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79
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Updated on
18/04/2026 [07:49:00]
Absstract of: 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
Absstract of: WO2026079735A1
A method for a user equipment comprises the steps of: receiving, from a base station, configuration information about the number Nt (where Nt is a natural number of 1 or more) of a plurality of time instances; receiving, from the base station, configuration information about the number Nb (where Nb is a natural number of 1 or more) of beams to be reported for each of the plurality of time instances; generating an inference result report comprising inference results for Nt*Nb beams; and transmitting the inference result report to the base station, wherein the inference result report comprises one or more reporting units, each of the one or more reporting units comprises inference results for K (where K is a natural number of 1 or more) or less beams, the number of the one or more reporting units is M*Nt, and M may be defined as M=ceiling (Nb/K).
Absstract of: WO2026079733A1
This method of a terminal may comprise the steps of: identifying, from inference result reporting for a plurality of time instances, a candidate set related to the number of beams to be included in a reporting unit for differential reporting; receiving, from a base station, information indicating a first number belonging to the candidate set; and transmitting, to the base station, an inference result report including the reporting unit that includes inference results for the number of beams corresponding to the first number.
Absstract of: WO2026078301A1
Example embodiments of the present disclosure provide a solution for a performance test caused by a functionality change. In an example method, a terminal device determines a change in an artificial intelligence / machine learning model of a functionality of the terminal device that is connected to a radio access network. Then, the terminal device transmits a functionality applicability report for the model of the functionality, wherein the functionality applicability report includes a reason of a change in the functionality, an applicability indication for the functionality and model status of the AI/ML model. Next, the terminal device receives a configuration message indicating at least one test configuration for a performance testing procedure for the AI/ML model of the functionality, wherein the at least one test configuration includes at least one procedure parameter for the performance testing procedure determined based on the reason, the applicability indication and the model status.
Absstract of: US20260105373A1
Method comprising determining, in a trusted execution environment, values of hyperparameters of a machine learning model based on private data stored in the trusted execution environment, wherein the hyperparameters include system-specific hyperparameters and model-specific hyperparameters; training, in the trusted execution environment, the machine learning model to which the determined values of the system-specific and model-specific hyperparameters are applied to obtain, after one or more epochs of training, a sufficiently trained machine learning model; outputting the sufficiently trained machine learning model from the trusted execution environment; and, inhibiting output of the determined values of the system-specific hyperparameters from the trusted execution environment, wherein the system-specific hyperparameters are not accessible in the outputted sufficiently trained machine learning model.
Absstract of: US20260105359A1
A trained machine learning model identifies that a real-world apparatus has a failed component, which trained machine learning model has been trained with a training corpus that includes content generated by synthesizing a plurality of synthesized operating examples for a given apparatus, wherein at least some of the plurality of synthesized operating examples are generated via a simulation modeling environment that receives as input characterizing information that corresponds to any of a variety of failure states for a component of the given apparatus.
Absstract of: US20260105354A1
Embodiments of the present disclosure include techniques for automatically generating machine learning models. In one embodiment, sets of hyperparameters corresponding to machine learning models trained on one training data set are provided as an input. The hyperparameters are iteratively selected using an algorithm, such as a bandit algorithm, and used to train an ML model using another training data set. The performance of the trained ML model is evaluated on each iteration until the ML model performance is above a threshold. The resulting model may be used to train a resulting model. In some embodiments, ML models are combined across iterations to improve performance.
Absstract of: US20260105364A1
A method including receiving a reservoir model of a target under-ground region. The method also includes extracting, from the reservoir model, a historic pressure distribution in grid cells of the target underground region. The method also includes extracting, from the reservoir model, distances. Each distance represents a distance between a grid cell and a corresponding lineament in the target underground region. The method also includes receiving historic earthquake data of past earthquakes in the target underground region. The method also includes generating a vector. The vector includes features and corresponding values for at least i) the historic pressure distribution, ii) the distances, and iii) the historic earthquake data. The method also includes training a trained machine learning algorithm by recursively executing a machine learning algorithm on the vector until convergence.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: 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.
Absstract of: AU2024354389A1
An example method for automatic generation of content based on an aggregation of trending events is provided. The method includes determining, by a computing device, a topic of interest. The method also includes determining additional information related to the topic of interest. The determining of the additional information includes, generating a prompt based on the topic of interest, submitting the prompt to an information search and retrieval system, and retrieving the additional information as an output of the information search and retrieval system. The method also includes generating, by a generative artificial intelligence model, a piece of annotated content associated with the topic of interest. The piece of annotated content comprises media content annotated with at least one selectable graphical object that links to the additional information. The method also includes providing, by the computing device, the piece of annotated content.
Absstract of: 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).
Nº publicación: US20260091748A1 02/04/2026
Applicant:
CAMBRIDGE MOBILE TELEMATICS INC [US]
Cambridge Mobile Telematics Inc
Absstract of: 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.
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