Alerta

METHOD FOR SECURELY DEPLOYING A MODEL, DEVICE AND STORAGE MEDIUM

Resumen de: US20260289400A1

A method for securely deploying a model, a device, and a storage medium are provided. The method includes: receiving, from a user side, a deployment request for a machine learning model, the deployment request including at least a model identification of the machine learning model that is encrypted and a key identification of a key used to encrypt the machine learning model; obtaining the machine learning model that is encrypted and the key based on the deployment request; decrypting, in a trusted execution environment, the machine learning model that is encrypted using the key to obtain the machine learning model; and deploying the machine learning model in the trusted execution environment.

METHOD OF MODEL TRAINING, ELECTRONIC DEVICE AND STORAGE MEDIUM

Resumen de: US20260289351A1

Provided is a method of model training, an electronic device and a storage medium, relating to the field of data processing technologies, and in particular, to the technical fields of artificial intelligence, big data, large models and deep learning. The method includes: obtaining target sample data corresponding to N branches required in a current training period of distributed training; performing T update operations on network parameters of a preset model deployed on the N training nodes for the distributed training based on T data blocks corresponding to the branches to obtain first full parameter sets corresponding to the branches; performing model parameter fusion on the first full parameter sets to obtain a fused full parameter set; and updating the network parameters of the preset model deployed on the training nodes based on the fused full parameter set and proceeding to the next training period to obtain a target model.

AUTONOMOUS MULTIFACTOR GENERATIVE ARTIFICIAL INTELLIGENCE FRAMEWORK

Resumen de: US20260289633A1

0000 Various embodiments are directed to apparatuses, methods, computer-readable media, computer program products, and systems related to detecting a process trigger that identifies a target entity; identifying, entity data associated with the target entity; generating, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset; generating, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset and the entity data; and transmitting the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.

UTILIZING ARTIFICIAL INTELLIGENCE FOR DATA MANAGEMENT AND DATA VISUALIZATION

Resumen de: US20260288716A1

A first dashboard is generated utilizing a machine learning model and user settings associated with a user. One or more follow-up prompts are received. Data needed to answer the one or more follow-up prompts is determined and obtained utilizing one or more data catalogs. One or more subsequent dashboards are generated utilizing the machine learning model and the user settings associated with the user.

OPTIMIZING ARTIFICIAL INTELLIGENCE GENERATED CODE ON THE COMPUTING CONTINUUM

Resumen de: US20260288429A1

Systems and methods for optimizing artificial intelligence generated code on the computing continuum. An instruction code that instructs a machine learning model (MLM) can be modified to obtain a modified instruction code that incorporates a dynamic control flow that limits processing of software code based on a target time period. One or more candidate codes based on the modified instruction code can be generated. One or more service paths within the dynamic control flow of the one or more candidate codes can be executed to obtain an optimized generated code having detailed responses within a threshold for downstream tasks by asynchronously performing sub-tasks from the one or more service paths to optimal computing nodes.

Machine Learning Approach to Multi-Domain Process Automation and User Feedback Integration

Resumen de: US20260289418A1

Embodiments relate to multi-domain process automation with user feedback integration. Some embodiments include a method performed by one or more computing devices. The one or more computing devices generate, using a machine learning (ML) model, predictions for records. The one or more computing devices receive at least one of single user feedback or multiple user feedback for the predictions. The one or more computing devices generate a user validated record pool based on the single user feedback or multiple user feedback. The one or more computing devices update the ML model using the user validated record pool.

SMART RING SYSTEM FOR MEASURING DRIVER IMPAIRMENT LEVELS AND USING MACHINE LEARNING TECHNIQUES TO PREDICT HIGH RISK DRIVING BEHAVIOR

Resumen de: US20260285324A1

A method for predicting risk exposure can include sensing, by a sensor of a ring worn by a user, a set of data. The sensor can be integrated with a microfluidic device. The method for predicting risk exposure further can include determining, by a trained machine learning (ML) model, a risk score based at least on the set of data. The method for predicting risk exposure also can include determining, by a trained ML model, a remediating action to reduce the risk score. The method for predicting risk exposure also can include, in response to determining the risk score, generating a notification to alert the user of the risk score and the remediating action. Other embodiments are disclosed.

