Machine learning

MACHINE LEARNING MODEL CORRECTION

Resumen de: US2024046349A1

A method, in some implementations, may include obtaining output from a machine learning (ML) model responsive to input data, obtaining initial training data representing training data used to train the ML model, generating, based on the output from the ML model and the initial training data, correction training data that represents a desired alteration to the output from the ML model responsive to one or more particular subgroups in the input data, generating, based on the correction training data, a correction ML model configured to receive, as input, the input data and to output correction values which, when combined with the output from the ML model, perform the desired alteration, and generating corrected output as a combination of the output from the ML model and the output correction values from the correction ML model, and providing, for display, the corrected output.

Systems and methods of property valuation

Resumen de: GB2636300A

The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non- transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

SYSTEMS AND METHODS FOR PROGRAMMATIC LABELING OF TRAINING DATA FOR MACHINE LEARNING MODELS VIA CLUSTERING AND LANGUAGE MODEL PROMPTING

Resumen de: AU2023383086A1

Embodiments introduce an approach to semi-automatically generate labels for data based on implementation of a clustering or language model prompting technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.

MACHINE LEARNING BASED OCCUPANCY FORECASTING

Resumen de: WO2025117106A1

Embodiments determine a final occupancy prediction for a check-in date for a plurality of hotel rooms. Embodiments receive historical reservation data including a plurality of booking curves for the hotel rooms corresponding to a plurality of reservation windows, the historical reservation data including a plurality of features. Based on the historical reservation data, embodiments generate a first occupancy prediction for the check-in date using a first model and generate a second occupancy prediction for the check-in date using a second model. Embodiments determine a best performing model from at least the first model and the second model uses a corresponding occupancy prediction corresponding to the best performing model as the final occupancy prediction for the check-in date.

CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE

Resumen de: US2025181978A1

Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

UTILIZING MACHINE LEARNING AND A SMART TRANSACTION CARD TO AUTOMATICALLY IDENTIFY OPTIMAL PRICES AND REBATES FOR ITEMS DURING IN-PERSON SHOPPING

Resumen de: US2025182156A1

A device may receive, from a client device of a customer, item data identifying a price of an item and customer data identifying the customer, where the item data may be received by a transaction card from a price tag of the item. The device may receive price data identifying prices associated with multiple items and other data identifying locations, availabilities, and terms of the multiple items, and may process the item data, the price data, and the other data, with a machine learning model, to identify an optimal price for the item. The device may provide, to the client device, data identifying the optimal price and data identifying a merchant associated with the optimal price, and may receive transaction data identifying the item, the optimal price, and the merchant when the customer purchases the item. The device may perform actions based on the transaction data.

CLOSED-LOOP OPTIMIZATION OF GENERAL REACTION CONDITIONS FOR HETEROARYL SUZUKI-MIYAURA COUPLING

Resumen de: AU2023366930A1

Disclosed are systems and methods for rapidly generating general reaction conditions using a closed-loop workflow leveraging matrix down-selection, machine learning, and robotic experimentation. In certain aspects, provided is a method, comprising: selecting a reaction pair comprising a first molecule and a second molecule; wherein the first molecule is selected from a first matrix and the second molecule is selected from a second matrix; selecting one or more reaction conditions for the reaction pair, the selection based on historic use of the one or more reaction conditions and a structural and functional diversity of the selected reaction pair; automatically performing, by a robotic system, an initial round of reactions between the selected reaction pair under the selected one or more reaction conditions.

NEURAL PROCESSING UNIT AND WI-FI SCHEDULING

Resumen de: WO2025117989A1

Technology disclosed herein may include an access point including a processing device. The processing device may generate, at an access point, a machine learning model previously trained using training traffic data; identify, at the access point, traffic data; provide, at the access point, the traffic data to the machine learning model; predict, at the access point, a traffic pattern using the machine learning model; and determine, at the access point, a scheduling characteristic based on the traffic pattern.

METHODS, SYSTEMS, AND DEVICES TO VALIDATE IP ADDRESSES

Resumen de: US2025184345A1

Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

METROLOGY AND PROCESS CONTROL FOR SEMICONDUCTOR MANUFACTURING

Resumen de: US2025181941A1

A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

FEATURE DIMENSIONALITY REDUCTION FOR MACHINE LEARNING MODELS

Resumen de: US2025181991A1

Provided is a method, system, and computer program product for performing automated feature dimensionality reduction without accuracy loss. A processor may determine a first training value associated with a first dataset of a machine learning model. The processor may rank features of the first dataset in relation to the first training value. The processor may compare the ranked features of the first dataset to a predetermined threshold. The processor may generate a second dataset from the first dataset by removing a third dataset, the third dataset having a set of features that did not meet the predetermined threshold. The processor may determine a second training value associated with the second dataset. The processor may compare the first training value to the second training value. In response to the second training value being lower than the first training value, the processor may analyze the third dataset with a dimensionality reduction algorithm.

