Wearable Sensor Wait Time Estimation
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Solution Overview
Problem
Conventional methods for determining restaurant wait times are limited, often requiring in-person inquiries or phone calls, and lack real-time, automated solutions for patrons.
Innovation Solution
A computer-implemented method using sensor data from mobile devices to estimate wait times by identifying sitting and arrival times, analyzing differences to determine wait times, and communicating these estimates to other devices for statistical processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (in-person inquiries or phone calls) are used to obtain wait time information, then information can be obtained from restaurant employees, but the process requires active user participation and is not real-time
Solution Approach 1:
The system enables wait time estimation without active user participation by automatically analyzing sensor data from mobile devices. The computing device independently processes location data, accelerometer data, and other sensor inputs to determine when a user arrives at a restaurant and when they are seated, eliminating the need for users to manually inquire about wait times or input data.
Solution Approach 2:
The patent replaces manual inquiry methods (mechanical human interaction) with automated sensor-based detection. Instead of physically visiting a restaurant to ask an employee or making a phone call, the system uses accelerometers, location services, and other sensors to automatically track user movement and infer wait time information through data analysis.
2Productivity
If automated sensor-based estimation is implemented, then real-time wait time information can be obtained without active user participation, but the system complexity increases
Solution Approach 1:
The system leverages existing multi-functional mobile devices that already contain accelerometers, location services, and sensors for other purposes. By repurposing these existing components for wait time estimation, the system avoids adding dedicated hardware complexity while achieving automated real-time tracking of user arrival and seating events.
Solution Approach 2:
The computing device acts as an intermediary that processes raw sensor data from mobile devices and translates it into meaningful wait time information. This intermediary layer simplifies the complexity by centralizing the analysis logic and presenting processed results to users, rather than requiring users to directly interpret raw sensor data.
3Loss of information
If in-person inquiries are made at the restaurant, then current wait time information can be obtained, but the process is time-consuming and not automated
Solution Approach 1:
The system performs preliminary tracking of user location and movement before the user even arrives at the restaurant. By continuously monitoring sensor data, the system is already aware of user arrival and can immediately begin calculating wait time, eliminating the need for users to spend time inquiring about current wait conditions upon arrival.
Solution Approach 2:
The system provides continuous feedback about wait time based on real-time sensor data analysis. By monitoring user movement patterns, location changes, and time stamps, the system continuously updates wait time estimates and communicates them to users, replacing the need for discrete in-person inquiries with ongoing automated information delivery.
Data Source
AI summary
Computer-implemented methods and systems of estimating wait times and food serving times at a restaurant using wearable devices include identifying from portions of sensor data that a user is seated at a restaurant table at an estimated sitting time. In addition, portions of sensor data can be used to identify that a user has started eating at a given restaurant. Time-correlated location data can be used to determine an estimated arrival time of the user at a current location. An estimated wait time can be determined from the difference between the estimated sitting time and the estimated arrival time. An estimated food serving time can be determined from the difference between estimated eating time and arrival time or eating time and sitting time. Data indicative of the estimated times can be communicated to other computing devices, evaluated across multiple users, and/or used to develop relevant notifications for surfacing to other users.