Machine learning

SYSTEMS AND METHODS FOR ANALYZING AND MITIGATING COMMUNITY-ASSOCIATED RISKS

Resumen de: US2025348967A1

A computer system for analyzing and mitigating risks associated with a building is provided. The computer system is configured to: (i) receive environment data from the at least one sensor; (ii) receive building data from the at least one database; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the building based upon the environment data and the building data; (iv) generate a building risk profile that includes the at least one potential risk associated with the building; and/or (v) generate a risk mitigation output based upon at least one of the building risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a user, and an event are also provided.

METHODS AND SYSTEMS FOR DETERMINING DENTAL CARIES

Resumen de: US2025349433A1

A sequencing module configured to provide metatranscriptomic reads from an oral sample from a subject; metatranscriptomic reads from the sequencing of oral sample and identify and cluster microbes identified in the oral sample into taxon clusters (TCs) using the metatranscriptomic reads mapped to a metagenomic library; generate TC-specific orthogroups for each of the TCs via protein clustering; determine KEGG orthology for each of the TC-specific orthogroups, or genes directly; generate phylogenomic functional categories (PGFCs) from grouping of gene expression counts by the KEGG modules for each of the TCs; retain the PGFCs having an MCR above an MCR threshold to obtain input data; and identify or predict, using a classifier model including variables selected by a feature selection machine learning algorithm, dental caries in said subject based on the input data.

MACHINE LEARNING MODEL FOR DETERMINING A TIME INTERVAL TO DELAY BATCHING DECISION FOR AN ORDER RECEIVED BY AN ONLINE CONCIERGE SYSTEM TO COMBINE ORDERS WHILE MINIMIZING PROBABILITY OF LATE FULFILLMENT

Resumen de: US2025348921A1

An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.

ENTERPRISE NETWORK THREAT DETECTION

Resumen de: US2025348819A1

In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

RETRIEVAL AUGMENTED GENERATION IN ARTIFICIAL INTELLIGENCE MODELS

Resumen de: US2025348765A1

Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input prompt for machine learning is received, and the input prompt is decomposed to generate a set of sub-prompts. A sequence of requests for sub-prompts of the set of sub-prompts that have sequential dependency is generated, and a parallel request for sub-prompts of the set of sub-prompts that do not have sequential dependency is generated. Based on evaluating the sequence of requests and the parallel request, an execution plan for using one or more machine learning models to generate a response to the input prompt is generated. The response to the input prompt is output according to the execution plan.

SYSTEMS AND METHODS FOR MITIGATING TRAVEL-RELATED TRANSACTION FRAUD RISK USING MACHINE LEARNING MODEL.

Resumen de: US2025348879A1

A computing system for automated fraud risk reduction for travel-related transactions, the computing system including at least one processing circuit including at least one processor and at least one memory, the at least one memory storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive data corresponding to a first travel-related transaction, process, using a first machine learning model, the data to automatically generate an output data set comprising a plurality of characteristics relating to the first travel-related transaction, the first machine learning model configured to generate the output data set by identifying the plurality of characteristics to include responsive to determining the plurality of characteristics are potentially relevant to a determination of whether the first travel-related transaction is fraudulent, and provide the generated output data set for use in analyzing whether the first travel-related transaction is fraudulent.

MACHINE LEARNING APPROACH FOR DESCRIPTIVE, PREDICTIVE, ANDPRESCRIPTIVE FACILITY OPERATIONS

Resumen de: US2025348062A1

A digital twin of a facility defines relationships between different components of the facility and a system of record for the facility. Information from different monitoring systems for the facility are related to events by the digital twin of the facility. Historical operation information for the facility is used to train a machine learning model. The trained machine learning model facilitates operations at the facility by providing descriptive information, predictive information, and/or prescriptive information on the operations at the facility.

System and Method for Surfacing Cyber-Security Threats with a Self-Learning Recommendation Engine

Resumen de: US2025350634A1

Techniques for performing cyber-security alert analysis and prioritization according to machine learning employing a predictive model to implement a self-learning feedback loop. The system implements a method generating the predictive model associated with alert classifications and/or actions which automatically generated, or manually selected by cyber-security analysts. The predictive model is used to determine a priority for display to the cyber-security analyst and to obtain the input of the cyber-security analyst to improve the predictive model. Thereby the method implements a self-learning feedback loop to receive cyber-security alerts and mitigate the cyberthreats represented in the cybersecurity alerts.

