Robotic Assistant for Retail Navigation via Regional Entropy
Solution Overview
Problem
Human assistance in facilities like retail stores and warehouses is labor-intensive and prone to errors, struggling to efficiently guide users through large product arrays and changing user preferences.
Innovation Solution
A robotic system generates item layout maps, determines product attributes, and creates guidance instructions based on ontology entries that indicate regional entropy values, allowing it to navigate users to specific product locations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human assistants are used to provide user assistance in facilities, then users can receive help with navigation and product information, but the assistance becomes labor-intensive and difficult to scale
Solution Approach 1:
The system enables self-service by having users interact with an automated robotic assistant that provides navigation and product information independently. The robotic assistant autonomously processes user requests, retrieves product data from databases, and provides guidance without requiring human assistant intervention, thus achieving both high quality assistance and scalable throughput.
Solution Approach 2:
The patent replaces the mechanical human assistant system with an automated robotic system equipped with sensors, processors, and communication modules. This substitution transforms the assistance delivery mechanism from human-based to machine-based, enabling automated navigation guidance, product information retrieval, and user interaction while maintaining service quality and dramatically increasing productivity.
2Adaptability or versatility
If human assistants are hired and trained to handle various assistance tasks, then the scope of tasks can be expanded, but the capacity to provide sustained assistance decreases due to human limitations
Solution Approach 1:
The robotic assistant is designed with universal functionality to perform multiple assistance tasks including navigation guidance, product information provision, comparison assistance, and recommendation generation. The system integrates diverse capabilities within a single platform, allowing it to handle various user requests across different product categories and assistance types without the limitations of human specialization.
Solution Approach 2:
The system achieves sustained high-throughput operation through automated self-service mechanisms. The robotic assistant autonomously manages user interactions, retrieves information from databases, processes requests, and provides guidance without fatigue or capacity limitations. This self-service capability enables the system to maintain consistent performance levels over extended periods and handle large volumes of user requests simultaneously.
3Reliability
If human assistants are used to navigate users through large product arrays, then users can receive personalized assistance, but errors increase and efficiency decreases at scale
Solution Approach 1:
The robotic assistant incorporates feedback mechanisms to continuously improve assistance accuracy. The system processes user responses, analyzes interaction patterns, and adjusts its guidance recommendations based on user preferences and behavior. This feedback loop enables the system to learn from each interaction, refine its navigation instructions, and provide increasingly accurate and personalized assistance while maintaining high operational efficiency.
Data Source
AI summary
In an example embodiment, a method may generate an item layout map for a shelving unit, where the shelving unit stores item types associated with a product category. The method may determine product attributes associated with the product category and divider configurations of the shelving unit, where a divider configuration divides the shelving unit into one or more candidate regions. The method may generate an ontology entry associated with a product attribute and the divider configuration based on the item layout map and the divider configuration of the shelving unit, the ontology entry indicating a regional entropy values of a product attribute for the candidate regions generated by the divider configuration. In some instances, the method may generate a guidance instruction related to the product attribute of the product category based on the ontology entry and provide the guidance instruction to a user.