Alerta

SYSTEM

Resumen de: US20260289330A1

A system includes a processor that is configured to preprocess information acquired from a plurality of information sources and analyze the preprocessed information by using a machine learning algorithm, analyze a question of a user by using a natural language processing technique, input a prompt to a generative AI model to instruct the generative AI model to generate an appropriate answer to the question, and analyze a text input of the user by using a sentiment analysis algorithm and adjust the generated answer based on an emotion estimated from the text input.

METHODS AND SYSTEMS FOR USING CONTENT CONTRIBUTION VALUE TO DETERMINE MEMORY, STORAGE, CONTEXT WINDOW, AND MODEL PARAMETERS IN MACHINE LEARNING MODELS

Resumen de: US20260288521A1

Systems, methods, and computer-readable media determine allocation of model memory, storage, parameters, context window capacity, tokens, or training and inference resources based on contribution values of input data. A system can encode input data, segment the input data into input sequences, test a model with the input sequences, obtain output token or encoding sequences and logit values, and compute entropy, significance, weight of evidence, explanatory power, rarity, or salience to determine expected contribution values. Input sequences having low or negative contribution value can be discarded or reordered, and remaining input sequences can be retained for training, inference, fine tuning, reinforcement learning, or context-window assembly. The resulting allocation can reduce processing of low-utility data while optimizing memory, storage, and parameter resources of the model in training.

SENTENCE GENERATION MODEL GENERATION DEVICE, SENTENCE GENERATION MODEL GENERATION METHOD, AND SENTENCE GENERATION DEVICE

Resumen de: US20260289073A1

The sentence generation model generation device (10) includes a learning unit (11) configured to, in response to input of an input sentence, identify, from the input sentence, a claim and a premise that supports the claim from the same standpoint as the claim or from a different standpoint than the claim, perform at least one of labeling the claim and the premise and labeling a relation between the claim and the premise, and conduct machine learning using the input sentence as an explanatory variable and the text of the labeled portion as an objective variable, to perform training of a sentence generation model for generating and outputting a text sentence related to a structure of the input sentence and generate the sentence generation model.

DYNAMICALLY MONITORING VARIATIONS IN PROCESS EXPLAINABILITY WITHIN A DISTRIBUTED COMPUTING ENVIRONMENT

Resumen de: 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.

IDENTIFICATION OF MARKER SEQUENCES FOR RAPID CLASSIFICATION OF MICROBIAL PATHOGENS THROUGH MACHINE LEARNING ANALYSIS OF GENOME ASSEMBLIES

Resumen de: US20260290509A1

0000 The disclosure provides methods to identify marker sequences for rapid classification of microbial strains through machine learning analysis of genome assemblies.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

SYSTEM AND METHOD FOR MATCHING ENTITIES USING MACHINE LEARNING

Resumen de: US20260288480A1

0000 A system and method may identify the best matching entities using artificial intelligence, by for example determining, from a set of features describing a number of entities, a subset of features which when input to a machine learning module allow the machine learning module to match a pair of entities based on the ability of the second entity in the pair to successfully take over responsibilities handled by the first entity in the pair; training a machine learning module using the determined features; and for a first entity described by the determined set of features, using the machine learning module to determine a list of entities matching the first entity, each entity in the list of entities associated with a matching score.

FEDERATED MACHINE LEARNING-BASED ANOMALY DETECTION ACROSS MULTIPLE DATA CENTERS

Resumen de: US20260289386A1

An apparatus comprises at least one processing device configured to generate graph structure representations of a first set of logs associated with information technology assets in a first data center, to train a first machine learning-based anomaly detection model utilizing the graph structure representations, to train a second machine learning-based anomaly detection model by distilling knowledge of the first machine learning-based anomaly detection model, and to update the second machine learning-based anomaly detection model based on an aggregation of parameters of the second machine learning-based anomaly detection model and additional parameters of additional machine learning-based anomaly detection models trained utilizing logs associated with information technology assets of additional data centers. The at least one processing device is also configured to perform anomaly detection on a second set of logs associated with the information technology assets in the first data center utilizing the updated second machine learning-based anomaly detection model.

STACKING OF HETEROGENEOUS MACHINE LEARNING MODELS FOR DETECTION OF FRAUDULENT TRANSACTIONS

Resumen de: US20260289568A1

A system is adapted to automatically detect fraudulent transactions. The system includes a fraud management computer system programmed for: receiving a plurality of transactions, some legitimate and some fraudulent; with the transactions and a K-fold training module, training a heterogeneous group of machine learning models to classify the transactions as legitimate or fraudulent, where the K-fold training module generates, for each machine learning model, a mean and a standard deviation; with the means and standard deviations, determining a weight for each machine learning model; and, with the weights for each machine learning model, defining a meta machine learning model to classify the transactions as legitimate or fraudulent; receiving a real-time transaction and, with the trained machine learning models, generating determinations of whether it is legitimate or fraudulent; with the meta machine learning model and the determinations, classifying the real-time transaction as legitimate or fraudulent; and, if fraudulent, generating an alert.

SYSTEM AND METHOD FOR AUTOMATED MACHINE LEARNING MODEL TRAINING

Resumen de: US20260289326A1

A system for automated machine learning model training comprising processor configured to receive multi-modal training data and user-defined parameters through end-user abstraction interface, autonomously determine machine learning domain applicable to multi-modal input data by analyzing data annotations. Autonomously prune redundant data elements from multi-modal input data to generate training dataset, automatically identify hyperparameters for training dataset, automatically split multi-modal input data into plurality of data groups using dynamically determined split ratios. Select a machine learning framework and neural network architecture based on the determined machine learning domain. Perform batch normalization on training dataset, train machine learning model using training dataset, selected framework and architecture, and identified hyperparameters, training includes dynamic adjustment of learning rates and evaluate machine learning model during training using validation data from plurality of data groups, wherein training is terminated when loss function indicates minimal reduction in difference between actual and predicted value.

EFFICIENT SPECULATIVE DECODING USING DRAFTING TREES FOR MACHINE LEARNING MODELS

Nº publicación: WO2026193870A1 24/09/2026

Solicitante:

QUALCOMM INC [US]
ZHANG WENHAO [CN]
LI ZHIGUO [CN]
LV SHAOCHUN [CN]
QUALCOMM INCORPORATED
ZHANG, Wenhao
LI, Zhiguo
LV, Shaochun

Resumen de: WO2026193870A1

Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a drafting tree comprising a first plurality of draft tokens is generated using a generative machine learning model. A plurality of confidence scores comprising a respective confidence score for each respective token of the first plurality of draft tokens is determined. A plurality of acceptance predictions comprising a respective acceptance prediction for each respective token of the first plurality of draft tokens is generated based on the plurality of confidence scores and a structure of the drafting tree, and a subset of draft tokens is selected from the first plurality of draft tokens based on the plurality of acceptance predictions. An output token is generated based on processing the subset of draft tokens using the generative machine learning model.