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AI-Powered Personal Memory Assistant with Context-Aware Smart Reminders and Multimodal Information Extraction
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, NLP, Computer Vision
DOI: https://doi.org/10.64388/IREV9I9-1715480
Abstract
Human memory limitations often lead to everyday challenges such as misplacing essential items, forgetting important deadlines, and losing track of valuable digital information. To address these issues, this study presents an AI-powered Personal Memory Assistant designed to support and enhance daily cognitive tasks. The system is developed as a secure, full-stack web application using the Flask framework and incorporates Fernet encryption to ensure data privacy. It enables users to store, organize, and retrieve both physical object locations (such as keys or documents) and digital content (such as links and articles) through a user-friendly, natural language chatbot interface. The proposed system integrates two key intelligent components. The Smart Predictive Reminder Engine analyzes user behavior patterns to identify frequently misplaced items and proactively suggests reminders, thereby helping users build consistent routines. Additionally, the Intelligent Document Reminder System utilizes Optical Character Recognition (OCR) to extract and interpret information from uploaded images, such as medical prescriptions, tickets, or bills. By understanding the context such as event type, urgency, and recurrence the system automatically generates appropriate reminders. Overall, the solution provides a practical and accessible approach to reducing cognitive load, improving personal organization, and minimizing the risk of human error in everyday memory-related tasks.
Keywords
Artificial Intelligence (AI), Cognitive Augmentation, Personal Memory Systems, Natural Language Processing (NLP), Optical Character Recognition (OCR), Context-Aware Computing, Predictive Reminder Systems, Pattern Recognition, Flask-Based Web Application, End-to-End Encryption, Human–Computer Interaction (HCI), Intelligent Assistive Systems
How to cite this paper
@article{1715480,
author = {K Bharath, Dr. K Ponmozhi},
title = {AI-Powered Personal Memory Assistant with Context-Aware Smart Reminders and Multimodal Information Extraction},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2229-2236},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715480.pdf},
abstract = {Human memory limitations often lead to everyday challenges such as misplacing essential items, forgetting important deadlines, and losing track of valuable digital information. To address these issues, this study presents an AI-powered Personal Memory Assistant designed to support and enhance daily cognitive tasks. The system is developed as a secure, full-stack web application using the Flask framework and incorporates Fernet encryption to ensure data privacy. It enables users to store, organize, and retrieve both physical object locations (such as keys or documents) and digital content (such as links and articles) through a user-friendly, natural language chatbot interface. The proposed system integrates two key intelligent components. The Smart Predictive Reminder Engine analyzes user behavior patterns to identify frequently misplaced items and proactively suggests reminders, thereby helping users build consistent routines. Additionally, the Intelligent Document Reminder System utilizes Optical Character Recognition (OCR) to extract and interpret information from uploaded images, such as medical prescriptions, tickets, or bills. By understanding the context such as event type, urgency, and recurrence the system automatically generates appropriate reminders. Overall, the solution provides a practical and accessible approach to reducing cognitive load, improving personal organization, and minimizing the risk of human error in everyday memory-related tasks.},
keywords = {Artificial Intelligence (AI), Cognitive Augmentation, Personal Memory Systems, Natural Language Processing (NLP), Optical Character Recognition (OCR), Context-Aware Computing, Predictive Reminder Systems, Pattern Recognition, Flask-Based Web Application, End-to-End Encryption, Human–Computer Interaction (HCI), Intelligent Assistive Systems},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715480}
}