18273479. TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM simplified abstract (NEC Corporation)
Contents
- 1 TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM - 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
TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM
Organization Name
Inventor(s)
Shigeyoshi Shima of Tokyo (JP)
TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18273479 titled 'TRAINING DATA GENERATING SYSTEM, METHOD, COMPUTER-READABLE MEDIUM, MODEL GENERATING SYSTEM, AND LOG GENERATING SYSTEM
Simplified Explanation
The patent application abstract describes a training data generating system that identifies existing applications with similar features to a target application and generates training data for the target application based on the existing applications.
- Existing tag information acquiring unit: Identifies existing applications and acquires tag information indicating features of those applications.
- Target tag information acquiring unit: Acquires tag information of the target application.
- Selecting unit: Calculates similarity between target and existing tag information to select similar applications.
- Training data generating unit: Generates training data for the target application based on the selected similar applications.
Potential Applications
This technology could be applied in various fields such as machine learning, artificial intelligence, and data analysis to improve the efficiency of generating training data for new applications.
Problems Solved
This technology solves the problem of manually generating training data for new applications by leveraging existing applications with similar features to automate the process.
Benefits
The benefits of this technology include saving time and resources in generating training data, improving the accuracy of user operation models, and enhancing the performance of new applications.
Potential Commercial Applications
A potential commercial application of this technology could be in software development companies looking to streamline the process of creating user operation models for new applications.
Possible Prior Art
One possible prior art for this technology could be existing systems that use machine learning algorithms to generate training data for new applications based on historical user data.
Unanswered Questions
How does the system handle cases where there are multiple existing applications with similar features to the target application?
The system may prioritize the most similar existing application or use a weighted approach to incorporate multiple existing applications in generating training data for the target application.
What measures are in place to ensure the accuracy and reliability of the generated training data?
The system may include validation processes, data cleaning techniques, and quality assurance checks to ensure the accuracy and reliability of the training data generated for the target application.
Original Abstract Submitted
A training data generating system includes an existing tag information acquiring unit, a target tag information acquiring unit, a selecting unit, and a training data generating unit. The existing tag information acquiring unit identifies, as an existing application, an application for which training data for a user operation model has already been generated based on a user operation history and, for each existing application, acquires tag information indicating a feature of the application as existing tag information. The target tag information acquiring unit acquires tag information of a target application as target tag information. The selecting unit calculates a degree of similarity between the target tag information and each piece of the existing tag information and, based on the degree of similarity, selects a similar application from the existing applications. The training data generating unit generates training data for the target application from training data for the similar application.
