Alerta

Hellinger Decision Trees for Fraud Detection

Resumen de: US20260260174A1

A Hellinger decision tree can detect fraudulent transactions in a data set of financial transactions. Applying the Hellinger decision tree uses a Hellinger distance. The Hellinger decision tree can be part of a machine learning algorithm. In an example, the Hellinger decision tree is a positive and unbalanced Hellinger decision tree used with an imbalanced positive and unlabeled data.

INFERENCE DATA SIMILARITY FEEDBACK FOR MACHINE LEARNING MODEL PERFORMANCE MONITORING IN BEAM PREDICTION

Resumen de: US20260260136A1

0000 In an aspect, a UE may obtain a first indication of a first set of characteristics associated with a plurality of reference datasets. The UE may calculate a respective level of similarity between an inference dataset and each of the plurality of reference datasets based on the first set of characteristics and a second set of characteristics associated with the inference dataset. The UE may output at least one second indication of the respective level of similarity between the inference dataset and each of the plurality of reference datasets.

ADVERSARIAL TRAINING OF A MODEL FOR QUESTION ANSWERING

Resumen de: US20260260122A1

Embodiments of the present disclosure provide a solution for adversarial model training. A method includes: generating a prompt input using an adversarial machine learning model; providing the prompt input to a target machine learning model, to generate a response to the prompt input; determining a first reward score for the response with respect to the prompt input; and fine-tuning the target machine learning model according to a first optimization objective, the first optimization objective being configured to increase or maximize the first reward score for the target machine learning model.

METHOD AND APPARATUS FOR SUPPORT OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML)-BASED POSITIONING

Resumen de: GB2639745A

According to various examples of the present disclosure, there is provided a location management function (LMF) entity configured to: subscribe to or request artificial intelligence/machine learning (AI/ML) -related services from a network data analytics function (NWDAF) entity in relation to an AI/ML model for determining positioning of a user equipment (UE); and receive, from the NWDAF entity, an indication that training has been performed for the AI/ML model. According to various examples of the present disclosure, there is provided a network data analytics function (NWDAF) entity configured to: receive, from a location management function (LMF) entity, a subscription to or request for artificial intelligence/machine learning (AI/ML) -related services in relation to an AI/ML model for determining positioning of a UE; obtain data for training the AI/ML model from at least one other entity; train the AI/ML model based on the obtained data; and transmit, to the LMF entity, an indication that training has been performed for the AI/ML model; wherein the NWDAF entity includes a model training logical function (MTLF).

SYSTEMS AND METHODS FOR GENERATING AND EXECUTING FUNCTION CALLS USING MACHINE LEARNING

Resumen de: US20260252397A1

0000 The methods, systems, and computer networking apparatuses described herein enable language models to receive input (e.g., a query or a request) from a user or application, and without any additional training data or instructions, determine to generate a function call based on the received input from the user and generate the function call based on the determination. In some embodiments, a language model may further access an external tool or application to request an output as a response to the generated function call. The disclosed methods, systems, and networking apparatuses improve the technical field by incorporating language model capabilities within the function calling process, and allowing for function-related information to be provided to a language model via input received in any number of formats or types, including structured or unstructured input, language or non-language input, or any combination thereof.

Transformer-Based Assistant for Identifying, Organizing, and Responding to Customer Concerns

Resumen de: US20260252607A1

0000 Transformer-based agent assistant systems as machine learning-based customer service tools that analyze past customer-agent conversations to build a knowledge base of problem-resolution steps are disclosed. The system may include a natural language processing (NLP) model and a transformer-based model to extract and generate customer concerns and resolutions. One embodiment also includes a head-topic and subtopic detection module for identifying trends in customer concerns. Another embodiment uses a question-answering model and a zero-shot-NLI (natural language inference) classifier for entity extraction and detection. The system is designed to be flexible, incorporating new data over time, and can retrieve company documentation or FAQs for the agent based on cosine similarity.

CONTINUOUS-FLOW DYNAMIC TITRATION CALORIMETER INCORPORATING MICROFLUIDICS AND MACHINE LEARNING-BASED CORRECTION, AND METHOD FOR USING SAME

Resumen de: WO2026174682A1

The present invention provides a continuous-flow dynamic titration calorimeter incorporating microfluidics and machine learning-based correction, and a method for using same. An apparatus comprises: a thermal insulation apparatus, used for accommodating a calorimetric structure, performing thermal insulation on the calorimetric structure, collecting the ambient temperature of the calorimetric structure, and transmitting the ambient temperature to a control and signal processing system; and the calorimetric structure, comprising a material preheating aluminum block and a constant-temperature aluminum block that are arranged at the left and right sides, wherein a material inlet/outlet connection block A is provided in a front slot of the material preheating aluminum block, a material inlet/outlet connection block B is provided in a rear slot of the material preheating aluminum block, a thermoelectric generator sheet A is provided at a front part of the constant-temperature aluminum block, a thermoelectric generator sheet B is provided at a rear part of the constant-temperature aluminum block, and a thin film heating sheet is provided above the thermoelectric generator sheet A. The technical solution of the present invention integrates microfluidics, thermoelectric power-generation measurement, and machine learning-based correction, and provides a low-cost, miniaturized and high-precision continuous-flow dynamic titration calorimetry method.

