Alerta

CAUSAL ENCODING OF CHANNEL STATE INFORMATION

Resumen de: US20260322433A1

A computer-implemented method is provided performed by a first computing device for causal encoding of channel state information, CSI, is provided. The method includes obtaining an indication of incorrect reporting of CSI; retrieving parameter data; obtaining a measurement of CSI data; and activating a machine learning, ML, model for CSI reporting. The ML model including causal relationships among the parameter data and the measurement of CSI data. The method further includes encoding the measurement of CSI data to obtain encoded CSI measurement data; applying the ML model to the parameter data and the encoded CSI measurement data to obtain an adapted CSI measurement data; and transmitting the adapted CSI measurement data to a second computing device. A computer implemented, method performed by a second computing device, and related methods and apparatus are also provided.

DEVICE ATTRIBUTE DETERMINATION BASED ON PROTOCOL STRING CONVENTIONS

Resumen de: US20260322819A1

0000 A system and method for determining device attributes based on protocol string conventions. A method includes applying at least one machine learning model to an application data set extracted based on at least one first pair of strings, each first pair of strings including a protocol string and a key string indicated in respective fields of communications session data corresponding to a device, wherein each machine learning model is trained based on a training data set including second pairs of strings device attribute labels, wherein each device attribute label corresponds to one of the second pairs of strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first pair of strings; and determining, based on the output of the at least one machine learning model, at least one device attribute of the device.

ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML) MANAGEMENT OF VEHICLE ADVANCED DRIVER ASSIST SYSTEM (ADAS) DRIVE POLICIES

Resumen de: TW202528173A

Various embodiments include methods and vehicles that may include managing driving policies in a vehicle advanced driver assist system (ADAS) that include obtaining vehicle sensor data, determining a current vehicle context based on the vehicle sensor data, selecting a modified vehicle driving policy from a plurality of saved modified vehicle driving policies based on the determined current vehicle context, and controlling vehicle behavior by the ADAS based upon the selected modified vehicle driving policy. Methods may also include receiving user voice inputs from a vehicle microphone, using a generative AI to infer relevance of the user voice inputs to vehicle driving policies or actions of the ADAS, adjusting a vehicle driving policy of the ADAS based on the inferred relevance of the user voice inputs, and commanding vehicle behavior based upon the adjusted vehicle driving policy.

ELECTRONIC DEVICE AND IMAGE EDITING METHOD FOR ELECTRONIC DEVICE

Resumen de: EP4822508A1

0001 This electronic device may comprise: a memory for storing instructions; a display; and a processor. The instructions, when executed by the processor, may cause the electronic device to: determine a keyword corresponding to a first user input on the basis of reception of the first user input; obtain at least one image through crawling on the basis of the determined keyword; determine at least one graphical object corresponding to the at least one image; receive a second user input related to the at least one graphical object; and generate an artificial intelligence image using a machine learning model on the basis of any one of the second user input or information about the at least one graphical object.

SYSTEMS AND METHODS FOR LIST-BASED DATASET OPTIMIZATION FOR TRAINING OF MACHINE LEARNING ALGORITHMS

Resumen de: US20260311059A1

0000 The present disclosure relates generally to systems and methods for generating and encoding columns corresponding to element sets that include elements that do not co-occur within the original list columns in the original dataset. This updated dataset may be used to dynamically train machine learning algorithms that support text input while minimizing the amount of data that needs to be included in the dataset.

AUTOMATED MANAGEMENT OF CONFIGURATION DRIFT FOR MACHINE LEARNING MODELS

Resumen de: US20260311039A1

An apparatus comprises at least one processing device configured to monitor performance metrics associated with execution of a machine learning model deployed on an information technology asset, to determine configuration information characterizing a current configuration for the information technology asset, and to generate, utilizing performance prediction models that take as input the determined configuration information, predicted performance metrics associated with execution of the machine learning model utilizing the current configuration of the information technology asset. The at least one processing device is further configured to modify, in response to determining utilizing the monitored and predicted performance metrics that the execution of the machine learning model deployed on the information technology asset is experiencing a configuration drift-induced performance issue, at least one of (i) the current configuration of the information technology asset and (ii) the machine learning model deployed on the information technology asset.

