Sentiment-Driven Inquiry Flow for Automated Follow-Up Content
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Solution Overview
Problem
In federated computing systems, traditional feedback methods are inefficient and inaccurate due to timing issues, fixed question types, group dynamics, and response honesty, leading to inadequate digital content generation.
Innovation Solution
Implementing open-ended inquiries on user devices, analyzing sentiments through response analyzers, and performing follow-up actions based on sentiment analysis to create digital content automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If feedback is elicited at the end of the project using fixed questions in group settings, then the feedback collection process is simple and efficient, but the accuracy and relevance of responses deteriorate due to timing issues, group dynamics, and fixed question constraints
Solution Approach 1:
The system dynamically adjusts feedback collection timing to capture sentiments at multiple points throughout the project lifecycle rather than only at the end. This allows real-time detection of sentiment changes and enables more accurate measurement of project-related sentiments while maintaining operational efficiency through automated scheduling.
Solution Approach 2:
The system changes the parameter of question flexibility by transitioning from fixed questions to open-ended inquiries. This allows respondents to express their true sentiments in their own words, significantly improving response accuracy and relevance while the automated system manages the increased complexity of processing unstructured data.
2Ease of operation
If fixed questions are used for feedback collection, then the feedback process is structured and easy to analyze, but the relevance and accuracy of responses worsen when questions do not fit the situation or respondent feelings
Solution Approach 1:
The system dynamically selects and adapts questions based on project context, phase, and individual respondent characteristics. This maintains the structural benefits of structured feedback while improving relevance by adjusting question content to match the specific situation and respondent state.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple question templates and sentiment analysis rules before feedback collection begins. This allows the system to quickly adapt to different situations and respondents while maintaining ease of operation through pre-established frameworks.
3Productivity
If feedback is collected in group settings, then the feedback process is efficient and social, but response honesty deteriorates as respondents feel less free to express true sentiments
Solution Approach 1:
The system introduces an intermediary layer of automated sentiment analysis that mediates between the respondent and the feedback collection process. Respondents provide input without direct social pressure from peers, while the automated system analyzes sentiments objectively, preserving both efficiency and honesty.
Solution Approach 2:
The system enables self-service feedback collection where respondents can independently provide input without group setting constraints. The automated system then processes these individual responses, maintaining efficiency through automation while ensuring response honesty through private, independent input collection.
4Ease of operation
If manual interpretation of feedback by managers and HR is used, then personalized follow-up is possible, but the complexity and time required for processing feedback increases significantly
Solution Approach 1:
The system implements self-service automated content generation that creates personalized follow-up digital content without requiring manual intervention. The sentiment analysis engine automatically generates appropriate follow-up actions based on detected sentiments, maintaining personalized follow-up capability while dramatically reducing processing complexity and time.
Solution Approach 2:
The system establishes an automated feedback loop where sentiment analysis results directly trigger appropriate follow-up actions. This closed-loop system maintains the personalized attention benefit of manual processing while using automated feedback mechanisms to reduce complexity and enable scalable implementation.
Data Source
AI summary
The techniques herein include an inquiry controller determining whether timing criteria have been met for sending an inquiry to a particular user account. In response to determining that the timing criteria for sending an inquiry to the particular user account is satisfied, the inquiry is sent to and received by a particular user device associated with the particular user account. When a response is received, a response analyzer analyzes it and selects a first follow-up action, with associated first digital content, for the system to perform for the particular user account based on the analysis. The chosen action may include automatically creating a calendar item in a digital calendar associated with the user account, updating a digital task list for the user account, generating and transmitting a second inquiry to the particular user device, and the like. The follow-up action is then performed.