System and method for monitoring and predicting customer occupancy

Abstract

In an embodiment, a seat occupancy system may include at least one sensor configured to output sensor data indicative of a customer occupancy parameter for each of a plurality of seats within a dining environment; at least one processor; and a memory storing instructions executable by the processor. The processor may receive sensor data and a map indicating a first layout of a dining environment, the first layout including locations for each of the plurality of seats and for a performance effect. The processor may also determine that the customer occupancy parameter for the at least one seat for the first layout of the dining environment does not match the target customer occupancy parameter for a period of time, and in response, generate a second layout including a new location for the at least one seat, a new location for the performance effect, or both.

Description

Background Art

[0001] This section is intended to introduce the reader to various aspects of the technology that may be related to the various aspects of the technology presented, which are described and / or claimed below. It is believed that this discussion is helpful in providing the reader with background information to promote a better understanding of the various aspects of the present disclosure. Therefore, it should be understood that these statements are to be read in this light, and not as an admission of prior art.

[0002] In a restaurant or restaurant facility, a customer may place an order at an ordering station (e.g., a kiosk, a register) and navigate through a dining environment to select a table for their dining experience. However, some tables in a dining environment may be more desirable than other tables due to their location. For example, a table near a window may be more desirable than a table near a station. In some cases, environmental factors (e.g., a roller coaster, sunlight) may change the desirability of a dining table. For example, during certain times of the day, sunlight shining through a window may change a highly desirable dining table into a less desirable dining table. Therefore, it is currently recognized that monitoring the movement of customers in a dining environment may be beneficial to understand customer preferences and predict future customer occupancy. Summary of the invention

[0003] Certain embodiments commensurate in scope with the originally claimed subject matter are summarized below. These embodiments are not intended to limit the scope of the claimed subject matter, but rather these embodiments are intended only to provide a brief overview of possible forms of the subject matter. Indeed, the subject matter may encompass a wide variety of forms that may be similar or different from the embodiments set forth below.

[0004] In one embodiment, a seat occupancy system may include: at least one sensor configured to output sensor data indicating a corresponding customer occupancy parameter for each of a plurality of seats within a dining environment; at least one processor; and a memory storing instructions executable by the at least one processor. The processor may receive the sensor data and receive a map indicating a first layout of the dining environment, the first layout including a corresponding position for each of the plurality of seats and a corresponding position for a show effect. The processor may also determine that the corresponding customer occupancy parameter for at least one of the plurality of seats does not match a target customer occupancy parameter for a period of time in the first layout of the dining environment, and in response, generate a second layout of the dining environment, the second layout including a new corresponding position for at least one of the plurality of seats, a new corresponding position for a show effect, or both, wherein the second layout is different from the first layout.

[0005] In one embodiment, a method of operating a seat occupancy system may include: using at least one processor to receive sensor data, the sensor data being captured by at least one of a plurality of sensors and indicating a corresponding customer occupancy parameter for each of a plurality of seats within an environment over a period of time; and using at least one processor to receive additional sensor data, the additional sensor data being captured by at least one of the plurality of sensors and indicating a plurality of environmental factors within the environment during the period of time. The method may also use the at least one processor to generate or access a first map, the first map representing the environment and including corresponding locations of show effects within the environment during the period of time, corresponding locations of each environmental factor, and corresponding locations of each of the plurality of seats; and the method may also use the at least one processor to generate a second map based on the sensor data, the additional sensor data, and the first map, the second map representing a recommended layout for the environment.

[0006] In one embodiment, a seat occupancy system may include at least one processor and a memory storing instructions executable by the at least one processor to cause the at least one processor to: receive a map indicating an environment, the environment including a plurality of seats, environmental factors, and show effects. The at least one processor may also determine, based at least in part on sensor data, a corresponding customer occupancy for each of the plurality of seats over a period of time, and determine that the corresponding customer occupancy for at least one of the plurality of seats during the period of time is below a threshold customer occupancy. The at least one processor may also determine a new corresponding position for the environmental factor, a new corresponding position for the show effect, or a combination thereof that is predicted to improve the corresponding customer occupancy for the at least one of the plurality of seats, and update the map with the corresponding new position for the environmental factor, the corresponding new position for the show effect, or a combination thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] These and other features, aspects and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like reference numerals represent like parts throughout the drawings, and in which:

[0008] Figure 1 is a schematic diagram of an embodiment of a seat occupancy system that can be used in a dining environment according to an aspect of the present disclosure;

[0009] Figure 2 According to one aspect of the present disclosure, a method for monitoring the location of customers in a dining environment is provided. Figure 1 A schematic illustration of a seat occupancy system;

[0010] Figure 3According to one aspect of the present disclosure, Figure 1 A schematic representation of the layout of the dining environment generated by the seat occupancy system;

[0011] Figure 4 According to one aspect of the present disclosure, a method for using Figure 1 A flowchart of an embodiment of a process for designing a dining environment using a seating occupancy system based on the present invention; and

[0012] Figure 5 The operation according to one aspect of the present disclosure is Figure 1 A flow chart of one embodiment of a process for a seat occupancy system. DETAILED DESCRIPTION

[0013] When introducing elements of various embodiments of the present disclosure, the articles "a," "an," and "the" are intended to mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be inclusive, and mean that there may be additional elements besides the listed elements. Additionally, it should be understood that references to "one embodiment" or "an embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

[0014] One or more specific embodiments of the present disclosure will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation may be described in this specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. In addition, it should be appreciated that such development work may be complex and time consuming, but will be no more than a routine task of design, fabrication, and manufacturing for a person of ordinary skill who benefits from the present disclosure.

[0015] The present disclosure generally relates to systems and methods for a seat occupancy system for a dining environment. The seat occupancy system may include at least one sensor that generates sensor data related to the dining environment and at least one processor for analyzing the sensor data. Within the dining environment, customers may prefer certain seats or certain areas, such as seats adjacent to a window rather than seats adjacent to an entrance or exit. Over time, sensor data may be collected and analyzed to determine trends in customer preferences. Further, the sensor data may be used to redesign the dining environment, design a new dining environment, or provide a performance effect within the dining environment.

[0016] For example, sensor data can be used to redesign a dining environment so that the placement of tables and chairs improves the customer dining experience. In another example, sensor data can be used to design a new dining environment so that the placement of tables, chairs, windows, doorways, etc. is optimized for the customer dining experience. Still, in another example, sensor data can be used to place performance effects within the dining environment to provide customers with a unique dining experience.

