Dynamic Value Pattern Auto-Fill for UI Data Entry
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Solution Overview
Problem
Data entry operations in software applications can be time-consuming due to repetitive entry of identical or obvious data, leading to inefficient use of time and effort, as existing systems do not automatically fill values for attributes that are already stored in the database.
Innovation Solution
A method and system that dynamically create a value pattern for attributes in a user interface by receiving a first value, querying a database to fetch proposed values, and automatically filling the second value if its frequency of occurrence exceeds a threshold, thereby reducing manual data entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is performed for all fields, then data can be entered flexibly and accurately, but time consumption increases and productivity decreases
Solution Approach 1:
The system performs preliminary action by pre-fetching and storing frequently occurring attribute values in a value pattern data structure before they are needed. When a user enters a value for one attribute, the system proactively retrieves and displays suggested values for related attributes from the pre-computed value pattern, eliminating the need for manual entry of obvious values.
Solution Approach 2:
The system implements self-service by automatically filling attribute values based on the entered values and the pre-computed value patterns. The system serves itself by using the entered data to trigger automatic value completion for related attributes, reducing the burden on the user without requiring additional manual input.
2Productivity
If the application automatically fills all obvious values, then data entry time is reduced, but the system complexity increases
Solution Approach 1:
The system computes and stores value patterns in advance during system initialization or background processing, rather than computing them in real-time when needed. This preliminary computation of attribute value relationships and frequency statistics reduces the complexity of runtime operations while maintaining high productivity.
Solution Approach 2:
The system creates a simplified representation (copy) of the database data in the form of value patterns that map attribute relationships to frequent value combinations. This copy allows the system to perform automatic value filling without needing to query the entire database in real-time, reducing computational complexity while maintaining efficiency.
3Measurement precision
If the system queries the database for every attribute value, then accurate and up-to-date data is retrieved, but response time increases
Solution Approach 1:
The system performs preliminary action by pre-computing and caching value patterns that map attribute relationships to frequent value combinations. This pre-computation eliminates the need for real-time database queries during user interaction, significantly improving response speed while maintaining data accuracy through the pre-fetched values.
Solution Approach 2:
The system creates a copy of the database data in the form of value patterns that store frequent attribute value combinations. This copied data structure allows rapid retrieval of suggested values without querying the original database in real-time, achieving both speed and accuracy by using pre-validated data from the value pattern cache.
Data Source
AI summary
Disclosed is a method and system to receive a first value of a first attribute in a user interface and create a value pattern for a second attribute in the user interface based on the first value dynamically. A second value for the second attribute is automatically proposed from the created value pattern.