Machine Learning Model for Natural Language Database Query Translation
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Solution Overview
Problem
Existing technologies face challenges in efficiently accessing data corresponding to user queries from databases, particularly due to the limited surface area for projecting virtual interfaces, which restricts the types and number of user interactions and applications that rely on these interfaces for input and output.
Innovation Solution
A method is described that involves training a machine learning model to translate user queries from natural language into database queries. This is achieved by using a prompt file containing example query pairs in natural language and database language, allowing the model to generate accurate predictions for user queries and fetch relevant data from databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual interfaces are projected onto limited surface area (e.g., user's palm), then portability and user interaction flexibility are improved, but the number and types of user interactions and applications are limited
Solution Approach 1:
The patent transitions from physical surface constraints to virtual/digital dimension by using machine learning models to process natural language queries. This allows the system to overcome the limited projection surface area by operating in the digital realm where surface area constraints do not apply, enabling complex data retrieval and interaction types beyond what the physical palm surface can accommodate.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the user's natural language input and the database query system. This intermediary translates human-friendly queries into structured database queries, eliminating the need for users to learn complex query languages or interact with limited on-surface controls, thereby overcoming the surface area limitation while maintaining interaction flexibility.
2Measurement precision
If traditional database query methods are used, then data retrieval accuracy is ensured, but computational resources and time required for query processing increase
Solution Approach 1:
The patent employs a machine learning model that has been pre-trained on extensive query data to automatically generate accurate database queries from natural language input. This preliminary training action enables the system to quickly translate user queries into precise database queries without requiring complex real-time processing, thereby maintaining high accuracy while improving processing efficiency.
Solution Approach 2:
The patent replaces traditional mechanical query processing methods (manual query construction, complex parsing algorithms) with a machine learning-based semantic understanding system. This substitution allows the system to interpret natural language queries and generate accurate database queries more efficiently, reducing both computational resources and processing time while maintaining or improving accuracy.
Data Source
AI summary
Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a method for using a machine learning model to provide data corresponding to a query comprises receiving a query for data stored in one or more databases. A prompt file is determined for the query. The prompt file and the query are provided as input for a machine learning model configured to generate a prediction for the query. Training the machine learning model can include updating network parameters in the machine learning model based on the prompt file. The prediction generated for the query is received. The prediction comprises a predicted database query corresponding to the query, and a level of accuracy is determined for the predicted database query.