Systems and methods for monitoring and predicting guest occupancy

Abstract

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

Description

[Background technology]

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

[0002] In a restaurant or dining establishment, guests may place an order at an ordering station (e.g., kiosk, register) and move through the dining environment to select a table for their dining experience. However, within a dining environment, certain tables may be more desirable than others due to their location. For example, a table by a window may be more desirable than a table closer to the station. In some cases, environmental factors (e.g., roller coaster, sunlight) may change the desirability of a table. For example, sunlight streaming through a window may change a highly desirable table to a less desirable table at certain times of the day. Accordingly, it is now recognized that it may be beneficial to monitor guest movements within a dining environment to understand guest preferences and predict future guest occupancy. Summary of the Invention

[0003]

[0013] The following summarizes certain embodiments commensurate in scope with the originally claimed subject matter. These embodiments are not intended to limit the scope of the claimed subject matter, but rather to provide a brief outline of possible forms of the subject matter. Indeed, the subject matter may include a variety of forms that may be similar to or different from the embodiments set forth below.

[0004] In one embodiment, a seat occupancy system can include at least one sensor configured to output sensor data indicative of a respective guest occupancy parameter for each of a plurality of seats in a dining environment, at least one processor, and a memory storing instructions executable by the at least one processor. The processor can receive the sensor data and receive a map indicative of a first layout of the dining environment including a respective position of each of the plurality of seats and a respective position of a show effect. The processor can also determine that the respective guest occupancy parameter for at least one of the plurality of seats does not match a target guest 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 that differs from the first layout of the dining environment, including a new respective position of the at least one of the plurality of seats, a new respective position of the show effect, or both.

[0005] In some embodiments, the method may include receiving, using at least one processor, sensor data captured by at least one of the plurality of sensors indicative of a respective guest occupancy parameter for each seat of a plurality of seats in the environment over a period of time, and receiving, using the at least one processor, further sensor data captured by the at least one of the plurality of sensors indicative of a plurality of environmental factors in the environment over the period of time. The method may also include generating, using the at least one processor, or accessing a first map representing the environment, the first map including a respective position of each seat of the plurality of seats, a respective position of each environmental factor, and a respective position of a show effect in the environment over the period of time, and generating, using the at least one processor, a second map representing a recommended layout of the environment based on the sensor data, the further sensor data, and the first map.

[0006] In one embodiment, a seat occupancy system can 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 illustrating an environment including a plurality of seats, environmental factors, and show effects. The at least one processor can also determine a respective guest occupancy rate for each seat of the plurality of seats over a period of time based at least in part on the sensor data, and determine that the respective guest occupancy rate for at least one seat of the plurality of seats over the period of time is below a threshold guest occupancy rate. The at least one processor can also determine new respective locations of environmental factors, new respective locations of show effects, or a combination thereof that are predicted to improve the respective guest occupancy rate for the at least one seat of the plurality of seats, and update the map with the new respective locations of environmental factors, new respective locations of show effects, or a combination thereof.

[0007] These and other features, aspects, and advantages of the present disclosure will be better understood from the following detailed description when read in conjunction with the accompanying drawings, in which like reference characters refer to like elements throughout. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a schematic diagram of an embodiment of a seat occupancy system that can be used in a dining environment, according to aspects of the present disclosure. [Figure 2] 2 is a schematic diagram of the seat occupancy system of FIG. 1 monitoring the location of guests within a dining environment, according to an embodiment of the present disclosure. [Figure 3] 2 is a schematic diagram of a dining environment layout that can be generated by the seat occupancy system of FIG. 1 according to an embodiment of the present disclosure. [Figure 4] 2 is a flowchart of an embodiment of a process for designing a dining environment using the seat occupancy system of FIG. 1 according to aspects of the present disclosure. [Figure 5] 2 is a flowchart of an embodiment of a process for operating the seat occupancy system of FIG. 1 according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] When introducing elements of various embodiments of the present disclosure, the articles "a," "an," and "the" are intended to mean the presence of one or more of the element. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements. Furthermore, 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 contain the recited features.

[0010]

[0013] The following description will discuss one or more specific embodiments of the present disclosure. In the interest of brevity in describing these embodiments, not all features of an actual implementation may be described herein. It should be understood that the development of any such implementation, as in any engineering or design project, requires numerous implementation-specific decisions to achieve the developer's particular objectives, including compliance with system- and business-related constraints that may vary from implementation to implementation. Moreover, it should be understood that such a development effort might be complex and time-consuming, but would be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill in the art having the benefit of this disclosure.

[0011] The present disclosure generally relates to systems and methods for seating occupancy systems for dining environments. The seating occupancy system may include at least one sensor that generates sensor data about the dining environment and at least one processor that analyzes the sensor data. Within a dining environment, guests may prefer a particular seat or a particular area, such as a window seat, over a seat next to an exit or entrance. The sensor data can be collected over a period of time and analyzed to determine trends in guest preferences. Additionally, the sensor data can be used to redesign a dining environment, design a new dining environment, or provide a show effect within the dining environment.

[0012] For example, the sensor data can be used to redesign a dining environment so that the placement of tables and chairs improves the dining experience of guests. In another example, the 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 dining experience of guests. In yet another example, the sensor data can be used to place show effects within the dining environment to provide a unique dining experience for guests.

[0013] This embodiment can be superior to traditional dining environments because it is tailored to guest preferences. The seating occupancy system can include sensors uniquely positioned within the dining environment and process the sensor data to determine patterns or trends in the sensor data over time. For example, the seating occupancy system can use the sensor data to train a machine learning model to provide redesign recommendations or alternative layouts for the dining environment based on guest preferences. Furthermore, the seating occupancy system can filter certain sensor data (e.g., duplicate sensor data) to increase processing efficiency and / or periodically delete sensor data to prevent the database from filling up. Indeed, understanding guest preferences can create unique dining experiences for guests.

