Device-Agnostic Replay of User Interactions via Pseudocode Scripts
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Solution Overview
Problem
Inexperienced users face labor-intensive and error-prone tasks when performing repetitive data analysis operations across different data sets, as existing data analytics visualizations lack efficient methods to capture and replicate user interactions across devices.
Innovation Solution
A system that captures user interactions on a data analytics interface and transmits them in a device-agnostic manner, using pseudocode scripts to enable visualization and replay on multiple devices, reducing the need for repeated actions and minimizing user error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually perform repetitive data analysis operations on different data sets, then data analysis can be completed, but the process becomes labor intensive and error-prone
Solution Approach 1:
The system captures user interactions as actionable events and replays them on different data sets, creating a copy of the analytical process that can be executed automatically. This eliminates manual repetition while maintaining analysis quality, directly resolving the contradiction between reducing labor intensity and maintaining reliability.
Solution Approach 2:
The system changes the parameter of user interaction from manual execution to automated replay through pseudocode scripts. By transforming the state of interaction from human-operated to system-executed, it reduces labor intensity while preserving the reliability of the original user actions.
2Adaptability or versatility
If users repeat the same data analytic operations on different data sets, then comprehensive analysis is achieved, but the task becomes labor intensive
Solution Approach 1:
The system creates a universal replay mechanism that can apply the same user interaction sequence across multiple different data sets. The pseudocode script framework enables one set of analytical operations to be universally applied to any data set, achieving adaptability while eliminating time consumption through automation.
Solution Approach 2:
The system performs preliminary capture of user interactions and stores them as actionable events before they are needed for replay. This preliminary action allows the system to have analysis operations ready to execute on different data sets without requiring users to repeat the manual process, reducing time consumption while maintaining versatility.
3Productivity
If users manually perform data analysis operations, then analysis results can be obtained, but the process is susceptible to user error
Solution Approach 1:
The system implements feedback by capturing the exact user interactions and replaying them systematically. This creates a closed-loop system where the original user actions serve as the feedback mechanism, ensuring that the same reliable operations are repeated without human error, thereby maintaining productivity while improving reliability.
4Adaptability or versatility
If the same user interactions are replayed on different devices, then cross-platform compatibility is achieved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary layer of pseudocode scripts that mediate between user interactions and device execution. This intermediary translates device-specific actions into a universal format that can be replayed on any device, achieving cross-platform compatibility while managing system complexity through standardized communication protocols.
Data Source
AI summary
Systems and methods are disclosed for providing visualizations across devices of user interactions with a data analytics service. User interactions with a data analytics user interface on a first device are detected. Device agnostic commands are generated for performing the user interactions on a second device. The device agnostic commands are transmitted to a second device for replicating the user interactions on the second device.