Context-Aware Retail Robot for Product Finding and Inventory Guidance
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Solution Overview
Problem
Traditional retail environments face challenges such as difficulty in finding products, understaffing, and poor customer service due to inefficient inventory systems and lack of effective communication between customers and staff.
Innovation Solution
A context-aware interactive robot equipped with an adaptive, interactive, and cognitive reasoning engine (AICoRE) that uses natural language understanding, visual recognition, and autonomous navigation to provide customer and staff services, including product information, inventory management, and navigation assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional inventory systems are used in retail stores, then staff can access inventory information, but it is difficult for both customers and work staff to access and find the correct shelf location for a product
Solution Approach 1:
The patent introduces a robot as an intermediary device between the inventory system and users (customers/staff). The robot equipped with sensors, processors, and communication modules acts as a mobile intermediary that can locate products, retrieve inventory information, and guide users to shelf locations, thereby resolving the difficulty of accessing inventory information and finding product locations.
Solution Approach 2:
The patent replaces traditional static inventory systems and manual product location methods with an autonomous robotic system. The robot uses computer vision, LIDAR, and navigation algorithms to automatically locate products and guide users, substituting mechanical/manual inventory access methods with automated intelligent systems.
2Productivity
If retail stores are understaffed to reduce costs, then operational costs decrease, but potential customers with unanswered questions remain unserved
Solution Approach 1:
The patent implements a self-service robotic system that can independently interact with customers, answer questions, provide product information, and guide them to locations. The robot autonomously performs customer service tasks without requiring human staff intervention, enabling stores to maintain service quality while being understaffed.
Solution Approach 2:
The robotic system is designed with multi-functionality to perform various customer service tasks including answering questions, providing product information, locating items, and guiding customers. This universal capability allows a single robot to replace multiple specialized staff functions, maintaining service quality with reduced staffing.
3Ease of operation
If more staff are hired to improve customer service, then customer service quality improves, but operational costs increase
Solution Approach 1:
The patent creates a robotic copy of human customer service functionality. The robot replicates the information-gathering and guidance capabilities of human staff through sensors, processors, and communication systems, providing equivalent service quality without the ongoing costs of human employment such as wages, benefits, and training.
4Device complexity
If traditional product location methods are used, then simple systems are maintained, but finding a desired product at a large retail location is difficult
Solution Approach 1:
The patent enhances traditional 2D shelf location signs by introducing a mobile 3D robotic system with spatial awareness. The robot uses LIDAR, cameras, and navigation algorithms to operate in three-dimensional retail environments, providing accurate product location information that goes beyond static signage by adding mobility and interactive guidance capabilities.
Data Source
AI summary
A plurality of images captured using a camera included in a robotic system are analyzed. A spatial map is generated using a sensor included in the robotic system. A semantic location map is generated using at least the analyzed plurality of captured images and the generated spatial map. A natural language input referencing a desired product item is received from a user. A speech recognition result is recognized from the natural language input and sent to a reasoning engine. In response to sending the recognized speech recognition result, one or more commands for the robotic system are received from the reasoning engine. The received one or more commands are performed and feedback to the user based on at least one of the one or more commands is provided.