Dynamic IVR Prompt Generation Using Contextual Language Analysis
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Solution Overview
Problem
Traditional IVR systems lack contextual awareness and adaptability, forcing customers into predetermined communication flows that are tedious and time-consuming, failing to personalize interactions based on individual customer knowledge and geographic location.
Innovation Solution
A personalized IVR system that uses machine learning to analyze historic communications and geo-localized data to generate dynamically tailored prompts, combining personalized and geo-localized contextual vectors to enhance interaction efficiency and customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional IVR systems use pre-recorded messages in a pre-determined sequence, then system simplicity and ease of operation are maintained, but communication efficiency and customer satisfaction deteriorate due to tedious and non-personalized interactions
Solution Approach 1:
The patent implements dynamic prompt generation that adapts the conversation flow in real-time based on customer responses and contextual information. Instead of fixed pre-determined sequences, the system dynamically selects and generates appropriate prompts, transitioning from static to dynamic operation to improve communication efficiency while managing complexity through automated contextual analysis
Solution Approach 2:
The system performs automated contextual analysis of customer inputs and self-generates appropriate responses without requiring human agent intervention for each interaction. The contextual vector extraction and prompt generation happen automatically, allowing the system to serve itself in creating personalized communication flows, thereby improving efficiency without proportionally increasing operational complexity
2Adaptability or versatility
If IVR systems use standardized pre-recorded messages for all customers, then ease of manufacture and system simplicity are maintained, but adaptability and personalization capability worsen
Solution Approach 1:
The patent extracts personalized contextual vectors specific to each customer based on their communication history, preferences, and behavior patterns. Instead of applying uniform treatment to all customers, the system creates localized personalized experiences by analyzing individual customer contexts and generating tailored prompts, thereby improving adaptability while managing complexity through targeted personalization
Solution Approach 2:
The system performs preliminary contextual analysis by extracting and storing contextual vectors from customer communication histories before actual interactions occur. This advance preparation of customer profiles and contextual information enables rapid personalization during live interactions without adding significant complexity to the real-time system operation
Solution Approach 3:
The patent introduces contextual vectors as an intermediary representation that bridges customer data and communication prompts. These vectors serve as a mediator that translates raw customer information into personalized communication strategies, enabling adaptability while managing complexity through an intermediate abstraction layer
3Loss of information
If IVR systems ignore customer communication history and context, then system simplicity and ease of operation are maintained, but loss of information and customer experience quality worsen
Solution Approach 1:
The patent extracts relevant contextual information from customer communication histories by generating contextual vectors that capture essential patterns and preferences. Instead of processing entire communication histories, the system extracts and retains only the most relevant contextual features, thereby reducing information loss while managing data processing complexity through selective extraction
Solution Approach 2:
The system transforms raw communication history data into condensed contextual vector representations by changing the parameter form from detailed historical records to aggregated contextual features. This parameter transformation retains essential information while reducing complexity, enabling effective use of contextual data without proportionally increasing processing demands
Data Source
AI summary
The present teaching relates to personalized IVR communications with a customer at a geo-locale. A first set of transcripts of the current and historic communications involving the customer and a second set of transcripts of historic communications associated with the geo-locale are analyzed to compute a personalized contextual vector, a geo-localized contextual vector, and a current text vector. The computed vectors are used by a language model to generate a personalized and geo-locale aware prompt, which is used to generate an IVR communication and is sent to the customer as a response.