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ON-DEVICE HYBRID MACHINE LEARNING MODEL FOR CALL OPTIMIZATION

Resumen de: WO2026058994A1

Embodiments of the present disclosure disclose method and apparatus optimizing call quality in a user equipment (UE). The method includes: identifying a mobile originated (MO) call or a mobile terminated (MT) call satisfying one or more criteria; capturing a plurality of parameters associated with the MO call or the MT call and the UE, based on the MO call or the MT call satisfying the one or more criteria and correlating the plurality of parameters with historical call data to identify one or more patterns influencing the call quality; analyzing, using a hybrid machine learning (ML) model, the one or more identified patterns and predicting call quality issues for the MO call or the MT call; and adjusting UE resources based on the predicted call quality issues and real time context data and adjusting includes providing recommendations for a user of the UE.

INTELLIGENT RESPONSES TO DATA REQUESTS

Resumen de: US20260080320A1

An embodiment includes generating, responsive to detecting a data request by a system, a response attribute by a machine learning model based on the data request wherein the machine learning model is trained on a historical attribute metric. The embodiment includes determining a validation metric corresponding to the machine learning model, wherein different machine learning models correspond to different validation metrics. The embodiment also includes deciding, by the system to modify the response attribute, by selecting the machine learning model with a greatest validation metric determined for the response attribute.

DOMAIN-AWARE NEUROSYMBOLIC AGENTS FOR IMPROVING PROBLEM-SOLVING ACCURACY AND CONSISTENCY

Resumen de: US20260080313A1

A system receives domain specific questions from users and answers them. The system stores domain specific information comprising domain specific facts and domain specific programs. The system receives an input request to perform a domain specific task for the particular domain. The system provides the input request to a machine learning model trained to predict a score indicating whether the input request should be processed by a symbolic processor or by a neural network. If the score predicted by the machine learning model indicates that the input request should be processed by the symbolic processor, the system determines whether a stored domain specific program can solve the input request. If none of the stored domain specific programs can solve the input request, the system generates a new program for solving the input request using a machine learning based language model and the set of domain specific facts.

SYSTEM AND METHOD FOR GENERATION PREDICTION, FAULT DETECTION AND PERFORMANCE ENHANCEMENT OF PHOTOVOLTAIC GENERATING STATION

Resumen de: WO2026058273A1

The present invention relates to a system and method for predicting photovoltaic (PV) power generation, detecting faults, and enhancing the performance of PV generating stations The system comprises a data collection module (14) that acquires actual data on environmental conditions and PV system performance and transmits sensor data to a cloud platform for analysis, the data analysis module (15) processes data to predict PV power generation, optimize system performance, and identify potential issues, and user interface (16) display system performance, provide accurate understandings, and enable remote monitoring. The system and method utilize advanced machine learning techniques to improve the accuracy of PV power generation predictions, detect faults, and optimize system performance, resulting in increased energy production and reduced operational costs.

SYSTEM AND METHOD FOR MANAGING COMPUTING DEVICES

Resumen de: EP4711895A2

A system and method for easily managing a data center with multiple computing devices such as cryptocurrency miners from different manufactures is disclosed. A first computer includes a management application to manage the selected computing devices and periodically read and store status information from them into a database. Controls are presented to enable selection of one or more of the devices and to apply an operating mode, including manual, semi-automatic, automatic, and intelligent modes. Machine learning may be used to determine recommended settings for the selected set of computing devices.

METHOD AND DEVICE FOR PROVIDING ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MEDIA SERVICE USING USER EQUIPMENT CAPABILITY NEGOTIATION IN WIRELESS COMMUNICATION SYSTEM

Resumen de: WO2024237615A1

Disclosed is a method and device for efficiently providing an artificial intelligence/machine learning (AI/ML) media service by a user equipment (UE), including receiving service access information from a network server providing the AI/ML media service, receiving a trained configuration AI model used to determine a capability of the UE associated with an AI split inferencing between the UE and the network server, performing inferencing for a capability discovery based on the trained configuration AI model, and transmitting capability metrics of the UE to the network server based on a result of the inferencing.

MULTI-TASK REAL-TIME INFERENCE SCHEDULING SYSTEM MACHINE TOOL AND METHOD THEREOF

Resumen de: EP4711869A1

The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.

