system

Abstract

The system according to this embodiment aims to propose menus that take into account customer preferences and allergy information, and to streamline inventory management and ingredient ordering. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, and a management unit. The reception unit receives input of customer preferences and allergy information. The suggestion unit suggests a meal menu based on the customer preferences and allergy information. The management unit streamlines store inventory management and food ordering based on the menu suggested by the suggestion unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

[0007] The system according to this embodiment can suggest menus that take into account customer preferences and allergy information, and can streamline inventory management and ingredient ordering. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 2 has 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 AI ​​assistant for restaurant orders according to an embodiment of the present invention is a system that suggests menus considering customer preferences and allergy information, optimizes inventory management and ordering based on sales data, and automates communication with customers. The AI ​​assistant for restaurant orders receives input from the customer regarding their preferences and allergy information. For example, if a customer inputs "I have a nut allergy," that information is entered into the reception department. Next, the suggestion department suggests a menu suitable for the customer based on the input information. For example, it suggests a menu that does not contain nuts. Furthermore, based on the suggested menu, the management department optimizes the store's inventory management and ingredient ordering. For example, it grasps the inventory status of ingredients required for the suggested menu in real time and orders the necessary ingredients appropriately. The suggestion department also learns menus to suggest in the future based on customer feedback. For example, if a customer provides feedback such as "This dish was delicious," the department learns that information and reflects it in future suggestions. Furthermore, the suggestion department suggests menus based on the customer's past order history. For example, it suggests similar dishes based on dishes the customer has ordered in the past. The suggestion department also estimates the customer's emotions and adjusts the menu suggestions based on the estimated emotions. For example, if a customer inputs "I'm tired today," the system will suggest relaxing dishes. The management department analyzes the times or days of the week when specific dishes sell well and maintains appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, it will keep a larger stock of that dish. Furthermore, it has a communication department that communicates with customers via voice and makes restaurant reservations based on customer requests. For example, if a customer requests by voice, "I'd like to make a reservation for tomorrow night," the system will make the reservation accordingly. In this way, the AI ​​assistant for restaurant ordering can provide menu suggestions that take customer preferences and allergy information into consideration, optimize inventory management and ordering, and automate communication with customers.

[0029] The AI ​​assistant for ordering in a restaurant according to this embodiment comprises a reception unit, a suggestion unit, and a management unit. The reception unit receives input of customer preferences and allergy information. For example, if a customer inputs "I have a nut allergy," the reception unit can receive that information. The reception unit can also receive input of customer preferences. For example, if a customer inputs "I like spicy food," the reception unit can receive that information. The suggestion unit suggests menu items based on the customer's preferences and allergy information. For example, the suggestion unit can suggest menu items that do not contain nuts. The suggestion unit can also suggest spicy dishes based on the customer's preferences. Furthermore, the suggestion unit can also suggest menu items based on the customer's past ordering history. For example, the suggestion unit can suggest similar dishes based on dishes the customer has ordered in the past. The management unit optimizes store inventory management and ingredient ordering based on the menus suggested by the suggestion unit. For example, the management unit can grasp the inventory status of ingredients required for the suggested menus in real time and order the necessary ingredients appropriately. The management unit can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. This enables the AI ​​assistant for restaurant orders according to the embodiment to optimize menu suggestions, inventory management, and ordering, taking into account customer preferences and allergy information.

[0030] The reception desk accepts customer preferences and allergy information. For example, if a customer enters "I have a nut allergy," the reception desk can accept that information. The reception desk can also accept customer preferences. For example, if a customer enters "I like spicy food," the reception desk can accept that information. To efficiently collect this information, the reception desk provides a user-friendly interface. Specifically, it uses touchscreens, voice input, and smartphone apps to allow customers to easily enter information. Furthermore, the reception desk stores customer input information in a database for use in subsequent processing. For example, by reusing allergy information and preference data entered by customers in the past, it can save the trouble of re-entering information when ordering next time. In addition, the reception desk utilizes AI-powered natural language processing technology to verify the accuracy of the entered information. This allows the AI ​​to appropriately interpret and process even ambiguous customer input as accurate information. For example, even if a customer enters "I can't eat nuts," the AI ​​can interpret this as "nut allergy" and accept it as accurate allergy information. This allows the reception department to accurately and efficiently collect customer preferences and allergy information, which can then be used to assist the subsequent processing by the proposal and management departments.