[0014] With this in mind, FIG. 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, diner, amusement park, or cruise ship. The dining environment 50 can include spaces, such as walkable areas, where guests can wait in line or queue, place an order, select a table 54, visit a station 56, and / or otherwise move within the dining environment 50. The dining environment 50 can include entrances 52 (e.g., entrances and / or exits, doorways) for guests to enter and exit the dining environment 50. Near the entrances 52, guests can create orders with vendors in the dining environment 50 (e.g., via a point-of-sale terminal, cash register, server). The guests can then move through the dining environment 50 to select a table 54 for their dining experience.

[0015] However, the dining environment 50 may be sensitive to or affected by environmental factors 51 external to and / or beyond the control of the dining environment 50. For example, the environmental factors 51 may include light, sound, temperature, air currents, vibrations, weather phenomena (e.g., rain, wind, snow, cloud cover), or the like that may affect particular areas of the dining environment 50 during particular periods of time. It should be understood that the environmental factors 51 may be caused by natural phenomena (e.g., sunlight) and / or machines (e.g., lights, speakers, air conditioning systems). Indeed, in one example, the environmental factors 51 may be caused by vehicles (e.g., trains, planes, cars) traveling by 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 aircraft may be making noise throughout the day. In another example, the dining environment 50 may be adjacent to a roller coaster, and the ride vehicles may periodically pass by the dining environment 50. The ride vehicle may be visible through the windows of the dining environment 50, providing enjoyment to guests within the dining environment 50, but the dining environment 50 may also be subject to certain environmental factors 51 (e.g., vibrations, noise) from the ride vehicle.

[0016] A guest may consider environmental factors 51 and the location of the table 54 when selecting a table 54. That is, a guest may determine that a particular table 54 is more desirable than other tables 54, which is referred to herein as a "guest preference." A table 54 may include or be accompanied by one or more seats, which may be movable (e.g., not fixed to the ground or the table 54) and / or fixed (e.g., bolted or fixed to the ground or the table 54, picnic benches, metal chairs, wooden chairs). In some embodiments, a guest may visit a station 56, which may be a temporary location typically visited before or after selecting a table 54. The station 56 may include a restroom, a hand sanitizing station, a condiment station, a utensil station, a trash station, a drink fountain, or the like. For example, a guest may visit a condiment station to obtain ketchup, barbecue sauce, salt, or pepper, etc., before occupying a table 54. A guest may also visit a restroom before sitting at a table 54.

[0017] In some cases, at least one sensor 58 coupled to each table 54 and / or each seat can detect guest occupancy. The sensor 58 can be coupled to the top surface of the table 54 (e.g., facing the guest), coupled to the underside of the table 54 (e.g., facing the floor), integrated within the table 54, or a combination thereof. For example, a sensor 58 integrated within a table 54 can receive weight from personal belongings, items from an order, or a combination thereof, indicating guest occupancy. Additionally or alternatively, the sensor 58 can be coupled to the seat, such as the top surface of the seat, the underside of the seat, the back of the seat, integrated within the seat, or a combination thereof. For example, the sensor 58 can output a sensor signal indicating guest occupancy when a guest sits in the seat.

[0018] The sensors 58 may include pressure sensors, weight sensors, light sensors, microphones, temperature sensors, flow meters, motion sensors, position sensors, cameras, or any combination thereof to generate sensor data regarding guest occupancy within the dining environment 50. In some cases, the sensor data may be binary (e.g., 0, 1) to reduce complexity, thereby reducing false positives or the time and processing power utilized in analyzing the sensor data. For example, the sensors 58 may include weight sensors integrated with seats such that the sensor data includes a 1 when a guest occupies the seat and a 0 when the seat is vacant. In other cases, the sensor data may include a numerical value, an image, or the like. For example, the sensors 58 may include light sensors configured to detect light and / or light characteristics (e.g., brightness, color) at the tables 54 and / or seats. The sensors 58 may also include microphones configured to detect sound and / or sound characteristics (e.g., volume, pitch) at the tables 54 and / or seats. The sensors 58 may also include temperature sensors configured to measure the temperature at the tables 54 and / or seats. The sensors 58 may also include flow meters configured to measure airflow at the tables 54 and / or seats. The sensors 58 may also include motion sensors (e.g., accelerometers) configured to detect movement of the tables 54 and / or seats (e.g., movement by guests, vibrations induced by nearby vehicles). The sensors 58 may also include position sensors (e.g., a global position sensor system) that provide the position of each of the tables 54 and / or seats in a coordinate system (e.g., a global coordinate system, a relative coordinate system) that can be mapped and / or coordinated with other structures (e.g., walls, entrances 52) within the dining environment 50. The sensors 58 may also include one or more cameras configured to capture image data (e.g., pictures or images) of the dining environment 50 over a period of time. One or more cameras configured to capture additional image data may also be positioned elsewhere around the dining environment 50 (e.g., mounted on the ceiling or walls).Thus, the image data may include an image(s) taken initially (e.g., when the guest arrives at dining environment 50) and an image(s) taken over a period of time to monitor the guest's movements within dining environment 50. The image data may include one or more attributes of the guest, such as hair color, clothing color, gait, personal items or accessories, to track the guest's movements within dining environment 50. Additionally, the camera may operate in the visible light spectrum, the infrared (IR) spectrum, or the ultraviolet (UV) spectrum. In this manner, the sensor data may track the guest's movements within dining environment 50 without tracking the guest's personally identifiable information (PII).

[0019] In some cases, sensors 58 may be positioned or integrated throughout dining environment 50 to monitor environmental factors 51 and / or other characteristics, such as electricity usage, associated with the operation of dining environment 50. For example, sensors 58 may track electricity usage of air conditioning systems, lighting systems, etc. Electricity usage may be analyzed in combination with environmental factors 51 for energy conservation measures. In this manner, seat occupancy system 10 may have, or be able to have, a complete understanding of dining environment 50.

[0020] The sensors 58 may transmit sensor data to the control system 60 for processing (e.g., image analysis, machine learning, artificial intelligence, computer vision). The sensors 58 may be communicatively coupled to the control system 60 by a wired or wireless connection. For example, the sensors 58 may be communicatively coupled to the control system 60 via Bluetooth, Wifi, or other suitable wireless connection. In the illustrated example, the sensors 58 may be communicatively coupled to the control system 60 by one or more wires. For ease of illustration, only certain connections are shown.