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.

SYSTEMS AND METHODS FOR ONTOLOGY-ORIENTED GRAPH DATASET MODELING, DATA RETRIEVAL, AND HYBRID STRUCTURING OF GENERATIVE ARTIFICIAL INTELLIGENCE OUTPUTS

Resumen de: WO2026175686A1

The present disclosure relates to systems and methods for ontology-oriented graph dataset modeling, data retrieval, and hybrid structuring of generative machine learning outputs, which address the challenges of querying and extracting clinical insights from large, complex datasets. By combining knowledge graphs and large language models, embodiments of the present disclosure enable lay users to perform natural language queries on large, structured datasets and receive evidence-backed results with visual context. Embodiments include an orchestrator module powered by one or more LLMs and an instruction prompt, which selects an appropriate context retrieval tool based on a user's query. The context retrieval tool retrieves results from an ontologically-oriented graph, generates a knowledge graph, and provides a response to the query. This approach improves the efficiency and accuracy, allowing users to discover novel and unknown relationships within the dataset without needing comprehensive schema knowledge, thereby overcoming the high barrier of entry for extracting insights.

Self-Explaining Decision Architecture and Methods of Use

Resumen de: US20260252951A1

A Self-Explaining Decision Architecture (SEDA) for machine learning-based decision-making systems capable of generating intuitive explanations for its decisions in real time. SEDA makes use of a feature extraction subsystem and a sequence interpretation subsystem to identify patterns in data followed by a decision generation subsystem that determines appropriate actions based on those patterns. Internal state information from each of these subsystems is used to generate explanations of the system's decisions. Using this information to create explanations provides insight as to the data elements the system focused on when making decisions as well as the reasoning that was used. In at least one embodiment the system uses deep learning components including a combined convolutional neural network and long short-term memory network with attention mechanisms.

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.

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.

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.

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.

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.

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

Resumen de: 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.

Configuring Applications Based On A User'S Wakefulness State

Resumen de: 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

Resumen de: 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.

SYSTEMS AND METHODS FOR IDENTITY GRAPH BASED FRAUD DETECTION

Resumen de: 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.

Automated analytical content generation using large language models

Resumen de: AU2025271014A1

Aspects of the present disclosure relate to automated analytical content generation. Embodiments include receiving data from one or more data sources. Embodiments further include extracting trends from the data using a heuristic algorithm. Embodiments further include providing an input based on the extracted trends to a generative machine learning model that has been configured to generate content based on extracted trends. Embodiments further include receiving, from the generative machine learning model based on the input, content that represents the extracted trends. Embodiments further include displaying the content via a user interface. ov o v RECEIVE DATA FROM ONE OR MORE DATA SOURCES EXTRACT TRENDS FROM THE DATA USING A HEURISTIC ALGORITHM PROVIDE AN INPUT BASED ON THE EXTRACTED TRENDS TO A GENERATIVE MACHINE LEARNING MODEL THAT HAS BEEN CONFIGURED TO GENERATE CONTENT BASED ON EXTRACTED RECEIVE, FROM THE GENERATIVE MACHINE LEARNING MODEL BASED ON THE INPUT, CONTENT THAT REPRESENTS THE EXTRACTED DISPLAY THE CONTENT VIA A USER INTERFACE RECEIVE DATA FROM ONE OR MORE DATA SOURCES ov o v

AGENTIC DATA FABRIC WITH PREDICTIVE INTELLIGENCE

Resumen de: WO2026174258A1

Generating profiles that consolidate tenant data with interaction and transaction records from heterogeneous data sources in real time. Executing AI agents including an orchestration agent that orchestrates other agents. Providing a model interface layer that securely connects to an external machine learning model of a third-party agent while enforcing data governance rules, wherein the machine learning model can securely access customer data from the profiles of the multi-tenant platform under contextaware policies. Providing secure access to data from the profiles to the model through the model interface layer without exporting the data into a separate repository, such that the model processes live enterprise data in place. Receiving a predictive output derived from the exported data. Updating a profile by writing the predictive output as a new attribute of that profile, thereby enriching the profile with machine-generated insights in real time to create an enriched profile.

METHOD FOR OPERATING A BEAM DEVICE, COMPUTER PROGRAM AND BEAM DEVICE FOR CARRYING OUT THE METHOD AS WELL AS A METHOD FOR GENERATING A TRAINING DATA SET AND A METHOD FOR TRAINING A MACHINE LEARNING MODEL

Nº publicación: US20260246526A1 20/08/2026

Solicitante:

CARL ZEISS MICROSCOPY GMBH [DE]
Carl Zeiss Microscopy GmbH

Resumen de: US20260246526A1

The system described herein relates to operating a beam device for obtaining information about an object. Moreover, the invention relates to a computer program product having a program code, which, when executed, controls the beam device in such a way that the method for operating the beam device is carried out. Additionally, the invention relates to a method for generating a training data set for a processing unit and/or for a machine learning model. Furthermore, the invention relates to a method for training a machine learning model of a beam device. The processing unit determines which machine learning model of a plurality of machine learning models is to be used for determining control values of control parameters. The control values of the control parameters are used to operate the control unit for generating the information about the object.