[0031] The suggestion department proposes meal menus based on the customer's preferences and allergy information. For example, the suggestion department can suggest menus that do not contain nuts. It can also suggest spicy dishes based on the customer's preferences. Furthermore, the suggestion department can suggest menus based on the customer's past ordering history. For example, it can suggest similar dishes based on dishes the customer has ordered in the past. The suggestion department uses AI to analyze the customer's preferences and allergy information and select the optimal menu. Specifically, the AI ​​searches for appropriate dishes from the menu database based on the customer's input information and makes suggestions. For example, if a customer inputs "I like spicy food," the AI ​​will prioritize selecting and suggesting spicy dishes from the menu. The AI ​​also analyzes the customer's past ordering history to identify dishes that the customer tends to like. For example, if there is a dish that the customer has ordered many times in the past, it can suggest a new menu item that is similar to that dish. Furthermore, the suggestion department can also propose special menus according to the season and events. For example, it may suggest cold or refreshing dishes in the summer and warm or hearty dishes in the winter. This allows the proposal department to suggest the most suitable menu, taking into account customer preferences and allergy information, thereby increasing customer satisfaction.

[0032] The management department optimizes store inventory management and ingredient ordering based on menus proposed by the proposal department. For example, the management department can monitor the inventory status of ingredients needed for proposed menus in real time and order the necessary ingredients appropriately. The management department can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. The management department uses AI to analyze inventory data and achieve efficient inventory management. Specifically, the AI ​​analyzes past sales data and seasonal demand fluctuations to calculate the optimal order quantity. For example, based on past data, it predicts when specific ingredients are consumed in large quantities and orders the appropriate amount accordingly. The AI ​​also monitors inventory levels in real time and can automatically place orders if inventory is likely to be insufficient. This prevents stockouts and ensures that appropriate inventory levels are always maintained. Furthermore, the management department manages ingredient expiration dates and minimizes waste. For example, it can adjust the system to prioritize the use of ingredients nearing their expiration date, reducing discards. This allows the management department to achieve efficient inventory management and optimized ordering, thereby improving the operational efficiency of the stores.

[0033] The suggestion department can learn from customer feedback to determine which menus to suggest for future visits. For example, if a customer provides feedback such as, "This dish was delicious," the suggestion department can learn this information and incorporate it into future suggestions. Conversely, if a customer provides feedback such as, "I didn't really like this dish," the suggestion department can learn this information and exclude it from future suggestions. Furthermore, the suggestion department can improve the menu suggestions based on customer feedback. For example, the suggestion department can analyze customer feedback and increase the variety of menus it suggests. This enables menu suggestions that reflect customer feedback.

[0034] The suggestion department can propose menus based on the customer's past ordering history. For example, it can suggest similar dishes based on dishes the customer has ordered in the past. Furthermore, the suggestion department can analyze the customer's past ordering history and propose menus tailored to their preferences. In addition, the suggestion department can propose seasonal or event-specific menus based on the customer's past ordering history. For example, it can propose seasonal or event-specific menus based on dishes the customer has ordered in the past. This enables menu suggestions that take the customer's past ordering history into consideration.

[0035] The management department can monitor inventory levels in real time. For example, the management department can monitor the inventory status of ingredients needed for a proposed menu in real time and order the necessary ingredients appropriately. The management department can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. Furthermore, the management department can monitor inventory levels in real time and set alerts to prevent stockouts. For example, the management department can issue an alert when inventory falls below a certain level, allowing for a quick response. This enables proper inventory management by providing real-time information on inventory levels.

[0036] The communications department can communicate with customers via voice and make reservations at stores in response to customer requests. For example, if a customer requests a reservation for tomorrow night via voice, the communications department can make the reservation accordingly. The communications department can also confirm and modify reservations based on customer requests. For example, if a customer requests to change a reservation via voice, the communications department can make the change accordingly. Furthermore, the communications department can collect customer feedback and use it to improve services. For example, if a customer provides feedback such as "This service was good," the communications department can collect that information and use it to improve the service. This makes it possible to make reservations in response to customer requests through voice communication.

