Voice Database Selection via Staged Attribute Filtering
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Solution Overview
Problem
Existing methods for selecting data records from large relational databases, such as those containing music pieces, are not efficient for reliable and convenient operation, especially when dealing with numerous records exceeding 15,000 entries.
Innovation Solution
A method and system that utilize voice inputs to select data records from a relational database by first stipulating a general attribute, then a specific attribute, allowing for the creation of subsets based on these inputs, enabling fast and accurate selection of desired records without considering the initial attribute's value initially.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice control is used to select data records from a large relational database (e.g., more than 15,000 entries), then the operation becomes convenient for the user, but the system complexity and processing time increase significantly
Solution Approach 1:
The patent segments the database selection process into multiple stages: first filtering by a general attribute (e.g., genre) to create a first subset, then filtering by a specific attribute (e.g., album) to create a second subset, and finally selecting from the second subset based on the value of the general attribute. This segmentation reduces the complexity of searching through all 15,000+ records at once by breaking it into smaller, manageable filtering steps.
Solution Approach 2:
The patent performs preliminary filtering actions before the final selection. The voice recognition system first identifies the type of the first attribute (e.g., genre type) without considering its value, creating a preliminary subset. This preliminary action prepares the data structure in advance, making the subsequent selection process more efficient and manageable.
2Quantity of substance
If the database contains more than 15,000 data records, then the system provides comprehensive data coverage, but the selection process becomes slower and less efficient
Solution Approach 1:
The patent divides the large database into progressively smaller subsets through multiple filtering stages. Starting with over 15,000 records, the system first filters by attribute type to create a first subset, then by specific attribute values to create a second subset, finally selecting from this reduced set. This segmentation maintains comprehensive data coverage while dramatically improving selection speed by reducing the search space at each stage.
Solution Approach 2:
The system performs preliminary filtering by attribute types before final selection. By first identifying which attribute type is being queried (e.g., genre, artist, album) and creating a subset based on that type alone, the system prepares the data structure in advance, enabling faster subsequent filtering and selection from the reduced dataset.
3Device complexity
If traditional selection methods are used without staged filtering, then the system structure remains simple, but the reliability of selection from large datasets decreases
Solution Approach 1:
The patent implements a multi-stage filtering system that segments the selection process into distinct reliability-enhancing steps: (1) filter by general attribute type to create first subset, (2) filter by specific attribute to create second subset, (3) select from second subset based on general attribute value. This segmentation ensures that each filtering stage operates on a manageable dataset, reducing errors and improving the reliability of the final selection from large databases.
Data Source
AI summary
A method for selecting at least one data record from a relational database having a plurality of data records each having at least two attributes each with a type of the attribute and a value of the attribute includes: providing a first subset of data records from the relational database on the basis of a stipulation for the type of a first attribute and independently of a stipulation for the value of the first attribute; selecting data records from the first subset on the basis of a stipulation for a second attribute; providing the selected data records in a second subset; and then selecting the at least one data record from the second subset on the basis of the stipulation for the value for the first attribute.