system

Abstract

The system according to this embodiment aims to provide consistently high-quality service in restaurant customer service. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a suggestion unit, a reception unit, and a response unit. The collection unit collects customer information. The learning unit learns the information collected by the collection unit. The suggestion unit makes personalized suggestions based on the information learned by the learning unit. The reception unit accepts customer orders. The response unit supports multiple languages.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the quality of the customer service in a restaurant varies depending on the skills of the staff and the congestion situation, and it is difficult to provide a consistent high-quality service.

[0005] The system according to the embodiment aims to provide a consistent high-quality service in the customer service of a restaurant.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a learning unit, a proposal unit, a reception unit, and a response unit. The collection unit collects customer information. The learning unit learns the information collected by the collection unit. The proposal unit makes personalized proposals based on the information learned by the learning unit. The reception unit accepts customer orders. The response unit supports multiple languages. [Effects of the Invention]

[0007] The system according to this embodiment can provide consistently high-quality service in restaurant customer service. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The restaurant service system according to an embodiment of the present invention is a system that utilizes AI agent technology to innovate the restaurant service experience and provide the best customer experience at every table. Unlike conventional service where the quality of service fluctuates depending on the staff's skills and busy times, the restaurant service system uses an AI agent to instantly provide consistently high-quality service. Specifically, it realizes a wide range of service functions, including order taking, menu explanation, personalized suggestions, and multilingual support. The restaurant service system learns the customer's past ordering history and preferences to provide personalized hospitality at every table. For example, it can flexibly respond to diverse needs such as food allergies and preferences for specific dishes. Furthermore, the restaurant service system is available 24 hours a day, 365 days a year, solving the problem of staff shortages and contributing to increased operational efficiency and profitability of restaurants. For example, the restaurant service system learns the customer's past ordering history and preferences. This allows it to make personalized suggestions based on past ordering history and preferences when a customer returns. For example, it can suggest new menu items related to a customer who likes a particular dish. Next, the restaurant service system takes the order. When customers are seated at a table, the restaurant service system takes their order and explains the menu. For example, it can provide detailed descriptions of specific dishes on the menu and information about the ingredients used. Furthermore, the restaurant service system is multilingual. Even when foreign tourists visit, they can order smoothly without experiencing language barriers. For example, it can provide menu explanations in multiple languages, such as English and Chinese. In addition, the restaurant service system is available 24 hours a day, 365 days a year, solving the problem of staff shortages. This improves the operational efficiency of the restaurant and increases profitability. For example, even during times when regular staff are unavailable, such as late at night or early in the morning, the restaurant service system can handle requests, maintaining customer satisfaction. In this way, by utilizing AI agent technology, the restaurant service system can revolutionize the restaurant service experience and provide the best possible customer experience at every table.This allows restaurant service systems to innovate the restaurant experience and deliver the best possible customer experience by collecting and learning customer information, making personalized suggestions, taking orders, and supporting multiple languages.

[0029] The restaurant service system according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, a reception unit, and a response unit. The collection unit collects customer information. Customer information includes, but is not limited to, personal information, purchase history, and preference information. The collection unit collects, for example, the customer's past order history and preferences. The collection unit can collect information, for example, by extracting from a database or based on survey results. The learning unit learns the information collected by the collection unit. The learning unit learns the information using, for example, machine learning or data mining algorithms. The learning unit can learn customer preferences, for example, based on the customer's past order history and preferences. The suggestion unit makes personalized suggestions based on the information learned by the learning unit. The suggestion unit can, for example, make menu suggestions or special offers based on the customer's preferences. The suggestion unit can make the best suggestions to the customer, for example, based on the customer's past order history and preferences. The reception unit takes customer orders. The reception unit can, for example, take orders when a customer is seated at a table and explain the menu. The reception area can take orders using, for example, a tablet device. The reception area can also take orders verbally, for example. The customer service area supports multiple languages. The customer service area can provide menu explanations in multiple languages, for example, English, Chinese, and Japanese. The customer service area allows, for example, foreign tourists to order smoothly without experiencing language barriers. As a result, the restaurant service system according to this embodiment can innovate the restaurant service experience and provide the best possible customer experience by collecting and learning customer information, making personalized suggestions, taking orders, and supporting multiple languages.

[0030] The data collection unit collects customer information. This information includes, but is not limited to, personal information, purchase history, and preference information. For example, the data collection unit collects customers' past order history and preferences. Specifically, it collects detailed information such as menus that customers have ordered in the past, how often they ordered them, and their preferences and allergies to specific ingredients. This information is automatically collected when customers make online reservations or use restaurant membership cards. More detailed preference information can also be obtained when customers answer questionnaires in the restaurant. The data collection unit stores this information in a database and updates it as needed. For example, if a new menu is added or a customer's preferences change, the data collection unit quickly updates the information to maintain up-to-date data. Furthermore, the data collection unit can also collect data from external sources, such as customers' social media posts and reviews on review sites. This allows for a more accurate understanding of customer preferences and ratings, and builds a foundation for providing personalized services.

[0031] The learning unit learns from the information collected by the collection unit. The learning unit learns from information using, for example, machine learning and data mining algorithms. Specifically, it can learn customer preferences based on past order history and preferences. For example, it can analyze menu items that customers frequently order and their tendency to prefer certain ingredients to extract customer preference patterns. Based on these patterns, the learning unit predicts menu items that customers are likely to order next and new menu items that customers might be interested in. Furthermore, the learning unit considers that customer preferences change over time and continuously updates the data to improve its learning model. For example, by reflecting seasonal changes in preferences and the impact of specific events and campaigns, it can make more accurate predictions. In addition, the learning unit analyzes differences in preferences between different customer groups and builds a foundation for making optimal suggestions for each group. This allows the learning unit to play a crucial role in providing personalized services to each individual customer.

[0032] The suggestion department provides personalized suggestions based on information learned by the learning department. For example, the suggestion department can offer menu suggestions and special offers based on customer preferences. Specifically, it suggests the best menu for the customer based on menus the customer has previously ordered and preferences predicted by the learning department. For example, if a customer has previously ordered spicy dishes, the suggestion department can suggest new spicy dishes or drinks that pair well with spicy food. The suggestion department can also provide customers with special offers tailored to specific events or seasons. For example, it can suggest a special dessert menu for Valentine's Day or offer discounts on cold drinks during the summer. The suggestion department makes these suggestions easily accessible to customers by notifying them on their smartphones or tablets. Furthermore, the suggestion department can collect customer feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion department to always provide customers with the best possible suggestions and improve customer satisfaction.

[0033] The reception area takes customer orders. For example, when a customer is seated at a table, the reception area can take their order and explain the menu. Specifically, orders can be taken using a tablet device. Customers can view the menu and place their orders through the tablet. The tablet displays detailed menu descriptions and images, making it easy for customers to choose. The reception area can also take orders verbally. For example, when a waiter visits a customer's table to take a verbal order, they can use the tablet to enter the order details and send them to the kitchen. Furthermore, the reception area can handle customer requests and allergy information. For example, if a customer wants to exclude certain ingredients or requests a special cooking method, the reception area can accurately record this information and pass it on to the kitchen. This allows the reception area to take customer orders quickly and accurately and provide service that meets customer needs.

