Context-Sensitive Predictive Text Dictionary for Mobile Input
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Solution Overview
Problem
Existing predictive text systems fail to adapt to changes in a user's physical and social context, leading to inefficient text input as they do not provide context-specific vocabulary suggestions, which can result in users turning off the feature due to irrelevant suggestions.
Innovation Solution
A context-sensitive predictive text dictionary that updates and prioritizes suggestions based on the user's current context, allowing for the download of context-specific dictionaries and adaptive storage to provide relevant and correct suggestions, such as those related to specific social groups, locations, or commercial sponsorships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predictive text dictionary is used to suggest words based on keystrokes, then text input speed is improved, but the suggestions become irrelevant when context changes (physical location, social group, time)
Solution Approach 1:
The predictive text system dynamically adapts to changing contexts by automatically downloading context-specific dictionaries when the device detects changes in physical location, social group, or time. The system transitions from a static dictionary to a dynamic one that updates based on contextual parameters, ensuring suggestions remain relevant while maintaining fast text input.
Solution Approach 2:
The system changes the parameter of the dictionary itself based on contextual parameters. When context changes (location, social group, time), the system switches between different dictionary versions or downloads new ones, effectively using parameter changes in the dictionary content to match parameter changes in the user's context.
2Adaptability or versatility
If context-specific dictionaries are downloaded to improve suggestion relevance, then adaptability is improved, but device storage requirements increase
Solution Approach 1:
The system implements a nested dictionary structure where a comprehensive base dictionary contains all possible words, and context-specific dictionaries contain only the subset of words relevant to particular contexts. The context-specific dictionaries are nested within or referenced from the base dictionary, allowing the system to load only necessary portions while maintaining access to the full vocabulary when needed.
Solution Approach 2:
The system performs preliminary actions by pre-downloading or pre-loading context-specific dictionaries before they are actually needed, based on predicted context changes. This allows the dictionaries to be ready in advance, reducing latency when context changes occur while managing storage through selective pre-loading based on usage patterns.
3Measurement precision
If the predictive text system continuously updates based on user context, then suggestion accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The system applies local quality by making the predictive text system context-aware and location-specific. Different dictionary versions or configurations are applied locally based on the detected context (physical location, social group, time), so that processing is optimized for each specific context rather than using a single universal processing approach for all situations.
Data Source
AI summary
A method for a mobile communication apparatus including enabling the mobile communication apparatus for character input, comparing inputted characters with words in a list, prioritizing and listing matches of the comparison in a priority list depending on current context. Further, displaying the priority list, enabling selection from the displayed priority list and enabling updating of the list depending on current context. A corresponding application, apparatus, system, user interface and computer program is also disclosed.