ADAPTIVE TRANSMISSION AND TRANSMISSION PATH SELECTION BASED ON PREDICTED CHANNEL STATE

Resumen de: US20260292652A1

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter node may predict a future state associated with a wireless channel at a future time instance using a machine learning model, wherein the future state is predicted based at least in part on one or more of weights associated with the machine learning model, a current state associated with the wireless channel, or one or more previous states associated with the wireless channel. The transmitter node may select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel. The transmitter node may perform the transmission using the one or more parameters. Numerous other aspects are described.

Systems and Methods for Adaptive Avatar Dialog Generation and Behavioral Modeling

Resumen de: US20260284519A1

0000 A deep-learning system enables a coach to guide interactions between a user and an avatar while adaptively refining behavioral characteristics associated with the avatar. During a session, a coach provides prompts, feedback, and scoring through a dedicated interface, and the system generates avatar-matched dialog based on predefined behavioral traits, objectives, and personality attributes. The system compares automated scoring with coach-provided evaluations to identify substantive differences and dynamically update adaptive model parameters used in future dialog generation. The invention further supports real-time response suggestions, behavioral reinforcement, avatar movement controls, voice transformation, and personality-based language modification. Training data generated during sessions may be reviewed and incorporated into ongoing deep-learning processes, enabling continuous improvement of avatar behavior modeling for coaching, simulation, education, therapeutic, or interactive communication environments.

MACHINE LEARNING TRAINING DURATION CONTROL

Resumen de: US20260289413A1

Some embodiments select a machine learning model training duration based at least in part on a fractal dimension calculated for a training data dataset. Model training durations are based on one or more characteristics of the data, such as a fractal dimension, a data distribution, or a spike count. Default long training durations are sometimes replaced by shorter durations without any loss of model accuracy. For instance, the time-to-detect for a model-based intrusion detection system is shortened by days in some circumstances. Model training is performed per a profile which specifies particular resources or particular entities, or both. Realistic test data is generated on demand. Test data generation allows the trained model to be exercised for demonstrations, or for scheduled confirmations of effective monitoring by a model-based security tool, without thereby altering the model's training.

ARTIFICIAL INTELLIGENCE BASED RULE GENERATION FOR DATABASE CHANGE DEPLOYMENT

Resumen de: US20260288683A1

0000 Embodiments provide systems, methods, and computer program products that utilize artificial intelligence/machine learning to process database change data and correlated performance data to predict the impact of database changes and generate rules with respect to database changes to prevent undesired behavior or promote increased performance.

SMART RING SYSTEM FOR MONITORING SLEEP PATTERNS AND USING MACHINE LEARNING TECHNIQUES TO PREDICT HIGH RISK DRIVING BEHAVIOR

Resumen de: US20260285376A1

A method can include acquiring, by a sensor of a sleep detecting device, a set of user data indicative of sleep activity of a user. The method further can include acquiring data indicative of an activity of the user. The method also can include predicting, based at least on the set of user data and the activity of the user, a level of risk exposure for the activity of the user. The method also can include in response to predicting, generating an output indicative of the level of risk exposure of the user. Other embodiments are disclosed herein.

METHODS AND SYSTEMS FOR CONCRETE MATERIALS AND CONCRETE MIXTURE CHARACTERIZATION

Resumen de: US20260289427A1

Systems, methods, and computer-readable media of characterizing concrete materials or concrete mixtures may include: obtaining, using sensors that comprise at least two different types of sensors, sensor data corresponding to the concrete materials or the concrete mixtures; analyzing, using a trained machine learning model, the sensor data to generate the characterization of the concrete materials or the concrete mixture; and outputting the characterization of the concrete materials or the concrete mixture. Training the trained machine learning model may include: obtaining a set of training data for historical concrete materials or a historical concrete mixture, including a plurality of characterizations of the historical concrete materials or the historical concrete mixture; classifying the set of training data into a plurality of subsets each corresponding to a different characterization or range of characterizations of the plurality of characterizations; and generating the trained machine learning model using the plurality of subsets of training data.

SYMBOLIC MACHINE LEARNING ARCHITECTURE

Resumen de: US20260289401A1

A machine learning system can include a symbolic representation generator and an inference component. The symbolic representation can generate a symbolic memory network using input obtained from an environment. The inference component can generate a causal model by experimenting upon components, operations, and parameters of the symbolic representation generator. The causal model can influence the generation of the symbolic memory network. In particular, the machine learning system can identify an analog in the symbolic memory network as being entangled, identify a disentangled version of the identified entangled analog in the symbolic memory network, and, in response to the identification of the disentangled version of the identified entangled analog, release the identified entangled analog from the symbolic memory network and provide instructions to take an action in an environment based on the disentangled version of the identified entangled analog.