[0034] The customer service unit supports multiple languages. For example, it can provide menu descriptions in multiple languages, such as English, Chinese, and Japanese. Specifically, it can display menus in the customer's chosen language using a tablet or smartphone app. This allows foreign tourists to order smoothly without experiencing language barriers. Furthermore, the customer service unit may also train waiters to communicate in multiple languages. For example, waiters who speak English or Chinese can directly assist foreign customers and take their orders. In addition, the customer service unit can use voice recognition technology to automatically detect the customer's language and respond in the appropriate language. This allows customers to communicate smoothly in their own language, making their restaurant experience more comfortable. Through these functions, the customer service unit removes language barriers and provides the best possible service to all customers.

[0035] The data collection unit can collect customers' past order history and preferences. For example, the data collection unit can extract customers' past order history from a database and analyze their preferences. The data collection unit can also collect customer preferences based on survey results, for example. The data collection unit can identify preferences for specific foods or drinks based on customers' past order history, for example. This allows for personalized suggestions by collecting customers' past order history and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customers' past order history into AI and have the AI ​​perform preference analysis.

[0036] The learning unit can learn from the collected information and make suggestions based on customer preferences. The learning unit can learn from the collected information using, for example, machine learning algorithms. The learning unit can also analyze customer preferences using, for example, data mining techniques. The learning unit can learn customer preferences based on, for example, the customer's past order history. This improves customer satisfaction by learning from the collected information and making suggestions based on customer preferences. Some or all of the above processes in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the collected information into AI and have the AI ​​learn customer preferences.

[0037] The suggestion department can make personalized suggestions based on customer preferences. For example, the suggestion department can make menu suggestions based on customer preferences. For example, the suggestion department can also suggest special offers to customers. For example, the suggestion department can make the best suggestions to customers based on their past order history. By making personalized suggestions based on customer preferences, customer satisfaction is improved. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input customer preference information into AI and have the AI ​​make personalized suggestions.

[0038] The reception area can take orders and explain the menu once customers are seated at a table. The reception area can take orders using, for example, a tablet device. The reception area can also take orders verbally. The reception area can provide, for example, detailed descriptions of specific dishes from the menu and information about the ingredients used in those dishes. This allows for a smooth ordering process by taking orders and explaining the menu once customers are seated at a table. Some or all of the above processes at the reception area may be performed using, for example, AI, or not using AI. For example, the reception area can input customer order information into the AI ​​and have the AI ​​take orders and explain the menu.

[0039] The support unit can provide menu descriptions in multiple languages. For example, it can provide menu descriptions in multiple languages ​​such as English, Chinese, and Japanese. The support unit allows foreign tourists to order smoothly without experiencing language barriers. For example, the support unit can provide detailed descriptions of specific dishes from the menu, as well as information about the ingredients used in the dishes, in multiple languages. By providing menu descriptions in multiple languages, it can also accommodate foreign tourists. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input menu descriptions in multiple languages ​​into AI and have the AI ​​execute the descriptions.

[0040] The customer service unit is available 24 hours a day, 365 days a year, and can solve the problem of staff shortages. The customer service unit can respond even during times when regular staff are unavailable, such as late at night or early in the morning. The customer service unit can take orders and explain the menu at any time when customers arrive. The customer service unit can maintain customer satisfaction by providing 24 / 7 service. This improves the operational efficiency of the restaurant by providing 24 / 7 service and solving the problem of staff shortages. Some or all of the above processes in the customer service unit may be performed using AI, for example, or not. For example, the customer service unit can input customer order information into AI and have the AI ​​perform 24 / 7 service.

[0041] The data collection unit can analyze the customer's past visit frequency and select the optimal information collection method. For example, if a customer visits frequently, the data collection unit can provide a detailed questionnaire to collect deeper information. For example, if a customer is visiting for the first time, the data collection unit can collect basic information through simple questions. For example, if a customer visits regularly, the data collection unit can provide customized questions based on past visit history and update the information. This enables efficient information collection by analyzing the customer's past visit frequency and selecting the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer visit frequency data into AI and have the AI ​​select the optimal information collection method.

[0042] The data collection unit can filter information based on the customer's current health status and dietary restrictions during data collection. For example, if a customer has allergies, the data collection unit collects allergy information and filters the suggested dishes. For example, if a customer has specific dietary restrictions, the data collection unit collects that information and suggests an appropriate menu. For example, if a customer desires a meal tailored to their health condition, the data collection unit collects that information and suggests a health-conscious menu. This allows for appropriate information collection by filtering based on the customer's health status and dietary restrictions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the customer's health status and dietary restrictions into the AI ​​and have the AI ​​perform the filtering.

[0043] The data collection unit can prioritize collecting highly relevant information by considering the customer's geographical location. For example, if the customer is a tourist, the data collection unit will prioritize collecting information about local specialty dishes. If the customer is a local resident, the data collection unit will prioritize collecting information about seasonal menus. If the customer is from a specific region, the data collection unit will prioritize collecting information related to the food culture of that region. By prioritizing the collection of highly relevant information while considering the customer's geographical location, customer satisfaction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant information.

[0044] The data collection unit can analyze customers' social media activity and collect relevant information during data collection. For example, the data collection unit can analyze photos of food shared by customers on social media to collect information about their food preferences. For example, the data collection unit can analyze restaurant reviews mentioned by customers on social media to collect feedback on service. For example, the data collection unit can collect information about food and beverages that customers follow on social media and use it for recommendations. This enables recommendations based on customer preferences by analyzing customers' social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input customer social media data into AI and have the AI ​​collect relevant information.

[0045] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can extract patterns related to customer preferences based on past learning data and optimize the algorithm. For example, the learning unit can analyze customer order history based on past learning data and optimize the algorithm. For example, the learning unit can reflect customer feedback based on past learning data and optimize the algorithm. By optimizing the learning algorithm by referring to past learning data, the accuracy of learning is improved. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0046] The learning unit can improve the accuracy of its learning based on the customer's eating history during the learning process. For example, the learning unit can improve the accuracy of its learning by identifying the customer's preferred dishes and ingredients based on their past eating history. For example, the learning unit can improve the accuracy of its learning by reflecting allergy information based on the customer's eating history. For example, the learning unit can improve the accuracy of its learning by considering specific dietary restrictions based on the customer's eating history. This makes it possible to provide personalized suggestions by improving the accuracy of learning based on the customer's eating history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's eating history data into AI and have AI perform the improvement of learning accuracy.

[0047] The learning unit can weight the training data based on the customer's visit timing during training. For example, the learning unit may weight the training data by giving more emphasis to data from times when customers frequently visit. For example, if a customer visits during a particular season, the learning unit may weight the training data by giving more emphasis to data from that season. For example, if a customer visits during a particular event, the learning unit may weight the training data by giving more emphasis to data related to that event. This improves the accuracy of training by weighting the training data based on the customer's visit timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer visit timing data into AI and have AI perform the weighting of the training data.

