Predictive Text Input Using Context-Sensitive Candidate Grouping
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Solution Overview
Problem
Existing text input methods for devices without letter/character keys, such as portable electronics and game consoles, are inefficient as they require a large number of keystrokes to enter text, and prior art systems have not sufficiently reduced this burden.
Innovation Solution
A predictive text input method using a context-sensitive approach that suggests words based on transitional probabilities and grammatical constructions, allowing users to select predicted words with minimal keystrokes through a two-dimensional user interface, utilizing a processor and dictionary to generate and arrange candidate words dynamically based on the input context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predictive text input method is implemented, then the number of keystrokes required for text input is reduced, but the device complexity increases due to the need for prediction algorithms and dictionary structures
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing transition probabilities between words in a dictionary structure before text input begins. This allows the prediction algorithm to quickly retrieve likely word continuations without performing complex calculations during actual text input, thereby reducing keystrokes while managing system complexity through pre-computation
Solution Approach 2:
The patent introduces an intermediary prediction algorithm that acts as a mediator between the user's input intent and the actual text entry. The algorithm uses transition probabilities from a dictionary to suggest likely word continuations, allowing users to select from predicted options rather than typing each character, thus improving productivity while containing complexity through the use of a structured intermediary system
2Productivity
If context-sensitive prediction is used to suggest words, then text input efficiency is improved, but the cognitive load on the user increases due to selecting from multiple candidates
Solution Approach 1:
The system applies local quality by providing different levels of prediction assistance based on the specific input context. The prediction algorithm analyzes the local linguistic context (surrounding words, grammatical structure) to generate context-sensitive suggestions, allowing users to receive help where needed while maintaining simplicity in contexts where prediction is less certain, thereby balancing efficiency gains with cognitive load management
3Ease of operation
If a two-dimensional user interface is used to display candidate words, then navigation efficiency is improved, but the interface complexity increases compared to traditional one-dimensional lists
Solution Approach 1:
The patent transitions from a traditional one-dimensional list of candidate words to a two-dimensional grid layout for displaying predictions. This dimensional change allows users to navigate through candidate words using both horizontal and vertical directions, potentially reducing the number of navigation steps required to reach target words while organizing the interface in a more structured, manageable manner that balances navigation efficiency with interface complexity
Data Source
AI summary
The invention relates to a method and device incorporating an improved text input mechanism. The text input method comprising the steps determining a list of candidates for the present context, a candidate being a possible textual continuation of the present context and having a relevance score assigned; arranging the candidates in groups, each group having a group designator; displaying the group designators; prompting the user to select a group and receiving the user selection; displaying candidates of the selected group, the displayed candidates being arranged according to their respective scores; prompting the user to select a candidate and receiving the user selection; accepting the selected candidate as text input and updating the context.