DYNAMIC FILTER RECOMMENDATIONS

Resumen de: US2025181587A1

A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.

Method And System For Key Predictors And Machine Learning For Configuring Cell Performance

Resumen de: US2025183392A1

A method of managing battery performance may include obtaining, via a measurement device, measurements of one or more parameters relating to one or more cells; generating or updating, based on the measurements, a machine learning model; and generating, using the machine learning model, cell performance prediction data for use in managing at least one cell. Each cell includes a cathode, a separator, and a silicon-dominant anode. The measurements of the one or more parameters correspond to a plurality of different types of data. The measurements include one or more of: measurements of cells or cell components before formation or cycling, measurements from formation cycles for one or more cells, measurements from a number of cycles after formation for one or more cells, and measurements of characteristics of cell components prior to cell assembly.

MODULAR MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025181676A1

A computer system is provided that is designed to handle multi-label classification. The computer system includes multiple processing instances that are arranged in a hierarchal manner and execute differently trained classification models. The classification task of one processing instance and the executed model therein may rely on the results of classification performed by another processing instance. Each of the models may be associated with a different threshold value that is used to binarize the probability output from the classification model.

DEEP LEARNING SYSTEMS AND METHODS FOR PREDICTING IMPACT OF CARDHOLDER BEHAVIOR BASED ON PAYMENT EVENTS

Resumen de: WO2024025710A1

A system is configured to retrieve a set of customer raw transaction data, wherein the transactions are devoid of any target transactions of interest. An impact neural network model is applied to the transaction data using a "notTargef ' variable. The "notTargef ' variable indicates that the target transaction of interest is not included in the transaction data. The model predicts a first result based on the "notTargef' variable. The model is applied to the transaction data using an "isTargef ' variable. The "isTargef ' variable indicates that the target transaction of interest is included in the set of customer raw transaction data. The model predicts a second result based on the "isTargef ' variable. The system determines a difference between the second and first results. The difference is a predicted incremental impact on cardholder behavior. The system presents the predicted incremental impact on cardholder behavior to an issuer associated with the transaction data.

FORECASTING USING DIFFERENTIAL-BASED MACHINE-LEARNING ARCHITECTURE

Resumen de: WO2025108940A1

Conventional forecast models assume that the underlying data are stationary. In the case of non-stationary data, the conventional forecast model must be increased in complexity, or otherwise, must be frequently retrained. Embodiments are disclosed of a forecast model that accounts for non-stationary data, without increased complexity and without an increase in the frequency of retraining, by recasting the forecasting problem as a matter of tracking differentials in the data. In particular, variables (e.g., covariates) are input into differential determinations and/or intermediate machine-learning models to determine differences in past and forecasted values of a plurality of features. These differences are then input into a machine- learning model to predict a change in the value of the target, which is aggregated with a past value of the target to produce a forecasted value of the target.

LIFELONG MACHINE LEARNING (LML) MODEL FOR PATIENT SUBPOPULATION IDENTIFICATION USING REAL-WORLD HEALTHCARE DATA

Resumen de: US2025173563A1

A deep learning model implements continuous, lifelong machine learning (LML) based on a Bayesian neural network using a framework including wide, deep, and prior components that use available real-world healthcare data differently to improve prediction performance. The outputs from each component of the framework are combined to produce a final output that may be utilized as a prior structure when the deep learning model is refreshed with new data in a deep learning process. Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from models in the prior component into future predictions. The Bayesian deep neural network-based LML model increases accuracy in identifying patient profiles by continuously learning, as new data becomes available, without forgetting prior knowledge.

SYSTEM AND METHOD FOR IDENTIFYING NATURAL ALTERNATIVES TO SYNTHETIC ADDITIVES IN FOODS

Resumen de: US2025173661A1

A method of modifying a food item to contain plant-based ingredients includes identifying plant-based substances to replace an ingredient of the food item. The plant-based substances are clustered, via a machine learning model, into a plurality of clusters according to an objective based on properties of the plant-based substances. The plant-based substances of a selected cluster are classified into a plurality of classes, via a machine learning classifier, based on the objective and the properties of the plant-based substances of the selected cluster. A score is determined for each plant-based substance of a selected class based on metrics. A plant-based substance is determined based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient.

ARTIFICIAL INTELLIGENCE SYSTEM PROVIDING AUTOMATED DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS

Resumen de: US2025173627A1

Multiple distinct control descriptors, each specifying an algorithm and values of one or more parameters of the algorithm, are created. A plurality of tuples, each indicating a respective record of a data set and a respective descriptor, are generated. The tuples are distributed among a plurality of compute resources such that the number of distinct descriptors indicated in the tuples received at a given resource is below a threshold. The algorithm is executed in accordance with the descriptors' parameters at individual compute resources.

