Message Management Application for SDN Conversation Processing
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Solution Overview
Problem
Current software-defined networking (SDN) systems lack the ability to dynamically and effectively incorporate user interests and preferences into messaging and conversation transactions, leading to inefficient information processing and management.
Innovation Solution
The implementation of a method that identifies user-submitted conversational statements, parses them to determine items of interest, retrieves relevant information from external sources, and automatically generates response statements, enhancing conversation relevance and efficiency by integrating additional or alternative information based on user characteristics and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional networking switches treat all packets in the same manner using predefined rules, then device complexity is reduced and ease of operation is improved, but adaptability to user interests and conversation relevance deteriorates
Solution Approach 1:
The patent introduces an intermediary message management application that sits between users and the SDN controller. This application parses conversational statements, identifies items of interest, and translates user interests into actionable information requests. The intermediary handles the complexity of understanding user context and generating relevant information, while the SDN controller focuses on efficient information retrieval and delivery, thus resolving the contradiction between adaptability and device complexity.
Solution Approach 2:
The system segments the information processing function into distinct components: (1) message management application that handles conversational analysis and item identification, (2) SDN controller that manages information retrieval based on identified items, and (3) network infrastructure that delivers information. This segmentation allows each component to specialize in specific tasks, improving overall adaptability while distributing complexity across multiple manageable modules rather than concentrating it in a single device.
2Productivity
If SDN systems process all conversation information manually without automation, then information quality and relevance are maintained, but productivity and response time deteriorate
Solution Approach 1:
The message management application implements self-service by automatically parsing conversational statements, identifying items of interest, formulating information requests, and generating response statements without requiring manual intervention. The system serves itself by autonomously managing the entire information processing workflow from user input to formatted output, thereby improving productivity while maintaining information quality through systematic analysis and validation processes.
Solution Approach 2:
The system incorporates feedback mechanisms where the message management application continuously monitors conversation flow, evaluates identified items of interest against conversation context, and adjusts information requests accordingly. The SDN controller receives feedback about information delivery effectiveness and refines its retrieval strategies. This feedback loop ensures that automated processing maintains high information quality by continuously adapting to conversation needs while preserving processing efficiency.
3Loss of information
If the system retrieves and processes all possible information sources for every conversation item, then information completeness is improved, but use of energy and processing time worsen
Solution Approach 1:
The message management application applies partial action by selectively processing only the most relevant items of interest identified from conversational statements rather than exhaustively analyzing every piece of information. The system prioritizes items that are most critical to conversation flow and user interests, retrieving information from a subset of relevant sources rather than all possible sources. This approach maintains sufficient information completeness for effective conversation while significantly reducing processing energy consumption by avoiding unnecessary analysis of less important elements.
Data Source
AI summary
Identifying user input data on a mobile user device may provide a way to predict the types of questions and actions a user will take and offer information contemporaneously with such actions. One example method of operation includes identifying a computer hosted conversation with at least one user submitting conversational statements, parsing the conversational statements to identify at least one item of interest, retrieving a source of information corresponding to the item of interest and automatically creating a response statement including the item of interest.