Managing Resource Usage Monitoring Using Machine Learning and Rules-Based Models

Resumen de: US20260310543A1

0000 Techniques for improving the performance of a resource usage monitoring system by selectively verifying anomaly predictions are disclosed. A system inputs resource usage data to a machine learning model. The system determines remediation actions to perform based on whether or not the system verifies machine-learning-generated anomaly predictions. The system verifies the anomaly predictions by applying measured resource usage data to a rules-based verification model. If the system verifies a predicted anomaly, the system remediates faults in resource metering devices, such as sensors, memory chips, microcontrollers, and data transmission devices. If the system determines a predicted anomaly is not verified, the system remediates a fault in the machine learning model by generating a training record based on the measured and estimated resource usage values to retrain the machine learning model.

FEDERATED LEARNING FOR MULTI-LOCATION MACHINE LEARNING MODELS

Resumen de: US20260311040A1

Described are systems, methods, and apparatus that enable deployment and updating of multiple machine learning (“ML”) models to different edge locations so that each edge model is improved based on training/updating done at other edge locations. In addition to updating models at different edge locations, the disclosed implementations also eliminate any risk of data exposure due to transmission between edge locations and reduce the compute capacity required to update an ML model at an edge location.

METHODS AND SYSTEMS FOR DETERMINING CORRECTNESS OF MACHINE LEARNING MODEL OUTPUT

Resumen de: US20260310999A1

0000 The present disclosure provides methods and systems for generating a confidence label for a prediction produced by a predictive model. The method comprises: (a) generating training datasets for training a confidence model, the training datasets are generated using data collected from a cross validation process for evaluating the predictive model; (b) training the confidence model using the training datasets to learn a relationship between a score assigned by the predictive model to a prediction and a correctness measure of the prediction; and (d) feeding an input to the trained confidence model to output a confidence label. The input comprises a target precision or a target recall for a new prediction produced by the predictive model and a score assigned to the new prediction, and the confidence label indicates whether the new prediction is high confidence or low confidence.

SYSTEM AND METHOD FOR INTELLIGENT GENERATION OF PRIVILEGE LOGS

Resumen de: US20260310899A1

Systems, methods, and computer readable media for intelligent generation of privilege logs are provided. These techniques may include applying an unsupervised machine learning model to a corpus of documents to identify a plurality of topics associated with the corpus of documents, associating a plurality of categories with respective subsets of the plurality of topics, executing a first classifier training model to train a first classifier for a category, and a second classifier training model to train a second classifier for the category, determining a performance metric for the first classifier for the category and a performance metric for the second classifier for the category, selecting one of the first classifier or the second classifier for the category based on the performance metrics, applying the selected classifier to documents in the corpus of documents, and generating a privilege log based upon the classifiers applied to documents in the corpus of documents.

NEUROLOGICAL DISORDER CLASSIFICATION USING LOG-REGULARIZED LAPLACIAN SPARSE FUNCTIONAL CONNECTIVITY AND ENSEMBLE GRAPH-BASED DEEP LEARNING

Resumen de: US20260311879A1

0000 A computer-implemented method, system, and computer program product for classifying a neurological disorder. Resting-state functional magnetic resonance imaging (rs-fMRI) data and phenotypic data associated with a subject are received. A sparse functional connectivity (FC) matrix is then estimated from the rs-fMRI data by minimizing an objective function comprising a Laplacian graph trace term and a log-regularized weight term. The discriminative brain connectivity features are then generated from the sparse FC matrix using an Adaptive-Hierarchical Graph Convolutional Network (AH-GCN). Discriminative brain connectivity features refer to a set of high-level, reduced-dimensional latent representations that capture the most topologically significant functional patterns in the subject's brain. Phenotypic data may then be processed through a plurality of machine learning classifiers to generate phenotypic probability scores. Furthermore, the discriminative brain connectivity features and the phenotypic probability scores are synthesized via a stacking meta-classifier to generate a classification for the neurological disorder.

APPARATUSES, METHODS, AND COMPUTER PROGRAMS FOR A NETWORK NODE AND FOR USER EQUIPMENT AND FOR SELECTING A MACHINE-LEARNING MODEL

Resumen de: WO2026209468A1

Provided are apparatuses, methods, and computer programs for a network node and for user equipment. The method (10) for the network node (300) of a mobile communication system (500) comprises transmitting (12), to a user equipment (400), UE, information related to a configuration of a model index table comprising a plurality of index entries. Each index entry associates an index value with a model identifier of a machine-learning model and/or with at least one functionality identifier of the machine-learning model. The model index table partitions the plurality of index entries into at least a first index range associated with one or more mobility model identifiers and a second index range associated with one or more stationary model identifiers, so as to enable the UE to select a machine-learning model and/or functionality, based on a mobility status of the UE, from the first index range or from the second index range.

