Alerta

LOCAL ARTIFICIAL INTELLIGENCE (AI)/MACHINE LEARNING (ML) SERVICE FOR INTERNET OF THINGS (IOT) DEVICES

Absstract of: US20260244994A1

0000 A device may include a processing device. The processing device may receive, at the device from an internet of things (IoT) device, one or more of an artificial intelligence (AI) task or a machine learning (ML) task. The processing device may receive, at the device from an IoT device, input data related to the one or more of the AI task or the ML task. The processing device may perform, at the device, the one or more of the AI task or the ML task using the input data to generate output data. The processing device may send, from the device to the IoT device, the output data.

STABISMUS SURGERY OUTCOMES PREDICTION USING MACHINE LEARNING AND MULTIPLE PREOPERATIVE VARIABLES

Absstract of: WO2026172341A1

There is provided a system for generating a prediction for strabismus surgery, comprising: at least one processor executing a code for: receiving via a user interface and/or by reading data stored on a data storage device, input data including: a data- structure indicating a candidate surgery for strabismus surgery on at least one extraocular muscle of at least one eye of the subject, a preoperative deviation angle of the subject, and at least one parameter of the subject, and processing the input data by a machine learning model to generate a predicted change in the preoperative deviation angle in response to implementing the candidate surgery.

METHOD AND SYSTEM FOR GENERATING A MACHINE LEARNING MEDICAL DIFFERENTIAL DIAGNOSIS

Absstract of: WO2026171902A1

A computer implemented method for generating a machine learning medical differential diagnosis using a differential diagnosis coordination module that is in bidirectional communication with a plurality of agent modules that each perform a specific task. The method is performed by the differential diagnosis coordination module. The method comprises receiving patient profile data and performing a series of iterations that are carried out until a predetermined condition is satisfied. Each iteration comprises selecting an agent module from the plurality of agent modules, generating agent specific instructions that cause the selected agent module to perform its specific task according to the agent specific instructions, logging iteration attribute data that relates to attributes of a current iteration and that includes an output of the selected agent module generated according to the agent specific instructions, and updating the patient profile data based on the logged iteration attribute data. When the predetermined condition is satisfied, the method comprises outputting a medical differential diagnosis based on the logged iteration attribute data. The present disclosure can be used in a variety of applications including, but not limited to, several anticipated use cases in medical diagnostics/applications and in healthcare. The present disclosure can also help in patient/physician decision making and can be used with machine learning.

RANKING AND MARKING MESSAGES IN A GROUP-BASED COMMUNICATION SYSTEM USING MACHINE LEARNING TECHNIQUES

Absstract of: US20260244950A1

Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, AND INFORMATION PROCESSING METHOD

Absstract of: US20260244989A1

0000 There is provided a technology capable of making an extremely accurate and precise response to an inquiry including a cross-cutting issue like the one which requires a plurality of pieces of business knowledge. A device for managing a plurality of machine learning models respectively having specializations in specified fields, wherein the device includes at least a processor and a storage device; the processor: accepts information about specialization related to an inquiry from a user; selects a machine learning model candidate which contributes to generation of response content for the inquiry, from the plurality of machine learning models in the list based on the specialization related to the inquiry, the specialization information, and the key word; and decides a machine learning model to be used for the generation of the response content for the inquiry based on the selected machine learning model candidate.

MULTIMODAL DATA BASED GENERATION OF QUALITY ASSURED KNOWLEDGE GRAPH AND CONTEXTS FOR USER QUERIES

Absstract of: US20260245681A1

Gastrointestinal (GI) tract cancers represent a significant burden on global health, with their diagnosis often posing challenges due to overlapping symptoms and complex etiologies. Conventional methods are inaccurate in differentiating between various GI tract cancers and thus remain a formidable task for clinicians, often leading to delays in diagnosis and suboptimal management. Present disclosure provides a system and a method that receive multimodal data for generating a seed knowledge graph and patterns identification. Dynamic mapping is then performed using the identified patterns on the seed knowledge graph to obtain an updated seed knowledge graph using a deep learning model. The system then fuses the employs the updated seed knowledge graph with the multimodal data being processed to obtain multimodal patient profile. The system employs large language models (LLMs) to analyze patient data and generate insights and explainability, ensuring physicians understand the rationale behind diagnosis and treatment recommendations.

