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BUSINESS PROCESS MANAGEMENT USING ADVERSIAL AGENTS

Resumen de: WO2026050742A1

Systems and methods for improving business processes. In some embodiments, the method includes receiving business process data; generating, based on the business process data, a process map representing a business process using a first machine learning model deployed at the cloud-based cluster; generating, based on the process map, at least one alternative process map using a second machine learning model; evaluating the generated process maps using at least one business objective; selecting an improved process map from the generated process maps based on the evaluation; presenting, at an interactive user interface, at least one of the generated process maps, the at least one generated process map including the improved process map; receiving, at the interactive user interface, a user selection indicating a preferred process map from the at least one generated process map; and implementing the preferred process map.

APPARATUS AND METHOD FOR EMERGENCY DISPATCH

Resumen de: US20260067398A1

An emergency data manager includes a mapping module that is operative to generate a map view in a cloud-based user interface provided to a public safety answering point (PSAP) by the emergency data manager. The map view displays location indicators for emergencies being handled by the PSAP. Machine learning trained logic is operatively coupled to the mapping module and is operative to correlate incoming emergency data and provide contextual data to PSAP dispatchers via the cloud-based user interface. The contextual data includes time, location, and event type. The machine learning trained logic may be further operative to provide a dispatch recommendation based on the contextual data, or based on contextual data and a set of dispatch rules. The machine learning trained logic may be further operative to provide a simulation of an experienced PSAP call taker or dispatcher.

METHOD AND SYSTEM FOR DEPLOYMENT AND REPLACEMENT OF A MACHINE-LEARNING MODEL IN ACTIVE ON-LINE USE WITHOUT SERVICE INTERRUPTION

Resumen de: EP4703874A1

Computer-implemented method and system for deployment of a first machine-learning model, and replacement, without service interruption, of a second machine-learning model in active on-line use, comprising: receiving, at a controller, a replacement request; in response to said replacement request, triggering the deployment of the first model and triggering the calculation of features to be used, collecting output data from the first model, fitting and inserting one or more calibration functions downstream from the first model, and routing inference requests to the first model instead of the second model; wherein the triggered deployment of the first model comprises preloading the first model into CPU or GPU memory, and making available the calculated features and the preloaded first model by CPU or GPU, respectively, before the inference requests are routed to the first model, thus enabling that no additional latency is added when traffic is rerouted to the first model.

DOUBLE TIER MACHINE LEARNING IN-SPACE HYBRID SIMULATIONS METHODS

Resumen de: EP4704091A1

A machine learning simulation method of determining a physical state of interaction between atoms from one or more physical properties of the atoms is disclosed. The method including dynamically evolving a first subset of atoms via a first machine learning model within a central high-fidelity region based on the one or more physical properties of the atoms. The method further includes dynamically evolving a second subset of the atoms via a second machine learning model with a remaining low-fidelity region based on the one or more physical properties of the atoms. The method also includes dynamically evolving a third subset of atoms located between the central high-fidelity region and the remaining low-fidelity region based on an interpolation of the first and second machine learning models to determine the physical state between the atoms.

TRAINING A NEURAL DATABASE FOR ENTITY MATCHING

Resumen de: EP4703968A1

Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to predict one or more second entities that supply materials for the first entity and associated with the good or the service.

SYSTEM AND METHOD FOR DETECTION AND BLOCKING OF FLASH CALLS

Resumen de: EP4703921A1

There is provided a method for training a machine learning model for identifying flash calls in a set of Call Detail Records, CDRs, the method comprising: receiving a set of CDRs and a set of call network data; creating a first training set, the first training set comprising a first subset of CDRs from the set of CDRs and a second subset of CDRs from the set of CDRs, wherein the first subset of CDRs comprises a plurality of CDRs known to represent flash calls, and the second subset of CDRs comprises a plurality of CDRs known to represent legitimate calls; determining one or more characteristic features in the first training set, the one or more characteristic features comprising a first characteristic feature associated with the first subset of CDRs and a second characteristic feature associated with the second subset of CDRs, wherein the first characteristic feature is different from the second characteristic feature.

METHOD AND SYSTEM FOR AUTOMATED ERROR TRIAGING IN AN INDUSTRIAL PLANT

Resumen de: WO2026041226A1

The present invention relates to a method of performing error triaging in an industrial plant (106). The method involves receiving information (502) related to an incident in the industrial plant (106) and generating a knowledge graph (504) by analyzing a plurality of log files (108A-N). The knowledge graph (504) includes data on interdependencies among log events. The method further includes determining one or more nodes (506) of the knowledge graph (504) associated with a set of log events, generating a plurality of templates (508) for these log events using a first machine learning algorithm, and generating a summary report (516) for the incident by utilizing a large language model (514) to process the templates (508). This approach facilitates accurate and efficient identification, categorization, and reporting of errors within the industrial plant (106).

