System

Abstract

To provide a system capable of predicting a customer wait time and responding appropriately on the basis of the emotions of the customer.SOLUTION: A system according to an embodiment comprises an allocation unit, a prediction unit, a notification unit, an acquisition unit, an estimation unit, a determination unit, and an execution unit. The allocation unit allocates a waiting number to the customer. The prediction unit predicts a customer waiting time on the basis of the number allocated by the allocation unit. The notification unit notifies the customer of the waiting time predicted by the prediction unit. The acquisition unit acquires emotion estimation information for estimating the emotions of the customer. The estimation unit estimates the emotions of the customer on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines an action to take on the basis of the customer emotions estimated by the estimation unit. The execution unit executes the action determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately predict customer waiting times or respond based on emotions, so there is room for improvement.

[0005] The system according to the embodiment aims to predict customer waiting times and provide appropriate responses based on emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an allocation unit, a prediction unit, a notification unit, an acquisition unit, an estimation unit, a determination unit, and an execution unit. The allocation unit allocates waiting numbers to customers. The prediction unit predicts customer waiting times based on the numbers allocated by the allocation unit. The notification unit informs customers of the waiting times predicted by the prediction unit. The acquisition unit acquires emotion estimation information for estimating customer emotions. The estimation unit estimates customer emotions based on the emotion estimation information acquired by the acquisition unit. The determination unit decides a response based on the customer emotions estimated by the estimation unit. The execution unit executes the response decided by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can predict customer waiting times and provide appropriate responses based on emotions. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a system that smoothly manages waiting in a restaurant using an agent with an emotion engine. The system assigns a waiting number to a customer, predicts a waiting time, and notifies the customer. The system also estimates the customer's emotions, and determines and executes an appropriate response based on the emotions. For example, the agent can assign a waiting number to a customer, inform the customer of the expected waiting time, and provide information such as the restaurant's menu and special offers. Furthermore, the agent can grasp the customer's situation and waiting time in real time, and appropriately inform the customer of the expected waiting time and changes in the situation. The agent collects information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content, and estimates the customer's emotions based on this information. Based on the estimated emotions, the agent decides and executes a response to the customer. For example, if a customer feels anxious, the agent can provide information to reassure the customer. Specifically, the system provides the expected waiting time, restaurant menu information, special offer information, and the like. This improves customer satisfaction and improves the operation efficiency of the restaurant. This allows the system to smoothly manage waiting in line while taking into account the customer's emotions.

[0029] The waiting list management system according to the embodiment includes an allocation unit, a prediction unit, a notification unit, an acquisition unit, an estimation unit, a determination unit, and an execution unit. The allocation unit allocates a waiting number to a customer. For example, the allocation unit can allocate a number taking into consideration the arrival time of the customer and the reservation status. The prediction unit predicts the customer's waiting time based on the number allocated by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and a current situation. The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information of the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the emotion of the customer using AI. The determination unit determines how to respond based on the customer's emotions estimated by the estimation unit. For example, if the customer feels anxious, the determination unit can determine to provide information to reassure the customer. The execution unit executes the response determined by the determination unit. For example, the execution unit can provide the customer with information such as expected waiting time, restaurant menu information, and special offer information. In this way, the queue management system according to the embodiment can smoothly manage queues while taking into account the customer's emotions.

[0030] The allocation unit allocates waiting numbers to customers. For example, the allocation unit can allocate numbers taking into consideration the arrival time and reservation status of the customer. Specifically, the allocation unit inputs information into the reception system when the customer arrives and records the arrival time. If there is a reservation in advance, the allocation unit inputs the reservation information into the system and allocates an optimal waiting number by combining the arrival time and the reservation information. Based on this information, the allocation unit can adjust to minimize the waiting time of the customer. For example, during peak times, customers who are close to the arrival time are given priority, and customers who have reservations are notified in advance, thereby realizing smooth reception. Furthermore, the allocation unit can also take into consideration the specific needs and requests of the customer. For example, it can flexibly respond to customers who want a specific service or customers who want to use the service during a specific time period. As a result, the allocation unit can increase customer satisfaction and realize efficient waiting management.

[0031] The prediction unit predicts the customer's waiting time based on the number assigned by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and the current situation. Specifically, the prediction unit analyzes the current waiting situation in real time based on past visit data and statistical information on service provision time. By utilizing a machine learning algorithm using AI and learning past patterns and trends, a more accurate waiting time prediction can be made. For example, the trend of waiting time on a specific day of the week or time period is grasped, and a prediction is made in comparison with the current situation. In addition, the prediction unit can also take into account the current service provision situation and the operating status of the staff. For example, the number of staff, skill level, current service provision speed, etc. are monitored in real time and reflected in the waiting time prediction. As a result, the prediction unit can provide more accurate waiting time information to the customer and reduce the customer's anxiety. Furthermore, the prediction unit can continuously update the prediction result and respond to the latest situation. For example, when a new customer arrives or when the service provision time changes, the prediction result is immediately corrected and the latest waiting time information is provided to the customer. As a result, the prediction unit can always perform accurate and reliable waiting time prediction and improve customer satisfaction.

[0032] The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. Specifically, the notification unit transmits the predicted waiting time result in real time to the smartphone app registered by the customer. The app can quickly notify the customer of the waiting time information by using push notification or pop-up message. The notification unit can also customize the notification method according to the customer's preference. For example, the customer can select the desired notification method such as email, SMS, or voice call. This allows the customer to receive the waiting time information in the most optimal way for him or her. Furthermore, the notification unit continuously updates the information according to the fluctuation of the waiting time to provide the customer with the latest situation. For example, if the waiting time is shortened or the prediction is changed, the new information is immediately notified. This allows the customer to always be aware of the latest waiting time information and take appropriate action. The notification unit increases the convenience of the customer and plays a role in reducing anxiety and stress about the waiting time.

[0033] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. Specifically, the acquisition unit captures the customer's facial expression, movement, and voice in real time using devices such as a camera, microphone, and sensor. For example, the camera captures the customer's face and detects changes in facial expression. The microphone analyzes the customer's tone of voice and speaking style to capture changes in emotion. In addition, the customer's physiological reaction can be monitored using a heart rate sensor and a blood pressure monitor. This allows the acquisition unit to grasp the customer's emotional state from multiple angles. The acquisition unit can also analyze content posted by the customer on SNS and review sites to grasp the trend of emotion. For example, the acquisition unit can analyze comments and evaluations posted by the customer using natural language processing technology to determine the positive / negative trend of emotion. This allows the acquisition unit to comprehensively evaluate the customer's emotion and use it for the next step.

[0034] The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the customer's emotions using AI. Specifically, the estimation unit utilizes an emotion recognition model using deep learning to analyze the acquired data. For example, the facial expression recognition model analyzes the feature points of the customer's face to identify emotions such as smiling, anger, and sadness. The voice recognition model analyzes the tone, pitch, and rhythm of the voice to estimate the customer's emotional state. Furthermore, the model that analyzes physiological data judges stress and relaxation states based on data such as heart rate, blood pressure, and pupil response. These models are trained using a large amount of data in advance, and can estimate emotions with high accuracy. The estimation unit can combine these models to comprehensively evaluate the customer's emotions and grasp the emotional state in real time. For example, it can identify specific emotional states, such as when a customer is dissatisfied with the waiting time or when the customer is relaxed. This allows the estimation unit to provide basic information for taking appropriate measures according to the customer's emotions in the next step.

[0035] The determination unit determines the response based on the customer's emotion estimated by the estimation unit. For example, if the customer feels anxious, the determination unit can determine to provide information to reassure the customer. Specifically, the determination unit uses a rule-based system or AI to select an appropriate response according to the estimated emotional state. For example, if the customer feels anxious, the determination unit reduces the customer's anxiety by providing detailed expectations of waiting time and the progress of the service. In addition, if the customer is relaxed, the customer's satisfaction can be further increased by providing bonus information and promotion information. The determination unit can also take into account the customer's past behavior history and preference information when selecting these responses. For example, for a customer who has used a specific service in the past, a more personalized response can be provided by providing information related to that service. In this way, the determination unit can quickly determine the optimal response according to the customer's emotional state and improve customer satisfaction.

[0036] The execution unit executes the response determined by the determination unit. For example, the execution unit can provide the customer with an expected waiting time, restaurant menu information, special offer information, and the like. Specifically, the execution unit executes an action to provide appropriate information to the customer upon receiving an instruction from the determination unit. For example, when notifying the customer of an expected waiting time, the execution unit provides the customer with information in real time through a smartphone app or digital signage. Also, when providing restaurant menu information or special offer information, the execution unit can select information according to the customer's preferences and send a personalized message. Furthermore, the execution unit can collect customer feedback and evaluate the effectiveness of the response. For example, the execution unit can monitor how the customer responded to the provided information and reflect the response in the next response. This allows the execution unit to respond quickly and appropriately to the customer and improve customer satisfaction.

