Semantic Cache Architecture for Query Variations and Cache Misses

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Solution Overview

Problem

Current caching systems in cloud computing face inefficiencies due to cache misses when user-generated inputs, such as plaintext queries, have slight variations in word order or choice, leading to unnecessary database queries and excessive computational resource usage.

Innovation Solution

Implementing a two-tiered cache system with a first tier using key-value pairs and a second tier employing artificial intelligence (AI) for semantic searching to match similar meanings, allowing retrieval of cached data without exact matches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a traditional key-value cache system is used, then data retrieval is fast for exact matches, but cache misses occur frequently with slight input variations leading to unnecessary database queries

Engineering Contradiction:

Improvecache hit rateVSAvoidcache system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The cache system is divided into two distinct tiers: a traditional key-value cache for exact matches and a semantic cache for approximate matches. This segmentation allows each tier to specialize in its strength while working together to solve the overall problem of improving cache hit rates without excessive complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A semantic encoding layer acts as an intermediary between the user's natural language input and the cache system. This intermediary converts variable inputs into standardized semantic representations, enabling the cache to match semantically equivalent queries even when the wording differs.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If semantic caching is implemented to handle input variations, then cache hit rate improves, but computational overhead increases due to AI processing

Engineering Contradiction:

Improvedata retrieval speedVSAvoidcomputational resource usage

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

Semantic encodings are pre-computed and stored alongside cache entries during the caching process. When a query arrives, the system compares the encoded query against stored encodings rather than performing complex AI processing, significantly reducing the computational overhead at query time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies semantic matching selectively - first attempting a fast exact match in the key-value cache, and only if that fails does it proceed to the more computationally intensive semantic cache lookup. This partial application of semantic processing minimizes overall computational resource usage.

Inventive Principle:

Principle #16Partial or excessive action

3Ease of operation

If exact matching is used for cache keys, then cache implementation is simple, but slight variations in user input cause cache misses

Engineering Contradiction:

Improvecache lookup simplicityVSAvoidcache effectiveness

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system transforms the cache key from the original variable natural language input into a standardized semantic encoding. This parameter transformation preserves the essential meaning while eliminating variations in wording, order, and phrasing, allowing the cache to effectively match semantically equivalent queries.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12561322B2Systems and methods for semantic caching

Publication Date: 2026.02.24 SERVICENOW INC

AI summary

Systems and methods are provided to improve data retrieval from a cache memory by using semantic matching to retrieve data from the cache memory. The system includes a two-tiered cache system, with a first tier implementing “key-value” pairs, and a second tier that includes a table that is configured as an artificial intelligence (AI) search indexed source. When a new input does not have a matching “key” at the first tier, the system performs a semantic search at the second tier of the cache to determine if relevant data is stored in the cache. The current systems and methods increase the likelihood of obtaining data for queries from the cache memory, reduce the response time to the queries, improve search consistency, reduce computing resource utilization, improve system performance, and reduce costs.