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70
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Updated on
19/06/2025 [07:26:00]
Results 50 to 70 of 70
Absstract of: US2025165529A1
A method, computer system, and computer program product are provided for real-time video searching based on augmented knowledge graphs that are generated using machine learning models. Multimedia data is obtained comprising an image portion and an audio portion, and a user query with respect to the multimedia data is obtained. A knowledge graph of the multimedia data is generated using one or more machine learning models based on the image portion and the audio portion, wherein the knowledge graph includes a plurality of entities and relationships between entities. An augmented knowledge graph is generated, wherein the augmented knowledge graph augments the knowledge graph with additional entities and additional relationships between the additional entities using additional data that is obtained from a source external to the multimedia data. A response to the user query is provided based on the augmented knowledge graph.
Absstract of: US2025165816A1
In some implementations, a controller may receive a request for an inference. The controller may determine, based on the received request for the inference, a first inference model of a plurality of inference models, to generate the inference. The controller may obtain, from a memory associated with an inference cache, first attribute data regarding first attributes of the first inference model. A location of the first attribute data, in the memory, may be determined using the inference cache. The attributes may include weights associated with the first inference model, biases associated with the first inference model, and a structure of the first inference model. The controller may utilize the first attribute data to generate the inference based on the request.
Absstract of: US2025165865A1
A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.
Absstract of: US2025165848A1
Systems and methods for selecting machine learning features using iterative batch feature reduction. In some aspects, the system trains a plurality of candidate models based on a plurality of feature groups split from a first set of features. Each candidate model takes as input a feature group of no more than a first threshold number of features. For each candidate model in the plurality of candidate models, the system processes the candidate model to extract an explainability vector. Based on the explainability vector, the system selects a second threshold number of features from the feature group to generate a slim feature group. The system trains a slim candidate model which takes as input the slim feature group. The system generates a second set of features by combining features from a plurality of slim candidate models.
Absstract of: US2025165821A1
A method for making the function of a machine learning algorithm explainable, wherein the machine learning algorithm is designed to assign input data to one of at least two groups. The method includes: providing input data for the machine learning algorithm; for all of the input data provided, assigning the corresponding input data to one of the at least two groups by means of the machine learning algorithm; selecting data from a first group of the at least two groups; ascertaining, from a second group of at least two groups, data that are most similar to the selected data from all the data contained in the second group; comparing the selected data with the ascertained data to make the machine learning algorithm explainable; and providing corresponding comparison results.
Absstract of: US2025165819A1
A technique for providing real time feedback from a machine learning system is provided that includes a method and system for interactively training machine learning models. In particular, by separating processing and analysis using static and dynamic models that are trained differently, the disclosed technique enables interactive training and prediction of machine learning models to increase the speed of generating new predictions based on real time feedback. In some cases, a dynamic model is applied to the output of a static model to generate an analysis, a correction of the analysis is received, and the correction is used to retrain the dynamic model. An updated analysis is generated based on reapplying the dynamic model to the output of the static model without having to retrain the static model.
Absstract of: US2025165820A1
Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.
Absstract of: US2025165822A1
Pre-selecting a machine learning model based on determined dataset characteristics may be facilitated. In some embodiments, a time-series dataset may be received by a system. Based on the time-series dataset, the system generates a periodogram to determine power frequencies of a set of frequencies of the time-series dataset. The system may then determine an asymptotic p-value based on the periodogram, where the asymptotic p-value indicates a probability value that a seasonal cycle is part of the time-series dataset. In response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value fails to exceed the threshold p-value, the system determines a seasonality cycle of the time-series dataset. The system then generates, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies.
Absstract of: US2025165993A1
Systems and methods for training a machine learning model by extracting targets from records are disclosed. A method includes receiving data packets including data associated with previous interactions of a user, determining at least one party to the previous interactions with whom the user interacted at a level above a threshold, and comparing the at least one party to a plurality of parties identified in a target database to identify at least one potential target, for each potential target, determining a respective target metric by inputting the data packets into a machine learning model, providing an identification of the at least one potential target, with reference to the respective target metrics, to a user device associated with the user, receiving, via the user device, feedback regarding the identification, and training the machine learning model based on the feedback.