[0021] The sensors 58, the control system 60, and the show effects 66 may form or be part of the seating occupancy system 10. During operation, the seating occupancy system 10 generates and processes sensor data of the dining environment 50 to monitor guest occupancy and identify one or more guest preferences (e.g., trends or patterns in guest occupancy). For example, the sensor data (e.g., training sensor data sets, 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 machine learning algorithms or artificial intelligence to understand and / or make predictions related to one or more guest preferences in the dining environment 50 (e.g., desirable or undesirable tables, desirable temperature, location for show effects). For example, the control system 60 may utilize machine learning algorithms trained using historical sensor data and / or modeling data representative of the dining environment 50 to understand patterns of human behavior in selecting and / or moving away from tables 54. In another example, the seat occupancy system 10 may utilize machine learning algorithms trained using 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 the dining environment 50, other dining environments 50, and / or future dining environments 50, including generating show effects 66 in specific areas of the dining environment 50.

[0022] The control system 60 may include a memory 62 and one or more processors 64 (e.g., processing circuitry). 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 disk drive, a solid-state drive, or any other non-transitory computer-readable medium containing instructions for operating the seat occupancy system 10. The memory 62 may also include historical sensor data collected over time, predictions of guest preferences, status 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 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. Control system 60 can be a distributed computer system, including one or more processors and / or one or more cloud computer systems having one or more processors.

[0023] The dining environment 50 may include show effects 66 to create a unique dining experience. For example, the show effects 66 may include performances (e.g., by costumed human performers and / or animated robotic characters), lighting effects, visual effects, movement effects, smoke effects, sound effects, movable objects (e.g., robotic objects), and the like. In some embodiments, the show effects 66 may include a show or performance contained within an area of ​​the dining environment 50 (e.g., on a stage). The show effects 66 may also include audio played throughout the dining environment 50, such as forest sounds, weather sounds, vehicle sounds, or a narrative narrator. In some embodiments, at least a portion of the show effects 66 may be integrated into the tables 54, the seats, or a combination thereof. For example, the show effects 66 may be integrated into the seats to vibrate. In another example, the show effects 66 may include lights, speakers, tactile devices, or fluid (e.g., water) dispensers integrated into the tables 54 and / or the seats to entertain guests.

[0024] The control system 60 can set, adjust, and / or modify one or more parameters of the show effects 66 to control the appearance of a performance, the appearance of a lighting effect, the appearance of a visual effect, the volume of a sound effect, the intensity of a motion effect, or a combination thereof. As an example, the control system 60 can operate to activate the show effects 66 in parallel (e.g., simultaneously) with the environmental factors 51. For example, the dining environment 50 can be located adjacent to a roller coaster so that the environmental factors 51 are generated in an area of ​​the dining environment 50 as the ride vehicle passes through the dining environment 50. The show effects 66 can be designed to enhance (e.g., vibrations that enhance the passage of the ride vehicle) or counteract (e.g., white noise that quells a noisy environmental factor 51) the environmental factors 51. The control system 60 can identify areas of the dining environment 50 (e.g., using machine learning algorithms based on historical sensor data and / or modeling data) and generate appropriate show effects 66 within the areas (e.g., using machine learning algorithms, also based on historical sensor data and / or modeling data). For example, show effects 66 may include activating haptic devices on seats located within the area to cause the seats to vibrate and / or activating lights within tables 54 to flash, thereby providing guests with an interactive dining experience.

[0025] It should be appreciated that appropriate show effects 66 can be determined using a machine learning algorithm, and in this manner, the show effects 66 can be designed to encourage occupancy within the area. For example, a machine learning algorithm trained using historical sensor data can generate an output indicating that a particular show effect 66 within the area (e.g., an air current generated by a haptic device) caused a guest to move to another table 54 outside the area, moved the guest's seat away from the air current, caused the guest to exit the dining environment 50, and / or had some other adverse effect on the guest (e.g., a guest did not eat much food, as indicated via a weight sensor and / or image captured by a camera, a guest made negative sounds and / or spoke negative words, as detected via a microphone and analyzed as such via keyword or natural language processing techniques, a guest remained at a table 54 for a time outside of a desired time range, such as for a shorter or longer time than the desired time range). On the other hand, the output may also indicate that the guest remained at the table without moving seats as a result of other show effects 66 in the area (e.g., vibrations and / or flashing lights) or some other positive impact on the guest (e.g., the guest eating a lot of food indicated via a weight sensor and / or images captured by a camera, the guest making positive sounds and / or speaking positive words detected via a microphone and analyzed as such via keyword or natural language processing techniques, the guest remaining at the table 54 for a desired time range).

[0026] In another example, the show effect 66 can include a performance, and the control system 60 can control lighting effects, sound effects, and / or other interactive effects. Additionally, the control system 60 can monitor guest occupancy and related parameters during the performance to determine guest preferences, turnover, seat occupancy time, etc. As further described with reference to FIG. 2 , the seat occupancy system 10 can implement machine learning and / or computer vision techniques to understand patterns of human behavior and determine one or more guest preferences. In this manner, the seat occupancy system 10 can optimize the configuration of the dining environment 50. For example, the seat occupancy system 10 can generate and analyze sensor data to determine new table placements, different areas of the dining environment 50 for the show effect 66, new layouts of the dining environment 50, etc.

