Dialog Reorder via Automatic Timeline Normalization
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Solution Overview
Problem
Contact center agents face inefficiencies in parsing and summarizing lengthy, unstructured customer interactions across various communication channels, leading to time consumption and customer dissatisfaction due to the need for manual processing of irrelevant information.
Innovation Solution
An automatic timeline and topic normalization mechanism using language analysis technologies to reorder and simplify customer interactions, identifying key events and their relevance, and integrating external data for a clear, actionable summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual parsing and summarizing of dialog information is performed, then agents can understand customer interactions, but time consumption increases and efficiency decreases
Solution Approach 1:
The system performs preliminary automatic parsing, summarizing, and chronological reordering of dialog information before the agent needs to review it. The normalization mechanism pre-processes the free-form dialog text into a structured timeline with key events identified and organized, so the agent receives ready-to-analyze information rather than raw unstructured text.
Solution Approach 2:
An automatic normalization mechanism acts as an intermediary between the raw dialog data and the agent. This intermediary system uses language analysis technologies to transform unstructured customer communications into a normalized chronological format, filtering and organizing information before presentation to the agent, thereby reducing the agent's cognitive load and time requirements.
2Loss of information
If agents manually parse lengthy dialogs to understand customer issues, then complete understanding is achieved, but customer satisfaction decreases due to longer wait times
Solution Approach 1:
The normalization mechanism performs preliminary analysis of the dialog during the customer wait time, organizing chronological sequences and identifying key events before the agent begins the interaction. This allows the agent to be fully prepared with understood context, enabling immediate effective service without extending customer wait time.
Solution Approach 2:
The system performs self-service by automatically analyzing and organizing dialog information without requiring the agent's manual parsing effort. The normalization mechanism independently processes the free-form text, extracts meaningful events, and structures them chronologically, freeing the agent to focus on solution-oriented interactions rather than information gathering.
3Adaptability or versatility
If free-form customer interactions are accepted across multiple channels, then communication flexibility increases, but information structure and relevance decrease
Solution Approach 1:
The normalization mechanism changes the structural parameters of free-form dialog text by applying language analysis to extract chronological relationships and key events. It transforms unstructured text into a normalized format with defined temporal ordering and event identification, imposing structure on previously flexible free-form communications while preserving the original meaning and intent.
Solution Approach 2:
The automatic normalization mechanism serves as an intermediary that receives free-form interactions from various channels and outputs structured chronological information. This intermediary layer maintains adaptability to different input formats while providing consistent structured output, bridging the gap between communication flexibility and information structure.
4Loss of information
If agents review all dialog information to ensure completeness, then no information is missed, but irrelevant information increases noise and processing burden
Solution Approach 1:
The normalization mechanism extracts key events and meaningful information from the free-form dialog, separating relevant data from irrelevant content. It identifies and extracts chronological events while filtering out noise, presenting only the essential information in an organized timeline format, thereby reducing processing complexity while maintaining completeness of meaningful information.
Solution Approach 2:
The system segments the continuous free-form dialog into discrete chronological events and key information points. By dividing the unstructured text into segmented, ordered events with clear temporal relationships, the normalization mechanism makes the information more manageable and less complex for the agent to process while ensuring all relevant segments are captured.
Data Source
AI summary
An automatic timeline and topic normalization mechanism is described along with various methods and systems for administering the same. The temporal correction system proposed herein creates fully interpreted and reordered representations of events within and external to a dialog, reducing the amount of time and expensive resources typically required for reading, comprehension, and response to written communications.