[0037] The notification unit can notify the customer of the waiting time in real time. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The notification unit can also notify the customer of fluctuations in the waiting time in real time. For example, when the waiting time is shortened or extended, the notification unit can notify the customer of the information in real time. This makes it possible to notify the customer of the waiting time in real time. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0038] The notification unit can notify the customer of changes in the waiting state in real time. For example, the notification unit can notify the customer of an increase or decrease in the number of people waiting and a change in the waiting time in real time. The notification unit can also provide the customer with appropriate information according to the change in the waiting state. For example, when the waiting time is shortened or extended, the notification unit can notify the customer of the information in real time. This makes it possible to notify the customer of changes in the waiting state in real time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupillary response, brain waves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit can capture the customer's facial expression with a camera and acquire the facial expression data. The acquisition unit can also detect the customer's movement with a sensor and acquire the movement data. The acquisition unit can also record the customer's voice with a microphone and acquire the voice data. For example, the acquisition unit can measure the customer's heart rate and acquire the heart rate data. The acquisition unit can also measure the customer's blood pressure and acquire the blood pressure data. The acquisition unit can also detect the customer's pupillary response with a camera and acquire the pupillary response data. For example, the acquisition unit can measure the customer's brain waves and acquire the brain wave data. The acquisition unit can also measure the customer's body temperature and acquire the body temperature data. The acquisition unit can also detect the customer's breathing with a sensor and acquire the breathing data. For example, the acquisition unit can analyze the customer's posted content and acquire the text data. This allows the acquisition unit to acquire customer emotion estimation information from various information sources. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The allocation unit can analyze the customer's past visit history and allocate an optimal waiting number. For example, if the customer has visited the store frequently in the past, the allocation unit can allocate a waiting number preferentially. In addition, if the customer has been kept waiting for a long time in the past, the allocation unit can allocate an earlier waiting number. Furthermore, if the customer visits the store for the first time, the allocation unit can allocate a normal waiting number. For example, the allocation unit can analyze the customer's past visit history and allocate an optimal waiting number based on the history. This makes it possible to allocate an optimal waiting number based on the customer's past visit history. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The allocation unit can dynamically change the waiting number based on the arrival time of the customer and the reservation status. For example, when the customer arrives earlier than scheduled, the allocation unit can change the waiting number to be earlier. In addition, the allocation unit can delay the waiting number when the customer arrives late. Furthermore, the allocation unit can readjust the waiting number when the customer cancels the reservation. For example, the allocation unit can dynamically change the waiting number based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the waiting number based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The allocation unit can allocate a waiting number taking into consideration the geographical location information of the customer. For example, when the customer is nearby, the allocation unit can allocate an early waiting number. Also, when the customer is far away, the allocation unit can allocate a normal waiting number. Furthermore, when the customer is on the move, the allocation unit can allocate a waiting number based on an estimated arrival time. For example, the allocation unit can allocate a waiting number taking into consideration the geographical location information of the customer. In this way, a waiting number can be allocated based on the geographical location information of the customer. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The allocation unit can analyze the social media activity of the customer and allocate a waiting number. For example, if the customer has highly rated the restaurant on social media, the allocation unit can allocate a waiting number preferentially. Also, the allocation unit can allocate an early waiting number if the customer frequently checks in to the restaurant on social media. Furthermore, the allocation unit can allocate a regular waiting number if the customer posts about the restaurant on social media. For example, the allocation unit can analyze the social media activity of the customer and allocate a waiting number based on the activity. In this way, a waiting number can be allocated based on the social media activity of the customer. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI.

[0044] The prediction unit can analyze past data and the current situation to improve the prediction accuracy of the waiting time. The prediction unit can predict the current waiting time based on, for example, past waiting time data. The prediction unit can also predict the waiting time taking into account the number of people currently waiting. Furthermore, the prediction unit can analyze the stay time of each customer and predict the waiting time. For example, the prediction unit can analyze past data and the current situation and improve the prediction accuracy of the waiting time based on the analysis results. Thereby, the prediction accuracy of the waiting time can be improved by analyzing the past data and the current situation. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0045] The prediction unit can dynamically change the waiting time prediction based on the customer's arrival time and reservation status. For example, if the customer arrives earlier than scheduled, the prediction unit can predict the waiting time to be shorter. In addition, if the customer arrives late, the prediction unit can also predict the waiting time to be longer. Furthermore, if the customer cancels the reservation, the prediction unit can recalculate the waiting time. For example, the prediction unit can dynamically change the waiting time prediction based on the customer's arrival time and reservation status. This makes it possible to dynamically change the waiting time prediction based on the customer's arrival time and reservation status. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The prediction unit can predict the waiting time taking into account the geographical location information of the customer. For example, when the customer is nearby, the prediction unit can predict the waiting time to be short. Furthermore, when the customer is far away, the prediction unit can also predict the waiting time to be long. Furthermore, when the customer is on the move, the prediction unit can predict the waiting time based on the predicted arrival time. For example, the prediction unit can predict the waiting time taking into account the geographical location information of the customer. This makes it possible to predict the waiting time based on the geographical location information of the customer. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The prediction unit can analyze the social media activity of the customer and predict the waiting time. For example, if the customer has highly rated the restaurant on social media, the prediction unit can predict the waiting time to be short. In addition, the prediction unit can also predict the waiting time to be short if the customer frequently checks in to the restaurant on social media. Furthermore, the prediction unit can predict the normal waiting time if the customer posts about the restaurant on social media. For example, the prediction unit can analyze the social media activity of the customer and predict the waiting time based on the activity. This makes it possible to predict the waiting time based on the social media activity of the customer. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The notification unit can analyze the past notification history of the customer and select the optimal notification method. For example, the notification unit can preferentially use a notification method (SMS, app notification, etc.) that the customer has used in the past. The notification unit can also analyze the customer's reaction to notifications received in the past and select the optimal notification method. Furthermore, the notification unit can select the optimal notification method for a specific time period from the customer's past notification history. For example, the notification unit can analyze the customer's past notification history and select the optimal notification method based on the analysis results. This makes it possible to select the optimal notification method based on the customer's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The notification unit can dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the notification unit can notify the customer of a shorter waiting time. In addition, if the customer arrives late, the notification unit can also notify the customer of a longer waiting time. Furthermore, if the customer cancels a reservation, the notification unit can recalculate the waiting time and notify the customer. For example, the notification unit can dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The notification unit can notify the customer of the waiting time taking into consideration the geographical location information of the customer. For example, if the customer is nearby, the notification unit can notify the customer of a shorter waiting time. Furthermore, if the customer is far away, the notification unit can also notify the customer of a longer waiting time. Furthermore, if the customer is on the move, the notification unit can also notify the customer of the waiting time based on the predicted arrival time. For example, the notification unit can notify the customer of the waiting time taking into consideration the geographical location information of the customer. This makes it possible to notify the customer of the waiting time based on the geographical location information of the customer. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The notification unit can analyze the social media activity of the customer and notify the customer of the waiting time. For example, if the customer has highly rated the restaurant on social media, the notification unit can notify the customer of a shorter waiting time. Furthermore, if the customer frequently checks in to the restaurant on social media, the notification unit can also notify the customer of a shorter waiting time. Furthermore, if the customer posts about the restaurant on social media, the notification unit can also notify the customer of a normal waiting time. For example, the notification unit can analyze the social media activity of the customer and notify the customer of the waiting time based on the activity. This makes it possible to notify the customer of the waiting time based on the social media activity of the customer. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. The acquisition unit can, for example, analyze the timing when the customer felt stressed in the past and acquire emotion estimation information at that timing. The acquisition unit can also analyze the timing when the customer was relaxed in the past and acquire emotion estimation information at that timing. Furthermore, the acquisition unit can select the optimal acquisition method (facial expression, voice, heart rate, etc.) based on the past emotion data of the customer. For example, the acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method based on the analysis result. In this way, the optimal acquisition method can be selected based on the past emotion data of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The acquisition unit can dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. For example, when the customer arrives earlier than scheduled, the acquisition unit can advance the timing of acquiring the emotion deduction information. Furthermore, when the customer arrives late, the acquisition unit can delay the timing of acquiring the emotion deduction information. Furthermore, when the customer cancels the reservation, the acquisition unit can stop acquiring the emotion deduction information. For example, the acquisition unit can dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The acquisition unit can acquire emotion estimation information taking into account the geographical location information of the customer. For example, when the customer is nearby, the acquisition unit can acquire facial expression and voice information. Furthermore, when the customer is far away, the acquisition unit can also acquire heart rate and body temperature information. Furthermore, when the customer is moving, the acquisition unit can also acquire movement and breathing information. For example, the acquisition unit can acquire emotion estimation information taking into account the geographical location information of the customer. In this way, emotion estimation information can be acquired based on the geographical location information of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0055] The acquisition unit can analyze the social media activity of the customer and acquire emotion estimation information. For example, when the customer highly rates the restaurant on social media, the acquisition unit can acquire facial expression and voice information. In addition, when the customer frequently checks in to the restaurant on social media, the acquisition unit can also acquire heart rate and body temperature information. In addition, when the customer posts about the restaurant on social media, the acquisition unit can also acquire movement and breathing information. For example, the acquisition unit can analyze the social media activity of the customer and acquire emotion estimation information based on the activity. In this way, emotion estimation information can be acquired based on the social media activity of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0056] The estimation unit can analyze the past emotion data of the customer and improve the accuracy of emotion estimation. The estimation unit can optimize the emotion estimation algorithm based on the past emotion data of the customer, for example. The estimation unit can also analyze the past emotion data of the customer and improve the accuracy of emotion estimation. Furthermore, the estimation unit can reduce the emotion estimation error by using the past emotion data of the customer. For example, the estimation unit can analyze the past emotion data of the customer and improve the accuracy of emotion estimation based on the analysis result. This makes it possible to improve the accuracy of emotion estimation based on the past emotion data of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0057] The estimation unit can dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the estimation unit can change the result of the emotion estimation earlier. In addition, the estimation unit can delay the result of the emotion estimation if the customer arrives late. Furthermore, the estimation unit can recalculate the result of the emotion estimation if the customer cancels the reservation. For example, the estimation unit can dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The estimation unit can perform emotion estimation by taking into account the geographical location information of the customer. For example, when the customer is nearby, the estimation unit can perform emotion estimation quickly. Furthermore, when the customer is far away, the estimation unit can also perform emotion estimation at a normal speed. Furthermore, when the customer is moving, the estimation unit can also dynamically perform emotion estimation. For example, the estimation unit can perform emotion estimation by taking into account the geographical location information of the customer. Thereby, emotion estimation can be performed based on the geographical location information of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0059] The estimation unit can analyze the social media activity of the customer and perform emotion estimation. For example, the estimation unit can quickly perform emotion estimation when the customer highly rates the restaurant on social media. The estimation unit can also quickly perform emotion estimation when the customer frequently checks in to the restaurant on social media. Furthermore, the estimation unit can perform emotion estimation at a normal speed when the customer posts about the restaurant on social media. For example, the estimation unit can analyze the social media activity of the customer and perform emotion estimation based on the activity. This allows emotion estimation to be performed based on the social media activity of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0060] The determination unit can analyze the past response history of the customer and select the optimal response method. The determination unit can, for example, preferentially provide the response method that the customer has preferred in the past. The determination unit can also analyze the past response history of the customer and select the optimal response method. Furthermore, the determination unit can select the optimal response method for a specific time period from the past response history of the customer. For example, the determination unit can analyze the past response history of the customer and select the optimal response method based on the analysis result. This makes it possible to select the optimal response method based on the past response history of the customer. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The determination unit can dynamically change the response method based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the determination unit can change the response method to an earlier one. In addition, the determination unit can delay the response method when the customer arrives late. Furthermore, the determination unit can recalculate the response method when the customer cancels the reservation. For example, the determination unit can dynamically change the response method based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the response method based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0062] The determination unit can determine a response method by taking into account the geographical location information of the customer. For example, the determination unit can respond quickly when the customer is nearby. Furthermore, the determination unit can also respond at a normal speed when the customer is far away. Furthermore, the determination unit can dynamically respond when the customer is on the move. For example, the determination unit can determine a response method by taking into account the geographical location information of the customer. This makes it possible to determine a response method based on the geographical location information of the customer. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0063] The decision unit can analyze the social media activity of the customer and determine a response method. For example, the decision unit can respond quickly if the customer rates the restaurant highly on social media. The decision unit can also respond quickly if the customer frequently checks in to the restaurant on social media. Furthermore, the decision unit can respond at a normal speed if the customer posts about the restaurant on social media. For example, the decision unit can analyze the social media activity of the customer and determine a response method based on the activity. This makes it possible to determine a response method based on the social media activity of the customer. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The execution unit can analyze the past execution history of the customer and select the optimal execution method. The execution unit can, for example, preferentially provide an execution method that the customer has preferred in the past. The execution unit can also analyze the past execution history of the customer and select the optimal execution method. Furthermore, the execution unit can select the optimal execution method for a specific time period from the past execution history of the customer. For example, the execution unit can analyze the past execution history of the customer and select the optimal execution method based on the analysis results. This makes it possible to select the optimal execution method based on the past execution history of the customer. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The execution unit can dynamically change the execution method based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the execution unit can change the execution method to be earlier. Also, if the customer arrives late, the execution unit can delay the execution method. Furthermore, if the customer cancels the reservation, the execution unit can recalculate the execution method. For example, the execution unit can dynamically change the execution method based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the execution method based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The execution unit can determine the execution method by taking into account the geographical location information of the customer. The execution unit can execute quickly, for example, when the customer is nearby. The execution unit can also execute at a normal speed when the customer is far away. Furthermore, the execution unit can execute dynamically when the customer is on the move. For example, the execution unit can determine the execution method by taking into account the geographical location information of the customer. This makes it possible to determine the execution method based on the geographical location information of the customer. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The execution unit can analyze the social media activity of the customer and determine the execution method. The execution unit can execute quickly, for example, when the customer rates the restaurant highly on social media. The execution unit can also execute quickly, when the customer frequently checks in the restaurant on social media. Furthermore, the execution unit can execute at a normal speed, when the customer posts about the restaurant on social media. For example, the execution unit can analyze the social media activity of the customer and determine the execution method based on the activity. This makes it possible to determine the execution method based on the social media activity of the customer. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The allocation unit can analyze the customer's past visit history and allocate the optimal waiting number. For example, if the customer has visited the store frequently in the past, the allocation unit can allocate a waiting number preferentially. Also, if the customer has been kept waiting for a long time in the past, the allocation unit can allocate an earlier waiting number. Furthermore, if the customer is visiting the store for the first time, the allocation unit can allocate a normal waiting number. In this way, it is possible to allocate the optimal waiting number based on the customer's past visit history.