SYSTEMS AND METHODS FOR INTERCEPTING AND MODIFYING INFERENCING OF MACHINE LEARNING MODELS

Resumen de: US20260311037A1

Systems and methods are disclosed for modifying inferencing of machine learning (ML) models. Based on a first input and session context data, an ML model may generate a first plurality of output tokens. Based on supplemental context data, a first one or more tokens from the first plurality of output tokens may be identified. A second input may be generated based on the identified first one or more tokens, the supplemental context data, or both. The second input may be provided to the ML model. Based on the second input, session context data, supplemental context data, or a combination thereof, the ML model may generate a second plurality of output tokens. In response to the first input and the second input, an output based on the first plurality of tokens, the second plurality of tokens, and supplemental context data may be provided.

ARTIFICIAL INTELLIGENCE BASED METHODS AND SYSTEMS FOR ANOMALY PATTERN DETECTION IN DATASETS

Resumen de: WO2026212789A1

A process includes obtaining a dataset having a plurality of data records that include time series data, determining a typology feature for a typology of interest of the dataset based on an exponentially decaying velocity of at least a portion of the plurality of data records. In response to the typology feature satisfying a typology condition, the process may provide input data features associated with the dataset that include at least a data feature relating to the exponentially decaying velocity of the portion of the plurality of data records into a machine learning anomaly classification model. The machine learning anomaly classification model may be operated on the input data features. In response to an output of the machine learning anomaly classification model satisfying an anomaly condition, an anomaly indicator that indicates that the dataset is an anomaly is stored in storage.

MACHINE LEARNING GENERATION FOR REAL-TIME LOCATION

Resumen de: US20260311056A1

A system and method for determining location information of a portable device relative to an object is provided. In one embodiment, aspects of the system to determine location with respect to the portable device may be transferred to another system for training that system to determine location with respect to the portable device or another type of portable device.

PARALLEL PROCESSING FOR MODEL EXECUTION

Resumen de: US20260311057A1

The subject technology provides for distributed training and inferencing of a machine learning model across multiple processing resources. During a forward pass, each processing resource generates a portion of an update for its corresponding portion of a feature set. While performing this computation, each resource can approximate, compress, and transmit its portion of the features to other resources, and can receive an approximated and compressed portion generated elsewhere. Each resource then decompresses the received representation and updates its local feature portion without waiting for full-precision data from other nodes. By allowing approximation, compression, communication, and computation to occur in parallel, the system may reduce communication overhead that typically causes idle compute periods. This structure may improve sustained utilization and responsiveness when models operate across multiple distributed compute nodes.

GENERATING SPEECH TEST MATERIALS WITH CUSTOMIZABLE LINGUISTIC CHARACTERISTICS

Resumen de: WO2026210286A1

Presented herein are systems and methods for generating one or more test sentences. A labeled training set including one or more test sentences with corresponding labels is obtained. A machine learning model is trained based on the labeled training set to generate a pre-trained machine learning model. The pre-trained machine learning model is fine-tuned, based on a fine-tuning sentence set, to generate a fine-tuned machine learning model. A user prompt indicating a request for one or more test sentences exhibiting one or more linguistic characteristics is obtained. The fine-tuned machine learning model is configured to generate one or more test sentences based on the request indicated in the user prompt.

OPTIMIZING ARTIFICIAL INTELLIGENCE (AI) MODEL TRAINING AND INFERENCING PERFORMANCE THROUGH SERVICE CLASS PRIORITIZATION

Resumen de: US20260311043A1

0000 In an embodiment, optimizing AI model training and inferencing performance through service class prioritization includes detecting one or more patterns in a plurality of service classes previously executed on a computing system, wherein each service class in the plurality of service classes includes a respective set of one or more work items; determining, based on the one or more patterns, a pattern strength score for each service class in the plurality of service classes; selecting, based on the pattern strength score, a service class for inclusion in a training data set; and training a machine learning model using the training data set.

COMPUTER SYSTEM AND METHOD FOR DETERMINING A CAUSE OF AN ANOMALY EVENT FOR A NETWORK DEVICE

Resumen de: US20260312962A1

A computing system and process for training a machine-learning (ML) model for determining a cause of a detected anomaly event for at least one network device. Accessed are training datasets including a plurality of anomaly events for a plurality of network devices and a plurality of computer network events associated with one or more computer networks. A determination is made regarding an appearance frequency a certain anomaly event for a network device occurs within a defined time period a network event occurs. A determination is made regarding a probability a certain anomaly event for a network device is associated with a network event responsive to the determined appearance frequency, such that, when the ML model is executed, a determination is made for a likelihood a given anomaly event for a network device is caused by a given network event.