[0048] The learning unit can improve the accuracy of its learning by referring to relevant literature related to the customer during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to literature on dishes that the customer is interested in. For example, if the customer is interested in a particular ingredient, the learning unit can improve the accuracy of its learning by referring to literature on that ingredient. For example, if the customer is interested in a particular cooking method, the learning unit can improve the accuracy of its learning by referring to literature on that cooking method. By improving the accuracy of learning by referring to relevant literature related to the customer, more accurate suggestions become possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's relevant literature data into AI and have the AI ​​perform the improvement of learning accuracy.

[0049] The suggestion function can adjust the level of detail in a suggestion based on the importance of the dish. For example, for a main dish, the suggestion function provides a detailed description, increasing the level of detail. For a side dish, for example, the suggestion function provides a concise description, decreasing the level of detail. For a dessert, for example, the suggestion function provides a detailed description tailored to the customer's preferences, adjusting the level of detail. This improves customer satisfaction by adjusting the level of detail in the suggestion based on the importance of the dish. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input dish importance data into AI and have the AI ​​perform the adjustment of the level of detail in the suggestion.

[0050] The suggestion unit can apply different suggestion algorithms depending on the category of the dish when making a suggestion. For example, in the case of a main dish, the suggestion unit applies a suggestion algorithm based on the customer's past order history. For example, in the case of a side dish, the suggestion unit applies a suggestion algorithm based on the customer's current order. For example, in the case of a dessert, the suggestion unit applies a suggestion algorithm based on the customer's preferences. By applying different suggestion algorithms depending on the category of the dish, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input dish category data into AI and have the AI ​​perform the application of the suggestion algorithm.

[0051] The suggestion department can determine the priority of suggestions based on the timing of food service. For example, the suggestion department might prioritize suggesting side dishes before serving the main course. For example, it might prioritize suggesting the main course before serving dessert. For example, it might prioritize suggesting food before serving drinks. By prioritizing suggestions based on the timing of food service, customer satisfaction can be improved. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input food service timing data into the AI ​​and have the AI ​​determine the priority of suggestions.

[0052] The suggestion function can adjust the order of suggestions based on the relevance of the dishes. For example, the suggestion function may prioritize suggesting side dishes related to the main dish. For example, the suggestion function may prioritize suggesting drinks related to dessert. For example, the suggestion function may prioritize suggesting main dishes related to appetizers. By adjusting the order of suggestions based on the relevance of the dishes, customer satisfaction is improved. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input dish relevance data into AI and have AI perform the adjustment of the suggestion order.

[0053] The reception desk can select the optimal reception method by referring to the customer's past order history when taking an order. For example, the reception desk can automatically display dishes that the customer has frequently ordered in the past as suggestions. For example, the reception desk can prioritize suggesting ordering methods (voice, text, etc.) that the customer has used in the past. For example, the reception desk can predict and suggest ordering methods to be used at a particular time based on the customer's past order history. By selecting the optimal reception method by referring to the customer's past order history, customer satisfaction is improved. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's past order history data into AI and have the AI ​​select the optimal reception method.

[0054] The reception desk can filter orders based on the customer's current dietary restrictions when taking orders. For example, if a customer has allergies, the reception desk can filter orders based on allergy information. For example, if a customer has specific dietary restrictions, the reception desk can filter orders based on that information. For example, if a customer requests a meal tailored to their health condition, the reception desk can filter orders based on that information. This allows for appropriate orders to be placed by filtering based on the customer's current dietary restrictions. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer dietary restriction data into AI and have the AI ​​perform the filtering.

[0055] The reception desk can prioritize orders based on the customer's geographical location when taking orders. For example, if the customer is a tourist, the reception desk will prioritize orders related to local specialties. If the customer is a local resident, the reception desk will prioritize orders related to seasonal menus. If the customer is from a specific region, the reception desk will prioritize orders related to the local food culture. By prioritizing orders based on the customer's geographical location, customer satisfaction is improved. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into the AI ​​and have the AI ​​perform the task of taking relevant orders.

[0056] The reception desk can analyze a customer's social media activity when taking an order and accept relevant orders. For example, the reception desk can analyze photos of food shared by a customer on social media and accept orders related to their preferred dishes. For example, the reception desk can analyze restaurant reviews mentioned by a customer on social media and accept orders related to service. For example, the reception desk can accept orders related to food and drinks that a customer follows on social media. By analyzing a customer's social media activity and accepting relevant orders, it becomes possible to make suggestions based on the customer's preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer social media data into AI and have the AI ​​take relevant orders.

[0057] The response unit can select the optimal response method by referring to the customer's language history during a response. For example, the response unit selects the optimal response method based on the languages ​​the customer has used in the past. For example, the response unit provides a language switching function if the customer uses multiple languages. For example, if the customer selects a specific language, the response unit will respond in that language. By selecting the optimal response method by referring to the customer's language history, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's language history data into AI and have the AI ​​select the optimal response method.

[0058] The response unit can customize its response methods based on the customer's cultural background when responding to a customer. For example, if the customer has a specific cultural background, the response unit will respond in a way that respects that culture. For example, if the customer has a specific religious background, the response unit will respond in a way that respects that religion. For example, if the customer has a specific regional background, the response unit will respond in a way that respects that region. By customizing the response methods based on the customer's cultural background, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input customer cultural background data into AI and have the AI ​​perform the customization of the response methods.

[0059] The response unit can select the optimal response method by considering the customer's geographical location information when responding to a customer. For example, if the customer is a tourist, the response unit can provide information about local specialty dishes. For example, if the customer is a local resident, the response unit can provide information about seasonal menus. For example, if the customer is from a specific region, the response unit can provide information related to the food culture of that region. By selecting the optimal response method by considering the customer's geographical location information, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's geographical location information into AI and have AI select the optimal response method.

[0060] The response unit can analyze the customer's social media activity and propose a response method when responding. For example, the response unit can analyze photos of food shared by the customer on social media and provide information about their preferred cuisine. For example, the response unit can analyze restaurant reviews mentioned by the customer on social media and provide information about the service. For example, the response unit can provide information about the food and drinks that the customer follows on social media. This enables a response based on the customer's preferences by analyzing the customer's social media activity and proposing a response method. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's social media data into AI and have the AI ​​propose a response method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] Restaurant service systems can analyze customers' past order history and evaluate the popularity of specific dishes. For example, if a particular dish is frequently ordered by customers, it can be displayed at the top of the menu. If a particular dish is popular during a specific season, the menu can be adjusted accordingly. If a particular dish is popular during a specific event, a special menu can be offered to coincide with that event. By analyzing customers' past order history and optimizing the menu, customer satisfaction can be improved.

[0063] Restaurant service systems can analyze a customer's past visit history and provide services tailored to specific times of day. For example, if a customer frequently visits during lunchtime, the system can prioritize suggesting the lunch menu. If a customer frequently visits during dinnertime, the system can prioritize suggesting the dinner menu. If a customer visits during a specific event, the system can suggest a special menu tailored to that event. By analyzing a customer's past visit history and optimizing service accordingly, customer satisfaction can be improved.

