Alerta

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.

MACHINE LEARNING MODEL FOR PROCESSING A COMMUNICATION RECORD AND IDENTIFYING AN ACTION

Resumen de: US20260080457A1

In an embodiment, a computer-implemented method includes parsing a communication record, determining one or more intents of the communication record using a trained machine learning model, linking the communication record to one or more record IDs, identifying one or more fields within the communication record, presenting the communication record in a standardized form, including identification of intent, the standardized form resulting from the identified one or more fields, and determining a corresponding intent related action on the determined one or more intents.

Training a neural database for entity matching

Resumen de: AU2025201913A1

Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to predict one or more second entities that supply materials for the first entity and associated with the good or the service. Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to

CONSTRAINED NON-LINEAR HYBRID MODELS FOR PREDICTION FROM MULTIPLE DATA SOURCES

Resumen de: AU2024319668A1

In some aspects, a machine learning (ML) model can be trained for risk assessment. The ML model can be trained to determine a risk indicator for a target entity from predictor variables associated with the target entity. The predictor variables are obtained from multiple sources with varying availability, and the training of the ML model is accomplished based on a multi-dimensional representation of common information from the set of data sources. Once generated, the risk indicator can be transmitted to a remote computing device in a responsive message for use in controlling access of the target entity to a computing environment.

METHOD FOR SEARCHING FOR OBJECTS IN A DATABASE

Resumen de: WO2026057878A1

The invention relates to a computer-implemented method for searching for database objects in a database 50, having the steps of: receiving (S1), by means of an input interface (10), object data (Do) relating to a search object; determining (S2), by means of a machine learning, ML, coding module (30), a vectorial object coding for the search object using the object data (Do), the vectorial coding comprising at least one feature vector (Vo); determining (S3), by means of a search module (40), the similarity of the at least one feature vector (Vo) to feature vectors of the database objects (OD); and determining (S4), by means of the search module (40), a search result (E) from database objects (OD) on the basis of the determined similarity. Furthermore, a method according to the invention has the following steps: determining, by means of the ML coding module (30), a specific coding for each of the plurality of information categories, the ML coding module (30) comprising, for each information category, a special pre-trained ML model for specifically coding the object data; and determining, by means of the ML coding module (30), a universal coding based on the specific codings.

METHODS AND SYSTEMS FOR PREDICTING AN ENERGY CONSUMPTION OF A VESSEL

Resumen de: WO2026057401A1

Methods and server systems for predicting energy consumption of a vessel are described herein. The method performed by a server system includes accessing a set of stable vessel operating parameters from a plurality of vessel operating parameters of a vessel, recorded at predefined intervals for the vessel. Herein, the set of stable vessel operating parameters satisfies stability criteria. The method further includes determining a subset of stable vessel operating parameters from the set of stable vessel operating parameters. Herein, each stable vessel operating parameter in the subset of stable vessel operating parameters satisfies a performance threshold. The method further includes generating a set of features based on the subset of stable vessel operating parameters. The method further includes predicting, by a Machine Learning (ML) model, an energy consumption for the vessel based on applying the set of features on the ML model.

SYSTEM AND METHOD FOR GENERATION AND USE OF DATA MODELS FOR DETERMINATION OF INFERRED SKILLS

Resumen de: WO2026059589A1

Embodiments described herein are generally directed to computer-based data analytics and the processing of enterprise data, including the generation and use of data models for determining inferred characteristics associated with candidates. In accordance with an embodiment, the system utilizes data-processing pipelines and machine learning models to process structured, semi-structured, and/or unstructured sets of data, received from various sources; generate a multi-dimensional ontology and a taxonomy associated with the characteristics of open positions or potential candidates; identify, based on the data models, one or more additional or inferred characteristics associated with the candidates; and present the output by way of an analytics dashboard, scorecard, or other data visualization.