Machine Learning Based Occupancy Forecasting

Resumen de: US2025173660A1

Embodiments determine a final occupancy prediction for a check-in date for a plurality of hotel rooms. Embodiments receive historical reservation data including a plurality of booking curves for the hotel rooms corresponding to a plurality of reservation windows, the historical reservation data including a plurality of features. Based on the historical reservation data, embodiments generate a first occupancy prediction for the check-in date using a first model and generate a second occupancy prediction for the check-in date using a second model. Embodiments determine a best performing model from at least the first model and the second model uses a corresponding occupancy prediction corresponding to the best performing model as the final occupancy prediction for the check-in date.

Monitoring Machine Learning Models

Resumen de: US2025173585A1

A computer-implemented method for monitoring machine learning models in a distributed setup includes obtaining model activity data relating to activity of the machine learning models in the distributed setup; analyzing the obtained model activity data; and, based on the analysis of the model activity data, outputting model management data for managing the activity of the machine learning models in the distributed setup.

AUTOMATIC REVISIONS TO DOCUMENT CLAUSES BASED ON CLAUSE TYPE

Resumen de: US2025173380A1

A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Methods And Systems For Quantifying Partitioning Of Agents In Vivo Based On Partitioning Of Agents In Vitro

Resumen de: US2025172540A1

Small molecule therapeutics can concentrate in distinct intracellular environments, some bounded by membranes, and others that may be formed by membrane-less biomolecular condensates. The chemical environments within biomolecular condensates have been proposed to differ from those outside these bodies, but the internal chemical environments of diverse condensates have yet to be explored. Here we use small molecule probes to demonstrate that condensates formed in vitro with the scaffold proteins of different biomolecular condensates harbor distinct chemical solvating properties. The chemical rules that govern selective partitioning in condensates, which we term condensate chemical grammar, can be ascertained by deep learning, allowing efficient prediction of the partitioning behavior of small molecules. The rules learned from in vitro condensates were adequate to predict the partitioning of small molecules into nucleolar condensates in living cells. Different biomolecular condensates harbor distinct chemical environments, that the chemical grammar of condensates can be ascertained by machine learning.

AI-CONTROLLED SENSOR NETWORK FOR THREAT MAPPING AND CHARACTERIZATION AND RISK ADJUSTED RESPONSE

Resumen de: US2025175456A1

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT

Resumen de: US2025174364A1

A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

MACHINE LEARNING BASED METHODS OF ANALYSING DRUG-LIKE MOLECULES

Resumen de: US2025174314A1

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

GENERATING AND PROCESSING SIMULATED MEDICAL INFORMATION FOR PREDICTIVE MODELING

Resumen de: US2025174362A1

A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.

FORECASTING USING DIFFERENTIAL-BASED MACHINE-LEARNING ARCHITECTURE

Resumen de: EP4560561A1

Conventional forecast models assume that the underlying data are stationary. In the case of non-stationary data, the conventional forecast model must be increased in complexity, or otherwise, must be frequently retrained. Embodiments are disclosed of a forecast model that accounts for non-stationary data, without increased complexity and without an increase in the frequency of retraining, by recasting the forecasting problem as a matter of tracking differentials in the data. In particular, variables (e.g., covariates) are input into differential determinations and/or intermediate machine-learning models to determine differences in past and forecasted values of a plurality of features. These differences are then input into a machine-learning model to predict a change in the value of the target, which is aggregated with a past value of the target to produce a forecasted value of the target.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM

Resumen de: WO2025104804A1

In this information processing device, a complementation means complements ontology data by using a graph machine learning model that has learned the relationship between information items included in the ontology data. A natural language processing model generation means generates a natural language processing model on the basis of the complemented ontology data. The information processing device can assist decision making of a user.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TECHNIQUES TO PREDICT ITEM GROUP COMPOSITION

Resumen de: WO2024144913A1

Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.

BUILDING SYSTEM WITH DIGITAL TWIN BASED AGENT PROCESSING

Resumen de: US2025164950A1

A system can include one or more memory devices that can store instructions thereon. The instructions can, when executed by one or more processors, cause the one or more processors to receive timeseries data associated with a building, detect that a new building device has been added to the building, determine that a representation of the new building device is absent from a digital twin of the building, and execute a machine learning model to add the representation of the new building device to the digital twin of the building.

SYSTEMS AND METHODS FOR PROCESSING IMAGES TO DETERMINE IMAGE-BASED COMPUTATIONAL BIOMARKERS FROM LIQUID SPECIMENS

Resumen de: US2025166397A1

A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

SYSTEMS AND METHOD FOR CALCULATING LIABILITY OF A DRIVER OF A VEHICLE

Resumen de: US2025166083A1

Aspects of the present disclosure are related to systems, apparatus, and methods of generating or calculating liability and operational costs of a vehicle based on a driver's handling of the vehicle are described herein. Using a combination of vehicle sensors, video input, and on-board artificial intelligence and/or machine learning algorithms, the systems and methods of the present disclosure can identify risky events performed by the driver of a vehicle and generate, calculate, and evaluate driving scores for the driver of the vehicle and send the calculations to one or more entities.