ARTIFICIAL INTELLIGENCE-BASED QUERY AND RESPONSE SYSTEMS AND METHODS

Absstract of: US20260244947A1

An AI-based computing system for responding in real-time to an inbound message includes a processor configured to: a) transmit, to a representative computing device, an AI model generated proposed response message responsive to a query message derived from the inbound message, b) receive, from the representative computing device, feedback associated with the AI model generated proposed response message, the feedback provided by a representative, d) create a historical record including the AI model generated proposed response message and the feedback, e) generate a training dataset including at least the created historical record, and g) using machine learning and/or artificial intelligence techniques, re-train the AI model using the training dataset.

META-MODEL FRAMEWORK FOR SIMULTANEOUS DUAL SCENARIO PREDICTION

Absstract of: US20260244997A1

Embodiments relate to technological systems and methods for determining return on investment (ROI) of a program (e.g., medical intervention), which may be used to retrospectively or prospectively evaluate the value that the program delivered or will deliver to a patient (e.g., a participant in a program). In some embodiments, a first set of training data associated with a patient population is retrieved. The training data may be classified as belonging to a patient treatment group including patients participating in a program or a patient control group including patients not participating in the program. A plurality of base machine learning models may be trained using a dual-training process to generate both counterfactual values and ROI predictions associated with patients. A global machine learning model may then be trained on the counterfactual values and ROI predictions output by the base machine learning models.

PROGRAMMABLE LOGIC CONTROLLER, INFERENCE EXECUTION SYSTEM, INFERENCE EXECUTION METHOD, AND RECORDING MEDIUM

Absstract of: US20260244181A1

0000 A programmable logic controller includes a control application to generate execution instruction information indicating an instruction to execute an inference, a plurality of inference applications each to execute the inference and be capable of generating an inference result, and a machine learning platform to identify, in response to the execution instruction information transmitted from the control application, an inference application satisfying a predetermined rule from the plurality of inference applications to cause the identified inference application to execute the inference, and transmit an inference result generated by the identified inference application to the control application.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Absstract of: US20260245109A1

Provided are computer-implemented methods and systems for generating a prediction using a cold-start model, including: providing at least one data set from at least one data source, the at least one data set comprising historical transactional data for a first plurality of individuals, and contextual data for a second plurality of individuals, the first plurality of individuals comprising a subset of the second plurality of individuals; determining at least one activity from the at least one data set, the at least one activity comprising at least one feature of the corresponding data set; generating a cold start prediction comprising at least one candidate identifier; generating at least one attribution value based on the at least one feature of the at least one activity; and generating an explainable prediction. Also provided are computer-implemented methods and systems for generating a cold-start model.

A SYSTEM AND METHODS FOR TRAINING AI-MODELS FOR ELECTRICAL AND ELECTRONIC SYSTEM DESIGNING

Absstract of: WO2026172366A1

The present invention relates to methods and systems for training machine learning and artificial intelligence models for electronic and electrical system design. A first method includes receiving input data associated with electronic and electrical systems, extracting design parameters, configuring a baseline machine learning model based on a first design configuration, and iteratively refining the model by generating candidate design configurations, computing loss metrics, and determining a second design configuration exceeding a predefined threshold. An optimization module generates an optimization score defining improvement, and the model is updated when the score exceeds a predefined optimization threshold. A second method trains artificial intelligence models using multi-modal datasets including design, simulation, manufacturing, test, and reliability data, preprocessing the datasets, defining training tasks, training models using multi-task learning objectives, and performing transfer learning for specific application domains. A system provides an integrated platform with data registration, dataset ingestion, model selection, training optimization, and model registry interfaces for deploying AI models to electronic design tools.

SYSTEMS AND METHODS FOR PREDICTING FORMATION PROPERTY CHANGES AND USES THEREOF

Absstract of: WO2026170381A1

Machine learning methods may be used in formation stimulation. Such methods may include, for example: providing a training dataset comprising injection training parameters and formation training parameters and formation property alteration training parameters, wherein the formation property alteration training parameters are provided based on a first formation simulation of a first geological formation; training a machine-learning (ML) algorithm, using the training dataset to provide a trained ML model that predicts formation property alteration parameters.

