Date Input Correction Using Context and History Ranking
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Solution Overview
Problem
Different geographical locations use varying date formats, leading to potential misinterpretation and incorrect storage of dates in applications, especially in web applications where multiple users from diverse regions input dates.
Innovation Solution
A date correction system that monitors user inputs, generates candidate date formats, and uses context and historical selection data to rank and output possible date formats for user verification and correction, employing a space function to determine context and a time function to analyze user history for preferred formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the application accepts date inputs from users in different geographical locations, then the application's versatility and user base are improved, but the reliability of date interpretation and storage deteriorates due to format ambiguity
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate date interpretations before the user submits the final input. When a date is entered, the system immediately creates several possible interpretations (e.g., MM/DD/YYYY, DD/MM/YYYY, YYYY-MM-DD) and ranks them, allowing the user to select the correct one before storage occurs.
Solution Approach 2:
The system implements feedback by presenting ranked candidate interpretations to the user and using the user's selection to verify or correct the intended date. The selection feedback is then used to update the ranking model for future predictions, improving accuracy over time.
2Stability of the object's composition
If the application stores dates in a standardized format, then data consistency is improved, but the ease of operation deteriorates because users must understand and input dates in the correct format
Solution Approach 1:
The system introduces an intermediary processing layer between user input and database storage. This layer generates multiple candidate interpretations of the user's date input, ranks them based on probability, and presents them to the user for selection, thereby mediating between diverse user input formats and standardized storage requirements.
Solution Approach 2:
The system enables self-service by allowing users to automatically select from pre-ranked candidate date interpretations that match their intended input. The ranking is based on the system's learned understanding of user preferences and contextual information, reducing the cognitive load on users.
3Measurement precision
If the application provides multiple date format options to users, then the accuracy of date interpretation is improved, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The system segments the date interpretation task into distinct components: generating candidate interpretations, ranking candidates based on multiple factors (user history, contextual clues, statistical probability), and presenting options to the user. This segmentation makes the complex processing more manageable and modular.
Solution Approach 2:
The system dynamically changes parameters such as the weighting of different ranking factors based on contextual information. For example, if the user's location or previous selections indicate a preference for a particular date format, the system adjusts the parameters to favor that format in the ranking.
Data Source
AI summary
In one embodiment, a method monitors input by a user using an application and detects a first input for a date in the application. A list of candidates is generated for the first input, the list of candidates listing the date in different formats that are possible based on the date. The method uses a space function to retrieve context for the first input, the context selected from information proximate to the first input within a threshold. Also, the method uses a time function to retrieve a history of selections, the history of selections being selected from one or more prior lists of candidates from prior inputs. The list of candidates is ranked based on a ranking function that uses the context and the history of selections to rank the list of candidates. A portion of the ranked list is output on the application for selection by the user.