SYSTEMS AND METHODS FOR HUMAN-IN-THE-LOOP TRAINING OF A MACHINE LEARNING MODEL FOR EXTRACTING TARGETS FROM RECORDS

Resumen de: US2025165993A1

Systems and methods for training a machine learning model by extracting targets from records are disclosed. A method includes receiving data packets including data associated with previous interactions of a user, determining at least one party to the previous interactions with whom the user interacted at a level above a threshold, and comparing the at least one party to a plurality of parties identified in a target database to identify at least one potential target, for each potential target, determining a respective target metric by inputting the data packets into a machine learning model, providing an identification of the at least one potential target, with reference to the respective target metrics, to a user device associated with the user, receiving, via the user device, feedback regarding the identification, and training the machine learning model based on the feedback.

SYSTEMS AND METHODS FOR PRE-SELECTING A MACHINE LEARNING MODEL BASED ON DETERMINED DATASET CHARACTERISTICS

Resumen de: US2025165822A1

Pre-selecting a machine learning model based on determined dataset characteristics may be facilitated. In some embodiments, a time-series dataset may be received by a system. Based on the time-series dataset, the system generates a periodogram to determine power frequencies of a set of frequencies of the time-series dataset. The system may then determine an asymptotic p-value based on the periodogram, where the asymptotic p-value indicates a probability value that a seasonal cycle is part of the time-series dataset. In response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value fails to exceed the threshold p-value, the system determines a seasonality cycle of the time-series dataset. The system then generates, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies.

SYSTEMS AND METHODS FOR PROCESSING IMAGES TO CLASSIFY THE PROCESSED IMAGES FOR DIGITAL PATHOLOGY

Resumen de: US2025165820A1

Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

REAL TIME FEEDBACK FROM A MACHINE LEARNING SYSTEM

Resumen de: US2025165819A1

A technique for providing real time feedback from a machine learning system is provided that includes a method and system for interactively training machine learning models. In particular, by separating processing and analysis using static and dynamic models that are trained differently, the disclosed technique enables interactive training and prediction of machine learning models to increase the speed of generating new predictions based on real time feedback. In some cases, a dynamic model is applied to the output of a static model to generate an analysis, a correction of the analysis is received, and the correction is used to retrain the dynamic model. An updated analysis is generated based on reapplying the dynamic model to the output of the static model without having to retrain the static model.

METHOD FOR MAKING THE FUNCTION OF A MACHINE LEARNING ALGORITHM EXPLAINABLE

Resumen de: US2025165821A1

A method for making the function of a machine learning algorithm explainable, wherein the machine learning algorithm is designed to assign input data to one of at least two groups. The method includes: providing input data for the machine learning algorithm; for all of the input data provided, assigning the corresponding input data to one of the at least two groups by means of the machine learning algorithm; selecting data from a first group of the at least two groups; ascertaining, from a second group of at least two groups, data that are most similar to the selected data from all the data contained in the second group; comparing the selected data with the ascertained data to make the machine learning algorithm explainable; and providing corresponding comparison results.

SYSTEMS AND METHODS FOR ITERATIVE FEATURE SELECTION FOR MACHINE LEARNING MODELS

Resumen de: US2025165848A1

Systems and methods for selecting machine learning features using iterative batch feature reduction. In some aspects, the system trains a plurality of candidate models based on a plurality of feature groups split from a first set of features. Each candidate model takes as input a feature group of no more than a first threshold number of features. For each candidate model in the plurality of candidate models, the system processes the candidate model to extract an explainability vector. Based on the explainability vector, the system selects a second threshold number of features from the feature group to generate a slim feature group. The system trains a slim candidate model which takes as input the slim feature group. The system generates a second set of features by combining features from a plurality of slim candidate models.

GENERATING AN EMBEDDING SPACE FROM A TRAINING DATA SET FOR A MACHINE LEARNING ALGORITHM

Resumen de: US2025165865A1

A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.

STORING AND OBTAINING ATTRIBUTE DATA OF ATTRIBUTES OF MACHINE LEARNING MODELS

Resumen de: US2025165816A1

In some implementations, a controller may receive a request for an inference. The controller may determine, based on the received request for the inference, a first inference model of a plurality of inference models, to generate the inference. The controller may obtain, from a memory associated with an inference cache, first attribute data regarding first attributes of the first inference model. A location of the first attribute data, in the memory, may be determined using the inference cache. The attributes may include weights associated with the first inference model, biases associated with the first inference model, and a structure of the first inference model. The controller may utilize the first attribute data to generate the inference based on the request.

