Messaging Topic Suggestions via Unhandled Query Clustering

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Solution Overview

Problem

Existing messaging systems, such as chat bots, often fail to identify appropriate topics for user queries, leading to poor performance and usability due to the need for additional dialog to elicit a response, even after engaging in clarifying questions.

Innovation Solution

A system that generates vector representations for unhandled queries from chat transcripts, clusters them, and suggests corresponding topics for inclusion in the bot, using an authoring tool to enable selection and implementation of these topics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the bot uses a fixed set of predefined topics to respond to user queries, then the system structure remains simple and manageable, but the bot cannot identify appropriate topics for queries that do not align with existing topics, leading to poor performance and usability

Engineering Contradiction:

Improvetopic coverageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of unhandled queries by generating vector representations and clustering them to identify potential new topics before they are formally added to the bot's topic set. This proactive approach allows the bot to anticipate and prepare for future query types, improving adaptability without requiring complex real-time decision-making

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback loop where unhandled queries are analyzed, clustered, and used to generate suggested topics that are then reviewed and potentially added to the bot's topic set. This continuous improvement mechanism allows the bot to learn from its failures and expand its topic coverage over time based on actual user interactions

Inventive Principle:

Principle #23Feedback

2Reliability

If the bot engages in additional dialog to elicit an appropriate response when a query does not align with any topic, then the bot attempts to clarify user intent, but this results in increased conversation length and reduced efficiency

Engineering Contradiction:

Improveresponse accuracyVSAvoidconversation length

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system pre-processes unhandled queries by generating vector representations and clustering them to identify underlying topics before users need to engage in clarifying dialogue. This allows the bot to potentially match future similar queries to identified topics directly, avoiding the need for lengthy clarifying conversations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables the bot to self-improve by automatically analyzing unhandled queries, clustering them to identify patterns, and generating suggested topics without requiring manual intervention or extensive user clarification. This self-service capability reduces the need for additional dialog by empowering the bot to independently expand its topic knowledge

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If the bot maintains a comprehensive topic set to handle diverse queries, then the bot can respond to more query types, but the difficulty of detecting and measuring appropriate topics increases

Engineering Contradiction:

Improvequery handling capabilityVSAvoidtopic identification difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system introduces vector representations as an intermediary layer between user queries and topic identification. By converting queries and topics into vector space, the system enables efficient similarity-based matching that scales well with topic set growth, reducing the difficulty of detecting appropriate topics even as the topic set expands

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces traditional keyword-based or rule-based topic matching with vector representation and clustering mechanisms. This substitution enables more nuanced and accurate topic identification by capturing semantic relationships, making it easier to detect appropriate topics even in a comprehensive topic set

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250219980A1Topic suggestion in messaging systems

Publication Date: 2025.07.03 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Embodiments are provided for suggesting topics in a messaging system. A set of queries is received from a chat transcript history, where the set of queries includes a set of unhandled queries, and each unhandled query comprises a query for which a bot did not identify a corresponding topic (e.g., queries that did not trigger selection of a topic by the bot). A vector representation is generated for each unhandled query in the set of unhandled queries. The vector representations for the set of unhandled queries are clustered to generate one or more clusters of vector representations, each cluster corresponding to a group of unhandled queries. A corresponding suggested topic is generated for each cluster and provided to an authoring tool that comprises one or more interactive elements to enable an author to select at least one of the suggested topics for implementation in the bot.