Sunday, December 14, 2025

Project Proposal Title: AI Smart Task Manager People routinely forget daily tasks such as taking medication, paying bills, attending appointments, or completing work assignments

 


Project Title: AI Smart Task Manager

1. Project Proposal

1.1. Introduction

In today’s fast-paced world, individuals often juggle multiple responsibilities and frequently forget essential daily tasks, leading to reduced productivity and increased stress. Traditional to-do list apps lack intelligence—they merely store tasks without understanding user habits, priorities, or context.

This project proposes the development of an AI Smart Task Manager—a mobile and web application that uses artificial intelligence to intelligently manage, remind, and prioritize user tasks based on behavior patterns, time sensitivity, and contextual cues.

1.2. Problem Statement

People routinely forget daily tasks such as taking medication, paying bills, attending appointments, or completing work assignments. Existing task managers are static—they do not adapt to user behavior, miss contextual awareness (e.g., location, time of day), and fail to predict or suggest tasks proactively.

1.3. Objectives

  • Develop an intelligent task management system powered by AI.

  • Automate task prioritization using user behavior analytics.

  • Provide context-aware reminders (time, location, calendar events).

  • Enable natural language input for task creation.

  • Reduce cognitive load and improve task completion rates.

1.4. Scope

The system will:

  • Allow users to add, edit, delete, and categorize tasks.

  • Use AI (machine learning + NLP) to infer task urgency and deadlines.

  • Send smart notifications based on user routines and external triggers (e.g., "You’re near the pharmacy—don’t forget to pick up your prescription").

  • Sync across devices (mobile + web).

  • Support voice and text input for task entry.

The system will not:

  • Integrate with third-party enterprise tools (e.g., Jira, Asana) in Phase 1.

  • Store sensitive personal data beyond what’s necessary for task management.

  • Replace medical or legal scheduling systems.

1.5. Target Users

  • University students

  • Working professionals

  • Elderly individuals managing daily routines

  • Anyone seeking an intelligent, proactive task assistant

1.6. Technologies

  • Frontend: React (Web), React Native (Mobile)

  • Backend: Node.js / Django

  • Database: PostgreSQL or Firebase

  • AI/ML: Python (scikit-learn, spaCy, or TensorFlow Lite for on-device inference)

  • NLP: Natural Language Understanding for parsing task inputs (e.g., “Call mom tomorrow at 5 PM” → structured task)

  • Cloud: Firebase Cloud Messaging (FCM) for notifications

  • Deployment: Docker, AWS/GCP

1.7. Expected Outcomes

  • A fully functional MVP with core AI-driven task management.

  • Improved user task completion rate (measurable via user testing).

  • A novel algorithm for dynamic task prioritization.

  • A foundation for future enhancements (e.g., habit tracking, team collaboration).

2. Software Requirements Specification (SRS)

Based on IEEE 830 Standard

2.1. Introduction

2.1.1 Purpose

This document specifies the functional and non-functional requirements for the AI Smart Task Manager application, serving as a blueprint for design, development, and testing.

2.1.2 Scope

As outlined in the proposal, the system enables intelligent task creation, prioritization, and reminders using AI. It supports multi-platform access and personalization.

2.1.3 Definitions

  • NLP: Natural Language Processing

  • ML: Machine Learning

  • Task: A unit of work with title, deadline, priority, and context

  • Smart Reminder: A context-aware notification triggered by time, location, or user behavior

2.2. Overall Description

2.2.1 Product Perspective

Standalone application with cloud backend. Integrates with device calendar, location services, and notification systems.

2.2.2 User Classes

User Type

Description

Regular User

Creates and manages personal tasks

Admin (optional)

Manages system analytics (for research phase)

2.2.3 Operating Environment

  • Mobile: Android 10+, iOS 14+

  • Web: Chrome, Firefox, Safari (latest)

  • Internet connectivity required for sync and AI cloud inference (optional offline mode)

2.2.4 Assumptions & Dependencies

  • Users grant location and notification permissions.

  • AI model training data will be simulated or collected ethically during testing.

  • Third-party APIs: Google Maps (for geofencing), Calendar API.

