Text Suggestion Region Consistency for Predictable Input
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Solution Overview
Problem
Existing text entry methods on computing devices, such as mobile phones and tablets, require users to attend to multiple text suggestion regions due to changing lexical contexts, reducing the speed and efficiency of text input as users cannot predict where a candidate character string will be displayed.
Innovation Solution
A computing device consistently outputs a particular candidate character string within the same text suggestion region each time it is selected, based on previous selection history, allowing users to predict the location and select it without attending to other regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If candidate character strings are displayed in text suggestion regions based on lexical context probability, then the accuracy of text suggestions is improved, but the user cannot predict the location of candidate strings and must attend to multiple regions, reducing input speed
Solution Approach 1:
The system performs preliminary analysis of lexical context and user input patterns before displaying suggestions, pre-determining the most likely candidate strings and their optimal display locations based on historical data and context probability, so that suggestions are already positioned for quick access before the user needs them
Solution Approach 2:
The text suggestion system segments candidate character strings into different priority groups and assigns them to different text suggestion regions based on their probability and relevance, with high-probability candidates in easily accessible regions and lower-probability candidates in other regions, allowing users to focus on specific regions rather than scanning all possibilities
2Adaptability or versatility
If text suggestion regions are dynamically updated based on lexical context, then the relevance of suggestions is improved, but the changing locations require users to attend to each region, increasing time spent
Solution Approach 1:
The system incorporates feedback from user selection patterns and interaction history to learn which candidate strings users frequently select and where they prefer to find them, continuously adapting the display strategy to minimize search time while maintaining high relevance through context-aware suggestion selection
Solution Approach 2:
The system pre-calculates and maintains a ranked list of candidate character strings based on lexical context analysis, so that when suggestions are displayed, they are already ordered by relevance and positioned in optimal locations, eliminating the need for users to search through unorganized options
Data Source
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AI summary
In one example, a method includes outputting, by a computing device and for display, a graphical user interface including a plurality of text suggestion regions. The method further includes receiving, by the computing device, an indication of gesture input detected at a presence-sensitive input device, and selecting, based at least in part on the indication of the gesture input, a candidate character string from a plurality of candidate character strings. The method further includes determining that the candidate character string was previously selected while being displayed within a particular text suggestion region from the plurality of text suggestion regions, and outputting, for display and based at least in part on determining that the candidate character string was previously selected while being displayed within the particular text suggestion region, the candidate character string within the particular text suggestion region.