MANAGING CLINICAL TRIAL PROGRESSION USING MACHINE LEARNING-BASED DATA

Resumen de: WO2024238507A1

Systems and methods for managing progression of a clinical trial. Input data for a machine learning model is formed, based on longitudinal data for clinical trial cohort. The input data corresponds to input features and the cohort includes a plurality of subjects. A clinical outcome output is generated for each subject, using the machine learning model and a portion of the input data corresponding to each subject. Feature importance values are generated, based on the machine learning model generating the clinical outcome output for each subject. The feature importance values include, for each subject, a set of feature importance values for a set of input features. A ratio of interest is computed using the plurality of feature importance values. An output is generated using the ratio of interest in which the output indicates whether the cohort should proceed to a next phase of the clinical trial.

METHOD AND APPARATUS FOR TRANSMITTING/RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

Resumen de: EP4712540A1

A method performed by a terminal in a wireless communication system, according to at least one of embodiments disclosed in the present specification, may comprise: configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmission and reception points (TRPs) for positioning; acquiring input data subsets on the basis of TRP subsets of the multiple TRPs; acquiring positioning information output from the at least one AI/ML model on the basis of the input data subsets; and transmitting a positioning-related report to a network on the basis of the positioning information, wherein the positioning-related report may include information on at least one of the TRP subsets or the input data subsets.

MODEL INFERENCE METHOD AND DEVICE

Resumen de: EP4711986A1

A model inference method and apparatus are disclosed, and relates to the field of machine learning technologies. A client and a server use respective deployed models to process different parts of user data, to obtain respective output results. In addition, the client obtains the output result of the server, and obtains an inference result based on the output results of the server and the client. Compared with a case in which the server needs to obtain all the user data in an inference process, in this application, the server obtains only a part of the user data. As the server cannot obtain, based on the part of the user data, all content included in the user data, security of the user data is ensured. In addition, the client needs to send only the part of the user data to the server, so that a bandwidth resource occupied by data transmission between the client and the server and time consumed by the transmission can be reduced, and inference efficiency can be improved.

APPARATUS AND METHOD FOR MACHINE LEARNING-BASED DOMAIN DERIVATION

Resumen de: US2025158959A1

Provided are an electronic device for deriving a domain connected to the IP address based on Open Source INTelligence (OSINT) information and for deriving the domain connected to the IP address based on an artificial intelligence (AI) model. and a method for the same.

COMPUTER SYSTEM AND METHOD FOR PROVIDING A SUBJECT-RELATED DATA DEVELOPMENT PLATFORM

Resumen de: US20260074078A1

A method comprises receiving at least one input data object containing subject-related information according to at least one of information types encoded in at least one of data formats; and processing the at least one input data object for standardizing the subject-related information. The method further includes subjecting the subject-related information to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format; storing the uniform dataset in one or more secured data repositories connected to a network; and providing a secured virtual environment accessible to users connected to the network, the secured virtual environment enabling importation of datasets stored in the one or more secured data repositories and a use of imported datasets as part of one or more user-controlled subject-related data development operations for generating at least one workspace-developed data object.

USING MARKOV CHAINS TO EXPLAIN MACHINE LEARNING MODEL PREDICTIONS AND TO EVALUATE AUTONOMOUS COMPUTER AGENTS

Resumen de: US20260073258A1

Historical performance information of a plurality of autonomous agents configured to handle a plurality of tasks is accessed. The historical performance information indicates, for each autonomous agent, a successful outcome or a failed outcome for each of the tasks handled by the autonomous agent. A Markov chain comprising a plurality of states is constructed based on the autonomous agents. Each autonomous agent corresponds to a different state of the states. For each autonomous agent, a first score and a second score are calculated based on the Markov chain. The first score corresponds to an expected number of transitions from the autonomous agent to other autonomous agents until the successful outcome or the failed outcome is reached, The second score corresponds to a probability of the autonomous agent ultimately achieving the successful outcome. The autonomous agents are evaluated based on the first score and the second score.

