system

Abstract

The system according to this embodiment aims to analyze the congestion status of a cafe in real time and calculate the optimal seating time. [Solution] The system according to the embodiment comprises an analysis unit, a calculation unit, a reception unit, and an queue management unit. The analysis unit analyzes the congestion status of the cafe in real time. The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The reception unit allows users to check seat availability and make reservations or check in via a smartphone app. The queue management unit provides priority seat usage to members.

Description

Technical Field

[0006] , , ,

[0005] , , ,

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to grasp the congestion situation of a café in real time and calculate the optimal seat usage time, and there is room for improvement in providing a comfortable café experience for users.

[0005] The system according to the embodiment aims to analyze the congestion situation of a café in real time and calculate the optimal seat usage time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a calculation unit, a reception unit, and an queue management unit. The analysis unit analyzes the congestion status of the cafe in real time. The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The reception unit allows users to check seat availability and make reservations or check in via a smartphone app. The queue management unit provides priority seating to members. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the congestion status of a cafe in real time and calculate the optimal seating time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-driven seating management platform according to an embodiment of the present invention is a system that provides benefits to both cafe users and owners. This system analyzes the cafe's congestion level in real time, calculates the optimal seating time, and aims to improve the turnover rate. Users can check seat availability, make reservations, and check in using a smartphone app, and there is also a priority seating system for members. Through this mechanism, the problem of people struggling to find a seat in a cafe is resolved, and users are provided with a comfortable cafe experience. For example, in order to monitor the cafe's congestion level in real time, the AI ​​analyzes the seating situation within the cafe. For example, the AI ​​analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. This allows for real-time monitoring of the cafe's congestion level. Next, the AI ​​calculates the optimal seating time. For example, it calculates the optimal seating time considering the user's stay time and the cafe's congestion level. This improves the cafe's turnover rate. Users can check seat availability, make reservations, and check in using a smartphone app. For example, they can open the app to check the current seat availability and reserve an available seat. They can also check in using the app when they arrive at the cafe. This allows users to secure a seat efficiently. Furthermore, there is a priority seating system for members. For example, when a member arrives at a cafe, they can use a seat with priority over other users. This allows members to use the cafe comfortably. This system solves the problem of cafes being difficult to find a seat in and provides users with a comfortable cafe experience. For example, even in a crowded cafe, users can use the app to check for available seats and make reservations, reducing waiting times. Cafe owners also benefit from increased turnover and higher revenue. In this way, an AI-driven seating management platform can provide benefits to both cafe users and owners.

[0029] The AI-driven seat management platform according to this embodiment comprises an analysis unit, a calculation unit, a reception unit, and an interruption unit. The analysis unit analyzes the congestion status of the cafe in real time. The analysis unit analyzes data obtained from cameras and sensors in the cafe, for example, to understand the current congestion status. For example, the analysis unit analyzes video data from surveillance cameras in the cafe to understand the seat usage status in real time. The analysis unit can also analyze data from temperature and humidity sensors to detect changes in the environment. Furthermore, the analysis unit can analyze data from voice sensors to understand the noise level in the cafe. The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seat usage time considering, for example, the user's stay time and the cafe's congestion status. For example, the calculation unit calculates the average user stay time based on past data and sets the optimal seat usage time based on that. Furthermore, the calculation unit can dynamically adjust the seat usage time considering the real-time congestion status. Furthermore, the calculation unit can optimize the seat usage time considering the user's reservation status. The reception desk allows users to check seat availability, make reservations, and check in via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve an available seat. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can check in by scanning a 2D code (e.g., QR code®). The reception desk can also automatically check in using location information. Furthermore, the reception desk can manage reservation status and send notifications to users. The queue desk provides priority seating to members. For example, when a member arrives at the cafe, the queue desk can give them priority seating over other users. For example, the queue desk can set priorities and assign seats based on the member's status. The queue desk can also prioritize seating for members, taking reservation status into consideration. Furthermore, the queue desk can offer special services to members.As a result, the AI-driven seat management platform according to this embodiment can provide benefits to both users and owners by analyzing the congestion status of a cafe in real time and calculating the optimal seat usage time.

[0030] The analysis unit analyzes the cafe's congestion level in real time. For example, it analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. Specifically, it collects video data from multiple surveillance cameras installed in the cafe and uses AI to analyze the people and seat occupancy status in the video. The AI ​​utilizes image recognition technology to determine whether seats are occupied or empty, and can also measure the number of users and their length of stay. It can also analyze data from temperature and humidity sensors to detect changes in the cafe's environment. For example, the temperature sensor monitors temperature changes in the cafe in real time, and the humidity sensor detects fluctuations in humidity. This allows the analysis unit to evaluate the comfort level of the cafe and adjust the air conditioning system as needed. Furthermore, it can analyze data from sound sensors to understand the noise level in the cafe. The sound sensor measures the volume of sound in the cafe and can issue a warning if the noise level exceeds a certain level. This allows the analysis unit to comprehensively monitor the cafe's environment and provide data to create a comfortable space for users.

[0031] The calculation unit calculates the optimal seating time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seating time by considering, for example, the user's length of stay and the cafe's congestion level. Specifically, it calculates the average length of stay for users based on past data and sets the optimal seating time based on that. For example, it analyzes the average length of stay for users during a specific time period from past data and optimizes the seating time during that time period. The calculation unit can also dynamically adjust seating time considering the real-time congestion level. For example, if the cafe is crowded, it encourages users to use the cafe for a short time to increase the number of available seats, making it easier for other users to use the cafe. On the other hand, if the cafe is not crowded, it allows users to use the cafe for a longer time, allowing them to have a comfortable experience. Furthermore, the calculation unit can also optimize seating time considering the user's reservation status. For example, during times with many reservations, it prioritizes providing seats to those with reservations, and during times with few reservations, it provides seats to walk-in users as well. In this way, the calculation unit can efficiently manage the cafe's seating use and provide a comfortable environment for users.

