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142
results.
Updated on
11/10/2026 [07:07:00]
Results 50 to 75 of 142
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260290509A1
0000 The disclosure provides methods to identify marker sequences for rapid classification of microbial strains through machine learning analysis of genome assemblies.
Absstract of: US20260289340A1
The disclosed embodiments include computer-implemented systems and processes that dynamically monitor variations in process explainability of within a distributed computing environment. For example, an apparatus may determine a first metric value characterizing a variation in an explainability of a machine-learning or artificial-intelligence process between a first temporal interval and a second temporal interval. When the first metric value is inconsistent with an exception criterion, the apparatus may obtain a second metric value characterizing an additional variation in the explainability of the machine-learning or artificial-intelligence process between the first temporal interval and a third temporal interval, and when the second metric value is inconsistent with the exception criterion, the apparatus may perform operations that modify at least one of (i) a value of a process parameter that characterizes the machine-learning or artificial-intelligence process or (ii) a composition of an input dataset associated with the machine-learning or artificial intelligence process.
Absstract of: 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.
Absstract of: 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.
Absstract of: WO2026198893A1
System, apparatus and method may control receiving time-indexed data comprising values of a plurality of features; generating derived features based on temporal changes of at least one of the plurality of features, the derived features including at least a first derivative of a first value; temporally aligning the plurality of features and the derived features to a reference date such that only data available prior to the reference date is used for training a machine learning model; training a machine learning model on the temporally aligned features and the derived features to predict a forward-looking value; and outputting the forward-looking value.
Absstract of: WO2026198682A1
A computer-implemented method for evaluating resource allocation requests using an ensemble machine learning model is disclosed. A resource request is received, comprising multiple data inputs from different data sources, where each data source provides a specific type of user feature. A first set of machine learning models, each trained on a single data source, generates corresponding first risk scores. A second decision tree-based machine learning model processes the data inputs, leveraging feature dependencies and learned missing feature patterns to generate a second risk score, enabling robust predictions even when certain user features are incomplete or missing. The first and second risk scores are ensembled using an ensemble technique to produce an overall risk score. The ensembled risk score is then transmitted to a resource hosting device, which approves, denies, or adjusts the requested resource based on the assessed risk.
Absstract of: WO2026197916A1
A method for training a machine learning model, comprising determining a plurality of input data objects from a first unlabelled set of data, and providing the plurality of input data objects to at least one reference model configured to generate a first output for each of the plurality of input data objects. A labelled set of training data is created using the generated first outputs, and a target model is trained using the labelled set of training data. The trained target model is applied to a second unlabelled set of data. A plurality of training data objects is determined from the second unlabelled set of data. The plurality of training data objects is provided as an input to the at least one reference model to generate a second output for each of the plurality of training data objects, and the labelled set of training data is updated.
Absstract of: WO2026195627A1
A system for predicting lifespan of a physical access control (PAC) reader device is described. The system collects data associated with a PAC reader. The system processes the data using one or more machine learning (ML) models to generate a prediction of a remaining lifespan of the PAC reader and one or more configuration changes for extending the remaining lifespan of the PAC reader. The system generates an alert comprising the prediction and the one or more configuration changes.
Absstract of: WO2026198742A1
A system and method of managing an electrical power grid. The method comprises: obtaining measurement data comprising a respective time series for each of a plurality of electrical loads connected to an electrical power grid, each time series comprising electrical power consumption measurements for the corresponding electrical load at each of a plurality of times; processing each of the time series using a machine learning model to determine a respective classification output for each of the electrical loads; processing the measurement data to predict one or more states of the electrical power grid at one or more corresponding times; and adjusting electrical power distribution to one or more of the electrical loads based on the classification outputs for the electrical loads and the predicted one or more states of the electrical power grid.
Absstract of: US20260288888A1
This disclosure relates generally to a method and system for generating spatially distributed response to query by intelligent selection of time-series remote sensing data. The current available methods for processing and analyzing geospatial data have limitations such as manual satellite data selection, limited natural language query support and the like. The method disclosed receives multi-contextual user query, intelligently selects the relevant time-series satellite data and climate information from repository using intents and entities generated utilizing pre-defined knowledge graph. The method intelligently selects time-series satellite data by creating and utilizing relationship strength-based node ranking graph. This time-series satellite data is combined with the climate data selected from climate repository to generate the spatially distributed response for the multi-contextual user query using machine learning model and large language model. The method enables stakeholders to access actionable insights without needing expertise in satellite data processing and using it for informed agricultural decision-making.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: US20260285373A1
0000 Embodiments of this disclosure 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 disclosure includes text information that facilitates understanding by a user.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Nº publicación: EP4811625A1 23/09/2026
Applicant:
2NEURON SOLUCOES EM INTELIGENCIA ARTIFICIAL LTDA [BR]
2Neuron Solucoes Em Inteligencia Artificial Ltda
Absstract of: 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.
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