[0070] The notification unit can notify the customer of the waiting time in real time. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The notification unit can also notify the customer of fluctuations in the waiting time in real time. For example, if the waiting time is shortened or extended, the notification unit can notify the customer of that information in real time. This makes it possible to notify the customer of the waiting time in real time.

[0071] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit can capture the customer's facial expression with a camera and acquire the facial expression data. The acquisition unit can also detect the customer's movement with a sensor and acquire the movement data. Furthermore, the acquisition unit can record the customer's voice with a microphone and acquire the voice data. This allows the acquisition unit to acquire the customer's emotion estimation information from a variety of information sources.

[0072] The allocating unit can allocate queue numbers taking into account the geographic location information of the customer. For example, the allocating unit can allocate an early queue number if the customer is nearby. The allocating unit can also allocate a regular queue number if the customer is far away. Furthermore, the allocating unit can allocate a queue number based on an estimated arrival time if the customer is on the move. In this way, queue numbers can be allocated based on the geographic location information of the customer.

[0073] The notification unit can analyze the social media activity of the customer and notify the customer of the waiting time. For example, the notification unit can notify the customer of a shorter waiting time if the customer has given a high rating to the restaurant on social media. The notification unit can also notify the customer of a shorter waiting time if the customer frequently checks in to the restaurant on social media. Furthermore, the notification unit can also notify the customer of a normal waiting time if the customer has posted about the restaurant on social media. This makes it possible to notify the customer of the waiting time based on the customer's social media activity.

[0074] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. For example, the acquisition unit can analyze the timing when the customer felt stressed in the past and acquire emotion estimation information at that timing. The acquisition unit can also analyze the timing when the customer felt relaxed in the past and acquire emotion estimation information at that timing. Furthermore, the acquisition unit can select the optimal acquisition method (facial expression, voice, heart rate, etc.) based on the past emotion data of the customer. This makes it possible to select the optimal acquisition method based on the past emotion data of the customer.

[0075] The process flow of the first embodiment will be briefly described below.

[0076] Step 1: The allocation unit allocates a waiting number to a customer. For example, the allocation unit can allocate a number taking into consideration the customer's arrival time and reservation status. Step 2: The prediction unit predicts the customer's waiting time based on the number assigned by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and the current situation. Step 3: The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. Step 4: The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, respiration, and posted content. Step 5: The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the emotion of the customer using AI. Step 6: The decision unit decides a response based on the customer's emotion estimated by the estimation unit. For example, the decision unit may decide to provide reassuring information to the customer if the customer feels anxious. Step 7: The execution unit executes the action determined by the determination unit. For example, the execution unit can provide the customer with information such as expected waiting time, restaurant menu information, and special offer information.

[0077] (Example 2) A system according to an embodiment of the present invention is a system that smoothly manages waiting in a restaurant using an agent with an emotion engine. The system assigns a waiting number to a customer, predicts a waiting time, and notifies the customer. The system also estimates the customer's emotions, and determines and executes an appropriate response based on the emotions. For example, the agent can assign a waiting number to a customer, inform the customer of the expected waiting time, and provide information such as the restaurant's menu and special offers. Furthermore, the agent can grasp the customer's situation and waiting time in real time, and appropriately inform the customer of the expected waiting time and changes in the situation. The agent collects information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content, and estimates the customer's emotions based on this information. Based on the estimated emotions, the agent decides and executes a response to the customer. For example, if a customer feels anxious, the agent can provide information to reassure the customer. Specifically, the system provides the expected waiting time, restaurant menu information, special offer information, and the like. This improves customer satisfaction and improves the operation efficiency of the restaurant. This allows the system to smoothly manage waiting in line while taking into account the customer's emotions.

