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78
resultados
Última actualización
17/04/2026 [07:15:00]
Resumen de: AU2026202443A1
Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes. ar a r
Resumen de: AU2024359770A1
A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.
Resumen de: EP4725391A1
0001 A method and device for diagnosing renal disease are disclosed. A control method of a diagnostic device according to one embodiment comprises: obtaining a retinal image of a subject; and obtaining renal disease diagnostic information regarding the subject using a machine learning model based on the retinal image, wherein the machine learning model includes a first model and a second model, wherein the first model is a neural network model, and wherein the second model is a regression-based machine learning model.
Resumen de: US20260101217A1
0000 Provided are a method and apparatus for monitoring a model in beam management by using artificial intelligence and machine learning. The method may include: in relation to a reference signal configured for a terminal, receiving second reference signal resource set configuration information of the reference signal for monitoring an AI/ML model; on the basis of the second reference signal resource set configuration information, measuring signal strength or signal quality for the reference signal; and reporting the performance result of the AI/ML model by comparing a measured value of the reference signal with a predicted value of the reference signal inferred via the AI/ML model.
Resumen de: US20260099769A1
In an aspect, an apparatus for machine operator feedback correlation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive, through a sensing device, performance data of at least a machine operator. At least a processor is configured to classify performance data to a performance category through a performance classifier. At least a processor is configured to calculate a performance determination. At least a processor is configured to generate a feedback correlation through a machine operator feedback correlation machine learning model. At least a processor is configured to provide a feedback correlation to a user through a display device.
Resumen de: WO2026076047A1
A computer-implemented method for generating a response to a query. The method comprises receiving one or more query tokens, the one or more query tokens indicative of the query, providing the one or more query tokens as input to a machine learning model, outputting, from a first head of the machine learning model, a first embedding based upon the one or more query tokens, generating an intermediate input embedding based upon the one or more query tokens and the first embedding, outputting, from a second head of the machine learning model, output data based upon the intermediate input embedding, and generating the response to the query based upon the output data.
Resumen de: WO2026075835A1
In some examples of the techniques described herein, one or more network settings may be associated with an identifier. In some approaches, a network entity may indicate one or more identifiers to a wireless device for checking an artificial intelligence or machine learning (AI/ML) model on the wireless device. For instance, a network entity may help to maintain a correspondence or alignment between identifiers and corresponding network settings during training data collection or inference. Network settings may change over time, and a network entity may control AI/ML positioning running at the wireless device. For instance, the network entity may indicate the wireless device to activate, deactivate, select, or switch an AI/ML model, or to fall back to a non-AI/ML-based positioning procedure. One or more operations may be utilized to enable life cycle management (LCM) based on the associated identifiers.
Resumen de: WO2026075809A1
Aspects presented herein may enable a user equipment (UE) to determine a collaboration level to be applied to at least one artificial intelligence (AI) or machine learning (ML) (AI/ML) model/functionality based on a set of defined conditions, thereby improving and promoting collaborations between the UE and a network entity related to AI/ML positioning and/or sensing. In one aspect, a UE communicates, with a network entity, a request for a performance of at least one AI/ML-based functionality. The UE selects, based on a set of conditions, a collaboration level from a set of collaboration levels to be applied to the performance of at least one AI/ML-based functionality. The UE performs the at least one AI/ML-based functionality based on the selected collaboration level.
Resumen de: WO2026075975A1
Systems and methods disclosed herein comprise providing operational history and an electrolyte of a used Li-ion battery to a machine-learning model; receiving, from the machine-learning model, an estimate of a state of health (SoH) of the used Li-ion battery; reading parameters of the used Li-ion battery; providing the parameters and the estimate of the SoH of the used Li-ion battery to a machine-learning model trained to output a rate of degradation of the SoH of the used Li-ion battery in response to receiving parameters and a SoH; receiving, from the machine-learning model, a rate of degradation of the SoH of the used Li-ion battery; generating, based on the estimate of the SoH and the rate of degradation, a recommendation for an application of the used Li-ion battery, the application being a second-life application, recycling, or end-of-life; and providing the used Li-ion battery and a recommendation to a facility.
Resumen de: WO2026075831A1
A method comprises determining, by a network entity, a device group of at least one selected wireless device, wherein the one or more selected wireless devices are a subset of available wireless devices in an environment; and configuring the one or more selected wireless devices to send measurement data generated by the one or more selected wireless devices to a consumer entity configured to use training examples to train a machine learning (ML) system to generate output data, the training examples being based on the measurement data, the measurement data comprising measurements of wireless signals received by the one or more selected wireless devices, the output data indicating physical positions of one or more User Equipment (UE) devices in the environment or the output data being input data to a process that determines the physical positions of the one or more UE devices.
Resumen de: US20260099711A1
Various techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status.
