Message Classification Feedback Using Express User Indicators
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Solution Overview
Problem
Existing methods for handling electronic messages based on regulatory policies are prone to user errors due to reliance on manual instructions, which can lead to incorrect or incomplete application of classifications, and policies become outdated as message characteristics change over time.
Innovation Solution
Implement a machine learning system that uses a machine learning policy to automatically determine classifications for electronic messages based on express user indications and message attributes, updating policies dynamically through feedback, and applying appropriate classifications using an enforcer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual instructions are used to determine message classifications, then user control over classification is maintained, but user errors occur due to manual intervention
Solution Approach 1:
The system extracts message attributes automatically and uses the machine learning policy to determine classifications without requiring manual user instructions. The system serves itself by autonomously analyzing message content, extracting relevant attributes, and applying appropriate classifications based on the trained policy, thereby eliminating user errors while maintaining classification control.
Solution Approach 2:
The patent replaces the mechanical manual process of user instruction with an automated machine learning system. The machine learning scanner and attribute extraction mechanisms substitute human manual classification efforts, using algorithmic processing to determine message classifications based on extracted attributes and trained policies, thus improving reliability while reducing dependency on manual user control.
2Adaptability or versatility
If static policies are used for message classification, then policy implementation is simple, but policies become outdated as message characteristics change
Solution Approach 1:
The patent implements dynamic policies through the machine learning trainer that continuously retrains the machine learning policy using extracted message attributes and feedback from classified messages. This allows the policy to adapt and evolve as message characteristics change over time, transforming static classification rules into dynamic, self-updating policies that maintain relevance and accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where classified messages and their attributes are fed back to the machine learning trainer for continuous policy improvement. The feedback loop enables the policy to learn from actual message patterns and adjust accordingly, ensuring the policy remains adaptable to changing message characteristics while managing complexity through automated feedback processing.
3Measurement precision
If comprehensive message analysis is performed to improve classification accuracy, then processing time increases
Solution Approach 1:
The patent extracts only the most relevant attributes from messages that are necessary for classification decisions, rather than analyzing every aspect of each message. The attribute extraction mechanism identifies and extracts key features that the machine learning policy deems important for accurate classification, thereby achieving high classification precision while minimizing processing time by focusing on essential message characteristics.
Data Source
AI summary
In some embodiments, a method comprises receiving an electronic message. In response to determining that the electronic message includes an express indication from a user that a classification applies or does not apply, the method comprises identifying message attributes of the electronic message that correspond to policy attributes of a machine learning policy and determining values of the policy attributes based on the identified message attributes. The method additionally comprises providing information to a machine learning trainer adapted to train the machine learning policy based on the information. The information comprises the values of the policy attributes and information indicating the classification that applies or does not apply to the electronic message, where such information is based on the express indication that the user included in the electronic message.