Context-Aware Text Suggestion System for Meeting Documents
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Solution Overview
Problem
Current text suggestion systems fail to provide context-aware suggestions for documents related to meetings or other specific user situations, often resulting in irrelevant suggestions due to a lack of consideration for the document's context.
Innovation Solution
A computer-implemented method that determines a user's situation, such as attending a meeting, and generates context-based text suggestions by creating a corpus of text from sources associated with the situation, including meeting transcripts, emails, and webpages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general language model is used for text suggestion, then text suggestion can be provided for any document type, but the text suggestion becomes irrelevant to the specific context of meeting notes or emails
Solution Approach 1:
The system applies different text suggestion strategies based on the local context of the document being edited. When meeting notes or meeting-related emails are detected, the system uses meeting-specific text from transcripts and sources to generate suggestions, rather than applying a uniform general language model approach to all documents.
Solution Approach 2:
The system dynamically adapts its text suggestion behavior based on the detected document type and context. It switches between general language model suggestions and meeting-specific suggestions by analyzing the document content and detecting whether it relates to meetings, thereby making the suggestion system flexible and context-aware.
2Loss of information
If manual navigation through different sources is required to find information, then complete information can be accessed, but the process becomes tedious and inefficient
Solution Approach 1:
The system automatically accesses and processes meeting transcripts and related sources without requiring manual user intervention. It autonomously extracts relevant text and generates suggestions, eliminating the need for users to manually navigate through different sources while ensuring complete information is utilized.
Solution Approach 2:
The system performs preliminary processing of meeting transcripts and sources by pre-extracting and storing relevant text segments before the user needs them. This preparation work is done in advance, so when the user is drafting documents, the system can quickly retrieve and suggest appropriate text without requiring real-time manual searching.
3Ease of manufacture
If general text suggestion is provided without context awareness, then the suggestion system is simple to implement, but the suggestions are not relevant to the specific meeting context
Solution Approach 1:
The system integrates multiple functions into a single text suggestion framework: it can handle both general documents and meeting-specific documents using the same interface. By detecting document type and automatically switching between general language model suggestions and meeting-specific suggestions, it maintains implementation simplicity while gaining context-awareness capabilities.
Data Source
AI summary
Generating text suggestions based on context can leverage sources associated with the context to generate more accurate and informed text suggestions. For example, the context can be a user situation, such as the user is attending a meeting. Obtaining text from sources associated with the user situation can generate a corpus of text that can be leveraged for generating the context-based text suggestions.