[0037] The management department can analyze the times of day or days when specific dishes sell well and adjust inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can keep a larger stock of that dish. The management department can also analyze the times of day or days when specific dishes sell well and optimize inventory levels based on that information. For example, if a particular dish sells well during the daytime, the management department can adjust inventory levels accordingly. Furthermore, the management department can predict the demand for specific dishes based on sales data and adjust inventory levels accordingly. For example, the management department can analyze past sales data to predict the demand for specific dishes and adjust inventory levels. This makes it possible to manage inventory while taking into account the times of day or days when specific dishes sell well.

[0038] The suggestion department can analyze a customer's past order history and propose menus tailored to the season and events. For example, it can suggest seasonal or event-specific menus based on dishes a customer has ordered in the past. It can also analyze a customer's past order history and propose menus tailored to seasonal characteristics and types of events. For example, it can suggest cold dishes in the summer and hot dishes in the winter. Furthermore, it can suggest menus suitable for specific events (e.g., Christmas) based on a customer's past order history. This enables menu suggestions tailored to the season and events. 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 customer past order history data into a generating AI and have the generating AI produce menu suggestions tailored to the season and events.

[0039] The reception desk can analyze a customer's past input history and provide an efficient input interface. For example, the reception desk can automatically display preferences and allergy information that the customer has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest preferences and allergy information that the customer will use at specific times of day based on their past input history. This makes it possible to provide an optimal input interface based on past input history. Some or all of the above processing 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 input history data into a generating AI and have the generating AI perform the task of providing an efficient input interface.

[0040] The reception desk can filter input based on the customer's current health status and dietary restrictions. For example, if the customer has a specific health condition (e.g., diabetes), the reception desk can filter the input based on that information. Similarly, if the customer has specific dietary restrictions (e.g., low-carbohydrate), the reception desk can filter the input based on that information. Furthermore, if the customer has specific allergies (e.g., gluten), the reception desk can filter the input based on that information. This enables filtering of input according to health status and dietary restrictions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the customer's health status and dietary restrictions into a generating AI and have the generating AI perform the filtering of the input.

[0041] The reception desk can prioritize receiving input about regionally specific ingredients and dishes, taking into account the customer's geographical location. For example, if the customer is in a specific region, the reception desk can prioritize receiving input about regionally specific ingredients and dishes. Furthermore, if the customer is traveling, the reception desk can prioritize receiving input about regionally specific ingredients and dishes of their travel destination. Additionally, if the customer is participating in a specific event (e.g., a local festival), the reception desk can prioritize receiving input about ingredients and dishes related to that event. This enables input that takes geographical location into account. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI prioritize input about regionally specific ingredients and dishes.

[0042] The reception desk can analyze customers' social media activity and automatically input relevant preferences and allergy information. For example, the reception desk can analyze photos and comments of meals shared by customers on social media and automatically input preferences and allergy information. It can also automatically input preferences and allergy information based on information about dishes and ingredients that customers follow on social media. Furthermore, the reception desk can analyze food-related groups and events that customers participate in on social media and automatically input preferences and allergy information. This enables the automatic input of preferences and allergy information based on social media activity. 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 activity data into a generating AI and have the generating AI perform the automatic input of preferences and allergy information.

[0043] The proposal department can improve the accuracy of its proposals by analyzing past customer feedback. For example, the proposal department can prioritize suggesting menus that customers have previously given high ratings to. It can also exclude menus that customers have previously given low ratings to. Furthermore, the proposal department can suggest similar menus based on past customer feedback. This makes it possible to improve the accuracy of proposals based on past feedback. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past customer feedback data into a generating AI and have the generating AI perform the task of improving the accuracy of proposals.

[0044] The suggestion unit can customize menus based on the customer's current health condition and dietary restrictions when making suggestions. For example, if a customer has a specific health condition (e.g., high blood pressure), the suggestion unit can suggest a menu suitable for that condition. It can also suggest a menu suitable for a customer with specific dietary restrictions (e.g., vegan). Furthermore, if a customer has a specific allergy (e.g., dairy products), the suggestion unit can suggest a menu suitable for that allergy. This allows for menu customization according to health conditions and dietary restrictions. 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 data on the customer's health condition and dietary restrictions into a generating AI and have the generating AI perform menu customization.

[0045] The suggestion unit can suggest regionally specific dishes while considering the customer's geographical location. For example, if the customer is in a specific region, the suggestion unit can suggest regionally specific dishes. Furthermore, if the customer is traveling, the suggestion unit can suggest regionally specific dishes of their travel destination. Additionally, if the customer is participating in a specific event (e.g., a local festival), the suggestion unit can suggest dishes related to that event. This enables the suggestion of regionally specific dishes that take geographical location into account. 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 the customer's geographical location into a generating AI and have the generating AI suggest regionally specific dishes.

