18275949. DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM simplified abstract (NEC Corporation)
Contents
- 1 DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM
Organization Name
Inventor(s)
Noritaka Yamashita of Tokyo (JP)
DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18275949 titled 'DATA GENERATION SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM
Simplified Explanation
The feature extraction means extracts a feature of data from at least one data of market data and test data. The data selection means selects one or more other data containing a feature corresponding to the extracted feature of the one data. The complementary data calculating means calculates complementary data that complements the market data or the test data from the one data and the selected other data. The integrated data generation means generates integrated data that integrates the calculated complementary data with at least one or both of the market data and the test data.
- Feature extraction: Extracting a specific feature from market data and test data.
- Data selection: Choosing other data that contains a feature related to the extracted feature.
- Complementary data calculation: Calculating additional data to complement the market or test data.
- Integrated data generation: Creating integrated data by combining the calculated complementary data with the market or test data.
Potential Applications
This technology could be applied in financial analysis, predictive modeling, and data integration in various industries.
Problems Solved
This technology helps in enhancing data analysis accuracy, improving decision-making processes, and optimizing data integration methods.
Benefits
The benefits of this technology include improved data quality, enhanced predictive modeling capabilities, and more accurate decision-making based on integrated data.
Potential Commercial Applications
A potential commercial application of this technology could be in the financial sector for algorithmic trading strategies.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms for feature extraction and data integration in financial markets.
Unanswered Questions
How does this technology handle large volumes of data efficiently?
This technology may face challenges in processing and integrating large datasets in real-time. Implementing scalable algorithms and efficient data processing techniques could address this issue.
What are the potential limitations of this technology in terms of data accuracy and reliability?
There may be limitations in the accuracy and reliability of the integrated data generated by this technology, especially when dealing with noisy or incomplete datasets. Conducting thorough data validation and quality checks could help mitigate these limitations.
Original Abstract Submitted
The feature extraction means extracts a feature of data from at least one data of market data and test data. The data selection means selects one or more other data containing a feature corresponding to the extracted feature of the one data. The complementary data calculating means calculates complementary data that complements the market data or the test data from the one data and the selected other data. The integrated data generation means generates integrated data that integrates the calculated complementary data with at least one or both of the market data and the test data.