SYSTEM AND METHOD FOR MEMORY CREATION

Resumen de: US2025350815A1

The present disclosure generally relates to generating a video corresponding to a memory (e.g., an event or context) from media assets on a device. In some embodiments, the device receives user inputs requesting a video based on a natural language description of a memory. The device sends information of the natural language description to a first machine-learning (ML) model, and receives query tokens, which are used to find media items on the device that match the query tokens. The device sends information representing the found media items to another ML model that determines traits from the media items. These traits are sent to a third ML model to generate a story outline, and the video is generated by comparing the descriptions of shots in the story outline to visual embeddings of the found media assets to curate and arrange them into the video consistent with the story outline.

METHODS AND SYSTEMS FOR OPTIMIZING SUPPLEMENT DECISIONS

Resumen de: US2025349438A1

A system for optimizing supplement decisions is disclosed. The system includes a computing device configured to receive a longevity inquiry from a remote device. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a second longevity element compatible with the ADME factor as a function of the first longevity element. The system selects the second longevity element as a tolerant longevity element. A method for optimizing supplement decisions is also disclosed.

Secure Messaging in a Machine Learning Blockchain Network

Resumen de: US2025348617A1

Multi-layer ensembles of neural subnetworks are disclosed. Implementations can classify inputs indicating various anomalous sensed conditions into probabilistic anomalies using an anomaly subnetwork. Determined probabilistic anomalies are classified into remedial application triggers invoked to recommend or take actions to remediate, and/or report the anomaly. Implementations can select a report type to submit, or a report recipient, based upon the situation state, e.g., FDA: Field Alert Report (FAR), Biological Product Deviation Report (BPDR), Medwatch, voluntary reporting by healthcare professionals, consumers, and patients (Forms 3500, 3500A, 3500B, Reportable Food Registry, Vaccine Adverse Event Reporting System (VAERS), Investigative Drug/Gene Research Study Adverse Event Reports, Potential Tobacco Product Violations Reporting (Form 3779), USDA: APHIS Center for Veterinary Biologics Reports, Animal and Plant Health Inspection Service: Adverse Event Reporting, FSIS Electronic Consumer Complaints, DEA Tips, Animal Drug Safety Reporting, Consumer Product Safety Commission Reports, State/local reports: Health Department, Board of Pharmacy.

Training generative artificial intelligence models

Resumen de: GB2640912A

A method for training generative AI / machine learning (ML) models wherein the training includes iterative steps and reinforcement learning wherein a reinforcement learning reward value for one model is based on a likelihood value obtained by another model. The method includes a set of rules and iterative training steps to train the generative ML models. Each iterative training step assigns to each model a role which includes an actor model and a judge model. Each step then prompts the assigned actor model with an input to generate content that complies with a constitution. Each step also prompts the assigned judge model with content generated by the actor model and determines a likelihood of compliance that the content complies with the constitution. Each iterative training step also provides a reinforcement learning reward for training which is based on the likelihood of compliance determined by the judge model. The method may allow for the models to be trained using reinforcement learning via the reward and by switching the roles of the models. The models may be used in natural language processing tasks (e.g. reasoning, decision making etc).

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR IMPROVING MACHINE LEARNING MODELS

Resumen de: US2025165874A1

Methods, systems, and computer program products are provided for improving machine learning models which include receiving a data set including data records; inputting the data set to a pre-trained first machine learning model to generate first embeddings; inputting the first embeddings to a second machine learning model to generate second embeddings in a user-specific embedding space; inputting the plurality of second embeddings to a third machine learning model to extract feature data associated with a feature; inputting an output from a machine learning system and the feature data to a fourth machine learning model to generate a relevance score for each entity; determining a subset of entities based on the relevance score; communicating a feedback request to a user; receiving feedback data from the user; and training at least one of the models based on the feedback data.

