Alerta

Prediction of Alzheimer's Disease

Resumen de: US2025342959A1

A method for diagnosing Alzheimer's Disease or determining susceptibility to Alzheimer's Disease includes steps of obtaining a blood sample from a target subject and extracting cell-free (cf) DNA from the blood sample as extracted cf DNA. The degree of methylation in one or a plurality of Alzheimer indicator genes in the extracted cf DNA is identified. Each Alzheimer indicator gene identified is an indicator of the presence of or risk of developing Alzheimer's Disease where the plurality of Alzheimer indicators genes have been identified by a machine learning technique or by logistic regression. The target subject is identified as being at risk for Alzheimer's Disease if the amount of methylation of one or more Alzheimer's indicator genes differs from the amount of methylation established in control subjects not having Alzheimer's Disease to a statistically significant degree.

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 The method further includes generating a historical user feature matrix (FM), generating a historical content feature matrix (FM), and transforming the historical user FM and the historical content FM into a suitable format for the ML model.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.

DEVICE AND COMPUTER PROGRAM FOR COMPRESSING A MACHINE LEARNING MODEL WHILE PRESERVING PERFORMANCE GOALS

Resumen de: US2025342396A1

A computing system is provided for evaluating performance of a compressed machine learning model. A sequence of target logits are obtained, and a sequence of compressed-model logits are calculated using the compressed machine learning model. A comparison value is determined based on the sequence of target logits and the sequence of compressed-model logits.

SYSTEMS AND METHODS FOR GENERATING A LIFESTYLE-BASED DISEASE PREVENTION PLAN

Resumen de: US2025342936A1

A system for generating a lifestyle-based disease prevention plan, the system including a computing device configured to receive at least a user biomarker input, produce a user profile as a function of the at least a user biomarker input, and generate a lifestyle-based disease prevention plan as a function of the user profile including training a machine learning process with a lifestyle training data set where the lifestyle training data set further comprises lifestyle elements correlated to a plurality of outputs containing diseases prevented and producing the lifestyle-based disease prevention plan as a function of the user profile and machine learning process.

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.

ESTIMATING THE RISK OF MEMBERSHIP INFERENCE ATTACKS ON MACHINE LEARNING MODELS

Resumen de: US2025343816A1

In various examples there is a method of empirically measuring a level of security’ of a training pipeline. The training pipeline is configured to train machine learning models using confidential training data. The method comprises storing a representation of a joint distribution of false positive rate and false negative rate of membership inference attacks on a plurality of machine learning models trained using the training pipeline. The method uses the representation to compute a posterior distribution of the level of security’ from observations of the membership inference attack on the plurality’ of machine learning models trained using the training pipelines. A confidence interval of the level of security is computed from the posterior distribution and the confidence interval is stored.

PREDICTING TROPOSPHERIC DUCTING EVENTS

Resumen de: US2025344080A1

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

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.

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.

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.

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.

TASK PROCESSING METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM

Resumen de: WO2025227741A1

Embodiments of the present disclosure provide a task processing method and apparatus, a device, and a storage medium. The method comprises: in response to receiving a request for a digital assistant, acquiring a processing configuration associated with the digital assistant; processing the request on the basis of the processing configuration to determine a reply of the digital assistant to the request, wherein processing the request on the basis of the processing configuration at least comprises: on the basis of at least one inference rule, performing inference on the request by calling a first-type machine learning model; and in response to a failure to process the request on the basis of the processing configuration, performing inference on the request by calling a second-type machine learning model to determine a reply of the digital assistant to the request, wherein the resource consumption of calling the second-type machine learning model is greater than that of calling the first-type machine learning model. The first-type machine learning model can be preferentially used, thereby effectively shortening the waiting time of a user, and quickly generating a reply.