INTERACTIVE REAL-TIME VIDEO SEARCH BASED ON KNOWLEDGE GRAPH

Resumen de: US2025165529A1

A method, computer system, and computer program product are provided for real-time video searching based on augmented knowledge graphs that are generated using machine learning models. Multimedia data is obtained comprising an image portion and an audio portion, and a user query with respect to the multimedia data is obtained. A knowledge graph of the multimedia data is generated using one or more machine learning models based on the image portion and the audio portion, wherein the knowledge graph includes a plurality of entities and relationships between entities. An augmented knowledge graph is generated, wherein the augmented knowledge graph augments the knowledge graph with additional entities and additional relationships between the additional entities using additional data that is obtained from a source external to the multimedia data. A response to the user query is provided based on the augmented knowledge graph.

Closed-Loop Intelligence

Resumen de: US2025165438A1

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

Closed-Loop Intelligence

Resumen de: US2025165439A1

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

SECURITY-RELATED EVENT ANOMALY DETECTION

Resumen de: US2025168179A1

The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. An anomaly score received for a production event is determined based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

MACHINE LEARNING BASED METHOD FOR CONSIDERING CONSTRAINT VIOLATIONS WHEN SELECTING OPTIMAL ACTIONS TO PERFORM IN A COMMUNICATIONS NETWORK

Resumen de: WO2025105999A1

Embodiments described herein relate to methods and apparatuses for initiating control of one or more parameters in a communications network. A computer-implemented method comprises obtaining a first state of the communications network; for each action in a set of actions: predicting a next state of the communications network utilizing a first ML model based on the first state and the action; predicting, utilizing a second ML model, whether taking the action whilst the communications network is in the first state will violate one or more constraints; determining a cost value associated with the action by evaluating a cost function, wherein the cost function comprises: a first component representing one or more performance metrics of the communications network in the next state; and a second component dependent on whether taking the action in the first state will violate the one or more constraints; selecting a selected action from the set of actions, wherein the selected action is associated with a best cost value; and initiating performance of the selected action in the communications network.

DATA ANALYTICS SYSTEM AND METHOD FOR MACHINE LEARNING SOLUTIONS WITH ZERO CODING

Resumen de: WO2025104745A1

A novel system and method for the development of machine learning solutions without the need for any coding expertise The system includes a highly advanced interactive dashboard designed for data visualization. One of its key features is ensuring that user inputs remain error-free and only request relevant options based on the provided data. This approach revolutionizes the accessibility and usability of machine learning, making it more inclusive for a broader user base, regardless of their coding background. The interactive dashboard streamlines the 10 machine learning solution development process and significantly reduces the barriers to entry, making it a promising innovation in the field of data science and artificial intelligence.

GENERATING AN EMBEDDING SPACE FROM A TRAINING DATA SET FOR A MACHINE LEARNING ALGORITHM

Resumen de: EP4557165A1

A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.

REUSE OF DATA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: EP4557178A1

Example embodiments may relate to systems, methods and/or computer programs for reusing data for training machine learning models. In an example, an apparatus comprises means for receiving a request to collect new user data for training a machine learning model associated with an application. The apparatus may also comprise means for identifying existing stored data suitable for training the machine learning model based upon an ontology. The apparatus may also comprise means for providing access to the identified existing stored data in response to identifying that the data is suitable for training the machine learning model.

/ Method and apparatus for artificial intelligence/machine learning based life cycle management

Resumen de: US2025158685A1

A system and a method are disclosed for AI/ML model LCM. A method performed by a UE includes transmitting, to a base station, a first signal including an indication of AI/ML use cases supported by the UE, and an indication of properties for the supported AI/ML use cases, receiving, from the base station, a second signal including at least one of a configuration, an activate command, or a trigger command of a CSI report to deploy at least one of a plurality of AI/ML functionalities based on the first signal, performing an inference operation for the CSI report based on the second signal, and transmitting, to the base station, a report based on the CSI report and the at least one of the plurality of AI/ML functionalities.

/ Method and apparatus for artificial intelligence/machine learning based life cycle management

Resumen de: US2025158685A1

A system and a method are disclosed for AI/ML model LCM. A method performed by a UE includes transmitting, to a base station, a first signal including an indication of AI/ML use cases supported by the UE, and an indication of properties for the supported AI/ML use cases, receiving, from the base station, a second signal including at least one of a configuration, an activate command, or a trigger command of a CSI report to deploy at least one of a plurality of AI/ML functionalities based on the first signal, performing an inference operation for the CSI report based on the second signal, and transmitting, to the base station, a report based on the CSI report and the at least one of the plurality of AI/ML functionalities.

ARTIFICIAL INTELLIGENCE IN AN AEROSOL DELIVERY DEVICE

Resumen de: US2025151806A1

An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based on the target variable, the target variable being a user profile depending on at least one of the properties, and times and durations of respective user puffs on the device.

