Semantic Embedding Clipboard History Search
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Solution Overview
Problem
Existing clipboard tools face challenges in efficiently managing and retrieving content items over time, as the clipboard history grows, making it difficult for users to find specific items due to the need for keyword-based searches that do not consider the abstract meaning of content.
Innovation Solution
A content management tool that captures and stores content items with associated metadata, generates semantic embeddings using semantic models, and allows users to search using natural language queries, providing a more accurate and efficient retrieval mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search is used to retrieve content items from clipboard history, then the search implementation is simple, but the search accuracy deteriorates as the clipboard history grows
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary layer between the content items and the search query. Instead of directly comparing keywords with content, the system transforms both the query and content items into semantic vector representations, enabling more accurate semantic-based matching while maintaining system manageability through established embedding models
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a semantic embedding-based retrieval system. This substitution transitions from simple string comparison to vector space semantic analysis, significantly improving search accuracy for retrieving content items with abstract meaning while managing complexity through efficient vector search techniques
2Quantity of substance
If clipboard history is extended to store more content items, then the availability of historical content increases, but the difficulty of finding specific items increases
Solution Approach 1:
The patent replaces manual scrolling and keyword searching with automated semantic embedding-based retrieval. Users can query the extended clipboard history using natural language, and the system efficiently retrieves relevant items through vector similarity search, making the operation as easy as typing a query regardless of the history size
Solution Approach 2:
The semantic embeddings serve as an intermediary that enables efficient querying of large content collections. By transforming both the stored content items and user queries into the same semantic vector space, the system facilitates easy retrieval even when the clipboard history contains a large number of diverse content items
Data Source
AI summary
Systems and methods for generating a design difference caption are provided. In particular, a computing device may receive a capture request to capture a content item, capture the content item with metadata associated with the content item, and generate one or more semantic embeddings of content data using one or more semantic models. The content data may include the content item and the metadata. The computing device may further store the one or more semantic embeddings in a content database, provide captured content history stored in the content database, the capture content history including a list of captured content items, receive a user query to search the captured content history; and provide search results based on the user query.