DATA PROCESSING METHOD AND APPARATUS

Resumen de: WO2026051507A1

A data processing method. The method comprises: a first instance performs a first processing process, the first processing process being a full inference process performed by means of a machine learning model; and a second instance performs a second processing process, the second processing process being an incremental inference process performed by means of the machine learning model, wherein the first instance and the second instance are different instances running on a same computing unit, and the first processing process and the second processing process are performed in parallel. In embodiments of the present application, different instances running on a same computing unit are used to respectively perform a full inference process and an incremental inference process by means of a machine learning model, thereby reducing overhead of data communication between devices and storage overhead, shortening an overall end-to-end delay, and correspondingly increasing the transactions per second (TPS).

INFORMATION PROCESSING METHOD AND RELATED DEVICE

Resumen de: WO2026051936A1

Embodiments of the present application provide an information processing method and a related device, applied to the field of artificial intelligence. The method comprises: acquiring initial feature information of input content; and inputting the initial feature information into a first machine learning model to obtain generated content. The generated content is generated after the first machine learning model executes a task. If the input content comprises an audio, initial feature information of the audio is discrete feature information, and thus the first machine learning model uses the discrete feature information to understand the audio, so that the first machine learning model has the capability to directly generate the audio. When the task comprises generating an audio, the generated content comprises an audio. If the input content comprises an image, initial feature information of the image is continuous feature information. By combining the discrete feature information of the audio with the continuous feature information of the image, end-to-end audio generation can be realized, and the image can be well understood, thereby improving the quality of the generated content.

User profiling using chain-of-thought knowledge graphs for querying a machine learning system

Resumen de: AU2025217419A1

Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query. Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning m

MACHINE LEARNING ACCELERATED SEMANTIC EQUIVALENCE DETECTION

Resumen de: US20260072913A1

Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

SYSTEM AND METHOD FOR CONTROLLING RESOURCE MANAGEMENT USING MACHINE LEARNING

Resumen de: AU2024327251A1

In some aspects, a computing system can use a machine learning model for resource management. For example, the system can receive a request for a set of steps associated with a target model output of a machine learning model. The request can include a starting input feature set and a number of steps. For each of the number of steps, the system can calculate a change to one or more features from the starting input feature set to arrive at the target model output based on a current position in feature space of the machine learning model. The system can update a feature vector by applying the change to the features of the starting input feature set and transmitting the set of steps. The system can then cause a resource of the external computing system to transition toward a position defined by the target model output.

METHOD FOR IDENTIFYING KEY INFLUENCING FACTORS ON ECOLOGICAL BENEFITS OF URBAN BLUE-GREEN SPACES

Resumen de: WO2026051144A1

Disclosed in the present invention is a method for identifying key influencing factors on ecological benefits of urban blue-green spaces. The method comprises: first, constructing an evaluation indicator set for ecological benefits of urban blue-green spaces; then, calculating data of indicators and performing standardization processing, and establishing a standardized data matrix for the ecological benefits of the urban blue-green spaces; next, using an analytic hierarchy process to determine subjective weights of the indicators, using an entropy weight method to determine objective weights, and obtaining weights of the indicators by means of combination weighting; afterwards, constructing an entropy weighted TOPSIS model to calculate a weighted standardized data matrix for the ecological benefits of the urban blue-green spaces, so as to complete the evaluation of the ecological benefits of the urban blue-green spaces; subsequently, establishing an influencing factor set for the ecological benefits of the urban blue-green spaces; and finally, on the basis of evaluation results of the ecological benefits of the urban blue-green spaces, using a GWR-XGBoost model to identify key influencing factors. In the present invention, a multi-criteria decision analysis method, a geographically weighted regression model and a machine learning technique are combined, thereby enabling the rapid and accurate acquisition of key influencing factors on ecological benefits of urban blue-green sp

ECOLOGICAL BENEFIT PREDICTION METHOD FOR URBAN BLUE-GREEN SPACE

Resumen de: WO2026051143A1

Disclosed in the present invention is an ecological benefit prediction method for an urban blue-green space, comprising the following steps: S1, establishing an urban blue-green space morphological feature index set and an ecological benefit index set; S2, calculating urban blue-green space morphological feature indexes, ecological benefit indexes, and a total ecological benefit, calculating the correlation and importance among the morphological feature indexes, and screening for key morphological feature indexes; S3, using a random forest algorithm to train key morphological feature index data and total ecological benefit data to obtain a response relationship between morphology and ecology; S4, calculating morphological index data after implementation of an urban blue-green space planning scheme; and S5, on the basis of the response relationship between morphology and ecology, predicting ecological benefits after the implementation of the urban blue-green space planning scheme by means of the morphological index data after the implementation of the urban blue-green space planning scheme. In the present invention, a random forest machine learning algorithm is used to predict the ecological benefits after the implementation of the urban blue-green space planning scheme, thereby providing a reference for ecological environment benefit evaluation of urban development.