[0046] The suggestion department can analyze the customer's social media activity and suggest relevant menu items when making a suggestion. For example, it can analyze photos and comments of meals shared by the customer on social media and suggest relevant menu items. It can also suggest relevant menu items based on information about dishes and ingredients that the customer follows on social media. Furthermore, it can analyze food-related groups and events that the customer participates in on social media and suggest relevant menu items. This makes it possible to suggest menu items based on social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the customer's social media activity data into a generating AI and have the generating AI generate menu suggestions.

[0047] The management department can improve the accuracy of inventory management by analyzing past sales data during management. For example, the management department can predict the demand for a specific dish based on past sales data and adjust inventory accordingly. Furthermore, the management department can predict seasonal demand based on past sales data and adjust inventory accordingly. In addition, the management department can predict demand for a specific event (e.g., Christmas) based on past sales data and adjust inventory accordingly. This enables improved accuracy of inventory management based on past sales data. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of inventory management.

[0048] The management department can adjust inventory levels according to the season and events during management. For example, the management department can forecast seasonal demand and adjust inventory levels accordingly. It can also forecast demand for specific events (e.g., Christmas) and adjust inventory levels accordingly. Furthermore, the management department can adjust the inventory levels of specific ingredients according to the season and events. This makes it possible to adjust inventory levels according to the season and events. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input seasonal and event data into a generating AI and have the generating AI perform inventory level adjustments.

[0049] The management department can prioritize the management of regionally specific ingredients by considering geographical location information during management. For example, the management department can predict the demand for regionally specific ingredients and prioritize inventory management. The management department can also prioritize inventory management by considering the supply situation of regionally specific ingredients. Furthermore, the management department can prioritize inventory management by considering the seasonal supply situation of regionally specific ingredients. This makes it possible to manage the inventory of regionally specific ingredients while considering geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location information into a generating AI and have the generating AI perform inventory management of regionally specific ingredients.

[0050] The management department can analyze social media activity and manage the inventory of related ingredients during management. For example, the management department can analyze trends on social media and manage the inventory of related ingredients. It can also analyze customer posts on social media and manage the inventory of related ingredients. Furthermore, the management department can analyze event information on social media and manage the inventory of related ingredients. This makes it possible to manage ingredient inventory based on social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input social media activity data into a generating AI and have the generating AI perform inventory management of related ingredients.

[0051] The communications department can analyze past interaction history to provide the optimal response during communication. For example, the communications department can provide the optimal response based on the customer's preferred response methods in the past. It can also provide the optimal response by avoiding response methods that the customer was dissatisfied with in the past. Furthermore, the communications department can analyze the customer's past interaction history and provide the optimal response for similar situations. This enables the provision of the optimal response based on past interaction history. Some or all of the above processes in the communications department may be performed using AI, for example, or not. For example, the communications department can input past interaction history data into a generating AI and have the generating AI execute the optimal response.

[0052] The communications department can customize its responses based on the customer's current situation and requests during communication. For example, if a customer has a specific request (e.g., making a reservation), the communications department can provide a response that meets that request. Furthermore, if a customer is in a specific situation (e.g., participating in an event), the communications department can provide a response that is appropriate to that situation. In addition, the communications department can analyze the customer's current situation and requests to provide the optimal response. This allows for the customization of responses based on the customer's current situation and requests. Some or all of the above processes in the communications department may be performed using AI, or not. For example, the communications department can input customer current situation and request data into a generating AI and have the generating AI perform the customization of the response.

[0053] The communications department can provide region-specific information while considering geographical location information during communication. For example, if a customer is in a specific region, the communications department can provide region-specific information. Furthermore, if a customer is traveling, the communications department can provide region-specific information for their travel destination. Additionally, if a customer is participating in a specific event (e.g., a local festival), the communications department can provide information related to that event. This enables the provision of region-specific information that takes geographical location information into account. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department can input geographical location information into a generating AI and have the generating AI perform the provision of region-specific information.

