Table Sensor System for Automated Service Detection
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Solution Overview
Problem
In the hospitality industry, managing waiting periods during customer table service can be inefficient, as staff often need to manually check on multiple tables, leading to wasted time and a disrupted dining experience, while customers may wait unnecessarily for service after finishing their meal.
Innovation Solution
A computer-implemented method and system that uses sensors to analyze load data from tables to differentiate between background noise and consumption patterns, enabling the detection of service requirements through machine learning and natural language processing, allowing for automated event detection and notification of staff when service is needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If staff manually check on multiple tables, then service coverage is improved, but time consumption and operational complexity increase
Solution Approach 1:
The table sensor system performs self-monitoring and automatically detects when service is needed, eliminating the need for staff to continuously check on tables. The system serves itself by autonomously tracking table status and generating notifications only when necessary.
Solution Approach 2:
Manual staff checking is replaced with an automated sensor-based detection system. The mechanical action of staff physically visiting tables is substituted with electronic sensors that continuously monitor table status and automatically identify service requirements.
2Reliability
If staff manually check on multiple tables, then service coverage is improved, but device complexity and operational burden increase
Solution Approach 1:
The sensor system automatically monitors tables and identifies service needs without requiring staff to manually check each table. The system self-manages the monitoring task, reducing operational complexity for staff while maintaining comprehensive service coverage.
Solution Approach 2:
The complex manual process of checking multiple tables is replaced with an automated electronic sensor network that continuously monitors table status, simplifying operations and reducing the burden on staff.
3Measurement precision
If sensors continuously monitor table load data, then service detection accuracy is improved, but background noise from customer behavior interferes with measurement precision
Solution Approach 1:
The system performs preliminary learning during the waiting period to establish baseline background noise patterns before service begins. This preliminary characterization of normal customer behavior allows the system to later distinguish actual service needs from routine background activity.
Solution Approach 2:
The system uses feedback from the learning phase to continuously refine its understanding of background noise patterns. By comparing current sensor readings against learned baselines, the system can filter out normal customer behavior and accurately detect deviations that indicate service requirements.
Data Source
AI summary
Method and system are provided for customer table service management. The method includes receiving sensor load data over time from a customer table. The method analyzes the sensor load data during a waiting time between a time of one or more customers arriving at the table and a time of consumables being served to the table to learn background noise data of the one or more customers. The method further analyzes the sensor load data during a dining time after the time of consumables being served to the table to detect one or more events that require a service action, wherein analyzing the sensor load data during the dining time removes the learnt background noise data to distinguish sensor load data changes relating to consumption of the consumables on the table. The method outputs event detection notifications to prompt the required service action.