Bookmark Annotation Engine Using Context Data
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Solution Overview
Problem
Existing systems lack efficient methods for annotating and retrieving bookmarks using voice or text inputs, requiring multiple user interactions and manual intervention, which hinders quick access to relevant web-based or application resources.
Innovation Solution
A computing system that automatically generates annotations for bookmarks using context data from various sources, including URLs, search queries, and metadata, allowing for voice or text inputs to create and retrieve bookmarks with minimal human intervention, using an annotation engine to derive importance values and similarity scores for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual bookmark creation and annotation methods are used, then user control over bookmarks is maintained, but the time and number of user interactions required to create and retrieve bookmarks increases
Solution Approach 1:
The system performs preliminary actions by automatically generating annotations and extracting keywords from context data (URLs, search queries, metadata) before the user needs to retrieve bookmarks. This preparation work is done in advance, so when retrieval is needed, the system can quickly match user input against pre-generated annotations without requiring manual annotation creation at that moment.
Solution Approach 2:
The bookmark system serves itself by automatically creating annotations and extracting keywords without requiring user intervention. The annotation engine processes context data and generates annotations autonomously, and the retrieval system automatically matches user input against annotations, eliminating the need for manual bookmark management while maintaining user control through the retrieval interface.
2Productivity
If automated annotation generation is implemented, then the number of user interactions is reduced, but the computational resources required for processing increase
Solution Approach 1:
The system extracts only the most essential and relevant information from context data for annotation generation. Instead of processing all available data, the annotation engine identifies and extracts key entities, keywords, and meaningful terms from URLs, search queries, and metadata, reducing the computational burden while maintaining annotation quality.
Solution Approach 2:
The annotation system applies different processing strategies to different parts of the context data based on their relevance. High-weight features from search queries and prominent entities from URLs receive more detailed processing, while less critical metadata receives lighter processing. This localized quality approach optimizes computational resource allocation.
3Measurement precision
If multiple annotations are generated for each bookmark, then retrieval accuracy is improved, but the complexity of managing and processing annotations increases
Solution Approach 1:
The system pre-ranks and prioritizes annotations during the annotation generation phase, assigning weights based on relevance to the bookmark content. This preliminary ranking is stored with the annotations, so during retrieval, the system can quickly access the most relevant annotations without having to re-evaluate all annotations, reducing processing complexity while maintaining high retrieval accuracy.
Solution Approach 2:
The system generates multiple annotations but only actively uses and processes the top-ranked subset during retrieval operations. By generating more annotations than strictly necessary (excessive action) but only processing the essential portion (partial action), the system ensures high retrieval accuracy while managing complexity through selective processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a first input associated with a bookmark and, in response to receiving the first input, obtaining context data for deriving annotations for the bookmark. An annotation engine of a system obtains multiple annotations that are derived using information included in the context data and a transcription of the first input associated with the bookmark. The annotation engine generates an importance value for each annotation. The importance value characterizes a relationship between each annotation and at least one of: the information included in the context data; or the transcription of the first input. The annotation engine generates a set of annotations for retrieving the bookmark. Each annotation in the set of annotations is generated from the obtained multiple annotations and each annotation has an importance value that exceeds a threshold value.