship representation may be a graph comprising nodes representing validation data samples, and edges with weights relative to the relationship between data samples.

CLASSIFYING A TYPE OF APPLIANCE ATTACHMENT

Resumen de: WO2025082993A1

The subject-matter of the present disclosure relates to a computer-implemented method of classifying a type of attachment installed on an appliance. The computer-implemented method comprises: sensing (S200), by a sensor (18) of the appliance, data representing physical parameters associated with operating the appliance; classifying (S202), using a machine learning model, a type of attachment installed on the appliance based on the sensed data; and outputting (S204) a signal indicating the type of attachment installed on the appliance based on the detection.

SYSTEM AND METHODS FOR HETEROGENEOUS CONFIGURATION OPTIMIZATION FOR DISTRIBUTED SERVERS IN THE CLOUD

Resumen de: US2025130845A1

A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

SYSTEM AND METHOD FOR SELECTING MACHINE LEARNING TRAINING DATA

Resumen de: US2025131336A1

Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.

Machine Learning Based Overbooking Modeling

Resumen de: US2025131342A1

Embodiments optimize hotel room reservations for a hotel. For a first day of a plurality of future days, embodiments automatically determine, based on an objective function, an overbooking limit for each category of hotel rooms for the hotel, where the hotel includes a plurality of different room categories. Embodiments receive a first reservation request for the first day for a first category room. When the determined overbooking limit for the first category room has not been reached, embodiments accept the first reservation request. When the accepted first reservation request is being checked in to the hotel on the first day, embodiments automatically determine, based on the objective function, to reject the first reservation request, accept the first reservation request, or upgrade the first reservation request to a higher category room.

AGENTIC ARTIFICIAL INTELLIGENCE WITH DOMAIN-SPECIFIC CONTEXT VALIDATION

Resumen de: US2025131028A1

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.

AI-POWERED AUTONOMOUS PLANT-GROWTH OPTIMIZATION SYSTEM THAT AUTOMATICALLY ADJUSTS INPUT VARIABLES TO YIELD DESIRED HARVEST TRAITS

Resumen de: US2025131268A1

Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

SMART SKILL COMPETENCY EVALUATION SYSTEM

Resumen de: WO2025085353A1

A computing device and methods of making and using a computing device having machine learning capabilities to analyze course text content based on prompting to generate a list of course learning objectives, and in particular embodiments, having machine learning capabilities to analyze presentation content text against each of the course learning objectives to generate a course competency score with supportive reasoning for each course learning objective and an overall presentation score.

CROSS-MODEL SCORE NORMALIZATION

Resumen de: US2025131337A1

Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.

ENSEMBLE MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025132051A1

Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.

SYSTEM AND METHOD FOR MANAGING AN ON-SENSOR MACHINE LEARNING (ML) MODEL

Resumen de: WO2025083451A1

The present disclosure discloses a system and method for managing an on-sensor machine learning (ML) model is disclosed. The method comprises the step of monitoring performance parameters of plurality of on-sensor ML models present in an industrial plant. The method further comprises the step of detecting a degradation of at least one on-sensor ML model based on the monitored ML model performance parameters of the plurality of on-sensor ML models. The degradation of the at least one on-sensor ML model comprises at least one of: data distribution change, training serving skew, model drift, occurrence of outlier event, and data quality issue. The method finally discloses the step of updating the at least one on-sensor ML model based on the ML model upgradation parameters retrieved from one of the plurality of sources.

CLASSIFYING A TYPE OF APPLIANCE ATTACHMENT

Resumen de: EP4542451A1

The subject-matter of the present disclosure relates to a computer-implemented method of classifying a type of attachment installed on an appliance. The computer-implemented method comprises: sensing (S200), by a sensor (18) of the appliance, data representing physical parameters associated with operating the appliance; classifying (S202), using a machine learning model, a type of attachment installed on the appliance based on the sensed data; and outputting (5204) a signal indicating the type of attachment installed on the appliance based on the detection.

CONFIGURING AN ELECTRONIC DEVICE USING ARTIFICIAL INTELLIGENCE

Resumen de: 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

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.

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.

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).

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.

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.

CLASSIFYING AN ENTITY FOR FOLFOX TREATMENT

Resumen de: 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

Resumen de: 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

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

Nº publicación: US2025124353A1 17/04/2025

Solicitante:

SNAP INC [US]
Snap Inc

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.