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MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES

Resumen de: US2025335796A1

Systems and methods include machine learning models operating at different frequencies. An example method includes obtaining images at a threshold frequency from one or more image sensors positioned about a vehicle. Location information associated with objects classified in the images is determined based on the images. The images are analyzed via a first machine learning model at the threshold frequency. For a subset of the images, the first machine learning model uses output information from a second machine learning model, the second machine learning model being performed at less than the threshold frequency.

CODE QUALITY MANAGEMENT USING MACHINE LEARNING

Resumen de: US2025335331A1

A method comprises causing scanning of at least a portion of code in response to one or more changes to the code, processing data generated as a result of the scanning, and analyzing the data using at least one machine learning algorithm to predict whether the one or more changes will cause a reduction in quality of the code. In response to a prediction that the one or more changes will cause a reduction in the quality of the code, a placeholder in a code development application to address the reduction is generated.

MACHINE-LEARNING MODELS FOR DYNAMIC CORRECTIVE ACTIONS

Resumen de: US2025335829A1

A system collects user data describing characteristics of multiple users. A first machine-learning model assesses this data to predict churn scores of the users. When a user sends an error signal concerning their experience with the system, the system retrieves a identified churn score for this user and applies a second machine-learning model. This second model takes as input user data and their churn score to select a corrective action among a set of corrective actions aimed at reducing the user's churn score. After implementing the selected corrective action, the system collects and updates the user's data to reflect their continued engagement or departure. The system uses this updated user data to retrain the first or second model to improve the predictive accuracy of the first or second model.

SYSTEMS AND METHODS FOR DETERMINING A USER SPECIFIC MISSION OPERATIONAL PERFORMANCE METRIC, USING MACHINE-LEARNING PROCESSES

Resumen de: US2025335326A1

Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.

TECHNIQUES FOR DETECTING ANOMALIES IN DATA FILES

Resumen de: US2025335588A1

Described are examples for detecting anomalies in files. Each of multiple files can be processed to generate corresponding flat files. Values can be extracted from multiple lines of each of the flat files into a parameter vector. Patterns can be generated from the multiple lines based on the values extracted. The parameter vector and the patterns can be provided as input to a machine learning (ML) model to obtain a set of rules for the files based on conditional probabilities. The set of rules can be applied to a set of one or more files to detect anomalies in values in the set of one or more files.

AI-Based Energy Edge Platform, Systems, and Methods Having a Digital Twin of Decentralized Infrastructure

Resumen de: US2025334943A1

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a plurality of digital twins of energy operating assets, the plurality of digital twins of energy operating assets including at least one energy generation digital twin, energy storage digital twin, energy delivery digital twin, and/or energy consumption digital twin, and a set of energy simulation systems configured to generate a simulation of energy-related behavior of at least one of the plurality of digital twins of energy operating assets, and a machine-learning system configured to generate a predicted state of at least one of the energy operating assets. The simulation of energy-related behavior is based on historical patterns, current states, and the predicted state of at least one of the energy operating assets.

SYSTEMS AND METHODS FOR STRATEGIC APPLICATION MODERNIZATION ASSESSMENT

Resumen de: US2025335160A1

Systems and methods for application modernization using machine learning (ML) are disclosed herein. An example system receives software development information corresponding to one or more applications, the software development information including human-readable code. The system provides the software development information to an ML model. The ML model is trained using application modernization training data corresponding to best practices for modernizing historical applications based upon historical software development information. The ML model includes a large language model trained to interpret the human-readable code. The ML model generates application modernization information corresponding to at least one application of the one or more applications. The application modernization information includes technical requirements of a corresponding application, and application modernization recommendations of the corresponding application based upon the one or more technical requirements. In response to generating the application modernization information, the system provides the application modernization information to a computing device.

SELECTABLE ENCRYPTION FOR 5G OPEN RADIO ACCESS NETWORK

Resumen de: WO2025226317A2

Techniques for encrypting data within a 5G Open Radio Access Network (O-RAN) includes receiving, at a first module of the 5G O-RAN, a first set of one or more data packets encrypted using mathematical encryption. The method also includes determining, using a machine-learning model trained to detect cybersecurity threats, the existence of a cybersecurity threat associated with the voice or data transaction, and in response, determining to switch encryption from the mathematical encryption to quantum encryption. The method further includes encrypting the one or more data packets using a quantum encryption key to generate quantum-encrypted data packets, transmitting the quantum encryption key from the first module of the 5G O-RAN core to a second module of the 5G O-RAN over a quantum key distribution (QKD) channel, and transmitting the quantum-encrypted data packets from the first module of the 5G O-RAN to the second module of the 5G O-RAN.

