Qualcomm incorporated (20240211793). FEATURE MAP DECOMPOSITION AND OPERATOR DECOMPOSITION IN MACHINE LEARNING OPERATIONS simplified abstract
Contents
FEATURE MAP DECOMPOSITION AND OPERATOR DECOMPOSITION IN MACHINE LEARNING OPERATIONS
Organization Name
Inventor(s)
Yeonseok Kim of Bucheon-si (KR)
Kyu Woong Hwang of Daejeon (KR)
FEATURE MAP DECOMPOSITION AND OPERATOR DECOMPOSITION IN MACHINE LEARNING OPERATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211793 titled 'FEATURE MAP DECOMPOSITION AND OPERATOR DECOMPOSITION IN MACHINE LEARNING OPERATIONS
The present disclosure provides techniques for processing streaming data using machine learning models.
- Generating feature maps for sets of streaming data using a machine learning model.
- Combining results of operations on each item in the streaming data into the feature maps.
- Generating a result based on a combination of the feature maps for the total set of data.
Potential Applications: - Real-time data analysis - Predictive maintenance in industrial settings - Fraud detection in financial transactions
Problems Solved: - Efficient processing of large volumes of streaming data - Real-time decision-making based on data analysis - Automation of data processing tasks
Benefits: - Improved accuracy in data analysis - Faster insights from streaming data - Enhanced decision-making capabilities
Commercial Applications: Title: Real-time Data Analysis Solutions for Industry 4.0 Description: This technology can be used in various industries such as manufacturing, finance, and healthcare for real-time data analysis, predictive maintenance, and fraud detection, leading to improved operational efficiency and cost savings.
Questions about the technology: 1. How does this technology improve the efficiency of processing streaming data? 2. What are the key advantages of using machine learning models for real-time data analysis?
Frequently Updated Research: Stay updated on the latest advancements in machine learning models for processing streaming data to ensure optimal performance and accuracy in data analysis.
Original Abstract Submitted
certain aspects of the present disclosure provide techniques for processing streaming data using machine learning models. an example method generally includes generating a first feature map for a first set of streaming data using a machine learning model. to generate the first feature map, results of one or more operations performed on each respective item in the first set of streaming data are combined into the first feature map, and the results of the one or more operations performed for each respective item in the first set of streaming data are combined into the first feature map. a second feature map is generated for a second set of streaming data using the machine learning model. a result of processing the total set of data through the machine learning model is generated based at least on a combination of the first feature map and the second feature map.