SYSTEMS AND METHODS FOR TEXT CLASSIFICATION

Resumen de: EP4647949A1

A computer-implemented method for text classification may include prompting, by a computing device, a large language model (LLM) to extract a set of explanatory statements for a training dataset, wherein each explanatory statement describes a difference between a grouping of samples within the training dataset. The method may also include prompting, by the computing device, the LLM to generate a set of queries based on the set of explanatory statements, wherein each query evaluates the difference described by an explanatory statement. The method may include training, by the computing device, a machine-learning (ML) model using the set of queries as features. Furthermore, the method may include classifying, by the computing device using the ML model, an unknown sample as a security threat. Finally, the method may include performing, by the computing device, a security action to mitigate the security threat. Various other methods, systems, and computer-readable media are also disclosed.

PREDICTING TROPOSPHERIC DUCTING EVENTS

Resumen de: EP4648457A1

Disclosed is a method comprising collecting input data (521) comprising at least weather forecast information for an area in which one or more cells (104B, 104C, 104D) are located; providing the input data (521) to a prediction algorithm (520), wherein the prediction algorithm (520) comprises: a machine learning model (500) trained to predict tropospheric ducting events impacting the one or more cells (104B, 104C, 104D), and a cell site database (511) indicating a location and one or more configuration parameters of the one or more cells (104B, 104C, 104D); and receiving, from the prediction algorithm (520), output data (522) indicating one or more predicted tropospheric ducting events expected to impact the one or more cells (104B, 104C, 104D) based on the input data (521).

SYSTEM AND METHOD FOR MEMORY CREATION

Resumen de: EP4647971A1

The present disclosure generally relates to generating a video corresponding to a memory (e.g., an event or context) from media assets on a device. In some embodiments, the device receives user inputs requesting a video based on a natural language description of a memory. The device sends information of the natural language description to a first machine-learning (ML) model, and receives query tokens, which are used to find media items on the device that match the query tokens. The device sends information representing the found media items to another ML model that determines traits from the media items. These traits are sent to a third ML model to generate a story outline, and the video is generated by comparing the descriptions of shots in the story outline to visual embeddings of the found media assets to curate and arrange them into the video consistent with the story outline.

MANAGING A PLURALITY OF WIRELESS DEVICES THAT ARE OPERABLE TO CONNECT TO A COMMUNICATION NETWORK

Resumen de: US2025344079A1

A method for managing a plurality of wireless devices. The method includes obtaining a plurality of base Machine Learning (ML) models, wherein each base ML model is operable to provide an output on the basis of which at least one RAN operation performed by a wireless device may be configured. The method further includes transmitting characterising information for individual models of the plurality of base ML models and configuration information for the plurality of base ML models over the RAN. The method further includes receiving an indication of which one or more of the plurality of base ML models the wireless device will be using as an ensemble ML model in connection with a RAN operation performed by the wireless device, and setting a value of at least one configuration parameter associated with the RAN operation performed by the wireless device based on the received indication.

SYSTEMS AND METHODS FOR BINDING AT LEAST ONE UNIQUE SCHEMA-SPECIFIC IDENTIFIER TO A CATEGORY

Resumen de: WO2025231033A1

A method including receiving activity data related to a first activity utilizing an unbound schema-specific identifier; training a machine learning engine based on at least one input to obtain a trained machine learning engine that is trained to identify a category associated with the entity; where the at least one input includes: an entity data feature vector, a historical user activity data feature vector, and/or a historical user schema-specific identifier data feature vector; predicting via the trained machine learning engine, a category associated with the first activity; binding the unbound schema-specific identifier to the category to generate a category bound schema-specific identifier; receiving a request to perform a second activity using the bound schema-specific identifier; determining if a second entity associated with the request to perform the second activity is associated with the category; performing one of: approving or denying the request to perform the second activity.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: US2025342281A1

The present disclosure relates to an information processing device, an information processing method, and a program capable of effectively detecting counterfeit data using a more versatile method.A contribution indicating how much each feature in a training dataset contributes to a predicted label output from a trained model is calculated, the training dataset including both a legitimate sample including only legitimate data and a counterfeit sample at least partially including counterfeit data. Then, clustering is executed to classify each sample of the training dataset into a plurality of clusters using unsupervised learning with the contribution as input, and feature variability between the clusters in the result of the clustering is compared to identify a cluster to which the counterfeit sample included in the training dataset belongs. The present technology can be applied to, for example, a machine learning system that generates a fraud detection model.