[0017] The presented embodiments may be superior to traditional dining environments because the presented embodiments are tailored to customer preferences. The seat occupancy system may include sensors that are specifically placed within the dining environment, and the system may process sensor data to determine patterns or trends in the sensor data over time. For example, the seat occupancy system may use sensor data to train a machine learning model and provide redesign recommendations or alternative layouts for the dining environment based on customer preferences. Further, the seat occupancy system may filter certain sensor data (e.g., duplicate sensor data) to increase processing efficiency, and / or the system may periodically delete sensor data to prevent the database from becoming full. In fact, understanding customer preferences can create a unique dining experience for customers.

[0018] Considering the previous content, Figure 1 is a schematic diagram of an embodiment of a seat occupancy system 10 that can be used within a dining environment 50, such as a restaurant, food hall, food court, dining room, amusement park, cruise ship, or the like. The dining environment 50 may include an open space, such as a walkable area, where customers may stand in a queue or line up, place an order, select (one or more) tables 54, visit (one or more) stations 56, and / or otherwise navigate through the dining environment 50. The dining environment 50 may include an entrance 52 (e.g., an entrance and / or exit; a doorway) for customers to enter or exit the dining environment 50. Near the entrance 52, customers may create an order at a vendor in the dining environment 50 (e.g., via a point of sale terminal, a register, a waiter). The customers may then navigate through the dining environment 50 to select a table 54 for their dining experience.

[0019] However, the dining environment 50 may be influenced or affected by environmental factors 51, which may be external to the dining environment 50 and / or uncontrollable by the dining environment 50. For example, the environmental factors 51 may include light, sound, temperature, air flow, vibration, weather events (e.g., rain, wind, snow, cloud cover), or the like that may affect certain areas of the dining environment 50 during certain time periods. It should be appreciated that the environmental factors 51 may be created by natural events (e.g., sunlight) and / or machines (e.g., light emitters, speakers, air conditioning systems). In fact, in one example, the environmental factors 51 may be created by vehicles (e.g., trains, airplanes, cars) passing through the dining environment 50 and / or facilities adjacent to the dining environment 50. For example, the dining environment 50 may be located near an airport, and airplanes may generate noise throughout the day. In another example, the dining environment 50 may be located adjacent to a roller coaster, and the ride vehicle may periodically pass through the dining environment 50. The ride vehicle may be visible through the windows of the dining environment 50 to provide entertainment to patrons in the dining environment 50 , but certain environmental factors 51 (e.g., vibrations, noise) from the ride vehicle may be experienced within the dining environment 50 .

[0020] When selecting a dining table 54, the customer may take environmental factors 51 and the location of the dining table 54 into consideration. That is, the customer may determine that some dining tables 54 are more desirable than other dining tables, which is referred to herein as "customer preferences". The dining table 54 may include or be associated with one or more seats, which may be movable chairs (e.g., not fixed to the ground or to the dining table 54) and / or fixed chairs (e.g., bolted or fastened to the ground or to the dining table 54; picnic benches, metal chairs, wooden chairs). In one embodiment, the customer may visit a station 56, which may be a temporary location that the customer typically visits before or after selecting a dining table 54. The station 56 may include a restroom, a hand sanitizer station, a condiment station, a tableware station, a garbage station, a water dispenser, or the like. For example, the customer may visit a condiment station before occupying the dining table 54 to get ketchup, barbecue sauce, salt, pepper, or the like. The customer may also visit a restroom before sitting at the dining table 54.

[0021] In some examples, at least one sensor 58 coupled to a respective table 54 and / or a respective seat may detect customer occupancy. The sensor 58 may be coupled to a top surface of the table 54 (e.g., relative to the customer), a bottom surface of the table 54 (e.g., toward the floor), integrated within the table 54, or a combination thereof. For example, the sensor 58 integrated within the table 54 may receive a weight from personal belongings, items from an order, or a combination thereof, which weight indicates customer occupancy. Additionally or alternatively, the sensor 58 may be coupled to a seat (e.g., on a top surface of a seat, a bottom surface of a seat, a back side of a seat), integrated within a seat, or a combination thereof. For example, when a customer sits in a seat, the sensor 58 may output a sensor signal indicating customer occupancy.

[0022] The sensor 58 may include a pressure sensor, a weight sensor, a light sensor, a microphone, a temperature sensor, a flow meter, a motion sensor, an orientation sensor, a camera, or any combination thereof to generate sensor data about customer occupancy within the dining environment 50. In some instances, the sensor data may be binary (e.g., 0, 1) to reduce complexity, thereby reducing false positives or the amount of time and processing power used to analyze the sensor data. For example, the sensor 58 may include a weight sensor integrated with a seat so that when a customer occupies a seat, the sensor data includes 1, and when the seat is empty, the sensor data includes 0. In other instances, the sensor data may include a numerical value, an image, or the like. For example, the sensor 58 may include a light sensor configured to detect light and / or characteristics of the light (e.g., brightness, color) at a dining table 54 and / or a seat. The sensor 58 may include a microphone configured to detect sound and / or characteristics of the sound (e.g., volume, tone) at a dining table 54 and / or a seat. The sensor 58 may include a temperature sensor configured to measure the temperature at a dining table 54 and / or a seat. The sensor 58 may include a flow meter configured to measure air flow at the table 54 and / or the seat. The sensor 58 may include a motion sensor (e.g., an accelerometer) configured to detect movement of the table 54 and / or the seat (e.g., by a customer; vibration caused by a nearby vehicle). The sensor 58 may include a position sensor (e.g., a global positioning system) that provides a corresponding position of the table 54 and / or the seat in a coordinate system (e.g., a global coordinate system; a relative coordinate system), which may be mapped to and / or coordinated with other structures (e.g., walls, entrance 52) in the dining environment 50. The sensor 58 may include one or more cameras configured to capture image data (e.g., images or videos) of the dining environment 50 over time. One or more cameras configured to capture additional image data may be positioned at other locations around the dining environment 50 (e.g., mounted to a ceiling or wall). Thus, the image data may include image(s) captured initially (e.g., when a patron arrives in the dining environment 50), as well as image(s) captured over time to monitor the movement of patrons within the dining environment 50. To track the movement of patrons, the image data may include one or more attributes of the patrons, such as hair color, clothing color, gait, personal items, accessories, or the like, to track the movement of patrons within the dining environment 50. Further, the camera may operate in the visible spectrum, infrared (IR) spectrum, or ultraviolet (UV) spectrum. In this manner, the sensor data may track the movement of patrons within the dining environment 50 without tracking the patrons' personally identifiable information (PII).

[0023] In some examples, sensors 58 may be positioned or integrated throughout the dining environment 50 to monitor environmental factors 51 and / or other attributes (such as power usage) related to the operation of the dining environment 50. For example, the sensors 58 may track the use of electricity by the air conditioning system, lighting system, or the like. The use of electricity may be analyzed in conjunction with the environmental factors 51 for energy conservation methods. In this way, the seat occupancy system 10 may have or provide a complete understanding of the dining environment 50.