[0032] The reception desk allows users to check seat availability, make reservations, and check in via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve an available seat. Specifically, users can download the smartphone app and create an account to check the cafe's seat availability in real time. Based on data provided by the analytics department, the app displays current seat availability, and users can select and reserve their desired seat. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can provide a function to check in by scanning a QR code. Users can easily check in by scanning a QR code placed at the cafe entrance with their smartphone. The reception desk can also perform automatic check-in using location information. For example, it can provide a function to automatically check in based on the user's smartphone location when they arrive near the cafe. Furthermore, the reception desk can manage reservation status and send notifications to users. For example, it can send notifications of reservation confirmation, changes, and cancellations via the app to provide users with the latest information. In this way, the reception desk can provide users with a convenient and smooth reservation and check-in experience.

[0033] The priority seating service provides members with preferential seating. For example, when a member arrives at the cafe, they can be given priority seating over other users. Specifically, members can receive the benefit of preferential seating by registering for the cafe's membership program. For example, the priority seating service sets priorities and assigns seats based on the member's status. Member status varies depending on usage frequency and membership rank, with higher-ranking members receiving priority seating. The priority seating service can also provide seats to members preferentially, taking reservation status into consideration. For example, during peak hours, seats can be reserved preferentially for members, while general users can be asked to wait if there are few available seats. Furthermore, the priority seating service can also provide special services to members. For example, by setting up a member-only seating area or offering member-only menus and discount services, member satisfaction can be increased. In this way, the priority seating service can provide excellent service to members and improve the overall satisfaction of the cafe's users.

[0034] The analysis unit can analyze data obtained from cameras and sensors within the cafe to understand the current level of crowding. For example, the analysis unit can analyze video data from surveillance cameras within the cafe to understand seat usage in real time. The analysis unit can also analyze data from temperature and humidity sensors to detect changes in the environment. For example, the analysis unit can analyze data from temperature sensors to understand temperature changes within the cafe. The analysis unit can also analyze data from humidity sensors to understand humidity changes within the cafe. Furthermore, the analysis unit can analyze data from sound sensors to understand the noise level within the cafe. For example, the analysis unit can analyze data from sound sensors to understand the noise level within the cafe. In this way, by analyzing data obtained from cameras and sensors within the cafe, the current level of crowding can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from cameras and sensors into a generating AI, which can then perform an analysis of congestion levels.

[0035] The calculation unit can calculate the optimal seating time considering the user's stay duration and the cafe's congestion level. For example, the calculation unit calculates the optimal seating time considering the user's stay duration and the cafe's congestion level. For example, the calculation unit calculates the average user stay duration based on past data and sets the optimal seating time based on that. The calculation unit can also dynamically adjust the seating time considering the real-time congestion level. For example, the calculation unit dynamically adjusts the seating time considering the real-time congestion level. Furthermore, the calculation unit can optimize the seating time considering the user's reservation status. For example, the calculation unit optimizes the seating time considering the user's reservation status. This improves the cafe's turnover rate by calculating the optimal seating time considering the user's stay duration and the cafe's congestion level. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the user's stay duration and the cafe's congestion level into a generating AI and have the generating AI perform the calculation of the optimal seating time.

[0036] The reception desk allows users to check seat availability and reserve available seats via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve available seats. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can check in by scanning a QR code. The reception desk can also automatically check in using location information. This allows users to secure seats efficiently. Some or all of the above processes at the reception desk may be performed using AI, or not. For example, the reception desk can input seat availability and reservation data from the smartphone app into a generating AI and have the generating AI manage the reservations.

[0037] The reception desk allows customers to check in using an app upon arrival at the cafe. For example, the reception desk can check in using an app upon arrival at the cafe. For example, the reception desk can check in by scanning a QR code. The reception desk can also automatically check in using location information. This allows users to efficiently secure seats. Some or all of the above processes at the reception desk may be performed using AI, or not using AI. For example, the reception desk can input check-in data from a smartphone app into a generating AI and have the generating AI manage the check-ins.

[0038] The queue management system allows members to use seats preferentially over other users when they arrive at the cafe. For example, the queue management system can set priorities and assign seats based on the member's status. The queue management system can also prioritize seating for members by considering reservation status. This allows members to use the cafe comfortably. Some or all of the above processing in the queue management system may be performed using AI, for example, or not using AI. For example, the queue management system can input member arrival data into a generating AI and have the generating AI manage preferential seating.

[0039] The analysis unit can analyze audio data within the cafe and estimate the level of crowding from the content of user conversations. For example, the analysis unit can use microphones within the cafe to analyze the volume of user conversations and estimate the level of crowding. The analysis unit can also analyze the content of user conversations using natural language processing techniques and estimate the level of crowding. Furthermore, the analysis unit can analyze the frequency and tone of user conversations and estimate the level of crowding. In this way, by analyzing audio data within the cafe, the level of crowding can be estimated from the content of user conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from the cafe into a generating AI and have the generating AI perform the crowding level estimation.

[0040] The analysis unit can analyze environmental data such as temperature and humidity inside the cafe and evaluate the level of comfort. For example, the analysis unit can analyze data obtained from a temperature sensor inside the cafe and evaluate the level of comfort. The analysis unit can also analyze data obtained from a humidity sensor inside the cafe and evaluate the level of comfort. Furthermore, the analysis unit can analyze data obtained from an air quality sensor inside the cafe and evaluate the level of comfort. In this way, the level of comfort can be evaluated by analyzing environmental data such as temperature and humidity inside the cafe. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input environmental data from inside the cafe into a generating AI and have the generating AI perform the evaluation of the level of comfort.

