Alerta

REGRESSION ANALYSIS METHOD, REGRESSION ANALYSIS SYSTEM, AND REGRESSION ANALYSIS PROGRAM

Resumen de: EP4654095A1

A more versatile technique is provided for constructing a regression model having a correspondence relationship with variation of an explanatory variable and variation of a response variable. A regression analysis method includes: retrieving, by a computer, training data from a storage device storing the training data, the training data being used as a response variable and an explanatory variable of a regression model; and performing, by the computer, machine learning by the regression model using the training data to minimize a cost function including a regularization term. The regularization term includes a first term that increases a cost more in an interval where a coefficient is positive than in an interval where the coefficient is negative, and a second term that increases the cost more in the interval where the coefficient is negative than in the interval where the coefficient is positive.

KNOWLEDGE GRAPH COMPLETION METHOD AND SYSTEM USING MACHINE LEARNING

Resumen de: KR20250163538A

머신러닝 기반 지식 그래프 완성 방법 및 시스템이 개시된다. 일 실시예에 따른 지식 그래프 완성 시스템에 의해 수행되는 지식 그래프 완성 방법은, 지식 그래프의 자연어 서술 정보와 구조적 정보를 이용한 대조 학습을 통해 지식 그래프 완성 모델을 학습하는 단계; 및 상기 학습된 지식 그래프 완성 모델을 통해 추론을 위한 지식 그래프로부터 누락된 새로운 지식을 추론하여 지식 그래프를 완성하는 단계를 포함할 수 있다.

HOMOMORPHICALLY ENCRYPTED DATA SYNTHESIS

Resumen de: WO2025237802A1

Disclosed is a method for training a machine learning model for generating synthetic data, the method comprising: Providing an encrypted data set, resulting from encryption of an original data set, to a server, the encrypted data set comprising a set of entries, wherein each entry of the encrypted data set comprises values for a set of attributes; application of a homomorphic machine learning algorithm for data synthesis comprising a set of homomorphic group operations on the encrypted data set, such a homomorphic algorithm as understood herein encompasses any algorithm the execution of which causes the decrypted results to coincide with the results of applying the same homomorphic group operations on the original data set.

EXPERIMENTAL CONTENT GENERATION LEARNING MODEL FOR RAPID MACHINE LEARNING IN A DATA-CONSTRAINED ENVIRONMENT

Resumen de: WO2025240061A1

Various embodiments are directed to an example apparatus, computer-implemented method, and computer program product for rapid machine learning in a data-constrained environment. Such embodiments may include using a decision space generation model to generate candidate content data objects based on content generation objectives. Such embodiments may further include generating a first plurality of rated content data objects for a first target client based on a first experimental classification group and generating a second plurality of rated content data objects for a second target client based on a second experimental classification group. Such embodiments may further generate, based on a learning model, the first experimental classification group, and the second experimental classification group, a custom output content set including one or more of the first plurality of rated content data objects and one or more of the second plurality of rated content data objects.

UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS

Resumen de: US2025355960A1

The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.

SYSTEMS AND METHODS FOR EVALUATING DATA FOR TRAINING MACHINE-LEARNING MODELS

Resumen de: WO2025237631A1

Improved systems and methods for constructing a machine-learning model associated with lithography are disclosed. The method may include accessing a first set of data comprising metrology data, training a machine-learning model iteratively based on the first set of data, the machine-learning model associated with a lithography process, obtaining information generated by the machine- learning model from each of multiple iterations during the training, and identifying outlier data from the first set of data based on the obtained information.

AI/ML POSITIONING TRAINING AND INFERENCE CONSISTENCY USING DATASET INDEXING

Resumen de: WO2025240170A1

Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.

AI/ML POSITIONING TRAINING AND INFERENCE CONSISTENCY USING DATASET INDEXING

Resumen de: US2025358769A1

Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.

