Nippon telegraph and telephone corporation (20240241490). INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM simplified abstract
Contents
INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM
Organization Name
nippon telegraph and telephone corporation
Inventor(s)
Yukitsugu Sasaki of Musashino-shi, Tokyo (JP)
Tsuyoshi Toyoshima of Musashino-shi, Tokyo (JP)
Kensuke Takahashi of Musashino-shi, Tokyo (JP)
Masaru Sakai of Musashino-shi, Tokyo (JP)
INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240241490 titled 'INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM
The patent application describes an information processing device that collects and combines observable data from a managed target at specific time intervals. This data is then used to update a causal structure matrix through a learning process involving a generator and discriminator.
- Working unit collects and combines observable data from a managed target.
- Processing unit updates a causal structure matrix using a generator and discriminator.
- Causal structure matrix represents the relationship between the observable data.
- Output unit provides the causal structure based on the matrix.
Potential Applications: - Data analysis and prediction in various industries. - Enhancing decision-making processes based on causal relationships. - Improving efficiency in data processing and learning algorithms.
Problems Solved: - Enhances the understanding of complex data relationships. - Facilitates the identification of causal links within datasets. - Streamlines the process of updating causal structures based on new data.
Benefits: - Increased accuracy in predicting outcomes. - Improved data analysis capabilities. - Enhanced decision-making based on causal relationships.
Commercial Applications: Title: Advanced Data Analysis and Prediction System This technology can be utilized in industries such as finance, healthcare, and marketing for predictive analytics, risk assessment, and targeted advertising strategies. The market implications include improved efficiency, better decision-making, and competitive advantages for businesses.
Prior Art: Readers can explore prior research on causal inference, machine learning algorithms, and data processing techniques to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in causal inference methods, machine learning models, and data analysis tools to enhance the capabilities of this technology.
Questions about Information Processing Device: 1. How does the device differentiate between true and false data generated by the discriminator? The discriminator evaluates the pseudo-generated data based on its similarity to real data, identifying discrepancies that indicate falseness.
2. What are the key advantages of using a causal structure matrix in data analysis? A causal structure matrix helps in understanding the relationships between variables, enabling more accurate predictions and informed decision-making processes.
Original Abstract Submitted
an information processing device includes: a working unit configured to collect and combine various observable data acquired from a managed target at predetermined time intervals; a processing unit configured to input the combined observable data and update a causal structure matrix by repeatedly learning with a generator that generates pseudo-generated data using the causal structure matrix and a discriminator that identifies whether the pseudo-generated data is false or not, wherein the causal structure matrix represents a causal structure between the combined observable data; and an output unit configured to output the causal structure between the observable data based on the causal structure matrix.