[0041] The analysis unit can analyze the Wi-Fi connection status within the cafe and estimate the user's stay time. For example, the analysis unit can analyze the Wi-Fi connection data within the cafe and estimate the user's stay time. The analysis unit can also analyze the connection time of the user's device and estimate the stay time. For example, the analysis unit can analyze the connection time of the user's device and estimate the stay time. Furthermore, the analysis unit can analyze the frequency of the user's Wi-Fi connection and estimate the stay time. For example, the analysis unit can analyze the frequency of the user's Wi-Fi connection and estimate the stay time. In this way, by analyzing the Wi-Fi connection status within the cafe, the user's stay time can be estimated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the Wi-Fi connection data within the cafe into a generating AI and have the generating AI perform the stay time estimation.

[0042] The analysis unit can analyze the lighting conditions inside the cafe and adjust the lighting according to the level of crowding. For example, the analysis unit can analyze data obtained from lighting sensors inside the cafe and adjust the lighting according to the level of crowding. The analysis unit can also adjust the lighting according to the length of time users are in the cafe. For example, the analysis unit can adjust the lighting according to the length of time users are in the cafe. Furthermore, the analysis unit can adjust the lighting according to the emotions of users. For example, the analysis unit can adjust the lighting according to the emotions of users. In this way, by analyzing the lighting conditions inside the cafe, the lighting can be adjusted according to the level of crowding. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lighting data from inside the cafe into a generating AI and have the generating AI perform the lighting adjustments.

[0043] The calculation unit can predict future congestion levels and calculate seat usage time by referring to past congestion data. For example, the calculation unit can predict future congestion levels based on past congestion data. The calculation unit can also calculate future seat usage time based on past user dwell times. For example, the calculation unit can calculate future seat usage time based on past user dwell times. Furthermore, the calculation unit can analyze past congestion patterns and calculate future seat usage time. For example, the calculation unit can analyze past congestion patterns and calculate future seat usage time. In this way, by referring to past congestion data, it is possible to predict future congestion levels and calculate seat usage time. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past congestion data into a generating AI and have the generating AI perform predictions of future congestion levels.

[0044] The calculation unit can calculate the optimal seat usage time individually, taking into account the user's seat usage history. For example, the calculation unit can calculate the optimal seat usage time based on the user's past seat usage history. The calculation unit can also calculate the optimal seat usage time based on the user's past stay time. For example, the calculation unit can calculate the optimal seat usage time based on the user's past stay time. Furthermore, the calculation unit can analyze the user's past usage patterns and calculate the optimal seat usage time. For example, the calculation unit can analyze the user's past usage patterns and calculate the optimal seat usage time. In this way, the optimal seat usage time can be calculated individually, taking into account the user's seat usage history. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's seat usage history data into a generating AI and have the generating AI perform the calculation of the optimal seat usage time.

[0045] The calculation unit can calculate seat usage time, taking into account the cafe's operating hours and special events. For example, the calculation unit can calculate seat usage time based on the cafe's operating hours. The calculation unit can also calculate seat usage time, taking into account special events at the cafe. Furthermore, the calculation unit can also calculate seat usage time based on the cafe's operating days. This allows for the calculation of a more appropriate seat usage time by considering the cafe's operating hours and special events. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the cafe's operating hours and special events into a generating AI and have the generating AI perform the seat usage time calculation.

[0046] The calculation unit can calculate the total seat usage time for a group, taking into account the user group composition. For example, the calculation unit can calculate the total seat usage time for a group based on the user group composition. The calculation unit can also calculate the seat usage time by considering the number of people in the group. For example, the calculation unit can calculate the seat usage time by considering the number of people in the group. Furthermore, the calculation unit can also calculate the seat usage time based on the group's stay time. For example, the calculation unit can calculate the seat usage time based on the group's stay time. In this way, the total seat usage time for a group can be calculated by considering the user group composition. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user group composition data into a generating AI and have the generating AI perform the calculation of the total seat usage time for the group.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze audio data within the cafe and estimate the level of crowding from the content of user conversations. For example, the analysis unit can use microphones in the cafe to analyze the volume of user conversations and estimate the level of crowding. It can also analyze the content of user conversations using natural language processing technology and estimate the level of crowding. Furthermore, the analysis unit can analyze the frequency and tone of user conversations and estimate the level of crowding. In this way, by analyzing audio data within the cafe, it is possible to estimate the level of crowding from the content of user conversations.

[0049] The reception area allows users to check seat availability and reserve an available seat via a smartphone app. For example, users can open the app, check current seat availability, and reserve an available seat. They can also check in using the app upon arrival at the cafe. For example, they can check in by scanning a QR code. Automatic check-in using location information is also possible. This allows users to secure a seat efficiently.

[0050] The analysis unit can analyze environmental data such as temperature and humidity inside the cafe and evaluate the level of comfort. For example, it can analyze data obtained from temperature sensors inside the cafe to evaluate comfort. It can also analyze data obtained from humidity sensors inside the cafe to evaluate comfort. Furthermore, it can analyze data obtained from air quality sensors inside the cafe to evaluate comfort. In this way, by analyzing environmental data such as temperature and humidity inside the cafe, the level of comfort can be evaluated.

[0051] The calculation unit can refer to past congestion data, predict future congestion levels, and calculate seat usage time. For example, it can predict future congestion levels based on past congestion data. It can also calculate future seat usage time based on past user dwell times. Furthermore, it can analyze past congestion patterns and calculate future seat usage time. In this way, by referring to past congestion data, it is possible to predict future congestion levels and calculate seat usage time.

[0052] The calculation unit can calculate the optimal seat usage time for each individual user, taking into account their seat usage history. For example, it can calculate the optimal seat usage time based on the user's past seat usage history. It can also calculate the optimal seat usage time based on the user's past dwell time. Furthermore, it can analyze the user's past usage patterns to calculate the optimal seat usage time. In this way, by considering the user's seat usage history, the optimal seat usage time can be calculated for each individual user.