[0064] Restaurant service systems can analyze customers' past order history and optimize the timing of serving specific dishes. For example, if a particular dish is popular as an appetizer, it can be served as an appetizer. If a particular dish is popular as a main course, it can be served as a main course. If a particular dish is popular as a dessert, it can be served as a dessert. By analyzing customers' past order history and optimizing the timing of dish service, this system improves customer satisfaction.

[0065] Restaurant service systems can analyze customers' past visit history and optimize the frequency of serving specific dishes. For example, if a particular dish is frequently ordered, it can be displayed at the top of the menu. If a particular dish is popular during a specific season, its serving frequency can be adjusted accordingly. If a particular dish is popular during a specific event, its serving frequency can be adjusted to match that event. By analyzing customers' past visit history and optimizing serving frequency, this system improves customer satisfaction.

[0066] Restaurant service systems can analyze customers' past order history and optimize how specific dishes are served. For example, if a particular dish is popular as a sharing plate, it can be served as such. If a particular dish is popular as an individual plate, it can be served that way. If a particular dish is popular as takeout, it can be served as takeout. By analyzing customers' past order history and optimizing the serving method, this system improves customer satisfaction.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects customer information. This information includes, for example, personal information, purchase history, and preference information. The collection unit can collect customers' past order history and preferences, and can also collect information based on database extraction and survey results. Step 2: The learning unit learns from the information collected by the collection unit. The learning unit learns from the information using machine learning and data mining algorithms, and can learn customer preferences based on the customer's past order history and preferences. Step 3: The suggestion department makes personalized suggestions based on the information learned by the learning department. The suggestion department can make menu suggestions and special offers based on customer preferences, and can make the best suggestions for the customer based on the customer's past order history and preferences. Step 4: The reception desk takes customer orders. The reception desk can take orders and explain the menu once customers are seated at a table. The reception desk can take orders using a tablet device, and can also take orders verbally. Step 5: The counter supports multiple languages. The counter can provide menu explanations in multiple languages, such as English, Chinese, and Japanese, allowing foreign tourists to order smoothly without experiencing language barriers.

[0069] (Example of form 2) The restaurant service system according to an embodiment of the present invention is a system that utilizes AI agent technology to innovate the restaurant service experience and provide the best customer experience at every table. Unlike conventional service where the quality of service fluctuates depending on the staff's skills and busy times, the restaurant service system uses an AI agent to instantly provide consistently high-quality service. Specifically, it realizes a wide range of service functions, including order taking, menu explanation, personalized suggestions, and multilingual support. The restaurant service system learns the customer's past ordering history and preferences to provide personalized hospitality at every table. For example, it can flexibly respond to diverse needs such as food allergies and preferences for specific dishes. Furthermore, the restaurant service system is available 24 hours a day, 365 days a year, solving the problem of staff shortages and contributing to increased operational efficiency and profitability of restaurants. For example, the restaurant service system learns the customer's past ordering history and preferences. This allows it to make personalized suggestions based on past ordering history and preferences when a customer returns. For example, it can suggest new menu items related to a customer who likes a particular dish. Next, the restaurant service system takes the order. When customers are seated at a table, the restaurant service system takes their order and explains the menu. For example, it can provide detailed descriptions of specific dishes on the menu and information about the ingredients used. Furthermore, the restaurant service system is multilingual. Even when foreign tourists visit, they can order smoothly without experiencing language barriers. For example, it can provide menu explanations in multiple languages, such as English and Chinese. In addition, the restaurant service system is available 24 hours a day, 365 days a year, solving the problem of staff shortages. This improves the operational efficiency of the restaurant and increases profitability. For example, even during times when regular staff are unavailable, such as late at night or early in the morning, the restaurant service system can handle requests, maintaining customer satisfaction. In this way, by utilizing AI agent technology, the restaurant service system can revolutionize the restaurant service experience and provide the best possible customer experience at every table.This allows restaurant service systems to innovate the restaurant experience and deliver the best possible customer experience by collecting and learning customer information, making personalized suggestions, taking orders, and supporting multiple languages.

[0070] The restaurant service system according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, a reception unit, and a response unit. The collection unit collects customer information. Customer information includes, but is not limited to, personal information, purchase history, and preference information. The collection unit collects, for example, the customer's past order history and preferences. The collection unit can collect information, for example, by extracting from a database or based on survey results. The learning unit learns the information collected by the collection unit. The learning unit learns the information using, for example, machine learning or data mining algorithms. The learning unit can learn customer preferences, for example, based on the customer's past order history and preferences. The suggestion unit makes personalized suggestions based on the information learned by the learning unit. The suggestion unit can, for example, make menu suggestions or special offers based on the customer's preferences. The suggestion unit can make the best suggestions to the customer, for example, based on the customer's past order history and preferences. The reception unit takes customer orders. The reception unit can, for example, take orders when a customer is seated at a table and explain the menu. The reception area can take orders using, for example, a tablet device. The reception area can also take orders verbally, for example. The customer service area supports multiple languages. The customer service area can provide menu explanations in multiple languages, for example, English, Chinese, and Japanese. The customer service area allows, for example, foreign tourists to order smoothly without experiencing language barriers. As a result, the restaurant service system according to this embodiment can innovate the restaurant service experience and provide the best possible customer experience by collecting and learning customer information, making personalized suggestions, taking orders, and supporting multiple languages.

[0071] The data collection unit collects customer information. This information includes, but is not limited to, personal information, purchase history, and preference information. For example, the data collection unit collects customers' past order history and preferences. Specifically, it collects detailed information such as menus that customers have ordered in the past, how often they ordered them, and their preferences and allergies to specific ingredients. This information is automatically collected when customers make online reservations or use restaurant membership cards. More detailed preference information can also be obtained when customers answer questionnaires in the restaurant. The data collection unit stores this information in a database and updates it as needed. For example, if a new menu is added or a customer's preferences change, the data collection unit quickly updates the information to maintain up-to-date data. Furthermore, the data collection unit can also collect data from external sources, such as customers' social media posts and reviews on review sites. This allows for a more accurate understanding of customer preferences and ratings, and builds a foundation for providing personalized services.

[0072] The learning unit learns from the information collected by the collection unit. The learning unit learns from information using, for example, machine learning and data mining algorithms. Specifically, it can learn customer preferences based on past order history and preferences. For example, it can analyze menu items that customers frequently order and their tendency to prefer certain ingredients to extract customer preference patterns. Based on these patterns, the learning unit predicts menu items that customers are likely to order next and new menu items that customers might be interested in. Furthermore, the learning unit considers that customer preferences change over time and continuously updates the data to improve its learning model. For example, by reflecting seasonal changes in preferences and the impact of specific events and campaigns, it can make more accurate predictions. In addition, the learning unit analyzes differences in preferences between different customer groups and builds a foundation for making optimal suggestions for each group. This allows the learning unit to play a crucial role in providing personalized services to each individual customer.

