Conversation Metadata Assignment for Semantic Chat Clustering
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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 conversational data, which is often free-form and lacks semantic structure.
Innovation Solution
Automatically map conversations to high-level semantic groups, cluster similar conversations, and assign representative utterances to create structured metadata, enabling KPI determination and automation recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI systems (chat-bots) are used to conduct text-based chat conversations, then information can be provided to users for predetermined situations, but the functionality is limited and the chat-bot may not have access to all applicable information
Solution Approach 1:
The patent introduces an intermediary system that bridges the chat-bot and the unstructured conversational data. This intermediary automatically maps conversations to high-level semantic groups and clusters similar conversations, enabling the chat-bot to access and utilize unstructured data without manual intervention, thus expanding its functionality while maintaining automation
2Loss of information
If unstructured conversational data is used for business intelligence, then insights can be derived from conversations, but the free-form nature limits the ability to provide insights or visibility regarding the substance or semantics
Solution Approach 1:
The system performs preliminary actions by automatically mapping conversations to high-level semantic groups before analysis. This pre-processing step organizes unstructured conversational data into structured metadata, making it easier to derive insights and measure semantic content without manual intervention
3Measurement precision
If manual review of conversational data is performed, then insights can be obtained, but it requires significant time and effort
Solution Approach 1:
The system enables self-service by automatically mapping conversations to semantic groups and clustering similar conversations without human intervention. This automated approach maintains measurement precision through consistent application of mapping rules while eliminating the time-consuming manual review process
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 group of conversation records involves receiving a user input modification pertaining to a group of semantically similar conversations, automatically reassigning a conversation to a different group of semantically similar conversations based on its representative utterance in a manner that is influenced by the user input modification, and automatically updating, at a database system, a record associated with the conversation to include metadata identifying the different group of semantically similar conversations.