Document Preview Caching With Preloaded Fragments for Faster Review
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Solution Overview
Problem
Conventional document viewing systems are inefficient in loading and processing large volumes of documents, leading to significant delays and wasted user time due to repetitive document opening and client-side processing, especially in eDiscovery and document search scenarios.
Innovation Solution
The implementation of document models that are processed into document models for low-latency display, utilizing object models to generate document models efficiently, allowing for simultaneous pre-loading and rendering of multiple documents without repetitive client-side processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If documents are loaded and processed sequentially on the client side, then each document can be reviewed in detail, but the total review time increases significantly due to repetitive loading and processing delays
Solution Approach 1:
The system pre-loads document fragments into client-side cache memory before they are actually needed for review. When a user navigates to a document, its fragments are already cached and ready for immediate display, eliminating the traditional loading delay. This preliminary action maintains review quality while dramatically reducing waiting time.
Solution Approach 2:
Documents are divided into multiple fragments that can be loaded, cached, and managed independently. This segmentation allows the system to pre-load specific portions of documents without loading entire documents, reducing memory usage and enabling more efficient parallel processing of multiple document fragments across the corpus.
2Productivity
If multiple documents are pre-loaded concurrently in the web browser, then review speed increases, but system memory consumption and processing complexity increase
Solution Approach 1:
By dividing documents into fragments, the system can manage multiple document portions in cache simultaneously without overwhelming system resources. Each fragment is independently manageable, allowing concurrent pre-loading of multiple fragments while maintaining controlled memory consumption and processing complexity.
Solution Approach 2:
The system dynamically adjusts caching parameters such as cache size limits, fragment priority levels, and pre-loading thresholds based on available system resources and user behavior patterns. This adaptive parameter adjustment optimizes the balance between review speed and system complexity.
3Loss of information
If the entire document is loaded into memory for processing, then complete document analysis is possible, but memory usage increases and loading time increases
Solution Approach 1:
Document fragments are pre-processed and cached in advance before the user needs to review them. This preliminary processing extracts and stores essential document information in an optimized format, ensuring that when the user accesses the document, the information is already prepared and ready for immediate display without full document loading.
Solution Approach 2:
The system extracts only the essential portions of documents (fragments) that are needed for review purposes, rather than loading entire documents into memory. This extraction approach maintains the necessary document information while significantly reducing memory consumption and loading time.
Data Source
AI summary
Systems and methods for previewing documents associated with a headless object are provided. A client device may generate a primary user interface having a first viewport with a first object focus. In response to a first event, the first object focus may be set to a document model corresponding to the object. The document model may be loaded into a data memory of the client device, and the first object focus may be set to display a rendering of the document model. In response to a second event, a secondary interface of the client device may display the rendering of the document model that was loaded into the data memory.