MACHINE LEARNING-BASED ARC FAULT DETECTION AND NUISANCE TRIP AVOIDANCE

Resumen de: WO2026060353A1

Machine learning-based arc fault detection and nuisance trip avoidance includes (i) obtaining data representing properties of an electrical signal sensed by an arc-fault circuit interrupter, (ii) interpreting the properties of the electrical signal as indicating an arc fault, the interpreting being based on application of an initial artificial intelligence (AI) model, (iii) based on the interpreting, opening a switch of the arc-fault circuit interrupter to interrupt the conduction of a supply of power to a load output terminal, (iv) receiving an indication that the properties of the electrical signal do not reflect an arc fault, such indication being that the arc-fault circuit interrupter is to refrain from arc-fault-based opening of the switch, (v) obtaining an updated AI model, where the updated AI model undergoes training using the data representing the properties of the electrical signal as an example of the absence of an arc fault, and (vi) deploying the updated AI model in place of the initial AI model.

MACHINE LEARNING-BASED METHOD FOR QUANTITATIVELY ANALYZING β-SHEET STRUCTURE IN SILK FIBROIN MATERIAL

Resumen de: WO2026056648A1

Provided is a machine learning-based method for quantitative analysis and comparison of beta-sheet structures in silk fibroin materials, relating to the fields of material characterization technology and biomedical materials. The method includes small-angle X-ray scattering detection experimentation and big data-based machine learning model analysis. The SAXS experimentation should be performed on a device that meets a detection sensitivity requirement to obtain scattered light intensity I data. After the machine learning model inputs SAXS experimentation q-I data of an unknown silk fibroin material, a shape and a feature dimension of a beta-sheet structure thereof are output. This method has higher convenience. The method can visually reflect the statistical results of the structural features in the sample without sample pretreatment, and can reflect nanostructure information in a scale range of <500 nm. Analyzing light scattering results by means of a machine learning method more accurately infers detailed structural information of a material, thereby optimizing material design.

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 GENERATION AND USE OF DATA MODELS FOR DETERMINATION OF INFERRED SKILLS

Resumen de: US20260080325A1

Embodiments described herein are generally directed to computer-based data analytics and the processing of enterprise data, including the generation and use of data models for determining inferred characteristics associated with candidates. In accordance with an embodiment, the system utilizes data-processing pipelines and machine learning models to process structured, semi-structured, and/or unstructured sets of data, received from various sources; generate a multi-dimensional ontology and a taxonomy associated with the characteristics of open positions or potential candidates; identify, based on the data models, one or more additional or inferred characteristics associated with the candidates; and present the output by way of an analytics dashboard, scorecard, or other data visualization.

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED CLASSIFICATION OF SIGNAL DATA SIGNATURES FEATURING USING A MULTI-MODAL ORACLE

Resumen de: US20260080009A1

The disclosed systems and methods provide a novel technical solution via mechanisms for identifying which models are truly high-performing and the set of models that would provide the most accurate single prediction for a signal data signature (SDS). The disclosed systems and methods provides a computerized framework that can document the depictions of individual model performance. Moreover, the disclosed framework can identify all high performing models according to positive results, negative results, as well as generalized results. The framework can additionally operate to combine high performing models into a single predictive oracle to render a final prediction based on input from many models.

AIRCRAFT HARDWARE COMPONENT ROTABILITY CLASSIFICATION USING MACHINE LEARNING

Resumen de: US20260079985A1

An application extracts a plurality of features of a hardware component of an aircraft. The application inputs a first subset of features of the plurality of features into a first machine learning model, and receives as output a first determination of whether the hardware component is rotable. The application inputs a second subset of features of the plurality of features into a second machine learning model, and receives as output a second determination of whether the hardware component is rotable. The applications determines, based on the first determination and the second determination, a final determination of whether the hardware component is rotable, and adds a data structure for the hardware component with the final determination in a searchable database. The application receives a query from a user that is associated with the hardware component, runs a search, outputs whether the hardware component is rotable.

TRAFFIC PATTERN-BASED PREDICTION OF CLOUD USAGE COSTS

Resumen de: US20260081894A1

Traffic log data generated by cloud firewalls executing in a cloud environment during a time period that indicate classes and corresponding amounts of network traffic detected across sessions as well as usage cost data recorded for the cloud firewalls during the time period are obtained. The traffic log data are preprocessed to generate training data comprising feature vectors indicating the aggregate amount of network traffic detected for each traffic class during a corresponding time interval within the time period and are labeled with the associated usage cost. A machine learning model is trained on the labeled traffic log data to learn the impact each traffic class has on the accumulated usage costs. The trained model generates predicted usage costs based on distributions of detected network traffic across traffic classes that are analyzed to correlate traffic patterns with usage costs to determine the optimal size(s) of cloud firewalls to deploy.