ULTRA-HIGH-THROUGHPUT MOLECULAR RETRIEVAL SYSTEM FOR VIRTUAL SCREENING OF LARGE CHEMICAL LIBRARIES

Resumen de: WO2026213359A1

A system and method may include a machine learning model configured to generate vector embeddings of molecules based on molecular representations, a storage system configured to store embeddings corresponding to a plurality of molecules in the chemical library, and a similarity search engine configured to compute similarity between a query molecule embedding and stored embeddings. A system may include a computing infrastructure configured to perform the similarity computation across at least one distributed computing node, wherein the system is configured to identify molecules having similarity above a threshold relative to the query molecule.

SYSTEM AND METHOD FOR ANALYZING AND NORMALIZING SLEEP DATA

Resumen de: WO2026213356A1

A system and method for standardizing and analyzing sleep data from a plurality of heterogeneous third-party device sources are disclosed. The system ingests sleep data from wearable devices, mobile applications, and aggregated health data platforms under a Bring Your Own Device (BYOD) framework. A machine learning-based standardization module normalizes heterogeneous sleep data into a unified dataset by compensating for differences in data formats, sampling rates, and device-specific collection methodologies, and predicts missing values to ensure dataset completeness. A composite predictive model comprising an ensemble of machine learning algorithms cross-validates sleep parameters, applies oversampling to address imbalanced training data, and generates a sleep score representing the user's sleep quality. A feedback mechanism receives user-reported sleep quality input and incorporates it into continuous refinement of the standardization module. Personalized model adjustments using parallel multi-device recordings further improve predictive accuracy for individual users.

ONBOARD FAULT DIAGNOSIS AND RECOVERY IN WIRELESS SENSOR NODE DEVICE AND METHOD

Resumen de: WO2026212573A1

Wireless sensor node devices and methods in wireless sensor node devices are disclosed A wireless sensor node device comprises: a sensor configured to generate sensor data; a wireless network interface configured to transmit the sensor data from the sensor node device; and a controller comprising: a machine learning classifier configured to identify anomalous data in the sensor data and to classify the anomalous data; a fault diagnosis module configured to diagnose a fault source based on a classification of the anomalous data; and a recovery module configured to generate a recovery action indication based on the fault source.

ML-BASED REFINEMENT MODEL FOR DEVELOPMENT OF A WELLNESS ASSESSMENT METHOD

Resumen de: WO2026210466A1

The present disclosure relates to a machine learning (ML) based refinement model for development of a wellness assessment method. More particularly, the model acquires genetic, metabolic, anthropometry, clinical data and live tracking data from health devices to provide personalized diet and exercise charts for subjects. Furthermore, the method is a tier-based process wherein tier 1 employs LASSO for regression analysis; tier-2 involves ML based decision models for data validation and tier-3 incorporates CNN and LLM to decide the subtype a subject belongs to. The proposed Machine Learning (ML) based model incorporates Natural Language Processing (NLP) and Large Language Models (LLM) to assess behavioral and recorded language data and links the same to the subtypes generated using genetic, metabolomic and anthropometry data.

ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR VALUE CHAIN NETWORKS

Resumen de: AU2026234064A1

Abstract A robot fleet management platform, includes a set of datastores that store a governance library that defines a set of governance standards that include at least one set of security standards, legal standards, ethical standards, regulatory standards, quality standards, or engineering standards that are applied to decisions made by one or more respective intelligence services; and includes: a set of one or more processors that execute a set of computer-readable instructions, wherein the set of one or more processors collectively execute: a governance-enabling intelligence layer that receives and responds to intelligence requests received from respective intelligence service clients, wherein the intelligence layer includes: a set of artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twin service, a robot process automation service, or a machine vision service; and an intelligence layer controller that coordinates performance of respective intelligence services on behalf of the respective intelligence service clients and performance of a set of analyses corresponding to the respective intelligence services based in part on the set of governance standards, wherein the intelligence layer returns decisions determined collectively by the artificial intelligence service in response to the intelligence requests, such that the decisions are determined based on a set of intelligence service dat

AI Superintelligence Application State Machine

Nº publicación: US20260311258A1 08/10/2026

Solicitante:

ARUNACHALAM LAKSHMI [US]
ARUNACHALAM Lakshmi

Resumen de: US20260311258A1

0000 The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.