METHODS AND APPARATUS FOR DETECTION OF MALICIOUS DOCUMENTS USING MACHINE LEARNING

Resumen de: US2025342250A1

An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

COPILOT IMPLEMENTATION: RESTRICTING OPERATION TO A DOMAIN OF COMPETENCE

Resumen de: US2025342171A1

Apparatus and methods are disclosed for implementing a copilot as a network of microservices including specialized large language models (LLMs) or other trained machine learning (ML) tools. The microservice network architecture supports flexible, customizable, or dynamically determinable dataflow from client input to corresponding output. Compared to much larger competing LLMs, comparable or superior performance is achieved for certain tasks, while significantly reducing computation time and hardware requirements, even to a single compute node with a single GPU. Examples incorporate a qualification microservice to test data, destined for a downstream microservice, for conformance with the copilot's competency. A knowledge graph of a corpus of documents is built, visualized, and pruned. The data is tested for conformance with the pruned graph representation, and non-conforming data is excluded from the dataflow. Variations and additional techniques are disclosed.

QUANTUM FEATURE MAPS

Resumen de: AU2024261237A1

In a general aspect, a quantum feature-map transforms an input dataset with original features to a preprocessed dataset with quantum-enhanced features. In some cases, pre-processing an input dataset for a machine learning model includes obtaining the input dataset comprising a plurality of data points; encoding the plurality of data points as parameters of a quantum logic circuit; and executing the quantum logic circuit on a quantum computing resource. Expectation values of a set of observables are obtained based on an output quantum state generated by executing the quantum logic circuit. The set of observables includes observables of first degree and observables of second degree. A pre-processed dataset is generated based on the expectation values and provided as an input to the machine learning model.

AUTOMATED MACHINE LEARNING TO GENERATE RECOMMENDATIONS FOR WEBSITES OR APPLICATIONS

Resumen de: US2025342373A1

Implementations described herein relate to methods, systems, and computer-readable media for automated generation and use of a machine learning (ML) model to provide recommendations. In some implementations, a method includes receiving a recommendation specification that includes a content type and an outcome identifier, and determining model parameters for a ML model based on the recommendation specification. The method further includes generating a historical user feature matrix (FM), generating a historical content feature matrix (FM), and transforming the historical user FM and the historical content FM into a suitable format for the ML model. The method further includes obtaining a target dataset that includes historical results for the outcome identifier for a plurality of pairs of user identifiers and content items of the content type. The method further includes training the ML model using supervised learning to generate a ranked list of content items for each user identifier.

MACHINE-LEARNING-BASED TECHNIQUES FOR DETERMINING RESPONSE TEAM PREDICTIONS FOR INCIDENT ALERTS IN A COMPLEX PLATFORM

Resumen de: US2025342374A1

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.

PRODUCING AN AUGMENTED DATASET TO IMPROVE PERFORMANCE OF A MACHINE LEARNING MODEL

Nº publicación: US2025342394A1 06/11/2025

Solicitante:

ZETANE SYSTEMS INC [CA]
ZETANE SYSTEMS INC

Resumen de: US2025342394A1

Producing an augmented dataset to improve performance of a machine learning model. A test series is created for a first type of data transformation. the test series defining a set of test values for at least one parameter characterizing the first type of data transformation. Test datasets are generated based on a source dataset, each of the test datasets corresponding to a respective test value of the set of test values for said at least one parameter characterizing the first type of data transformation. Each of the test datasets is input to the machine learning model to produce a corresponding model output. At least one score is determined for each test dataset based at least in part on the corresponding model output. Robustness metrics of the first type of data transformation are determined based on a function which maps said at least one score of each of the test datasets to said at least one parameter characterizing the first type of data transformation. A set of one or more data augmentations are determined to be applied to the source dataset based at least in part on said one or more robustness metrics of the first type of data transformation. An augmented dataset is generated based on the source dataset using the determined set of one or more data augmentations.