Absstract of: US2025166397A1
A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.
Absstract of: US2025166083A1
Aspects of the present disclosure are related to systems, apparatus, and methods of generating or calculating liability and operational costs of a vehicle based on a driver's handling of the vehicle are described herein. Using a combination of vehicle sensors, video input, and on-board artificial intelligence and/or machine learning algorithms, the systems and methods of the present disclosure can identify risky events performed by the driver of a vehicle and generate, calculate, and evaluate driving scores for the driver of the vehicle and send the calculations to one or more entities.
Absstract of: US2025164950A1
A system can include one or more memory devices that can store instructions thereon. The instructions can, when executed by one or more processors, cause the one or more processors to receive timeseries data associated with a building, detect that a new building device has been added to the building, determine that a representation of the new building device is absent from a digital twin of the building, and execute a machine learning model to add the representation of the new building device to the digital twin of the building.
Absstract of: WO2024144913A1
Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.
Absstract of: WO2025105999A1
Embodiments described herein relate to methods and apparatuses for initiating control of one or more parameters in a communications network. A computer-implemented method comprises obtaining a first state of the communications network; for each action in a set of actions: predicting a next state of the communications network utilizing a first ML model based on the first state and the action; predicting, utilizing a second ML model, whether taking the action whilst the communications network is in the first state will violate one or more constraints; determining a cost value associated with the action by evaluating a cost function, wherein the cost function comprises: a first component representing one or more performance metrics of the communications network in the next state; and a second component dependent on whether taking the action in the first state will violate the one or more constraints; selecting a selected action from the set of actions, wherein the selected action is associated with a best cost value; and initiating performance of the selected action in the communications network.
Absstract of: WO2025104745A1
A novel system and method for the development of machine learning solutions without the need for any coding expertise The system includes a highly advanced interactive dashboard designed for data visualization. One of its key features is ensuring that user inputs remain error-free and only request relevant options based on the provided data. This approach revolutionizes the accessibility and usability of machine learning, making it more inclusive for a broader user base, regardless of their coding background. The interactive dashboard streamlines the 10 machine learning solution development process and significantly reduces the barriers to entry, making it a promising innovation in the field of data science and artificial intelligence.
Absstract of: WO2025104804A1
In this information processing device, a complementation means complements ontology data by using a graph machine learning model that has learned the relationship between information items included in the ontology data. A natural language processing model generation means generates a natural language processing model on the basis of the complemented ontology data. The information processing device can assist decision making of a user.
Absstract of: EP4557178A1
Example embodiments may relate to systems, methods and/or computer programs for reusing data for training machine learning models. In an example, an apparatus comprises means for receiving a request to collect new user data for training a machine learning model associated with an application. The apparatus may also comprise means for identifying existing stored data suitable for training the machine learning model based upon an ontology. The apparatus may also comprise means for providing access to the identified existing stored data in response to identifying that the data is suitable for training the machine learning model.
Absstract of: EP4557165A1
A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.
Absstract of: US2025158685A1
A system and a method are disclosed for AI/ML model LCM. A method performed by a UE includes transmitting, to a base station, a first signal including an indication of AI/ML use cases supported by the UE, and an indication of properties for the supported AI/ML use cases, receiving, from the base station, a second signal including at least one of a configuration, an activate command, or a trigger command of a CSI report to deploy at least one of a plurality of AI/ML functionalities based on the first signal, performing an inference operation for the CSI report based on the second signal, and transmitting, to the base station, a report based on the CSI report and the at least one of the plurality of AI/ML functionalities.
Nº publicación: KR20250070592A 20/05/2025
Applicant:
삼성전자주식회사
Absstract of: US2025158685A1
A system and a method are disclosed for AI/ML model LCM. A method performed by a UE includes transmitting, to a base station, a first signal including an indication of AI/ML use cases supported by the UE, and an indication of properties for the supported AI/ML use cases, receiving, from the base station, a second signal including at least one of a configuration, an activate command, or a trigger command of a CSI report to deploy at least one of a plurality of AI/ML functionalities based on the first signal, performing an inference operation for the CSI report based on the second signal, and transmitting, to the base station, a report based on the CSI report and the at least one of the plurality of AI/ML functionalities.
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