[0078] The waiting list management system according to the embodiment includes an allocation unit, a prediction unit, a notification unit, an acquisition unit, an estimation unit, a determination unit, and an execution unit. The allocation unit allocates a waiting number to a customer. For example, the allocation unit can allocate a number taking into consideration the arrival time of the customer and the reservation status. The prediction unit predicts the customer's waiting time based on the number allocated by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and a current situation. The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information of the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the emotion of the customer using AI. The determination unit determines how to respond based on the customer's emotions estimated by the estimation unit. For example, if the customer feels anxious, the determination unit can determine to provide information to reassure the customer. The execution unit executes the response determined by the determination unit. For example, the execution unit can provide the customer with information such as expected waiting time, restaurant menu information, and special offer information. In this way, the queue management system according to the embodiment can smoothly manage queues while taking into account the customer's emotions.

[0079] The allocation unit allocates waiting numbers to customers. For example, the allocation unit can allocate numbers taking into consideration the arrival time and reservation status of the customer. Specifically, the allocation unit inputs information into the reception system when the customer arrives and records the arrival time. If there is a reservation in advance, the allocation unit inputs the reservation information into the system and allocates an optimal waiting number by combining the arrival time and the reservation information. Based on this information, the allocation unit can adjust to minimize the waiting time of the customer. For example, during peak times, customers who are close to the arrival time are given priority, and customers who have reservations are notified in advance, thereby realizing smooth reception. Furthermore, the allocation unit can also take into consideration the specific needs and requests of the customer. For example, it can flexibly respond to customers who want a specific service or customers who want to use the service during a specific time period. As a result, the allocation unit can increase customer satisfaction and realize efficient waiting management.

[0080] The prediction unit predicts the customer's waiting time based on the number assigned by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and the current situation. Specifically, the prediction unit analyzes the current waiting situation in real time based on past visit data and statistical information on service provision time. By utilizing a machine learning algorithm using AI and learning past patterns and trends, a more accurate waiting time prediction can be made. For example, the trend of waiting time on a specific day of the week or time period is grasped, and a prediction is made in comparison with the current situation. In addition, the prediction unit can also take into account the current service provision situation and the operating status of the staff. For example, the number of staff, skill level, current service provision speed, etc. are monitored in real time and reflected in the waiting time prediction. As a result, the prediction unit can provide more accurate waiting time information to the customer and reduce the customer's anxiety. Furthermore, the prediction unit can continuously update the prediction result and respond to the latest situation. For example, when a new customer arrives or when the service provision time changes, the prediction result is immediately corrected and the latest waiting time information is provided to the customer. As a result, the prediction unit can always perform accurate and reliable waiting time prediction and improve customer satisfaction.

[0081] The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. Specifically, the notification unit transmits the predicted waiting time result in real time to the smartphone app registered by the customer. The app can quickly notify the customer of the waiting time information by using push notification or pop-up message. The notification unit can also customize the notification method according to the customer's preference. For example, the customer can select the desired notification method such as email, SMS, or voice call. This allows the customer to receive the waiting time information in the most optimal way for him or her. Furthermore, the notification unit continuously updates the information according to the fluctuation of the waiting time to provide the customer with the latest situation. For example, if the waiting time is shortened or the prediction is changed, the new information is immediately notified. This allows the customer to always be aware of the latest waiting time information and take appropriate action. The notification unit increases the convenience of the customer and plays a role in reducing anxiety and stress about the waiting time.

[0082] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. Specifically, the acquisition unit captures the customer's facial expression, movement, and voice in real time using devices such as a camera, microphone, and sensor. For example, the camera captures the customer's face and detects changes in facial expression. The microphone analyzes the customer's tone of voice and speaking style to capture changes in emotion. In addition, the customer's physiological reaction can be monitored using a heart rate sensor and a blood pressure monitor. This allows the acquisition unit to grasp the customer's emotional state from multiple angles. The acquisition unit can also analyze content posted by the customer on SNS and review sites to grasp the trend of emotion. For example, the acquisition unit can analyze comments and evaluations posted by the customer using natural language processing technology to determine the positive / negative trend of emotion. This allows the acquisition unit to comprehensively evaluate the customer's emotion and use it for the next step.

[0083] The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the customer's emotions using AI. Specifically, the estimation unit utilizes an emotion recognition model using deep learning to analyze the acquired data. For example, the facial expression recognition model analyzes the feature points of the customer's face to identify emotions such as smiling, anger, and sadness. The voice recognition model analyzes the tone, pitch, and rhythm of the voice to estimate the customer's emotional state. Furthermore, the model that analyzes physiological data judges stress and relaxation states based on data such as heart rate, blood pressure, and pupil response. These models are trained using a large amount of data in advance, and can estimate emotions with high accuracy. The estimation unit can combine these models to comprehensively evaluate the customer's emotions and grasp the emotional state in real time. For example, it can identify specific emotional states, such as when a customer is dissatisfied with the waiting time or when the customer is relaxed. This allows the estimation unit to provide basic information for taking appropriate measures according to the customer's emotions in the next step.

[0084] The determination unit determines the response based on the customer's emotion estimated by the estimation unit. For example, if the customer feels anxious, the determination unit can determine to provide information to reassure the customer. Specifically, the determination unit uses a rule-based system or AI to select an appropriate response according to the estimated emotional state. For example, if the customer feels anxious, the determination unit reduces the customer's anxiety by providing detailed expectations of waiting time and the progress of the service. In addition, if the customer is relaxed, the customer's satisfaction can be further increased by providing bonus information and promotion information. The determination unit can also take into account the customer's past behavior history and preference information when selecting these responses. For example, for a customer who has used a specific service in the past, a more personalized response can be provided by providing information related to that service. In this way, the determination unit can quickly determine the optimal response according to the customer's emotional state and improve customer satisfaction.

[0085] The execution unit executes the response determined by the determination unit. For example, the execution unit can provide the customer with an expected waiting time, restaurant menu information, special offer information, and the like. Specifically, the execution unit executes an action to provide appropriate information to the customer upon receiving an instruction from the determination unit. For example, when notifying the customer of an expected waiting time, the execution unit provides the customer with information in real time through a smartphone app or digital signage. Also, when providing restaurant menu information or special offer information, the execution unit can select information according to the customer's preferences and send a personalized message. Furthermore, the execution unit can collect customer feedback and evaluate the effectiveness of the response. For example, the execution unit can monitor how the customer responded to the provided information and reflect the response in the next response. This allows the execution unit to respond quickly and appropriately to the customer and improve customer satisfaction.

[0086] The determination unit can determine the provision of information based on the customer's emotions as a response based on the customer's emotions, and the execution unit can provide the customer with the information based on the customer's emotions. For example, when a customer feels anxious, the determination unit decides to provide information to reassure the customer. For example, the determination unit decides to provide the customer with information such as expected waiting time, restaurant menu information, and special offer information. The execution unit provides the customer with the information determined by the determination unit. For example, the execution unit can provide the customer with information such as expected waiting time, restaurant menu information, and special offer information. This makes it possible to provide information according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0087] The determination unit determines to provide menu information or special offer information based on the customer's emotion, and the execution unit can provide the customer with the menu information or special offer information based on the customer's emotion. For example, when the customer feels anxious, the determination unit determines to provide menu information or special offer information to reassure the customer. For example, the determination unit determines to provide special offer coupons or discount information to the customer. The execution unit provides the customer with the menu information or special offer information determined by the determination unit. For example, the execution unit can provide special offer coupons or discount information to the customer. This makes it possible to provide menu information or special offer information according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The determination unit can determine the provision of information according to the customer's emotion and the customer's waiting time as a response according to the customer's emotion, and the execution unit can provide the customer with information according to the customer's emotion and the customer's waiting time. For example, when a customer feels anxious, the determination unit determines to provide information to reassure the customer. For example, the determination unit determines to provide the customer with an expected waiting time, restaurant menu information, special offer information, and the like. The execution unit provides the customer with the information determined by the determination unit. For example, the execution unit can provide the customer with an expected waiting time, restaurant menu information, special offer information, and the like. This makes it possible to provide information according to the customer's emotion and waiting time. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The determination unit determines the provision of information according to the customer's emotion, the customer's waiting time, and the customer's attributes as a response according to the customer's emotion, and the execution unit can provide the customer with information according to the customer's emotion, the customer's waiting time, and the customer's attributes. For example, when a customer feels anxious, the determination unit determines to provide information to reassure the customer. For example, the determination unit determines to provide the customer with information such as the expected waiting time, restaurant menu information, and special offer information. The execution unit provides the customer with the information determined by the determination unit. For example, the execution unit can provide the customer with information such as the expected waiting time, restaurant menu information, and special offer information. This makes it possible to provide information according to the customer's emotion, waiting time, and attributes. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The notification unit can notify the customer of the waiting time in real time. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The notification unit can also notify the customer of fluctuations in the waiting time in real time. For example, when the waiting time is shortened or extended, the notification unit can notify the customer of the information in real time. This makes it possible to notify the customer of the waiting time in real time. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0091] The notification unit can notify the customer of changes in the waiting state in real time. For example, the notification unit can notify the customer of an increase or decrease in the number of people waiting and a change in the waiting time in real time. The notification unit can also provide the customer with appropriate information according to the change in the waiting state. For example, when the waiting time is shortened or extended, the notification unit can notify the customer of the information in real time. This makes it possible to notify the customer of changes in the waiting state in real time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupillary response, brain waves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit can capture the customer's facial expression with a camera and acquire the facial expression data. The acquisition unit can also detect the customer's movement with a sensor and acquire the movement data. The acquisition unit can also record the customer's voice with a microphone and acquire the voice data. For example, the acquisition unit can measure the customer's heart rate and acquire the heart rate data. The acquisition unit can also measure the customer's blood pressure and acquire the blood pressure data. The acquisition unit can also detect the customer's pupillary response with a camera and acquire the pupillary response data. For example, the acquisition unit can measure the customer's brain waves and acquire the brain wave data. The acquisition unit can also measure the customer's body temperature and acquire the body temperature data. The acquisition unit can also detect the customer's breathing with a sensor and acquire the breathing data. For example, the acquisition unit can analyze the customer's posted content and acquire the text data. This allows the acquisition unit to acquire customer emotion estimation information from various information sources. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The allocation unit can estimate the emotion of the customer and adjust the waiting number based on the estimated emotion of the customer. For example, when the customer feels anxious, the allocation unit can allocate an early waiting number. Also, when the customer is relaxed, the allocation unit can allocate a normal waiting number. Furthermore, when the customer is in a hurry, the allocation unit can allocate a waiting number preferentially. For example, the allocation unit can estimate the emotion of the customer and adjust the waiting number based on the emotion. This allows the waiting number to be adjusted based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The allocation unit can analyze the customer's past visit history and allocate an optimal waiting number. For example, if the customer has visited the store frequently in the past, the allocation unit can allocate a waiting number preferentially. In addition, if the customer has been kept waiting for a long time in the past, the allocation unit can allocate an earlier waiting number. Furthermore, if the customer visits the store for the first time, the allocation unit can allocate a normal waiting number. For example, the allocation unit can analyze the customer's past visit history and allocate an optimal waiting number based on the history. This makes it possible to allocate an optimal waiting number based on the customer's past visit history. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI.

