Description:StorePulse is a cutting-edge, indigenous AI-based solution designed to transform the way businesses analyze and optimize their store operations. Built to provide actionable insights and advanced analytics, StorePulse empowers businesses with a comprehensive understanding of customer behaviour, operational efficiency, and sales performance.
With its powerful features, StorePulse offers footfall tracking, enabling businesses to gauge store traffic patterns and peak hours. It also provides detailed customer demographic insights, such as age and gender, helping businesses tailor their offerings and marketing strategies to match customer profiles. Additionally, StorePulse ensures operational transparency with features like store opening and closing time tracking, ensuring adherence to operational schedules.
The solution goes beyond conventional analytics by providing customized AI-based alerts, enabling businesses to address issues in real time. For example, sudden dips in foot traffic or operational delays can trigger instant notifications, allowing swift remedial action. This real-time responsiveness is invaluable for maintaining customer satisfaction and operational efficiency.
StorePulse is versatile and scalable, making it ideal for tracking single or multiple stores across a unified platform. Whether it's a standalone retail store or a chain of outlets, businesses can rely on StorePulse for centralized, data-driven decision-making.
Industries such as retail, hospitality, and malls can reap significant benefits from StorePulse’s capabilities. Retailers can optimize staffing, inventory, and promotions, while hospitality businesses can enhance guest experiences. Shopping malls can leverage insights to improve tenant performance and foot traffic.
StorePulse’s innovative approach empowers businesses with the intelligence they need to thrive in a competitive market. By combining data-driven insights with advanced AI, StorePulse paves the way for smarter, more efficient store operations.

4. Advantages of the Inventions:
1. StorePulse provides detailed demographic data, including age and gender, helping businesses understand their customer base and create targeted marketing strategies to boost engagement and conversions.
2. Track the number of visitors in real time to identify peak hours, optimize staff allocation, and enhance customer experience. This data also supports better decision-making for promotions and in-store activities.
3. Monitor store opening and closing times to ensure compliance with operating schedules. This helps improve accountability and enhances operational consistency across locations.
4. Receive customized, AI-powered notifications for anomalies or actionable events, such as unexpected drops in foot traffic or delayed store openings. These alerts enable businesses to respond proactively and minimize disruptions.
5. Manage and analyze data from multiple store locations on a single platform. This unified approach provides a holistic view of performance and simplifies comparisons across outlets.
6. By linking customer behavior insights with sales data, businesses can refine product offerings, optimize pricing strategies, and improve marketing campaigns for better ROI.
7. StorePulse is adaptable to various industries, including retail, hospitality, malls, and more. It supports both small standalone stores and large multi-location enterprises with equal efficiency.
8. Intuitive dashboards and analytics tools turn raw data into actionable insights, enabling quicker decision-making and performance tracking.
9. Insights from StorePulse can help businesses reduce costs by optimizing staffing, managing inventory efficiently, and addressing underperforming areas proactively.
10. Leverage AI and advanced analytics to stay ahead of market trends, ensuring that businesses remain competitive and customer-focused in an ever-evolving landscape.

5. Detailed of the Invention:

The StorePulse system begins with the Company Onboarding and AI Model Setup Flow, where businesses register on the platform, entering relevant company and store details. After store details such as name, location, and other specific information are entered, the company selects the required AI models from a list of available options. These models are tailored to address specific needs such as customer demographics analysis, operational efficiency monitoring, and safety. Following this, a custom configuration plan is generated for each store to ensure an optimal setup, considering unique store requirements.

Once the configuration plan is defined, the Local Processing Unit (LPU), an edge device, is deployed at the store. This unit is responsible for processing the incoming video streams locally, reducing latency and bandwidth dependency. Simultaneously, IP cameras are installed and configured to stream video data from the store to the LPU using RTSP (Real-Time Streaming Protocol).
The next step is the AI model loading phase, where the selected models are deployed onto the LPU. A thorough integration and validation phase is conducted to ensure seamless communication between the cameras and the models. After successful testing, the system goes live, and real-time AI processing begins.

The Demographic and Footfall Analysis Flow starts with the LPU capturing RTSP video streams. Frames are extracted from the live video feed and sent to a backend server via FTP for storage and further processing. The system utilizes a Facial Recognition System (FRS) to analyze customer faces, matching them against a database of previously recorded faces to avoid duplicate counting. New faces are added to the database and counted as unique customers. Additionally, the system analyzes customer attributes, such as age and gender, storing these insights in a central database (e.g., PostgreSQL).

