Alerta

SCALABLE MACHINE LEARNING PLATFORM WITH INTEGRATED FEATURE GENERATION AND REAL-TIME MODEL SERVING

Resumen de: US20260252904A1

0000 A system and method for machine learning platform management includes a unified architecture for developing and deploying machine learning models at scale. The platform integrates feature generation, model training, and inference services through a centralized interface. The system processes source data through a feature platform to generate training datasets and real-time features. A multi-stage training pipeline enables automated model experimentation through configurable workflows combining core frameworks and user modeling code. The platform implements specialized inference services optimized for high-throughput ranking and recommendation use cases, with distributed feature stores and local caching for efficient feature serving. A comprehensive monitoring system tracks model performance, feature distributions, and prediction quality through automated anomaly detection. The platform enables rapid experimentation while maintaining production reliability through automated deployment orchestration, optimized inference engines, and continuous feedback loops for model improvement.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATIC DATA RECONCILIATION

Resumen de: US20260252914A1

A machine learning based (ML-based) method and system for automatic data reconciliation across multiple datasets, is disclosed. The process begins by obtaining inputs related to first data in a natural language, which are pre-processed to generate refined first data. An ML model is employed to generate first rules for a rule model based on this pre-processed first data, facilitating the matching of at least two datasets to identify second data comprising matched records. During subsequent matchings, the ML model analyses third data, consisting of unmatched records, to produce matching suggestions, confidence scores, and rationales. As the system evolves, it generates second rules informed by user feedback and ongoing matchings. The rule model is iteratively updated to enhance accuracy by examining correlations between first and second rules. Ultimately, this system provides users with efficient, automated, data reconciliation outputs, enabling improved integration and accuracy between at least two datasets.

BIAS REDUCTION FOR MACHINE LEARNING FEATURE SELECTION

Resumen de: WO2026178346A1

A method may include splitting the dataset into holdout sets. Multiple training and testing sets may be generated by splitting, for each holdout fold in the holdout sets, a remaining plurality of samples not included in the holdout fold into a training and testing set. Multiple iterations of computational feature selection may be performed using the holdout sets and the training and testing sets. Each iteration may include training and validating a first instance of a machine learning model on a training and testing set before training, using the same samples in the training and testing set, a second instance of the machine learning model to operate on a subset of features selected during the training. Each iteration may further include validating of the first instance of the machine learning model before applying the second instance of the machine learning model to a holdout fold from the holdout sets.

METHOD FOR OBTAINING A MACHINE LEARNING MODEL FOR ESTIMATION OF TOTAL ORGANIC CARBON (TOC) FROM HYPERSPECTRAL DATA OF ROCK SAMPLES

Resumen de: US20260252966A1

0000 Method for estimating total organic carbon (TOC) of rock samples in an automated manner and without the use of destructive techniques. The technique obtains hyperspectral data from rock samples and trains and uses machine learning algorithms, among them artificial neural networks (ANN), to estimate TOC based on the obtained hyperspectral data. The method described herein may be applied to rock samples from different sedimentary basins, provided that the algorithms are trained with samples from all basins. The method has been shown to be generalizable to other rock samples belonging to the basin or sedimentary basins used in the training of the algorithms. The method eliminates the subjectivity of the human analyst and optimizes the time and resources expended in conventional TOC estimation.

PRIVACY PROTECTION IN VERTICAL FEDERATED LEARNING

Resumen de: US20260252959A1

Measures for privacy protection that comprise, at a machine learning model implementation entity having a local machine learning model resulting from vertical federated learning of a global machine learning model, said local machine learning model being defined by local model parameters, said machine learning model implementation entity being in a prediction phase of said global machine learning model in which at least two machine learning model implementation entities including said machine learning model implementation entity determine machine learning model prediction results for a data sample, said data sample including a plurality of features disjoint among said at least two machine learning model implementation entities, at least one feature of said plurality of features of said data sample being assigned to said machine learning model implementation entity, calculating, for each of said at least one feature, a modified feature, and determining a machine learning model prediction result for said data sample.

AUTOMATED GENERATION OF COVERAGE DATA FOR CIRCUIT DESIGNS USING MACHINE LEARNING

Resumen de: US20260252774A1

Automated verification and generation of coverage data for a circuit design includes generating, by computer hardware, valid value ranges for coverpoint variables of a circuit design based on constraints for the coverpoint variables. A regression test is run on the circuit design using a verification testbench to generate test results and sampled values for the coverpoint variables. The sampled values of the coverpoint variables from the regression test are classified by a machine learning model based on the test results. Coverage data for the regression test is generated by the computer hardware by assigning the sampled values of the coverpoint variables to different ones of a plurality of bins based on the classifying.

