Alerta

SYSTEM AND METHOD FOR MACHINE LEARNING BASED GENERATION OF MEDIA STRUCTURED PRODUCTS

Absstract of: US2025232323A1

A method, computer program product, and computer system for identifying, by a computing device, event specific data of a past media program. A size of delivery of a future media program may be predicted based upon, at least in part, the event specific data of the past media program. A financial media product may be generated based upon, at least in part, the predicted size of delivery of the future media program. An actual size of delivery may be identified. A first action may be executed if the actual size of delivery is greater than the predicted size of delivery based upon, at least in part, the financial media product. A second action may be executed if the actual size of delivery is less than the predicted size of delivery based upon, at least in part, the financial media product.

METHOD, MEDIUM, AND SYSTEM FOR GENERATING SYNTHETIC DATA

Absstract of: US2025232353A1

Systems and methods for generating synthetic data are disclosed. A system may include one or more memory devices storing instructions and one or more processors configured to execute the instructions. The instructions may instruct the system to categorize consumer data based on a set of characteristics. The instructions may also instruct the system to receive a first request to generate a first synthetic dataset. The first request may specify a first requirement for at least one of the characteristics. The instructions may further instruct the system to retrieve, from the consumer data, a first subset of the consumer data satisfying the first requirement. The instructions may also instruct the system to provide the first subset of consumer data as input to a data model to generate the first synthetic dataset, and to provide the first synthetic dataset as training data to a machine-learning system.

System and Method for ML-Based Engineering Library Translation and Integration, and for Schema & File Format Mapping

Absstract of: US2025232119A1

A method includes using an artificial intelligence/machine learning, AI/ML, model to map content between an interface of a first entity for interaction with other entities and an interface of a second entity for interaction with other entities, and/or classify content of the interface of the first entity and/or of the interface of the second entity; and obtaining, from the AI/ML model, a first output indicative of a result of the mapping of the content, and/or of a result of the classification of the content.

GENERATING AND DEPLOYING PACKAGES FOR MACHINE LEARNING AT EDGE DEVICES

Absstract of: US2025232226A1

A provider network implements a machine learning deployment service for generating and deploying packages to implement machine learning at connected devices. The service may receive from a client an indication of an inference application, a machine learning framework to be used by the inference application, a machine learning model to be used by the inference application, and an edge device to run the inference application. The service may then generate a package based on the inference application, the machine learning framework, the machine learning model, and a hardware platform of the edge device. To generate the package, the service may optimize the model based on the hardware platform of the edge device and/or the machine learning framework. The service may then deploy the package to the edge device. The edge device then installs the inference application and performs actions based on inference data generated by the machine learning model.

COMPUTER-BASED SYSTEMS FOR DYNAMIC PERSONA-BASED ACCESS TO COMPUTER NETWORK RESOURCES BASED ON MACHINE LEARNING TECHNIQUES AND METHODS OF USE THEREOF

Absstract of: US2025233925A1

At least some embodiments are directed to a system that receives a profile values associated from new user profiles of a computer network or system. A machine learning system determines a set of existing profiles that share at least one common profile value with the new user profile. A second machine learning model determines a set of existing user entitlements associated with the set of existing profiles. The new user profile is processed by a natural language processing engine to determine a set of new user entitlements from the set of existing user entitlements. The system provides the new user with access to electronic resources of the computer network. The system tracks the new user computer network or system activities and updates the new user profile based on the set of new user entitlements and the new user activity on the computer network or system.

METHOD AND SYSTEM FOR A CROSS-SILO SERVERLESS COLLABORATIVE LEARNING IN A MALICIOUS CLIENT THREAT-MODEL

Absstract of: US2025232188A1

Traditional deep learning techniques are performed by high-performance system with direct access to the data to train large models. An approach of training the model from a collaboration of similar stakeholders where they pool together their data in a central server. However, data privacy is lost by exposing said models and data security while accessing heterogeneous data. Embodiments of the present disclosure provide a method and system for a cross-silo serverless collaborative learning among a plurality of clients in a malicious client threat-model based on a decentralized Epsilon cluster selection. Protocols are initialized and considered to iteratively train local models associated with each client and aggregate the local models as private input based on the multi-party computation to obtain global model. Non-linear transformation of a silhouette score to an Epsilon probability without implementing a server to select rth model from an active set to assign as the global model.

