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System, Method, and Computer Program Product for Determining Feature Importance

Resumen de: US20260220533A1

Provided is a system that includes a processor to receive a dataset comprising a plurality of feature values of a plurality of features; determine, for each feature of the plurality of features, a plurality of sequence deviation metrics; generate a plurality of sets of features for the plurality of sequence deviation metrics, wherein each set of features comprises a ranked set of features for a sequence deviation metric; train a plurality of machine learning models based on the plurality of sets of features, wherein the plurality of machine learning models comprises a machine learning model for each sequence deviation metric; determine a performance metric of each trained machine learning model for each sequence deviation metric; and select a ranked set of features for a sequence deviation metric that corresponds to a trained machine learning model that has a highest performance metric. Methods and computer program products are also provided.

EXPLAINABLE MACHINE-LEARNING TECHNIQUES FROM MULTIPLE DATA SOURCES

Resumen de: US20260220531A1

0000 In some aspects, a computing system can generate and optimize a hybrid machine learning model for risk assessment based on predictor variables associated with a target entity. The hybrid machine learning model can be trained using training vectors with sets of training predictor variables and training outputs corresponding to the respective sets of training predictor variables. The predictor variables associated with the target entity may include unknown values and the training predictor variables or trainings output may also include unknown values. Additionally, the computing system can generate explanatory data for the target entity to indicate relationships between changes in the risk indicator and changes in the predictor variables associated with the target entity. The risk indicator and the explanatory data can be used in controlling access of the target entity to interactive computing environments.

METHOD FOR IMPROVING THE CLASSIFICATION OF A DIGITAL DOCUMENT USING A PLURALITY OF LEARNING MODELS

Resumen de: US20260220960A1

0000 A method for improving the classification of a document from a plurality of machine learning models, includes receiving a digital document; executing a first learning function generated from a first machine learning model trained from a first training domain processing the first input data sequence and making it possible to classify a document type and generate a prediction of a first automatic action to be performed on the digital document, acquiring a first corrective action from a user relating to the modification of the movement of the first digital document to a second directory; generating a first annotation; modifying the first training domain by adding the first annotation; generating a retraining of the first machine learning model.

SYSTEMS AND METHODS FOR PREDICTION OF ANTIBIOTIC RESISTANCE FROM BACTERIAL GENOMES

Resumen de: US20260220336A1

Systems, methods, and devices disclosed herein provide antibiotic resistance predictions using a pan-antibiotic resistance prediction (PARP) model. The PARP model includes a machine learning system trained with a training data set of genetic information associated with a plurality of bacterial species, and/or antibiotic feature information associated with a plurality of antibiotics. The PARP model is deployed to a cloud-based service for scalability which provides access to the PARP model for clinic devices, hospital devices, and/or laboratory devices. For instance, a web-based portal of the cloud-based service receives a genomic sequence associated with a particular bacterial isolate, uploaded via a remote device. The PARP model outputs a predictive indication of an antibiotic resistance, for the particular bacterial isolate. The predictive indication can include a bar graph (e.g., presented at a graphical user interface) showing, for the particular bacterial isolate, susceptibility/resistance predictions for a plurality of antibiotics.

SYSTEMS AND METHODS FOR TRANSFORMING AND STORING DATA FROM MULTIPLE STUDIES

Resumen de: US20260221293A1

There is disclosed a method and system for combining datasets. Study results may be retrieved. Each study result may include datapoints. Each datapoint may include attributes. Study questions may be extracted from the study results. The questions may be converted into a standardized format. Categories may be assigned to the questions. The questions may be grouped together into groups. A response scale may be determined for each of the groups. The responses may be rescaled using the corresponding response scale. A final dataset may be generated by combining the study results. Features may be selected using the final dataset. A machine learning algorithm may be trained using the features of the final dataset.

REAL-TIME FINANCIAL INTELLIGENCE

Resumen de: US20260220709A1

0000 At least one example describes mechanisms for real-time financial intelligence (RFI) that leverage one or more machine-learning (ML) techniques to process financial data. In at least one example, RFI may dynamically generate dimensions and their associated contextual information within the financial data to enable dimensional intelligence. In at least one example, dimensional intelligence may include performing intelligent data analytics across the dimensions, enabling generation of meaningful insights, efficient reporting, identifying patterns, and supporting decision-making. In at least one example, RFI may offer an interactive graphical user interface-financial intelligent chat (Fine-Chat), enabling users to input financial queries in a natural language format. In at least one example, via these financial queries, Fine-Chat may perform dimensional intelligence, dimensional analysis, intelligent data analytics and generates real-time financial reports, financial recommendations. Fine-Chat may generate a response to a financial query including at least one result in a natural language format.

AN IMPROVED METHOD OF, AND APPARATUS FOR, MEDICAL DATA OUTCOME ANALYSIS

Resumen de: WO2026158807A1

An Improved Method of, and Apparatus for, Medical Data Outcome Analysis There is provided a computer-implemented method for determining an uncertainty metric for a medical data abstraction process utilising a federated machine learning model executed on a computing node and having a plurality of personal abstraction models (PAMs) associated therewith, wherein each PAM comprises an instance of the federated machine learning model local to and executed on a client computing node and trained using a local training dataset stored on the respective client node, wherein the method is executed by at least one hardware processor and comprises the steps of: inputting medical data relating to a patient medical record into the federated machine learning model to generate an outcome classification prediction for one or more medical parameters; inputting the medical data relating to the patient medical record into each trained PAM to generate an outcome classification prediction for the one or more medical parameters for each trained PAM; utilising the determined outcome classification predictions from each trained PAM in an ensemble voting process to determine an uncertainty metric of the outcome classification prediction in step a); d) determining whether the uncertainty metric meets one or more predefined criteria and, if so, accepting the determined outcome classification prediction and abstracting the patient medical record in an abstraction database.