[0027] Additionally or alternatively, the seating occupancy system 10 may be configured to provide guests and / or vendors with real-time (e.g., real-time or near-real-time) information (e.g., graphical or visual representations of sensor data via a display) to facilitate operation of the dining environment 50. For example, a guest may access the seating occupancy system 10 using an application on a mobile device to view real-time wait times for the dining environment 50, one or more available tables 54, or a combination thereof. The seating occupancy system 10 may store or access a map of the dining environment 50. The map of the dining environment 50 may include a schematic diagram or an image (e.g., a still or video image), etc. Some features of the map may be based on sensor data collected over a period of time (e.g., the arrangement of tables 54 and seats according to the sensor data). The map may associate objects in the dining environment 50 with respective identifiers, such as letters, numbers, or shapes. For example, the tables 54 may be labeled A through F. Additionally, the seats at the tables 54 may be assigned letters, numbers, or both. The seat occupancy system 10 may determine and display to vendors (e.g., based on sensor data) dirty tables, empty tables, occupied tables, or a combination thereof to optimize operations within the dining environment 50. The map may also associate occupancy parameters with each table 54 and / or seat, which may include guest occupancy time, guest throughput, turnover rate, average seated time per guest, or percentage of guest occupancy time, etc. For example, the seat occupancy system 10 may indicate that table A is occupied for 50 percent of the operating hours and table B is occupied for 90 percent of the operating hours by identifying table A's percentage of guest occupancy time as 50 percent and table B's percentage of guest occupancy time as 90 percent.It should be understood that any other data or information described herein or derivable from the information described herein (e.g., other occupancy parameters, including occupancy parameters indicative of food consumption, such as the percentage of guests at a table 54 and / or seat who have completed their meal and / or the average amount of food, such as by weight, consumed per guest at a table 54 and / or seat) can be presented via the display. It should also be understood that the map displayed to guests and / or vendors can include any of the details or formats shown in Figures 1-3 (including combinations thereof).

[0028] 1 is illustrative only, and it should be understood that the seating occupancy system 10 may be used with any of a variety of suitably arranged dining environments 50. Furthermore, certain components of the seating occupancy system 10 may be shared among / communicate with multiple dining environments 50 or may be specialized for a unique dining environment 50.

[0029] With this in mind, FIG. 2 is an example diagram of a guest 80 moving through dining environment 50. For example, guest 80 may use a mobile device 82 (e.g., a cell phone) to create an order and select a table 54 within dining environment 50. In another example, guest 80 may create an order at a point-of-sale terminal located near entrance 52. Guest 80 may then enter dining environment 50 and move through dining environment 50 (as indicated by line 83) to select a table 54. In yet another example, a host or server of dining environment 50 may escort guest 80 to table 54. As described herein, seat occupancy system 10 tracks the movements of guest 80 through one or more sensors 58 within dining environment 50.

[0030] For example, guest 80 may move through dining environment 50 along a path (as shown by line 83). Guest 80 may be seated at first table 54a, as represented by point 84. However, environmental factors 51 may affect guest 80, causing guest 80 to leave first table 54a and move to second table 54b. For example, sunlight coming through a window may illuminate first table 54a, making the guest uncomfortable while dining. In another example, the location of first table 54a adjacent to entrance 52 may create noise that creates an unpleasant dining experience. Therefore, guest 80 may change to second table 54b (as represented by point 86). In some embodiments, second table 54b may be located next to a particular type of structure or away from another particular type of structure, which guest 80 may prefer. Patterns or trends in the sensor data over a period of time (e.g., all guests moving away from or avoiding the first table 54a during a particular time period during sunny weather) can indicate guest preferences, and these sensor data patterns or trends can be used to implement the machine learning methods disclosed herein.

[0031] The seating occupancy system 10 can identify guest preferences by monitoring the movements of guests 80 within the dining environment 50. The seating 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 table 54 over time. For example, the sensor data can indicate guest occupancy time. The seating occupancy system 10 can determine the proportion or percentage of time that a table 54 is occupied compared to the total operating time (e.g., business hours) of the dining environment 50. In some embodiments, the seating occupancy system 10 can use the sensor data in combination with a map to determine one or more guest preferences. For example, the seating occupancy system 10 can determine that a particular table 54 located near a high-traffic area, such as the entrance 52 or station 56, has a low guest occupancy time. For example, the third table 54c located near the entrance 52 can have a guest occupancy time of 10 percent. The seating occupancy system 10 may generate sensor data indicating that a guest 80 has occupied the third table 54c for a period of time and compare this period to the total operating time to determine a 10 percent guest occupancy time. In another example, a fourth table 54d located near the station 56 may have a 20 percent guest occupancy time. Guests may visit the station 56 before or after selecting the table 54, making the station 56 a high-traffic area. Guests may not like others walking around the table 54 or making noise near the table 54. Therefore, high-traffic areas may have a lower guest occupancy time.

[0032] In one embodiment, the seating occupancy system 10 can identify that a particular table 54 in the dining environment 50 has a high guest occupancy time. For example, the fifth table 54e, located away from the entrance 52, can have a 50 percent guest occupancy time. The seating occupancy system 10 can identify the location of the fifth table 54e away from the entrance 52 as a preference for the guest 80. In another example, the sixth table 54f can have a 80 percent guest occupancy time. The sixth table 54f can be located near a window, and the seating occupancy system 10 can determine access to the window as a guest preference. The guest occupancy time or other occupancy parameters can be correlated to time of day, environmental factors 51, show effects 66, etc. For example, a first table 54a may have a 10 percent occupancy rate during morning hours on a sunny day (e.g., bright sunshine), a 60 percent occupancy rate during morning hours on a cloudy day, an 80 percent occupancy rate during evening hours on a sunny day, a 90 percent occupancy rate during a performance provided as one of the show effects 66, etc., while other tables 54 may indicate other guest occupancy times or other occupancy parameters under such conditions.