[0095] The allocation unit can dynamically change the waiting number based on the arrival time of the customer and the reservation status. For example, when the customer arrives earlier than scheduled, the allocation unit can change the waiting number to be earlier. In addition, the allocation unit can delay the waiting number when the customer arrives late. Furthermore, the allocation unit can readjust the waiting number when the customer cancels the reservation. For example, the allocation unit can dynamically change the waiting number based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the waiting number based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The allocation unit can estimate the emotion of the customer and determine the priority of the waiting number based on the estimated emotion of the customer. For example, when the customer is stressed, the allocation unit can preferentially allocate the waiting number. Also, when the customer is relaxed, the allocation unit can allocate the normal waiting number. Furthermore, when the customer is in a hurry, the allocation unit can allocate the waiting number with the highest priority. For example, the allocation unit can estimate the emotion of the customer and determine the priority of the waiting number based on the emotion. This makes it possible to determine the priority of the waiting number based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The allocation unit can allocate a waiting number taking into consideration the geographical location information of the customer. For example, when the customer is nearby, the allocation unit can allocate an early waiting number. Also, when the customer is far away, the allocation unit can allocate a normal waiting number. Furthermore, when the customer is on the move, the allocation unit can allocate a waiting number based on an estimated arrival time. For example, the allocation unit can allocate a waiting number taking into consideration the geographical location information of the customer. In this way, a waiting number can be allocated based on the geographical location information of the customer. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The allocation unit can analyze the social media activity of the customer and allocate a waiting number. For example, if the customer has highly rated the restaurant on social media, the allocation unit can allocate a waiting number preferentially. Also, the allocation unit can allocate an early waiting number if the customer frequently checks in to the restaurant on social media. Furthermore, the allocation unit can allocate a regular waiting number if the customer posts about the restaurant on social media. For example, the allocation unit can analyze the social media activity of the customer and allocate a waiting number based on the activity. In this way, a waiting number can be allocated based on the social media activity of the customer. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI.