SYSTEMS AND METHODS FOR OBJECT FORMULATION UTILIZING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES

Resumen de: WO2025226533A1

A method may receive, by one or more processors, relevant data from a plurality of data sources. A method may input the relevant data, by the one or more processors into a machine learning model, for generating iterations of object models and iterations of object designs. A method may assess, by the one or more processors utilizing an artificial intelligence module, one or more of object performance metrics, user experience indicators, or industry acceptance probabilities based on user feedback and state pattern. A method may cause, by the one or more processors, iterative refinement of the object models and the object designs.

SYSTEMS AND METHODS FOR STRATEGIC APPLICATION MODERNIZATION ASSESSMENT

Resumen de: WO2025226527A1

Systems and methods for application modernization using machine learning (ML) are disclosed herein. An example system receives software development information corresponding to one or more applications, the software development information including human-readable code. The system provides the software development information to an ML model. The ML model is trained using application modernization training data corresponding to best practices for modernizing historical applications based upon historical software development information. The ML model includes a large language model trained to interpret the human-readable code. The ML model generates application modernization information corresponding to at least one application of the one or more applications. The application modernization information includes technical requirements of a corresponding application, and application modernization recommendations of the corresponding application based upon the one or more technical requirements. In response to generating the application modernization information, the system provides the application modernization information to a computing device.

CELL MANUFACTURING MANAGEMENT PLATFORM USING MACHINE LEARNING

Resumen de: WO2025224675A1

A cell manufacturing management platform facilitates management of a cell manufacturing process. The cell manufacturing management platform tracks events associated with a cell manufacturing process and coordinates between disparate entities involved in the process. The cell manufacturing management platform utilizes machine learning techniques to generate inferences associated with event scheduling in a manner that optimizes an efficiency metric and reduces likelihood of exceptions occurring. Machine learning models may furthermore be used to generate various alerts or other actions associated with the process. A user interface enables different participating entities to track progress of the process and upcoming events.

SYSTEM AND METHOD FOR GENERATING FORMULATIONS USING MACHINE LEARNING ARCHITECTURES

Resumen de: WO2024130398A1

There is provided systems and methods for generating formulations with improved performance and lower resource consumption. The population of formulations may be created by a Differential Evolution (DE) process. In each successive generation, lower performing formulations may be replaced with new formulations predicted to have better performance by a modeling pipeline. The modeling pipeline may perform a search for ML architectures and hyperparameter optimization for a suitable ML architecture, and then train one or more machine learning models to minimize error (otherwise maximizing the resulting score for a formulation). The machine learning models may be an ensemble of Random Forest models. These new formulations may form part of the next generation in the DE process. This modified version of the Differential Evolution process using machine learning techniques may result in populations of formulations which are superior in performance, and/or require fewer iterations of generations to achieve formulations which meet performance objectives.

MACHINE LEARNING BASED OCCUPANCY GRID GENERATION

Resumen de: CN120303583A

In some aspects, a device may receive sensor data associated with a vehicle and a set of frames. The device may aggregate sensor data associated with the set of frames using the first gesture to generate an aggregated frame, where the aggregated frame is associated with the set of cells. The device may obtain an indication of a respective placeholder flag from each cell of the set of cells, where the respective placeholder flag includes a first placeholder flag or a second placeholder flag, and where the set of cells from the set of cells is associated with the first placeholder flag. The device may train a machine learning model using data associated with the aggregated frame to generate a placeholder grid based on a loss function that calculates only losses from respective cells of the set of cells. Numerous other aspects are described.

MULTI-STAGE MACHINE LEARNING MODEL CHAINING

Resumen de: CN120390929A

A skill chain including a set of ML model evaluations is generated, the input is processed with the set of ML model evaluations, and the skill chain is used to eventually generate a model output accordingly. Each ML model evaluates a "model skill" corresponding to the skill chain. The intermediate output generated by the first ML evaluation for the first model skills of the skill chain may then be processed as an input for the second ML evaluation for the second model skills of the skill chain, thereby ultimately generating a model output for the given input. Such a skill chain may include any number of skills according to any of the various structures and do not need to be evaluated using the same ML model.

REPLACEMENT COMPONENT MANAGEMENT USING MACHINE LEARNING

Resumen de: US2025328809A1

A method comprises predicting one or more device types for which one or more components thereof will be replaced, wherein the predicting is performed using at least a first machine learning algorithm, identifying locations of respective devices of a plurality of devices corresponding to the one or more device types, and determining one or more component distribution sources that are in proximity to the locations of the respective devices, wherein the determining is performed using at least a second machine learning algorithm. At least one device of the respective devices qualifying for at least one replacement component is identified. The method further comprises causing dispatching of the at least one replacement component to a location of the at least one device from a component distribution source of the one or more component distribution sources in proximity to the location of the at least one device.