[0053] The analysis unit can analyze the Wi-Fi connection status within a cafe and estimate the user's stay time. For example, it can analyze Wi-Fi connection data within the cafe to estimate the user's stay time. It can also analyze the connection time of the user's device to estimate the stay time. Furthermore, it can analyze the frequency of the user's Wi-Fi connection to estimate the stay time. In this way, by analyzing the Wi-Fi connection status within a cafe, it is possible to estimate the user's stay time.

[0054] The calculation unit can calculate the total seat usage time for a group, taking into account the user group composition. For example, it can calculate the total seat usage time for a group based on the user group composition. It can also calculate the seat usage time considering the number of people in the group. Furthermore, it can calculate the seat usage time based on the group's length of stay. In this way, by taking into account the user group composition, the total seat usage time for a group can be calculated.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The analysis unit analyzes the cafe's congestion level in real time. The analysis unit analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. For example, it analyzes video data from surveillance cameras to understand seat usage in real time. It can also analyze data from temperature and humidity sensors to detect changes in the environment. Furthermore, it can analyze data from sound sensors to understand the noise level within the cafe. Step 2: The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seat usage time considering the user's stay time and the cafe's congestion level. For example, it calculates the average user stay time based on past data and sets the optimal seat usage time based on that. It can also dynamically adjust the seat usage time considering the real-time congestion level. Furthermore, it can optimize the seat usage time considering the user's reservation status. Step 3: The reception staff allows users to check seat availability, make reservations, and check in via a smartphone app. The reception staff can open the app to check current seat availability and reserve an available seat. Users can also check in using the app upon arrival at the cafe. For example, they can check in by scanning a QR code. Automatic check-in using location information is also possible. Furthermore, the system can manage reservation status and send notifications to users. Step 4: The queue management system provides members with priority seating. When members arrive at the cafe, the queue management system allows them to use seats before other users. For example, it can assign seats based on the member's status, or it can prioritize seating for members considering reservation status. In addition, it can offer special services to members.

[0057] (Example of form 2) The AI-driven seating management platform according to an embodiment of the present invention is a system that provides benefits to both cafe users and owners. This system analyzes the cafe's congestion level in real time, calculates the optimal seating time, and aims to improve the turnover rate. Users can check seat availability, make reservations, and check in using a smartphone app, and there is also a priority seating system for members. Through this mechanism, the problem of people struggling to find a seat in a cafe is resolved, and users are provided with a comfortable cafe experience. For example, in order to monitor the cafe's congestion level in real time, the AI ​​analyzes the seating situation within the cafe. For example, the AI ​​analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. This allows for real-time monitoring of the cafe's congestion level. Next, the AI ​​calculates the optimal seating time. For example, it calculates the optimal seating time considering the user's stay time and the cafe's congestion level. This improves the cafe's turnover rate. Users can check seat availability, make reservations, and check in using a smartphone app. For example, they can open the app to check the current seat availability and reserve an available seat. They can also check in using the app when they arrive at the cafe. This allows users to secure a seat efficiently. Furthermore, there is a priority seating system for members. For example, when a member arrives at a cafe, they can use a seat with priority over other users. This allows members to use the cafe comfortably. This system solves the problem of cafes being difficult to find a seat in and provides users with a comfortable cafe experience. For example, even in a crowded cafe, users can use the app to check for available seats and make reservations, reducing waiting times. Cafe owners also benefit from increased turnover and higher revenue. In this way, an AI-driven seating management platform can provide benefits to both cafe users and owners.

[0058] The AI-driven seat management platform according to this embodiment comprises an analysis unit, a calculation unit, a reception unit, and an interruption unit. The analysis unit analyzes the congestion status of the cafe in real time. The analysis unit analyzes data obtained from cameras and sensors in the cafe, for example, to understand the current congestion status. For example, the analysis unit analyzes video data from surveillance cameras in the cafe to understand the seat usage status in real time. The analysis unit can also analyze data from temperature and humidity sensors to detect changes in the environment. Furthermore, the analysis unit can analyze data from voice sensors to understand the noise level in the cafe. The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seat usage time considering, for example, the user's stay time and the cafe's congestion status. For example, the calculation unit calculates the average user stay time based on past data and sets the optimal seat usage time based on that. Furthermore, the calculation unit can dynamically adjust the seat usage time considering the real-time congestion status. Furthermore, the calculation unit can optimize the seat usage time considering the user's reservation status. The reception desk allows users to check seat availability, make reservations, and check in via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve an available seat. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can check in by scanning a 2D code (e.g., a QR code). The reception desk can also automatically check in using location information. Furthermore, the reception desk can manage reservation status and send notifications to users. The queue desk provides members with priority seating. For example, when a member arrives at the cafe, the queue desk can give them priority seating over other users. For example, the queue desk can set priorities and assign seats based on the member's status. The queue desk can also prioritize seating for members, taking reservation status into consideration. Furthermore, the queue desk can offer special services to members.As a result, the AI-driven seat management platform according to this embodiment can provide benefits to both users and owners by analyzing the congestion status of a cafe in real time and calculating the optimal seat usage time.

[0059] The analysis unit analyzes the cafe's congestion level in real time. For example, it analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. Specifically, it collects video data from multiple surveillance cameras installed in the cafe and uses AI to analyze the people and seat occupancy status in the video. The AI ​​utilizes image recognition technology to determine whether seats are occupied or empty, and can also measure the number of users and their length of stay. It can also analyze data from temperature and humidity sensors to detect changes in the cafe's environment. For example, the temperature sensor monitors temperature changes in the cafe in real time, and the humidity sensor detects fluctuations in humidity. This allows the analysis unit to evaluate the comfort level of the cafe and adjust the air conditioning system as needed. Furthermore, it can analyze data from sound sensors to understand the noise level in the cafe. The sound sensor measures the volume of sound in the cafe and can issue a warning if the noise level exceeds a certain level. This allows the analysis unit to comprehensively monitor the cafe's environment and provide data to create a comfortable space for users.