APPARATUS AND METHOD FOR IDENTIFYING BASE STATION BEHAVIOUR

Resumen de: US20260292508A1

0000 A method for identifying anomalous base station behaviour in a communications network comprises generating an ensemble algorithm ML model, performing first training of plural decision trees in the ML model and performing second training of the decision trees such that feature values of diversion nodes are included in feature contributions of anomaly detection nodes. The method further includes processing communications network report data using the ML model, identifying a base station exhibiting anomalous behaviour and generating an alert. A machine learning agent, a communications network, a computer program and a computer-readable storage medium are also disclosed.

APPARATUSES AND METHODS FOR MACHINE LEARNING MODEL RELIABILITY ASSESSMENT

Resumen de: US20260289336A1

Example embodiments enable reliability assessment of a machine learning model for mobility management related predictions. A client node may be configured to obtain configuration information for reliability assessment of a machine learning model, the configuration information comprising one or more parameters for configuration of an evaluation window, one or more evaluation functions and one or more reliability acceptance thresholds; obtain verification data based on the evaluation window and evaluation criteria for determining at least one reliability measure; determine at least one reliability measure based on the verification data and the one or more evaluation functions; determine qualification of the machine learning model based on the at least one reliability measure and the one or more reliability acceptance thresholds; send a result of the qualification to the network node; receive feedback from the network node based on the result indicating if the client node is authorized to use the machine learning model; and perform a mobility management related prediction based on the machine learning model when authorized by the network node. Apparatuses and methods are disclosed.

METHOD FOR ESTABLISHING ROBOT COMMUNITY, ROBOT COMMUNITY SYSTEM AND OPERATING METHOD

Resumen de: EP4811249A1

0001 A method for establishing a robot community, a robot community system and an operating method. For establishing the robot community, each of intelligent robots (101) is assigned with a specific attribute, and accordingly a training data is prepared for training the intelligent robot (101) through a machine-learning algorithm. The robot communication system then deploys multiple intelligent robots (101) into a social media and generates community contents associated with each of the intelligent robots (101) according to the attribute and basic data of each of the intelligent robots (101) by a generative intelligent model. Therefore, the robot community system allows users to interact with the intelligent robots (101) and establish social relationships between the users and the intelligent robots (101) in the social media.

QUANTUM COMPUTING FOR ACCELERATING MACHINE LEARNING MODEL TRAINING

Resumen de: WO2026075653A2

A system and method to accelerate machine learning training comprises receiving, by a non-quantum computer, a set of data for training a machine learning model; dividing the set of data into a plurality of data batches, each data batch determined according to predetermined requirements of a quantum computer; calculating, by the non-quantum computer, a Jacobian matrix for each of the plurality of data batches, the Jacobian matrix and a loss vector being part of a linear matrix equation for updating weights in the model; arranging, by the non-quantum computer, input data of a polynomial function formatted for a quantum computer; solving, by the quantum computer, a vector of weight updates; and updating the weights in the model.

PREDICTION METHOD AND PREDICTION SYSTEM

Resumen de: EP4811366A1

To design a material required to satisfy a plurality of properties efficiently. A method performed by a prediction system according to an embodiment of the present invention includes acquiring a composition and a manufacturing condition of a material of interest; and predicting a plurality of properties of the material of interest by inputting the acquired composition and manufacturing condition of the material of interest into a machine learning model for predicting properties of a material based on its composition and manufacturing condition. The machine learning model is based on a mixture of continuous probability distributions.

SYSTEM AND METHOD FOR MONITORING THE OPERATING CONDITION OF ROTARY ELECTRICAL MACHINES AND AUTOMATIC DETECTION OF MECHANICAL AND ELECTRICAL FAULTS

Resumen de: EP4811625A1

The present invention pertains to methods for operation, maintenance, and monitoring of rotating electrical machines, and relates to a system and a method for monitoring the operational condition and detecting mechanical, electrical, load, and process faults in rotating electrical machines. The proposed system and method, together, provide a more effective way to detect faults early on, with greater installation convenience and scalability than prior art methods, and are more efficient in avoiding production losses, improving operational performance and preventing damage to equipment and risks to operators. The developed method continuously collects electrical current and voltage signals that power the machine, utilizing a data acquisition module and current and voltage transformers. The collected data undergoes stages of compression, encryption, application of Fast Fourier Transform (FFT), subsampling, feature extraction, anomaly detection using statistical and machine learning techniques, and fault classification using machine learning techniques. The proposed system consists of one or more data acquisition modules, one or more gateway devices, a processing center on a cloud computing platform, and an operator interface. In an industrial application, the method can be continually improved with the collection of new data and with validation information from an operator in the face of an identified fault.