Resumen de: WO2026075381A1
The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a first user equipment (UE) in a wireless communication system, according to various embodiments of the present disclosure, may comprise the steps of: receiving, from a second base station, an artificial intelligence (AI) model on the basis of first network-side training information associated with a first base station; receiving, from the second base station, second network-side training information associated with the second base station; when the first network-side training information corresponds to the second network-side training information, transmitting, to the second base station, information indicating that the AI model is applicable; and receiving, from the second base station, information for configuring inference using the AI model.
Resumen de: EP4723506A1
Provided are a method and apparatus for training a model for artificial intelligence and/or machine learning (AI/ML)-based communication. A terminal receives, from a base station, data for AI/ML model training, and performs AI/ML model training based on the received data. After performing the AI/ML model training, the terminal transmits, to the base station, a first message indicating termination of collection of the data.
Resumen de: EP1000000A1
The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.
Resumen de: EP1000000A1
The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.
Resumen de: US20260094166A1
A computer-implemented method for augmenting customer support is disclosed in which a granular taxonomy is formed to classify tickets based on customer issue topic. A dashboard and user interface of performance metrics may be generated for the topics in the taxonomy.Recommendations may also be generated to aid servicing customer support issues for topics in the taxonomy. This may include generating information to aid in determining topics for generating automated responses or generating recommended answers for particular topics. In some implementations, an archive of historic tickets is used to generate training data for a machine learning model to classify tickets.
Resumen de: US20260094427A1
A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.
Resumen de: AU2024407921A1
A method includes receiving a user input and generating a set of user input tokens based on the user input. The method also includes generating a set of enhanced input tokens by providing the set of user input tokens as input to a first machine learning model. A state is determined based on a previous state and at least one of the set of user input tokens or the set of enhanced input tokens. Predetermined data is retrieved from a database based on the state and at least one of the set of user input tokens or the set of enhanced input tokens. The method also includes generating a set of response tokens by providing the set of user input tokens and the predetermined data as input to a second machine learning model. Based on the set of response tokens, a response is sent to a user device.
Resumen de: US20260094061A1
Methods and systems are provided for classifying free-text content using machine learning. Free-text content (e.g., customer feedback) and parameter values organized according to a schema are received. A free-text corpus is generated, and an artificial-text corpus is generated by applying rules to the parameter values. The artificial-text corpus is generated by converting the parameter values into a finite set of words based on the rules and concatenating the words of the finite set of words into a fixed sequence wordlist. Feature vectors (e.g., sentence embeddings) based on the free-text corpus and the artificial-text corpus are combined and forwarded to a machine learning model for classification. The machine learning model may be trained with a bias towards a specified metric (e.g., precision, recall, F1 score). The model may be trained using transfer learning with training data from a different category of free-text content (e.g., a different category of customer feedback).
Resumen de: US20260091748A1
A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and available values for one or more additional data types. The mobile device executes a first machine-learning model selected from a plurality of machine-learning models based on the one or more additional data types for which there are available values to generate a first confidence of a total loss event.
Resumen de: WO2026072270A1
A system and method for privacy-preserving identity resolution using deep learning enables accurate matching of personally identifiable information (PH) while maintaining data security. The system employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process data by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches. The system enables identity resolution without requiring Pll data movement from first-party environments. The invention facilitates building accurate first-party identity graphs and enables secure collaboration between parties without exposing underlying Pll data.
Resumen de: WO2026072162A1
The computer-based methods and systems presented in this disclosure provide prediction occurrence of an event for an individual on a user device of the individual. The system receives, from a plurality of remote devices, pieces of input data about the individual. The system pre-processes the pieces of input data to make them ready to be processed by respective input modules of a machine learning model running on the system. Each input module is associated with a respective marker and processes the pre-processed data for that marker. Outputs of the input modules are further processed by the model. The model provides an output indicating respective probabilities that particular events happen. The system can generate one or more alerts based on the output of the model, and can send the alerts to contacts of the individual.
Resumen de: WO2026072207A1
Certain aspects of the present disclosure provide techniques for performing wireless communication. In some aspects, the techniques include obtaining a first metric associated with a model that is associated with wireless communication; and obtaining, in response to a trigger condition associated with the first metric being satisfied, a second metric associated with the model, the second metric providing a different measure of the model than the first metric.
Resumen de: US20260094032A1
first group of AI agents to train and report, per each AI agent of the first group, a respective first partial AI or machine learning (ML) (AI/ML) model to the AI manager, receive the first partial model from each AI agent of the first group, generate a first version of a global model from the first partial models, if the first version of the global model is determined to be trustworthy, select a second group of AI agents to train and report, per each AI agent of the second group, a respective second partial AI/ML model to the AI manager, receive the second partial models and aggregate the second partial models and the first version of the global model into a second version of the global model.
Nº publicación: US20260094677A1 02/04/2026
Solicitante:
CAMERON INT CORPORATION [US]
CAMERON INTERNATIONAL CORPORATION
Resumen de: US20260094677A1
Methods of predicting physicochemical properties of a chemical system using a family of surrogate or reduced order models, trained on first principle simulation results. The models are created using machine learning techniques. The chemical system can be a complex multicomponent and multiphase system such as produced water.
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