Topic Guidance System for In-Person Conversations
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Solution Overview
Problem
Existing methods fail to effectively guide users on selecting suitable topics for conversation with acquaintances, often leading to undesirable discussions or overly cautious interactions, as they cannot dynamically determine partner-specific desirable and undesirable topics in real-time.
Innovation Solution
A system that analyzes social data from a partner using cognitive techniques, such as natural language processing and machine learning, to identify topics and their desirability, providing users with a list of topics to discuss or avoid during in-person conversations, and dynamically adjusts based on conversation progress and biometric feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users rely on general conversation guidelines, then conversation coverage is broad, but conversation quality and partner satisfaction deteriorate due to inability to avoid undesirable topics
Solution Approach 1:
The system performs preliminary analysis of social data (posts, comments, likes) before the conversation occurs to pre-identify topics the partner is likely to find desirable or undesirable. This advance preparation allows the user to select appropriate topics without needing to know the partner's preferences in real-time, thus improving conversation quality while avoiding the loss of topic suitability information.
Solution Approach 2:
The system cushions against potential conversation failures by identifying and flagging undesirable topics in advance based on the partner's social media behavior patterns. This beforehand cushioning prevents the user from accidentally bringing up topics the partner would find unpleasant, thereby ensuring higher conversation quality and partner satisfaction.
2Ease of operation
If users avoid all potentially sensitive topics, then conversation comfort is improved, but conversation depth and engagement deteriorate due to excessive caution
Solution Approach 1:
Instead of applying a uniform cautious approach to all topics, the system analyzes each topic individually based on the partner's specific social media behavior patterns. Topics are locally evaluated and categorized as desirable, neutral, or undesirable, allowing the user to comfortably discuss desirable topics in depth while avoiding undesirable ones, thus maintaining both conversation comfort and engagement.
Solution Approach 2:
The system changes the parameter of topic selection from a binary safe/unsafe classification to a nuanced desirable/neutral/undesirable spectrum based on the partner's social media engagement patterns. This parameter change allows for more nuanced topic selection that improves conversation comfort while maintaining engagement through desirable topics.
3Measurement precision
If the system analyzes detailed social data to determine topic preferences, then topic selection accuracy is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system extracts only the essential features from the partner's social media data that are relevant to topic preferences, such as frequently discussed topics, emotional reactions to different topics, and engagement patterns. By taking out only the necessary information rather than analyzing the entire social media profile, the system achieves high topic preference accuracy while keeping complexity manageable.
Solution Approach 2:
The system performs partial analysis of social media data, focusing only on the most relevant aspects (posts, comments, likes related to various topics) rather than comprehensively analyzing every piece of data. This partial action approach achieves sufficient topic preference accuracy without the excessive complexity of complete data analysis.
4Adaptability or versatility
If the system provides real-time topic guidance during conversation, then conversation adaptability is improved, but response time and processing overhead worsen
Solution Approach 1:
The system performs preliminary analysis of the conversation context and partner preferences before the user needs to select a topic. By preparing topic recommendations in advance based on the partner's known preferences and the current conversation flow, the system provides adaptive topic guidance without causing delays in the conversation.
Solution Approach 2:
The system merges the topic selection function with the conversation flow analysis, combining multiple functions into a unified process. This merging allows the system to adapt to conversation changes while minimizing processing overhead by handling topic recommendations as an integrated part of the conversation management rather than a separate real-time analysis.
Data Source
AI summary
Social data of a conversation partner is analyzed who is physically situated relative to a user to have an in-person conversation with the user. From the analysis, a list of topics and a sentiment corresponding to each topic on the list of topics are computed. An evaluation is made that a first value of a first sentiment corresponding to a first topic in the list of topics exceeds a threshold. The user is provided a notification about the first topic and the first sentiment, causing the user to discuss the first topic with the partner in the in-person conversation. When a second topic has a second sentiment below the threshold, the user is caused to drop the second topic from the in-person conversation.