Master Pattern Generation for Database Entry
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Solution Overview
Problem
The burden of repetitive and redundant data entry in databases is significant, especially when dealing with large numbers of data fields, leading to time-consuming and error-prone processes, as existing data entry applications fail to provide comprehensive suggestions that account for relationships between fields.
Innovation Solution
A method and system for identifying master patterns in a database, which are used as templates for data entry, by generating similarity patterns through comparisons and consolidating them to eliminate duplicates, ensuring only unique and broad patterns are used for data entry facilitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If field-based suggestions are provided based on frequency of prior usage, then data entry for specific fields is facilitated, but the suggestions fail to account for relationships between fields and become less useful when entry choices are large
Solution Approach 1:
The patent segments the data entry process into two levels: field-level suggestions (individual field-based) and record-level suggestions (master pattern-based). This segmentation allows the system to handle both specific field requirements and comprehensive cross-field relationships, resolving the contradiction by providing multiple layers of suggestion mechanisms that operate independently but complement each other.
Solution Approach 2:
The master pattern suggestion mechanism serves multiple functions simultaneously: it provides comprehensive cross-field suggestions, maintains consistency across related fields, and scales effectively regardless of the number of entry choices. This multi-functionality resolves the contradiction by creating a single mechanism that handles both simple and complex suggestion scenarios.
2Reliability
If repetitious data entry is required for redundant fields, then database completeness is maintained, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs preliminary action by identifying master patterns in existing records before new data entry is required. These master patterns are pre-computed and stored, allowing the system to automatically generate suggestions for new records without requiring users to manually review or re-enter redundant information, thus reducing time while maintaining consistency.
Solution Approach 2:
The patent uses copying by creating master patterns that serve as templates for new data records. Instead of requiring users to manually re-enter redundant information, the system copies the structure and relationships from existing master patterns to generate suggestions for new records, significantly reducing user effort while maintaining data consistency.
3Adaptability or versatility
If master patterns are generated by comparing all records to all other records, then comprehensive patterns are identified, but computational complexity increases with database size
Solution Approach 1:
The system performs preliminary action by pre-processing the database to identify and store master patterns before actual data entry operations. This pre-computation approach allows the system to handle large datasets efficiently, as the complex pattern identification is performed once during setup rather than repeatedly during each data entry operation.
Solution Approach 2:
The patent extracts only the essential pattern information from the full database comparisons and stores it as master patterns. This extraction approach reduces the computational complexity for subsequent operations, as the system only needs to work with the extracted pattern data rather than re-processing the entire database for each new record.
Data Source
AI summary
A master pattern is identified in a target database for use as a template for data entry. The target database is qualified based on one or more database prerequisites. A plurality of similarity patterns is generated based on comparisons of records in the target database. These similarity patterns may be qualified and consolidated based on one or more pre-configured pattern significance guidelines to produce broad and unique patterns that may be used as master patterns. The master patterns may be recommended to the user during data entry in the target database.