Conversation Metadata Mapping for KPI-Aware Chat Analytics
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Solution Overview
Problem
Existing chat-bots lack access to comprehensive information, limiting their ability to provide insights or drive key performance indicators (KPIs) due to unstructured and free-form conversational data, which impedes user experience and productivity improvements.
Innovation Solution
A system that automatically maps unstructured conversations to high-level semantic groups, clusters similar conversations, and assigns representative utterances to facilitate performance metric determination, enabling structured metadata for tracking KPIs and automating responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If chat-bots are used to conduct text-based conversations with users, then automated information provision is achieved, but the chat-bot functionality is limited due to lack of access to comprehensive information
Solution Approach 1:
The patent introduces an intermediary system that sits between the chat-bot and the unstructured conversational data. This intermediary automatically maps unstructured conversations to structured metadata formats, extracts key information, and makes it accessible to the chat-bot. This mediator enables the chat-bot to access comprehensive information without requiring manual structuring of the data source.
2Loss of information
If unstructured conversational data is used for business intelligence, then insights can be derived, but the ability to track KPIs and provide visibility is limited
Solution Approach 1:
The patent segments unstructured conversational data into structured components by automatically mapping conversations to metadata formats with distinct fields for different information types. This segmentation enables precise extraction and tracking of specific KPIs from the conversational data, transforming unstructured text into measurable business intelligence metrics.
3Loss of information
If manual review of conversations is performed to extract insights, then comprehensive analysis is possible, but productivity and time consumption are reduced
Solution Approach 1:
The patent implements a self-service system where the conversational data automatically maps itself to structured metadata formats without requiring manual intervention. The system autonomously extracts insights, identifies key information, and structures data for KPI tracking, eliminating the need for manual review while maintaining comprehensive analysis capabilities.
4Device complexity
If unstructured conversations are stored without metadata, then data storage is simple, but analytics and recommendations cannot be effectively performed
Solution Approach 1:
The patent applies preliminary action by automatically mapping unstructured conversations to structured metadata formats at the time of data ingestion or processing. This pre-structuring of data enables future analytics, recommendations, and KPI tracking to be performed efficiently without requiring complex post-processing or manual data preparation.
Data Source
AI summary
Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, the plurality of utterances including at least a first set of utterances by a first actor and a second set of utterances corresponding to a second actor, obtaining a summarization of semantic content of the conversation based at least in part on an initial subset of the plurality of utterances using a summarization model, identifying, from among the first set of utterances corresponding to the first actor, a representative utterance that is closest to the summarization of the semantic content of the conversation, and automatically updating the record associated with the conversation at a database system to include metadata identifying the representative utterance by the first actor.