[0073] The suggestion department provides personalized suggestions based on information learned by the learning department. For example, the suggestion department can offer menu suggestions and special offers based on customer preferences. Specifically, it suggests the best menu for the customer based on menus the customer has previously ordered and preferences predicted by the learning department. For example, if a customer has previously ordered spicy dishes, the suggestion department can suggest new spicy dishes or drinks that pair well with spicy food. The suggestion department can also provide customers with special offers tailored to specific events or seasons. For example, it can suggest a special dessert menu for Valentine's Day or offer discounts on cold drinks during the summer. The suggestion department makes these suggestions easily accessible to customers by notifying them on their smartphones or tablets. Furthermore, the suggestion department can collect customer feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion department to always provide customers with the best possible suggestions and improve customer satisfaction.

[0074] The reception area takes customer orders. For example, when a customer is seated at a table, the reception area can take their order and explain the menu. Specifically, orders can be taken using a tablet device. Customers can view the menu and place their orders through the tablet. The tablet displays detailed menu descriptions and images, making it easy for customers to choose. The reception area can also take orders verbally. For example, when a waiter visits a customer's table to take a verbal order, they can use the tablet to enter the order details and send them to the kitchen. Furthermore, the reception area can handle customer requests and allergy information. For example, if a customer wants to exclude certain ingredients or requests a special cooking method, the reception area can accurately record this information and pass it on to the kitchen. This allows the reception area to take customer orders quickly and accurately and provide service that meets customer needs.

[0075] The customer service unit supports multiple languages. For example, it can provide menu descriptions in multiple languages, such as English, Chinese, and Japanese. Specifically, it can display menus in the customer's chosen language using a tablet or smartphone app. This allows foreign tourists to order smoothly without experiencing language barriers. Furthermore, the customer service unit may also train waiters to communicate in multiple languages. For example, waiters who speak English or Chinese can directly assist foreign customers and take their orders. In addition, the customer service unit can use voice recognition technology to automatically detect the customer's language and respond in the appropriate language. This allows customers to communicate smoothly in their own language, making their restaurant experience more comfortable. Through these functions, the customer service unit removes language barriers and provides the best possible service to all customers.

[0076] The data collection unit can collect customers' past order history and preferences. For example, the data collection unit can extract customers' past order history from a database and analyze their preferences. The data collection unit can also collect customer preferences based on survey results, for example. The data collection unit can identify preferences for specific foods or drinks based on customers' past order history, for example. This allows for personalized suggestions by collecting customers' past order history and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customers' past order history into AI and have the AI ​​perform preference analysis.

[0077] The learning unit can learn from the collected information and make suggestions based on customer preferences. The learning unit can learn from the collected information using, for example, machine learning algorithms. The learning unit can also analyze customer preferences using, for example, data mining techniques. The learning unit can learn customer preferences based on, for example, the customer's past order history. This improves customer satisfaction by learning from the collected information and making suggestions based on customer preferences. Some or all of the above processes in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the collected information into AI and have the AI ​​learn customer preferences.

[0078] The suggestion department can make personalized suggestions based on customer preferences. For example, the suggestion department can make menu suggestions based on customer preferences. For example, the suggestion department can also suggest special offers to customers. For example, the suggestion department can make the best suggestions to customers based on their past order history. By making personalized suggestions based on customer preferences, customer satisfaction is improved. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input customer preference information into AI and have the AI ​​make personalized suggestions.

[0079] The reception area can take orders and explain the menu once customers are seated at a table. The reception area can take orders using, for example, a tablet device. The reception area can also take orders verbally. The reception area can provide, for example, detailed descriptions of specific dishes from the menu and information about the ingredients used in those dishes. This allows for a smooth ordering process by taking orders and explaining the menu once customers are seated at a table. Some or all of the above processes at the reception area may be performed using, for example, AI, or not using AI. For example, the reception area can input customer order information into the AI ​​and have the AI ​​take orders and explain the menu.

[0080] The support unit can provide menu descriptions in multiple languages. For example, it can provide menu descriptions in multiple languages ​​such as English, Chinese, and Japanese. The support unit allows foreign tourists to order smoothly without experiencing language barriers. For example, the support unit can provide detailed descriptions of specific dishes from the menu, as well as information about the ingredients used in the dishes, in multiple languages. By providing menu descriptions in multiple languages, it can also accommodate foreign tourists. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input menu descriptions in multiple languages ​​into AI and have the AI ​​execute the descriptions.

[0081] The customer service unit is available 24 hours a day, 365 days a year, and can solve the problem of staff shortages. The customer service unit can respond even during times when regular staff are unavailable, such as late at night or early in the morning. The customer service unit can take orders and explain the menu at any time when customers arrive. The customer service unit can maintain customer satisfaction by providing 24 / 7 service. This improves the operational efficiency of the restaurant by providing 24 / 7 service and solving the problem of staff shortages. Some or all of the above processes in the customer service unit may be performed using AI, for example, or not. For example, the customer service unit can input customer order information into AI and have the AI ​​perform 24 / 7 service.

[0082] The data collection unit can estimate the customer's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit can adjust the timing of information collection during the meal to avoid interrupting the meal. For example, if the customer is in a hurry, the data collection unit can collect information immediately after ordering to prepare for prompt service. For example, if the customer is dissatisfied, the data collection unit can collect information after the meal to obtain feedback for service improvement. This improves customer satisfaction by adjusting the timing of information collection based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input customer emotion data into AI and have the AI ​​perform the adjustment of information collection timing.

[0083] The data collection unit can analyze the customer's past visit frequency and select the optimal information collection method. For example, if a customer visits frequently, the data collection unit can provide a detailed questionnaire to collect deeper information. For example, if a customer is visiting for the first time, the data collection unit can collect basic information through simple questions. For example, if a customer visits regularly, the data collection unit can provide customized questions based on past visit history and update the information. This enables efficient information collection by analyzing the customer's past visit frequency and selecting the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer visit frequency data into AI and have the AI ​​select the optimal information collection method.

[0084] The data collection unit can filter information based on the customer's current health status and dietary restrictions during data collection. For example, if a customer has allergies, the data collection unit collects allergy information and filters the suggested dishes. For example, if a customer has specific dietary restrictions, the data collection unit collects that information and suggests an appropriate menu. For example, if a customer desires a meal tailored to their health condition, the data collection unit collects that information and suggests a health-conscious menu. This allows for appropriate information collection by filtering based on the customer's health status and dietary restrictions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the customer's health status and dietary restrictions into the AI ​​and have the AI ​​perform the filtering.

[0085] The data collection unit can estimate the customer's emotions and prioritize the information to collect based on the estimated emotions. For example, if the customer is enjoying themselves, the data collection unit will prioritize collecting information about their preferred food and drinks. If the customer is dissatisfied, the data collection unit will prioritize collecting feedback about the service. If the customer is relaxed, the data collection unit will prioritize collecting information about suggestions for their next visit. By prioritizing the information to collect based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input customer emotion data into an AI and have the AI ​​determine the priority of the information.

