AI Persuasion System Dynamic Audio Analysis
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Solution Overview
Problem
Current persuasion technologies lack interactive integration among the persuader, target, and computer system, reducing their effectiveness in human-computer persuasion, especially for complex products or services that require human interaction.
Innovation Solution
The AI-based computer-aided persuasion system (CAPS) dynamically generates persuasion references by analyzing audio streams from both the agent and target using deep learning models, updating a conversation matrix, and providing guidance based on sentiment and emotion classifiers to enhance interaction and persuasion accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based dynamic analysis and integration are implemented, then persuasion accuracy and effectiveness are improved, but system complexity increases
Solution Approach 1:
The system segments the persuasion process into distinct analytical components: audio stream processing, sentiment analysis, conversation matrix updates, and persuasion reference generation. Each component handles a specific aspect of the interaction, allowing complex AI-based analysis to be broken down into manageable modules that can be developed and maintained independently while collectively achieving high persuasion accuracy.
Solution Approach 2:
The system introduces an AI-based computer system as an intermediary between the persuader (agent) and the target. This intermediary dynamically analyzes audio streams, updates conversation matrices, and generates persuasion references that guide the agent's communication strategy. The intermediary enables real-time adaptive persuasion without requiring direct complex interactions between all system components.
2Productivity
If real-time audio stream analysis is performed, then interaction effectiveness is improved, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing audio streams into structured data formats, pre-defining sentiment classification categories, and pre-establishing conversation matrix structures. These preliminary preparations enable faster real-time analysis during actual persuasion interactions, reducing computational overhead and time consumption while maintaining high interaction effectiveness.
Solution Approach 2:
The system maintains continuous analysis of audio streams throughout the persuasion interaction, continuously updating the conversation matrix and generating real-time persuasion references. This continuous useful action ensures that the system adapts dynamically to changing interaction contexts without interrupting the flow of communication, thereby maintaining high productivity and effectiveness throughout the entire persuasion process.
Data Source
AI summary
Methods and systems are provided for the AI-based computer-aided persuasion system (CAPS). The CAPS obtains inputs from both the target and the agent for an object, dynamically generates persuasion references based on analysis of the input. The CAPS obtains content output by analyzing the agent audio stream and the target audio stream using a recurrent network (RNN) model, obtains sentiment classifiers based on a convolutional neural network (CNN) model, updates a conversation matrix, and generates a persuasion reference based on the updated conversation matrix. The persuasion reference is based on an acceptance likelihood result generated from the conversation matrix using the RNN model. The CAPS further generates a target profile using CNN with input of target Big Data, wherein the target profile includes one or more objects, and wherein the agent is selected based on the generated profile and one or more selected objects.