[0033] The seating occupancy system 10 may identify that tables 54 near windows or in the center of the dining environment 50 have slower turnover. Additionally, in some embodiments, the seating occupancy system 10 may utilize a map to identify large or small tables (e.g., more or fewer seats, table size) and correlate sensor data to determine guest occupancy times. Additionally, the seating occupancy system 10 may communicatively couple to point-of-sale terminals and / or vendors to receive signals indicative of guest orders and correlate particular order items with high or low guest occupancy times or other occupancy parameters. For example, an order containing pasta may take longer to prepare and / or eat and therefore correlate with high guest occupancy times or a good occupancy parameter. In another example, an order containing a children's meal may correlate with low guest occupancy times because families may want to eat quickly and move on. By utilizing different types of sensor data, the seating occupancy system 10 may accurately understand the dining environment 50 and predict guest behavior. Additionally, patterns or trends in the sensor data over time (e.g., that certain size tables and / or certain meals result in certain guest occupancy times or other occupancy parameters) may be utilized to provide output, including recommendations. For example, one or more algorithms, such as one or more machine learning algorithms, may generate output including recommendations regarding the 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, window size, brightness of light emitted by illuminators, volume of sound emitted by speakers, intensity of haptic effects provided by haptic devices), and operational characteristics of dining environment 50 (e.g., meals to be served). The recommendations may include a map with recommended or new layouts for dining environment 50. Recommendations may be determined and provided periodically (e.g., weekly, monthly, yearly) and / or in response to a particular event, such as identifying at least one seat having negative or undesirable occupancy parameters (e.g., not matching target occupancy parameters).

[0034] 3 is an example diagram of a layout of the dining environment 50 that the seat occupancy system 10 can generate based on sensor data. For example, the seat occupancy system 10 can identify particular tables 54 with low guest occupancy times in order to integrate a show effect 66 to increase the desirability of the table 54 (and thus increase guest occupancy time). In another example, the seat occupancy system 10 can determine areas of show effect 66 to increase the desirability of the particular table 54. In yet another example, the seat occupancy system 10 can assign prices to particular desirable tables to generate additional revenue for vendors.

[0035] In some embodiments, the seat occupancy system 10 can recommend changes to the current layout of the dining environment 50 based on the sensor data to improve the guest dining experience. 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 guest occupancy time. In practice, tables 54a, 54b can be positioned further away from the entrance 52 than the location of the third table 54c described with reference to FIG. 2 (based on the sensor data of the third table 54c in FIG. 2 and / or based on sensor data of other tables indicating a suitable distance resulting in a desired guest occupancy time or other desired occupancy parameter). In another example, the high-traffic area can be included in an area of ​​the dining environment 50 by positioning the station 56 adjacent to the entrance 52 (based on sensor data indicating that this results in a desired guest occupancy time or other desired occupancy parameter). In another example, the seat occupancy system 10 can identify an area of ​​the dining environment 50 for a show effect 66a. For example, the show effect 66a can include a show or performance that creates an interactive dining experience for guests. The seat occupancy system 10 can identify particular tables 54 (e.g., tables 54c, 54d, 54e) that have low guest occupancy times or other undesirable occupancy parameters and place show effects 66a adjacent to the tables 54 to increase their desirability. Indeed, guests with children may desire tables 54c, 54d, 54e that are closer to the performance. Furthermore, in some embodiments, the seat occupancy system 10 can assign prices 90 to tables 54c, 54d, 54e because their desirability is enhanced by their proximity to show effects 66a. The prices 90 can vary depending on the occupancy parameters (e.g., a higher price for better occupancy parameters over a period of time), and thus the prices 90 can take into account any of the environmental factors 51 that affect the desirability of a particular table 54.

[0036] In some embodiments, the seat occupancy system 10 may determine that adding show effects 66 to tables 54 (e.g., tables 54f, 54g, 54h) located near the environmental factors 51 can improve occupancy parameters. For example, the environmental factors 51 may include a roller coaster that periodically passes by the dining environment 50. The show effects 66 may include flashing lights, vibrating seats, or roller coaster sounds. The seat occupancy system 10 may generate the show effects 66 simultaneously as the roller coaster passes by the dining environment 50. Thus, the roller coaster entertains guests, improving their dining experience, which may be reflected in the seat occupancy parameters. Furthermore, the seat occupancy system 10 may be communicatively coupled to one or more ride sensors and / or a ride controller of the roller coaster. By communicatively coupling to the ride sensors and / or a ride controller of the roller coaster, the seat occupancy system 10 may receive signals indicative of the timing and position of the roller coaster. As a result, the seat occupancy system 10 can determine or predict which tables 54 and / or seats will benefit from the show effect 66, as well as provide appropriate timing for the show effect 66. For example, as the ride vehicle passes through the dining environment 50, particular tables and / or seats (e.g., based on their respective locations relative to the ride vehicle) can experience the show effect 66.

[0037] It can be beneficial to determine when a show effect 66 can be generated based on occupancy parameters. For example, the seat occupancy system 10 can receive sensor data indicating whether the table 54 is occupied by a guest. If the table 54 is occupied by a guest, the seat occupancy system 10 can generate a show effect 66 at or near the table 54 (e.g., a light near the table 54, a seat at the table 54). For example, a show effect 66, such as a vibrating seat, can be dampened by the guest's weight. Thus, the show effect 66 can be noticeable to the guest but not to surrounding guests. If the table 54 is not occupied by a guest, the seat occupancy system 10 can not generate the show effect 66 because it could be distracting to other guests due to noise generated by the vibrating seat or flashing lights in the surrounding area that are visible to other guests. Additionally or alternatively, tables 54 and / or seats not occupied by guests may be subject to wear from the show effects 66 over time; therefore, it may be beneficial for the seating occupancy system 10 to monitor guest occupancy within the dining environment 50 and selectively generate show effects 66 based on guest occupancy. Additionally, the dining environment 50 may conserve energy by selectively generating show effects 66. The seating occupancy system 10 may also determine, via the machine learning methods disclosed herein, based on sensor data, that show effects 66 should be provided at particular locations and / or at particular times. For example, the seating occupancy system 10 may determine that show effects 66 or particular show effects 66 (e.g., haptic effects at seats) should be provided to guests only during the time the guest is seated, during and after the meal, etc., to increase the likelihood of desirable occupancy parameters.

[0038] In some embodiments, the seating occupancy system 10 can design the dining environment 50 to optimize power usage within the dining environment 50. For example, the seating occupancy system 10 can utilize sensor data to predict the number of guests that will pass through the dining environment 50 during a certain period (e.g., hour, day, month, year) and optimize different energy systems to provide different amounts of energy (e.g., air conditioning, electricity). For example, during the summer months, guest occupancy and electricity usage may be high. On the other hand, certain times of the day, such as the morning, may have lower guest occupancy, and therefore energy usage may be reduced. For example, during lunchtime, guest occupancy may be higher, and therefore the seating occupancy system 10 may increase electricity usage to improve the guest dining experience.

