Conversation Slipstream Propagation via Graph Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often overlook important communications due to the vast volume of messages on electronic communication platforms, leading to incomplete conversations and a lack of access to relevant information, especially when joining ongoing discussions or collaborating across different projects or organizations.
Innovation Solution
A method and system that construct a conversations graph using machine learning to aggregate and analyze communications across multiple platforms, generating a conversation slipstream that connects logically related communications, allowing for the creation of enhanced conversations by combining new communications with relevant prior discussions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users participate in multiple conversations across different projects or organizations, then the volume of communications increases, but the ability to access relevant information and maintain context decreases
Solution Approach 1:
The patent segments the vast volume of communications by creating a conversation graph that divides communications into discrete nodes (individual communications) and edges (relationships). This segmentation allows the system to manage large volumes of communications by breaking them into manageable units that can be individually analyzed and connected based on logical relationships, thereby maintaining adaptability to access relevant information without being overwhelmed by the total volume.
Solution Approach 2:
The conversation graph serves as an intermediary structure between the user and the vast volume of communications. Instead of directly managing all communications, the system uses the graph as a mediator that organizes, stores, and retrieves communications based on their relationships. This intermediary structure enables efficient access to relevant information by translating complex communication patterns into a manageable graphical representation.
2Productivity
If users join ongoing discussions, then collaboration continues, but loss of context and incomplete conversations increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing all communications in a conversation graph before users need to access them. When a user joins an ongoing discussion, the relevant context has already been captured, organized, and made accessible through the graph structure. This preliminary organization of communications ensures that context is preserved and immediately available, eliminating the need for users to manually catch up on previous discussions.
Solution Approach 2:
The conversation graph provides feedback to users about the state of ongoing discussions by visually representing which communications have been read, which are pending, and how they relate to each other. This feedback mechanism helps users understand the current context of discussions they are joining, reducing information loss by clearly indicating what has already been communicated and what requires their attention.
3Adaptability or versatility
If traditional messaging platforms are used, then communication exchange is simple, but aggregation and analysis across platforms is limited
Solution Approach 1:
The conversation graph implements universality by creating a platform-agnostic structure that can aggregate communications from multiple different messaging platforms. The graph nodes and edges are designed to represent communications in a unified way regardless of their source platform, enabling cross-platform aggregation without requiring separate systems for each platform. This multi-functional approach allows a single system to handle diverse communication sources while maintaining consistent analysis capabilities.
Solution Approach 2:
The conversation graph acts as an intermediary layer between different messaging platforms and the analysis system. Instead of directly integrating with each platform's complex architecture, the graph provides a standardized intermediate representation that simplifies cross-platform aggregation. Communications from various platforms are translated into the graph's unified structure, reducing the overall system complexity by abstracting away platform-specific details.
4Reliability
If users manually track conversations across multiple platforms, then completeness improves, but time and effort increase
Solution Approach 1:
The system implements self-service by automatically capturing, organizing, and analyzing communications across platforms without requiring manual user intervention. The conversation graph autonomously builds and maintains the communication network by processing incoming messages, identifying relationships, and updating the graph structure. This automation ensures conversation completeness while eliminating the time and effort users would otherwise spend manually tracking communications across multiple platforms.
Solution Approach 2:
The patent replaces the mechanical manual process of tracking conversations with an automated computational system. Instead of users manually recording and analyzing communications, the system uses algorithms to automatically process, store, and analyze communication patterns in the conversation graph. This substitution of manual mechanical tracking with automated computational processing maintains complete conversation records while dramatically reducing the time and effort required.
Data Source
AI summary
Conversation slipstream propagation can include using machine learning to construct a conversations graph representing conversations conducted over an electronic communications network by a plurality of participants and collected from one or more messaging platforms. A conversation slipstream comprising one or more communications extracted from the conversations can be generated in response to receiving a secondary communication over the electronic communications network. Each of the one or more communications extracted is represented by a sub-graph of the conversations graph, each sub-graph corresponding to a graph of the secondary communication. The conversation slipstream can be presented to at least one participant to the secondary communication.