Predictive Hotel Arrival Using Offline Calendar Analysis
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Solution Overview
Problem
Current techniques for predicting a guest's arrival at a hotel room are not accurate enough to enable timely provision of services such as housekeeping, maintenance, and optimal hotel system operations, especially when Internet access is unavailable.
Innovation Solution
A hotel computer system uses calendar information from a guest's mobile device to predict their arrival time, analyzing this data along with historical unlock patterns and external factors like weather and traffic, without requiring Internet connectivity, to perform actions such as adjusting hotel services and door lock polling frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calendar information and historical patterns are analyzed offline without Internet access, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing calendar information, historical unlock patterns, and guest preferences before the guest arrives. This offline analysis enables accurate predictions without requiring complex real-time Internet processing during the actual arrival moment.
Solution Approach 2:
The patent introduces an intermediary offline analysis layer that processes data locally using simplified algorithms. This intermediary system bridges the gap between limited offline computational resources and the need for accurate predictions, avoiding the need for complex real-time Internet-based processing.
2Speed
If door lock polling frequency is increased to detect arrival, then detection speed is improved, but energy consumption increases
Solution Approach 1:
The door lock polling frequency is made dynamic rather than static. The system adjusts the polling frequency based on the predicted arrival time - increasing frequency near the predicted arrival moment for fast detection, and decreasing it during periods when the guest is unlikely to arrive, thus optimizing energy consumption.
Solution Approach 2:
The system uses feedback from the predicted arrival time to dynamically adjust the polling strategy. When a guest's arrival is predicted, the system increases polling frequency in the vicinity of that time window, and reduces it otherwise, creating an energy-efficient adaptive detection mechanism.
3Ease of operation
If services are prepared in advance based on predicted arrival, then guest experience is improved, but risk of premature service increases
Solution Approach 1:
The system performs preliminary preparation of services based on predicted arrival times, but incorporates confidence intervals and probabilistic thresholds. Services are triggered only when the prediction confidence exceeds certain thresholds, preventing premature service while still enabling advance preparation when appropriate.
Solution Approach 2:
The system changes parameters such as prediction confidence thresholds and service trigger conditions based on different scenarios. By adjusting these parameters dynamically, the system balances the trade-off between providing timely services and avoiding premature preparation, thereby maintaining both guest experience and service timing accuracy.
Data Source
AI summary
A method, system, and computer program product for predicting a hotel room arrival for a guest at a hotel. The method comprises a hotel computer system receiving calendar information for the guest from a mobile device for the guest of the hotel while the guest is in the hotel. The method predicts the hotel room arrival for the guest at the hotel using the calendar information. The method performs an action in the hotel based on the hotel room arrival predicted for the guest.