[0024] The sensor 58 may transmit sensor data to the control system 60 for processing (e.g., image analysis, machine learning, artificial intelligence, computer vision). The sensor 58 may be communicatively coupled to the control system 60 via a wired or wireless connection. For example, the sensor 58 may be communicatively coupled to the control system 60 via Bluetooth, Wifi, or other suitable wireless connection. In the illustrated example, the sensor 58 may be communicatively coupled to the control system 60 via one or more wires. To facilitate discussion of the figures, only certain connections may be illustrated.

[0025] The sensors 58, the control system 60, and the show effects 66 may form or be part of the seat occupancy system 10. In operation, the seat occupancy system 10 generates and processes sensor data of the dining environment 50 to monitor customer occupancy and identify one or more customer preferences (e.g., trends or patterns in customer occupancy). For example, the sensor data (e.g., a training set of sensor data; historical sensor data) may be used to train a machine learning model stored within the control system 60, and / or the sensor data (e.g., additional sensor data) may be used to update the machine learning model over time. In practice, the control system 60 may utilize a machine learning algorithm or artificial intelligence to understand and / or make predictions related to one or more customer preferences in the dining environment 50 (e.g., a desirable or less desirable table, a desirable temperature, a location for a show effect). For example, the control system 60 may utilize a machine learning algorithm trained by historical sensor data and / or modeling data representing the dining environment 50 to understand patterns of human behavior for selecting a table 54 and / or for leaving a table 54. In another example, the seat occupancy system 10 may utilize a machine learning algorithm trained with historical sensor data and / or modeling data representing other dining environments 50 (e.g., at least 10, 100, 500, or more dining environments 50) to design and / or optimize a dining environment 50, other dining environments 50, and / or future dining environments 50, including the generation of performance effects 66 in certain areas of the dining environment 50.

[0026] The control system 60 may include a memory 62 and one or more processors 64 (e.g., processing circuit modules). The memory 62 may include volatile memory (such as random access memory (RAM)) and / or non-volatile memory (such as read-only memory (ROM), an optical drive, a hard drive, a solid-state drive, or any other non-transitory computer-readable medium including instructions for operating the seat occupancy system 10). The memory 62 may also include historical sensor data collected over time, predictions of customer preferences, condition data (e.g., weather forecasts), maps (e.g., facility maps of the dining environment 50), patterns of human behavior, machine learning algorithms, and / or other types of information for the control system 60. The processor 64 may be configured to execute the instructions. For example, the processor 64 may include one or more application specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more general-purpose processors, or any combination thereof. The control system 60 may be a distributed computing system including one or more processors and / or one or more cloud computing systems having one or more processors.

[0027] The dining environment 50 may include show effects 66 that create a unique dining experience. For example, the show effects 66 may include performances (e.g., performed by human performers in costumes and / or by animated robotic characters), lighting effects, visual effects, movement effects, smoke effects, audio effects, movable objects (e.g., robotic objects), and the like. In one embodiment, the show effects 66 may include a show or performance contained in an area (e.g., on a stage) of the dining environment 50. The show effects 66 may also include audio played throughout the dining environment 50, such as forest sounds, weather sounds, vehicle sounds, spoken words that provide a narrative, or the like. In one embodiment, at least some of the show effects 66 may be integrated into the dining table 54, the seat, or a combination thereof. For example, the show effects 66 may be integrated into the seat so that the seat vibrates. In another example, the show effects 66 include lights, speakers, tactile devices, fluid (e.g., water) dispensers, or the like integrated into the dining table 54 and / or the seat to provide entertainment to customers.

[0028] The control system 60 may set, adjust, and / or change one or more parameters of the show effect 66, such as to control the appearance of the show, the appearance of the lighting effects, the appearance of the visual effects, the volume of the audio effects, the intensity of the moving effects, or a combination thereof. As an example, the control system 60 may be operable to activate the show effect 66 in parallel (e.g., at the same time) with the environmental factor 51. For example, the dining environment 50 may be located adjacent to a roller coaster, so that when the ride vehicle passes through the dining environment 50, the environmental factor 51 is generated in the area of ​​the dining environment 50. The show effect 66 may be intended to enhance the environmental factor 51 (e.g., vibrations to make the ride vehicle pass by more intense) or to counteract the environmental factor 51 (e.g., white noise to reduce noisy environmental factors 51). The control system 60 may identify the area of ​​the dining environment 50 (e.g., based on historical sensor data and / or modeling data; using a machine learning algorithm) and generate an appropriate show effect 66 in the area (e.g., also based on historical sensor data and / or modeling data; using a machine learning algorithm). For example, the show effects 66 may include activation of haptic devices in seats in the area (causing the seats to vibrate) and / or activation of lights in the dining tables 54 (causing the lights to flash). In this way, customers may experience an interactive dining experience.

[0029] It should be appreciated that appropriate performance effects 66 can be determined using a machine learning algorithm, and in this manner, the performance effects 66 can be designed to encourage occupancy in the area. For example, a machine learning algorithm trained by historical sensor data can generate an output indicating that certain performance effects 66 in the area (e.g., air flow generated by a haptic device) caused patrons to move to another table 54 outside the area, move their seats away from the air flow, leave the dining environment 50, and / or have some other negative impact on the patrons (e.g., the patrons ate less of their meals, such as indicated via a weight sensor and / or images captured by a camera; the patrons made negative sounds and / or said negative words, such as detected via a microphone and analyzed via keywords or natural language processing techniques; the patrons stayed at the table 54 for some amount of time outside of the expected time range, such as shorter or longer than the expected time range). However, the output may indicate that other performance effects 66 in the area (e.g., vibrating and / or flashing lights) caused patrons to remain at their tables and not move their seats or some other positive impact on the patrons (e.g., patrons ate more of their meals, such as indicated via weight sensors and / or images captured by cameras; patrons made positive sounds and / or said positive words, as detected via a microphone and analyzed via keywords or natural language processing techniques; patrons remained at tables 54 for a desired time frame).

[0030] In another example, the show effects 66 may include a performance, and the control system 60 may control lighting effects, sound effects, and / or other interactive effects. Further, the control system 60 may monitor customer occupancy and related parameters during the performance to determine customer preferences, turnover, seat occupancy time, or the like. Figure 2 As further described, the seat occupancy system 10 may implement machine learning and / or computer vision techniques to understand patterns of human behavior and determine one or more customer preferences. In this manner, the seat occupancy system 10 optimizes the configuration of the dining environment 50. For example, the seat occupancy system 10 may generate and analyze sensor data to determine new table arrangements, different areas of the dining environment 50 for a show effect 66, a new layout for the dining environment 50, or the like.

