Parallel Search Pipelines for Ranking Transient Content
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Solution Overview
Problem
Traditional operating system (OS) search techniques fail to index and retrieve transient user interactions, such as web form submissions and in-game interactions, which are not stored in files, leading to a significant loss of user-generated content.
Innovation Solution
Capture screenshots intermittently and preprocess them to identify regions of interest, apply machine learning models for entity recognition and text extraction, and index using both semantic and full-text search methods, enabling retrieval of transient content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional file-based search techniques are used, then search implementation is simple, but transient user interactions cannot be indexed and retrieved
Solution Approach 1:
The system performs preliminary actions by capturing screenshots and indexing them before they are lost. The screenshot capture mechanism proactively records user interactions at the moment they occur, and the indexing system immediately processes these screenshots to create searchable representations, ensuring transient content is preserved before it disappears from the display.
Solution Approach 2:
The patent introduces an intermediary mechanism using screenshots as a mediator between user interactions and the search system. Instead of directly indexing complex transient interactions, the system captures visual representations (screenshots) that serve as intermediaries, making transient content accessible to the search index and retrievable through standard search queries.
2Measurement precision
If multiple indexing pipelines and search pipelines are implemented, then search accuracy and user preference alignment improve, but system complexity and processing time increase
Solution Approach 1:
The system segments the search process into multiple independent indexing pipelines and search pipelines. Each pipeline processes data through different algorithms and generates separate search result orderings. This segmentation allows each pipeline to be optimized independently and enables parallel processing, reducing the complexity burden while maintaining high accuracy through multiple perspectives on the same data.
Solution Approach 2:
The patent implements partial action by running multiple search pipelines that generate multiple orderings of search results. Instead of requiring a single perfect ranking algorithm, the system executes several pipelines that each provide a partial ordering, then combines these orderings to produce the final result. This excessive action approach ensures comprehensive coverage of user preferences while managing complexity through parallelization.
3Reliability
If screenshots are captured intermittently and processed through multiple pipelines, then retrieval accuracy of transient content improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing of screenshots as they are captured, before they are needed for search queries. By pre-processing and indexing transient content at the moment of capture, the system eliminates the need for time-consuming processing during search operations, reducing perceived processing time while maintaining high retrieval reliability.
Solution Approach 2:
The patent maintains continuity of useful action by keeping multiple search pipelines running continuously in the background. These pipelines continuously process new screenshots and maintain updated indices, ensuring that when a search query arrives, the data is already processed and ready for immediate retrieval, eliminating processing delays.
Data Source
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AI summary
The disclosed techniques run multiple indexing pipelines and multiple search pipelines on a local device. Search results are presented in a first ordering according to a default search pipeline using data generated by one or more of the indexing pipelines. Other orderings of search results generated by other search pipelines are similarly generated. A user selection of one of the search results is received. Search pipelines that ranked the selected result higher than the default pipeline are identified as being better aligned with actual user preferences. Different indexing pipelines may utilize different indices, different thresholds, among other variations. Search result feedback may be aggregated from multiple users to identify more accurate, more efficient, or otherwise better search pipelines. This feedback may also be used to personalize the default search pipeline for a particular user or a particular class of users.