SYSTEMS, METHODS AND TESTING APPARATUS FOR ASSESSING PLATELET SAMPLES USING MACHINE LEARNING FOR CANCER ESTIMATION

Resumen de: WO2026156436A1

An artificial intelligence-based method for detecting a disease comprises a meta- classification model comprising a plurality of base classification models each generating a respective output and wherein each of such outputs is input to a meta-model, the meta-model comprising an artificial neural network. Variants are described in respect of the meta-model stacked ensemble pipeline, dimensionality / complexity control, and specific architectures designed for tracking disease trajectory through identifying gene pathway crosstalk. The method is particularly suitable for detecting cancer in blood samples comprising tumor educated platelets.

METHODS AND SYSTEMS FOR EMPOWERING MACHINE UNLEARNING VIA CONTRASTIVE LEARNING

Resumen de: WO2026157104A1

A method for unlearning data in a machine learning system involves generating a first unlearned model by performing a first unlearning iteration on a global model, based on a request from a target client. The server transmits the unlearned model to the client, which verifies, using a metric, whether further unlearning is required. If needed, the target client sends a notification to a network controller, which selects an unlearning function from a function pool and transmits it to the server. The server performs a second unlearning iteration using the selected function, generating a second unlearned model. The second model is transmitted to the target client for verification. Upon confirmation that the model meets unlearning requirements, the server broadcasts the refined model to non-target clients. The method ensures effective data removal while maintaining model integrity for continued use across the system.

MULTI-FACTOR ANTI-PHISHING SYSTEMS AND METHODS

Resumen de: US20260222445A1

The present application describes a multi-factor anti-phishing system and method, featuring real-time local analysis of webpages on user devices. The system comprises a feature extraction system integrated with a web browser to extract a comprehensive set of features from visited webpages. These features include URL and host characteristics, content and structure indicators, resource files and scripts, form and action elements, and embedded media analysis. A machine learning (ML) model, trained on these features, analyzes the extracted data to predict phishing risks. The system uses a cloud secure enclave to manage allow lists and block lists, process encrypted feedback, and retrain the ML model, such that sensitive information remains confidential. The retrained ML model is periodically distributed to user devices to enhance phishing detection capabilities.

SYSTEMS AND METHODS FOR MACHINE LEARNING IMPLEMENTED STATE TRANSITION PLANNING

Resumen de: WO2026156458A1

Systems and methods for the generation of a state transition plan by using a diffusion model to predict state representations. The diffusion model can be constrained using at least one constraint function during training where the at least one constraint function models a start and an end state in the reverse diffusion process. The diffusion model is trained to learn a constrained score function corresponding to a score function comprising a score offset based on the at least one constraint function. At inference, the diffusion model can be provided with observed states of one or more agents to generate predicted states for the one or more agents.

SYSTEMS AND METHODS FOR GENERATING PERSONALIZED CONTENT

Resumen de: US20260220503A1

In some embodiments, apparatuses and methods are provided herein useful to generate personalized content. In some embodiments, a system comprising a processing resource; a machine readable medium storing instructions that, when executed, cause the processing resource to: aggregate, session data during a client session; update, periodically and during the interaction session, one or more inference indicators associated with the client in an inference cache storage, the one or more inference indicators being determined via a trained machine learning model using the session data and historical data in a historical data database; identify a trigger event based on client interactions; retrieve, in response to the trigger event, at least one inference indicator associated with the client from the inference cache storage; and generate a personalized content for display on the client device based on the at least one inference indicator retrieved from the inference cache storage.

CONTROL DEVICE FOR RADIO ACCESS NETWORK AND COMPUTER-READABLE STORAGE MEDIUM

Resumen de: EP4783645A1

0001 A control apparatus for a radio access network (RAN), includes: collection means configured to control the RAN based on a learning model and to collect first learning data from the RAN; determination means configured to determine usefulness of the first learning data in machine learning for the learning model; and processing means configured to perform processing to select second learning data from the first learning data for transmission to another control apparatus that performs the machine learning, based on the usefulness of the first learning data.

EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S)

Resumen de: EP4783078A2

Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM

Nº publicación: EP4783079A1 29/07/2026

Solicitante:

MITSUBISHI ELECTRIC CORP [JP]
Mitsubishi Electric Corporation

Resumen de: EP4783079A1

An information processing device includes: a machine learning unit (12) to learn a relationship between an evaluation value and a parameter on a basis of a search point of the parameter and an evaluation value of the search point, and predict the evaluation value for a search candidate point of the parameter; and a search progress acquiring unit (13) to acquire progress information indicating progress of a search on a basis of the search point, the evaluation value of the search point, the search candidate point, and the evaluation value of the search candidate point predicted by the machine learning unit (12).