VALIDATING VECTOR CONSTRAINTS OF OUTPUTS GENERATED BY MACHINE LEARNING MODELS

Resumen de: WO2025221872A1

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

ESTIMATING MEMORY IMPAIRMENT FROM A MOBILE MULTIMODAL DIGITAL SCREENING

Resumen de: WO2025221739A1

Methods are provided for determining a memory impairment probability. The method comprises receiving one or more audio recordings of an interaction by a user with a computing device during an assessment; extracting acoustic and speech timing features from the one or more audio recordings; applying a machine learning model to the acoustic and speech timing features to generate a memory impairment probability for the user; and providing a clinical decision support recommendation for a provider based on the memory impairment probability.

SYSTEMS AND METHODS FOR BATTERY PERFORMANCE PREDICTION

Resumen de: WO2025221413A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

AI-BASED SMART HEALTH SCREENING SYSTEM FOR IDENTIFYING INDIVIDUALS AT RISK OF DEVELOPING CANCERS, CARDIOVASCULAR DISEASE AND TYPE 2 DIABETES

Resumen de: WO2025219769A1

The invention is an AI-driven health screening system for early detection of cancers, cardiovascular diseases, and type 2 diabetes. It integrates multiple screening methods, using machine learning, deep learning, and natural language processing to analyze genetic and health data. A federated learning framework ensures high prediction accuracy while maintaining data privacy. The system also employs a blockchain-based electronic health record (EHR) for secure data management. AI models, including neural networks and support vector machines, assess risk factors and provide personalized healthcare recommendations. Designed with a three-tier architecture, it supports deployment as a web service, software, or integration into existing programs. The system enhances early disease detection, optimizes healthcare resources, and improves patient outcomes by offering a scalable, efficient, and non-invasive screening solution.

SYSTEMS AND METHODS FOR PREEMPTIVE COMMUNICATION OF ROAD CONDITION DATA

Resumen de: US2025329252A1

A system and method for communicating road condition data. The system and method includes a plurality of inter-changeable housings, including a sensor housing comprising a sensor configured to generate sensor data; a data processing housing comprising a processor configured to receive the sensor data and vehicle-originated data, and apply one or more layers of a machine learning architecture to the sensor data and the vehicle-originated data to generate at least a portion of vehicle instruction data; and a wireless communication housing comprising a wireless interface circuit configured to receive the vehicle-originated data and to transmit the vehicle instruction data generated by the processor.

VALIDATING VECTOR CONSTRAINTS OF OUTPUTS GENERATED BY MACHINE LEARNING MODELS

Resumen de: US2025328822A1

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

GENERATING CONVERSATION CONTENT FOR TRAINING CONVERSATIONAL ARTIFICIAL INTELLIGENCE

Resumen de: US2025328814A1

According to one embodiment, a method, computer system, and computer program product for generating natural conversation content for training conversational artificial intelligence (AI) systems is provided. The present invention may include receiving conversation content comprising one or more conversation sequences; assigning one or more labels to one or more utterances comprising the conversation sequences using a machine learning-based intent classifier to produce a plurality of labeled conversation content; determining if a confidence score for at least one of the assigned labels is below a predetermined threshold; determining at least one variant operation of a plurality of variant operations to perform on the labeled conversation content using a natural conversation variator; and performing the at least one operation of the plurality of variant operations on the labeled conversation content using the natural conversation variator to generate one or more variations of the labeled conversation content.

BENCHMARKING ALGORITHMS FOR DATA QUALITY MONITORING

Resumen de: WO2025221286A1

In a general aspect, benchmarking for data quality monitoring is described. In some embodiments, a system identifies a base data set to be used as input to a machine learning (ML) model. The system generates a modified base data set by causing synthetic anomaly injection operations to be performed on data of the base data set. The system causes the ML model to run, using the base data set as input, to determine a first output of the ML model, and to run, using the modified base data set as input, to determine a second output of the ML model. The system determines a set of performance metrics representing performance of the ML model at detecting data anomalies and outputs a representation of the set of performance metrics.

Machine Learning System Using Quantum Computing

Resumen de: US2025328793A1

Methods and systems for training and using a binary classifier implemented using quantum computing techniques are disclosed. The described approach involves deriving, from an input data set, a plurality of training samples, each training sample comprising a data vector having a plurality of features and a class label. Each data vector is processed using a quantum classification process including: encoding the data vector as an Ising Hamiltonian; implementing the Ising Hamiltonian on a set of real or virtual qubits of a quantum processing unit or an emulation thereof to form a quantum system representing the data vector; executing operations on the (emulation of the) quantum processing unit to prepare the ground state of the quantum system; determining one or more properties of the ground state; and identifying one of a set of possible ground states corresponding to the data vector based on the one or more properties. The system then determines, based on the identified ground states and class labels for the training samples, a mapping that maps ground states to class labels. The mapping is stored and used for classifying further data samples.

SYSTEMS AND METHODS FOR SELF-LEARNING ARTIFICIAL INTELLIGENCE OF THINGS (AIOT) DEVICES AND SERVICES

Nº publicación: US2025328785A1 23/10/2025

Solicitante:

SHORELINE IOT INC [US]
SHORELINE IOT, INC

Resumen de: US2025328785A1

The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.