Social Profiling for Electronic Message Prioritization
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Solution Overview
Problem
Current methods for managing and prioritizing electronic messages, such as email, are inefficient and prone to errors, as they rely on manual sorting and machine learning algorithms that can reinforce poor patterns or fail to accurately assess urgency and importance, especially when messages are from unfamiliar or dramatic sources.
Innovation Solution
A computerized method and system that associate electronic messages with social profiles based on user behavior tags, identifying clusters of similar messages and calculating social profiles for users with social affinity, allowing for improved filtering, forwarding, and communication among affiliated users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting and tagging methods are used, then users can accurately assess message importance and urgency, but the process is slow and effortful
Solution Approach 1:
The system performs preliminary analysis of message content, sender behavior, and recipient communication patterns before the user needs to process the message. Social profiles are pre-calculated based on historical interactions, and messages are automatically tagged with relevance scores, allowing users to quickly review pre-sorted message lists rather than manually analyzing each message from scratch
Solution Approach 2:
The system automatically generates social profiles and message tags by analyzing communication patterns between users and their contacts. The algorithm self-adjusts by learning from user feedback and correction actions, progressively improving its prioritization accuracy without requiring manual intervention for each message assessment
2Productivity
If machine learning algorithms are used to automate message analysis, then processing speed increases, but the system may fail when messages are from uninformed or overdramatic sources and can reinforce poor patterns
Solution Approach 1:
The system incorporates feedback loops where user corrections and adjustments to automated tags and prioritization are fed back into the learning algorithm. This allows the system to learn from both successful and unsuccessful automated assessments, progressively improving its ability to distinguish between genuine urgency and dramatic but unimportant messages while reducing reinforcement of poor patterns
Solution Approach 2:
The system dynamically adjusts analysis parameters and weighting factors based on the specific characteristics of each message and sender. Rather than applying fixed rules, the algorithm modifies its assessment criteria in real-time based on contextual factors, allowing it to adapt to different communication styles and situations while maintaining high processing speed
3Extent of automation
If messages are sorted by source or topic algorithms, then automated categorization is achieved, but initial sorting may be wrong and requires user correction
Solution Approach 1:
The system performs preliminary sorting based on social profiles and communication patterns before user review. Messages are pre-grouped by relevance to the recipient's interests and recent interactions, providing a head-start on organization that reduces the need for extensive user correction while maintaining high accuracy through socially-informed categorization
Data Source
AI summary
A method of associating an electronic message social profile with electronic messages. The method comprises identifying a plurality of users having a social affinity to one another, tagging each of a plurality of electronic messages received at a plurality of messaging accounts of the plurality of users with at least one user behavior tag indicative of a behavioral messaging action performed by one of the plurality of users, identifying a cluster of electronic messages having a common content from the plurality of electronic messages according to a similarity analysis, calculating, using a processor, an electronic message social profile for members of the cluster based on a combination of respective the at least one user behavior tag of the members of the cluster, and associating the electronic message social profile with the members of the cluster.