Probability Distribution Model for Dialog System Troubleshooting
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Solution Overview
Problem
Conventional automated troubleshooting systems using IVR rely on Boolean logic, leading to incorrect beliefs about device states due to errors in speech recognition and network tests, resulting in failed dialogs and low customer satisfaction.
Innovation Solution
Implementing a probability distribution model that maintains multiple guess states over time for the state of the product or service, using a Bayesian network to establish speech-based and non-speech-based channels of interaction, and responding based on a probability distribution to troubleshoot effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional IVR systems use Boolean logic to maintain a single guess for device state, then the system operates with simple decision-making, but the system forms incorrect beliefs about product state due to errors in speech recognition and network test inputs
Solution Approach 1:
The patent transforms the device state representation from discrete Boolean values to continuous probability distributions. Instead of maintaining a single guess for device state, the system maintains probability distributions that capture uncertainty, allowing it to handle errors in speech recognition and network tests by representing multiple possible states simultaneously with associated confidence levels.
Solution Approach 2:
The system dynamically updates probability distributions over time as new information arrives from speech recognition, network tests, and user responses. This dynamic maintenance of multiple guess states allows the system to adapt its beliefs about device state based on incoming evidence, resolving the contradiction between simple operation and accurate belief formation.
2Reliability
If the system uses ad hoc techniques like thresholds on ASR confidence scores and confirmations to reduce incorrect beliefs, then the chances of forming incorrect beliefs are reduced, but the dialog length increases leading to user frustration
Solution Approach 1:
The system performs preliminary probabilistic reasoning to maintain multiple guess states in advance, allowing it to anticipate and handle potential errors from speech recognition and network tests without requiring lengthy confirmations during the dialog. By pre-computing and maintaining probability distributions, the system resolves ambiguity efficiently without extending dialog duration.
3Productivity
If conventional IVR systems ask questions from a fixed list to self-classify problems, then the system can route calls efficiently, but the system cannot adequately address complex troubleshooting scenarios requiring multiple guess states
Solution Approach 1:
The probability distribution model serves multiple functions: it enables efficient call routing by classifying problems while simultaneously supporting complex troubleshooting scenarios by maintaining multiple guess states about device conditions. This universal approach allows the same mechanism to handle both simple routing and complex diagnostic situations, resolving the contradiction between productivity and adaptability.
Data Source
AI summary
Disclosed herein are systems, methods, and computer-readable media for troubleshooting based on a probability distribution model. The method for troubleshooting based on a probability distribution model includes establishing a speech-based channel of interaction, establishing at least one non-speech-based channel of interaction, maintaining a probability distribution over time for each of a plurality of component variables describing the state of the product or service and state of the conversation, and troubleshooting a product or service by responding based on the probability distribution.