[0099] The prediction unit can estimate the customer's emotion and adjust the predicted waiting time based on the estimated customer's emotion. For example, if the customer feels anxious, the prediction unit can predict a shorter waiting time. Also, if the customer is relaxed, the prediction unit can predict a normal waiting time. Furthermore, if the customer is in a hurry, the prediction unit can predict the shortest waiting time. For example, the prediction unit can estimate the customer's emotion and adjust the predicted waiting time based on the emotion. This makes it possible to adjust the predicted waiting time based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The prediction unit can analyze past data and the current situation to improve the prediction accuracy of the waiting time. The prediction unit can predict the current waiting time based on, for example, past waiting time data. The prediction unit can also predict the waiting time taking into account the number of people currently waiting. Furthermore, the prediction unit can analyze the stay time of each customer and predict the waiting time. For example, the prediction unit can analyze past data and the current situation and improve the prediction accuracy of the waiting time based on the analysis results. Thereby, the prediction accuracy of the waiting time can be improved by analyzing the past data and the current situation. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0101] The prediction unit can dynamically change the waiting time prediction based on the customer's arrival time and reservation status. For example, if the customer arrives earlier than scheduled, the prediction unit can predict the waiting time to be shorter. In addition, if the customer arrives late, the prediction unit can also predict the waiting time to be longer. Furthermore, if the customer cancels the reservation, the prediction unit can recalculate the waiting time. For example, the prediction unit can dynamically change the waiting time prediction based on the customer's arrival time and reservation status. This makes it possible to dynamically change the waiting time prediction based on the customer's arrival time and reservation status. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The prediction unit can estimate the customer's emotion and adjust the order of displaying the predicted waiting time results based on the estimated customer's emotion. For example, if the customer feels anxious, the prediction unit can display the predicted waiting time results first. Also, if the customer is relaxed, the prediction unit can display the predicted waiting time results in a normal order. Furthermore, if the customer is in a hurry, the prediction unit can preferentially display the predicted waiting time results. For example, the prediction unit can estimate the customer's emotion and adjust the order of displaying the predicted waiting time results based on the emotion. This makes it possible to adjust the order of displaying the predicted waiting time results based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The prediction unit can estimate the customer's emotion and adjust the order of displaying the predicted waiting time results based on the estimated customer's emotion. For example, if the customer feels anxious, the prediction unit can display the predicted waiting time results first. Also, if the customer is relaxed, the prediction unit can display the predicted waiting time results in a normal order. Furthermore, if the customer is in a hurry, the prediction unit can preferentially display the predicted waiting time results. For example, the prediction unit can estimate the customer's emotion and adjust the order of displaying the predicted waiting time results based on the emotion. This makes it possible to adjust the order of displaying the predicted waiting time results based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] The prediction unit can predict the waiting time taking into account the geographical location information of the customer. For example, when the customer is nearby, the prediction unit can predict the waiting time to be short. Furthermore, when the customer is far away, the prediction unit can also predict the waiting time to be long. Furthermore, when the customer is on the move, the prediction unit can predict the waiting time based on the predicted arrival time. For example, the prediction unit can predict the waiting time taking into account the geographical location information of the customer. This makes it possible to predict the waiting time based on the geographical location information of the customer. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0105] The prediction unit can analyze the social media activity of the customer and predict the waiting time. For example, if the customer has highly rated the restaurant on social media, the prediction unit can predict the waiting time to be short. In addition, the prediction unit can also predict the waiting time to be short if the customer frequently checks in to the restaurant on social media. Furthermore, the prediction unit can predict the normal waiting time if the customer posts about the restaurant on social media. For example, the prediction unit can analyze the social media activity of the customer and predict the waiting time based on the activity. This makes it possible to predict the waiting time based on the social media activity of the customer. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The notification unit can estimate the customer's emotion and adjust the waiting time notification method based on the estimated customer's emotion. For example, when the customer feels anxious, the notification unit can notify the customer of detailed waiting time information. Furthermore, when the customer is relaxed, the notification unit can also notify the customer of concise waiting time information. Furthermore, when the customer is in a hurry, the notification unit can also quickly notify the customer of waiting time information. For example, the notification unit can estimate the customer's emotion and adjust the waiting time notification method based on the emotion. This makes it possible to adjust the waiting time notification method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The notification unit can analyze the past notification history of the customer and select the optimal notification method. For example, the notification unit can preferentially use a notification method (SMS, app notification, etc.) that the customer has used in the past. The notification unit can also analyze the customer's reaction to notifications received in the past and select the optimal notification method. Furthermore, the notification unit can select the optimal notification method for a specific time period from the customer's past notification history. For example, the notification unit can analyze the customer's past notification history and select the optimal notification method based on the analysis results. This makes it possible to select the optimal notification method based on the customer's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0108] The notification unit can dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the notification unit can notify the customer of a shorter waiting time. In addition, if the customer arrives late, the notification unit can also notify the customer of a longer waiting time. Furthermore, if the customer cancels a reservation, the notification unit can recalculate the waiting time and notify the customer. For example, the notification unit can dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the notification content of the waiting time based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0109] The notification unit can estimate the emotion of the customer and determine the priority of the notification based on the estimated emotion of the customer. For example, when the customer feels anxious, the notification unit can give priority to notifying the customer of the waiting time information. In addition, when the customer is relaxed, the notification unit can also give priority to notifying the customer of the waiting time information in the normal order. Furthermore, when the customer is in a hurry, the notification unit can also give priority to notifying the customer of the waiting time information. For example, the notification unit can estimate the emotion of the customer and determine the priority of the notification based on the emotion. This makes it possible to determine the priority of the notification based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The notification unit can notify the customer of the waiting time taking into consideration the geographical location information of the customer. For example, if the customer is nearby, the notification unit can notify the customer of a shorter waiting time. Furthermore, if the customer is far away, the notification unit can also notify the customer of a longer waiting time. Furthermore, if the customer is on the move, the notification unit can also notify the customer of the waiting time based on the predicted arrival time. For example, the notification unit can notify the customer of the waiting time taking into consideration the geographical location information of the customer. This makes it possible to notify the customer of the waiting time based on the geographical location information of the customer. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0111] The notification unit can analyze the social media activity of the customer and notify the customer of the waiting time. For example, if the customer has highly rated the restaurant on social media, the notification unit can notify the customer of a shorter waiting time. Furthermore, if the customer frequently checks in to the restaurant on social media, the notification unit can also notify the customer of a shorter waiting time. Furthermore, if the customer posts about the restaurant on social media, the notification unit can also notify the customer of a normal waiting time. For example, the notification unit can analyze the social media activity of the customer and notify the customer of the waiting time based on the activity. This makes it possible to notify the customer of the waiting time based on the social media activity of the customer. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0112] The acquisition unit can estimate the emotion of the customer and adjust the timing of acquiring the emotion estimating information based on the estimated emotion of the customer. For example, when the customer feels anxious, the acquisition unit can frequently acquire the emotion estimating information. Furthermore, when the customer is relaxed, the acquisition unit can also acquire the emotion estimating information at a normal timing. Furthermore, when the customer is in a hurry, the acquisition unit can quickly acquire the emotion estimating information. For example, the acquisition unit can estimate the emotion of the customer and adjust the timing of acquiring the emotion estimating information based on the emotion. This makes it possible to adjust the timing of acquiring the emotion estimating information based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. The acquisition unit can, for example, analyze the timing when the customer felt stressed in the past and acquire emotion estimation information at that timing. The acquisition unit can also analyze the timing when the customer was relaxed in the past and acquire emotion estimation information at that timing. Furthermore, the acquisition unit can select the optimal acquisition method (facial expression, voice, heart rate, etc.) based on the past emotion data of the customer. For example, the acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method based on the analysis result. In this way, the optimal acquisition method can be selected based on the past emotion data of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The acquisition unit can dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. For example, when the customer arrives earlier than scheduled, the acquisition unit can advance the timing of acquiring the emotion deduction information. Furthermore, when the customer arrives late, the acquisition unit can delay the timing of acquiring the emotion deduction information. Furthermore, when the customer cancels the reservation, the acquisition unit can stop acquiring the emotion deduction information. For example, the acquisition unit can dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the acquisition of the emotion deduction information based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0115] The acquisition unit can estimate the customer's emotion and determine the priority of information to be acquired based on the estimated customer's emotion. For example, when the customer feels anxious, the acquisition unit can preferentially acquire facial expression and voice information. Furthermore, when the customer is relaxed, the acquisition unit can also preferentially acquire heart rate and body temperature information. Furthermore, when the customer is in a hurry, the acquisition unit can also preferentially acquire movement and breathing information. For example, the acquisition unit can estimate the customer's emotion and determine the priority of information to be acquired based on the emotion. This makes it possible to determine the priority of information to be acquired based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0116] The acquisition unit can acquire emotion estimation information taking into account the geographical location information of the customer. For example, when the customer is nearby, the acquisition unit can acquire facial expression and voice information. Furthermore, when the customer is far away, the acquisition unit can also acquire heart rate and body temperature information. Furthermore, when the customer is moving, the acquisition unit can also acquire movement and breathing information. For example, the acquisition unit can acquire emotion estimation information taking into account the geographical location information of the customer. In this way, emotion estimation information can be acquired based on the geographical location information of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0117] The acquisition unit can analyze the social media activity of the customer and acquire emotion estimation information. For example, when the customer highly rates the restaurant on social media, the acquisition unit can acquire facial expression and voice information. In addition, when the customer frequently checks in to the restaurant on social media, the acquisition unit can also acquire heart rate and body temperature information. In addition, when the customer posts about the restaurant on social media, the acquisition unit can also acquire movement and breathing information. For example, the acquisition unit can analyze the social media activity of the customer and acquire emotion estimation information based on the activity. In this way, emotion estimation information can be acquired based on the social media activity of the customer. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0118] The estimation unit can estimate the emotion of the customer and adjust the emotion estimation algorithm based on the estimated emotion of the customer. For example, the estimation unit can sensitively adjust the emotion estimation algorithm when the customer feels anxious. Also, the estimation unit can set the emotion estimation algorithm to a normal setting when the customer is relaxed. Furthermore, the estimation unit can quickly adjust the emotion estimation algorithm when the customer is in a hurry. For example, the estimation unit can estimate the emotion of the customer and adjust the emotion estimation algorithm based on the emotion. This allows the emotion estimation algorithm to be adjusted based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The estimation unit can analyze the past emotion data of the customer and improve the accuracy of emotion estimation. The estimation unit can optimize the emotion estimation algorithm based on the past emotion data of the customer, for example. The estimation unit can also analyze the past emotion data of the customer and improve the accuracy of emotion estimation. Furthermore, the estimation unit can reduce the emotion estimation error by using the past emotion data of the customer. For example, the estimation unit can analyze the past emotion data of the customer and improve the accuracy of emotion estimation based on the analysis result. This makes it possible to improve the accuracy of emotion estimation based on the past emotion data of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0120] The estimation unit can dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the estimation unit can change the result of the emotion estimation earlier. In addition, the estimation unit can delay the result of the emotion estimation if the customer arrives late. Furthermore, the estimation unit can recalculate the result of the emotion estimation if the customer cancels the reservation. For example, the estimation unit can dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the result of the emotion estimation based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0121] The estimation unit can estimate the emotion of the customer and adjust the display method of the emotion estimation result based on the estimated emotion of the customer. For example, when the customer feels anxious, the estimation unit can display the emotion estimation result in detail. Furthermore, when the customer is relaxed, the estimation unit can also display the emotion estimation result briefly. Furthermore, when the customer is in a hurry, the estimation unit can also display the emotion estimation result quickly. For example, the estimation unit can estimate the emotion of the customer and adjust the display method of the emotion estimation result based on the emotion. This makes it possible to adjust the display method of the emotion estimation result based on the emotion of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] The estimation unit can perform emotion estimation by taking into account the geographical location information of the customer. For example, when the customer is nearby, the estimation unit can perform emotion estimation quickly. Furthermore, when the customer is far away, the estimation unit can also perform emotion estimation at a normal speed. Furthermore, when the customer is moving, the estimation unit can also dynamically perform emotion estimation. For example, the estimation unit can perform emotion estimation by taking into account the geographical location information of the customer. Thereby, emotion estimation can be performed based on the geographical location information of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0123] The estimation unit can analyze the social media activity of the customer and perform emotion estimation. For example, the estimation unit can quickly perform emotion estimation when the customer highly rates the restaurant on social media. The estimation unit can also quickly perform emotion estimation when the customer frequently checks in to the restaurant on social media. Furthermore, the estimation unit can perform emotion estimation at a normal speed when the customer posts about the restaurant on social media. For example, the estimation unit can analyze the social media activity of the customer and perform emotion estimation based on the activity. This allows emotion estimation to be performed based on the social media activity of the customer. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0124] The decision unit can estimate the customer's emotion and adjust the response method based on the estimated customer's emotion. For example, if the customer feels anxious, the decision unit can provide information to reassure the customer. Furthermore, if the customer is relaxed, the decision unit can provide a normal response method. Furthermore, if the customer is in a hurry, the decision unit can provide a quick response method. For example, the decision unit can estimate the customer's emotion and adjust the response method based on the emotion. This makes it possible to adjust the response method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0125] The determination unit can analyze the past response history of the customer and select the optimal response method. The determination unit can, for example, preferentially provide the response method that the customer has preferred in the past. The determination unit can also analyze the past response history of the customer and select the optimal response method. Furthermore, the determination unit can select the optimal response method for a specific time period from the past response history of the customer. For example, the determination unit can analyze the past response history of the customer and select the optimal response method based on the analysis result. This makes it possible to select the optimal response method based on the past response history of the customer. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0126] The determination unit can dynamically change the response method based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the determination unit can change the response method to an earlier one. In addition, the determination unit can delay the response method when the customer arrives late. Furthermore, the determination unit can recalculate the response method when the customer cancels the reservation. For example, the determination unit can dynamically change the response method based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the response method based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0127] The determination unit can estimate the emotion of the customer and determine the priority of the response based on the estimated emotion of the customer. For example, the determination unit can prioritize the response when the customer feels anxious. Also, the determination unit can respond in the normal order when the customer is relaxed. Furthermore, the determination unit can respond with the highest priority when the customer is in a hurry. For example, the determination unit can estimate the emotion of the customer and determine the priority of the response based on the emotion. This makes it possible to determine the priority of the response based on the emotion of the customer. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0128] The determination unit can determine a response method by taking into account the geographical location information of the customer. For example, the determination unit can respond quickly when the customer is nearby. Furthermore, the determination unit can also respond at a normal speed when the customer is far away. Furthermore, the determination unit can dynamically respond when the customer is on the move. For example, the determination unit can determine a response method by taking into account the geographical location information of the customer. This makes it possible to determine a response method based on the geographical location information of the customer. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0129] The decision unit can analyze the social media activity of the customer and determine a response method. For example, the decision unit can respond quickly if the customer rates the restaurant highly on social media. The decision unit can also respond quickly if the customer frequently checks in to the restaurant on social media. Furthermore, the decision unit can respond at a normal speed if the customer posts about the restaurant on social media. For example, the decision unit can analyze the social media activity of the customer and determine a response method based on the activity. This makes it possible to determine a response method based on the social media activity of the customer. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI.

