Query Detection Engine for Emergent Incident Query Prediction
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Solution Overview
Problem
Cloud service-based customer services face challenges in identifying and promptly handling emergent incident queries due to the volume and complexity of decentralized data, leading to delays and potential misinterpretations, which can overwhelm systems and impact customer satisfaction.
Innovation Solution
An enhanced and adaptive query detection engine that predicts and identifies emergent queries by clustering historical data, generating embeddings, and determining confidence scores to map new queries, alerting service agents to emerging issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems such as chatbots are used to handle customer queries, then service efficiency is improved, but query misinterpretation increases leading to customer frustration
Solution Approach 1:
The patent introduces an intermediary human review layer for queries that fall into uncertain categories or exhibit emergent patterns. The system automatically routes these queries to human agents who can provide accurate interpretation, thus maintaining both high automated processing efficiency and high interpretation accuracy.
Solution Approach 2:
The patent replaces simple rule-based chatbot mechanics with advanced NLP models including transformer-based language models and embedding-based semantic analysis. This substitution enables more accurate understanding of customer intent while maintaining automated efficiency.
2Quantity of substance
If vast amounts of data are stored across various cloud platforms, then data availability is improved, but data integration complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data sources and formats through standardized preprocessing pipelines. The system uses unified embedding models that work across different data types, reducing integration complexity while maintaining broad data availability.
Solution Approach 2:
The patent transforms heterogeneous data from various cloud platforms into a unified parameter space using embedding vectors. By changing the representation parameters of different data sources to a common vector format, the system simplifies integration while preserving data availability.
3Device complexity
If traditional query analysis methods are used, then system simplicity is maintained, but emergent query detection capability is insufficient
Solution Approach 1:
The patent performs preliminary clustering and embedding generation on historical query data to establish baseline patterns before new queries arrive. This preliminary action enables the system to quickly detect emergent queries by comparing them against pre-computed clusters, improving detection capability without significantly increasing operational complexity.
Solution Approach 2:
The patent applies advanced NLP processing selectively - using full transformer models only when needed for ambiguous or potentially emergent queries, while simpler methods handle routine queries. This partial application of complex methods maintains system simplicity for common cases while providing strong emergent detection capability when required.
Data Source
AI summary
Various embodiments of the present technology generally relate to systems and methods for providing a query detection engine and its related functions. In an example, a method includes receiving, by a query detection engine, a plurality of queries and processing the queries to generate processed queries. For each of the processed queries, the query detection engine, generates an embedding and then groups the embeddings into clusters such that each cluster contains a subset of processed queries. The query detection engine then generates a cluster topic for each of the clusters. Once a new query is received, the query detection engine maps the new query to an appropriate cluster and generates a confidence score for the mapping of the new query to the appropriate cluster. Based on the confidence score, the query detection engine determines that the new query is an emergent query and generates an alert of the emergent query.