[0054] The communications department can analyze social media activity during communication and provide relevant information. For example, the communications department can provide relevant information based on information shared by customers on social media. It can also provide relevant information based on accounts and groups that customers follow on social media. Furthermore, the communications department can analyze events and activities that customers participate in on social media and provide relevant information. This makes it possible to provide relevant information based on social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can input social media activity data into a generating AI and have the generating AI perform the task of providing relevant information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The proposal department can analyze customers' past order history and suggest menus tailored to the season and events. For example, it can suggest seasonal or event-specific menus based on dishes customers have ordered in the past. Furthermore, the proposal department can analyze customers' past order history and suggest menus that match the characteristics of each season and the type of event. For example, it can suggest cold dishes in the summer and hot dishes in the winter. It can also suggest menus suitable for specific events (e.g., Christmas). This enables the suggestion of menus tailored to the season and events.

[0057] The management department can monitor inventory levels in real time. For example, they can check the inventory status of ingredients needed for a proposed menu in real time and order the necessary ingredients appropriately. They can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, they can secure a larger inventory of that dish. Furthermore, they can monitor inventory levels in real time and set alerts to prevent stockouts. For example, they can issue an alert when inventory falls below a certain level, allowing for a quick response. This enables proper inventory management by providing real-time information on inventory levels.

[0058] The communications department can communicate with customers via voice and make reservations at stores in response to their requests. For example, if a customer requests a reservation for tomorrow night, the department can make the reservation accordingly. It can also confirm and modify reservations based on customer requests. For instance, if a customer requests to change a reservation, the department can do so. Furthermore, it can collect customer feedback to improve services. For example, if a customer provides feedback stating that they appreciated the service, this information can be collected and used to improve the service. This allows for reservations tailored to customer needs through voice communication.

[0059] The proposal department can improve the accuracy of its proposals by analyzing past customer feedback. For example, it can prioritize proposing menu items that customers have previously given high ratings to. It can also exclude menu items that customers have previously given low ratings to. Furthermore, it can suggest similar menu items based on past customer feedback. This makes it possible to improve the accuracy of proposals based on past feedback.

[0060] The proposal department can customize menus based on the customer's current health condition and dietary restrictions when making a proposal. For example, if a customer has a specific health condition (e.g., high blood pressure), a menu suitable for that condition can be proposed. Similarly, if a customer has specific dietary restrictions (e.g., vegan), a menu suitable for those restrictions can be proposed. Furthermore, if a customer has specific allergies (e.g., dairy products), a menu suitable for those allergies can be proposed. This allows for menu customization according to health conditions and dietary restrictions.

[0061] The proposal department can suggest regionally specific dishes by considering the customer's geographical location when making a proposal. For example, if a customer is in a specific region, it can suggest dishes unique to that region. If a customer is traveling, it can suggest dishes unique to their destination. Furthermore, if a customer is participating in a specific event (e.g., a local festival), it can suggest dishes related to that event. This makes it possible to suggest regionally specific dishes that take geographical location into consideration.

[0062] The proposal department can analyze customers' social media activity and suggest relevant menu items when making proposals. For example, it can analyze photos and comments on meals shared by customers on social media and suggest relevant menu items. It can also suggest relevant menu items based on information about dishes and ingredients that customers follow on social media. Furthermore, it can analyze food-related groups and events that customers participate in on social media and suggest relevant menu items. This makes it possible to propose menu items based on social media activity.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives customer preferences and allergy information. For example, if a customer enters "I have a nut allergy," that information can be received. Similarly, if a customer enters "I like spicy food," that information can also be received. Step 2: The suggestion department proposes meal menus based on the customer's preferences and allergy information. For example, they can suggest menus that do not contain nuts or spicy dishes. They can also suggest similar dishes based on the customer's past ordering history. Step 3: The management department optimizes store inventory management and ingredient ordering based on the menus proposed by the proposal department. For example, they monitor the inventory status of ingredients needed for the proposed menus in real time and order the necessary ingredients appropriately. They also analyze the times and days of the week when specific dishes sell well and use that information to maintain appropriate inventory levels.