INFORMATION PROCESSING SYSTEM AND PROGRAM

Resumen de: EP4645322A1

Comprising at least one processor obtaining a combination of information identifying each of the raw materials received from the user and the amount of each of the raw materials, and obtaining a predicted value of a physical property of the property name to be predicted for a composition comprising each of the raw materials by inputting into a first machine learning model at least one of the chemical fingerprints, SMILES strings or chemical graph structure data or product name or substance name corresponding to each of the raw materials and the amount of each of the raw materials, or by inputting into a second machine learning model a set of values based on at least one of the chemical fingerprints, SMILES strings or chemical graph structure data or product name or substance name corresponding to each of the raw materials and the amount of each of the raw materials,wherein the first machine learning model is a model in which parameters are adjusted so that it can predict outputs from inputs by means of a learning data set that takes as inputs at least one of the chemical fingerprints, SMILES strings or chemical graph structure data or product names or substance names corresponding to each of the raw materials and the amount of each of the said raw materials, and as takes as outputs physical property values of the target property names to be predicted, and the second machine learning model is a model in which parameters are adjusted so that it can predict outputs from inputs b

METHOD AND APPARATUS FOR BEAM MANAGEMENT BY USING ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING

Resumen de: EP4645709A1

Provided are a method and an apparatus for performing beam management in a wireless communication system. The method of a terminal may include triggering at least one beam failure recovery (BFR) for a cell that performs beam management using an artificial intelligence and/or machine learning model, deactivating the artificial intelligence and/or machine learning model based on the number of the at least one beam failure recovery triggered during a specific time duration, and transmitting deactivation information of the artificial intelligence and/or machine learning model to a base station. The method of the base station may include transmitting, to the terminal, configuration information related to the artificial intelligence and/or machine learning model, receiving, from the terminal, the deactivation information of the artificial intelligence and/or machine learning model, and, based on the received deactivation information, stopping beam generation related to the artificial intelligence and/or machine learning model.

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

Resumen de: CN120266139A

Systems and methods for predicting group composition of items are disclosed. A system for predicting group composition of items may include a memory storing instructions and at least one processor configured to execute the 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 a localized machine learning model based on the entity identification information, the localized machine learning model trained to predict a category of an item based on transaction information applied to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate a predicted category of items associated with the transaction, the model input including the received entity identification information and timestamps but not information distinguishing items associated with the transaction.

DETERMINING CONFIDENCE TO START VALUES FOR POWER GENERATION SYSTEMS USING MACHINE LEARNING MODELS

Resumen de: CN120500694A

Systems and methods for applying a machine learning (ML) model to determine a startup confidence value for a generator are presented herein. The computing system may identify a first plurality of parameters of the first generator. The plurality of parameters may identify operation of the first generator. The computing system may apply the first plurality of parameters to the ML model to determine a first confidence value that identifies a first likelihood that the first generator is started at initiation. The computing system may provide an output based on a first confidence value of the first generator.

Apparatus and Method for Combining Optical Access Network with Machine Learning Model

Resumen de: KR20250155192A

본 개시의 실시 예는, 광액세스망과 머신 러닝 모델을 결합하는 방법에 있어서, 사용자 요구사항을 기반으로 하여 기계 학습(machine learning : ML) 모델을 선택하는 과정; 상기 선택된 ML 모델의 평가 지표값과 미리 설정된 기준값을 비교하는 과정; 및 상기 평가 지표값이 상기 기준값을 초과하는지에 기초하여, 상기 선택된 ML 모델 및 상기 선택된 ML 모델로부터 미세조정된 ML 모델 중 적어도 하나를 아티팩트 스토어에 등록하는 과정을 포함하되, 상기 아티팩트 스토어에 등록된 ML 모델은 상기 사용자의 요구사항에 기초로 하여 가상 수동형 광 네트워크(passive optical network : PON) 및 물리 PON에 결합된다.

MEDICAL QUERY PROCESSING WITH SEQUENCE PROCESSING MODELS

Resumen de: WO2025227051A1

Provided are a number of different systems and methods that enhance the performance and reliability of sequence processing models, particularly when applied to the processing of medical data and queries. The proposed techniques collectively address the challenges of integrating vast, evolving external knowledge sources, refining responses in the face of uncertainty, and efficiently managing complex, multi-modal datasets such as are commonly found in the medical field. Specifically, by leveraging iterative self-training, uncertainty-guided retrieval, multi-stage prompting, and/or advanced model architectures, the disclosed technology improves how machine learning models can process, analyze, and/or utilize complex information, resulting in improved computer systems applicable to a number of different applications or use cases, including medical diagnostics and/or other medical applications.

