Dynamic Electronic Message Prioritization via User Behavior Analysis
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Solution Overview
Problem
Users face challenges in efficiently prioritizing electronic messages such as emails, SMS, and voicemails due to the time-consuming nature of manual examination and the inflexibility of existing message handling software, which often fails to accurately reflect message priority based on the user's perspective.
Innovation Solution
A system and method that dynamically prioritize electronic messages by analyzing properties like sender, recipient, subject line, and past activity, incorporating factors such as request detection, social weight, temporal urgency, and relevance, using a combination of server and client-side modules to generate and update priority indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of each message is performed to determine priority, then message priority accuracy is improved, but user time and effort increase
Solution Approach 1:
The system enables messages to self-prioritize by automatically analyzing message properties (sender, subject, content) and user behavior patterns without requiring manual user intervention. The prioritization algorithm processes messages autonomously, assigning priority levels based on learned user preferences and message characteristics.
Solution Approach 2:
The patent replaces the mechanical manual examination process with an automated computational system that uses machine learning algorithms and natural language processing to analyze message content, sender behavior, and user response patterns, substituting human cognitive effort with automated intelligence.
2Ease of manufacture
If fixed rules with keywords or addresses are used to identify high priority messages, then setup time is reduced, but priority accuracy and flexibility deteriorate
Solution Approach 1:
The system transitions from static fixed rules to dynamic adaptive prioritization that continuously learns from user behavior. Priority criteria evolve over time based on user interactions, message responses, and changing preferences, allowing the system to adapt to new patterns without manual reconfiguration.
Solution Approach 2:
The system incorporates feedback loops where user responses to prioritized messages are analyzed to refine and improve future prioritization accuracy. The algorithm learns from whether users act on prioritized messages, adjusting weightings and criteria based on this feedback to enhance priority prediction over time.
3Productivity
If sender priority indicators are used, then message handling speed is improved, but priority accuracy from user perspective deteriorates
Solution Approach 1:
The system moves beyond the single dimension of sender-provided priority indicators to multiple dimensions including message content analysis, sender-user relationship patterns, temporal factors, and user behavior history. This multi-dimensional approach provides a more comprehensive and accurate priority assessment from the user's perspective.
Solution Approach 2:
The system introduces an intermediary prioritization algorithm that mediates between sender indicators and user needs, translating sender priorities through the lens of user behavior patterns and message context to determine final priority assignments that better reflect user importance.
4Measurement precision
If dynamic prioritization based on user behavior is implemented, then priority accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the prioritization task into distinct functional modules: message property extraction, user behavior analysis, priority calculation, and result presentation. Each module handles a specific aspect independently, making the overall complex system manageable through modular design and clear separation of concerns.
Data Source
AI summary
A system, method, and computer program product dynamically prioritizes electronic messages. An electronic message having one or more properties is received. These message properties can include, a particular sender or body text. Information describing past activity of a recipient user of the electronic message is accessed. A priority is determined for the electronic message, where the determining is based at least in part on a comparison of a property of the electronic message with the accessed information. The priority determination may include detecting the presence of a request in the electronic message, determining the social weight of the sender of the electronic message, determining the temporal urgency of the electronic message, or determining the relevance of the electronic message, for example. An indication of the priority of the message is presented to the recipient user.