The Main AI Model Processing Flow initiates once the store is live, with cameras and LPU active. Video streams are continuously captured, frames are extracted, and preprocessed (resized, normalized, and region-of-interest filtered). Based on the store’s configuration, the relevant AI models are activated for tasks like tampering detection, food delay tracking, and cross-contamination checks. Model results are aggregated, and alerts are triggered for any detected anomalies, sent through APIs or message queues. All processed data, including insights and alerts, are logged to a central database for tracking and analytics. The system’s results are visualized in real-time on an interactive dashboard, providing actionable insights. A feedback loop is also in place to facilitate model retraining and updates based on new data or performance feedback, ensuring continuous improvement of the system’s AI capabilities.

6. Figure to be attached:

7. What problems of prior art does it claim to solve :

1. Real-time monitoring of store visitors to identify traffic patterns and peak hours.

2. Provides insights into customer age and gender for targeted marketing and personalized customer experiences.

3. Tracks store opening and closing times to ensure operational compliance and efficiency.

4. Delivers customized alerts for unusual activity, such as unexpected drops in footfall or delays in operations.

5. Manage single or multiple stores on a unified platform for consistent and scalable performance monitoring.

6. Offers actionable insights through easy-to-understand, data-driven visualizations for better decision-making.

7. Enables real-time responses to operational issues, optimizing resource allocation and minimizing disruptions.

8. Suitable for retail, hospitality, malls, and other industries seeking to enhance store performance and customer engagement.

8. Solution does the invention provide:

- Real-time footfall tracking to monitor store traffic and peak hours.
- Provides customer demographics insights, including age and gender, for targeted marketing.
- Monitors store opening and closing times to ensure operational efficiency.
- AI-powered alerts for real-time notifications on unusual activity or operational issues.
- Centralized platform for managing single or multiple store locations.
- Advanced analytics and dashboards that deliver actionable insights for decision-making.
- Proactive problem-solving through real-time data analysis, allowing quick response to issues.
- Adaptable to various industries such as retail, hospitality, and malls for optimized performance.

9. Related prior art/ document/patents:

10. Technical Advance Over Prior art:

1. Combines footfall, customer demographics, and operational data into a single platform for streamlined analysis.

2. Utilizes advanced AI to provide tailored insights and alerts specific to business needs.

3. Delivers live data analysis and real-time notifications for immediate action on operational issues.

4. Easily manages and monitors multiple stores from a centralized platform.

5. Provides in-depth insights into customer age, gender, and behavior for targeted strategies.

6. Enables quick responses to emerging issues with real-time insights and AI-powered alerts.

7. Transforms raw data into intuitive, actionable dashboards for better decision-making.

11. Novelty factor or core concepts of Invention:

StorePulse is an AI-powered platform that transforms operations by leveraging advanced machine learning, computer vision, and real-time analytics. It integrates seamlessly with existing infrastructure, providing businesses with actionable insights to optimize operational efficiency, enhance safety, and improve customer experiences. The system is designed to be modular and scalable, allowing it to evolve as new needs arise or industries are added.

1. StorePulse processes video streams in real-time using computer vision models deployed on edge devices. This allows for immediate detection of events and anomalies, with no reliance on cloud processing, ensuring low latency and real-time decision-making.

- Edge Computing: Real-time video analysis happens locally on edge devices, reducing latency and bandwidth costs.
- Continuous Monitoring: The platform continuously processes video feeds to provide immediate insights and alerts.

2. StorePulse supports customizable AI models, allowing businesses to adapt the platform to different operational needs. New models can be easily integrated or replaced without disrupting existing functions, offering flexibility across a variety of applications.

- Customizable Models: Tailored AI models for specific tasks can be deployed and updated as needed.
- Flexible and Scalable: The platform can be scaled and adapted to new industries or operational challenges.

3. Advanced Analytics and Predictive Insights
The platform not only tracks real-time data but also uses predictive analytics to inform future decisions. By analyzing historical and current data, it helps businesses anticipate issues, optimize workflows, and improve overall performance.

- Predictive Analytics: Forecasts potential issues like staffing shortages or bottlenecks.
- Data-Driven Decisions: Provides actionable insights to improve staffing, layout, and operational efficiency.

12. What Aspects can be considered as novel/ What other aspects can be covered in building claims which provide Maximum benefit also Make Invention Commercially attractive:

Here are the commercial benefits of the StorePulse platform, focusing on its key features:

1. Reduced dependency on cloud services, lowering operational costs and improving system responsiveness. Enhances data privacy by processing sensitive information locally.

2. Flexible customization of AI models allows businesses to scale and adapt the system to changing needs without additional investment. A future-proof solution that grows with the business.