[0065] (Example of form 2) The AI ​​assistant for restaurant orders according to an embodiment of the present invention is a system that suggests menus considering customer preferences and allergy information, optimizes inventory management and ordering based on sales data, and automates communication with customers. The AI ​​assistant for restaurant orders receives input from the customer regarding their preferences and allergy information. For example, if a customer inputs "I have a nut allergy," that information is entered into the reception department. Next, the suggestion department suggests a menu suitable for the customer based on the input information. For example, it suggests a menu that does not contain nuts. Furthermore, based on the suggested menu, the management department optimizes the store's inventory management and ingredient ordering. For example, it grasps the inventory status of ingredients required for the suggested menu in real time and orders the necessary ingredients appropriately. The suggestion department also learns menus to suggest in the future based on customer feedback. For example, if a customer provides feedback such as "This dish was delicious," the department learns that information and reflects it in future suggestions. Furthermore, the suggestion department suggests menus based on the customer's past order history. For example, it suggests similar dishes based on dishes the customer has ordered in the past. The suggestion department also estimates the customer's emotions and adjusts the menu suggestions based on the estimated emotions. For example, if a customer inputs "I'm tired today," the system will suggest relaxing dishes. The management department analyzes the times or days of the week when specific dishes sell well and maintains appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, it will keep a larger stock of that dish. Furthermore, it has a communication department that communicates with customers via voice and makes restaurant reservations based on customer requests. For example, if a customer requests by voice, "I'd like to make a reservation for tomorrow night," the system will make the reservation accordingly. In this way, the AI ​​assistant for restaurant ordering can provide menu suggestions that take customer preferences and allergy information into consideration, optimize inventory management and ordering, and automate communication with customers.

[0066] The AI ​​assistant for ordering in a restaurant according to this embodiment comprises a reception unit, a suggestion unit, and a management unit. The reception unit receives input of customer preferences and allergy information. For example, if a customer inputs "I have a nut allergy," the reception unit can receive that information. The reception unit can also receive input of customer preferences. For example, if a customer inputs "I like spicy food," the reception unit can receive that information. The suggestion unit suggests menu items based on the customer's preferences and allergy information. For example, the suggestion unit can suggest menu items that do not contain nuts. The suggestion unit can also suggest spicy dishes based on the customer's preferences. Furthermore, the suggestion unit can also suggest menu items based on the customer's past ordering history. For example, the suggestion unit can suggest similar dishes based on dishes the customer has ordered in the past. The management unit optimizes store inventory management and ingredient ordering based on the menus suggested by the suggestion unit. For example, the management unit can grasp the inventory status of ingredients required for the suggested menus in real time and order the necessary ingredients appropriately. The management unit can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. This enables the AI ​​assistant for restaurant orders according to the embodiment to optimize menu suggestions, inventory management, and ordering, taking into account customer preferences and allergy information.

[0067] The reception desk accepts customer preferences and allergy information. For example, if a customer enters "I have a nut allergy," the reception desk can accept that information. The reception desk can also accept customer preferences. For example, if a customer enters "I like spicy food," the reception desk can accept that information. To efficiently collect this information, the reception desk provides a user-friendly interface. Specifically, it uses touchscreens, voice input, and smartphone apps to allow customers to easily enter information. Furthermore, the reception desk stores customer input information in a database for use in subsequent processing. For example, by reusing allergy information and preference data entered by customers in the past, it can save the trouble of re-entering information when ordering next time. In addition, the reception desk utilizes AI-powered natural language processing technology to verify the accuracy of the entered information. This allows the AI ​​to appropriately interpret and process even ambiguous customer input as accurate information. For example, even if a customer enters "I can't eat nuts," the AI ​​can interpret this as "nut allergy" and accept it as accurate allergy information. This allows the reception department to accurately and efficiently collect customer preferences and allergy information, which can then be used to assist the subsequent processing by the proposal and management departments.

[0068] The suggestion department proposes meal menus based on the customer's preferences and allergy information. For example, the suggestion department can suggest menus that do not contain nuts. It can also suggest spicy dishes based on the customer's preferences. Furthermore, the suggestion department can suggest menus based on the customer's past ordering history. For example, it can suggest similar dishes based on dishes the customer has ordered in the past. The suggestion department uses AI to analyze the customer's preferences and allergy information and select the optimal menu. Specifically, the AI ​​searches for appropriate dishes from the menu database based on the customer's input information and makes suggestions. For example, if a customer inputs "I like spicy food," the AI ​​will prioritize selecting and suggesting spicy dishes from the menu. The AI ​​also analyzes the customer's past ordering history to identify dishes that the customer tends to like. For example, if there is a dish that the customer has ordered many times in the past, it can suggest a new menu item that is similar to that dish. Furthermore, the suggestion department can also propose special menus according to the season and events. For example, it may suggest cold or refreshing dishes in the summer and warm or hearty dishes in the winter. This allows the proposal department to suggest the most suitable menu, taking into account customer preferences and allergy information, thereby increasing customer satisfaction.