[0086] The data collection unit can prioritize collecting highly relevant information by considering the customer's geographical location. For example, if the customer is a tourist, the data collection unit will prioritize collecting information about local specialty dishes. If the customer is a local resident, the data collection unit will prioritize collecting information about seasonal menus. If the customer is from a specific region, the data collection unit will prioritize collecting information related to the food culture of that region. By prioritizing the collection of highly relevant information while considering the customer's geographical location, customer satisfaction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant information.

[0087] The data collection unit can analyze customers' social media activity and collect relevant information during data collection. For example, the data collection unit can analyze photos of food shared by customers on social media to collect information about their food preferences. For example, the data collection unit can analyze restaurant reviews mentioned by customers on social media to collect feedback on service. For example, the data collection unit can collect information about food and beverages that customers follow on social media and use it for recommendations. This enables recommendations based on customer preferences by analyzing customers' social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input customer social media data into AI and have the AI ​​collect relevant information.

[0088] The learning unit can estimate customer emotions and select training data based on the estimated customer emotions. For example, if a customer is relaxed, the learning unit selects training data based on past positive feedback. For example, if a customer is dissatisfied, the learning unit selects training data based on past negative feedback. For example, if a customer is excited, the learning unit selects training data based on past exciting experiences. This improves the accuracy of learning by selecting training data based on customer emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input customer emotion data into an AI and have the AI ​​perform the selection of training data.

[0089] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can extract patterns related to customer preferences based on past learning data and optimize the algorithm. For example, the learning unit can analyze customer order history based on past learning data and optimize the algorithm. For example, the learning unit can reflect customer feedback based on past learning data and optimize the algorithm. By optimizing the learning algorithm by referring to past learning data, the accuracy of learning is improved. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0090] The learning unit can improve the accuracy of its learning based on the customer's eating history during the learning process. For example, the learning unit can improve the accuracy of its learning by identifying the customer's preferred dishes and ingredients based on their past eating history. For example, the learning unit can improve the accuracy of its learning by reflecting allergy information based on the customer's eating history. For example, the learning unit can improve the accuracy of its learning by considering specific dietary restrictions based on the customer's eating history. This makes it possible to provide personalized suggestions by improving the accuracy of learning based on the customer's eating history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's eating history data into AI and have AI perform the improvement of learning accuracy.

[0091] The learning unit can estimate the customer's emotions and adjust the learning frequency based on the estimated emotions. For example, if the customer is relaxed, the learning unit can set a low learning frequency to avoid excessive information gathering. If the customer is excited, for example, the learning unit can set a high learning frequency and update information in real time. If the customer is dissatisfied, for example, the learning unit can set a moderate learning frequency to collect information for service improvement. This improves the efficiency of learning by adjusting the learning frequency based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input customer emotion data into AI and have the AI ​​adjust the learning frequency.

[0092] The learning unit can weight the training data based on the customer's visit timing during training. For example, the learning unit may weight the training data by giving more emphasis to data from times when customers frequently visit. For example, if a customer visits during a particular season, the learning unit may weight the training data by giving more emphasis to data from that season. For example, if a customer visits during a particular event, the learning unit may weight the training data by giving more emphasis to data related to that event. This improves the accuracy of training by weighting the training data based on the customer's visit timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input customer visit timing data into AI and have AI perform the weighting of the training data.

[0093] The learning unit can improve the accuracy of its learning by referring to relevant literature related to the customer during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to literature on dishes that the customer is interested in. For example, if the customer is interested in a particular ingredient, the learning unit can improve the accuracy of its learning by referring to literature on that ingredient. For example, if the customer is interested in a particular cooking method, the learning unit can improve the accuracy of its learning by referring to literature on that cooking method. By improving the accuracy of learning by referring to relevant literature related to the customer, more accurate suggestions become possible. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the customer's relevant literature data into AI and have the AI ​​perform the improvement of learning accuracy.

[0094] The proposal unit can estimate the customer's emotions and adjust the way the proposal is presented based on those emotions. For example, if the customer is relaxed, the proposal unit will present the proposal in a gentle manner. If the customer is in a hurry, the proposal unit will present the proposal in a concise and quick manner. If the customer is excited, the proposal unit will present the proposal in an energetic manner. By adjusting the way the proposal is presented based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input customer emotion data into an AI and have the AI ​​adjust the way the proposal is presented.

[0095] The suggestion function can adjust the level of detail in a suggestion based on the importance of the dish. For example, for a main dish, the suggestion function provides a detailed description, increasing the level of detail. For a side dish, for example, the suggestion function provides a concise description, decreasing the level of detail. For a dessert, for example, the suggestion function provides a detailed description tailored to the customer's preferences, adjusting the level of detail. This improves customer satisfaction by adjusting the level of detail in the suggestion based on the importance of the dish. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input dish importance data into AI and have the AI ​​perform the adjustment of the level of detail in the suggestion.

[0096] The suggestion unit can apply different suggestion algorithms depending on the category of the dish when making a suggestion. For example, in the case of a main dish, the suggestion unit applies a suggestion algorithm based on the customer's past order history. For example, in the case of a side dish, the suggestion unit applies a suggestion algorithm based on the customer's current order. For example, in the case of a dessert, the suggestion unit applies a suggestion algorithm based on the customer's preferences. By applying different suggestion algorithms depending on the category of the dish, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input dish category data into AI and have the AI ​​perform the application of the suggestion algorithm.

[0097] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on those emotions. For example, if the customer is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the customer is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the customer is excited, the suggestion unit will provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into an AI and have the AI ​​adjust the length of the suggestion.

[0098] The suggestion department can determine the priority of suggestions based on the timing of food service. For example, the suggestion department might prioritize suggesting side dishes before serving the main course. For example, it might prioritize suggesting the main course before serving dessert. For example, it might prioritize suggesting food before serving drinks. By prioritizing suggestions based on the timing of food service, customer satisfaction can be improved. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input food service timing data into the AI ​​and have the AI ​​determine the priority of suggestions.

[0099] The suggestion function can adjust the order of suggestions based on the relevance of the dishes. For example, the suggestion function may prioritize suggesting side dishes related to the main dish. For example, the suggestion function may prioritize suggesting drinks related to dessert. For example, the suggestion function may prioritize suggesting main dishes related to appetizers. By adjusting the order of suggestions based on the relevance of the dishes, customer satisfaction is improved. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input dish relevance data into AI and have AI perform the adjustment of the suggestion order.

[0100] The reception desk can estimate the customer's emotions and adjust the timing of order taking based on the estimated emotions. For example, if the customer is relaxed, the reception desk will adjust the timing of order taking to match the progress of the meal. If the customer is in a hurry, the reception desk will take the order immediately. If the customer is dissatisfied, the reception desk will take the order after the meal. By adjusting the timing of order taking based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input customer emotion data into AI and have the AI ​​adjust the timing of order taking.

[0101] The reception desk can select the optimal reception method by referring to the customer's past order history when taking an order. For example, the reception desk can automatically display dishes that the customer has frequently ordered in the past as suggestions. For example, the reception desk can prioritize suggesting ordering methods (voice, text, etc.) that the customer has used in the past. For example, the reception desk can predict and suggest ordering methods to be used at a particular time based on the customer's past order history. By selecting the optimal reception method by referring to the customer's past order history, customer satisfaction is improved. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's past order history data into AI and have the AI ​​select the optimal reception method.

