Parallel Search Pipelines for Transient Content Retrieval
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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 index regions of interest using machine learning models to identify entities and extract text, employing semantic and full-text searches to retrieve relevant content, while maintaining user privacy by processing data locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional file-based search techniques are used, then search simplicity is maintained, but transient user interactions cannot be indexed and retrieved
Solution Approach 1:
The system performs preliminary actions by capturing screenshots at scheduled intervals and pre-processing them to extract text, identify regions of interest, and generate embeddings before the actual search occurs. This ensures transient content is preserved and indexed in advance, making it retrievable when needed without requiring complex real-time capture mechanisms during search operations.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes a screenshot capture service, text extraction service, and embedding generation service. These intermediary components bridge the gap between simple file-based search and the need to index transient visual content, transforming screenshots into searchable data representations without requiring direct complex interactions with the original transient content.
2Measurement precision
If multiple indexing pipelines with different parameters are implemented, then search result accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The indexing process is segmented into multiple independent pipelines, each with different parameters (e.g., embedding dimensions, threshold values, region detection methods). Each pipeline processes screenshots separately and generates its own index, allowing parallel execution. This segmentation enables the system to evaluate multiple indexing strategies simultaneously without sequential processing delays, improving search result relevance while managing processing time through parallelization.
Solution Approach 2:
The system implements multiple indexing pipelines that vary key parameters such as embedding dimensionality, similarity thresholds, and region of interest detection sensitivity. By changing these parameters across different pipelines, the system can optimize for different search scenarios (e.g., speed vs. precision) and select the most appropriate pipeline based on query characteristics, balancing processing time and search result accuracy.
3Productivity
If screenshots are captured intermittently rather than continuously, then system performance is maintained, but content retrieval completeness may be reduced
Solution Approach 1:
Instead of continuous capture, the system employs periodic screenshot capture at strategically determined intervals. The capture frequency is adjusted based on system performance constraints and content retention requirements. This periodic action maintains productivity by avoiding continuous processing overhead while ensuring sufficient content is captured to meet retrieval completeness requirements through optimized capture scheduling.
Data Source
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.