Semantic Analysis for Context-Aware Reminder Generation
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Solution Overview
Problem
Existing automatic reminder generation systems are limited in extracting information from user input, relying mainly on templates or keywords, which restricts the scope of input and fails to capture contextual information across multiple sentences, making them ineffective for users with busy schedules or those who struggle with remembering tasks.
Innovation Solution
A method and apparatus for automatically converting note-to-self reminders in electronic devices using semantic representation and context analysis, which parses and generates reminders based on anaphora and deictic representations, allowing for natural language and free-form text input, and resolving contextual information like pronouns, purpose, and temporal expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If template or keyword-based extraction is used for reminder generation, then the system is simple to implement, but the information extraction scope is restricted and contextual information is lost
Solution Approach 1:
The patent introduces semantic representation as an intermediary layer between raw text input and reminder generation. This mediator transforms unstructured text into structured semantic representations that preserve contextual information while enabling systematic processing, thus resolving the contradiction between implementation simplicity and information preservation
Solution Approach 2:
The system changes the parameter of text representation from simple keywords to comprehensive semantic representations that include anaphora and deictic information. This parameter change enables the system to capture contextual relationships while maintaining a structured approach to reminder generation
2Ease of operation
If template-based extraction is used, then the processing method is simple, but the system cannot handle free-form text input and multiple sentence contexts
Solution Approach 1:
The patent implements a dynamic processing approach where the system adapts its analysis depth and methods based on the input characteristics. The semantic representation framework dynamically handles both simple and complex inputs, allowing the system to maintain processing efficiency while accommodating diverse input formats and contexts
Solution Approach 2:
The semantic representation framework serves multiple functions: it handles keyword extraction, anaphora resolution, deictic interpretation, and contextual relationship mapping within a single unified system. This multi-functionality enables the system to process both simple and complex inputs through the same mechanism, enhancing versatility without sacrificing operational simplicity
3Measurement precision
If comprehensive semantic analysis is performed, then contextual accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the semantic analysis process into distinct components: anaphora representation analysis, deictic representation analysis, and contextual relationship extraction. This segmentation allows the complex task of semantic analysis to be broken down into manageable steps, improving contextual accuracy while controlling processing complexity through structured decomposition
Data Source
AI summary
A method for automatically converting note-to-self to action reminders in an electronic device is provided. The method includes receiving an input comprising at least one word from a user of the electronic device, analyzing an anaphora representation or a deictic representation for each of the at least one word, and generating a reminder based on a context from the anaphora representation or the deictic representation.