ENSEMBLE MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025157670A1

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.

VIRTUAL WORLD ENVIRONMENTS FOR ENTERPRISE DECISION-MAKING

Resumen de: US2025157088A1

In an example embodiment, machine learning is utilized to create a virtual world where a user can view and interact with data in a graphical environment. This virtual world may be termed a “Story Verse” environment, which can create multiple different virtual world universes capable of segmenting the traditional complexities of Enterprise system data into an easily usable and holistic set. In a further example embodiment, the virtual world is presented in a way that data is represented as real world objects, such as amusement park rides, clouds, etc.

HIGH-FIDELITY PREDICTION WITH TRUST REGIONS

Resumen de: US2025156747A1

According to one embodiment, a method, computer system, and computer program product for making high-fidelity predictions with trust regions is provided. The embodiment may include identifying a data set. The embodiment may also include partitioning the data set into two or more clusters. The embodiment may further include creating two or more disjoint polytopic regions in a multi-dimensional space, wherein a cluster from the two or more disjoint polytopic regions corresponds to a trust region from the two or more polytopic regions. The embodiment may also include training a machine learning model based on the two or more disjoint polytopic regions. The embodiment may further include drawing a conclusion based on the trained machine learning model.

SYNTHETIC DATA GENERATION USING LARGE LANGUAGE MODELS

Resumen de: US2025156644A1

In various examples, synthetic question-answer (QA) pairs may be generated using question and answer generation models comprising corresponding language models (e.g., autoregressive LLMs). A repository of textual data representing a particular knowledge base may be used to source synthetic QA pairs by partitioning textual data from the repository into units of text (e.g., paragraphs) that represent context. For each unit of text, the question generation model may be prompted to generate a synthetic question from that unit of text, and the answer generation model may be prompted to generate a synthetic answer to the synthetic question. Textual entailment and/or human evaluations may be used to filter out low quality, incorrect, and/or non-productive QA pairs that may be a result of hallucinations. As such, the synthetic QA pairs may be used as, and/or may be used to generate, training data for one or more machine learning models.

Suggesting Actions Based on Machine Learning

Resumen de: US2025156203A1

This document describes techniques for suggesting actions based on machine learning. These techniques determine a task that a user desires to perform, and presents a user interface through which to perform the task. To determine this task, the techniques can analyze content displayed on the user device or analyze contexts of the user and user device. With this determined task, the techniques determine an action that may assist the user in performing the task. This action is further determined to be performable through analysis of functionalities of an application, which may or may not be executing or installed on the user device. With some subset of the application's functionalities determined, the techniques presents the subset of functionalities via the user interface. By so doing, the techniques enable a user to complete a task more easily, quickly, or using fewer computing resources.

Technologies for Predictive Management of Customer Account Balance Attrition

Resumen de: US2025156939A1

Technologies for predictive management of customer account balance attrition include a compute device. The compute device includes circuitry configured to obtain data indicative of one or more attributes of a customer of a financial institution. The circuitry may also be configured to generate, from the obtained data, a feature set for use by an ensemble of machine-learning models trained to predict customer behavior, provide the feature set to the ensemble of machine-learning models to produce a prediction of whether the customer of the financial institution will be lost to (e.g., at least a portion of the customer's money will be transferred to) a competitor financial institution, obtain the prediction from the ensemble of machine-learning models, and perform, in response to a determination that the that the customer is predicted to be lost, a remedial action to reduce a likelihood of losing the customer.

MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF

Resumen de: US2025156677A1

The presently disclosed technology relates to medical image processing. An example method includes receiving medical image data which represents an anatomical structure and processing the received image data through convolutional neural network (CNN) to generate predictions. The predictions can include abnormality location proposals and abnormality class probabilities associated with each abnormality location proposals.

SYSTEM AND METHOD FOR AUTOMATED MAPPING OF DATA TYPES FOR USE WITH DATAFLOW ENVIRONMENTS

Resumen de: US2025156160A1

In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

Automatically Recognizing and Surfacing Important Moments in Multi-Party Conversations

Resumen de: US2025157474A1

A system and a method are disclosed for identifying a subjectively interesting moment in a transcript. In an embodiment, a device receives a transcription of a conversation, and identifies a participant of the conversation. The device accesses a machine learning model corresponding to the participant, and applies, as input to the machine learning model, the transcription. The device receives as output from the machine learning model a portion of the transcription having relevance to the participant, and generates for display, to the participant, information pertaining to the portion.

REUSE OF DATA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: US2025156763A1

Example embodiments may relate to systems, methods and/or computer programs for reusing data for training machine learning models. In an example, an apparatus comprises means for receiving a request to collect new user data for training a machine learning model associated with an application. The apparatus may also comprise means for identifying existing stored data suitable for training the machine learning model based upon an ontology. The apparatus may also comprise means for providing access to the identified existing stored data in response to identifying that the data is suitable for training the machine learning model.

MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF

Resumen de: US2025156676A1

The presently disclosed technology relates to medical image processing. An example method includes receiving medical image data which represents an anatomical structure and processing the received image data through convolutional neural network (CNN) to generate predictions. The predictions can include abnormality location proposals and abnormality class probabilities associated with each abnormality location proposals.

Automatically assigning hybrids or seeds to fields for planting

Resumen de: AU2025202916A1

A computer-implemented method comprising: using field assignment instructions in a server computer system, receiving, over a digital data communication network at the server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers; using the field assignment instructions in the server computer system, obtaining over the digital data communication network at the server computer system, other input data comprising relative maturity values, historic yield values for the fields of the growers, and mean yield values for regions in which the fields of the growers are located; using the field assignment instructions in the server computer system, calculating pair datasets consisting of permutations of product assignments of two (2) products to two (2) fields from among the fields of the growers, and corresponding converse assignments of the same products and fields; inputting features of the pair dataset(s) to a trained machine learning model, to yield predicted probability of success (POS) values for each of the product assignments and its corresponding converse assignment; blending the predicted POS values for all fields with field classification data using an operations research model of other field data, to result in creating and storing score values for each of the product assignments and the corresponding converse assignments; using the field assignment instructions in the server computer syst

High Frequency QRS in Biometric Identification

Resumen de: AU2025202887A1

Abstract Disclosed is sampling HF-QRS signals from a number of subjects (or derived values or features), and using e.g. deep learning-convolutional neural networks to find features or values which are (i) sufficiently similar for the same subject over all samples, yet (ii) sufficiently different among different subjects to allow identification. Also disclosed is finding signatures which are sufficiently stable over a particular period such that these signatures are within a deviation threshold, and then monitoring all subjects to be identified at least as often as the period used to establish the deviation threshold.

FLEXIBLE MACHINE LEARNING MODEL COMPRESSION

Resumen de: WO2025101721A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compresses a machine learning model having a plurality of parameters. In one aspect, one of the methods includes obtaining trained values of a set of parameters for at least a portion of a machine learning model; identifying one or more dense ranges for the trained values; determining a least number of bits required to represent each trained value within the one or more dense ranges; identifying a second format having a range that is smaller than a range of the first format; and generating a compressed version of the at least a portion of the machine learning model.

BIOLOGICAL AGENT AEROSOL CLASSIFICATION/IDENTIFICATION USING MACHINE LEARNING ALGORITHMS

Resumen de: WO2025101490A1

In accordance with various embodiments, a system and a method for identifying a particle as a bioactive stimulant are provided. The system includes a processor configured to execute machine-readable instructions borne by a non-transitory computer-readable memory device to cause the processor to process one or more steps of the method disclosed herein. The system/method include the steps to: receive a dataset comprising scattered light signals and/or fluorescent light signals of the particle; analyze the dataset using one or more machine learning models, wherein the one or more machine learning models is trained using elastic scattering light intensity data and fluorescent light intensity data of a library of biological molecules; generate a probability score that the particle is bioactive based on the analysis of the dataset; determine, via classification of the probability score, that the particle is bioactive; and/or output a result indicating that the particle is the bioactive stimulant.

GENERATING INFORMED PRIORS FOR HYPERPARAMETER SELECTION

Resumen de: WO2025101345A1

A system iteratively evaluates the target machine learning model using evaluation hyperparameter values of the target machine learning model to measure performance of the target machine learning model for different combinations of the evaluation hyperparameter values. The system trains a surrogate machine learning model using the different combinations of the evaluation hyperparameter values as features and the performance of the target machine learning model based on a corresponding combination of the evaluation hyperparameter values as labels. The system generates a feature importance vector of the surrogate machine learning model based on the training of the surrogate machine learning model, generate informed priors based on the feature importance vector, and generates the target hyperparameter values of the target machine learning model based on the informed priors.

EXPLAINING BLACK BOX NLP MODELS VIA ENSURING INTERMEDIATE HUMAN-UNDERSTANDABLE REPRESENTATIONS WITH PROTOTYPE-BASED LEARNING

Resumen de: WO2025099498A1

A computer-implemented, machine learning method for providing human-understandable intermediate representations using prototype-based learning includes receiving textual input and generating a rationale using the textual input, where the rationale includes an explanation for the textual input. A closest prototype is identified based on the rationale and a defined distance measure. A classification label and a set of rationales for the closest prototype is provided to a user. The method has applications including, but not limited to, use cases in medicine / healthcare (e.g., for disease classification), resource allocation and data security, data integrity, crime investigation, for example, to optimize predictions or support decision-making.

