Semantic Annotation for Customer Interaction Analytics
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Solution Overview
Problem
Existing customer interaction management (CIM) systems lack adaptive natural language generation, continuous feedback, and the ability to understand customer segments and goals due to the absence of semantically annotated data, limiting their effectiveness in personalizing interactions and improving sales outcomes.
Innovation Solution
Implementing a customer interaction semantics (CIS) system that semantically annotates interaction data, allowing for 'meaning-based' analytics and inference, and providing a semantics engine to manage interactions across channels, enabling real-time feedback and adaptive strategy development through a network-connected augmented dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If typical IM systems provide real-time offers and suggestions through augmented dashboard, then agent work is enriched with customer information, but the systems cannot adapt and personalize interactions to each unique customer due to lack of semantic annotation
Solution Approach 1:
The system performs preliminary semantic annotation of customer interaction data before actual customer interactions occur. Interaction segments are pre-annotated with semantic tags representing customer goals, information state, and agent strategy, enabling the IM system to quickly retrieve and apply relevant annotated segments during real-time interactions without delaying agent work.
Solution Approach 2:
The patent introduces semantic annotation as an intermediary layer between raw interaction data and the IM system's decision-making processes. This semantic layer translates unstructured interaction segments into structured, meaningful representations that enable both real-time dashboard enrichment and adaptive personalization, bridging the gap between operational efficiency and customer adaptability.
2Adaptability or versatility
If organizations implement multiple customer interaction channels, then customer reach is expanded, but coordinating interactions across channels and time becomes complex and costly
Solution Approach 1:
The semantic annotation framework creates a universal representation layer that works across all customer interaction channels (chat, email, phone, web). By annotating interaction segments with channel-agnostic semantic tags representing customer goals and information state, the system enables consistent coordination and strategy application across diverse channels without requiring channel-specific coordination mechanisms.
Solution Approach 2:
The system creates semantic copies of customer interaction data that can be reused across different channels and time periods. Annotated interaction segments representing successful strategies or customer states can be copied and applied to similar situations in other channels, reducing coordination complexity and enabling consistent customer experience management across the organization.
3Reliability
If organizations retrain company representatives to use new interaction strategies, then interaction quality improves, but significant costs and time are incurred
Solution Approach 1:
The system implements continuous feedback loops where interaction segments are automatically annotated and analyzed to identify effective strategies. This feedback is immediately available to agents through the augmented dashboard, enabling real-time strategy adjustment without requiring formal retraining. The semantic annotation system continuously learns from successful interactions and propagates these insights across the agent workforce.
Solution Approach 2:
The IM system enables agents to self-update their interaction strategies by providing them with annotated segments and recommendations from the semantic annotation framework. Instead of requiring centralized retraining, agents can independently access and apply newly discovered interaction patterns and strategies through the dashboard, reducing both time and cost while maintaining interaction quality.
4Device complexity
If standard natural language understanding systems are used without semantic annotation, then system simplicity is maintained, but adaptive information retrieval about customer interactions cannot be achieved
Solution Approach 1:
The patent segments customer interactions into discrete, annotatable units (interaction segments) that can be individually tagged with semantic information. This segmentation approach allows semantic annotation to be applied in a structured, manageable way rather than attempting to annotate entire interaction transcripts, reducing the complexity burden while preserving critical semantic information about customer goals, information state, and agent strategy.
Data Source
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AI summary
Computer-implemented method and apparatus for customer interaction management using interaction semantics to leverage knowledge across interaction media including web chat. An agent device displays to the agent (i) a plurality of instructions and directions about how to interact with the customer with real-time updates, (ii) a plurality of feedback to inform the agent in real-time of their current performance, and (iii) an annotation engine for providing semantic annotations of the interaction in real-time or offline so that stored interactions are annotated to better assist the development of machine learning systems that provide direction and feedback to agents. The present invention provides an interface for business users to set strategies to be used by agents during their interactions and to view the outcomes of particular strategies being implemented. The present invention provides an organic manner in which an organization can semantically annotate interaction data in a semi-automated process and also provide analytics about the use of particular interaction strategies at the semantic level.