[0069] The management department optimizes store inventory management and ingredient ordering based on menus proposed by the proposal department. For example, the management department can monitor the inventory status of ingredients needed for proposed menus in real time and order the necessary ingredients appropriately. The management department can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. The management department uses AI to analyze inventory data and achieve efficient inventory management. Specifically, the AI ​​analyzes past sales data and seasonal demand fluctuations to calculate the optimal order quantity. For example, based on past data, it predicts when specific ingredients are consumed in large quantities and orders the appropriate amount accordingly. The AI ​​also monitors inventory levels in real time and can automatically place orders if inventory is likely to be insufficient. This prevents stockouts and ensures that appropriate inventory levels are always maintained. Furthermore, the management department manages ingredient expiration dates and minimizes waste. For example, it can adjust the system to prioritize the use of ingredients nearing their expiration date, reducing discards. This allows the management department to achieve efficient inventory management and optimized ordering, thereby improving the operational efficiency of the stores.

[0070] The suggestion department can learn from customer feedback to determine which menus to suggest for future visits. For example, if a customer provides feedback such as, "This dish was delicious," the suggestion department can learn this information and incorporate it into future suggestions. Conversely, if a customer provides feedback such as, "I didn't really like this dish," the suggestion department can learn this information and exclude it from future suggestions. Furthermore, the suggestion department can improve the menu suggestions based on customer feedback. For example, the suggestion department can analyze customer feedback and increase the variety of menus it suggests. This enables menu suggestions that reflect customer feedback.

[0071] The suggestion department can propose menus based on the customer's past ordering history. For example, it can suggest similar dishes based on dishes the customer has ordered in the past. Furthermore, the suggestion department can analyze the customer's past ordering history and propose menus tailored to their preferences. In addition, the suggestion department can propose seasonal or event-specific menus based on the customer's past ordering history. For example, it can propose seasonal or event-specific menus based on dishes the customer has ordered in the past. This enables menu suggestions that take the customer's past ordering history into consideration.

[0072] The management department can monitor inventory levels in real time. For example, the management department can monitor the inventory status of ingredients needed for a proposed menu in real time and order the necessary ingredients appropriately. The management department can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can secure a larger inventory of that dish. Furthermore, the management department can monitor inventory levels in real time and set alerts to prevent stockouts. For example, the management department can issue an alert when inventory falls below a certain level, allowing for a quick response. This enables proper inventory management by providing real-time information on inventory levels.

[0073] The communications department can communicate with customers via voice and make reservations at stores in response to customer requests. For example, if a customer requests a reservation for tomorrow night via voice, the communications department can make the reservation accordingly. The communications department can also confirm and modify reservations based on customer requests. For example, if a customer requests to change a reservation via voice, the communications department can make the change accordingly. Furthermore, the communications department can collect customer feedback and use it to improve services. For example, if a customer provides feedback such as "This service was good," the communications department can collect that information and use it to improve the service. This makes it possible to make reservations in response to customer requests through voice communication.

[0074] The management department can analyze the times of day or days when specific dishes sell well and adjust inventory levels based on that information. For example, if a particular dish sells well on Friday nights, the management department can keep a larger stock of that dish. The management department can also analyze the times of day or days when specific dishes sell well and optimize inventory levels based on that information. For example, if a particular dish sells well during the daytime, the management department can adjust inventory levels accordingly. Furthermore, the management department can predict the demand for specific dishes based on sales data and adjust inventory levels accordingly. For example, the management department can analyze past sales data to predict the demand for specific dishes and adjust inventory levels. This makes it possible to manage inventory while taking into account the times of day or days when specific dishes sell well.

[0075] The suggestion unit can estimate the customer's emotions and adjust the menu suggestions based on those emotions. For example, if the customer inputs "I'm tired today," the suggestion unit can suggest relaxing dishes. Similarly, if the customer inputs "I'm not feeling well today," the suggestion unit can suggest energizing dishes. Furthermore, the suggestion unit can estimate the customer's emotions in real time and adjust the menu suggestions based on those emotions. For example, the suggestion unit can estimate emotions from the customer's facial expressions and voice and suggest menu items based on those emotions. This enables menu suggestions tailored to the customer's emotions. 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-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI perform emotion-based menu suggestions.