SYSTEMS, APPARATUS AND METHODS FOR TREATMENT OF RETINAL AND MACULAR DISEASES USING ARTIFICIAL INTELLIGENCE

Resumen de: AU2024267218A1

An optical coherence tomography (OCT) device includes artificial intelligence for recommending a treatment plan for a patient with a retinal or macular disease such as age-related macular degeneration (AMD). The OCT device includes a sensor configured to quantify an initial level of macular edema or retinal exudation. The OCT device receives treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient. The OCT device performs OCT on the patient subsequent to each anti-VEGF injection to determine subsequent levels of edema or retinal exudation. The OCT device collects a set of training data including: the initial and subsequent levels of edema or exudation, patient information, and treatment information. The OCT device applies the training data to a machine-learning model trained on training data for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient.

ADAPTIVE NAVIGATION AND CONTENT-FIRST DYNAMIC SYSTEM

Resumen de: US2025355683A1

A system displays a first set of generative interfaces in a user interface. Each generative interface includes user interface elements that contain content specifying information of the generative interface. Responsive to receiving a user interaction with a user interface element, the system activates a dynamic input phase that dynamically generates responses during runtime of receiving user inputs to the user interface. The system receives a second user input and applies a machine learning model to the generative interface comprising the interacted user interface element, the content contained in the interacted user interface element and the content from the second user input. The system receives content as an output and updates the user interface to display a second set of generative interfaces. The second set of generative interfaces may include one or more runtime-determined user interface elements, and each runtime-determined user interface element include information associated with the received content.

IDENTIFYING SEPARATION-RELATED PROBLEMS IN PETS

Resumen de: WO2025238443A1

The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.

RETRIEVAL AUGMENTED GENERATION IN ARTIFICIAL INTELLIGENCE MODELS

Resumen de: WO2025239978A1

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: WO2025235501A1

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.

NODE MANAGEMENT BASED ON COMPUTATIONAL COST

Resumen de: WO2025234985A1

A node within a machine learning model may self-determine its own nodal actions by calculating predicted computational costs for nodal actions in a group of nodal actions performable by the node. The node selects a nodal action from the group based on one or more comparisons among the predicted computational costs. The node then performs the nodal action that was selected based on the one or more comparisons of the predicted computational costs. A node manager within a machine learning model may manage a node within the machine learning model by calculating predicted computational costs for nodal commands in a group of nodal commands performable on the node. The node manager selects a nodal command to be performed on the node from the group based on one or more comparisons among the predicted computational costs. The node manager then causes performance of the selected nodal command on the node.

GENERATION OF TOY CONSTRUCTION MODELS

Resumen de: WO2025233314A1

A computer-implemented method of creating a digital representation of a toy construction model, the method comprising: receiving a user prompt to create a digital representation of a toy construction model, the user prompt including one or more desired attributes of the toy construction model; using a generative machine-learning model to create, based on the user prompt, a structured representation of a toy construction model, the toy construction model being constructed from a set of mutually interconnected toy construction elements, the structured representation being indicative of the toy construction elements of said set and of the mutual interconnections between respective ones of said toy construction elements; translating the created structured representation into a digital 2D or 3D representation of a visual appearance of the toy construction model and/or into a set of building instructions for creating the toy construction model.

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.

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.

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.

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.

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.

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.

SYSTEM AND METHOD FOR GENERATING COMPETING MODELS IN RASHOMON SETS FOR GRADIENT BOOSTING

Resumen de: US2025348794A1

A method may include: receiving a dataset comprising a plurality of samples and a loss function; training a first number first machine learning models using the dataset comprising, wherein each of the first machine learning models has a similar performance; selecting one of the first machine learning models with a smallest loss; computing a residual for each of the plurality of samples using the one first machine learning model; defining a new dataset comprising the plurality of samples and the residual for each samples; training the first machine learning model with the new dataset; generating a second plurality of machine learning models by repeating the selecting, the computing, the defining, and training for a number of boosting iterations; selecting a subset of the second plurality of machine learning model models having a specified property; and deploying the subset of second machine learning models to a downstream task.

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.

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.

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

Nº publicación: US2025348921A1 13/11/2025

Solicitante:

MAPLEBEAR INC [US]
Maplebear Inc

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.