[0102] The reception desk can filter orders based on the customer's current dietary restrictions when taking orders. For example, if a customer has allergies, the reception desk can filter orders based on allergy information. For example, if a customer has specific dietary restrictions, the reception desk can filter orders based on that information. For example, if a customer requests a meal tailored to their health condition, the reception desk can filter orders based on that information. This allows for appropriate orders to be placed by filtering based on the customer's current dietary restrictions. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer dietary restriction data into AI and have the AI ​​perform the filtering.

[0103] The reception desk can estimate the customer's emotions and determine the priority of orders to be accepted based on those emotions. For example, if the customer is in a hurry, the reception desk will set a high priority for the order. For example, if the customer is relaxed, the reception desk will set a low priority for the order. For example, if the customer is dissatisfied, the reception desk will set a moderate priority for the order. This improves customer satisfaction by determining the priority of orders based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input customer emotion data into an AI and have the AI ​​determine the order priority.

[0104] The reception desk can prioritize orders based on the customer's geographical location when taking orders. For example, if the customer is a tourist, the reception desk will prioritize orders related to local specialties. If the customer is a local resident, the reception desk will prioritize orders related to seasonal menus. If the customer is from a specific region, the reception desk will prioritize orders related to the local food culture. By prioritizing orders based on the customer's geographical location, customer satisfaction is improved. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into the AI ​​and have the AI ​​perform the task of taking relevant orders.

[0105] The reception desk can analyze a customer's social media activity when taking an order and accept relevant orders. For example, the reception desk can analyze photos of food shared by a customer on social media and accept orders related to their preferred dishes. For example, the reception desk can analyze restaurant reviews mentioned by a customer on social media and accept orders related to service. For example, the reception desk can accept orders related to food and drinks that a customer follows on social media. By analyzing a customer's social media activity and accepting relevant orders, it becomes possible to make suggestions based on the customer's preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer social media data into AI and have the AI ​​take relevant orders.

[0106] The response unit can estimate the customer's emotions and adjust the way it responds based on those emotions. For example, if the customer is relaxed, the response unit will respond in a gentle manner. If the customer is in a hurry, the response unit will respond in a concise and quick manner. If the customer is excited, the response unit will respond in an energetic manner. By adjusting the way the response is expressed based on the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input customer emotion data into AI and have the AI ​​adjust the way the response is expressed.

[0107] The response unit can select the optimal response method by referring to the customer's language history during a response. For example, the response unit selects the optimal response method based on the languages ​​the customer has used in the past. For example, the response unit provides a language switching function if the customer uses multiple languages. For example, if the customer selects a specific language, the response unit will respond in that language. By selecting the optimal response method by referring to the customer's language history, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's language history data into AI and have the AI ​​select the optimal response method.

[0108] The response unit can customize its response methods based on the customer's cultural background when responding to a customer. For example, if the customer has a specific cultural background, the response unit will respond in a way that respects that culture. For example, if the customer has a specific religious background, the response unit will respond in a way that respects that religion. For example, if the customer has a specific regional background, the response unit will respond in a way that respects that region. By customizing the response methods based on the customer's cultural background, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input customer cultural background data into AI and have the AI ​​perform the customization of the response methods.

[0109] The response unit can estimate the customer's emotions and determine the priority of the response based on the estimated emotions. For example, if the customer is in a hurry, the response unit will set a high priority for the response. For example, if the customer is relaxed, the response unit will set a low priority for the response. For example, if the customer is dissatisfied, the response unit will set a moderate priority for the response. This improves customer satisfaction by determining the priority of the response based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input customer emotion data into AI and have the AI ​​perform the determination of the priority of the response.

[0110] The response unit can select the optimal response method by considering the customer's geographical location information when responding to a customer. For example, if the customer is a tourist, the response unit can provide information about local specialty dishes. For example, if the customer is a local resident, the response unit can provide information about seasonal menus. For example, if the customer is from a specific region, the response unit can provide information related to the food culture of that region. By selecting the optimal response method by considering the customer's geographical location information, customer satisfaction is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's geographical location information into AI and have AI select the optimal response method.

[0111] The response unit can analyze the customer's social media activity and propose a response method when responding. For example, the response unit can analyze photos of food shared by the customer on social media and provide information about their preferred cuisine. For example, the response unit can analyze restaurant reviews mentioned by the customer on social media and provide information about the service. For example, the response unit can provide information about the food and drinks that the customer follows on social media. This enables a response based on the customer's preferences by analyzing the customer's social media activity and proposing a response method. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the customer's social media data into AI and have the AI ​​propose a response method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] Restaurant service systems can estimate customer emotions and optimize seating arrangements based on those estimates. For example, if a customer wants to relax, they can be seated in a quiet area. If a customer is in a hurry, they can be seated near an exit. If a customer is celebrating a special event, they can be seated in a private area. By optimizing seating arrangements based on customer emotions, this system improves customer satisfaction.

[0114] Restaurant service systems can analyze customers' past order history and evaluate the popularity of specific dishes. For example, if a particular dish is frequently ordered by customers, it can be displayed at the top of the menu. If a particular dish is popular during a specific season, the menu can be adjusted accordingly. If a particular dish is popular during a specific event, a special menu can be offered to coincide with that event. By analyzing customers' past order history and optimizing the menu, customer satisfaction can be improved.

[0115] A restaurant service system can estimate a customer's emotions and adjust the order in which dishes are served based on those emotions. For example, if a customer is relaxed, the dishes can be served slowly. If a customer is in a hurry, the dishes can be served quickly. If a customer is celebrating a special event, the dishes can be served in a special order. By adjusting the order of dishes based on the customer's emotions, this system can improve customer satisfaction.

[0116] Restaurant service systems can analyze a customer's past visit history and provide services tailored to specific times of day. For example, if a customer frequently visits during lunchtime, the system can prioritize suggesting the lunch menu. If a customer frequently visits during dinnertime, the system can prioritize suggesting the dinner menu. If a customer visits during a specific event, the system can suggest a special menu tailored to that event. By analyzing a customer's past visit history and optimizing service accordingly, customer satisfaction can be improved.

[0117] Restaurant service systems can estimate customer emotions and adjust menu descriptions based on those estimates. For example, if a customer is relaxed, a detailed explanation can be given. If a customer is in a hurry, a concise explanation can be given. If a customer is celebrating a special event, a special explanation can be given. By tailoring menu descriptions to customer emotions, this system improves customer satisfaction.

[0118] Restaurant service systems can analyze customers' past order history and optimize the timing of serving specific dishes. For example, if a particular dish is popular as an appetizer, it can be served as an appetizer. If a particular dish is popular as a main course, it can be served as a main course. If a particular dish is popular as a dessert, it can be served as a dessert. By analyzing customers' past order history and optimizing the timing of dish service, this system improves customer satisfaction.