ARTIFICIAL-INTELLIGENCE-ENHANCED BIAS RESPONSE PROTOCOLS

Resumen de: WO2026057724A1

Bias response methods, systems, and computer program products for detecting and responding to behavioral biases in user plans. A method may include receiving a plan on behalf of a user, calculating an estimated net consequence (ENC) of the plan using machine learning models trained on historical data, and comparing the plan against bias patterns to determine if the plan has recognizable biases. The method may also include generating notifications or tracking user responses to refine response protocols or establish new bias patterns. A system may implement AI enhancement protocols to improve bias detection, analysis, or response capabilities. The system may refine logical bases for plans through user interactions, monitor actual outcomes over time, adjust estimation protocols based on discrepancies between estimated and actual consequences, or improve a bias filter with more or better bias pattern definition.

AUTOMATED MACHINE LEARNING MODEL EXPLANATION GENERATION

Resumen de: US20260080283A1

A processing system including at least one processor may obtain description information of a first machine learning model, obtain a set of interpretation criteria for the first machine learning model, and generate, via a second machine learning model, an explanation text providing an interpretation of the first machine learning model in accordance with the set of interpretation criteria and the description information of the first machine learning model.

SYSTEMS AND METHODS FOR IDENTIFYING COMPLEMENTARY OBJECTS HAVING SIMILAR STYLES

Resumen de: US20260080282A1

Described are systems and methods for determining complementary and/or matching objects based on an input query object. The described systems and methods can generate an embedding representative of the provided object, which can be transformed to generate a style embedding by a trained system, such as a machine learning system. The style embedding can then be used to identify one or more complementary objects from a corpus of classified objects. Aspects of the present disclosure also relate to creation of the training dataset, as well as training the machine learning system.

MODELING CLOUD INEFFICIENCIES USING DOMAIN-SPECIFIC TEMPLATES

Resumen de: US20260079768A1

Systems and methods are provided for identifying cloud inefficiencies. The method includes obtaining telemetric log data for services, distinct from the server, executing on cloud computing systems. The method also includes determining disaggregation data for the services based on the telemetric log data by applying disaggregation algorithms. The method also includes forming feature vectors based on the telemetric log data. The method also includes identifying software of service types and cloud wastage templates by inputting the feature vectors to trained classifiers, wherein the cloud wastage templates follow conventions of a domain specific language (DSL) that describe the cloud computing systems. Each classifier is a machine-learning model trained to identify cloud wastages for predetermined states of the cloud computing systems. The method also includes determining cloud states of computing resources used by the services based on the disaggregation data. The method also includes cataloging cloud inefficiencies using the cloud wastage templates.

MACHINE-LEARNING PREDICTION OR SUGGESTION BASED ON OBJECT IDENTIFICATION

Resumen de: US20260080314A1

Disclosed herein are system, computer-program product (non-transitory computer-readable medium), and method embodiments for machine-learning prediction or suggestion based on object identification. A system including at least one processor may be configured to cross-reference an identifier of a selected object with a list of known unique identifiers. The selected object may be selected via received selection. The at least one processor may further retrieve a set of values associated with the identifier of the selected object, upon determining that the list of known unique identifiers includes the identifier of the selected object, and perform machine-learning to derive a predicted-value set based at least in part on the set of values associated with the identifier of the selected object and a category applicable to the selected object. The at least one processor may determine that the predicted-value set satisfies a predetermined confidence condition, and output at least part of the predicted-value set.

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.

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.

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.

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.

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.

SYSTEM AND METHOD FOR MANAGING COMPUTING DEVICES

Nº publicación: EP4711895A2 18/03/2026

Solicitante:

CORE SCIENT INC [US]
Core Scientific, Inc

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.