Semantic Entity Extraction from Text Messages
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Solution Overview
Problem
Users face difficulties in easily recalling and searching for semantic entities, such as place names or addresses, from text messages due to complex processes required to retrieve and re-construct conversation contexts.
Innovation Solution
An electronic device method and system that recognizes text-based inputs, extracts semantic entities, and provides them through an application, allowing users to search and rank these entities based on relevance, time, reliability, and sender/recipient, facilitating intuitive content retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for semantic entities in text messages, then they can find the content, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary extraction of semantic entities from text messages and stores them in a database before users need to search. This advance preparation eliminates the need for users to manually scan through conversations, significantly reducing search time while maintaining accurate retrieval of entities like place names, addresses, and shop names.
Solution Approach 2:
The patent introduces an intermediary semantic entity extraction system that acts as a mediator between raw text messages and user search queries. This intermediary layer automatically processes messages, identifies semantic entities, and organizes them for efficient retrieval, simplifying the user's search process while improving accuracy.
2Ease of operation
If users recall conversation context to find semantic entities, then they can locate the content, but the operation becomes complex
Solution Approach 1:
The system extracts semantic entities from the complex context of text messages and separates them into a standalone database. This extraction process removes the complexity of conversation context from the user's search task, allowing users to directly search for entities without needing to recall or navigate through conversation details.
Solution Approach 2:
The patent segments the complex message data into distinct semantic entities (such as place names, addresses, shop names) and stores them separately with metadata. This segmentation transforms an unmanageable mass of text into organized, searchable units, greatly simplifying the user operation while reducing the perceived complexity.
3Productivity
If the system extracts and stores all semantic entities, then search capability improves, but data storage requirements increase
Solution Approach 1:
The system creates a simplified copy of semantic entity information from the original text messages, storing only the essential elements (entity name, type, metadata) in a database rather than preserving the complete message context. This copying approach enables efficient search operations while significantly reducing the data volume that needs to be stored and processed.
Data Source
AI summary
Disclosed is a method of extracting and using a semantic entity from a text message by an electronic device. The method includes: recognizing a text-based input; extracting a semantic entity from the text-based input; and providing the extracted semantic entity through an application in response to the application having a semantic setting that corresponds to the extracted semantic entity.