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Updated on
20/04/2026 [09:26:00]
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: US20260105374A1
The disclosed technology can include a system capable of receiving a corpus of documents including a first subset of documents and a second subset of documents, where the first subset of documents and the second subset of documents are received at different time intervals, generating a credibility score and an impact score for each document of the first subset of documents, selecting a training subset from the first subset of documents based on the credibility score and the impact score, training a machine learning algorithm based on the training subset, generating, using the machine learning algorithm, a plurality of hypotheses, and evaluating the plurality of hypotheses against the second subset of documents.
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: 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: US20260105383A1
An approach is provided for prediction-guided machine learning model ensembling. Label groupings within an output range of a base machine learning model are determined. Accuracies of the base model across the label groupings are evaluated. One or more of the label groupings are identified whose respective evaluated accuracy does not satisfy end user-defined criteria. Using a reduced training set, a specialized machine learning model for a given label grouping included in the identified one or more label groupings is trained. A majority of samples of the reduced training set are from the given label grouping. During inference, it is determined that an initial prediction by the base model is within an output range specified by the given label grouping. A weighting for an ensembling using the base model and the specialized model is determined. Using the ensembling, the initial prediction is refined to generate a final prediction.
Absstract of: US20260106884A1
First event data, indicative of a first activity on a computer network and second event data indicative of a second activity on the computer network, is received. A first machine learning anomaly detection model is applied to the first event data, by a real-time analysis engine operated by the threat indicator detection system in real time, to detect first anomaly data. A second machine learning anomaly detection model is applied to the first anomaly data and the second event data, by a batch analysis engine operated by the threat indicator detection system in a batch mode, to detect second anomaly data. A third anomaly is detected using an anomaly detection rule. The threat indictor system processes the first anomaly data, the second anomaly data, and the third anomaly data using a threat indicator model to identify a threat indicator associated with a potential security threat to the computer network.
Absstract of: US20260105369A1
A method for searching for an output machine learning (ML) algorithm to perform an ML task is described. The method comprising: receiving data specifying an input ML algorithm; receiving data specifying a search algorithm that searches for candidate ML algorithms and an evaluation function that evaluates the performance of candidate ML algorithms; generating data representing a symbolic tree from the input ML algorithm; generating data representing a hyper symbolic tree from the symbolic tree; searching an algorithm search space that defines a set of possible concrete symbolic trees from the hyper symbolic tree for candidate ML algorithms and training the candidate ML algorithms to determine a respective performance metric for each candidate ML algorithm; and selecting one or more trained candidate ML algorithms among the trained candidate ML algorithms based on the determined performance metrics.
Absstract of: US20260105329A1
Systems and methods for event outcome validation are provided. The system receives a user input indicative of an event and at least one anticipated outcome of the event to be wagered on by the user. The system receives confirmation data associated with an outcome of the event from at least one confirmation data source confirming the outcome of the event and classifies the confirmation data utilizing at least one machine learning algorithm. The system determines a threshold of confirmation data sources to validate the outcome of the event and utilizes the at least one machine learning algorithm to determine a reduced threshold of confirmation data sources to validate the outcome of the event based on at least one of the classified confirmation data and a confirmation rating of the at least one confirmation data source. The system validates the outcome of the event based on the reduced threshold.
Absstract of: US20260105384A1
In an example embodiment, a model generation component may additionally assign various cloud resources to a machine learned model so that the training or retraining of the model can be performed using these resource. The containers may be weighted to handle model generation work of different weight. Having one single configuration for a container responsible for generating all models leads to overuse of hardware resources because machine learning algorithms are very resource intensive, and thus dynamically selecting the weight improves hardware utilization.
Absstract of: US20260105259A1
A computerized method for extracting domain specific insights from a corpus of files containing large documents comprising: breaking down large chunks of text into smaller sentences/short paragraphs in a domain specific way, identifying and removing domain noise; identifying the sentence intents of the non-noise sentences; tagging the sentences with other domain specific attributes; defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes; applying a pre-defined ontology to tag documents with domain specific hashtags; and combining the hashtags using machine learning techniques into insights.
Absstract of: US20260105335A1
A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.
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: US20260105166A1
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.
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: 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: WO2026080665A1
Some non-limiting aspects of the present disclosure describes generating dataset for implementing a rules-driven query system on a machine learning model. With this method, the system can interchange data easily since little modifications would be needed to shift this approach to using any other information. In this way, the present disclosure describes reducing bottlenecks for sales personnel and technicians to access parts data to complete quotations and service.
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: 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: 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: 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: 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.
Nº publicación: US20260099711A1 09/04/2026
Applicant:
TRADING TECH INTERNATIONAL INC [US]
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.
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