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED DATA MATCHING AND RECONCILIATION OF INFORMATION

Absstract of: US2025231922A1

A method for machine learning-based data matching and reconciliation may include: ingesting a plurality of records from a plurality of data sources; identifying company associated with each of the plurality of records; assigning a unique identifier to each uniquely identified company; matching each of the records to one of the uniquely identified companies using a trained company matching machine learning engine; identifying a primary company record in the matching records and associating other matching records with the primary company record; matching each of the records to a contact using a trained contact matching machine learning engine; identifying a primary contact record in the matching records and associating other matching records with the primary contact record; synchronizing the plurality of records in a graph database using the unique identifier; receiving feedback on the matching companies and/or matching contacts; and updating the trained company matching machine learning engine.

METHOD AND SYSTEM FOR A CROSS-SILO SERVERLESS COLLABORATIVE LEARNING IN A MALICIOUS CLIENT THREAT-MODEL

Absstract of: EP4586142A1

Traditional deep learning techniques are performed by high-performance system with direct access to the data to train large models. An approach of training the model from a collaboration of similar stakeholders where they pool together their data in a central server. However, data privacy is lost by exposing said models and data security while accessing heterogeneous data. Embodiments of the present disclosure provide a method and system for a cross-silo serverless collaborative learning among a plurality of clients in a malicious client threat-model based on a decentralized Epsilon cluster selection. Protocols are initialized and considered to iteratively train local models associated with each client and aggregate the local models as private input based on the multi-party computation to obtain global model. Non-linear transformation of a silhouette score to an Epsilon probability without implementing a server to select r^th model from an active set to assign as the global model.

SYSTEMS AND METHODS FOR IMPLEMENTING DRUG MECHANISMS OF ACTION WITH MACHINE LEARNING

Absstract of: EP4586260A1

A computer-implemented method for generating machine-learning training data includes obtaining (654) mechanism of action (MOA) data that is indicative of a hierarchical tree structure of relationships between the MOA data (138); and generating (656) linear representations of branches of the hierarchical tree structure. It also includes determining (606) association rules for the MOA data by applying one or more frequent pattern-mining algorithm to the linear representations. It also includes determining (308, 608), as at least a portion of the generated machine learning training data, MOA clusters (400) by applying a clustering model (150) to the linear representations and the association rules. The method also includes determining the hierarchical tree structure by extracting (104) a plurality of nodes from the MOA data (138), and generating (602) the hierarchical tree structure based on the extracted nodes.

MEDICAL INFORMATION PROCESSING DEVICE, MEDICAL INFORMATION PROCESSING METHOD, AND PROGRAM

Absstract of: EP4586156A1

A medical information processing device of an embodiment includes an acquirer, an inference basis determiner, a concept reflection degree calculator, and a dependency determiner. The acquirer is configured to acquire a machine learning model and training data used to train the machine learning model. The inference basis determiner is configured to determine an inference basis for each piece of the training data using the machine learning model to generate inference basis visualization results. The concept reflection degree calculator is configured to determine a concept emphasized by the machine learning model during inference based on the inference basis visualization results and calculate a concept reflection degree of each piece of the training data related to the concept. The dependency determiner is configured to generate visualization information of a dependency between the concept and a feature interpretable by a user in the training data based on the concept reflection degree and the feature interpretable by the user.

