Adaptive Query Detection Engine for Emergent Incident Alerts
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Solution Overview
Problem
Cloud-based customer service platforms face challenges in identifying and promptly addressing emergent incident queries due to the complexity and decentralization of data, leading to delays and 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 misinterpretation of customer queries occurs leading to customer frustration
Solution Approach 1:
The patent introduces an adaptive query detection engine as an intermediary layer between automated systems and customer queries. This engine analyzes query patterns, identifies emergent incidents, and routes complex or ambiguous queries to human agents, thereby maintaining automation efficiency while improving interpretation accuracy for difficult cases.
Solution Approach 2:
The system performs preliminary analysis of customer queries using the detection engine to identify emergent incidents before they are fully processed. By detecting patterns and anomalies in advance, the system can prepare appropriate response strategies and escalate to human agents proactively, preventing misinterpretation before it occurs.
2Adaptability or versatility
If data is stored across various cloud platforms to enable scalability, then service flexibility is improved, but data integration complexity increases making it difficult to identify customer issues
Solution Approach 1:
The adaptive query detection engine is designed to work across multiple cloud platforms and data sources simultaneously. It provides a universal interface that can ingest and analyze queries from disparate sources without requiring complex integration logic at each data source, thereby maintaining service flexibility while reducing integration complexity.
Solution Approach 2:
The detection engine serves as a central intermediary that consolidates and standardizes data from various cloud platforms. By creating a unified view of customer queries and incidents, it simplifies the integration complexity while preserving the scalability benefits of distributed cloud storage.
3Measurement precision
If more data is analyzed to improve incident detection accuracy, then detection precision is improved, but processing time increases causing delays in addressing customer needs
Solution Approach 1:
The system applies partial analysis to most queries using the detection engine, focusing computational resources on identifying emergent incidents rather than analyzing every query in depth. This approach achieves sufficient detection accuracy for routine cases while reserving full analysis power for complex or critical incidents, thereby reducing overall processing time.
Solution Approach 2:
The adaptive query detection engine operates continuously in the background, constantly monitoring query patterns and learning from new data. This continuous operation allows the system to maintain high detection accuracy over time without requiring periodic batch processing, thus avoiding delays while improving precision through ongoing analysis.
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.