IVR Workflow Library Creation from Agent Interactions
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Solution Overview
Problem
Current IVR systems lack the ability to accurately and efficiently leverage historical call-center experience for real-time responses and fail to convert this experience into machine-storable and machine-readable information for future access by call-center agents.
Innovation Solution
A method is developed to monitor and record call-center agent-customer interactions using audio and voice-to-text systems, converting them into text files and IVR workflows through natural language processing (NLP) algorithms, and storing these workflows in a searchable AI library for future reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IVR systems use traditional methods for handling call-center interactions, then system simplicity is maintained, but the ability to leverage historical experience and improve response accuracy is limited
Solution Approach 1:
The system creates machine-storable copies of historical call-center interactions by converting audio recordings into text files and structured data. These copies can be stored, searched, and reused to improve future responses without adding physical complexity to the original interaction system.
Solution Approach 2:
The patent replaces manual analysis and memorization of historical interactions with automated voice-to-text conversion and NLP algorithms. This substitution of mechanical human processes with computational systems enables efficient leverage of historical experience while maintaining system manageability.
2Loss of time
If call-center interactions are converted into machine-storable information using audio and voice-to-text systems, then historical experience can be leveraged for future responses, but processing time and computational resources increase
Solution Approach 1:
The system performs voice-to-text conversion and NLP processing during or immediately after calls, preparing historical interaction data in advance for future retrieval. This preliminary action ensures that when agents need historical references, the data is already processed and ready, minimizing access time.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts audio to text and structures it into searchable formats. This intermediary system acts as a bridge between raw audio recordings and the agent interface, enabling efficient access without requiring complex real-time processing during agent queries.
3Loss of information
If a library of AI files is created and stored for searchable access, then knowledge retention and response consistency improve, but storage requirements and data management complexity increase
Solution Approach 1:
The system creates structured text copies of audio interactions that can be stored efficiently in digital libraries. These text-based copies are much more compact and searchable than audio files, enabling comprehensive knowledge retention without proportionally increasing storage requirements.
Solution Approach 2:
The patent transforms unstructured audio data into structured text with defined parameters such as key phrases, topics, and metadata. This parameterization enables efficient indexing and searching, reducing the complexity of data management while improving knowledge retrieval effectiveness.
Data Source
AI summary
A method for filtering a plurality of agent-customer interactions to determine whether one or more of a plurality of agent-customer interactions should be stored in a library of Artificial Intelligence (AI) files related to an interactive voice response system (IVR) is provided. The method may include receiving an identification of a plurality of IVR flashpoints, monitoring and/or reviewing the plurality of agent-customer interactions, and determining whether one of the plurality of agent-customer interactions meets a threshold number of the IVR flashpoints. For each of the plurality of agent-customer interactions that meets a threshold number of the IVR flashpoints, the method may further direct the IVR to convert the interaction into an IVR workflow and store the IVR workflow in the library of AI related to IVR.