Message Thread Augmentation via Tone-Based Scoring
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Solution Overview
Problem
Personal assistant modules struggle to differentiate between 'chatty' and 'searchy' messages in message exchange threads, leading to inappropriate responses that may not enhance the conversation or provide relevant information.
Innovation Solution
A method that calculates an 'idle chatter score' and a 'search query suitability score' for messages using machine learning models, allowing for the selective incorporation of conversational responses or search query results based on these scores to augment the message exchange thread.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personal assistant modules respond to all messages in message exchange threads, then user engagement is maintained, but irrelevant or inappropriate responses are generated for non-informational messages
Solution Approach 1:
The system applies different response strategies to different types of messages within the same message exchange thread. By analyzing message characteristics (informational vs. non-informational), the system locally adapts its response behavior - generating informative responses for queries while providing appropriate non-committal responses for chatty messages, thus maintaining both user engagement and response appropriateness
2Loss of information
If personal assistant modules provide informative responses to all messages, then information needs are met, but conversational flow is disrupted for casual chatter
Solution Approach 1:
The system differentiates between informational and non-informational messages and applies appropriate response strategies locally. For informational messages, it provides detailed responses to meet information needs. For non-informational chatty messages, it provides brief, conversational responses that maintain flow without disrupting the natural rhythm of casual conversation
3Measurement precision
If machine learning models analyze every message to determine tone, then response accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies machine learning analysis selectively rather than uniformly to all messages. By identifying message characteristics that indicate informational vs. non-informational content, the system performs detailed tone analysis only when necessary, while using quicker classification methods for obviously casual messages, thus balancing accuracy with processing efficiency
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for automatically augmenting message exchange threads based on a detected tone of messages exchanged between participants. In various implementations, a message contributed to a message exchange thread involving one or more message exchange clients by a participant may be determined. In various implementations, an idle chatter score associated with the message may be calculated. In various implementations, either a conversational response to the message or content responsive to a search query generated based on the message may be selectively incorporated into the message exchange thread based at least in part on the idle chatter score. In some implementations, a search query suitability score associated with the message may also be calculated.