Data Formatting Rule Generation via User Edit Heuristics
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Solution Overview
Problem
Existing data formatting methods are inefficient as they require manual conversion and lack automation in adapting to user edits, leading to inconsistent data formats across different systems and applications.
Innovation Solution
A machine learning heuristic-based formatting manager automatically determines data formatting rules by analyzing user edits, allowing for the conversion of data from one format to another, and applies these rules to additional data items with a confidence level indicator for user review and update.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual data formatting methods are used, then data can be converted between formats, but the process is inefficient and lacks automation
Solution Approach 1:
The system automatically detects user formatting edits and self-generates formatting rules without requiring manual intervention. The formatting manager monitors user actions, extracts patterns, and applies rules autonomously, making the system serve itself rather than requiring continuous user direction.
Solution Approach 2:
The patent replaces manual mechanical formatting operations with an automated machine learning-based system. Instead of users manually converting formats, the system uses pattern recognition and heuristic algorithms to automatically detect and apply formatting rules, substituting human effort with computational intelligence.
2Adaptability or versatility
If different transformation functions are used to format data, then various formatting requirements can be met, but consistency across different systems is lost
Solution Approach 1:
The system continuously monitors user formatting edits and uses this feedback to learn and refine formatting rules. By observing actual user behavior patterns, the system adapts its rule generation process to maintain consistency while accommodating different formatting requirements, creating a closed-loop learning system.
Solution Approach 2:
The system dynamically adjusts formatting parameters based on detected patterns. Instead of using fixed transformation functions, the system modifies formatting parameters adaptively according to user edits and contextual information, allowing it to maintain consistency across different data types and systems while remaining versatile.
3Productivity
If automated formatting rules are generated from user edits, then formatting efficiency improves, but the complexity of the system increases
Solution Approach 1:
The system divides the complex formatting task into discrete, manageable components: edit detection, pattern extraction, rule generation, and rule application. By segmenting the process into distinct modules, the system manages complexity while maintaining high automation capability and productivity.
4Loss of time
If data formatting is automated based on user edits, then time consumption is reduced, but the precision of format determination must be ensured
Solution Approach 1:
The system performs preliminary analysis of user edits to pre-determine formatting rules before actual data formatting is needed. By提前 analyzing patterns and generating rules in advance, the system reduces real-time processing time while ensuring accurate format determination through thorough preliminary validation.
Data Source
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AI summary
Data formatting rules to convert data from one form to another form are automatically determined based on a user's edits. A machine learning heuristic is applied to a user's edits to determine a data formatting rule that may be applied to data. For example, a user may make edits that add/remove characters from data, concatenate data, extract data, rename data, and the like. The machine learning heuristic may be automatically triggered in response to an event (e.g. after a predetermined number of edits are made to a same type of data) or manually triggered (e.g. selecting a user interface option). The data formatting rule may be applied to other data and the results of the formatting reviewable by the user. Based on further edits/reviews, the data formatting rule may be updated. The data formatting rules may be stored for later use.