SYSTEMS AND METHODS FOR PHARYNGEAL PHENOTYPING IN OBSTRUCTIVE SLEEP APNEA

Absstract of: US2025226094A1

Systems and methods for pharyngeal phenotyping in obstructive sleep apnea are described herein. An example method includes receiving manometry data for a subject; extracting a plurality of features from the manometry data, where the extracted features include one or more of a high-level breath feature, a frequency feature, or a largest negative connected component (LNCC) feature; inputting the extracted features into a trained machine learning model; and predicting, using the trained machine learning model, at least one of a location of pharyngeal collapse for the subject or a degree of pharyngeal collapse for the subject.

Methods For Building And Integrating A Machine Learning-Based Predictor To Provision The CPU Resource For Cloud Databases

Absstract of: US2025224993A1

Techniques are provided for optimizing resources (e.g., CPU, memory, IO) allocated to a database server using one or more machine learning models. A database management system executes a database workload for the database server. During execution of the workload, a monitoring service collects metrics for the database workload and sends the metrics to a resource allocation prediction service. The resource allocation prediction service implements one or more machine learning models to generate optimized resource allocation predictions. A generated resource allocation prediction is sent to a change recommendation generation service that generates change instructions for updating the resources allocated to the database server in order to align the current resource allocation of the database server with the resource allocation prediction.

PRIORITIZING ALERTS IN INFORMATION TECHNOLOGY SERVICE MANAGEMENT SYSTEMS

Absstract of: US2025225416A1

A plurality of correlations is determined including by applying a machine learning model to a first plurality of features extracted from a plurality of information technology and operations management alerts and information technology service management reporting data. Each correlation of the plurality of correlations is between a corresponding one of the plurality of information technology and operations management alerts and at least one corresponding portion of the information technology service management reporting data. The information technology service management reporting data includes at least one urgency indicator. A prioritized list of information technology and operations management alerts is generated based at least in part on the determined plurality of correlations and the at least one urgency indicator. The prioritized list of information technology and operations management alerts is organized based at least in part on relative priorities of the alerts.

System and Method to Predict Service Level Failure in Supply Chains

Absstract of: US2025225474A1

A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.

INTELLIGENT SELECTOR CONTROL FOR USER INTERFACES

Absstract of: US2025225418A1

Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.

System and Method to Predict Service Level Failure in Supply Chains

Absstract of: US2025225473A1

A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.

MEDICAL INFORMATION PROCESSING DEVICE, MEDICAL INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM

Absstract of: US2025225439A1

A medical information processing device of an embodiment includes processing circuitry. The processing circuitry is configured to acquire a machine learning model and training data used to train the machine learning model, determine an inference basis for each piece of the training data using the machine learning model to generate inference basis visualization results, determine a concept emphasized by the machine learning model during inference based on the inference basis visualization results, calculate a concept reflection degree of each piece of the training data related to the concept, and generate visualization information of a dependency between the concept and a feature interpretable by a user in the training data based on the concept reflection degree and the feature interpretable by the user.

MACHINE LEARNING ENGINE USING FOLLOWING LINK SELECTION

Absstract of: US2025225523A1

A machine learning engine may be trained using artificial intelligence techniques and used according to techniques discussed herein. While an initial electronic transaction for a resource may be permitted, a subsequent related transaction to the initial electronic transaction may be analyzed in view of additional electronic information that was not available at the time of the initial transaction. Analysis of the subsequent related transaction, using the machine learning engine, may indicate a new classification related to the resource and/or the acquisition of the resource. Based on this new classification, usage of the resource may be restricted and/or denied, and the initial transaction for the resource may even be canceled retroactively.

MACHINE LEARNING BASED FUNCTION TESTING

Absstract of: US2025225441A1

A method for determining the performance metric of a function may include interpolating the performance metric of the function relative to a known performance metric of a reference function. The performance metric of the function may be interpolated based on a first difference in a performance of the function measured by applying a first machine learning model and a performance of the function measured by applying a second machine learning model. The performance metric of the function may be further interpolated based on a second difference in a performance of the reference function measured by applying the first machine learning model and a performance of the reference function measured by applying the second machine learning model. The function may be deployed to a production system if the performance metric of the function exceeds a threshold value. Related systems and articles of manufacture, including computer program products, are also provided.

