Predictive Text Dictionary Population via User Profile Customization
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Solution Overview
Problem
Existing text entry methods on computing devices with limited keyboards, such as multi-tap and T9, are inefficient due to predictive text dictionaries that may not contain relevant words, leading to incorrect suggestions and increased user effort in populating dictionaries on devices with limited memory.
Innovation Solution
A system that populates a predictive text dictionary based on user preferences and interests by maintaining a database of words related to categories and locations, allowing for automatic updating of the dictionary on devices via a server connection, ensuring that suggested words are relevant and reducing the need for manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large, generic predictive text dictionary is used, then more words are available for suggestion, but many irrelevant words are suggested and memory is consumed
Solution Approach 1:
The patent applies local quality by customizing the predictive text dictionary according to user-specific characteristics such as name, location, and interests. Instead of using a uniform generic dictionary for all users, the system tailors the dictionary content to each individual user, ensuring that suggested words are locally relevant to their context and needs.
Solution Approach 2:
The system dynamically changes the parameters of the predictive text dictionary based on user profile data. By modifying the dictionary composition according to user-specific parameters (name, location, interests), the system optimizes the relevance of suggestions while managing memory usage efficiently.
2Quantity of substance
If a pared-down predictive text dictionary is used to save memory, then fewer words are suggested, but relevant words may be missing
Solution Approach 1:
The patent ensures high reliability of suggestions by customizing the dictionary to each user's specific context. By incorporating user profile information such as name, location, and interests, the system prioritizes relevant words in the limited memory space, ensuring that the most useful words are always available for prediction.
Solution Approach 2:
The system automatically generates and updates the user-specific predictive text dictionary without requiring manual intervention. It uses user profile data to self-configure the dictionary content, ensuring that relevant words are included while maintaining efficient memory usage.
3Ease of operation
If manual population of predictive text dictionary is required, then user preferences can be included, but time and effort are consumed
Solution Approach 1:
The system automatically populates the predictive text dictionary using user profile information without requiring manual input from the user. It extracts relevant words based on the user's name, location, and interests, and generates the customized dictionary autonomously, eliminating the time and effort previously needed for manual population.
Solution Approach 2:
The system performs preliminary action by pre-populating the predictive text dictionary during device setup or initialization using available user profile data. This automated pre-population ensures that relevant words are ready for use before the user needs them, eliminating the need for time-consuming manual entry.
4Stability of the object's composition
If generic predictive text dictionary is used across multiple devices, then consistency is maintained, but user-specific relevance is lost
Solution Approach 1:
The patent resolves this contradiction by generating unique predictive text dictionaries for each device based on user profile information. Instead of using a generic dictionary across all devices, the system customizes the dictionary content to reflect user-specific characteristics, ensuring that each device provides locally relevant suggestions tailored to the individual user.
Solution Approach 2:
The system segments the predictive text dictionary into user-specific instances for each device. By dividing the generic dictionary into personalized versions based on user profile data, the system maintains consistency in approach across devices while achieving user-specific relevance in the actual dictionary content.
Data Source
AI summary
A method and system for populating a predictive text dictionary is provided. A connection between a handheld electronic device and a network is detected. The handheld electronic device is operable to allow a user to enter text. The handheld electronic device has a predictive text dictionary that is operable to receive and employ sets of words. User preferences for the handheld electronic device are retrieved. The predictive text dictionary of the handheld electronic device is populated with a set of words at least partially based on the user preferences.