[0031] Additionally or alternatively, the seat occupancy system 10 may be configured to provide real-time (e.g., real-time or near real-time) information (e.g., a graphical or visual representation of sensor data via a display) to customers and / or vendors to facilitate the operation of the dining environment 50. For example, customers may access the seat occupancy system 10 using an application on their mobile device to view real-time waiting times for the dining environment 50, one or more open tables 54, or a combination thereof. The seat occupancy system 10 may store or have access to a map of the dining environment 50. The map of the dining environment 50 may include a schematic diagram, an image (e.g., a still or moving image), or the like. Certain features of the map may be based on sensor data collected over time (e.g., the placement of tables 54 and seats according to sensor data). The map may associate objects within the dining environment 50 with corresponding identifiers, such as letters, numbers, or shapes. For example, the tables 54 may be labeled AF, respectively. Further, the seats of the tables 54 may be assigned letters, numbers, or both. The seat occupancy system 10 may determine (e.g., based on sensor data) and display dirty tables, empty tables, occupied tables, or a combination thereof to vendors to optimize operations within the dining environment 50. The map may also associate occupancy parameters for each table 54 and / or seat, which may include customer occupancy time, customer throughput, turnover rate, average seat time per customer, percentage of time occupied by customers, or the like. For example, the seat occupancy system 10 may identify the percentage of time occupied by customers for table A as 50 percent, and the percentage of time occupied by customers for table B as 90 percent, indicating that table A is occupied 50 percent of the operating time, and table B is occupied 90 percent of the operating time. It should be appreciated that any other data or information described herein, or any other data or information derivable from the information described herein (e.g., other occupancy parameters, including occupancy parameters indicative of food consumption, such as the percentage of patrons who complete their meals for tables 54 and / or seats; and / or the average amount of food consumed per patron at tables 54 and / or seats, such as by weight) may be presented via the display. It should be appreciated that the map displayed to the patrons and / or vendors may include Figure 1-3 Any of the details or formats shown in (including any combination thereof).

[0032] It should be understood that Figure 1 The layout and arrangement of the dining environment in is merely exemplary, and the seat occupancy system 10 can be used in any of a variety of dining environments arranged in any suitable manner. In addition, certain components of the seat occupancy system 10 can be shared between multiple dining environments 50 / communicate with multiple dining environments 50, or can be dedicated to its own dining environment 50.

[0033] Considering the foregoing, Figure 2 8 is an example illustration of a customer 80 moving within a dining environment 50. For example, a customer 80 may use a mobile device 82 (e.g., a mobile phone) to create an order and select a table 54 within the dining environment 50. In another example, the customer 80 may create an order at a point of sale terminal located near the entrance 52. The customer 80 may then enter the dining environment 50 and move within the dining environment 50 to select a table 54 (as illustrated by line 83). Still, in another example, the customer 80 may be directed to the table 54 by a host or waiter of the dining environment 50. As described herein, the seat occupancy system 10 tracks the movement of the customer 80 through one or more sensors 58 within the dining environment 50.

[0034] For example, customer 80 may travel along a passage within dining environment 50 (as illustrated by line 83). Customer 80 may be seated at first dining table 54a, as indicated by point 84. However, environmental factors 51 may influence customer 80 to leave first dining table 54a and move to second dining table 54b. For example, sunlight shining through a window may hit first dining table 54a and make the customer uncomfortable during the dining experience. In another example, the location of first dining table 54a adjacent to entrance 52 may cause noise, which results in an unpleasant dining experience. Therefore, customer 80 may switch to second dining table 54b (as indicated by point 86). In one embodiment, second dining table 54b may be located adjacent to a particular type of structure or away from another particular type of structure, which may be preferred by customer 80. Patterns or trends in sensor data over time (e.g., all customers move away from or avoid first dining table 54a at a particular time of day during sunny weather) may indicate customer preferences, and patterns or trends in sensor data may be used to implement machine learning techniques disclosed herein.

[0035] The seat occupancy system 10 can identify customer preferences by monitoring the movement of customers 80 within the dining environment 50. The seat occupancy system 10 can generate sensor data (via sensors 58 coupled to the tables 54 and / or seats), and analyze the sensor data to determine occupancy parameters at each corresponding table 54 over time. For example, the sensor data can indicate customer occupancy time. The seat occupancy system 10 can determine the ratio or percentage of the time that the table 54 is occupied compared to the total operating time (e.g., business hours) of the dining environment 50. In one embodiment, the seat occupancy system 10 can use the sensor data in conjunction with a map to determine one or more customer preferences. For example, the seat occupancy system 10 can determine low customer occupancy time for certain tables 54 located near high traffic areas (such as the entrance 52 or the station 56). For example, the third table 54c located near the entrance 52 may have a customer occupancy time of 10 percent. The seat occupancy system 10 can generate sensor data indicating that the customer 80 occupies the third table 54c for a period of time, and compare the period of time with the total time of operation to determine the 10 percent customer occupancy time. In another example, a fourth table 54d located near station 56 may have a customer occupancy time of 20 percent. Customers may visit station 56 before or after selecting table 54, making station 56 a high traffic area. Customers may not like having other people walking around table 54 or causing noise at table 54. Therefore, high traffic areas may result in low customer occupancy times.

[0036] In one embodiment, the seat occupancy system 10 may identify high customer occupancy times for certain tables 54 within the dining environment 50. For example, the fifth table 54e located away from the entrance 52 may have a 50 percent customer occupancy time. The seat occupancy system 10 may identify the location of the fifth table 54e away from the entrance 52 as a preference of the customer 80. In another example, the sixth table 54f may have an 80 percent customer occupancy time. The sixth table 54f may be located near a window, and the seat occupancy system 10 may determine window access as a customer preference. Customer occupancy times or other occupancy parameters may be related to the time of day, environmental factors 51, show effects 66, etc. For example, the first table 54a may have a 10 percent occupancy during the morning time on a sunny day (e.g., bright sunshine), a 60 percent occupancy during the morning time on a cloudy day, an 80 percent occupancy during the evening time on a sunny day, etc., a 90 percent occupancy during a performance provided as one of the show effects 66, etc. However, other tables 54 may demonstrate other customer occupancy times or other occupancy parameters under such conditions.