Query Processing with Machine Learning

Resumen de: US2025156430A1

The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

Systems and methods for intelligent machine learning-based malware detection

Resumen de: US2025156546A1

Systems and methods for training a machine learning model for malware detection include steps of collecting a training dataset comprising a plurality of malicious files and a plurality of benign files from one or more sources; extracting features from each file in the training dataset, wherein the features include at least one of n-gram features, entropy features, or domain features; labeling each file in the training dataset as malicious or benign based on a predefined criterion; and applying a supervised machine learning technique to learn patterns in the extracted features and generate a trained machine learning model configured to predict whether a file is malicious or benign based on an incremental packet-based analysis.

TRANSFORMER-BASED ASSISTANT FOR IDENTIFYING, ORGANIZING, AND RESPONDING TO CUSTOMER CONCERNS

Resumen de: WO2025102019A1

Transformer-based agent assistant systems as machine learning-based customer service tools that analyze past customer-agent conversations to build a knowledge base of problem-resolution steps are disclosed. The system may include a natural language processing (NLP) model and a transfomer-based model to extract and generate customer concerns and resolutions. One embodiment also includes a head-topic and subtopic detection module for identifying trends in customer concerns. Another embodiment uses a question-answering model and a zero-shot-NLI (natural language inference) classifier for entity extraction and detection. The system is designed to be flexible, incorporating new data over time, and can retrieve company documentation or FAQs for the agent based on cosine similarity.

STATE MACHINE DRIVEN AUTOMATED AGENT WITH NESTED AUDITING

Resumen de: US2025156703A1

A system providing a state-driven automated agent. The state machine can include multiple nested state machines to receive, process, and generate responses to a user inquiry received through a chat application. The state machine has a plurality of states, and navigates between states based on one or more machine learning (ML) models. In some instances, each state is associated with a machine learning model that can predict what state the state machine should transition to from its current state. The machine learning model(s) can be implemented using one or more large language models (LLM). The state-driven automated agent includes an auditing state that analyzes a predicted response to a user inquiry and identifies errors with the response. The errors may be identified and automatically self-corrected.

DEVICES, DATA STRUCTURE, AND COMPUTER-IMPLEMENTED METHODS FOR MACHINE LEARNING USING SEMANTIC TECHNOLOGIES

Resumen de: US2025156758A1

Devices, data structure, and computer-implemented methods for machine learning. A method for machine learning includes providing a data structure of a database, which data structure includes a set of nodes and a set of relations, and a set of tuples. Each respective tuple includes at least two nodes, and at least one relation. The method includes predicting a plurality of tuples depending on the data structure, wherein each respective tuple includes at least two nodes and at least one relation, predicting, whether the respective tuples of the plurality of tuples classifies as a member of the set of tuples, selecting a tuple from the plurality of tuples depending on the uncertainties predicted for the respective tuples, acquiring a label that indicates whether the selected tuple classifies as a member of the set of tuples, and adding the selected tuple to the set of tuples based on the label.

DEVICES, DATA STRUCTURE, AND COMPUTER-IMPLEMENTED METHODS FOR MACHINE LEARNING USING SEMANTIC TECHNOLOGIES

Resumen de: EP4553716A1

Devices, data structure, and computer-implemented methods for machine learning, wherein a first method for machine learning comprises providing (402) a data structure of a database, wherein the data structure comprises a set of nodes, in particular a set of entities, or a set of subjects and objects, wherein the data structure comprises a set of relations, in particular a set of edges, or a set of predicates, and wherein the data structure comprises a set of tuples, wherein a respective tuple of the set of tuples comprises at least two nodes of the set of nodes, and at least one relation of the set of relations, in particular two entities of the set of entities and an edge of the set of edges, or a subject, and an object of the set of subjects and object, and a predicate of the set of predicates, wherein the method comprises predicting (404) a plurality of tuples depending on the data structure, wherein a respective tuple of the plurality of tuples comprises at least two nodes of the set of nodes, and at least one relation of the set of relations, in particular two entities of the set of entities and an edge of the set of edges, or a subject, and an object of the set of subjects and objects, and a predicate of the set of predicates, wherein the method comprises predicting (406), for the plurality of tuples, an uncertainty about whether the respective tuple of the plurality of tuples classifies as a member of the set of tuples or not, selecting (408) a tuple from the plurality

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, PROGRAM, AND ISING MODEL DEVELOPMENT ASSISTANCE METHOD

Nº publicación: EP4553723A1 14/05/2025

Solicitante:

RESONAC CORP [JP]
Resonac Corporation

Resumen de: EP4553723A1

With respect to an information processing device that supports creation of an Ising model for causing an annealing-type optimization machine to solve an optimum solution search problem, the information processing device includes a transforming unit configured to binarize an explanatory variable included in a training data set created using a trained machine learning model; a training unit configured to train an Ising model by performing machine learning with a relationship between the binarized explanatory variable and a predicted value of the training data set; and an output unit configured to output the trained Ising model.