ENHANCED FEATURE INDICATION FOR AI/ML POSITIONING FUNCTIONALITIES AND MODELS

Resumen de: WO2026043591A1

Aspects presented herein may enable a user equipment (UE) to indicate a correlation or a mapping between components of different feature groups (FGs) and differentiation of components in each FG to an artificial intelligence (AI) or machine learning (ML) (AI/ML) model/functionality level. In one aspect, a UE transmits, to a network entity, one or more first indications for a first positioning FG, where a number of the one or more first indications is indicative of a respective AI/ML model or functionality among a plurality of AI/ML models or functionalities associated with the first positioning FG and supported by the UE. The UE receives, from the network entity based on the one or more first indications, a second indication of at least one configuration for AI/ML positioning.

BALANCED TRAINING DATASETS FOR PREDICTING AIRCRAFT COMPONENT FAULTS

Resumen de: US20260054855A1

The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.

WIRELESS DEVICE POWER OPTIMIZATION UTILIZING ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING

Resumen de: US20260059440A1

A method of reducing a power consumption of wireless communication circuitry of an edge device according to one embodiment includes determining a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the edge device and adjusting a wake-up interval of the wireless communication circuitry of the edge device based on the DTIM interval to reduce the power consumption of the wireless communication circuitry of the edge device.

USER PROFILING USING CHAIN-OF-THOUGHT KNOWLEDGE GRAPHS FOR QUERYING A MACHINE LEARNING SYSTEM

Resumen de: US20260057254A1

Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.

THREAT DETECTION PLATFORMS FOR DETECTING, CHARACTERIZING, AND REMEDIATING EMAIL-BASED THREATS IN REAL TIME

Resumen de: US20260058966A1

A method for behavior-based threat detection may include obtaining a first set of data corresponding to at least one of an employee or an enterprise associated with the employee. The method may include training a machine learning model for at least one of the employee or the enterprise associated with the employee by providing the first set of data to the machine learning model as training data, the machine learning model configured to identify deviations between behavioral traits of email communications and behavioral traits of the employee or the enterprise. The method may include receiving an email communication addressed to the employee. The method may include determining that the email communication represents a security risk by applying the machine learning model to the email communication. The method may include performing a remediation action on the email communication based on determining that the email communication represents a security risk.

SYSTEM AND METHOD FOR OPERATING SYSTEM DISTRIBUTION AND VERSION IDENTIFICATION USING COMMUNICATIONS SECURITY FINGERPRINTS

Resumen de: US20260058949A1

A system and method for inferring an operating system version for a device based on communications security data. A method includes identifying a plurality of sequences in communications security data sent by the device; determining an operating system type of an operating system used by the device based on the identified plurality of sequences; applying a version-identifying model to the identified plurality of sequences, wherein the version-identifying model is a machine learning model trained to output a version identifier, wherein the applied version-identifying model is associated with the determined operating system type; and determining the operating system version of the device based on the output of the version-identifying model.

VIRTUAL METROLOGY APPARATUS, VIRTUAL METROLOGY METHOD, AND VIRTUAL METROLOGY PROGRAM

Resumen de: US20260056009A1

A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.

METHOD AND SYSTEM FOR PROVIDING INTELLIGENT RESPONSE AGENT BASED ON SOPHISTICATED REASONING AND INFERENCE FUNCTION

Resumen de: US20260056983A1

A method and system for providing an intelligent response agent based on a sophisticated reasoning and speculation function can generate and provide response data for queries related to specialized documents using a deep-learning neural network that implements a stepwise process for a sophisticated reasoning and speculation function.

BATCH SELECTION POLICIES FOR TRAINING MACHINE LEARNING MODELS USING ACTIVE LEARNING

Resumen de: US2024354655A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method comprises: generating a set of candidate batches of model inputs; generating, for each candidate batch of model inputs, a respective score for the candidate batch of model inputs that characterizes: (i) an uncertainty of the machine learning model in generating predicted labels for the model inputs in the candidate batch of model inputs, and (ii) a diversity of the model inputs in the candidate batch of model inputs; and selecting the current batch of model inputs from the set of candidate batches of model inputs based on the scores; and training the machine learning model on at least the current batch of model inputs.