[0037] The seat occupancy system 10 may identify a slower turnover rate for tables 54 near windows or in the center of the dining environment 50. Still, in one embodiment, the seat occupancy system 10 may utilize a map to identify larger or smaller tables (e.g., more or fewer seats, the size of the table), and correlate the sensor data to determine customer occupancy time. Further, the seat occupancy system 10 may be communicatively coupled to a point-of-sale terminal and / or vendor to receive a signal indicating a customer order and associate certain order items with greater or lower customer occupancy times or other occupancy parameters. For example, an order with pasta may be associated with a higher customer occupancy time or better occupancy parameter because the item may take longer to make and / or take longer to eat. In another example, an order with a children's meal may be associated with a lower customer occupancy time because the family may want to eat quickly and move on. By utilizing different types of sensor data, the seat occupancy system 10 can accurately understand the dining environment 50 and predict customer behavior. Further, patterns or trends in the sensor data over time (e.g., certain sized tables and / or certain meals lead to certain customer occupancy times or other occupancy parameters) can be used to provide an output with recommendations. For example, one or more algorithms (such as one or more machine learning algorithms) may generate an output with recommendations for placement of structures (e.g., entrances 52, tables 54, seats, stations 56, windows, a stage or other area for show effects 66, output devices for generating show effects 66), characteristics of structures (e.g., table size, size of windows, brightness of light emitted by light emitters, volume of sound emitted by speakers, intensity of haptic effects provided by haptic devices), operating characteristics of dining environment 50 (e.g., meals served), etc. The recommendations may include a map with a recommended or new layout for dining environment 50. The recommendations may be determined and provided periodically (e.g., weekly, monthly, yearly) and / or in response to certain events (e.g., identification of at least one seat with negative or undesirable occupancy parameters (e.g., not matching target occupancy parameters)).

[0038] Figure 3 is an example illustration of a layout of a dining environment 50 that may be generated by the seat occupancy system 10 based on sensor data. For example, the seat occupancy system 10 may identify certain tables 54 with low customer occupancy times for integration with a show effect 66 to increase the desirability of the tables 54 (and, therefore, to increase customer occupancy time). In another example, the seat occupancy system 10 may determine areas for the show effect 66 to increase desirability for certain tables 54. Still, in another example, the seat occupancy system 10 may assign prices to certain desirable tables to generate additional revenue for vendors.

[0039] In one embodiment, the seat occupancy system 10 can recommend changes in the current layout of the dining environment 50 based on the sensor data to improve the dining experience of customers. For example, tables 54 (e.g., tables 54a, 54b) located near high-traffic areas can be moved away from the high-traffic areas to increase customer occupancy time. In fact, as described in reference Figure 2 Compared to the position of the third dining table 54c described above, the dining tables 54a and 54b may be located further from the entrance 52 (based on the Figure 2 54c in the third table 54c and / or sensor data for other tables, the sensor data indicating a suitable distance that results in a desirable customer occupancy time or other desirable occupancy parameter). In another example, the station 56 may be adjacent to the entrance 52, thereby containing a high traffic area to the area of ​​the dining environment 50 (based on the sensor data, the sensor data indicating that this results in a desirable customer occupancy time or other desirable occupancy parameter). In another example, the seat occupancy system 10 may identify an area of ​​the dining environment 50 for a performance effect 66a. For example, the performance effect 66a may include a performance or show to create an interactive dining experience for customers. The seat occupancy system 10 may identify certain tables 54 (e.g., tables 54c, 54d, 54e) with low customer occupancy times or other undesirable occupancy parameters, and place the performance effect 66a adjacent to the table 54 to increase desirability. In fact, customers with children may desire tables 54c, 54d, 54e that are closer to the show. Further, in one embodiment, the seating occupancy system 10 may assign a price 90 to the tables 54c, 54d, 54e because the close proximity to the show effect 66a may increase desirability. The price 90 may vary with the occupancy parameters (e.g., higher prices for better occupancy parameters over time), and thus, the price 90 may take into account any of the environmental parameters 51 that affect the desirability of certain tables 54.

[0040] In one embodiment, the seat occupancy system 10 may determine that the occupancy parameters can be improved by adding a show effect 66 to a table (e.g., table 54f, 54g, 54h) located near the environmental factor 51. For example, the environmental parameter 51 may include a roller coaster that periodically passes through the dining environment 50. The show effect 66 may include flashing lights, vibrating seats, roller coaster sounds, or the like. The seat occupancy system 10 may generate the show effect 66 at the same time that the roller coaster may pass through the dining environment 50. Therefore, customers can be entertained by the roller coaster and their dining experience can be improved, which can be reflected in the occupancy parameters. Further, the seat occupancy system 10 can be communicatively coupled to one or more ride sensors and / or ride controllers of the roller coaster. By communicatively coupling to the ride sensors and / or ride controllers of the roller coaster, the seat occupancy system 10 can receive a signal indicating the timing and location of the roller coaster. Then, the seat occupancy system 10 can determine or predict which tables 54 and / or seats can benefit from the show effect 66 and also provide the appropriate timing of the show effect 66. For example, as the ride vehicle passes through the dining environment 50 , certain tables and / or seats may experience the performance effects 66 (eg, based on their respective orientations relative to the ride vehicle).

[0041] It may be beneficial to determine when the performance effect 66 may be generated based on the occupancy parameter. For example, the seat occupancy system 10 may receive sensor data indicating whether the table 54 is occupied by a customer. If the table 54 is occupied by a customer, the seat occupancy system 10 may generate a performance effect 66 at or near the table 54 (e.g., lighting near the table 54, the seat of the table 54). For example, the weight of the customer may surround the performance effect 66, such as a vibrating seat. Therefore, the performance effect 66 may be noticeable to the customer, but not to the surrounding customers. If the table 54 is not occupied by a customer, the seat occupancy system 10 may not generate the performance effect 66 because the performance effect 66 may distract other customers, such as creating noise due to the vibrating seat or flashing lights in the peripheral vision area of ​​other customers. Additionally or alternatively, the performance effect 66 may wear the table 54 and / or the seat over time without customer occupancy, so it may be beneficial for the seat occupancy system 10 to monitor customer occupancy within the dining environment 50 and selectively generate the performance effect 66 based on customer occupancy. Further, the dining environment 50 can save energy by selectively generating the show effects 66. The seat occupancy system 10 can also determine, via the machine learning techniques disclosed herein and based on the sensor data, that the show effects 66 should be provided at certain locations and / or at certain times. For example, the seat occupancy system 10 can determine that the show effects 66 or certain show effects 66 (e.g., haptic effects in the seats) should only be provided to customers during the seated time, during their meal, after their meal, etc., to increase the likelihood of desirable occupancy parameters.