INTELLIGENT COLLABORATIVE OPERATION CONTROL STRATEGY FOR ELECTRO-HYDRAULIC SUSPENSION SYSTEM OF HIGH-HORSEPOWER TRACTOR

Resumen de: US20260070388A1

0000 An intelligent collaborative operation control strategy for an electro-hydraulic suspension system of a high-horsepower tractor is provided, including: S1, establishing an operation task database; S2, collecting information of the electro-hydraulic suspension system during an operation of the high-horsepower tractor in real time as real-time data; S3, preprocessing the real-time data; S4, analyzing a dataset and generating a preliminary control strategy for the electro-hydraulic suspension system based on parameters in the operation task database; S5, performing data interaction and collaborative control with operation units; S6, identifying deviations between operation status data and expected control parameters; S7, dynamically adjusting the preliminary control strategy. Through implementation of sensing technology and machine learning algorithms, real-time and precise adjustment of operation parameters for the suspension system of the tractor is achieved, significantly enhancing a level of automation and intelligence in operations, thereby effectively improving efficiency and economic benefits of agricultural operations.

MULTI-SCALE SPEAKER DIARIZATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Resumen de: US20260073937A1

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker diarization. The techniques include obtaining a speaker embedding for various reference times of a speech and for various differently-sized time intervals, identifying a plurality of clusters, each cluster associated with a different speaker of the speech. The techniques further include computing, using the speaker embeddings, a set of embedding weights for various differently-sized time intervals, and identifying, using the computed set of the embedding weights, one or more speakers speaking at a respective reference time.

PRESERVING PRIVACY IN GENERATING A PREDICTION MODEL FOR PREDICTING USER METADATA BASED ON NETWORK FINGERPRINTING

Resumen de: US20260074958A1

A method, an apparatus and a computer program product for machine learning based on network fingerprinting, while preserving privacy in generating a prediction model for predicting user metadata. Routing information of a device is obtained based probe packets sent by the device to a server that is connectable to the device via the Internet, such as a series of packet hops implemented to route the packets to the server or a series of Internet Protocol (IP) addresses of the series of packet hops until reaching the Internet. A fingerprint describing an architecture of connection path of the device to the Internet is created based on the routing information. The prediction model is trained using training dataset that includes pairs of fingerprints and labels using edge devices having known labels, that are indicative of a routing information of an edge device to the Internet.

SYSTEM FOR TIME BASED MONITORING AND IMPROVED INTEGRITY OF MACHINE LEARNING MODEL INPUT DATA

Resumen de: US20260073309A1

Embodiments of the invention are directed to systems, methods, and computer program products for providing intelligent system and methods for identifying and weighting volatile data in machine learning data sets. The system is adaptive, in that it can be adjusted based on the needs or goals of the user utilizing it, or may intelligently and proactively adapt based on the data set or machine learning model being employed. The system may be seamlessly embedded within existing applications or programs that the user may already use to interact with one or more entities, particularly those which aid in the managing of user resources.

Control Logic for Thrust Link Whiffle-Tree Hinge Positioning for Improved Clearances

Nº publicación: US20260073240A1 12/03/2026

Solicitante:

GENERAL ELECTRIC COMPANY [US]
General Electric Company

Resumen de: US20260073240A1

Systems and methods for optimizing clearances within an engine include an adjustable coupling configured to couple a thrust link to the aircraft engine, an actuator coupled to the adjustable coupling, where motion produced by the actuator adjusts a hinge point of the adjustable coupling, sensors configured to capture real time flight data, and an electronic control unit. The electronic control unit receives flight data from the sensors, implements a machine learning model trained to predict clearance values within the engine based on the received flight data, predicts, with the machine learning model, the clearance values within the engine based on the received flight data, determines an actuator position based on the clearance values, and causes the actuator to adjust to the determined actuator position.