AI Planogram Analytics for Retail Store Layout Bottlenecks
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Solution Overview
Problem
Retailers face challenges in making data-based decisions and verifying execution due to the complexity of retail environments, including volume of activity, employees, products, and customers, which current systems fail to address comprehensively.
Innovation Solution
A system and method utilizing AI and planograms to analyze data from sensors such as cameras, tracking product inventory, equipment, patrons, and employees, and providing insights through a graphical user interface for store performance and layout optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual monitoring and data collection methods are used in retail stores, then employees can directly observe and record store conditions, but the system cannot comprehensively address the complexity of retail environments including volume of activity, employees, products, and customers
Solution Approach 1:
The patent replaces manual mechanical monitoring methods with an automated computer vision system using cameras and AI algorithms. The system automatically captures images of store shelves, products, employees, and customers, then processes this visual data to generate insights about store conditions, inventory status, and customer behavior without requiring manual data collection
Solution Approach 2:
The patent introduces an intermediary AI processing layer between the physical store environment and the decision-making system. This intermediary automatically analyzes images from multiple cameras, extracts relevant data about products, employees, and customers, and transforms it into actionable insights, bridging the gap between raw visual data and meaningful business intelligence
2Loss of information
If comprehensive data collection from multiple sensors and cameras is implemented, then real-time insights into store performance and layout can be obtained, but the difficulty of detecting and measuring store conditions increases
Solution Approach 1:
The patent segments the complex task of store monitoring into distinct functional modules: camera modules capture specific aspects (shelves, products, employees, customers), image processing modules analyze each aspect separately using specialized AI algorithms, and a synthesis module combines the results. This segmentation allows each component to focus on a specific detection task, reducing the overall complexity while maintaining comprehensive coverage
Solution Approach 2:
The patent creates a universal AI processing platform that handles multiple types of data simultaneously. The same system processes images of products, employees, and customers using different but integrated algorithms, extracting various types of information (inventory status, employee behavior, customer flow) from a unified multi-sensor input stream, thereby simplifying the measurement process
3Productivity
If AI algorithms process large datasets from sensors to determine store performance conclusions, then actionable insights can be generated, but the quantity of data to be processed increases
Solution Approach 1:
The patent applies preliminary action by implementing real-time data processing and filtering at the source. As images are captured from cameras, AI algorithms immediately process and extract relevant information, filtering out unnecessary data before it accumulates. This preliminary processing reduces the volume of data that needs to be stored and analyzed further, while still capturing all essential insights about store conditions
Data Source
AI summary
A system and method for providing insights regarding retail store performance and/or store layout are described herein. One or more cameras in a store capture and transmit data associated with product inventory, equipment inventory, patrons, and employees in the store to a computing device. The computing device queries a repository to determine identifiable information associated with each of the product inventory, the equipment inventory, the patrons, and the employees. The data is combined with the identifiable information to form a data set. An artificial intelligence (AI) algorithm is applied to the data set to determine a conclusion regarding a performance and/or layout of the store. The data, the data set, and the conclusion are stored the repository. A representation of the conclusion is generated and presented via a graphical user interface (GUI) of the computing device to a user. The representation is a store-specific planogram.