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)
Customize this document with your university’s formatting guidelines.
Add diagrams: Use Case, ERD, System Architecture (I can help generate these).
Begin UI wireframing (Figma recommended).
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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