[0042] In one embodiment, the seat occupancy system 10 can design the dining environment 50 to optimize power usage within the dining environment 50. For example, the seat occupancy system 10 can use sensor data to forecast the number of customers passing through the dining environment 50 during a period of time (e.g., hours, days, months, years), and optimize different energy systems to provide different amounts of energy (e.g., air conditioning, electricity). For example, summer months can be associated with high customer occupancy and high power usage. However, during certain times of the day (such as, morning), customer occupancy may be low, so energy usage can be reduced. In an example, lunch time can be associated with high customer occupancy, and the seat occupancy system 10 can increase power usage to improve the dining experience for customers.

[0043] In one embodiment, the seat occupancy system 10 may design a layout for (one or more) future dining environments 50 with similar attributes. The attributes may include the type of vendor, the type of order, the maximum customer capacity (e.g., the number of customers), customer preferences, the type of show effect 66, the type of environmental factor 51, or the like. For example, a future dining environment 50 with similar customer occupancy may be designed based on sensor data obtained in one or more existing dining environments 50 to improve the dining experience of customers in the future dining environment 50. However, even if certain attributes are not similar, the seat occupancy system 10 may use machine learning to understand certain customer preferences, such as a preference for sitting next to a window or at a certain minimum distance away from a trash can, to design the future dining environment 50. Further, the seat occupancy system 10 may increase the desirability of certain seats using certain show effects 66 with specific placement and / or timing, which are informed by sensor data obtained in one or more existing dining environments and processed via machine learning. In this way, the seat occupancy system 10 may design and optimize the layout for the future dining environment 50 based on sensor data.

[0044] Figure 4 An example method 100 for designing a dining environment 50 using a seat occupancy system 10 is provided. At block 102, a control system 60 may receive sensor data indicating occupancy of customers within a dining environment 50. For example, the control system 60 may receive one or more sensor signals indicating occupied seats from sensors 58 coupled to tables 54 and / or seats and / or from sensors 58 positioned elsewhere in the dining environment 50. The sensor signals may be motion signals indicating that a seat was pulled by a customer followed by pressure signals indicating that a customer sat in the chair. The sensor signals may also be image data over time, such that the seat occupancy system 10 may track the movement of customers within the dining environment 50 over time. Multiple redundant sensor signals may accurately depict customer occupancy within the dining environment 50. Still, in another example, the control system 60 may retrieve historical sensor data indicating past customer occupancy from the memory 62. The seat occupancy system 10 may be programmed through machine learning, artificial intelligence, or computer vision capabilities to interpret and understand the dining environment 50 based on sensor data (e.g., real-time, historical).

[0045] At block 104, the seat occupancy system 10 may receive a map of the dining environment 50. For example, the map of the dining environment 50 may include a machine learning model representing the dining environment 50 generated from sensor data over time. In another example, the map may include real-time sensor data, thereby representing current customer occupancy. Further, the map may include multiple different types of sensor data, such as pressure data from a pressure sensor, motion data from a motion sensor, weight data from a weight sensor, image data from an image sensor, light data from a light sensor, audio data from a microphone, temperature data from a temperature sensor, air flow data from a flow meter, orientation data from an orientation sensor, or the like. The sensor data may also include or indicate environmental factors 51, such as sunlight in the dining environment 50 or a roller coaster passing through the dining environment. Further, the sensor data may include energy usage of the dining environment 50. In this way, the map may provide a complete representation of the dining environment 50. The seat occupancy system 10 may receive and / or store a map of the dining environment 50 in the memory 62.

[0046] At block 106, the seat occupancy system 10 may analyze the sensor data to determine whether the customer occupancy (e.g., customer occupancy parameters) in the location of the map corresponds to the target customer occupancy (e.g., target customer occupancy parameters). The seat occupancy system 10 may determine the customer occupancy based on the sensor data received at block 102 and the map received at block 104. The target customer occupancy may include a target number of customers sitting at the corresponding table 54 each day or other suitable parameters. The target customer occupancy may also include a target turnover rate, a threshold sitting time, a threshold percentage of the time that the table and / or seat is occupied, or the like. For example, the target customer occupancy may be a threshold percentage of the time that the seat is occupied throughout the day, such as 20 percent, 30 percent, 40 percent, 50 percent, 60 percent, etc. Still, in another example, the target customer occupancy may be the number of times that the table 54 is turned over per hour, such as 5, 6, 7, 8 times, etc. In one embodiment, the seat occupancy system 10 may apply the target customer occupancy to all tables 54 within the dining environment 50. In one embodiment, the seat occupancy system 10 may modify the target customer occupancy based on the location of the table 54 within the dining environment 50. For example, tables 54 located near the entrance 52 may have a lower target customer occupancy than tables 54 located near the show effect 66 .

[0047] If the usage in the location of the map corresponds to the target customer occupancy, then at block 108, the seat occupancy system 10 may output a signal. For example, the seat occupancy system 10 may output a signal to a display of a vendor indicating that the current layout of the dining environment 50 satisfies the target customer occupancy. The signal may also include a graphic or visual representation of the map and indicate customer occupancy for each table 54 and / or seat and / or any other data or information disclosed herein. Thus, the vendor may understand customer preferences and / or dining environment operations within the dining environment 50.

[0048] If the customer occupancy in the location does not correspond to the target customer occupancy, the seat occupancy system 10 may identify one or more alternative configurations of the dining environment 50. At block 110, the seat occupancy system 10 may recommend changes to the dining environment 50. The seat occupancy system 10 may use sensor data to identify more desirable or less desirable tables 54 and / or one or more customer preferences. The seat occupancy system 10 may then generate a different layout for the dining environment 50 by rearranging one or more tables 54, repositioning stations 56, removing or adding show effects 66, pulling blinds at predetermined times, providing instructions for dining operations, or a combination thereof. As discussed herein, the seat occupancy system 10 may use machine learning to make predictions of suitable changes that are expected to provide desirable customer occupancy (e.g., matching or corresponding to the target customer occupancy; based on sensor data), and generate recommendations based on the predictions.

[0049] Further, the seat occupancy system 10 can utilize sensor data to create layouts for other dining environments 50 and / or future dining environments 50. In one example, a vendor can use a mobile device to access the seat occupancy system 10 to input one or more planned attributes of a future dining environment 50. For example, the vendor can input planned environmental factors 51, target customer occupancy, available building area / lot size, or the like. The seat occupancy system 10 can use the attributes, sensor data (from one or more existing dining environments 50), and maps to generate a proposed layout for a future dining environment 50. For example, the seat occupancy system 10 can generate a proposed layout that includes an arrangement of tables 54, seats, entrances 52, stations 56, and show effects 66.

[0050] Thus, the seat occupancy system 10 can classify certain actions and / or combinations of actions taken by customers, which can enable the seat occupancy system 10 to accurately determine customer preferences for designing the layout of a dining environment 50 (including future dining environments 50) having a performance effect 66.

