Conversational Scheduling Assistant Sentiment Analysis
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Solution Overview
Problem
Current computing systems that utilize digital agents or personal digital assistants face challenges in determining user satisfaction, as traditional survey methods are cumbersome and result in limited feedback, making it difficult to improve system features and services.
Innovation Solution
A software agent that performs natural language processing on user communications to identify sentiment, concerns, and actions, generating action signals to measure user satisfaction, and uses this information to generate control signals for improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional survey methods are used to measure user satisfaction, then the system can obtain feedback from users, but the process becomes cumbersome and time-consuming, limiting the quantity of feedback collected
Solution Approach 1:
The system automatically analyzes user communications and generates satisfaction metrics without requiring users to manually complete surveys. The digital assistant monitors interactions, identifies satisfaction indicators, and produces feedback reports autonomously, freeing users from the cumbersome survey process while maintaining comprehensive feedback collection.
Solution Approach 2:
The patent replaces the mechanical survey process with automated natural language processing and sentiment analysis systems. Instead of manually collecting and processing survey responses, the system uses computational methods to analyze user communications and extract satisfaction metrics, significantly improving both the quantity of feedback and user convenience.
2Quantity of substance
If the system analyzes all user communications to measure satisfaction, then comprehensive feedback is obtained, but the processing time and computational resources increase
Solution Approach 1:
The system extracts and focuses on specific satisfaction indicators and key phrases within user communications rather than processing every detail of all communications. By identifying and analyzing only the relevant elements that indicate user satisfaction, the system maintains comprehensive feedback while reducing processing time and computational requirements.
Solution Approach 2:
The patent applies partial analysis by selectively processing communications based on predefined criteria and satisfaction indicators. Rather than uniformly analyzing all communications, the system prioritizes messages containing relevant satisfaction signals, achieving comprehensive feedback measurement with reduced processing overhead.
3Measurement precision
If the system processes both interactive and non-interactive communications, then a more comprehensive understanding of user satisfaction is achieved, but the complexity of the analysis increases
Solution Approach 1:
The system uses a unified natural language processing framework that handles both interactive and non-interactive communications through the same analysis pipeline. This multi-functional approach allows the system to process diverse communication types with consistent methodology, achieving comprehensive satisfaction measurement without proportionally increasing analysis complexity.
Solution Approach 2:
The patent adjusts analysis parameters and processing depth based on communication type. The system dynamically modifies processing characteristics for interactive versus non-interactive communications, applying appropriate analysis intensity to each communication type while maintaining overall measurement precision through a coordinated parameter adjustment strategy.
Data Source
AI summary
A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent.