Random Index Pattern Matching for Email Similarity
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Solution Overview
Problem
Conventional email applications lack the ability to automatically identify and categorize electronic messages that are related in content, relying on pre-defined categories and requiring users to manually search through large inboxes for similar information.
Innovation Solution
An apparatus and method that uses a processor to detect significant words in an electronic message, compare them to other messages, and present similar messages by reducing text to keywords, translating them into machine-readable formats, and using vector representations to compute cosine distances for similarity identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional email applications use pre-defined categories for classification, then the categorization structure is simple and easy to implement, but the system cannot automatically identify related electronic messages and requires manual searching
Solution Approach 1:
The patent extracts and removes stop words (common words like 'the', 'is', 'at') from the email text to create a filtered set of significant words. This extraction process simplifies the data representation while enabling automatic similarity computation, resolving the contradiction between automation capability and system complexity.
Solution Approach 2:
The patent introduces a random index matrix as an intermediary data structure that maps words to random indices. This intermediary enables efficient similarity computation between email messages without requiring complex direct comparisons, thus achieving automatic message identification while maintaining manageable system complexity.
2Productivity
If the system processes all words in email messages, then the content representation is complete and accurate, but the computation time increases significantly for large inboxes
Solution Approach 1:
The system extracts and removes stop words from the email text, retaining only significant words for similarity computation. This extraction dramatically reduces the number of words processed while maintaining the essential semantic information needed for accurate message identification, thus improving productivity without significant information loss.
Solution Approach 2:
The patent segments the word list into stop words and significant words, processing only the significant portion for similarity computations. This segmentation allows the system to maintain complete information representation for meaningful content while reducing computational load by excluding redundant stop words.
3Measurement precision
If the system uses detailed text analysis to identify message relationships, then the similarity identification is accurate, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only significant words from the text, eliminating stop words that do not contribute to message similarity. This extraction maintains measurement precision for accuracy while significantly reducing processing time by focusing computations only on meaningful content words.
Solution Approach 2:
The patent transforms the text representation by changing the parameter space from all words to a filtered set of significant words mapped to random indices. This parameter transformation preserves the essential information for accurate similarity measurement while reducing the dimensionality and computational complexity of the analysis.
Data Source
AI summary
An apparatus for identifying similar electronic messages includes a processor configured to detect a first electronic message, detect a set of one or more significant words in the first electronic message, compare the detected set of one or more significant words in the first electronic message to one or more sets of one or more significant words from a plurality of other electronic messages, and present at least a second electronic message from the plurality of other electronic messages that includes a set of one or more significant words that is similar to the detected set of one or more significant words in the first electronic message.