[0039] In some embodiments, the seat occupancy system 10 can design layouts for future dining environments 50 with similar attributes. Attributes can include vendor type, order type, maximum guest capacity (e.g., number of guests), guest preferences, type of show effect 66, or type of environmental factor 51. For example, to improve the guest dining experience at the future dining environment 50, a future dining environment 50 with a similar guest occupancy rate can be designed based on sensor data acquired at one or more existing dining environments 50. However, the seat occupancy system 10 can use machine learning to understand specific guest preferences, such as a preference for sitting by a window or being a certain minimum distance away from a trash can, and design the future dining environment 50 even if some attributes are not similar. Additionally, the seat occupancy system 10 can increase the desirability of certain seats by utilizing specific show effects 66 with specific placements and / or timings informed by sensor data acquired at one or more existing dining environments and processed via machine learning. In this manner, the seat occupancy system 10 can design and optimize the layout of future dining environments 50 based on sensor data.

[0040] FIG. 4 illustrates an example method 100 for designing a dining environment 50 using the seating occupancy system 10. In block 102, the control system 60 can receive sensor data indicative of guest occupancy within the dining environment 50. For example, the control system 60 can receive one or more sensor signals indicative of an occupied seat from sensors 58 coupled to the table 54 and / or seat and / or sensors 58 located at other locations within the dining environment 50. The sensor signals can be a pressure signal indicating that a guest has sat down in a chair followed by a movement signal indicating that the guest has pulled the seat. The sensor signals can also be image data over time such that the seating occupancy system 10 can track guest movements within the dining environment 50 over time. Multiple redundant sensor signals can accurately represent guest occupancy within the dining environment 50. In yet another example, the control system 60 can retrieve historical sensor data from the memory 62 indicative of past guest occupancy. The seating occupancy system 10 can be programmed to interpret and understand the dining environment 50 based on the sensor data (e.g., real-time, historical) using machine learning, artificial intelligence, or computer vision capabilities.

[0041] In block 104, the seating 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 to represent current guest occupancy. Additionally, 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, airflow data from a flow meter, or position data from a position sensor. The sensor data may also include or indicate environmental factors 51, such as sunlight within the dining environment 50 or a roller coaster passing by the dining environment. Additionally, the sensor data may include energy usage of the dining environment 50. In this manner, the map may provide a complete representation of the dining environment 50. The seating occupancy system 10 may receive and / or store in memory 62 the map of the dining environment 50.

[0042] In block 106, the seat occupancy system 10 may analyze the sensor data to determine whether a guest occupancy rate (e.g., a guest occupancy parameter) at a location of the map corresponds to a target guest occupancy rate (e.g., a target guest occupancy parameter). The seat occupancy system 10 may determine the guest occupancy rate based on the sensor data received in block 102 and the map received in block 104. The target guest occupancy rate may include a target number of guests to seat at each table 54 per day or other suitable parameters. The target guest occupancy rate may also include a target turnover rate, a threshold seating time, or a threshold percentage of time that a table and / or seat is occupied, etc. For example, the target guest occupancy rate may be a threshold percentage of time that a seat is occupied throughout the day, such as 20%, 30%, 40%, 50%, and 60%. In yet another example, the target guest occupancy rate may be a number of guest turnovers at a table 54 per hour, such as 5, 6, 7, and 8. In some embodiments, the seating occupancy system 10 may apply a target guest occupancy rate to all tables 54 within the dining environment 50. In some embodiments, the seating occupancy system 10 may vary the target guest occupancy rate based on the location of the table 54 within the dining environment 50. For example, a table 54 located near the entrance 52 may have a lower target guest occupancy rate than a table 54 located near the show effect 66.

[0043] If the utilization rate at a location on the map corresponds to the target guest occupancy rate, then in block 108, the seating occupancy system 10 may output a signal. For example, the seating occupancy system 10 may output a signal to a vendor's display indicating a message that the current layout of the dining environment 50 meets the target guest occupancy rate. The signal may also include a graphical or visual representation of the map, indicating the guest occupancy rate of each table 54 and / or seat, and / or any other data or information disclosed herein. Thus, the vendor may understand the operation of the dining environment and / or guest preferences within the dining environment 50.

[0044] If the guest occupancy at one location does not correspond to the target guest occupancy, the seating occupancy system 10 can identify one or more alternative configurations of the dining environment 50. In block 110, the seating occupancy system 10 can recommend changes to the dining environment 50. The seating occupancy system 10 can use the sensor data to identify more or less desirable tables 54 and one or more guest preferences. The seating occupancy system 10 can then generate different layouts of the dining environment 50 by rearranging one or more tables 54, rearranging stations 56, removing or adding show effects 66, drawing blinds at predetermined times, providing instructions for dining operations, or any combination thereof. As described herein, the seating occupancy system 10 can use machine learning to make predictions of suitable changes that are expected to result in a desirable guest occupancy (e.g., based on the sensor data to match or correspond to the target guest occupancy) and generate recommendations based on the predictions.

[0045] Additionally, the seating occupancy system 10 may utilize the sensor data to generate layouts for other dining environments 50 and / or future dining environments 50. In one example, a vendor may access the seating occupancy system 10 using a mobile device to input one or more planned attributes of the future dining environment 50. For example, the vendor may input the planned environmental factors 51, the target guest occupancy rate, or the available building square footage / unit size. The seating occupancy system 10 may use the attributes, the sensor data (from one or more existing dining environments 50), and the map to generate a proposed layout for the future dining environment 50. For example, the seating occupancy system 10 may generate a proposed layout that includes the placement of tables 54, seats, entrances 52, stations 56, and show effects 66.

[0046] Thus, by classifying the specific actions and / or combinations of actions taken by the guests, the seat occupancy system 10 is able to accurately determine guest preferences for designing the layout of the dining environment 50, including future dining environments 50 having show effects 66.

