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DESIGN THINKING BASED AUTOBILING SHOPPING MALL CART

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DESIGN THINKING BASED AUTOBILING SHOPPING MALL CART

ORDINARY APPLICATION

Published

date

Filed on 15 November 2024

Abstract

The Design thinking-based Auto billing Shopping Mall Cart is an innovative retail solution designed to enhance the shopping experience by integrating cutting-edge technologies such as RFID, IoT, and AI. This smart cart automates the billing process by automatically detecting and recording products as they are placed in the cart, eliminating the need for traditional checkout counters. Built with a user-centric approach using design thinking principles, the cart addresses key pain points such as long queues, billing errors, and inventory inefficiencies. It features real-time billing displays, intelligent navigation assistance, and digital payment integration, making shopping faster, more convenient, and enjoyable. For retailers, the cart offers real-time inventory tracking and customer behaviour insights, optimizing operations and enabling data-driven decision-making. Additionally, its eco-friendly materials and energy-efficient design promote sustainability. By combining advanced technology with human-cantered design, the Auto billing Shopping Mall Cart redefines modern retail, offering a seamless, efficient, and futuristic shopping experience for both customers and retailers.

Patent Information

Application ID202441088533
Invention FieldCOMPUTER SCIENCE
Date of Application15/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
MANOHARAN KAssociate Professor, SNS College of Technology, SaravanampattiIndiaIndia
Ms. Sowmiya RAssistant Professor / Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Revathi PAssistant Professor / Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Varshaa K SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Suviksha SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Suruthi GUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Sanjana M KUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Swetha SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Sree Ranjini RUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Mr. Sivaneshwaran DUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Vijitha VUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms.Raga Chandrika RUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms.Vincilin Sinthiya IUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia

Applicants

NameAddressCountryNationality
MANOHARAN KAssociate Professor, SNS College of Technology, SaravanampattiIndiaIndia
Ms. Sowmiya RAssistant Professor / Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Revathi PAssistant Professor / Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Varshaa K SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Suviksha SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Suruthi GUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Sanjana M KUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Swetha SUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Sree Ranjini RUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Mr. Sivaneshwaran DUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms. Vijitha VUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms.Raga Chandrika RUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia
Ms.Vincilin Sinthiya IUG Scholar, Artificial Intelligence and Data Science, SNS College of Engineering SNS Kalvi Nagar, Kurumbapalayam, Coimbatore-641107, Tamil Nadu, India.IndiaIndia

Specification

Description:3. PREAMBLE TO THE DESCRIPTION

In the fast-paced world of retail shopping, enhancing customer convenience and streamlining checkout processes have become essential to creating a superior shopping experience. The concept of an Autobilling Shopping Mall Cart, rooted in design thinking principles, aims to revolutionize the traditional shopping journey. By integrating advanced technologies such as IoT, RFID, and AI, this solution is poised to address key customer pain points, such as long checkout lines and manual billing processes.

Design thinking emphasizes empathy, ideation, and iterative problem-solving, ensuring that the cart aligns with user needs and expectations. This innovative cart not only simplifies the shopping process but also transforms it into a seamless and enjoyable experience. With real-time product scanning, digital payment integration, and intelligent navigation assistance, the cart empowers users with autonomy while reducing operational burdens for retailers.

This preamble sets the stage for exploring how human-centered design and cutting-edge technology converge to redefine shopping convenience and create a futuristic retail environment.

COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.
DESCRIPTION:
Introduction
The retail industry is undergoing rapid transformation, driven by evolving customer expectations and advancements in technology. One of the most significant challenges in traditional shopping is the inefficiency of manual billing and checkout processes. The Autobilling Shopping Mall Cart is a revolutionary solution designed to provide an enhanced shopping experience through automation, convenience, and personalization. Grounded in design thinking principles, this cart incorporates a deep understanding of customer needs, ideation, and iterative refinement to deliver a product that not only solves existing pain points but also elevates the overall shopping journey.



Design Thinking Process

1. Empathy: Understanding the User Needs
The journey began by observing and interacting with customers, retailers, and store staff to identify key pain points and opportunities for improvement.

Customer Pain Points:
Long and frustrating queues at billing counters.
Challenges in finding specific products in large stores.
Limited real-time feedback on purchases, offers, or total bills.

Retailer Pain Points:
High costs associated with manual billing errors and fraud.
Inefficient inventory tracking and replenishment processes.
Lack of data-driven insights into customer preferences and shopping behavior.

