Conversation Search via Topic Segmentation for Precise Retrieval
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Solution Overview
Problem
Existing generative AI conversations are challenging to navigate due to their organic nature, requiring extensive scrolling and keyword searches that yield irrelevant results, making it difficult to return to desired topics or content.
Innovation Solution
A search engine that partitions AI conversations into data topic segments based on transitions in business concepts, using key performance indicators and metrics to organize and categorize content, allowing users to quickly locate specific insights or visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If keyword search is used to find topics in conversations, then search coverage is improved, but search precision deteriorates due to multiple irrelevant results
Solution Approach 1:
The patent segments conversations into distinct topic segments based on topic transitions, creating organized units that can be searched independently. This segmentation allows the system to retrieve only relevant topic segments rather than returning all conversation portions containing search keywords, thereby improving precision while maintaining coverage.
Solution Approach 2:
The patent introduces topic segments as an intermediary layer between raw conversation data and search queries. These topic segments act as mediators that filter and organize conversation content, enabling precise retrieval without requiring users to manually filter through irrelevant keyword matches.
2Productivity
If conversations are organized by topic segments, then retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary topic segmentation during conversation processing, organizing content into topic segments before retrieval operations occur. This advance organization enables efficient retrieval without requiring complex real-time analysis during search operations.
Solution Approach 2:
The system automatically identifies topic transitions and segments conversations without requiring manual intervention or complex external processing. The topic segmentation occurs self-service through automated detection of topic changes in the conversation flow.
3Loss of information
If extensive scrolling is used to navigate conversations, then complete content review is possible, but time consumption increases
Solution Approach 1:
By segmenting conversations into topic-based units, the system allows users to directly navigate to specific topic segments rather than scrolling through entire conversations. This maintains the ability to review complete relevant content while eliminating time-wasting scrolling through irrelevant portions.
Solution Approach 2:
The patent adds a topical organization dimension to conversation navigation, transforming the traditional linear scrolling approach into a multi-dimensional access method where users can jump to topics of interest directly, significantly reducing navigation time.
Data Source
AI summary
According a present invention embodiment, a system for searching conversations for desired content monitors one or more conversations and identifies changes in topics in the one or more conversations based on inquiries from one or more users. The topics pertain to business concepts. The one or more conversations are partitioned into segments based on the identified changes in topics. The segments are assigned to the topics based on the segments containing content for the topics. A query including a topic is processed, and the segments of the one or more conversations pertaining to the topic of the query are retrieved based on the assignment of the segments to the topics. Embodiments of the present invention further include a method and computer program product for searching conversations for desired content in substantially the same manner described above.