Samsung SDS had 28 patents in artificial intelligence during Q2 2024. Samsung SDS Co Ltd filed patents related to calibrating prediction models, generating time series data, generating summaries, and event processing methods. These patents involve techniques such as detecting latent factors, pre-learning calibration information, generating synthetic time series samples, updating parameters based on differences, grouping events based on code values, and calculating likelihood and unlikelihood losses for summary models. GlobalData’s report on Samsung SDS gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Samsung SDS had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Apparatus and method for calibrating prediction models (Patent ID: US20240193403A1)
The patent filed by Samsung SDS Co Ltd describes an apparatus and method for calibrating prediction models of an inference service. The apparatus includes components such as a drift pattern creating unit, individual drift calibrating unit, and ensemble drift calibrating unit. These units work together to pre-learn calibration information based on a loss function between learning data and drift patterns, as well as determine similarity between input data and recovery data. The apparatus also includes a storage module for storing pre-learned ensemble drift calibrating unit until abnormality in input data is detected.
The method outlined in the patent involves detecting latent factors of learning data, creating possible drift patterns based on these factors, and pre-learning calibration information for each drift pattern. The method also includes determining similarity between input data and recovery data, adjusting weights of calibration information based on this similarity, and integrating the calibration information for each drift pattern to apply to the input data. Additionally, the method involves classifying data pairs based on drift levels and using a VAE-based generative model to more similarly reconstruct input data with the same data distribution as pre-learned learning data.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