3. Enhanced decision-making through integrated insights from multiple models. Allows businesses to optimize staffing, inventory, and customer engagement strategies, improving revenue generation and cost efficiency.

4. Continuous improvement of AI model accuracy without manual intervention, ensuring long-term value and operational precision as the system adapts to new data.

5. Proactively addressing potential operational issues (e.g., staffing shortages, bottlenecks) before they occur, reducing downtime, improving resource allocation, and enhancing operational efficiency.

6. Immediate response to critical events, such as safety violations or operational disruptions, ensuring swift corrective actions and reducing the risk of costly mistakes.

7. Fast deployment with minimal disruption to existing operations, reducing upfront costs and complexity. Businesses can quickly leverage the benefits without overhauling their systems.

8. Empower managers with real-time insights into foot traffic, customer behavior, and operational metrics, facilitating informed decision-making and better resource allocation.

9. Reduces the risk of compliance violations and legal issues by automating safety and hygiene checks, improving operational safety and protecting the business from potential liabilities.

10. Optimizes staffing and attendance management, improving productivity and reducing labor costs. Ensures businesses can quickly adjust their workforce to match operational demand.

11. Personalizes customer interactions based on behavior and demographics, leading to higher conversion rates, better customer satisfaction, and improved customer retention.

12. Saves time on manual reporting, ensures timely access to important operational data, and facilitates quicker decision-making, improving overall operational efficiency.

13. Streamlines operations with automated alerts and actions, improving efficiency, reducing errors, and ensuring smooth operations, thereby saving time and reducing labor costs.

14. Consolidates data from various systems into one platform, providing a holistic view of operations and enabling better decision-making, which improves resource management and operational effectiveness.

13. Suggested Embodiment:
A suggested embodiment of the StorePulse system involves the integration of edge-based AI models with real-time video streams from IP cameras deployed in a business environment. The system begins by registering the business on the StorePulse platform, where the user provides their details and selects the necessary AI models for deployment. The Local Processing Unit (LPU) is then set up on-site to handle video processing locally, minimizing the need for cloud dependency.

The LPU receives RTSP video streams from the IP cameras, which are processed in real-time to extract frames for analysis. These frames are then sent to a backend server via FTP for centralized storage and further processing. The system uses AI models to analyze the frames and provide actionable insights such as footfall tracking, customer behavior analysis, and operational performance monitoring.

The platform also includes an alert system that notifies managers of issues such as fire detection, staff absence, or safety violations. Data collected is stored in a secure database and presented on an intuitive real-time dashboard, allowing managers to monitor the system remotely and make informed decisions. The architecture is scalable, supporting seamless model updates and new feature integrations as business needs evolve.
14. What Does the Inventor want to protect:
The inventor seeks to protect the methodology and system architecture for integrating edge-based AI processing with real-time video streams to deliver actionable business insights. This includes the custom configuration and deployment of AI models, the local processing unit (LPU) handling video stream analysis, and the unique facial recognition-based demographic analysis for accurate footfall tracking. Additionally, protection is sought for the AI model inference framework, alert generation mechanisms, real-time dashboard updates, and the database integration. This invention optimizes operational efficiency, security, and customer engagement, ensuring the seamless interaction between hardware, software, and AI algorithms.

, Claims:1. Real-time monitoring of store visitors to identify traffic patterns and peak hours.

2. Provides insights into customer age and gender for targeted marketing and personalized customer experiences.

3. Tracks store opening and closing times to ensure operational compliance and efficiency.

4. Delivers customized alerts for unusual activity, such as unexpected drops in footfall or delays in operations.

5. Manage single or multiple stores on a unified platform for consistent and scalable performance monitoring.

6. Offers actionable insights through easy-to-understand, data-driven visualizations for better decision-making.

7. Enables real-time responses to operational issues, optimizing resource allocation and minimizing disruptions.

8. Suitable for retail, hospitality, malls, and other industries seeking to enhance store performance and customer engagement.

8. Solution does the invention provide:

- Real-time footfall tracking to monitor store traffic and peak hours.
- Provides customer demographics insights, including age and gender, for targeted marketing.
- Monitors store opening and closing times to ensure operational efficiency.
- AI-powered alerts for real-time notifications on unusual activity or operational issues.
- Centralized platform for managing single or multiple store locations.
- Advanced analytics and dashboards that deliver actionable insights for decision-making.
- Proactive problem-solving through real-time data analysis, allowing quick response to issues.
- Adaptable to various industries such as retail, hospitality, and malls for optimized performance.