Screen Capture Masking for Secure Support Session Handoffs
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Solution Overview
Problem
Current customer service solutions lack a seamless method for providing contextual information, such as screenshots, to human customer service representatives when AI chatbots cannot resolve issues, and screenshots often contain sensitive information posing a security risk.
Innovation Solution
Systems and methods for identifying and masking sensitive information within screen captures before storing them, using machine learning models to aggregate and normalize data, recognize connections, and proactively remove sensitive information from screen captures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If screenshots are collected and stored for customer support, then contextual information is improved, but security risk increases due to sensitive information exposure
Solution Approach 1:
The system extracts sensitive information from screenshots using machine learning models and removes it before storage or transmission. This separates the useful contextual information (the screenshot itself) from the harmful sensitive data, allowing the screenshot to be retained for support purposes while eliminating security risks.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between screenshot capture and storage/transmission. This intermediary automatically detects and masks sensitive information, serving as a buffer that protects both the customer's data and the support system while enabling seamless information transfer.
2Reliability
If manual screenshot processing is used, then security control is improved, but customer support efficiency deteriorates
Solution Approach 1:
The system implements self-service by enabling automated detection and masking of sensitive information through machine learning models. This eliminates the need for manual review by support agents, allowing the system to handle security control autonomously while maintaining high processing speed and efficiency.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated machine learning-based detection systems. This substitution transforms the security control mechanism from a labor-intensive manual process to an automated computational process, dramatically improving efficiency while maintaining or enhancing security reliability.
3Reliability
If sensitive information is masked automatically, then security is improved, but system complexity increases
Solution Approach 1:
The system changes the parameter of information processing by applying learned parameters from machine learning models to automatically identify and mask sensitive information. This allows the system to adapt to different types of sensitive data without requiring complex manual configuration, maintaining security while managing system complexity through data-driven parameter adjustment.
Data Source
AI summary
Systems and methods for linking a screen capture to a user support session are disclosed. The system may receive a screen capture initiation request from a user device. The system may capture a first data object indicative of a first graphical user interface associated with the user device. The system may provide, to the user device, the first graphical user interface for a predetermined period of time. The system may track, by the one or more processors, one or more inputs from the user device that indicate the presence of one or more articles of sensitive information within the graphical user interface. The system may mask the one or more articles of sensitive information within the graphical user interface, generate a second data object indicative of the graphical user interface having the masked articles of sensitive information, and store the second data object in a data repository.