[0051] The method 100 may be executed according to instructions stored on one or more tangible non-transitory machine-readable media, and / or may be executed by a processor 64 or processing circuit module of the control system 60 described herein or on another suitable controller. The blocks of the method 100 may be executed in any suitable order. In addition, certain blocks of the method 100 may be omitted, and / or other blocks may be added to the method 100.

[0052] Figure 5 An example method 130 of operating a seat occupancy system 10 to provide a customer and / or vendor with a map of a dining environment 50. For example, the seat occupancy system 10 may generate a map for a customer to create an order, reserve a seat, view wait times, or the like. The seat occupancy system 10 may also generate a map for a vendor to optimize dining operations and / or provide a deep understanding of dining operations. Customers and / or vendors may access the map via an application on their mobile device or other display system. The mobile device is configured to display a graphical user interface (GUI) having a graphic or visual representation of a map.

[0053] To generate the map, at block 132, the seat occupancy system 10 may receive real-time sensor data of the dining environment 50. The sensor data may include image data of customers within the dining environment 50 (e.g., in a queue, at a table, in a seat), weight data indicating customer occupancy at the table 54, pressure data indicating customer occupancy at the table 54, motion data indicating movement of customers within the dining environment 50, or a combination thereof. For example, the sensor 58 may include a weight sensor that is integrated with a seat and configured to output a sensor signal indicating a weight on the seat. The sensor 58 may include a camera that is configured to output a sensor signal indicating image data representing the dining environment 50. In an embodiment, the seat occupancy system 10 may use the weight sensor signal and the image data to determine customer occupancy. For example, the weight sensor signal may indicate customer occupancy, while the image data may indicate a bag on the seat. In another example, the weight sensor signal may indicate a weight value, and the seat occupancy system 10 may identify that the weight is below a threshold and therefore does not indicate customer occupancy.

[0054] At block 134, the seat occupancy system 10 may determine whether one or more tables 54 may be available for occupancy by a customer. In one embodiment, the seat occupancy system 10 may use a map in conjunction with the sensor data to determine whether one or more tables 54 are available. The seat occupancy system 10 may correlate each sensor signal with a corresponding table 54 and determine whether the table 54 is likely unoccupied based on the sensor signal.

[0055] If one or more tables 54 are not occupied, then at block 136, the seat occupancy system 10 may allow the customer to place an order. For example, the mobile device 82 may display a GUI with a graphical or visual representation of the vendor's menu to allow the customer to place an order. The customer may select one or more items for their order, and the seat occupancy system 10 may receive the input. Further, the seat occupancy system 10 may transmit the customer input to the vendor for order preparation. Thus, waiting time within the dining environment 50 may be reduced, and dining operations may be optimized.

[0056] After the order is placed, at box 138, the seat occupancy system 10 may allow the customer to select a table for their dining experience 50. For example, the mobile device 82 may display a GUI with a map that includes one or more tables 54 marked as open or occupied. The seat occupancy system 10 may cause the GUI to display a graphic or visual representation of the dining environment 50, marking premium tables with special performances, premium tables closest to performances, empty or occupied tables, one or more high-traffic tables, or the like. If a table is open, the seat occupancy system 10 may cause the GUI to allow the customer to enter a request for reserving the table. In one embodiment, the customer may select a table 54 to reserve, and the seat occupancy system 10 may receive the customer input.

[0057] In one embodiment, the seat occupancy system 10 may not identify one or more open tables 54 for occupancy by the customer. Therefore, the seat occupancy system 10 may cause the GUI to display the wait time and a message asking the customer if they would like to join the wait list.

[0058] In order to vacate the table, the seat occupancy system 10 may provide instructions for one or more dining operations to the vendor. It may be beneficial for the vendor to keep track of customer occupancy to optimize dining operations. For example, the seat occupancy system 10 may cause a graphical user interface of a mobile device associated with the vendor to display a map with real-time sensor data. The sensor data may include image data of the dining environment 50 and one or more tables 54 marked for cleaning. At box 140, the seat occupancy system 10 may identify one or more tables for cleaning, thereby vacating the tables 54 within the dining environment 50. In one embodiment, the vendor may assign an individual to clean the identified tables 54 and input (via the GUI) the task.

[0059] At box 142, the seat occupancy system 10 may update the map via real-time (or near real-time) sensor data. For example, the table 54 may be cleaned, thereby opening the table 54 for occupancy by a customer. In one embodiment, the vendor may indicate to the seat occupancy system 10 that the table 54 is clean and ready for occupancy by a customer. In one embodiment, the seat occupancy system 10 may analyze the sensor data (e.g., image data; weight data, such as the weight on the tabletop of the table 54) to identify the cleaned table. The seat occupancy system 10 may update the map based on the sensor data to reflect the cleaned table. In one embodiment, the seat occupancy system 10 may also update the map to reflect energy usage output, current wait time, received orders, or a combination thereof.

[0060] At block 144, the seat occupancy system 10 may output a signal indicating the updated map. For example, the seat occupancy system 10 may update the map displayed on the display of the customer's mobile device 82 and allow the customer to place an order. It should be appreciated that the method 130 may be performed for multiple customers at the same time, at overlapping times, and / or at different times (e.g., when the multiple customers enter the dining environment 50, multiple tables 54 are cleaned).

[0061] The method 130 may be stored on one or more tangible, non-transitory machine-readable media and / or may be executed by the processor 64 or processing circuit module of the control system 60 described above or on another suitable controller. The steps of the method 130 may be performed in the order disclosed above or in any other suitable order. In addition, certain steps of the method may be omitted, and / or other blocks may be added to the method 130.

[0062] As used herein, 'machine learning' and / or 'artificial intelligence' may refer to algorithms and statistical models used by computer systems to perform specific tasks with or without explicit instructions. For example, a machine learning process may generate a mathematical model based on a sample of clean data, referred to as "training data," to make predictions or decisions without being explicitly programmed to perform the task. The seat occupancy system 10 may generate (e.g., train and / or update, such as passively update) the model based on sensor data collected over time. In this way, the model may improve over time based on new sensor data collected over time. For example, the model may receive sensor data to provide output related to customer preferences and location for show performance, and the sensor data may then also be used to update and improve the model.

[0063] It should be appreciated that the seat occupancy system may be suitable for environments other than dining environments, such as gaming environments, ride attractions with waiting queues, or the like. The technical effects of the systems and methods described herein include utilizing sensor data and / or historical sensor data to determine and provide a dining environment layout with a performance effect to create an interactive dining experience for customers. The dining environment layout may be used to optimize the current dining environment or be modified for future dining environments. Further, the systems and methods described herein provide a real-time or near real-time map of the dining environment. Such on-demand and directed information may enable quick and easy access by customers to create orders, select tables, and / or select premium seats. Further, providing on-demand information to vendors may optimize dining operations, such as creating orders or cleaning tables.