[0119] Restaurant service systems can estimate customer emotions and prioritize services based on those emotions. For example, if a customer is relaxed, the service priority can be set lower. If a customer is in a hurry, the service priority can be set higher. If a customer is celebrating a special event, the service priority can be set specially. By prioritizing services based on customer emotions, customer satisfaction can be improved.

[0120] Restaurant service systems can analyze customers' past visit history and optimize the frequency of serving specific dishes. For example, if a particular dish is frequently ordered, it can be displayed at the top of the menu. If a particular dish is popular during a specific season, its serving frequency can be adjusted accordingly. If a particular dish is popular during a specific event, its serving frequency can be adjusted to match that event. By analyzing customers' past visit history and optimizing serving frequency, this system improves customer satisfaction.

[0121] Restaurant service systems can estimate customer emotions and offer special offers based on those emotions. For example, if a customer is relaxed, a special drink offer can be provided. If a customer is in a hurry, a special takeaway offer can be provided. If a customer is celebrating a special event, a special dessert offer can be provided. This improves customer satisfaction by providing special offers based on customer emotions.

[0122] Restaurant service systems can analyze customers' past order history and optimize how specific dishes are served. For example, if a particular dish is popular as a sharing plate, it can be served as such. If a particular dish is popular as an individual plate, it can be served that way. If a particular dish is popular as takeout, it can be served as takeout. By analyzing customers' past order history and optimizing the serving method, this system improves customer satisfaction.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects customer information. This information includes, for example, personal information, purchase history, and preference information. The collection unit can collect customers' past order history and preferences, and can also collect information based on database extraction and survey results. Step 2: The learning unit learns from the information collected by the collection unit. The learning unit learns from the information using machine learning and data mining algorithms, and can learn customer preferences based on the customer's past order history and preferences. Step 3: The suggestion department makes personalized suggestions based on the information learned by the learning department. The suggestion department can make menu suggestions and special offers based on customer preferences, and can make the best suggestions for the customer based on the customer's past order history and preferences. Step 4: The reception desk takes customer orders. The reception desk can take orders and explain the menu once customers are seated at a table. The reception desk can take orders using a tablet device, and can also take orders verbally. Step 5: The counter supports multiple languages. The counter can provide menu explanations in multiple languages, such as English, Chinese, and Japanese, allowing foreign tourists to order smoothly without experiencing language barriers.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, learning unit, suggestion unit, reception unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 38B of the smart device 14 and processes it with the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns customer preferences using a machine learning algorithm based on the collected information. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes personalized suggestions based on the learned information. The reception unit is implemented in the specific processing unit 46A of the smart device 14 and takes customer orders and explains the menu. The response unit is implemented in the specific processing unit 46A of the smart device 14 and can explain the menu in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, learning unit, suggestion unit, reception unit, and response unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer information using the camera 42 and microphone 238 of the smart glasses 214 and processes it with the control unit 46A. The learning unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and learns customer preferences using a machine learning algorithm based on the collected information. The suggestion unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and makes personalized suggestions based on the learned information. The reception unit is implemented, for example, in the control unit 46A of the smart glasses 214 and takes customer orders and explains the menu. The response unit is implemented, for example, in the control unit 46A of the smart glasses 214 and can explain the menu in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, learning unit, suggestion unit, reception unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 238 of the headset terminal 314 and processes it with the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns customer preferences using a machine learning algorithm based on the collected information. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes personalized suggestions based on the learned information. The reception unit is implemented in the specific processing unit 46A of the headset terminal 314 and takes customer orders and explains the menu. The response unit is implemented in the specific processing unit 46A of the headset terminal 314 and can explain the menu in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, learning unit, suggestion unit, reception unit, and response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 238 of the robot 414 and processes it with the control unit 46A. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns customer preferences using a machine learning algorithm based on the collected information. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes personalized suggestions based on the learned information. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and takes customer orders and explains the menu. The response unit is implemented by, for example, the control unit 46A of the robot 414 and can explain the menu in multiple languages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A collection department that collects customer information, A learning unit that learns the information collected by the aforementioned collection unit, A proposal unit that makes personalized suggestions based on the information learned by the learning unit, The reception area where customer orders are taken, It includes a support unit that supports multiple languages. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the customer's past order history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, The system learns from the collected information and makes suggestions based on customer preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We provide personalized suggestions based on customer preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Once the customer is seated at the table, we take their order and explain the menu. The system described in Appendix 1, characterized by the features described herein. (Note 6) The corresponding part is, Provide menu descriptions in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 7) The corresponding part is, It is available 24 hours a day, 365 days a year, and solves the problem of insufficient staffing for customer service. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate customer emotions and adjust the timing of information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the customer's past visit frequency and select the optimal method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the customer's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate customer emotions and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we prioritize collecting highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze customers' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During the learning process, the accuracy of the learning is improved based on the customer's meal history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, It estimates customer emotions and adjusts the learning frequency based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the training data is weighted based on when the customer visited. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During the learning process, we improve the accuracy of the learning by referring to relevant customer literature. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the dishes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Estimate the customer's emotions and adjust the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, prioritize them based on when the dishes will be served. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the dishes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is We estimate customer emotions and adjust the timing of order acceptance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When receiving an order, the system will refer to the customer's past order history to select the most suitable order processing method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is When taking an order, filtering is performed based on the customer's current dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is The system estimates customer emotions and prioritizes orders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reception unit is When taking an order, the system prioritizes orders that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is When taking an order, the system analyzes the customer's social media activity and accepts related orders. The system described in Appendix 1, characterized by the features described herein. (Note 32) The corresponding part is, We estimate the customer's emotions and adjust the way we respond based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The corresponding part is, When responding to a customer, the system will refer to the customer's language history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The corresponding part is, When responding to a customer, customize the response method based on the customer's cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 35) The corresponding part is, Estimate the customer's emotions and determine the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The corresponding part is, When responding to a customer, the most appropriate response method will be selected, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The corresponding part is, When responding to a customer, we analyze their social media activity and propose appropriate responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects customer information, A learning unit that learns the information collected by the aforementioned collection unit, A proposal unit that makes personalized suggestions based on the information learned by the learning unit, The reception area where customer orders are taken, It includes a support unit that supports multiple languages. A system characterized by the following features.

2. The aforementioned collection unit is Collect the customer's past order history and preferences. The system according to feature 1.

3. The aforementioned learning unit, The system learns from the collected information and makes suggestions based on customer preferences. The system according to feature 1.

4. The aforementioned proposal section is, We provide personalized suggestions based on customer preferences. The system according to feature 1.

5. The aforementioned reception unit is Once the customer is seated at the table, we take their order and explain the menu. The system according to feature 1.

6. The corresponding part is, Provide menu descriptions in multiple languages. The system according to feature 1.

7. The corresponding part is, It is available 24 hours a day, 365 days a year, and solves the problem of insufficient staffing for customer service. The system according to feature 1.

8. The aforementioned collection unit is We estimate customer emotions and adjust the timing of information collection based on those estimated emotions. The system according to feature 1.

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