Semantic Tagging for Email Context Resolution
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Solution Overview
Problem
Current search engines rely heavily on keyword-based systems, which are inefficient in retrieving relevant information from vast amounts of electronic communication data, and lack the ability to effectively mine information from communication messages, especially due to the lack of context and reliance on generic metadata generation.
Innovation Solution
A computer-based system that enhances search efficiency by automatically translating semantic concepts into tags, analyzing structured contextual information, and weighting tokens to improve tagging accuracy, enabling semantic search and reasoning across various communication platforms like emails, chat rooms, and text messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based searching is used, then search implementation is simple, but search efficiency and accuracy deteriorate
Solution Approach 1:
The patent transforms the search parameter from simple keywords to semantic tags with multiple attributes (entity type, communication role, context relationships). This parameter transformation enables the system to maintain implementation feasibility while dramatically improving search efficiency by enabling contextual understanding and semantic reasoning across communications.
Solution Approach 2:
The patent adds a semantic dimension to traditional keyword searching by introducing structured tags that capture entity types, communication roles, and contextual relationships. This dimensional expansion transforms flat keyword matches into multi-dimensional semantic queries, improving search efficiency without proportionally increasing complexity.
2Productivity
If semantic tagging is implemented, then search efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the semantic analysis process into distinct modular components: entity recognition module, role identification module, relationship extraction module, and tag generation module. Each module handles a specific aspect of semantic processing, which reduces overall system complexity by enabling independent development, testing, and optimization of each component while maintaining high search efficiency.
Solution Approach 2:
The patent introduces structured semantic tags as an intermediary layer between raw communication text and search queries. These tags serve as a standardized interface that simplifies the interaction between diverse communication formats and the search engine, reducing system complexity by providing a uniform processing target regardless of input variability.
3Measurement precision
If context analysis is added, then tagging accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary context analysis by pre-identifying entities, roles, and relationships during the tagging process before actual search operations. This preliminary structuring of semantic information enables accurate contextual understanding to be cached and reused across multiple searches, improving tagging accuracy while amortizing the processing time cost over multiple queries.
Solution Approach 2:
The patent applies context analysis selectively to different portions of communications based on their semantic importance. Rather than uniformly analyzing all text, the system focuses computational resources on critical segments containing entities, decisions, or key information, thereby achieving high tagging accuracy for search-critical elements while minimizing overall processing time.
Data Source
AI summary
A semantic tagging method may add context to a sentence in order to increase search efficiency. Regardless of an author's writing style, translating semantic concepts into tags may increase search efficiency. Automatic semantic tagging of documents may allow semantic search and reasoning. Text for semantic tagging may include an email, a website chat room, an internet forum, or a text message. Additional texts may include aggregating general consensus of an emailed topic across multiple emails, whether in the same email chain or separate emails. To increase search efficiency, the analysis of prior communications within the body of text may comprise analyzing structured contextual information to facilitate with homophora resolution. The structured contextual information may include at least one of a sender email address, one or more recipient email addresses, a subject field, a message date and time stamp, and an attachment title.