[0060] The calculation unit calculates the optimal seating time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seating time by considering, for example, the user's length of stay and the cafe's congestion level. Specifically, it calculates the average length of stay for users based on past data and sets the optimal seating time based on that. For example, it analyzes the average length of stay for users during a specific time period from past data and optimizes the seating time during that time period. The calculation unit can also dynamically adjust seating time considering the real-time congestion level. For example, if the cafe is crowded, it encourages users to use the cafe for a short time to increase the number of available seats, making it easier for other users to use the cafe. On the other hand, if the cafe is not crowded, it allows users to use the cafe for a longer time, allowing them to have a comfortable experience. Furthermore, the calculation unit can also optimize seating time considering the user's reservation status. For example, during times with many reservations, it prioritizes providing seats to those with reservations, and during times with few reservations, it provides seats to walk-in users as well. In this way, the calculation unit can efficiently manage the cafe's seating use and provide a comfortable environment for users.

[0061] The reception desk allows users to check seat availability, make reservations, and check in via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve an available seat. Specifically, users can download the smartphone app and create an account to check the cafe's seat availability in real time. Based on data provided by the analytics department, the app displays current seat availability, and users can select and reserve their desired seat. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can provide a function to check in by scanning a QR code. Users can easily check in by scanning a QR code placed at the cafe entrance with their smartphone. The reception desk can also perform automatic check-in using location information. For example, it can provide a function to automatically check in based on the user's smartphone location when they arrive near the cafe. Furthermore, the reception desk can manage reservation status and send notifications to users. For example, it can send notifications of reservation confirmation, changes, and cancellations via the app to provide users with the latest information. In this way, the reception desk can provide users with a convenient and smooth reservation and check-in experience.

[0062] The priority seating service provides members with preferential seating. For example, when a member arrives at the cafe, they can be given priority seating over other users. Specifically, members can receive the benefit of preferential seating by registering for the cafe's membership program. For example, the priority seating service sets priorities and assigns seats based on the member's status. Member status varies depending on usage frequency and membership rank, with higher-ranking members receiving priority seating. The priority seating service can also provide seats to members preferentially, taking reservation status into consideration. For example, during peak hours, seats can be reserved preferentially for members, while general users can be asked to wait if there are few available seats. Furthermore, the priority seating service can also provide special services to members. For example, by setting up a member-only seating area or offering member-only menus and discount services, member satisfaction can be increased. In this way, the priority seating service can provide excellent service to members and improve the overall satisfaction of the cafe's users.

[0063] The analysis unit can analyze data obtained from cameras and sensors within the cafe to understand the current level of crowding. For example, the analysis unit can analyze video data from surveillance cameras within the cafe to understand seat usage in real time. The analysis unit can also analyze data from temperature and humidity sensors to detect changes in the environment. For example, the analysis unit can analyze data from temperature sensors to understand temperature changes within the cafe. The analysis unit can also analyze data from humidity sensors to understand humidity changes within the cafe. Furthermore, the analysis unit can analyze data from sound sensors to understand the noise level within the cafe. For example, the analysis unit can analyze data from sound sensors to understand the noise level within the cafe. In this way, by analyzing data obtained from cameras and sensors within the cafe, the current level of crowding can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from cameras and sensors into a generating AI, which can then perform an analysis of congestion levels.

[0064] The calculation unit can calculate the optimal seating time considering the user's stay duration and the cafe's congestion level. For example, the calculation unit calculates the optimal seating time considering the user's stay duration and the cafe's congestion level. For example, the calculation unit calculates the average user stay duration based on past data and sets the optimal seating time based on that. The calculation unit can also dynamically adjust the seating time considering the real-time congestion level. For example, the calculation unit dynamically adjusts the seating time considering the real-time congestion level. Furthermore, the calculation unit can optimize the seating time considering the user's reservation status. For example, the calculation unit optimizes the seating time considering the user's reservation status. This improves the cafe's turnover rate by calculating the optimal seating time considering the user's stay duration and the cafe's congestion level. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the user's stay duration and the cafe's congestion level into a generating AI and have the generating AI perform the calculation of the optimal seating time.

[0065] The reception desk allows users to check seat availability and reserve available seats via a smartphone app. For example, the reception desk can open the app to check current seat availability and reserve available seats. The reception desk can also check in using the app upon arrival at the cafe. For example, the reception desk can check in by scanning a QR code. The reception desk can also automatically check in using location information. This allows users to secure seats efficiently. Some or all of the above processes at the reception desk may be performed using AI, or not. For example, the reception desk can input seat availability and reservation data from the smartphone app into a generating AI and have the generating AI manage the reservations.

[0066] The reception desk allows customers to check in using an app upon arrival at the cafe. For example, the reception desk can check in using an app upon arrival at the cafe. For example, the reception desk can check in by scanning a QR code. The reception desk can also automatically check in using location information. This allows users to efficiently secure seats. Some or all of the above processes at the reception desk may be performed using AI, or not using AI. For example, the reception desk can input check-in data from a smartphone app into a generating AI and have the generating AI manage the check-ins.

[0067] The queue management system allows members to use seats preferentially over other users when they arrive at the cafe. For example, the queue management system can set priorities and assign seats based on the member's status. The queue management system can also prioritize seating for members by considering reservation status. This allows members to use the cafe comfortably. Some or all of the above processing in the queue management system may be performed using AI, for example, or not using AI. For example, the queue management system can input member arrival data into a generating AI and have the generating AI manage preferential seating.

