Repeatable Query Prediction and Cached Search Retrieval

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Solution Overview

Problem

Existing search engines inefficiently handle repeatable search queries, requiring repeated user input and resource-intensive processing for the same queries, especially in unreliable networks.

Innovation Solution

A learning model identifies repeatable queries based on contextual data, providing user-selectable interface components for quick query issuance without manual input, and caching search results for efficient retrieval.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If search engines process each query repeatedly using full search algorithms, then search result accuracy is maintained, but computing resources and processing time are wasted on repeatable queries

Engineering Contradiction:

Improvesearch processing efficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by identifying repeatable queries through contextual analysis and machine learning models before full search processing occurs. Search results for predicted repeatable queries are pre-computed and cached, so when the actual query is submitted, the system can retrieve results instead of re-processing, thereby improving productivity while reducing energy loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The search processing is segmented into two paths: a fast retrieval path for identified repeatable queries that returns cached results, and a full processing path for non-repeatable queries. This segmentation allows the system to handle repeatable queries efficiently without compromising the accuracy of unique queries, resolving the contradiction between speed and resource usage

Inventive Principle:

Principle #1Segmentation

2Loss of energy

If search engines cache results for repeatable queries, then resource consumption is reduced, but network reliability issues may cause stale or incorrect results

Engineering Contradiction:

Improvenetwork resource consumptionVSAvoidsearch result accuracy

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system implements feedback mechanisms where user interactions with cached results are monitored. If users frequently modify queries or indicate dissatisfaction with cached results, the system adjusts its behavior to re-process queries or update caches more frequently. This feedback loop ensures reliability is maintained while still achieving resource savings through caching

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The caching strategy is dynamic rather than static. The system adjusts cache validity periods, refresh frequencies, and retrieval thresholds based on query patterns, network conditions, and user behavior. This dynamic approach allows the system to optimize between resource savings and result accuracy adaptively, resolving the contradiction between caching benefits and reliability concerns

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If the system implements contextual analysis and machine learning models to identify repeatable queries, then query prediction accuracy improves, but system complexity increases

Engineering Contradiction:

Improverepeatable query identification accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces intermediary components (contextual analysis layer and machine learning model) that bridge the gap between raw query input and search processing. These intermediaries analyze contextual data to identify repeatable queries with high accuracy, while their modular design allows them to be added without fundamentally restructuring the core search engine, thus managing complexity while improving precision

Inventive Principle:

Principle #24Intermediary (Mediator)

4Ease of operation

If users must manually input each search query, then query specificity is ensured, but user time and interaction effort increase for repeatable queries

Engineering Contradiction:

Improvequery submission convenienceVSAvoiduser input time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system creates copies of repeatable queries from contextual patterns and user behavior data. Instead of requiring users to re-type or re-input familiar queries, the system generates query copies based on historical patterns and presents them for quick selection. This copying mechanism dramatically reduces user input time while maintaining query specificity, as users can select from accurate, context-aware query suggestions

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250238472A1Identification and Issuance of Repeatable Queries

Publication Date: 2025.07.24 GOOGLE LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.