Alerta

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

Resumen de: 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

Resumen de: 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

Resumen de: 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.

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

Resumen de: 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

Resumen de: 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

Resumen de: 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

Resumen de: 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.

ARTIFICIAL INTELLIGENCE FOR PERSONALIZED GROWTH MODELS OF GEOGRAPHIC ATROPHY

Resumen de: 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.

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

Resumen de: 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

Resumen de: 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.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Resumen de: 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.

RNA SIGNATURE TEST FOR DIAGNOSIS OF THORACIC AORTIC ANEURYSM DISEASE

Resumen de: 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

Resumen de: 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

Resumen de: 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

Resumen de: 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.

ORTHOPEDIC SURGICAL PLANNING SYSTEM FOR AUTONOMOUS ROBOTIC SURGERY

Resumen de: 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.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Resumen de: US11966704B1

The techniques described herein relate to techniques for verifying a veracity of machine learning outputs. An example method includes receiving a first output generated by a first model responsive to a first input, the first output comprising one or more verifiable statements in text, verifying, using a second model and first reference data stored in at least one first datastore, the one or more verifiable statements to produce first verification results indicating which of them has been verified, when it is determined that at least one of them remains unverified based on the first verification results, identifying, using at least one of the first or second models, at least one second datastore having second reference data attesting to veracity of the first output; and verifying, using the second model and the second reference data, the at least one unverified statement to produce second verification results to be provided as output.

DYNAMIC VISUALIZATION FOR THE PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS AND ANY OTHER TYPE OF PREDICTIVE MODEL

Nº publicación: EP4792262A1 19/08/2026

Solicitante:

GOODER AI INC [US]
Gooder AI, Inc.

Resumen de: WO2025080778A1

A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.