[0068] The analysis unit can estimate the user's emotions and adjust the congestion analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the frequency of congestion analysis and provide real-time updates. For example, if the user is stressed, the analysis unit can increase the frequency of congestion analysis and provide real-time updates. The analysis unit can also decrease the frequency of analysis and limit updates to periodic ones if the user is relaxed. For example, if the user is relaxed, the analysis unit can decrease the frequency of analysis and limit updates to periodic ones. The analysis unit can also quickly analyze congestion and provide immediate results if the user is in a hurry. For example, if the analysis unit quickly analyzes congestion and provides immediate results if the user is in a hurry. This allows for more appropriate analysis by adjusting the congestion analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the method for analyzing congestion levels.

[0069] The analysis unit can analyze audio data within the cafe and estimate the level of crowding from the content of user conversations. For example, the analysis unit can use microphones within the cafe to analyze the volume of user conversations and estimate the level of crowding. The analysis unit can also analyze the content of user conversations using natural language processing techniques and estimate the level of crowding. Furthermore, the analysis unit can analyze the frequency and tone of user conversations and estimate the level of crowding. In this way, by analyzing audio data within the cafe, the level of crowding can be estimated from the content of user conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from the cafe into a generating AI and have the generating AI perform the crowding level estimation.

[0070] The analysis unit can analyze environmental data such as temperature and humidity inside the cafe and evaluate the level of comfort. For example, the analysis unit can analyze data obtained from a temperature sensor inside the cafe and evaluate the level of comfort. The analysis unit can also analyze data obtained from a humidity sensor inside the cafe and evaluate the level of comfort. Furthermore, the analysis unit can analyze data obtained from an air quality sensor inside the cafe and evaluate the level of comfort. In this way, the level of comfort can be evaluated by analyzing environmental data such as temperature and humidity inside the cafe. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input environmental data from inside the cafe into a generating AI and have the generating AI perform the evaluation of the level of comfort.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of congestion based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display the congestion status using a simple graphic. The analysis unit can also display a detailed congestion status if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display the congestion status using different colors. This allows for a more appropriate display by adjusting the display method of congestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the congestion status is displayed.

[0072] The analysis unit can analyze the Wi-Fi connection status within the cafe and estimate the user's stay time. For example, the analysis unit can analyze the Wi-Fi connection data within the cafe and estimate the user's stay time. The analysis unit can also analyze the connection time of the user's device and estimate the stay time. For example, the analysis unit can analyze the connection time of the user's device and estimate the stay time. Furthermore, the analysis unit can analyze the frequency of the user's Wi-Fi connection and estimate the stay time. For example, the analysis unit can analyze the frequency of the user's Wi-Fi connection and estimate the stay time. In this way, by analyzing the Wi-Fi connection status within the cafe, the user's stay time can be estimated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the Wi-Fi connection data within the cafe into a generating AI and have the generating AI perform the stay time estimation.

[0073] The analysis unit can analyze the lighting conditions inside the cafe and adjust the lighting according to the level of crowding. For example, the analysis unit can analyze data obtained from lighting sensors inside the cafe and adjust the lighting according to the level of crowding. The analysis unit can also adjust the lighting according to the length of time users are in the cafe. For example, the analysis unit can adjust the lighting according to the length of time users are in the cafe. Furthermore, the analysis unit can adjust the lighting according to the emotions of users. For example, the analysis unit can adjust the lighting according to the emotions of users. In this way, by analyzing the lighting conditions inside the cafe, the lighting can be adjusted according to the level of crowding. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input lighting data from inside the cafe into a generating AI and have the generating AI perform the lighting adjustments.

[0074] The calculation unit can estimate the user's emotions and adjust the optimal seat usage time based on the estimated emotions. For example, if the user is relaxed, the calculation unit can extend the seat usage time. For example, if the user is relaxed, the calculation unit can extend the seat usage time. The calculation unit can also shorten the seat usage time if the user is in a hurry. For example, if the calculation unit is stressed, the calculation unit can shorten the seat usage time. In addition, if the user is stressed, the calculation unit can adjust the seat usage time. For example, if the calculation unit is stressed, the calculation unit can adjust the seat usage time. This allows for more appropriate seat usage by adjusting the optimal seat usage time based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user emotion data into a generating AI and have the generating AI adjust the optimal seat usage time.

[0075] The calculation unit can predict future congestion levels and calculate seat usage time by referring to past congestion data. For example, the calculation unit can predict future congestion levels based on past congestion data. The calculation unit can also calculate future seat usage time based on past user dwell times. For example, the calculation unit can calculate future seat usage time based on past user dwell times. Furthermore, the calculation unit can analyze past congestion patterns and calculate future seat usage time. For example, the calculation unit can analyze past congestion patterns and calculate future seat usage time. In this way, by referring to past congestion data, it is possible to predict future congestion levels and calculate seat usage time. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past congestion data into a generating AI and have the generating AI perform predictions of future congestion levels.

[0076] The calculation unit can calculate the optimal seat usage time individually, taking into account the user's seat usage history. For example, the calculation unit can calculate the optimal seat usage time based on the user's past seat usage history. The calculation unit can also calculate the optimal seat usage time based on the user's past stay time. For example, the calculation unit can calculate the optimal seat usage time based on the user's past stay time. Furthermore, the calculation unit can analyze the user's past usage patterns and calculate the optimal seat usage time. For example, the calculation unit can analyze the user's past usage patterns and calculate the optimal seat usage time. In this way, the optimal seat usage time can be calculated individually, taking into account the user's seat usage history. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's seat usage history data into a generating AI and have the generating AI perform the calculation of the optimal seat usage time.

