Sentiment Classifier for Real-Time Workflow Flagging
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Solution Overview
Problem
Interactive voice response systems fail to identify and address user sentiment in real-time, leading to suboptimal responses that can frustrate users and result in negative reviews, unfulfilled business opportunities, and customer loss, due to the complexity and resource-intensive nature of machine learning algorithms required for sentiment prediction.
Innovation Solution
A method that preprocesses user utterances using a conversation manager, natural language processor, signal extractor, and utterance sentiment classifier to reduce data input and processing resources, allowing for real-time sentiment scoring and response optimization without the need for large training data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used to predict conversation sentiment, then sentiment identification capability is improved, but processing resources and complexity increase significantly
Solution Approach 1:
The patent segments the sentiment analysis task into multiple components: a conversation manager that handles high-level workflow decisions, a natural language processor that extracts features, and a simplified ML classifier that only processes pre-filtered inputs. This segmentation reduces the complexity burden on any single component while maintaining overall accuracy.
Solution Approach 2:
The patent introduces intermediate processing layers (conversation manager and natural language processor) that mediate between user input and the ML classifier. These intermediaries pre-process and filter data, reducing the complexity of the ML task while preserving sentiment identification accuracy.
2Measurement precision
If large volumes of training data are used to train sentiment prediction algorithms, then sentiment recognition accuracy is improved, but data processing time and resource consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing utterances and extracting relevant features before they reach the ML classifier. The conversation manager and natural language processor prepare data in advance, reducing the real-time processing burden and enabling faster sentiment prediction without sacrificing accuracy.
Solution Approach 2:
The patent extracts only the most relevant features and signals from utterances using the natural language processor, rather than feeding raw large-volume data to the ML algorithm. This extraction approach maintains recognition accuracy while significantly reducing processing time and resource consumption.
3Ease of operation
If real-time sentiment scoring is implemented for each conversation portion, then user experience improvement is enhanced, but system resource consumption increases
Solution Approach 1:
The patent applies partial action by implementing sentiment scoring selectively at key conversation portions rather than continuously analyzing every utterance. The conversation manager determines when sentiment scoring is necessary, reducing overall resource consumption while still improving user experience at critical moments.
Solution Approach 2:
The patent applies local quality by concentrating computational resources on specific conversation portions that require sentiment analysis, rather than uniformly processing all conversations. The system dynamically adjusts resource allocation based on conversation context and identified sentiment triggers.
Data Source
AI summary
Aspects of the disclosure relate to using an apparatus for flagging and removing real time workflows that produce sub-optimal results. Such an apparatus may include an utterance sentiment classifier. The apparatus stores a hierarchy of rules. Each of the rules is associated with one or more rule signals. In response to receiving the one or more utterance signals, the classifier iterates through the hierarchy of rules in sequential order to identify a first rule for which the one or more utterance signals are a superset of the rule's one or more rule signals. In response to receiving the one or more alternate utterance signals from the signal extractor, the classifier may iterate through the hierarchy of rules in sequential order to identify the first rule in the hierarchy for which the one or more alternate utterance signals are a superset of the first rule's one or more rule signals.