Query Clustering and Response Mapping for Efficient ML Frameworks
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Solution Overview
Problem
Existing systems face challenges in efficiently and reliably generating and delivering compute and energy-efficient responses for complex tasks across various processing frameworks, leading to inefficient use of resources and storage space.
Innovation Solution
A machine learning-based system generates embeddings for queries, forms clusters based on vector representations, and matches incoming requests with pre-generated query-response pairs, saving computational resources and energy while maintaining response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If servers execute computing tasks for client devices, then computing tasks can be processed, but compute and energy efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-generating query-response pairs for frequently asked questions and storing them in a database. When a client device sends a query, the system first checks if a pre-generated response exists before executing complex computing tasks, thereby avoiding unnecessary computation and reducing energy consumption while maintaining productivity.
Solution Approach 2:
The system creates a copy of common query-response pairs and stores them in a database for rapid retrieval. Instead of generating responses from scratch for frequent queries, the system retrieves pre-generated response copies, significantly reducing compute requirements and energy consumption while maintaining processing capability.
2Productivity
If servers generate responses for complex tasks, then task completion is achieved, but reliability deteriorates
Solution Approach 1:
The system performs preliminary action by pre-generating and storing responses for frequently asked questions before they are requested. This ensures that when a query arrives, a reliable pre-computed response is immediately available, improving response delivery reliability while maintaining productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where client devices can indicate when a pre-generated response does not accurately answer their query. This feedback is used to refine and update the pre-generated responses, improving reliability over time while maintaining efficient response generation.
3Ease of operation
If historical client queries are used to generate embeddings and clusters, then FAQ services are improved, but storage space and computational resources increase
Solution Approach 1:
The system extracts and stores only the most critical information (query-response pairs for frequently asked questions) in the database, rather than storing all historical queries and responses. This selective extraction reduces storage space and computational resources while maintaining effective FAQ services.
Solution Approach 2:
The system performs preliminary clustering and embedding generation for historical queries to identify and select only the most frequent and relevant question patterns. This preliminary action filters out redundant data, reducing the quantity of stored information while maintaining high-quality FAQ services.
Data Source
AI summary
The technical solutions provide ML-based generation of queries and responses for operational frameworks of entities. A processor can identify queries of a processing frameworks for an entity and construct embeddings for the queries. Each embedding can correspond to a vector of a query of the plurality of queries that form a plurality of clusters in a vector space. The processor can generate, for each cluster, using API calls to ML models, a cluster query and a cluster response for the cluster query and store, in a storage, a mapping of each cluster query to each cluster response. The processor can receive, from a client, a request and select, using the mapping, the cluster response based on a relation between the cluster query and the request. The processor can provide, to the client, the selected cluster response responsive to the request.