METHOD FOR DETERMINING SPARSE INTERACTION EFFECT OF BLACK-BOX ARTIFICIAL INTELLIGENCE MODEL

Resumen de: EP4700652A1

The present application relates to the technical field of machine learning. Disclosed are a method and system for interpreting a sparse interaction effect modeled by a black-box artificial intelligence model. The method and system can automatically analyze an interactive distribution modeled by a model. The implementation of the method and system comprises the following steps: providing data that needs to be assessed; using a black-box model to perform prediction on the data, so as to obtain a prediction result of the model; on the basis of an output of the black-box model, modeling the interaction effect between input units of samples, calculating the interaction intensity between combinations formed by the input units, and expressing the black-box model as an "AND addition relationships" and an "OR addition relationships" between the combinations of the input units; and performing optimization, such that the "AND addition relationships" and the "OR addition relationships" are sparser. The advantages of the present invention lie in that a quantification method for interpreting the interaction modeled by a black-box artificial intelligence model is provided, and in comparison with previous research, a sparser and concise interactive interpretation can be obtained.

BALANCED TRAINING DATASETS FOR PREDICTING AIRCRAFT COMPONENT FAULTS

Resumen de: EP4700611A1

The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.

USER PROFILING USING CHAIN-OF-THOUGHT KNOWLEDGE GRAPHS FOR QUERYING A MACHINE LEARNING SYSTEM

Resumen de: EP4700603A1

Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.

SYSTEMS AND METHODS IMPLEMENTING AN INTELLIGENT OPTIMIZATION PLATFORM

Resumen de: EP4700664A2

A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

SYSTEM FOR PREPARING MACHINE LEARNING TRAINING DATA FOR USE IN EVALUATION OF TERM DEFINITION QUALITY

Resumen de: EP4700604A1

A system for preparing machine learning training data for use in evaluation of term definition quality. The system can include a server having at least one server processor and at least one server memory for storing a plurality of terms with corresponding definitions, and a plurality of client devices each having at least one client memory device and at least one client processor. The client processor programmed to receive at least one of the plurality of terms and its corresponding definition from the server, display the term and its corresponding definition, and receive an indication of whether the definition satisfies one or more definition quality guidelines. The server memory includes instructions for causing the at least one server processor to receive the indications from the plurality of client devices and label each definition as satisfying each of the definition quality guidelines or not based on the received indications.

MACHINE LEARNING (ML)-BASED METHOD FOR DETERMINING A CONTROL CHANNEL ELEMENT (CCE) AGGREGATION LEVEL FOR A USER EQUIPMENT (UE) IN A PHYSICAL DOWNLINK CONTROL CHANNEL (PDCCH)

Resumen de: WO2024218535A1

The disclosure relates to a ML-based method for determining a CCE aggregation level for a UE in a PDCCH. The method comprises obtaining RBS traces. The method comprises training, using first data obtained from the traces, a machine learning model to predict a probability of discontinuous transmission (DTX) "isDTX probability". The method comprises inputting second data obtained from the traces into the machine learning model, obtaining the isDTX probability and expanding the second data with the isDTX probability. The method comprises, for each of a plurality of probability thresholds (PTs) and for each of a plurality of strategies, selecting a data having an isDTX probability greater or equal to the PT and best satisfying the strategy and using the data to train a classifier. The method comprises selecting one classifier and using the classifier for determining the CCE aggregation level for the UE in the PDCCH.

SYSTEMS AND METHODS FOR PREDICTING INCIDENT ADENOCARCINOMA OF THE ESOPHAGUS OR GASTRIC CARDIA USING MACHINE LEARNING

Resumen de: US20260051409A1

Systems and methods for predicting esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using machine learning are provided. An example system may obtain an electronic health record (EHR) dataset, identify missing values in the EHR dataset, and generate imputed values for the missing values using simple random sampling imputation. The system may train a model using an extreme gradient boosting algorithm and a training dataset including the EHR dataset to generate a trained model including multiple decision trees. Training the model includes tuning the model to achieve a greatest value of an area under a receiver operating characteristic curve associated with the model. The system may obtain a patient EHR dataset, generate a prediction associated with a risk of EAC and/or GCA by applying the trained model to the patient EHR dataset, and provide the prediction to a computing device to determine a patient treatment protocol.

FRAUD DETECTION SYSTEM AT BASE STATIONS

Resumen de: WO2026039018A1

The present invention relates to a system (1) which enables base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified.

METHOD AND SYSTEM FOR DETERMINING INTEGRATED CIRCUIT PARAMETERS USING MACHINE LEARNING

Nº publicación: WO2026039830A1 19/02/2026

Solicitante:

UNIV RICE WILLIAM M [US]
WILLIAM MARSH RICE UNIVERSITY

Resumen de: WO2026039830A1

A method includes determining, by a computer processor, a set of optimal active device parameters for an active device of an integrated circuit using an optimization engine, a first machine-learning model, and a set of target circuit performance metrics; determining, by the computer processor, a set of optimal passive network parameters for a passive network of the integrated circuit using the optimization engine, a second machine-learning model, and the set of target circuit performance metrics; and generating, by the computer processor, a circuit design for the integrated circuit based on the set of optimal active device parameters and the set of optimal passive network parameters.