IoT Sensor AI Utilization Prediction for Dining Spaces
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Solution Overview
Problem
Traditional methods for tracking usage and foot traffic in physical environments, such as dining spaces, are inaccurate due to the lack of digital mapping and reliance on manual data entry, which fails to capture the dynamic activity levels at different times and locations.
Innovation Solution
Implementing IoT sensor-based systems that use a variety of sensors (imaging, heat, pressure, etc.) to collect data and train AI models to predict utilization values, incorporating timing, weather, event, and infrastructure data to provide real-time or near-real-time utilization mapping and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual data entry is used to track usage and foot traffic, then implementation simplicity is maintained, but measurement precision and data accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical data entry with automated sensor-based data collection systems. Sensors (cameras, weight sensors, RFID readers) automatically capture foot traffic and usage data, eliminating the need for manual recording while significantly improving measurement precision and data accuracy.
Solution Approach 2:
The patent introduces digital mapping as an intermediary layer between physical environment and data analysis. The digital map serves as a virtual representation that connects sensor data to specific locations and activities, enabling automated tracking without manual intervention while maintaining high precision.
2Measurement precision
If comprehensive sensor data collection is implemented, then measurement precision and utilization tracking improve, but device complexity and infrastructure requirements worsen
Solution Approach 1:
The patent employs multi-functional sensors that can perform multiple detection tasks simultaneously. For example, cameras can detect both foot traffic count and specific activities, weight sensors can measure both occupancy and duration of stay. This reduces the total number of devices needed while maintaining comprehensive tracking precision.
Solution Approach 2:
The patent creates a digital copy (digital map) of the physical environment that mirrors its structure and characteristics. This digital representation allows complex spatial relationships to be analyzed computationally without requiring complex physical measurement infrastructure at every location.
3Productivity
If digital mapping and AI models are used for real-time prediction, then productivity and operational efficiency improve, but loss of information and data processing requirements worsen
Solution Approach 1:
The patent extracts only the essential features and characteristics needed for utilization prediction from the comprehensive sensor data. Instead of processing all raw data, the system identifies and extracts key parameters (occupancy levels, activity types, duration patterns) that are fed into AI models, reducing data processing burden while maintaining prediction accuracy.
Solution Approach 2:
The patent performs preliminary processing of sensor data by creating and maintaining a digital map structure before AI analysis. The digital map pre-organizes spatial relationships and location information, so when predictions are needed, the AI models work with pre-structured data rather than raw sensor inputs, improving processing efficiency.
Data Source
AI summary
Internet of Things (IoT) sensor based systems and methods of improving utilization of a physical dining environment using artificial intelligence (AI). The IoT sensor based systems and methods include collecting, by one or more processors, sensor data from one or more sensors positioned within the physical dining environment, where the sensor data corresponds to one or more locations within the physical dining environment; inputting, into an AI model executing on the one or more processors, the sensor data, where the AI model is trained with sensor data captured by the one or more sensors positioned within the physical dining environment; and generating, by the AI model and based on the sensor data, a prediction defining a utilization value of the physical dining environment.