Demographic and Context Language Model Segmentation for Text Input
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile devices without hardware text input devices require efficient text input methods, but existing methods fail to utilize user demographic and context information effectively, leading to lower probabilities of recommending the correct input words.
Innovation Solution
Implementing a method and apparatus that use demographic and context language models to predict and recommend text inputs through a virtual keyboard, considering user demographics such as gender, age, and context information like time and application, to enhance text input efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single language model is used for all users, then the device complexity is reduced, but the accuracy of text input recommendations decreases
Solution Approach 1:
The patent segments the language model into multiple specialized models: a demographic language model divided by gender, age, and region, plus a context language model for different applications. This segmentation allows each model to specialize in specific user groups or contexts, improving recommendation accuracy without requiring a single overly complex universal model.
Solution Approach 2:
The system dynamically selects which language model to use based on user input and context. The controller determines the user's demographic group and the current application context, then activates the most appropriate language model. This dynamic adaptation provides high accuracy recommendations while keeping individual model complexities manageable.
2Measurement precision
If demographic and context information is collected and processed, then the accuracy of text recommendations is improved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-segmenting the demographic language model into categories (gender, age, region) and pre-establishing context models for different applications. User demographic information and context information are collected and processed in advance to determine which pre-prepared model to use, avoiding the need for complex real-time analysis during text input.
Solution Approach 2:
The controller acts as an intermediary that receives user input, determines the appropriate demographic group and context, selects the corresponding language model, and outputs recommendations. This intermediary layer simplifies the overall system architecture by managing the complexity of model selection and coordination, rather than requiring a monolithic complex system.
3Measurement precision
If multiple language models are selected and processed, then the accuracy of text input predictions is improved, but the time required for processing increases
Solution Approach 1:
The system performs preliminary segmentation of the demographic language model into distinct categories (gender, age, region) and pre-establishes context models for different applications. This preliminary organization allows for rapid model selection during text input without requiring complex real-time analysis, reducing processing time while maintaining high prediction accuracy.
Solution Approach 2:
By segmenting the language model into multiple specialized models organized by demographic categories and context types, the system can quickly identify and activate the most relevant model based on user input. This segmentation avoids the need to process all possible models simultaneously, significantly reducing processing time while maintaining high prediction accuracy through targeted model selection.
Data Source
AI summary
An approach, performed by a device, for recommending at least one word to be input through a keyboard is provided. The approach displays the keyboard to be used to input at least one character or text on the device. The approach selects at least one language model. The approach recommends the at least one word related to at least one character or text inputted on the keyboard, based on the at least one inputted character or text and the at least one selected language model, in which the at least one selected language model comprises at least one of a language model based on demographic properties of a user of the device, a language model based on text input histories of the user of the device, and a language model based on a context in which the keyboard is executed.