Audio-Based Customer Identity Detection in Quick Service Ordering
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Solution Overview
Problem
Quick service restaurants and similar establishments face challenges in understanding customer preferences due to brief interactions, limiting the effectiveness of digital technologies designed for other retail sectors, which struggle to provide insights without usernames, addresses, or loyalty memberships.
Innovation Solution
A computer system connects to customer-facing devices to automate and assist the order process, using machine learning algorithms to determine customer identity and preferences from contextual cues, providing recommendations without collecting personally identifying information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional digital retail solutions are used in quick service restaurants, then technology infrastructure is established, but customer insights cannot be obtained without usernames, addresses, or loyalty memberships
Solution Approach 1:
The patent introduces an audio-based intermediary system that captures customer orders through voice commands in drive-thru environments. This intermediary layer enables the collection of order data without requiring traditional customer identifiers, thereby bridging the gap between digital infrastructure and customer insights in quick service restaurants
Solution Approach 2:
The patent replaces traditional mechanical identification methods (username entry, loyalty card scanning, address collection) with an audio-based identification system. By substituting the mechanical identification process with voice command analysis, the system can identify customers and provide insights without requiring personal information
2Speed
If brief in-person interactions are maintained for quick service efficiency, then service speed is improved, but customer preference understanding deteriorates
Solution Approach 1:
The patent implements preliminary action by capturing and analyzing customer audio orders at the point of ordering, before the transaction is completed. This allows the system to extract preference information during the natural ordering process without requiring extended interactions or follow-up surveys
Solution Approach 2:
The system enables self-service by automatically analyzing audio orders and extracting customer preferences without requiring employee intervention or extended customer engagement. The technology independently processes order data to generate insights while maintaining quick service turnover
3Reliability
If no personally identifying information is collected for privacy reasons, then customer privacy is protected, but personalized recommendations cannot be provided
Solution Approach 1:
The patent changes the identification parameters from personal identifiers (names, addresses, phone numbers) to audio-based identifiers derived from voice patterns and order characteristics. This parameter transformation enables personalized recommendations while maintaining privacy by using non-personal identifiable information for customer recognition
Solution Approach 2:
The audio order system serves as a privacy-preserving intermediary that enables personalization without direct collection of personally identifiable information. The system mediates between privacy protection requirements and personalization needs by using voice-based identification as an intermediate layer
Data Source
AI summary
A computer system may connect to various customer-facing devices and manage or automate the order process between a retail store and the customer. The computer system may perform the dialogue and receive an order for items from the retail store and may perform quality control monitoring of the dialogue between customers and employees taking orders. The ordering system may utilize the ordered items in combination with various contextual cues to determine a customer identity which may then be linked to past orders and/or various order preferences. Based on the determined customer identity, the system may provide recommendations of additional order items or order alterations to the customer before personally identifying information has been collected from the customer. The determination of the customer identity and the determination of recommendations may be performed by machine learning algorithms that were trained on customer data and the retail store products.