System, Method, and Computer Program Product for Privacy-Preserving Synthetic Data Generation

Resumen de: US20260252729A1

0000 Systems, methods, and computer program products are provided for privacy-preserving synthetic data generation. An example method includes receiving a request for generation of synthetic data from a user. A plurality of possible parameters for the synthetic data are generated. At least one parameter is determined based on the possible parameters and at least one input. At least one machine learning model is determined from a plurality of machine learning models based on the parameter(s). The machine learning models are trained based on real data. The synthetic data is generated based on the machine learning model(s) and the parameter(s). The synthetic data is verified based on at least one of the parameter(s), the real data, or any combination thereof. In response to verifying the synthetic data, the synthetic data is communicated to the user.

SYSTEM AND METHOD FOR ENTERPRISE APPLICATION DECISION CAPTURE AND LEARNING

Resumen de: US20260252925A1

0000 Embodiments described herein are generally related to data analytics environments, and are particularly directed to a system and method for enterprise application decision capture and learning. The described approach can be used to capture information about decisions made within an organization, and provide feedback to machine learning or other processes, for use in forming an understanding of the decisions, or generating recommendations for future actions. Decision objects can be used to capture and store decision records in a decision store, which indicates the fact that a decision has been made, together with a decision context. The decision store can include information descriptive of decisions made within various analytic applications, such as for example, ERP, HCM, HR, or SCM applications, or within a larger enterprise ecosystem, to provide a bottom-up approach in learning from the decisions and actions that users take within the organization.

METHOD FOR MOTION PLANNING FOR A VEHICLE AND METHOD FOR TRAINING A MACHINE LEARNING ALGORITHM FOR INFERRING MANEUVER COSTS IN THE CONTEXT OF MOTION PLANNING FOR A VEHICLE

Resumen de: WO2026175619A1

The invention relates to a computer-implemented method (2) for motion planning for a vehicle (1), wherein the following steps are carried out in each of a plurality of calculation cycles: providing (21) state information relating to the vehicle (1) and environment information relating to an environment of the vehicle (1); defining (22) multiple possible maneuvers (M1, M2, M3, M4) in view of the state information and the environment information; generating (23) a particular feature vector for each maneuver (M1, M2, M3, M4); on the basis of the particular feature vector, inferring (24) costs for each maneuver (M1, M2, M3, M4) with respect to an optimization problem which comprises one or more trajectory-based cost functionals, by means of a machine learning algorithm trained for this purpose; and, according to the inferred costs, selecting (25) one or more of the maneuvers (M1, M2, M3, M4), preferably together with a specific sequence, for a detailed cost evaluation on the basis of the optimization problem.

DIFFUSION OBFUSCATION TRANSFORM

Resumen de: US20260252732A1

0000 Provided are methods and systems for obtaining, by a computer system, input data for a machine learning model; generating, by the computer system, one or more obfuscation value distribution for input data obfuscation based on a diffusion process; training, by the computer system, the one or more obfuscation value distribution for data obfuscation; and storing, by the computer system, the trained one or more obfuscation value distribution in memory.

MACHINE LEARNING WITH PII PROTECTION AND EXPLAINBILITY

Resumen de: EP4797139A1

A computer-implemented method of processing input data, comprising receiving an encrypted model parameter update, wherein at least model parameters relating to personally identifiable information are encrypted using a homomorphic encryption algorithm, decrypting the encrypted model parameter update using the homomorphic encryption algorithm, applying the decrypted model parameter update to model parameters of a machine learning model, receiving, by the machine learning model, input data comprising personally identifiable information; processing, using a machine learning model, the input data to produce output data; and applying an explainability algorithm to the output data.

AIDING MACHINE LEARNING INFORMATION RETRIEVAL BY SYMBOLIC KNOWLEDGE REPRESENTATION

Resumen de: EP4797163A1

A computer-implemented method (100) for retrieving a response (3) to a query (1) from a machine learning/artificial intelligence model, ML/Al model (2), the method (100) comprising the steps of:• providing (110) the query (1) to the ML/Al model (2), thereby obtaining an initial response (3);• determining (120), from the initial response (3), instances of concepts (5*) of a given symbolic knowledge representation (4) that the initial response (3) relates to;• marking (130), in the symbolic knowledge representation (4), each concept (5*) that the initial response (3) relates to as instantiated;• determining (140), by a reasoning engine (6), concepts (5#) of the symbolic knowledge representation (4) that, given the set of presently instantiated concepts (5*), need to be instantiated as well;• determining (150) a supplemental query (1*) for information relating at least one concept (5#) that needs to be instantiated as well; and• providing (160) this supplemental query (1*) to the ML/Al model (2), thereby obtaining a supplemental response (3*) that augments the initial response (3).

