Context-Aware Collaborative Communication System for Real-Time Anticipatory Computing
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Solution Overview
Problem
Current communication tools, such as email, telephone, and video conferencing, do not effectively leverage computing power to provide relevant information during collaborative sessions, making it inconvenient to retrieve context-specific search results without disrupting the conversation.
Innovation Solution
A method and system that utilize context information from communication data to identify relevant concepts and provide contextual content to participants, allowing for seamless retrieval of relevant information without explicit user queries through a client device interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-based search interfaces are used during collaborative sessions, then users can input search queries verbally, but the conversation flow is disrupted and search term specification becomes cumbersome
Solution Approach 1:
The system performs preliminary actions by automatically analyzing conversation context and pre-identifying relevant search concepts before users need to query. The server continuously monitors communication information, extracts context, identifies concepts, and prepares search results in advance, eliminating the need for users to interrupt their conversation to input search terms.
Solution Approach 2:
The system enables self-service by automatically performing context analysis and search result generation without user intervention. The server autonomously retrieves context information from communication data, identifies relevant concepts, selects appropriate content, and presents search results to users without requiring them to explicitly input queries or specify search terms.
2Measurement precision
If text-based search queries are typed during collaborative sessions, then specific search instructions can be provided, but this is inconvenient and impractical on mobile devices
Solution Approach 1:
The system performs search operations automatically based on analyzed conversation context without requiring users to manually input queries. The server extracts context information from communication data, identifies relevant concepts, and generates search results autonomously, eliminating the need for users to type search queries on mobile devices while maintaining precision through context-based concept identification.
3Measurement precision
If explicit search term specification is required to retrieve relevant results, then accurate search queries can be formed, but this becomes excessively time-consuming
Solution Approach 1:
The system performs preliminary context analysis and concept identification continuously during collaborative sessions, preparing search results in advance before users need them. The server monitors communication information, retrieves context, identifies relevant concepts, and has search results ready for immediate presentation, eliminating the time users would otherwise spend formulating explicit search queries.
Solution Approach 2:
The system autonomously performs the complete search process including context retrieval, concept identification, and result selection without user intervention. This self-service approach maintains search accuracy by using context-based concept identification while dramatically reducing search time by eliminating the need for users to manually specify search terms.
4Extent of automation
If computational resources are allocated for real-time context analysis, then relevant content can be automatically presented, but system complexity increases
Solution Approach 1:
The system segments computational tasks by separating context analysis and content selection functions from client devices and concentrating them on the server. The server is responsible for retrieving context information from communication data, identifying concepts, selecting relevant content, and presenting results, while client devices only handle communication and display, reducing overall system complexity.
Solution Approach 2:
The server acts as an intermediary between communication participants and search services. It receives communication information, performs context analysis and concept identification, selects relevant content from search services, and presents results to users. This intermediary approach centralizes complex processing on the server, simplifying client device architecture while maintaining high automation levels.
Data Source
AI summary
Contextual content is provided to a first conversation participant via a client device of the first conversation participant. Communication information associated with a conversation is received via the first client device interface. Context information associated with the conversation is retrieved from the received communication information. One or more concepts associated with the conversation are identified based on the context information. Content is selected for presenting on the client device based on the identified concepts. The selected content is then presented to the first conversation participant in a second client device interface.