Adaptive Electronic Message Filtering via Dynamic Profile States
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Solution Overview
Problem
Current electronic message processing systems are limited in their ability to adaptively filter non-spam and non-malicious messages based on user profiles, relying on predefined criteria that do not account for real-time user activities and objectives.
Innovation Solution
A computer-based method and system that access user profiles to determine profile states and criteria, using content recognition models to identify and filter messages that match specific content types, thereby preventing content presentation and achieving user-defined objectives, such as financial or savings goals, by utilizing filter parameters triggered by user activity metrics and propensity models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined criteria lists are used for message filtering, then spam filtering capability is maintained, but adaptability to user-specific objectives and real-time activities is lost
Solution Approach 1:
The patent implements dynamic filter parameters that automatically adjust based on user profile states and activity metrics. The system transitions from static predefined criteria to dynamic criteria that evolve with user behavior, enabling adaptability to user objectives while maintaining manageable complexity through automated adjustments.
Solution Approach 2:
The system changes filtering parameters based on user profile states and activity metrics. Filter parameters are dynamically modified according to user objectives and real-time activities, allowing the system to adapt to different user needs without requiring complete system redesign.
2Productivity
If dynamic filtering based on user profiles is implemented, then message management effectiveness is improved, but processing complexity increases
Solution Approach 1:
The system performs self-service by automatically determining user profile states, evaluating activity metrics, and adjusting filter parameters without manual intervention. This automation improves message management efficiency while containing processing complexity through systematic self-adjustment mechanisms.
Solution Approach 2:
The system incorporates feedback loops where user activities and profile states continuously inform filter parameter adjustments. This feedback mechanism enables the system to learn from user behavior and improve message management efficiency while maintaining manageable complexity through iterative optimization.
3Measurement precision
If content recognition models are used to identify message types, then filtering accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies content recognition models selectively based on message relevance and user profile criteria. Rather than analyzing every message in detail, the system performs partial analysis on messages that match filter parameters, improving content identification accuracy for relevant messages while reducing overall computational energy requirements.
Data Source
AI summary
Systems and methods of the present disclosure enable improved content filtering by determining a profile state of the user profile based at least in part on at least one attribute of the user profile and determining a user profile criterion associated with the user profile based on the profile state including a threshold value to trigger a filter parameter associated with the profile objective, where the filter parameter comprises a content type to be processed so as to facilitate achieving the profile objective. The filter parameter is determined based on the profile state surpassing the user profile criterion. A content recognition model is used to identify a message content of each message. An electronic message that matches the content type is identified and filtered at the electronic messaging client to prevent the content from being presented.