In today's era dominated by pervasive computing devices and an array of social networks like Facebook, Instagram, TikTok, and YouTube, maintaining focus and productivity has become a herculean task. The relentless distractions from these platforms, designed to capture and hold our attention, substantially hinder our ability to concentrate on tasks and perform effectively. This pervasive issue not only challenges our time management skills but also significantly impacts our overall well-being.

Current screen time management applications, though providing useful statistics, fall short in several areas:

• They fail to recognize the varied purposes of app usage, such as distinguishing between work, educational, and leisure activities.
• They lack the capability to consider the user's location and time, missing contextual nuances like the non-distractive nature of listening to music while commuting.
• They do not account for the user's personal schedule and tasks, essential for tailoring app usage to suit individual responsibilities and commitments.
• They are not designed to proactively remind users about their prioritized tasks and responsibilities.

A more effective approach involves making app blocking sensitive to time, location, and context. The goal is to minimize or eliminate non-productive app time, with adaptive allowances based on the user's situation – like limiting entertainment apps during work hours but allowing them during commutes or leisure times.

This project is driven by the goal to enhance personal productivity and well-being. The effectiveness of this endeavor will be assessed through user surveys. The core challenge lies in developing an in-depth understanding of the user's habits, schedule, and work-life balance, and integrating these with historical and current lifestyle patterns. This challenge is compounded by the absence of any existing application with similar capabilities on the market. Our aim is not to impose app blocks but to encourage users to cultivate better usage habits through heightened awareness and prioritization.

We propose the development of an Android application endowed with features akin to the artificial intelligence system Jarvis, offering:

• Interactive Queries: Post-app usage, the app will prompt users with questions to gain insights into their behavior and to differentiate between productive and non-productive usage of various apps.
• Movement and Location Tracking: Utilizing accelerometer and location data, the app will detect the user's mode of movement (walking, driving, stationary, etc.) to fine-tune its functionality.
• Contextual App Usage Tracking: The app will monitor app usage in conjunction with the user's context, adjusting accessibility based on real-time circumstances.
• Adaptive Machine Learning: Central to the app is a machine learning model that continuously evolves by learning from user feedback and observed behavior patterns. Given the uniqueness of our use case, we will collect and utilize user-generated data to train and refine our model.

In essence, Time Jar is not merely a tool for managing screen time; it is a comprehensive solution designed to promote a healthier digital lifestyle, enhancing productivity and well-being through context-aware technology and user-centric design.