[0077] The calculation unit can estimate the user's emotions and adjust the seat usage time notification method based on the estimated user emotions. For example, if the user is relaxed, the calculation unit may delay the seat usage time notification. For example, if the user is relaxed, the calculation unit may delay the seat usage time notification. The calculation unit can also speed up the seat usage time notification if the user is in a hurry. For example, if the calculation unit is in a hurry, the calculation unit may speed up the seat usage time notification. Furthermore, if the user is stressed, the calculation unit may adjust the seat usage time notification method. For example, if the calculation unit is stressed, the calculation unit may adjust the seat usage time notification method. By adjusting the seat usage time notification method based on the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user emotion data into the generating AI and have the generating AI adjust the method of notifying users of their seat usage time.

[0078] The calculation unit can calculate seat usage time, taking into account the cafe's operating hours and special events. For example, the calculation unit can calculate seat usage time based on the cafe's operating hours. The calculation unit can also calculate seat usage time, taking into account special events at the cafe. Furthermore, the calculation unit can also calculate seat usage time based on the cafe's operating days. This allows for the calculation of a more appropriate seat usage time by considering the cafe's operating hours and special events. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input data on the cafe's operating hours and special events into a generating AI and have the generating AI perform the seat usage time calculation.

[0079] The calculation unit can calculate the total seat usage time for a group, taking into account the user group composition. For example, the calculation unit can calculate the total seat usage time for a group based on the user group composition. The calculation unit can also calculate the seat usage time by considering the number of people in the group. For example, the calculation unit can calculate the seat usage time by considering the number of people in the group. Furthermore, the calculation unit can also calculate the seat usage time based on the group's stay time. For example, the calculation unit can calculate the seat usage time based on the group's stay time. In this way, the total seat usage time for a group can be calculated by considering the user group composition. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user group composition data into a generating AI and have the generating AI perform the calculation of the total seat usage time for the group.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The analysis unit can analyze audio data within the cafe and estimate the level of crowding from the content of user conversations. For example, the analysis unit can use microphones in the cafe to analyze the volume of user conversations and estimate the level of crowding. It can also analyze the content of user conversations using natural language processing technology and estimate the level of crowding. Furthermore, the analysis unit can analyze the frequency and tone of user conversations and estimate the level of crowding. In this way, by analyzing audio data within the cafe, it is possible to estimate the level of crowding from the content of user conversations.

[0082] The calculation unit can estimate the user's emotions and adjust the optimal seat usage time based on those emotions. For example, if the user is relaxed, the seat usage time can be extended. Conversely, if the user is in a hurry, the seat usage time can be shortened. Furthermore, if the user is stressed, the seat usage time can be adjusted. By adjusting the optimal seat usage time based on the user's emotions, more appropriate seat usage becomes possible.

[0083] The reception area allows users to check seat availability and reserve an available seat via a smartphone app. For example, users can open the app, check current seat availability, and reserve an available seat. They can also check in using the app upon arrival at the cafe. For example, they can check in by scanning a QR code. Automatic check-in using location information is also possible. This allows users to secure a seat efficiently.

[0084] The analysis unit can analyze environmental data such as temperature and humidity inside the cafe and evaluate the level of comfort. For example, it can analyze data obtained from temperature sensors inside the cafe to evaluate comfort. It can also analyze data obtained from humidity sensors inside the cafe to evaluate comfort. Furthermore, it can analyze data obtained from air quality sensors inside the cafe to evaluate comfort. In this way, by analyzing environmental data such as temperature and humidity inside the cafe, the level of comfort can be evaluated.

[0085] The calculation unit can refer to past congestion data, predict future congestion levels, and calculate seat usage time. For example, it can predict future congestion levels based on past congestion data. It can also calculate future seat usage time based on past user dwell times. Furthermore, it can analyze past congestion patterns and calculate future seat usage time. In this way, by referring to past congestion data, it is possible to predict future congestion levels and calculate seat usage time.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of congestion status based on the estimated emotions. For example, if the user is feeling stressed, the congestion status can be displayed using a simple graphic. If the user is relaxed, a more detailed congestion status can be displayed. Furthermore, if the user is in a hurry, the congestion status can be displayed using color coding. In this way, by adjusting the display method of congestion status based on the user's emotions, a more appropriate display becomes possible.

[0087] The calculation unit can calculate the optimal seat usage time for each individual user, taking into account their seat usage history. For example, it can calculate the optimal seat usage time based on the user's past seat usage history. It can also calculate the optimal seat usage time based on the user's past dwell time. Furthermore, it can analyze the user's past usage patterns to calculate the optimal seat usage time. In this way, by considering the user's seat usage history, the optimal seat usage time can be calculated for each individual user.

[0088] The calculation unit can estimate the user's emotions and adjust the seat usage time notification method based on the estimated emotions. For example, if the user is relaxed, the seat usage time notification can be delayed. Conversely, if the user is in a hurry, the seat usage time notification can be brought forward. Furthermore, if the user is stressed, the seat usage time notification method can also be adjusted. By adjusting the seat usage time notification method based on the user's emotions, more appropriate notifications can be provided.

[0089] The analysis unit can analyze the Wi-Fi connection status within a cafe and estimate the user's stay time. For example, it can analyze Wi-Fi connection data within the cafe to estimate the user's stay time. It can also analyze the connection time of the user's device to estimate the stay time. Furthermore, it can analyze the frequency of the user's Wi-Fi connection to estimate the stay time. In this way, by analyzing the Wi-Fi connection status within a cafe, it is possible to estimate the user's stay time.