[0130] The execution unit can estimate the customer's emotion and adjust the execution method based on the estimated customer's emotion. For example, if the customer feels anxious, the execution unit can provide information to reassure the customer. Also, if the customer is relaxed, the execution unit can provide a normal execution method. Furthermore, if the customer is in a hurry, the execution unit can provide a quick execution method. For example, the execution unit can estimate the customer's emotion and adjust the execution method based on the emotion. This makes it possible to adjust the execution method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] The execution unit can analyze the past execution history of the customer and select the optimal execution method. The execution unit can, for example, preferentially provide an execution method that the customer has preferred in the past. The execution unit can also analyze the past execution history of the customer and select the optimal execution method. Furthermore, the execution unit can select the optimal execution method for a specific time period from the past execution history of the customer. For example, the execution unit can analyze the past execution history of the customer and select the optimal execution method based on the analysis results. This makes it possible to select the optimal execution method based on the past execution history of the customer. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0132] The execution unit can dynamically change the execution method based on the arrival time of the customer and the reservation status. For example, if the customer arrives earlier than scheduled, the execution unit can change the execution method to be earlier. Also, if the customer arrives late, the execution unit can delay the execution method. Furthermore, if the customer cancels the reservation, the execution unit can recalculate the execution method. For example, the execution unit can dynamically change the execution method based on the arrival time of the customer and the reservation status. This makes it possible to dynamically change the execution method based on the arrival time of the customer and the reservation status. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0133] The execution unit can estimate the customer's emotion and determine the priority of execution based on the estimated emotion of the customer. For example, the execution unit can execute with priority when the customer feels anxious. Also, the execution unit can execute in the normal order when the customer is relaxed. Furthermore, the execution unit can execute with the highest priority when the customer is in a hurry. For example, the execution unit can estimate the customer's emotion and determine the priority of execution based on the emotion. This makes it possible to determine the priority of execution based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0134] The execution unit can determine the execution method by taking into account the geographical location information of the customer. The execution unit can execute quickly, for example, when the customer is nearby. The execution unit can also execute at a normal speed when the customer is far away. Furthermore, the execution unit can execute dynamically when the customer is on the move. For example, the execution unit can determine the execution method by taking into account the geographical location information of the customer. This makes it possible to determine the execution method based on the geographical location information of the customer. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0135] The execution unit can analyze the social media activity of the customer and determine the execution method. The execution unit can execute quickly, for example, when the customer rates the restaurant highly on social media. The execution unit can also execute quickly, when the customer frequently checks in the restaurant on social media. Furthermore, the execution unit can execute at a normal speed, when the customer posts about the restaurant on social media. For example, the execution unit can analyze the social media activity of the customer and determine the execution method based on the activity. This makes it possible to determine the execution method based on the social media activity of the customer. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The allocation unit can analyze the customer's past visit history and allocate the optimal waiting number. For example, if the customer has visited the store frequently in the past, the allocation unit can allocate a waiting number preferentially. Also, if the customer has been kept waiting for a long time in the past, the allocation unit can allocate an earlier waiting number. Furthermore, if the customer is visiting the store for the first time, the allocation unit can allocate a normal waiting number. In this way, it is possible to allocate the optimal waiting number based on the customer's past visit history.

[0138] The determination unit can determine the entertainment content to be provided during the waiting time based on the customer's emotions. For example, if the customer feels bored, entertainment content such as games or videos can be provided. If the customer is seeking relaxation, relaxation music or a meditation guide can be provided. Furthermore, if the customer is seeking information, news or articles on topics of interest can be provided. This makes it possible to provide entertainment content according to the customer's emotions.

[0139] The notification unit can notify the customer of the waiting time in real time. For example, the notification unit can notify the customer of the waiting time through a smartphone app. The notification unit can also notify the customer of fluctuations in the waiting time in real time. For example, if the waiting time is shortened or extended, the notification unit can notify the customer of that information in real time. This makes it possible to notify the customer of the waiting time in real time.

[0140] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit can capture the customer's facial expression with a camera and acquire the facial expression data. The acquisition unit can also detect the customer's movement with a sensor and acquire the movement data. Furthermore, the acquisition unit can record the customer's voice with a microphone and acquire the voice data. This allows the acquisition unit to acquire the customer's emotion estimation information from a variety of information sources.

[0141] The allocating unit can allocate queue numbers taking into account the geographic location information of the customer. For example, the allocating unit can allocate an early queue number if the customer is nearby. The allocating unit can also allocate a regular queue number if the customer is far away. Furthermore, the allocating unit can allocate a queue number based on an estimated arrival time if the customer is on the move. In this way, queue numbers can be allocated based on the geographic location information of the customer.

[0142] The determination unit can determine the food and drink samples to be provided during the waiting time based on the customer's emotions. For example, if the customer feels hungry, a snack or drink sample can be provided. If the customer wants to relax, a relaxing drink such as herbal tea or smoothie can be provided. Furthermore, if the customer wants to try something new, a sample of a new menu item from the restaurant can be provided. This makes it possible to provide food and drink samples according to the customer's emotions.

[0143] The notification unit can analyze the social media activity of the customer and notify the customer of the waiting time. For example, the notification unit can notify the customer of a shorter waiting time if the customer has given a high rating to the restaurant on social media. The notification unit can also notify the customer of a shorter waiting time if the customer frequently checks in to the restaurant on social media. Furthermore, the notification unit can also notify the customer of a normal waiting time if the customer has posted about the restaurant on social media. This makes it possible to notify the customer of the waiting time based on the customer's social media activity.

[0144] The decision unit can determine the reward to be provided during the waiting time based on the customer's emotions. For example, if the customer feels anxious, a reward coupon for reassurance can be provided. If the customer is looking to relax, a discount coupon for relaxation services can be provided. Furthermore, if the customer is looking for a new experience, an invitation to a special event at the restaurant can be provided. This makes it possible to provide rewards according to the customer's emotions.

[0145] The acquisition unit can analyze the past emotion data of the customer and select the optimal acquisition method. For example, the acquisition unit can analyze the timing when the customer felt stressed in the past and acquire emotion estimation information at that timing. The acquisition unit can also analyze the timing when the customer felt relaxed in the past and acquire emotion estimation information at that timing. Furthermore, the acquisition unit can select the optimal acquisition method (facial expression, voice, heart rate, etc.) based on the past emotion data of the customer. This makes it possible to select the optimal acquisition method based on the past emotion data of the customer.

[0146] The determination unit can determine the format of information to be provided during the waiting time based on the customer's emotions. For example, if the customer prefers visual information, the information can be provided in the form of images or videos. If the customer prefers auditory information, the information can be provided in the form of audio messages or podcasts. Furthermore, if the customer prefers text information, the information can be provided in the form of articles or text messages. This makes it possible to provide information in a format that corresponds to the customer's emotions.

[0147] The process flow of the second embodiment will be briefly described below.

[0148] Step 1: The allocation unit allocates a waiting number to a customer. For example, the allocation unit can allocate a number taking into consideration the customer's arrival time and reservation status. Step 2: The prediction unit predicts the customer's waiting time based on the number assigned by the allocation unit. For example, the prediction unit can predict the waiting time by analyzing past data and the current situation. Step 3: The notification unit notifies the customer of the waiting time predicted by the prediction unit. For example, the notification unit can notify the customer of the waiting time through a smartphone app. Step 4: The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. For example, the acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, respiration, and posted content. Step 5: The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit can estimate the emotion of the customer using AI. Step 6: The decision unit decides a response based on the customer's emotion estimated by the estimation unit. For example, the decision unit may decide to provide reassuring information to the customer if the customer feels anxious. Step 7: The execution unit executes the action determined by the determination unit. For example, the execution unit can provide the customer with information such as expected waiting time, restaurant menu information, and special offer information.

