Word Recommendation Using Context-Aware Language Model Segmentation
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Solution Overview
Problem
Existing electronic devices limit the recommendation of words to users by not considering the user's usage environment, leading to unsuitable suggestions.
Innovation Solution
An electronic device that recommends words by using a language model combined with state information from external devices, allowing it to provide more tailored suggestions based on the user's context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model is used to recommend words, then word recommendation capability is provided, but the recommendations are not suitable for the user's usage environment
Solution Approach 1:
The system segments the language model into multiple specialized models, each trained for specific usage environments (messaging, web browsing, banking, note-taking). This allows the device to select the appropriate model segment based on the current usage context, improving adaptability without requiring one model to handle all scenarios perfectly.
Solution Approach 2:
The processor acts as an intermediary that receives usage environment information, determines which language model is most appropriate, and selects recommended words accordingly. This intermediary layer enables the system to bridge the gap between the language model and the usage environment, ensuring recommendations are contextually appropriate.
2Measurement precision
If multiple language models are integrated to improve recommendation accuracy, then word recommendation suitability is enhanced, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between different language models based on real-time usage environment detection. Rather than running all models simultaneously, the processor identifies the current context (messaging, browsing, banking, etc.) and activates only the relevant model, maintaining high accuracy while managing complexity through dynamic adaptation.
Solution Approach 2:
The system changes operational parameters by adjusting which language model is active based on usage environment parameters. When the usage context changes (e.g., from messaging to banking), the system switches to a different model configuration, optimizing performance for each scenario without requiring permanent integration of all models at full complexity.
Data Source
AI summary
An electronic device according to various embodiments includes: a display; a communication circuit configured to perform communication with at least one of at least one external server and/or at least one external electronic device; a processor operatively connected to the display and the communication circuit; and a memory operatively connected to the processor. The memory stores instructions which, when executed, cause the processor to control the electronic device to: display a user interface including a virtual keyboard through the display; display a string including at least one word on the user interface based at least in part on an input to the virtual keyboard; determine at least one recommended word associated with the string based at least in part on a language model; and display the determined at least one recommended word on the user interface. The language model may include a language model of the electronic device, and a language model that is received from the external server or the external electronic device based on state information of the external electronic device that is connected for communication through the communication circuit.