Secure Messaging Emotional Analytics Asynchronous Processing
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Solution Overview
Problem
Current secure messaging systems face challenges in modular subsystem isolation, latency remediation, and user experience improvement, particularly in effectively gathering and analyzing user feedback to understand emotional responses and their causes.
Innovation Solution
A system and method that utilize a user device to communicate and receive prompts related to user emotions, causes, and specific triggers, with a computing device analyzing response times to determine qualifying responses, incorporating an emotional analytics system and neural network for asynchronous processing and security features like multiple security tokens to enhance data privacy and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional secure messaging systems are used, then security is maintained, but latency is high and user experience is poor
Solution Approach 1:
The system segments the messaging functionality into modular subsystems (messaging module, emotional analytics module, feedback module) that can operate independently and asynchronously. This segmentation allows critical security functions to be isolated while enabling parallel processing of non-critical functions like emotional analysis, thereby reducing overall system latency without compromising security.
Solution Approach 2:
The system performs preliminary actions by pre-processing and analyzing user emotions and feedback in advance using neural networks and emotional analytics. This allows the system to prepare responses and notifications beforehand, reducing the perceived latency when actual user interactions occur, while security protocols continue to operate independently to maintain reliability.
2Loss of information
If comprehensive user feedback is gathered, then user experience understanding is improved, but system complexity increases
Solution Approach 1:
The system introduces an emotional analytics system as an intermediary layer between the user interface and the core messaging functionality. This intermediary uses neural networks to automatically analyze user emotions and feedback quality, filtering and processing complex user responses without requiring the core messaging system to handle the complexity directly. This maintains feedback quality while isolating system complexity to a dedicated modular component.
3Reliability
If modular subsystem isolation is implemented, then system reliability is improved, but integration complexity increases
Solution Approach 1:
The system implements a universal communication protocol and standardized interface layer that enables different modular subsystems (messaging, emotional analytics, feedback processing) to interact through common methods. This universal interface reduces integration complexity by providing consistent patterns for inter-module communication, while maintaining the reliability benefits of modular isolation. Each module remains independent but can be integrated through the standardized universal interface.
Data Source
AI summary
Provided herein are exemplary systems and methods for an intelligent secure networked system configured by at least one processor to execute instructions stored in memory, the system including a data retention system and an emotional analytics system, the emotional analytics system performing asynchronous processing to determine if interactions with a user's computing device are such that the user is responding from an emotional state of mind or a meditated state of mind.