[0090] The calculation unit can calculate the total seat usage time for a group, taking into account the user group composition. For example, it can calculate the total seat usage time for a group based on the user group composition. It can also calculate the seat usage time considering the number of people in the group. Furthermore, it can calculate the seat usage time based on the group's length of stay. In this way, by taking into account the user group composition, the total seat usage time for a group can be calculated.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The analysis unit analyzes the cafe's congestion level in real time. The analysis unit analyzes data obtained from cameras and sensors within the cafe to understand the current congestion level. For example, it analyzes video data from surveillance cameras to understand seat usage in real time. It can also analyze data from temperature and humidity sensors to detect changes in the environment. Furthermore, it can analyze data from sound sensors to understand the noise level within the cafe. Step 2: The calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit. The calculation unit calculates the optimal seat usage time considering the user's stay time and the cafe's congestion level. For example, it calculates the average user stay time based on past data and sets the optimal seat usage time based on that. It can also dynamically adjust the seat usage time considering the real-time congestion level. Furthermore, it can optimize the seat usage time considering the user's reservation status. Step 3: The reception staff allows users to check seat availability, make reservations, and check in via a smartphone app. The reception staff can open the app to check current seat availability and reserve an available seat. Users can also check in using the app upon arrival at the cafe. For example, they can check in by scanning a QR code. Automatic check-in using location information is also possible. Furthermore, the system can manage reservation status and send notifications to users. Step 4: The queue management system provides members with priority seating. When members arrive at the cafe, the queue management system allows them to use seats before other users. For example, it can assign seats based on the member's status, or it can prioritize seating for members considering reservation status. In addition, it can offer special services to members.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] Each of the multiple elements described above, including the analysis unit, calculation unit, reception unit, and interruption unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the congestion status in the cafe in real time using the camera 42 and sensors of the smart device 14. The calculation unit calculates the optimal seat usage time based on the data obtained from the analysis unit, using the specific processing unit 290 of the data processing unit 12. The reception unit, using the control unit 46A of the smart device 14, enables users to check seat availability and make reservations or check in via a smartphone app. The interruption unit, using the specific processing unit 290 of the data processing unit 12, provides members with priority seat usage. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the analysis unit, calculation unit, reception unit, and interruption unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the congestion status in the cafe in real time using the camera 42 and sensors of the smart glasses 214. The calculation unit calculates the optimal seat usage time based on the data obtained from the analysis unit, using the specific processing unit 290 of the data processing unit 12. The reception unit, using the control unit 46A of the smart glasses 214, enables users to check seat availability and make reservations or check in via a smartphone app. The interruption unit, using the specific processing unit 290 of the data processing unit 12, provides priority seat usage to members. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the analysis unit, calculation unit, reception unit, and interruption unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and sensors of the headset terminal 314 to analyze the congestion status in the cafe in real time. The calculation unit, using the specific processing unit 290 of the data processing unit 12, calculates the optimal seat usage time based on the data obtained from the analysis unit. The reception unit, using the control unit 46A of the headset terminal 314, enables users to check seat availability and make reservations or check in via a smartphone app. The interruption unit, using the specific processing unit 290 of the data processing unit 12, provides members with priority seat usage. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the analysis unit, calculation unit, reception unit, and interruption unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and sensors of the robot 414 to analyze the congestion situation in the cafe in real time. The calculation unit, using the specific processing unit 290 of the data processing unit 12, calculates the optimal seat usage time based on the data obtained from the analysis unit. The reception unit, using the control unit 46A of the robot 414, enables users to check seat availability and make reservations or check in via a smartphone app. The interruption unit, using the specific processing unit 290 of the data processing unit 12, provides members with priority seat usage. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0146] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] (Note 1) An analysis unit that analyzes the cafe's congestion status in real time, A calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit, The reception area allows users to check seat availability, make reservations, and check in via a smartphone app. It includes a section that provides priority seating to members. A system characterized by the following features. (Note 2) The aforementioned analysis unit, By analyzing data obtained from cameras and sensors inside the cafe, the current crowding situation can be determined. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit, The system calculates the optimal seating time, taking into account the user's length of stay and the cafe's congestion level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Users can check seat availability and reserve an available seat through a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Check in using the app when you arrive at the cafe. The system described in Appendix 1, characterized by the features described herein. (Note 6) The interrupt unit is, When a member arrives at the cafe, they will have priority seating over other users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate user emotions and adjust the congestion analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, By analyzing audio data from within the cafe, the system estimates the level of crowding based on the content of user conversations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system analyzes environmental data such as temperature and humidity inside the cafe to evaluate comfort levels. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how congestion levels are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Analyze the Wi-Fi connection status within the cafe to estimate the user's stay time. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system analyzes the lighting conditions inside the cafe and adjusts the lighting according to the level of crowding. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, It estimates the user's emotions and adjusts the optimal seat usage time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, By referring to past congestion data, we predict future congestion levels and calculate seat usage time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, The system calculates the optimal seat usage time for each individual user, taking into account their seat usage history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, The system estimates the user's emotions and adjusts the notification method for seat usage time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, We calculate seating time taking into account the cafe's operating hours and any special events. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, The system calculates the total seat usage time for the group, taking into account the user group composition. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the cafe's congestion status in real time, A calculation unit calculates the optimal seat usage time based on the data obtained by the analysis unit, The reception area allows users to check seat availability, make reservations, and check in via a smartphone app. It includes a section that provides priority seating to members. A system characterized by the following features.

2. The aforementioned analysis unit, By analyzing data obtained from cameras and sensors inside the cafe, the current crowding situation can be determined. The system according to feature 1.

3. The calculation unit, The system calculates the optimal seating time, taking into account the user's length of stay and the cafe's congestion level. The system according to feature 1.

4. The aforementioned reception unit is Users can check seat availability and reserve an available seat through a smartphone app. The system according to feature 1.

5. The aforementioned reception unit is Check in using the app when you arrive at the cafe. The system according to feature 1.

6. The interrupt unit is, When a member arrives at the cafe, they will have priority seating over other users. The system according to feature 1.

7. The aforementioned analysis unit, We estimate user emotions and adjust the congestion analysis method based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, By analyzing audio data from within the cafe, the system estimates the level of crowding based on the content of user conversations. The system according to feature 1.

9. The aforementioned analysis unit, The system analyzes environmental data such as temperature and humidity inside the cafe to evaluate comfort levels. The system according to feature 1.

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