2. Define: The Problem Statement
"How might we create a shopping cart that simplifies the billing process, enhances the shopping experience, and optimizes operational efficiency for retailers?"

3. Ideation: Concept Development
Through brainstorming sessions, the Autobilling Shopping Mall Cart was conceptualized to integrate technologies that automate billing, personalize the shopping experience, and offer real-time inventory management.

4. Prototyping and Testing
Multiple prototypes were developed to test the functionality, usability, and adaptability of the cart. Feedback loops from user testing were critical in refining the design and features.

Features of the Autobilling Shopping Mall Cart
1. RFID-Based Product Scanning
Each product in the store is tagged with an RFID chip.
As items are placed in the cart, RFID sensors embedded in the cart automatically scan and add them to the virtual cart displayed on a touchscreen interface.
If an item is removed, it is automatically deducted from the bill, ensuring accurate real-time tracking.

2. Real-Time Billing and Display
A built-in screen displays the running total of items, offering transparency to shoppers.
Customers can review, modify, or confirm their purchases before finalizing the bill.

3. Smart Navigation System
Integrated GPS and AI-powered systems guide customers to specific products within the store using an interactive map.
Voice commands and touch navigation improve accessibility for diverse user groups.

4. Digital Payment Integration
Multiple payment options, including QR code scanning, mobile wallets, and card readers, allow customers to complete transactions directly at the cart.
Contactless payment ensures hygiene and safety.

5. AI-Driven Personalized Shopping Experience
Based on purchase history, preferences, and ongoing promotions, the cart suggests complementary products, deals, and discounts.
Loyalty points and rewards are automatically applied during checkout.

6. IoT for Inventory Management
The cart updates inventory in real time as items are added or removed.
Retailers gain insights into popular products, restocking needs, and demand patterns.

7. Security Features
Weight sensors ensure that all scanned items correspond to the products in the cart.
Anti-theft mechanisms alert store personnel if unpaid items are removed from the cart.

8. Eco-Friendly and Energy-Efficient Design
Constructed from recyclable materials to reduce environmental impact.
Powered by long-lasting, rechargeable batteries.


Implementation Details

1. Technology Stack
Hardware: RFID readers, touchscreen display, GPS module, weight sensors, and IoT connectivity modules.
Software: AI algorithms for recommendations, real-time inventory management software, and mobile payment gateways.
Cloud Integration: Data storage and analytics for inventory tracking and customer insights.

2. User Interface
The touchscreen interface features an intuitive design with options for language selection, real-time bill view, and navigation assistance.

3. Maintenance and Upgrades
Modular design allows for easy maintenance and scalability.
Regular software updates ensure compatibility with emerging technologies and new features.


Fig 1: Circuit Diagram


Fig 2: Circuit Diagram








Fig 3: LCD Output

Benefits
For Customers:
1. Significant time savings by eliminating the need for checkout queues.
2. Enhanced convenience through real-time billing and navigation.
3. A personalized shopping experience with recommendations and offers.

For Retailers:
1. Reduced staffing needs and operational costs.
2. Improved accuracy in billing and inventory tracking.
3. Insights into customer behavior for better marketing strategies and stock management.

Future Enhancements
Integration with augmented reality (AR) for immersive product exploration.
Multilingual support to cater to diverse customer bases.
AI-powered voice assistants for enhanced interaction.

, Claims:Claim 1: The Autobilling Shopping Mall Cart uses RFID technology to automatically scan items as they are placed in the cart, allowing customers to complete billing directly on the cart without waiting in checkout lines.

Claim 2: The cart integrates smart navigation, real-time billing display, and personalized product recommendations, making the shopping journey faster, more convenient, and engaging.

Claim 3: The IoT-enabled cart provides real-time inventory updates, reduces manual labor for stock management, and offers data-driven insights into customer behavior, leading to streamlined operations.

Claim 4: Equipped with weight sensors and automated tracking systems, the cart minimizes errors by cross-verifying scanned items with actual contents, ensuring accurate billing.

Claim 5: Constructed with recyclable materials and powered by energy-efficient systems, the cart not only supports eco-friendly practices but also represents a leap forward in retail technology.

Documents

NameDate
202441088533-COMPLETE SPECIFICATION [15-11-2024(online)].pdf15/11/2024
202441088533-DRAWINGS [15-11-2024(online)].pdf15/11/2024
202441088533-FORM 1 [15-11-2024(online)].pdf15/11/2024

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