[0047] Method 100 may be performed according to instructions stored on one or more tangible, non-transitory, machine-readable media and / or may be performed by processor 64 or processing circuitry of control system 60 described herein, or on another suitable controller. The blocks of method 100 may be performed in any suitable order. Furthermore, some blocks of method 100 may be omitted and / or other blocks may be added to method 100.

[0048] 5 is an example method 130 of operating the seating occupancy system 10 to provide guests and / or vendors with a map of the dining environment 50. For example, the seating occupancy system 10 may generate a map that enables guests to place orders, reserve seats, check wait times, etc. The seating occupancy system 10 may also generate a map that enables vendors to optimize and / or understand dining operations. Guests and / or vendors may access the map via an application on a mobile device or other display system. The mobile device may be configured to display a graphical user interface (GUI) that includes a graphical or visual representation of the map.

[0049] At block 132, the seat occupancy system 10 can receive real-time sensor data of the dining environment 50 to generate the map. The sensor data can include image data of guests within the dining environment 50 (e.g., queue, table, seat), weight data indicative of guest occupancy at the table 54, pressure data indicative of guest occupancy at the table 54, motion data indicative of guest movement within the dining environment 50, or combinations thereof. For example, the sensor 58 can include a weight sensor integrated into the seat and configured to output a sensor signal indicative of a weight on the seat. The sensor 58 can also include a camera configured to output a sensor signal indicative of image data representing the dining environment 50. In some embodiments, the seat occupancy system 10 can determine guest occupancy using the weight sensor signal and the image data. For example, the weight sensor signal can indicate guest occupancy, while the image data can indicate a bag on the seat. In another example, the weight sensor signal can indicate a weight value, and the seat occupancy system 10 can identify that the weight is below a threshold and therefore does not indicate guest occupancy.

[0050] In block 134, the seat occupancy system 10 may determine whether one or more tables 54 are available for guest occupancy. In one embodiment, the seat occupancy system 10 may use a map in combination with sensor data to determine whether one or more tables 54 are available. The seat occupancy system 10 may correlate each sensor signal with a respective table 54 and determine whether the table 54 is likely to be vacant based on the sensor signals.

[0051] If one or more tables 54 are available, then in block 136, the seat occupancy system 10 may allow the guest to place an order. For example, the mobile device 82 may display a GUI including a graphical or visual representation of a vendor's menu to enable the guest to place an order. The guest may select one or more items to order, and the seat occupancy system 10 may receive the input. Further, the seat occupancy system 10 may transmit the guest's input to the vendor for order preparation. Thus, wait times within the dining environment 50 may be reduced, optimizing dining operations.

[0052] After the order is placed, in block 138, the seat occupancy system 10 may allow the guest to select a table for the dining experience 50. For example, the mobile device 82 may display a GUI having a map including one or more tables 54 labeled as available or in use. The seat occupancy system 10 may cause the GUI to display a graphical or visual representation of the dining environment 50, marking premium tables including special show effects, premium tables closest to show effects, available or in use tables, or one or more high traffic tables. If a table is available, the seat occupancy system 10 may cause the GUI to allow the guest to input a request to reserve the table. In some embodiments, the guest may select a table 54 for reservation, and the seat occupancy system 10 may receive the guest's input.

[0053] In some embodiments, the seat occupation system 10 may not identify one or more available tables 54 available for the guest to occupy. Therefore, the seat occupation system 10 may display a message in the GUI indicating the wait time and asking the guest if they would like to join the waiting list.

[0054] The seating occupancy system 10 can provide instructions to a vendor for one or more dining operations to free up tables. Vendors may find it beneficial to track guest occupancy to optimize dining operations. For example, the seating occupancy system 10 can display a map including real-time sensor data on a graphical user interface of a mobile device associated with the vendor. The sensor data can include image data of the dining environment 50 and one or more tables 54 labeled for clean-up. In block 140, the seating occupancy system 10 can free up tables 54 in the dining environment 50 by identifying one or more tables to be cleaned. In some embodiments, the vendor can assign an individual to clean the identified tables 54 and input the task (via the GUI).

[0055] In block 142, the seating occupancy system 10 can update the map using real-time (or near real-time) sensor data. For example, cleaning a table 54 can free up the table 54 for guest occupancy. In some embodiments, a vendor can indicate to the seating occupancy system 10 that the table 54 has been cleaned and is ready for guest use. In some embodiments, the seating occupancy system 10 can analyze sensor data (e.g., image data, weight data, such as the weight on the table 54 top) to identify cleaned tables. Based on the sensor data, the seating occupancy system 10 can update the map to reflect cleaned tables. In some embodiments, the seating occupancy system 10 can also update the map to reflect energy usage output, current wait times, received orders, or a combination thereof.

[0056] 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 guest's mobile device 82 and allow the guest to place an order. It should be understood that the method 130 may be performed for multiple guests simultaneously, at overlapping times, and / or at different times (e.g., as multiple guests enter the dining environment 50, as multiple tables 54 are being cleaned, etc.).

[0057] Method 130 may be stored on one or more tangible, non-transitory, machine-readable media and / or may be executed by processor 64 or processing circuitry of control system 60 described above, or on another suitable controller. The steps of method 130 may be performed in the order described above, or in any other suitable order. Additionally, some method steps may be omitted and / or other blocks may be added to method 130.

[0058] As used herein, "machine learning" and / or "artificial intelligence" can refer to algorithms and statistical models used by a computer system to perform a specific task, with or without explicit instructions. For example, a machine learning process can generate a mathematical model based on samples of clean data, known as "training data," to make predictions or decisions without being explicitly programmed to perform the task. The seat occupancy system 10 can generate (e.g., train and / or update, e.g., passively update) the model based on sensor data collected over time. In this manner, the model can improve over time based on new sensor data collected over time. For example, the model can receive sensor data to provide outputs related to guest preferences and locations for show effects, and subsequently use the sensor data to update and improve the model.