2.3. System Features & Requirements

2.3.1 Functional Requirements

ID

Feature

Description

FR1

User Registration/Login

Email/password or Google/Facebook OAuth

FR2

Task Creation

Via text, voice, or quick templates

FR3

NLP Task Parsing

Convert “Buy milk after work” → {action: “Buy milk”, context: “after work”, location: inferred}

FR4

Smart Prioritization

Dynamically rank tasks using ML model based on: deadline, frequency, user history

FR5

Context-Aware Reminders

Trigger reminders by:<br>• Time (e.g., 9 AM)<br>• Location (e.g., near gym)<br>• Event (e.g., after meeting ends)

FR6

Recurring Tasks

Support daily/weekly/custom repeats

FR7

Task Categories & Tags

e.g., Work, Health, Personal

FR8

Task History & Analytics

Show completion rate, missed tasks, peak productivity hours

FR9

Sync Across Devices

Real-time synchronization via cloud

FR10

Backup & Export

Export tasks as CSV or JSON

2.3.2 Non-Functional Requirements

Type

Requirement

Performance

App loads in <2s; reminders trigger within 30s of condition

Usability

Intuitive UI; <3 taps to add a task

Reliability

99% uptime; local caching for offline use

Security

Data encrypted in transit (TLS) and at rest; GDPR-compliant

Scalability

Support 10,000+ concurrent users (cloud-ready)

Maintainability

Modular code; logging and error tracking (Sentry/LogRocket)

2.4. AI/ML Component Specification

2.4.1 Task Prioritization Engine

  • Input: Task metadata + user interaction history

  • Model: Lightweight classifier (e.g., Random Forest or Logistic Regression)

  • Features:

    • Deadline proximity

    • Task category importance (user-defined)

    • Historical completion rate for similar tasks

    • Time of day preference

2.4.2 NLP Parser

  • Parses free-text input using rule-based + ML hybrid (e.g., spaCy + custom regex)

  • Extracts:

    • Action verb

    • Object

    • Time expression

    • Location hint

2.4.3 Context Detection

  • Uses device sensors + calendar:

    • Geofencing: Trigger when user enters/leaves location

    • Calendar integration: Schedule reminders relative to events

2.5. Comparative Analysis of Existing Systems

System

Strengths

Weaknesses

Gap Addressed by Our System

Todoist

Clean UI, cross-platform

No AI; static priorities

✅ AI-driven dynamic prioritization

Microsoft To Do

Integrates with Outlook

Limited context awareness

✅ Location/time/event-based triggers

Google Tasks

Simple, free

No smart suggestions

✅ Proactive task prediction

TickTick

Habit tracking, Pomodoro

No NLP for task input

✅ Natural language task creation

Any.do

Voice input, reminders

AI features limited to premium

✅ Open, intelligent core in free tier

Key Innovation: Our system uniquely combines NLP task entry, behavioral learning, and multi-context reminders in a single open architecture.

2.6. Development Roadmap (Milestones)

Phase

Timeline

Deliverables

1. Research & Design

Month 1

SRS, UI mockups, architecture diagram

2. Core Backend + Auth

Month 2

User system, task CRUD API

3. NLP & AI Module

Month 3

Task parser, priority model (Python microservice)

4. Mobile & Web Frontend

Month 4

React Native + React apps

5. Context Integration

Month 5

Geofencing, calendar sync, smart reminders

6. Testing & Evaluation

Month 6

Usability tests, accuracy metrics, dissertation

2.7. Evaluation Metrics

  • Task Parsing Accuracy: % of correctly structured tasks from NLP

  • Reminder Relevance Score: User rating (1–5) for smart notifications

  • Completion Rate Improvement: Before vs. after using the app (via A/B testing)

  • System Latency: Time from trigger to notification

2.8. Risks & Mitigation

Risk

Mitigation

Overcomplicated AI model

Start with rule-based + simple ML; iterate

Privacy concerns

On-device processing where possible; clear privacy policy

Low user engagement

Gamification (streaks, achievements)

Platform fragmentation

Use cross-platform frameworks (React Native, Flutter alternative)

3. Conclusion

The AI Smart Task Manager addresses a real-world problem with an innovative, user-centric approach. By integrating NLP, behavioral analytics, and context awareness, it goes beyond traditional tools to deliver a truly intelligent assistant. This SRS provides a complete foundation for development, testing, and academic evaluation.

Next Steps for You (Yew)

  1. Customize this document with your university’s formatting guidelines.

  2. Add diagrams: Use Case, ERD, System Architecture (I can help generate these).

  3. Begin UI wireframing (Figma recommended).

  4. Set up Git repository with project structure.

Would you like me to:

  • Generate UML diagrams?

  • Provide a sample database schema?

  • Write user stories or API specs?

  • Help with the dissertation structure (Chapters 1–5)?

Let me know—I'm ready to support your BIT final project!


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