CONTROL PARAMETER OPTIMIZATION

Resumen de: EP4797015A1

0001 The disclosure relates to computer systems 10 and computer-implemented methods 200, 300 for performing a process having a binary output value and for optimizing controllable input parameters of such processes. One computer system 10 comprises at least one processor 11 and memory 12 configured to implement a parameter optimization module 102 for optimizing input parameters of a process having a binary output value, comprising: a probabilistic machine learning model 1021 trained to model a relationship between at least a first controllable input parameter of the process, a second controllable input parameter of the process and the binary output value of the process, the relationship defining an optimized pair of input parameter values 104 comprising an optimized value for the first input parameter and an optimized value for the second input parameter; and a batched binary Bayesian Optimizer 1022 configured to generate, from the trained probabilistic machine learning model 1021, a list 105 comprising a plurality of training pairs of input parameter values, each training pair comprising a value for the first input parameter and a value for the second input parameter. The parameter optimization module 102 is configured to output the optimized pair of input parameter values 104 and the list 105 of training pairs of input parameter values, and to receive binary output values of the process performed using the training pairs of input parameter values. The computer system 10 i

MODEL MANAGEMENT METHOD AND APPARATUS

Resumen de: EP4797760A1

0001 This application provides a model management method and an apparatus, and relates to the field of communication technologies, to reduce a data security risk of an AI model in the wireless field. The method includes: obtaining a first communication data set used for updating a first machine learning model, and sending a second communication data set to a model training function entity. The first processing policy includes a first loss threshold, and the first machine learning model is used for managing a wireless communication service. The second communication data set is obtained by performing a processing operation on suspicious data in the first communication data set according to the first processing policy, and the suspicious data is wireless communication data whose loss value is greater than the first loss threshold.

INFORMATION PROCESSING METHOD AND RELATED DEVICE

Resumen de: EP4796407A1

Embodiments of this application disclose an information processing method and a related device. The method may be applied to the autonomous driving field in artificial intelligence. The method includes: inputting, to a deep learning model, first information corresponding to a traffic scene around an ego vehicle, and obtaining second information corresponding to the first information, where the first information includes first text information, the second information is obtained based on the deep learning model, and the second information corresponds to any one of the following tasks: making a decision on behavior of the ego vehicle, planning a trajectory for the ego vehicle, or controlling the ego vehicle. Input information of the deep learning model provided in this application includes text information that facilitates understanding by a user. Therefore, interpretability of a running process of the deep learning model is improved, to be specific, a decision-making, trajectory planning, or control process of an autonomous vehicle is more transparent, so that the user can more intuitively understand behavior of the autonomous vehicle.

METHOD AND RELATED DEVICE FOR ACQUIRING MATHEMATICAL MODEL, AND OPERATIONS OPTIMIZATION METHOD

Resumen de: EP4797146A1

0001 A mathematical model obtaining method and device, and an operations optimization method are provided. In the method, an artificial intelligence technology may be used to obtain a mathematical model corresponding to an operations optimization problem. The method includes: outputting at least one problem, and obtaining an answer to each problem, where the answer to each problem is for obtaining first description information, the first description information is description information for describing the operations optimization problem, and a first problem is a problem for obtaining the description information of the operations optimization problem; and obtaining first information based on the first description information, and inputting the first information into a machine learning model, to obtain a mathematical model, where the mathematical model is for solving the operations optimization problem, and the mathematical model includes an objective function and a constraint. The solution greatly reduces manpower costs consumed in a process of obtaining the mathematical model, and can be adapted to obtaining mathematical models in various domains, and has high generalization.

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.

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.

A METHOD FOR DESIGNING AND OPTIMIZING AN ELECTRICAL OR ELECTRONIC SYSTEM

Resumen de: 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

Resumen de: 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

Nº publicación: WO2026171971A1 20/08/2026

Solicitante:

AUMOVIO GERMANY GMBH [DE]
AUMOVIO GERMANY GMBH

Resumen de: 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.