ARTIFICIAL INTELLIGENCE FOR PERSONALIZED GROWTH MODELS OF GEOGRAPHIC ATROPHY

Absstract of: WO2026174075A1

A computer-implemented method for modeling progression of geographic atrophy (GA) in a subject, comprising: receiving an image sequence of a subject's eye over time, the sequence comprising a baseline image; segmenting, via a first deep learning model, each image of the sequence to delineate a GA lesion boundary; computing a lesion area based on the GA lesion boundary; longitudinally registering, via a second deep learning model, the sequence to align each image to the baseline image; fitting a growth model of GA progression based on the computed lesion area of the sequence; estimating, via a Bayesian hierarchical framework, model parameters of the growth model; iteratively updating the growth model based on at least one subsequent image, thereby forming a digital twin of GA progression; and generating, based on the digital twin, an individualized forecast of GA lesion area, the individualized forecast comprising an associated uncertainty interval.

Configuring Applications Based On A User'S Wakefulness State

Absstract of: US20260240490A1

0000 Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.

PREDICTING SUBJECTIVE RECOVERY FROM ACUTE EVENTS USING CONSUMER WEARABLES

Absstract of: US20260245733A1

0000 In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.

RNA SIGNATURE TEST FOR DIAGNOSIS OF THORACIC AORTIC ANEURYSM DISEASE

Absstract of: WO2026174031A1

A method for identifying RNA patterns in diagnosis and treatment of thoracic aortic aneurysm disease includes performing RNA sequencing on samples; analyzing the RNA sequencing data with the performance of differential gene expression analysis focusing on differentially regulated pathways to reveal complex regulatory mechanisms; developing a machine learning model to integrate pathway-level interactions; and combining pathway- specific analysis of the RNA patterns with the machine learning model to generate predictions identifying patients likely to be susceptible to thoracic aortic aneurysm disease.

Apparatus and methods for generating data intelligence

Absstract of: US20260244980A1

Apparatus for generating data intelligence and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive digital records, each of which includes a plurality of reference attributes, receive query data including a plurality of query attributes, identify one or more relevant digital records by matching one or more query attributes with one or more reference attributes, generate, using an output generation machine-learning model, one or more output data structures as a function of the one or more relevant digital records, calculate an intelligence metric as a function of each output data structure of the one or more output data structures, wherein the intelligence metric includes an estimated likelihood of positive outcome, and select at least a recommended output data structure as a function of the one or more intelligence metrics.

AUTOMATED WORKFLOW FOR SOCIAL DETERMINANTS OF HEALTH DATA MANAGEMENT

Absstract of: US20260245690A1

0000 The technology disclosed teaches a system and methods for generating a personalized care plan based on social determinants of health. The method further comprises pre-processing unstructured patient data corresponding to a patient to generate structured patient data and processing the structured patient data using a machine learning model, wherein the machine learning model is pre-trained to generate output data including at least one of a barrier to care, a disease risk factor, a discrepancy in the structured patient data, a risk score, and a recommended SDoH intervention. The method further includes creating a personalized care plan for the patient, based on the output data, including a personalized resource recommendation, wherein the personalized resource recommendation identifies an action plan responsive to an identified barrier to care.

METHOD AND SYSTEM FOR PREDICTING EIMERIA MAXIMA INFECTION OR PREVALENCE IN ANIMALS

Absstract of: WO2026171696A1

The method (900) for predicting Eimeria maxima infection or prevalence in animals comprises the steps of: - providing (905) a plurality of features and a plurality of empirically measured biomarker data; - training (910), using as input the plurality of features and historical biomarker data, a machine learning model to associate predetermined labels indicating whether the set of animals have Eimeria maxima infection or prevalence to said input; - receiving (915), measured biomarker data corresponding to one or more animals, wherein the measured biomarker data indicates blood concentrations of one or more biomarkers in the one or more animals; providing (920), the biomarker data as input to the trained machine learning model; receiving (925) at least one predetermined label indicating whether the one or more animals are positive for Eimeria maxima infection or prevalence or susceptible to mMX prevalence; and providing (930), upon a computer interface, at least one predetermined label received.

SYSTEMS AND METHODS FOR IDENTITY GRAPH BASED FRAUD DETECTION

Absstract of: US20260245097A1

A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud. The method also includes performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.

ORTHOPEDIC SURGICAL PLANNING SYSTEM FOR AUTONOMOUS ROBOTIC SURGERY

Absstract of: WO2026173966A1

Systems and methods may provide orthopedic surgical planning for autonomous robotic surgery. For example, a method can include displaying a user interface including a surgical planning program. The method can include receiving patient information at the surgical planning program, the patient information indicating that a patient has a sagittal plane knee malalignment. The method can include generating, using a machine learning trained model of the surgical planning program, a sagittal plane knee surgical plan based on the patient information, the sagittal plane knee surgical plan being specific to the sagittal plane knee malalignment and including a resection recommendation. The method can include displaying the sagittal plane knee surgical plan on the user interface for use with a robotic arm.

A METHOD FOR DESIGNING AND OPTIMIZING AN ELECTRICAL OR ELECTRONIC SYSTEM

Absstract of: WO2026172367A1

The present invention relates to methods for designing and optimizing electrical or electronic systems using machine learning. The method comprises receiving input data associated with a desired electrical and electronic system including a design configuration and at least one performance target, extracting a plurality of design parameters and design rules from the input data, initializing at least one machine learning model based on the extracted design parameters, determining one or more candidate design configurations by the machine learning model, simulating each candidate design configuration to generate simulation results associated with performance metrics, evaluating the simulation results against design rules to determine optimization metrics, and iteratively refining the extracted design parameters and model weights in response to the optimization metrics until a predetermined number of iteration cycles is completed. The candidate design configuration of the current iteration cycle is selected as the desired design configuration. The method enables automated multi-disciplinary design optimization across electrical, mechanical, thermal, and manufacturing engineering domains.

Method and System for Processing Sparse Time Series Data

Absstract of: US20260244954A1

Disclosed is a method for processing a sparse time series data. The method comprises receiving a dataset comprising the sparse time series data pertaining to real-world variables of a real-world system; transforming the sparse time series data to enable identifying data elements therein; processing the transformed time series data, for augmenting the transformed time series data and identifying the data elements and causal relationships between the data elements; reconstructing the transformed time series data into an operational model; and applying the operational model and the identified causal relationships to a machine learning algorithm to generate an output pertaining to the real-world variables of the real-world system.

METHOD OF ADVANCED CONFIGURATION SIGNALING FOR MULTIPLE TWO-SIDED MODELS COORDINATION

Absstract of: WO2026171971A1

The present disclosure describes a novel method of using the pre-configured AI/ML (artificial intelligence/machine learning) with cross-RAT model compatibility in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). With AI/ML model applied to radio access network, compatibility of supporting the configured two-sided models is challenging for a single or multiple UEs having different ML operational capabilities and environments. Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring cross-RAT model compatibility.

METHOD OF CELL-BASED MODEL CONFIGURATION SIGNALING

Nº publicación: WO2026171953A1 20/08/2026

Applicant:

AUMOVIO GERMANY GMBH [DE]
AUMOVIO GERMANY GMBH

Absstract of: WO2026171953A1

The present invention disclosure provides systems and methods for efficiently managing machine learning (ML) operations in communication networks using cell- independent and cell-dependent identification types. ML IDs, encompassing ML condition IDs, model IDs, and dataset IDs, are mapped to these identifiers to enable dynamic adaptability and optimal performance. A mapping relation table is utilized to associate ML IDs with cell-specific and network-wide configurations, facilitating seamless operation across cell boundaries. The invention also includes techniques for periodic and non-periodic feedback-based performance monitoring and signaling flow for mapping relation updates, ensuring enhanced resource utilization and reduced signaling overhead. These configurations enable flexible ML operation management, supporting UE mobility and adaptive decision-making across varying network conditions.