Document Data Entry Suggestions via Context Analysis
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Solution Overview
Problem
Data entry on slate devices with virtual keyboards is slow and cumbersome, especially for modifying documents on the go, as users often need to type full words or sentences to receive suggestions from existing autocorrect and text suggestion features.
Innovation Solution
The system generates a list of data entry suggestions for a designated area by analyzing data from other areas of the document and external sources, such as dictionaries, without requiring manual input, allowing users to select and enter suggestions directly into the data entry area, enhancing existing keyboard suggestions features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users type full words or sentences to get suggestions from existing autocorrect features, then suggestion accuracy is improved, but data entry speed and efficiency deteriorate
Solution Approach 1:
The system performs preliminary analysis of data from other areas of the document before the user needs to enter data in the current area. By pre-processing and analyzing available document data, the system prepares suggestion lists in advance, so when the user needs input, suggestions are already ready without requiring the user to type anything first.
Solution Approach 2:
The system uses the document's own existing data to generate suggestions for data entry areas. Instead of relying on external dictionaries or requiring user input, the system self-services by analyzing patterns, terms, and data from other areas within the same document to provide context-relevant suggestions.
2Manufacturing precision
If users manually type data into data entry areas, then data entry precision is improved, but time consumption increases
Solution Approach 1:
The system continuously analyzes data from other areas of the document and uses this feedback to generate and update suggestion lists. As the user works through the document, the system learns from entered data and refines suggestions for remaining areas, creating a dynamic feedback loop that improves both speed and precision.
Solution Approach 2:
The system prepares suggestion lists in advance by analyzing document data before the user needs to enter information. This preliminary analysis reduces the time users spend typing while maintaining precision through context-aware suggestions generated from existing document content.
3Reliability
If existing autocorrect features are used, then text correction capability is improved, but user efficiency deteriorates due to requiring manual typing
Solution Approach 1:
The system automatically generates suggestions by analyzing the document's own data without requiring user input or interaction. This self-service approach maintains reliable text correction capability while dramatically improving ease of operation, as users can simply select from pre-generated suggestions rather than typing manually.
Solution Approach 2:
The system performs preliminary analysis and generates correction suggestions before the user needs them. By pre-processing document data and preparing suggestion lists in advance, the system maintains accurate text correction capability while eliminating the need for manual typing, thus improving user efficiency.
Data Source
AI summary
A document having multiple data entry areas can be presented, and a list of suggestions for a designated data entry area can be generated. Generating the list can include analyzing a first set of data previously entered into one or more data entry areas of the document other than the designated data entry area and analyzing a second set of data including data outside the first set of data. The list of suggestions can be presented without having received manual entry of data in the designated data entry area, and user input selecting one of the suggestions can be received. Data represented by the selected suggestion can be entered in the data entry area in response to receiving the user input selecting the selected suggestion.