Predictive Application Control Framework for Productivity
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Solution Overview
Problem
Existing systems lack a predictive framework to manage and control device and application usage, leading to inefficiencies and productivity losses, particularly during working hours.
Innovation Solution
A decision intelligence (DI)-based computerized framework that predicts user behavior by analyzing usage patterns and provides non-native capabilities to control, manage, and modify how applications and devices are accessed and utilized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If usage history is analyzed to provide perspective on time lost, then understanding of past behavior is improved, but ability to predict and prevent future time loss is not achieved
Solution Approach 1:
The system performs preliminary analysis of usage patterns before the user actually loses time. By predicting future usage behavior based on historical data, the system provides warnings and suggestions in advance, allowing users to make better decisions before engaging in unproductive activities. This transforms reactive usage analysis into proactive time management.
Solution Approach 2:
The system implements a feedback loop where usage data is continuously collected, analyzed, and used to generate predictions that are presented back to the user. This feedback mechanism includes warnings about predicted time loss and suggestions for alternative actions, enabling users to adjust their behavior based on system insights rather than repeating past mistakes.
2Productivity
If Apps are designed to keep user attention for as long as possible, then user engagement is improved, but productivity is reduced
Solution Approach 1:
The system acts as an intermediary between the user and the Apps. Rather than allowing direct, uninterrupted interaction between users and potentially time-consuming applications, the prediction system inserts itself into the interaction flow, providing warnings and alternative suggestions that mediate the user's App usage decisions. This intermediary function helps balance engagement with productivity.
Solution Approach 2:
The system applies preliminary anti-action by predicting and preventing excessive App usage before it occurs. When the system detects patterns that lead to productivity loss, it presents warnings and alternative actions that counteract the tendency to spend excessive time on Apps, thereby preventing the harmful effect before it can manifest.
3Productivity
If a predictive framework is implemented to control device usage, then productivity is improved, but system complexity increases
Solution Approach 1:
The system employs self-service principles by automatically collecting usage data, analyzing patterns, generating predictions, and presenting recommendations without requiring manual configuration or intervention. The framework learns and adapts to user behavior autonomously, reducing the complexity burden on users while maintaining sophisticated predictive capabilities.
Data Source
AI summary
Disclosed are systems and methods that provide a computerized control and management framework that is configured to operate to control the manner in which applications and/or devices upon which such applications are executing can function, if at all. The disclosed framework can provide prediction features that leverage learned behaviors of users when interacting with an application and/or computing device (e.g., smart phone, for example). Accordingly, the disclosed framework can provide non-native capabilities to such Apps and/or devices to control, manage and/or modify how the Apps and/or associated devices can be accessed, utilized and/or interacted with by a user and/or other Apps, devices, platforms and systems.