SYSTEMS AND METHODS FOR IMPLEMENTING DRUG MECHANISMS OF ACTION WITH MACHINE LEARNING

Absstract of: US2025225445A1

A computer-implemented method for generating machine learning training data may include obtaining mechanism of action (MOA) data that is indicative of a hierarchical tree structure of relationships between the MOA data; generating linear representations of branches of the hierarchical tree structure; determining association rules for the MOA data by applying one or more frequent pattern mining algorithm to the linear representations; and determining, as at least a portion of the generated machine learning training data, MOA clusters by applying a clustering model to the linear representations and the association rules.

HYBRID MACHINE LEARNING METHODS OF TRAINING AND USING MODELS TO PREDICT FORMULATION PROPERTIES

Absstract of: AU2023407504A1

Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.

INFORMATION PROCESSING APPARATUS AND MACHINE LEARNING METHOD

Absstract of: US2025226063A1

A computer executes, among algorithms configured to obtain energy of a molecule through an iterative process and including a first algorithm and a second algorithm that uses quantum circuit data and is different from the first algorithm, the first algorithm based on molecular information specifying a molecule to be analyzed, to obtain a first iteration count of the first algorithm. The computer enters the first iteration count into a machine learning model trained with an iteration count of the first algorithm as an explanatory variable and an iteration count of the second algorithm as a response variable. The computer outputs an estimated value of a second iteration count of the second algorithm obtained from the machine learning model, for execution of the second algorithm based on the molecular information.

GEOSTEERING COPILOT ASSISTANT

Absstract of: WO2025147274A1

A method and a system comprising: disposing a bottom hole assembly (BHA) into a wellbore, wherein the BHA comprises a measurement assembly; acquiring one or more measurements with the measurement assembly; acquiring historical data from the wellbore; extracting relevant information from the historical data; training a machine learning (ML) model with the relevant information to form a trained ML model; and providing an answer to a question utilizing the trained ML model.

MACHINE-LEARNING FOR CONTENT INTERACTION

Absstract of: AU2022492000A1

A system can generate content recommendations and facilitate interactions using machine-learning. The system can receive a request from a provider entity. The system can receive entity data and interaction data associated with a target entity. The system can generate at least a first graph structure and a second graph structure. The system can generate a linked graph structure based on the first graph structure and the second graph structure. The system can determine among a plurality of operations, one or more target operations to perform on data included in the linked graph structure. The system can execute using a trained machine-learning model, the target operations to generate a content recommendation for facilitating an interaction. The system can provide a responsive message based on the content recommendation usable to facilitate the interaction.

System for adaptive prompt enhancement in generative artificial intelligence

Nº publicación: AU2024266910A1 10/07/2025

Applicant:

INTUIT INC
Intuit Inc

Absstract of: AU2024266910A1

Aspects of the present disclosure relate to generating optimized machine learning model prompts. Embodiments include providing an input prompt to a child machine learning model that directs the child machine learning model to generate an output. Embodiments further include generating a parent model prompt comprising instructions to generate a score for the input prompt based on one or more scoring criteria, the input prompt, and the output of the child machine learning model. Embodiments further include providing the parent model prompt to a parent machine learning model. Embodiments further include generating, by a generative machine learning model, an optimized prompt for the child machine learning model based on the generated score for the input prompt. Aspects of the present disclosure relate to generating optimized machine learning model prompts. Embodiments include providing an input prompt to a child machine learning model that directs the child machine learning model to generate an output. Embodiments further include generating a parent model prompt comprising instructions to generate a score for the input prompt based on one or more scoring criteria, the input prompt, and the output of the child machine learning model. Embodiments further include providing the parent model prompt to a parent machine learning model. Embodiments further include generating, by a generative machine learning model, an optimized prompt for the child machine learning model based on the gene