[0059] It should be understood that the seat occupancy system can also be adapted for environments other than dining environments, such as gaming environments or ride attractions with queues. 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 show effect, creating an interactive dining experience for guests. The dining environment layout can be used to optimize a current dining environment or modify it for future dining environments. Furthermore, the systems and methods described herein provide a real-time or near-real-time map of the dining environment. Such on-demand and instructional information can be quickly and easily accessed by guests to place orders, select tables, and / or select premium seating. Furthermore, providing on-demand information to vendors can optimize dining operations, such as placing orders or cleaning tables.

[0060] While 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, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present disclosure. It is to be understood that the features illustrated and described with reference to Figures 1-5 may be combined in any suitable manner.

[0061] The technology shown and claimed herein refers to and applies to tangible objects and specific examples of a practical nature that will materially improve the art, and thus are not abstract, intangible, or purely theoretical. Furthermore, if any claim appended at the end of this specification contains one or more elements designated as "means for (performing) ... (function)" or "step for (performing) ... (function)," such elements are to be construed pursuant to 35 U.S.C. 112(f). Conversely, for any claim containing elements designated in any other manner, such elements are not to be construed pursuant to 35 U.S.C. 112(f). [Explanation of symbols]

[0062] 10 Seat Occupancy System 50 Food Environment 51 Environmental Factors 52 Entrance 54 Tables 56 Stations 58 Sensors 60 Control System 62 memory 64 processors 66 Show Effect

Claims

1. 1. A seat occupancy system, comprising: at least one sensor configured to output sensor data indicative of a respective guest occupancy parameter for each seat of a plurality of seats within the dining environment; at least one processor; a memory for storing instructions; wherein the instructions Receives sensor data, receiving a map showing a first layout of the dining environment including a respective position of each seat of the plurality of seats and a respective position of a show effect; determining that a respective guest occupancy parameter for at least one seat of the plurality of seats does not match a target guest occupancy parameter for a period of time in the first layout of the dining environment; in response, generating a second layout of the dining environment that differs from the first layout, the second layout including a new respective position of at least one seat of the plurality of seats, a new respective position of the show effect, or both. and wherein the method is executable by the at least one processor to cause the at least one processor to: Seat occupancy system.

2. The seat occupancy system of claim 1 , wherein the map indicates the location of each of the environmental factors, the environmental factors including noise, vibration, airflow, light, or a combination thereof.

3. 3. The seat occupancy system of claim 2, wherein the environmental factor is generated by a ride vehicle in proximity 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 show effect while the ride vehicle is generating the environmental factor.

4. The instruction: identifying a guest vacancy for at least one seat among the plurality of seats based on the sensor data; shutting off the show effect in response to identifying the guest vacancy; 4. The seat occupancy system of claim 3, wherein the at least one processor is executable to cause the at least one processor to:

5. 3. 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 show effect based on the environmental factors.

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

7. The seat occupancy system of claim 1 , wherein the show effect includes a performance.

8. 2. 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 guest occupancy parameters and the second layout.

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

10. The instruction: identifying one or more vacant seats among the plurality of seats; displaying a graphical user interface (GUI) on the display of the guest's mobile device; and the GUI is executable by the at least one processor to cause the at least one processor to: the second layout of the dining environment including the one or more vacant seats of the plurality of seats; virtual buttons that allow the guest to place an order and select a table associated with the one or more available seats in the plurality of seats; The seat occupancy system of claim 1 , comprising:

11. 2. 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 based on the sensor data using one or more machine learning algorithms.

12. 1. A method of operating a seat occupancy system, comprising: receiving, using at least one processor, sensor data captured by at least one sensor of the plurality of sensors indicative of a respective guest occupancy parameter for each seat of the plurality of seats in the environment over a period of time; receiving, using the at least one processor, additional sensor data captured by at least one of the plurality of sensors 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, the first map including a respective position of each seat of the plurality of seats, a respective position of each environmental factor of the plurality of environmental factors, and a respective position of a show effect within the environment over the period of time; generating, using the at least one processor and one or more machine learning algorithms, a second map representing a recommended layout of the environment based on the sensor data, the further sensor data, and the first map; A method comprising:

13. The method of claim 12 , wherein the suggested layout includes new positions for each of the show effects.

14. using the at least one processor to compare the respective guest occupancy parameters for each seat of the plurality of seats to a target guest occupancy parameter; generating the second map in response to identifying, using the at least one processor and the one or more machine learning algorithms, that the respective guest occupancy parameters for at least one seat among the plurality of seats do not match the target guest occupancy parameters; 13. The method of claim 12, comprising:

15. The method of claim 12 , comprising generating, via the at least one processor, the show effect in parallel with at least one environmental factor of the plurality of environmental factors.

16. 16. The method of claim 15, comprising determining, via the at least one processor, that a guest is seated in a particular seat of the plurality of seats before generating the show effect in proximity to the particular seat of the plurality of seats.

17. 16. The method of claim 15, further comprising using the at least one processor and the one or more machine learning algorithms to generate a third map representing a further recommended layout for another environment based on the sensor data, the further sensor data, and the first map.

18. 1. A seat occupancy system, comprising: at least one processor; a memory for storing instructions; wherein the instructions receiving a map showing the environment including a plurality of seats, environmental factors, and show effects; determining a respective guest occupancy rate for each seat of the plurality of seats over a period of time based at least in part on the sensor data; determining that a respective guest occupancy rate for at least one seat of the plurality of seats over the time period is below a threshold guest occupancy rate; determining new respective locations of the environmental factors, new respective locations of the show effects, or a combination thereof, that are predicted to improve the respective guest occupancy rate for the at least one seat of the plurality of seats; updating the map with the new respective positions of the environmental factors, the new respective positions of the show effects, or a combination thereof; and wherein the method is executable by the at least one processor to cause the at least one processor to: Seat occupancy system.

19. The instruction: receiving, via said at least one processor, input indicative of one or more parameters of a new environment that is different from said environment; determining, via the at least one processor, a second map for the new environment based on the map, the sensor data, and the one or more parameters; 20. The seat occupancy system of claim 18, wherein the at least one processor is executable to cause the at least one processor to:

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

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