Email Wrong Transmission Determination via Similarity Models
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Solution Overview
Problem
Conventional methods for preventing wrong electronic mail transmissions are inadequate in accurately determining and correcting incorrect destination addresses, leading to potential miscommunications.
Innovation Solution
An electronic mail wrong transmission determination apparatus that creates feature information for email content, accumulates and analyzes destination data, and uses similarity models to assess the reliability of destination addresses, offering alternative candidates when a high likelihood of wrong transmission is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (vocabulary frequency comparison, politeness analysis) are used to prevent wrong transmission, then some level of wrong transmission prevention is achieved, but the accuracy in determining incorrect destination addresses is insufficient
Solution Approach 1:
The patent replaces conventional linguistic analysis methods (vocabulary frequency comparison, politeness analysis) with a machine learning-based similarity model that uses automatic feature extraction and classification algorithms to determine destination address correctness, thereby improving measurement precision through more robust computational methods
Solution Approach 2:
The patent transforms the destination address verification problem from a linguistic analysis task into a similarity-based classification task by changing the parameters from vocabulary statistics to feature vector similarities, enabling more accurate determination of correct destinations through mathematical distance metrics in feature space
2Measurement precision
If similarity-based destination address correction is implemented, then the accuracy of identifying correct destinations improves, but the complexity of the determination system increases
Solution Approach 1:
The patent performs preliminary feature extraction and similarity model creation before the actual wrong transmission determination, preparing classification models in advance based on historical email data, which reduces the computational complexity during real-time determination while maintaining high accuracy
Solution Approach 2:
The patent creates simplified similarity models that capture the essential patterns of correct destination addresses from historical data, using representative feature vectors and classification rules that replicate the behavior of complex analysis without requiring full re-analysis, thereby reducing system complexity while preserving accuracy
Data Source
AI summary
An electronic mail wrong transmission determination apparatus includes: a feature information creation unit which creates feature information related to contents of an electronic mail that is a transmission object; an accumulation unit which accumulates feature information related to contents of a transmitted electronic mail and a destination of the transmitted electronic mail in association with each other; a destination candidate selection unit which selects destination candidates that are similar in appearance to a destination of the electronic mail that is the transmission object, from destinations of transmitted electronic mails; a similarity model creation unit which creates a similarity model for each destination accumulated in the accumulation unit based on the feature information accumulated in the accumulation unit in association with the destination and based on the feature information accumulated in the accumulation unit in association with other destinations that differ from the destination; wherein the similarity model serves as a criterion of determination as to whether or not feature information related to contents of an arbitrary electronic mail belongs to a certain feature information region in the word space, which is defined according to the contents of the electronic mails transmitted to the destination so far; and a reliability calculation unit which calculates respective reliabilities of a destination and destination candidates of the electronic mail that is the transmission object, based on feature information related to contents of the electronic mail that is the transmission object, based on the similarity model related to the destination of the electronic mail that is the transmission object, and based on the similarity models related to the destination candidates.