No-Code Database Setup Support via Machine Learning
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Solution Overview
Problem
Users setting up databases with no-code or low-code technologies often struggle with specifying appropriate settings, particularly when it comes to field types, due to a lack of guidance and support.
Innovation Solution
A setup support system that utilizes a machine learning model trained on data from existing database settings to identify user setting operations and provide support in configuring database settings, including suggesting field types and layouts based on learned patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If no-code or low-code database creation is used, then ease of operation is improved, but users still struggle with specifying appropriate settings due to lack of guidance
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the user and the database settings. The model analyzes user inputs and automatically suggests appropriate settings, field types, and configurations, acting as a intelligent mediator that bridges the gap between simple no-code operation and expert-level configuration knowledge.
Solution Approach 2:
The system implements feedback by having the machine learning model continuously analyze user inputs and provide real-time suggestions for settings. The model learns from user interactions and adjusts its suggestions accordingly, creating a feedback loop that improves the setup process while maintaining ease of operation.
2Adaptability or versatility
If users specify all settings manually, then customization is improved, but time consumption and complexity increase
Solution Approach 1:
The machine learning model performs preliminary actions by pre-analyzing user requirements and automatically generating suggested settings, field types, and database configurations before the user needs to manually specify them. This reduces the time and effort required for customization while maintaining adaptability.
Solution Approach 2:
The system dynamically changes parameters by having the machine learning model adjust settings based on analyzed user inputs. The model can modify field types, data structures, and configuration parameters automatically, providing customized solutions without requiring users to manually specify every detail.
3Ease of operation
If machine learning model provides automated suggestions, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex machine learning model functionality as a separate, modular component that can be independently managed and updated. This allows the core database creation interface to remain simple and user-friendly, while the sophisticated ML capabilities are handled by a dedicated service layer that users interact with indirectly through suggestions.
Data Source
AI summary
Provided is a setup support system including at least one processor, the at least one processor being configured to: identify a setting operation performed by a user in order to set a setting of a database to be created with no-code or low-code; and support setting up of the database by the user based on the setting operation and a machine learning model which has learned training data created based on a setting for training.