Dynamic Rule Tree Prediction for Reduced-Keypad Alphanumeric Entry
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Solution Overview
Problem
Conventional alphanumeric keypads require excessive key presses to select desired characters when multiple characters are assigned to a key, and existing solutions like dictionary-based and linguistic-based methods are language-specific and ineffective for uncommon words or acronyms.
Innovation Solution
A method and apparatus that uses a dynamically built rule tree to predict alphanumeric characters based on previous selections, weighting characters by their selection frequency, allowing quick adaptation to different languages and vocabularies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple alphanumeric characters are assigned to a single key in a reduced-key keypad, then the number of keys is reduced, but the number of key presses required to select a specific character increases excessively
Solution Approach 1:
The system performs preliminary action by predicting the desired character before the user completes the key press sequence. The prediction is based on analyzing previously entered characters and using that context to anticipate the next character the user wants to input, thereby reducing the time the user needs to wait and the number of key presses required.
Solution Approach 2:
The system implements feedback by continuously monitoring the sequence of key presses and using this information to refine predictions. The prediction mechanism learns from user input patterns and adjusts its predictions based on the feedback from previous selections, improving accuracy over time and reducing the effort required to select characters.
2Loss of time
If dictionary-based or linguistic-based methods are used to predict characters, then the number of key presses is reduced, but the system becomes language-specific and ineffective for uncommon words or acronyms
Solution Approach 1:
The system applies dynamics by making the prediction mechanism adaptive and flexible rather than static and language-specific. The prediction algorithm dynamically adjusts based on the actual input patterns observed, allowing it to handle any language, uncommon words, or acronyms without requiring pre-programmed linguistic rules or dictionaries for each language.
Solution Approach 2:
The system achieves universality by creating a language-agnostic prediction mechanism that works across different languages and contexts. Instead of relying on language-specific dictionaries or linguistic rules, the system uses a general-purpose pattern recognition approach that can adapt to any language or vocabulary, making it universally applicable.
3Device complexity
If conventional multitap techniques are used to select characters, then the keypad layout remains simple, but the complexity of the input process increases significantly
Solution Approach 1:
The system applies self-service by enabling the prediction mechanism to automatically assist the user without requiring manual intervention to scroll through multiple characters. The system serves itself by using the input pattern to generate predictions that reduce the user's workload, allowing the user to simply confirm predictions rather than manually navigating through multiple character options.
Data Source
AI summary
A method and apparatus for predicting the desired alphanumeric character of a depressed multi-character key of a reduced-key keypad based upon the previous selection of characters. Rule trees defining the prediction associated with a depressed key based upon the previous selection of characters are dynamically built upon use to readily function with all language styles and vocabularies. Moreover, the rule trees are weighted to increase the probability of predicting the desired character and to be quickly adaptable to different users having different language styles or vocabularies.