Voice Context Recognition for Automated Bill Splitting
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Solution Overview
Problem
Existing methods for splitting bills among a group at a restaurant are tedious and time-consuming, especially in larger parties, as they require manual calculation and distribution of charges for menu items.
Innovation Solution
A system utilizing voice and context recognition to create an inventory of items ordered by each user, allowing for efficient splitting of bills through mobile devices and a waiter device, which can transmit and process orders and bills, enabling users to pay only for their ordered items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation and distribution of charges is used for splitting bills, then accuracy of charge allocation can be maintained, but time consumption and service delay increase significantly
Solution Approach 1:
The patent replaces the manual mechanical system of calculation and distribution with an automated electronic system. Mobile devices capture images of menu items, automatically calculate charges through image recognition and data processing, and electronically distribute charges to respective users, eliminating the time-consuming manual process while maintaining accuracy through systematic digital recording and computation.
Solution Approach 2:
The system enables self-service by allowing users to autonomously capture their own order information through mobile device cameras, automatically generate their charge allocations, and receive their portions of the bill without requiring server intervention for calculation and distribution, thus significantly reducing service time while maintaining accurate charge allocation.
2Productivity
If automated voice and context recognition is implemented for order capture, then service speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent utilizes mobile devices that already possess multiple functions (camera, microphone, processor, display) to perform the specialized tasks of voice capture, context recognition, order recording, and charge calculation. This approach avoids creating dedicated complex hardware while achieving automated order capture, as the mobile device's existing capabilities are leveraged for multiple purposes including voice recognition and image capture.
Solution Approach 2:
The system employs an intermediary processing layer that receives voice inputs and contextual information, processes them through recognition algorithms, and translates them into structured order data. This intermediary layer simplifies the interface between the complex recognition system and the user, converting unstructured speech and context into organized order information that can be easily processed and displayed.
3Measurement precision
If separate bills are generated for each user in a group, then payment accuracy for individual items is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the group order into individual user portions by capturing images and data specific to each user's selected items. The system divides the total bill into separate charge allocations for each user based on their individual orders, enabling precise payment tracking for each person while maintaining organized and manageable data structures that do not overly complicate the processing system.
Data Source
AI summary
Methods and systems for facilitating payment of a bill are described. The methods use voice and context recognition to create an inventory of items for a specific user. When it is time to split the bill among a group at a table, each user is apportioned their share of the bill based on their inventory so that each user pays for what they ordered. The users may decide to split the bill differently, such as equally among themselves.