[0149] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0151] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] For example, the allocation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit is realized using the camera 42 or the microphone 38B of the smart device 14. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the execution unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] For example, the allocation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit is realized using the camera 42 or the microphone 238 of the smart glasses 214. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the execution unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0171] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0184] For example, the allocation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit is realized using the camera 42 or the microphone 238 of the headset type terminal 314. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the execution unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a 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.

[0187] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0196] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0198] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0200] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] For example, the allocation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit is realized using the camera 42 or the microphone 238 of the robot 414. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the execution unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0203] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0207] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] In addition, 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 upon request from the data processing device 12.

[0212] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0214] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0218] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0220] (Appendix 1) an allocation unit that allocates waiting numbers to customers; a prediction unit that predicts a waiting time of the customer based on the number assigned by the assignment unit; a notification unit that notifies the customer of the waiting time predicted by the prediction unit; an acquisition unit that acquires emotion estimation information for estimating the emotion of the customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response based on the emotion of the customer estimated by the estimation unit; an execution unit that executes the response determined by the determination unit; A system characterized by: (Appendix 2) The determination unit is determining a response based on the customer's emotion to provide information based on the customer's emotion; The execution unit is Providing information to the customer based on the customer's emotions 2. The system of claim 1, (Appendix 3) The determination unit is determining whether to provide menu information or special offer information based on the customer's feelings; The execution unit is Providing menu information or special offer information to the customer based on the customer's feelings 3. The system of claim 2, (Appendix 4) The determination unit is determining, as a response to the customer's emotion, a response to provide information according to the customer's emotion and the waiting time of the customer; The execution unit is Provide the customer with information according to the customer's emotions and the customer's waiting time 3. The system of claim 2, (Appendix 5) The determination unit is determining, as a response to the customer's emotion, a provision of information according to the customer's emotion, the customer's waiting time, and the customer's attributes; The execution unit is To provide the customer with information according to the customer's emotions, the customer's waiting time, and the customer's attributes. 3. The system of claim 2, (Appendix 6) The notification unit is Notify the customer of their waiting time in real time 2. The system of claim 1, (Appendix 7) The notification unit is Notify changes in waiting status in real time 2. The system of claim 1, (Appendix 8) The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 9) The allocation unit is Estimate customer sentiment and adjust queue numbers based on estimated customer sentiment 2. The system of claim 1, (Appendix 10) The allocation unit is Analyze customers' past visit history and assign optimal queue numbers 2. The system of claim 1, (Appendix 11) The allocation unit is Dynamically change queue numbers based on customer arrival time or reservation availability 2. The system of claim 1, (Appendix 12) The allocation unit is Estimate customer sentiment and prioritize queue numbers based on estimated customer sentiment 2. The system of claim 1, (Appendix 13) The allocation unit is Consider the geographic location of your customers when assigning queue numbers 2. The system of claim 1, (Appendix 14) The allocation unit is Analyze your customers' social media activity and assign them queue numbers 2. The system of claim 1, (Appendix 15) The prediction unit is Estimate customer sentiment and adjust wait time predictions based on estimated customer sentiment 2. The system of claim 1, (Appendix 16) The prediction unit is Analyze past data and current conditions to improve the accuracy of waiting time predictions 2. The system of claim 1, (Appendix 17) The prediction unit is Dynamically change wait time estimates based on customer arrival time or reservation availability 2. The system of claim 1, (Appendix 18) The prediction unit is Infer customer sentiment and adjust the order in which wait time predictions are displayed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 19) The prediction unit is Infer customer sentiment and adjust the order in which wait time predictions are displayed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 20) The prediction unit is Generate wait time predictions based on customer geolocation 2. The system of claim 1, (Appendix 21) The prediction unit is Analyze customer social media activity to predict wait times 2. The system of claim 1, (Appendix 22) The notification unit is Estimate customer sentiment and adjust wait time notifications based on estimated customer sentiment 2. The system of claim 1, (Appendix 23) The notification unit is Analyze the customer's past notification history and select the optimal notification method 2. The system of claim 1, (Appendix 24) The notification unit is Dynamically change wait time notifications based on customer arrival time or reservation status 2. The system of claim 1, (Appendix 25) The notification unit is Estimate customer sentiment and prioritize notifications based on estimated customer sentiment 2. The system of claim 1, (Appendix 26) The notification unit is Consider customer geolocation when notifying them of wait times 2. The system of claim 1, (Appendix 27) The notification unit is Analyze customer social media activity and provide wait time notifications 2. The system of claim 1, (Appendix 28) The acquisition unit is To estimate a customer's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the customer. 2. The system of claim 1, (Appendix 29) The acquisition unit is Analyze past customer sentiment data and select the optimal acquisition method 2. The system of claim 1, (Appendix 30) The acquisition unit is Dynamically change the acquisition of emotion estimation information based on the customer's arrival time or reservation status 2. The system of claim 1, (Appendix 31) The acquisition unit is Estimate customer sentiment and prioritize information to be retrieved based on the estimated customer sentiment 2. The system of claim 1, (Appendix 32) The acquisition unit is Obtaining sentiment estimation information by considering the geographic location of the customer 2. The system of claim 1, (Appendix 33) The acquisition unit is Analyze your customers' social media activity to get sentiment insights 2. The system of claim 1, (Appendix 34) The estimation unit is Estimate customer sentiment and adjust sentiment estimation algorithms based on estimated customer sentiment 2. The system of claim 1, (Appendix 35) The estimation unit is Analyze customers' past emotion data to improve the accuracy of emotion estimation 2. The system of claim 1, (Appendix 36) The estimation unit is Dynamically change sentiment estimation results based on customer arrival time or reservation status 2. The system of claim 1, (Appendix 37) The estimation unit is Estimate customer sentiment and adjust how sentiment estimation results are displayed based on the estimated customer sentiment 2. The system of claim 1, (Appendix 38) The estimation unit is Perform sentiment estimation taking into account the geographic location of the customer 2. The system of claim 1, (Appendix 39) The estimation unit is Analyze your customers' social media activity and make sentiment estimates 2. The system of claim 1, (Appendix 40) The determination unit is Infer customer sentiment and tailor your response based on that sentiment 2. The system of claim 1, (Appendix 41) The determination unit is Analyze the customer's past response history and choose the most appropriate response method 2. The system of claim 1, (Appendix 42) The determination unit is Dynamically adapt your interactions based on customer arrival time or reservation status 2. The system of claim 1, (Appendix 43) The determination unit is Estimate customer sentiment and prioritize interactions based on the estimated customer sentiment 2. The system of claim 1, (Appendix 44) The determination unit is Consider your customer's geographic location to determine how to respond 2. The system of claim 1, (Appendix 45) The determination unit is Analyze your customers' social media activity and decide how to respond 2. The system of claim 1, (Appendix 46) The execution unit is Infer customer sentiment and adjust execution based on inferred customer sentiment 2. The system of claim 1, (Appendix 47) The execution unit is Analyze the customer's past execution history and select the optimal execution method 2. The system of claim 1, (Appendix 48) The execution unit is Dynamically change execution based on customer arrival time or reservation availability 2. The system of claim 1, (Appendix 49) The execution unit is Estimate customer sentiment and prioritize execution based on estimated customer sentiment 2. The system of claim 1, (Appendix 50) The execution unit is Consider your customers' geographic location to determine how to execute 2. The system of claim 1, (Appendix 51) The execution unit is Analyze your customers' social media activity and determine how to act 2. The system of claim 1, [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an allocation unit that allocates waiting numbers to customers; a prediction unit that predicts a waiting time of the customer based on the number assigned by the assignment unit; a notification unit that notifies the customer of the waiting time predicted by the prediction unit; an acquisition unit that acquires emotion estimation information for estimating the emotion of the customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response based on the emotion of the customer estimated by the estimation unit; an execution unit that executes the response determined by the determination unit; A system characterized by:

2. The determination unit is determining a response based on the customer's emotion to provide information based on the customer's emotion; The execution unit is Providing information to the customer based on the customer's emotions 2. The system of claim 1 .

3. The determination unit is determining whether to provide menu information or special offer information based on the customer's feelings; The execution unit is Providing menu information or special offer information to the customer based on the customer's feelings 3. The system of claim 2.

4. The determination unit is determining, as a response to the customer's emotion, a response to provide information according to the customer's emotion and the waiting time of the customer; The execution unit is Provide the customer with information according to the customer's emotions and the customer's waiting time 3. The system of claim 2.

5. The determination unit is determining, as a response to the customer's emotion, a provision of information according to the customer's emotion, the customer's waiting time, and the customer's attributes; The execution unit is To provide the customer with information according to the customer's emotions, the customer's waiting time, and the customer's attributes.

3. The system of claim 2.

6. The notification unit is Notify the customer of their waiting time in real time 2. The system of claim 1 .

7. The notification unit is Notify changes in waiting status in real time 2. The system of claim 1 .

8. The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, respiration, and posted content are acquired as the emotion estimation information.

2. The system of claim 1 .

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