METHODS AND SYSTEMS FOR OPTIMAL CARE SETTING FOR PATIENTS USING MACHINE LEARNING

Resumen de: WO2025106998A1

A method includes: receiving patient information including information about a current hospitalization of a patient; receiving hospital-unit-volume information corresponding to a rolling window of time, indicating a patient number admitted to, transferred into, or discharged from a hospital unit housing the patient; converting the patient information and the hospital-unit- volume information into a machine-interpretable format for at least one binary output machine learning algorithm; executing the at least one algorithm to generate binary models that predict the likelihood of discharging the patient within each of multiple timeframes; combining the binary models into a single combined model, including optimizing weightings of discharge predictions for each binary model; determining, using the single combined model, at least one weighted discharge readiness quantization; transforming the at least one weighted discharge readiness quantization into at least one discharge prediction; and controlling a user interface to provide the at least one discharge prediction to a user.

MACHINE LEARNING BASED METHOD FOR CONSIDERING CONSTRAINT VIOLATIONS WHEN SELECTING OPTIMAL ACTIONS TO PERFORM IN A COMMUNICATIONS NETWORK

Resumen de: WO2025105999A1

Embodiments described herein relate to methods and apparatuses for initiating control of one or more parameters in a communications network. A computer-implemented method comprises obtaining a first state of the communications network; for each action in a set of actions: predicting a next state of the communications network utilizing a first ML model based on the first state and the action; predicting, utilizing a second ML model, whether taking the action whilst the communications network is in the first state will violate one or more constraints; determining a cost value associated with the action by evaluating a cost function, wherein the cost function comprises: a first component representing one or more performance metrics of the communications network in the next state; and a second component dependent on whether taking the action in the first state will violate the one or more constraints; selecting a selected action from the set of actions, wherein the selected action is associated with a best cost value; and initiating performance of the selected action in the communications network.

AUTOMATED CONTENT MODALITY TRANSFORMATION

Resumen de: CA3292429A1

Aspects of the present disclosure relate to automated content modality transformation. Embodiments include receiving content that corresponds to a particular modality. Embodiments further include providing the content as input to a generative machine learning model that has been trained to generate versions of provided content items that correspond to a target modality. Embodiments further include receiving, as an output from the generative machine learning model, a new version of the content that corresponds to the target modality. Embodiments further include providing the content and the new version of the content as input to a given machine learning model that is trained to generate a confidence score that indicates a likelihood that the new version is an accurate representation of the content in the target modality. Embodiments further include performing one or more actions involving the new version of the content based on the confidence score.

SYSTEM AND METHOD FOR COLD-START MACHINE LEARNING MODELS

Resumen de: EP4797167A1

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.

METHOD FOR DETECTING PRESENCE OF AN ELECTRIC ARC AND ASSOCIATED DEVICE

Nº publicación: US20260276691A1 17/09/2026

Solicitante:

SAFRAN [FR]
CENTRE NAT RECH SCIENT [FR]
ECOLE SUPERIEURE PHYSIQUE & CHIMIE IND VILLE DE PARIS [FR]
UNIV SORBONNE [FR]
SAFRAN
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE
ECOLE SUPERIEURE DE PHYSIQUE ET DE CHIMIE INDUSTRIELLE DE LA VILLE DE PARIS
SORBONNE UNIVERSITE

Resumen de: US20260276691A1

A computer-implemented method for detecting the presence of an electric arc on a power line using a machine learning algorithm, carried out using a time-correlated measurement of the arc voltage Varc, of the current I and of the source voltage Vsource and also a knowledge model for linking the arc voltage Varc and the current I to constants depending on the knowledge model, implemented after a learning phase that involves adjusting a learning model that takes the current I and the source voltage Vsource at input and provides the voltage Varc and the constants of the knowledge model at output; the method including continuously detecting the presence of an arc using a decision function depending on a cost function used in the learning phase.