SYSTEMS AND METHODS FOR USING DIFFERENT TYPES OF SAMPLE CONTAINER CARRIERS IN DIAGNOSTIC LABORATORY SYSTEMS

Resumen de: WO2025226511A1

Diagnostic laboratory systems provided herein employ a machine learning software model to identify locations of sample containers and empty container slots in different types of sample container carriers. The model training data is based on images of different sample container carrier types each having at least two sample containers and at least one empty container slot. The images are overlaid with an estimated grid of slots based on identified locations of the at least two sample containers in the image and at least one pre-determined grid parameter. Image patches are extracted from the images based on the estimated grid. Each image patch includes a sample container or an empty container slot upon which locations of sample containers and empty container slots can be identified in sample container carriers received in a diagnostic laboratory system. Systems and methods of training a model and operating a diagnostic laboratory system are disclosed.

UNSUPERVISED DETERMINATION OF SIMILAR CHUNKS OF TEXT TO TUNE A TEXT SIMILARITY MODEL

Resumen de: US2025335786A1

Systems, media, and computer-implemented methods are provided for identifying similar chunks of text to tune a text similarity model, such as a text similarity model that is used to find content in response to queries. Using a masked language model, a machine learning model may be tuned on different content from that which the machine learning model was trained. The machine learning model as tuned may be used to determine vector embeddings for terms in chunks of content. Chunks may be matched to each other by finding a term in one chunk having a highest similarity score with a corresponding term in another chunk. Aggregate similarity scores may be determined between the chunks based on the term-to-term similarity scores. If an aggregate similarity score for a pair of chunks satisfies one or more conditions, a text similarity model may be tuned to identify the pair as similar.

ANOMALY-BASED MITIGATION OF ACCESS REQUEST RISK

Resumen de: US2025337742A1

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

CELL MANUFACTURING MANAGEMENT PLATFORM USING MACHINE LEARNING

Resumen de: US2025336521A1

A cell manufacturing management platform facilitates management of a cell manufacturing process. The cell manufacturing management platform tracks events associated with a cell manufacturing process and coordinates between disparate entities involved in the process. The cell manufacturing management platform utilizes machine learning techniques to generate inferences associated with event scheduling in a manner that optimizes an efficiency metric and reduces likelihood of exceptions occurring. Machine learning models may furthermore be used to generate various alerts or other actions associated with the process. A user interface enables different participating entities to track progress of the process and upcoming events.

MULTI-PASS PROCESSING FOR ARTIFICIAL INTELLIGENCE CHATBOTS

Resumen de: US2025335799A1

Methods, systems, and apparatus, including computer-readable media, for multi-pass processing for artificial intelligence chatbots. In some implementations, a system obtains code or instructions generated by one or more artificial intelligence or machine learning (AI/ML) models, where the code or instructions specify criteria to retrieve data from a data source to respond to a prompt from a user. The system determines that the code or instructions specify multiple stages of data processing. The system generates a set of results from the data source based on the generated code or instructions, and obtains a response to the prompt that the one or more AI/ML models generate using at least a portion of the set of results. The system generates an interpretation statement that describes each of the multiple stages of data processing and provides output that includes (i) the response to the prompt and (ii) the generated interpretation statement.

TASK PROCESSING FOR A DIGITAL ASSISTANT

Nº publicación: US2025335821A1 30/10/2025

Solicitante:

BEIJING ZITIAO NETWORK TECHNOLOGY CO LTD [CN]
Beijing Zitiao Network Technology Co., Ltd

Resumen de: US2025335821A1

Embodiments of this specification describe technologies for task processing. One method includes: in response to receiving a request for a digital assistant, obtaining a processing configuration associated with the digital assistant, the processing configuration comprising one or more inference rules, at least one of the one or more inference rules being configured to perform inference on the request using a corresponding first-type machine learning model; processing the request based on the processing configuration to determine a response of the digital assistant to the request; and in response to a failure to process the request based on the processing configuration, performing inference on the request by invoking a second-type machine learning model to determine a response of the digital assistant to the request, wherein a resource cost of invoking the second-type machine learning model is greater than a resource cost of invoking the first-type machine learning model.