[0064] Although only certain features of the present disclosure have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is to be understood, therefore, that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present disclosure. Figure 1-5 Any features shown and described may be combined in any suitable way.

[0065] The technology presented and claimed herein is cited and applied to specific examples and substantial objects that arguably improve the practical nature of the art, and is therefore not abstract, intangible, or purely theoretical. Further, if any claim appended to the end of this specification contains one or more elements designated as "a component for (performing) ... (function)" or "a step for (performing) ... (function)", it is intended that such elements are to be interpreted in accordance with 35 U.S.C. 112(f). However, for any claim containing elements designated in any other manner, it is intended that such elements are not to be interpreted in accordance with 35 U.S.C. 112(f).

Claims

1. A seat occupancy system, comprising: at least one sensor configured to output sensor data indicative of a respective patron occupancy parameter for each of a plurality of seats within the dining environment; at least one processor; as well as a memory storing instructions executable by the at least one processor to cause the at least one processor to: receiving the sensor data; receiving a map indicating a first layout of the dining environment, the first layout including a corresponding location for each of the plurality of seats and a corresponding location for a performance effect; determining that the corresponding patron occupancy parameter for at least one of the plurality of seats does not match a target patron occupancy parameter for a period of time in the first layout of the dining environment; and In response, a second layout of the dining environment is generated, the second layout including a new corresponding position for at least one of the plurality of seats, a new corresponding position for the show effect, or both, wherein the second layout is different from the first layout.

2. The seat occupancy system according to claim 1, wherein: The map indicates corresponding locations for environmental factors, and the environmental factors include noise, vibration, air flow, light, or any combination thereof.

3. The seat occupancy system according to claim 2, wherein: The environmental factor is caused by a ride vehicle proximate to the dining environment, and the instructions are executable by the at least one processor to cause the at least one processor to generate the performance effect when the ride vehicle generates the environmental factor.

4. The seat occupancy system according to claim 3, wherein: The instructions are executable by the at least one processor to cause the at least one processor to: identifying a customer vacancy for at least one of the plurality of seats based on the sensor data; and The performance effect is prevented in response to identifying the patron vacancy.

5. The seat occupancy system of claim 2, wherein: The instructions are executable by the at least one processor to cause the at least one processor to adjust one or more characteristics of the performance effect based on the environmental factors.

6. The seat occupancy system of claim 1, wherein: The performance effect includes vibrating the at least one seat among the plurality of seats.

7. The seat occupancy system of claim 1, wherein: The performance effect includes performance.

8. The seat occupancy system of claim 1, wherein: The instructions are executable by the at least one processor to cause the at least one processor to determine a respective price for each seat of the plurality of seats based on the respective customer occupancy parameter and the second layout.

9. The seat occupancy system of claim 1, wherein: The at least one sensor includes a pressure sensor, a weight sensor, a motion sensor, a camera, or any combination thereof.

10. The seat occupancy system of claim 1, wherein: The instructions are executable by the at least one processor to cause the at least one processor to: identifying one or more unoccupied seats among the plurality of seats; as well as Causing a display of a customer's mobile device to display a graphical user interface (GUI), the GUI comprising: the second layout of the dining environment having the one or more unoccupied seats of the plurality of seats; as well as A virtual button enables the customer to place an order and select a table associated with the one or more unoccupied seats of the plurality of seats.

11. The seat occupancy system of claim 1, wherein: The instructions are executable by the at least one processor to cause the at least one processor to generate the second layout using one or more machine learning algorithms based on the sensor data.

12. A method of operating a seat occupancy system, the method comprising: receiving, using at least one processor, sensor data captured by at least one sensor of a plurality of sensors and indicating respective patron occupancy parameters for each of a plurality of seats within an environment over a period of time; receiving, with the at least one processor, additional sensor data captured by at least one of the plurality of sensors and indicative of a plurality of environmental factors within the environment over the period of time; generating or accessing, using the at least one processor, a first map representing the environment and including respective locations of show effects within the environment during the period of time, respective locations of each of the plurality of environmental factors, and respective locations of each of the plurality of seats; as well as Based on the sensor data, the additional sensor data, and the first map, a second map is generated using the at least one processor and one or more machine learning algorithms, the second map representing a recommended layout for the environment.

13. The method of claim 12, wherein: The recommended layout includes the new corresponding positions of the performance effects.

14. The method of claim 12, comprising: comparing, using the at least one processor, the corresponding customer occupancy parameter for each seat in the plurality of seats to a target customer occupancy parameter; as well as In response to identifying that the corresponding customer occupancy parameter for at least one seat in the plurality of seats does not match the target customer occupancy parameter, generating the second map using the at least one processor and the one or more machine learning algorithms.

15. The method of claim 12, comprising: The performance effect is generated via the at least one processor in parallel with at least one of the environmental factors in the plurality of environmental factors.

16. The method of claim 15, comprising: Prior to generating the performance effect near a specific seat among the plurality of seats, determining, via the at least one processor, that a customer is sitting in the specific seat among the plurality of seats.

17. The method of claim 15, comprising: Based on the sensor data, the additional sensor data, and the first map, a third map is generated using the at least one processor and the one or more machine learning algorithms, the third map representing an additional recommended layout for another environment.

18. A seat occupancy system comprising: at least one processor; as well as a memory storing instructions executable by the at least one processor to cause the at least one processor to: receiving a map indicating an environment including a plurality of seats, environmental factors, and performance effects; determining a respective customer occupancy for each of the plurality of seats over a period of time based at least in part on the sensor data; determining that a corresponding customer occupancy for at least one of the plurality of seats during the period is below a threshold customer occupancy; determining a new corresponding position for the environmental factor, a new corresponding position for the show effect, or a combination thereof that is predicted to improve the corresponding customer occupancy for the at least one seat of the plurality of seats; as well as The map is updated with the new corresponding positions for the environmental factors, the new corresponding positions for the performance effects, or a combination thereof.

19. The seat occupancy system of claim 18, wherein: The instructions are executable by the at least one processor to cause the at least one processor to: receiving, via the at least one processor, an input indicative of one or more parameters of a new environment different from the environment; as well as Based on the map, the sensor data, and the one or more parameters, a second map for the new environment is determined via the at least one processor.

20. The seat occupancy system of claim 18, comprising one or more energy sensors configured to output a signal indicative of energy usage within the environment.

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