Digital Messaging System Context Extraction
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Solution Overview
Problem
Current digital messaging systems fail to effectively unify and search digital messages, including emails and digitized voice communications, due to limitations in context tracking and speech recognition accuracy, leading to locked information and inefficient information retrieval.
Innovation Solution
A digital messaging system that aggregates messages using context extraction and classification, enabling the unification of messages from various sources, including emails and voice communications, by attributing them to logical projects and using speech recognition to convert speech into keywords for improved searchability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If speech recognition algorithms are applied to convert speech into text, then voice communications become searchable, but speech recognition accuracy is highly affected by speaker training and context
Solution Approach 1:
The system extracts context information from email conversations before the speech recognition process. This preliminary extraction of context (such as topic, participants, and key information) is stored and then used to guide the speech recognition algorithm, providing it with expected vocabulary and context beforehand, thereby improving accuracy without requiring extensive speaker training
Solution Approach 2:
The system introduces an intermediary context extraction layer between the speech input and the search function. Instead of directly converting speech to text and searching, the system first extracts contextual metadata from the email conversation, then uses this context to enhance the speech recognition process, creating a mediating layer that improves both searchability and recognition accuracy
2Loss of information
If document management systems track tokens embedded when communication is initiated, then email conversations can be stored according to context, but the systems fail to see changes in content as the conversation evolves or to include new messages from initially unrelated parties
Solution Approach 1:
The system dynamically updates the context information as the email conversation evolves. Instead of using static tokens captured at the beginning, the system continuously analyzes new messages, extracts updated context information (such as new topics, participants, or key information), and re-associates messages with the appropriate conversation threads, making the system adaptive to changing conversation content
Solution Approach 2:
The system implements a feedback mechanism where each new email message is analyzed against existing conversation contexts. The context extraction process continuously monitors incoming messages, compares them with stored conversation patterns, and provides feedback to determine whether messages belong to existing threads or should create new contexts, enabling the system to adapt to evolving conversations
Data Source
AI summary
A digital messaging system is arranged to aggregate digital messages from a plurality of sources, the system comprising context extraction means arranged to extract context information from the messages and classification means arranged to classify the messages according to the extracted context information.