Multi-tier AI Sentiment Scoring for Interactive Response Systems
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Solution Overview
Problem
Interactive response systems, such as IVR and chatbots, struggle to identify and address user sentiment in real-time, leading to suboptimal responses that can frustrate users and result in negative reviews, as existing sentiment prediction methods require large training data sets and significant processing resources, making them inefficient and delayed.
Innovation Solution
The implementation of a system that includes a conversation manager, natural language processor, signal extractor, utterance sentiment classifier, and sequential neural network classifier to process user utterances, providing real-time sentiment scoring and response selection without the need for extensive training data, using pre-processing and AI to generate sentiment scores and labels for immediate response adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to predict sentiment of a conversation, then sentiment analysis capability is improved, but processing resources required and data input volume increase significantly
Solution Approach 1:
The patent segments the conversation into discrete utterances and processes each utterance independently through the ML algorithm. This segmentation allows the system to analyze sentiment at a granular level without requiring to process the entire conversation history, reducing computational complexity while maintaining analysis precision.
Solution Approach 2:
The patent implements preliminary processing of utterances by extracting key features and contextual information before inputting them to the ML algorithm. This preliminary action prepares the data in advance, allowing the ML model to make accurate sentiment predictions with reduced processing requirements during real-time interaction.
2Measurement precision
If machine learning is used to predict sentiment of a conversation, then sentiment analysis capability is improved, but processing time increases causing delayed output
Solution Approach 1:
By segmenting the conversation into individual utterances and processing them independently, the system achieves real-time sentiment analysis for each utterance without the computational burden of analyzing the entire conversation, thus maintaining both precision and speed.
Solution Approach 2:
The system performs preliminary feature extraction and context preparation before ML processing, enabling faster real-time sentiment prediction during user interactions while maintaining high accuracy through pre-processed input data.
3Loss of information
If existing sentiment rating systems are used after interaction, then user feedback is collected, but the system cannot identify which response triggered positive/negative review and real-time sentiment is not addressed
Solution Approach 1:
The system performs sentiment analysis on each utterance in real-time during the interaction, rather than collecting feedback after the interaction. This preliminary action allows the system to identify user sentiment as it occurs and determine which system responses triggered positive or negative reactions, enabling immediate adaptation.
Solution Approach 2:
The system implements real-time feedback by analyzing user sentiment during the interaction and using this information to adapt subsequent responses. This continuous feedback loop allows the system to identify which responses triggered positive or negative reviews and adjust its behavior accordingly, rather than relying on post-interaction rating systems.
Data Source
AI summary
Apparatus and methods for leveraging machine learning and artificial intelligence to assess a sentiment of an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score.