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MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: WO2026172331A1

Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.

PRIVACY PRESERVING WORKFLOW FOR REPRODUCIBLE MACHINE LEARNING TRAINING ITERATIONS

Resumen de: US20260244979A1

0000 Techniques for tracking machine learning model training iterations that preserve privacy, increase security, and reduce memory and other computing resources are disclosed herein. An example computer-implemented method comprises generating one or more hash values corresponding to raw data associated with a machine-learned model training process, the raw data comprising a first raw data point associated with a first key and the one or more hash values comprising a first hash value; storing the first hash values in a database in association with the first key; executing a first processing stage using the first raw data point to generate a processed data point; generating a hash value for the first processed data point; storing the second hash value in association with the first key; training a machine-learned model using processed data to generate a trained machine-learned model; and registering the trained machine-learned model.

MACHINE LEARNING-BASED AUTOMATED WELL LOG QUALITY CHECK AND RECONSTRUCTION

Resumen de: WO2026173837A1

A method for processing well log data includes obtaining input data including the well log data. The well log data may include a plurality of datasets. Each dataset of the plurality of datasets includes one or more log curves and is associated with a respective well of one or more wells. The method also includes harmonizing the plurality of datasets using a curated dictionary to produce harmonized datasets. The method further includes reconstructing the one or more log curves of each harmonized dataset of the harmonized datasets to produce reconstructed logs using a supervised machine-learning (ML) model. The method also includes generating normalized datasets based on the harmonized datasets and using log normalization. The method also includes generating an output based on the reconstructed logs and the normalized datasets.

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.

MODEL FILE LOADING METHOD AND SYSTEM, COMPUTER DEVICE, AND STORAGE MEDIUM

Resumen de: WO2026170718A1

The present application relates to the technical field of machine learning, and discloses a model file loading method and system, a computer device, and a storage medium. The method comprises: when an inference service starts, acquiring an update request; if it is detected that the update request carries a local storage path of a model file, using the local storage path as a target storage volume of a target scheduling unit; mounting the target storage volume in an init container of the target scheduling unit, and generating a target mount item of the init container; generating response information on the basis of the target storage volume and the target mount item, wherein the response information is used for updating the target scheduling unit to establish a communication link between the updated target scheduling unit and a local directory; and sending the response information to a first slave node, so that the first slave node updates the target scheduling unit, and loads the model file on the basis of the communication link. The present application can solve problems such as high transmission delay, redundant occupation of hard disk resources, and namespace limitations during model file loading.

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.

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.

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.

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.

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.

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

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.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Nº publicación: EP4792260A1 19/08/2026

Solicitante:

AMKS INVEST I LLC [US]
AMKS INVESTMENTS I LLC

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.