[0076] The suggestion department can analyze a customer's past order history and propose menus tailored to the season and events. For example, it can suggest seasonal or event-specific menus based on dishes a customer has ordered in the past. It can also analyze a customer's past order history and propose menus tailored to seasonal characteristics and types of events. For example, it can suggest cold dishes in the summer and hot dishes in the winter. Furthermore, it can suggest menus suitable for specific events (e.g., Christmas) based on a customer's past order history. This enables menu suggestions tailored to the season and events. 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 customer past order history data into a generating AI and have the generating AI produce menu suggestions tailored to the season and events.

[0077] The reception desk can estimate the customer's emotions and adjust the input method for preferences and allergy information based on the estimated emotions. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the input steps. If the customer is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the customer is in a hurry, the reception desk can prioritize voice input to allow for quick input of preferences and allergy information. This allows for adjustment of the input method according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, 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 reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the input method.

[0078] The reception desk can analyze a customer's past input history and provide an efficient input interface. For example, the reception desk can automatically display preferences and allergy information that the customer has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest preferences and allergy information that the customer will use at specific times of day based on their past input history. This makes it possible to provide an optimal input interface based on past input history. Some or all of the above processing 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 input history data into a generating AI and have the generating AI perform the task of providing an efficient input interface.

[0079] The reception desk can filter input based on the customer's current health status and dietary restrictions. For example, if the customer has a specific health condition (e.g., diabetes), the reception desk can filter the input based on that information. Similarly, if the customer has specific dietary restrictions (e.g., low-carbohydrate), the reception desk can filter the input based on that information. Furthermore, if the customer has specific allergies (e.g., gluten), the reception desk can filter the input based on that information. This enables filtering of input according to health status and dietary restrictions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the customer's health status and dietary restrictions into a generating AI and have the generating AI perform the filtering of the input.

[0080] The reception desk can estimate the customer's emotions and prioritize the input content based on the estimated emotions. For example, if the customer is tired, the reception desk can prioritize the input of important information (e.g., allergy information). If the customer is relaxed, the reception desk can prioritize the input of detailed preference information. Furthermore, if the customer is in a hurry, the reception desk can prioritize the input of minimal information. This makes it possible to prioritize input content according to 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 a generative AI and have the generative AI determine the priority of the input content.

[0081] The reception desk can prioritize receiving input about regionally specific ingredients and dishes, taking into account the customer's geographical location. For example, if the customer is in a specific region, the reception desk can prioritize receiving input about regionally specific ingredients and dishes. Furthermore, if the customer is traveling, the reception desk can prioritize receiving input about regionally specific ingredients and dishes of their travel destination. Additionally, if the customer is participating in a specific event (e.g., a local festival), the reception desk can prioritize receiving input about ingredients and dishes related to that event. This enables input that takes geographical location into account. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI prioritize input about regionally specific ingredients and dishes.

[0082] The reception desk can analyze customers' social media activity and automatically input relevant preferences and allergy information. For example, the reception desk can analyze photos and comments of meals shared by customers on social media and automatically input preferences and allergy information. It can also automatically input preferences and allergy information based on information about dishes and ingredients that customers follow on social media. Furthermore, the reception desk can analyze food-related groups and events that customers participate in on social media and automatically input preferences and allergy information. This enables the automatic input of preferences and allergy information based on social media activity. 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 activity data into a generating AI and have the generating AI perform the automatic input of preferences and allergy information.

[0083] The suggestion unit can estimate the customer's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the customer is relaxed, the suggestion unit can suggest a menu with detailed explanations. If the customer is in a hurry, the suggestion unit can suggest a concise and to-the-point menu. Furthermore, if the customer is excited, the suggestion unit can suggest a visually appealing menu. This allows for adjustment of the menu suggestion method according to 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI adjust the menu suggestion method based on emotions.

[0084] The proposal department can improve the accuracy of its proposals by analyzing past customer feedback. For example, the proposal department can prioritize suggesting menus that customers have previously given high ratings to. It can also exclude menus that customers have previously given low ratings to. Furthermore, the proposal department can suggest similar menus based on past customer feedback. This makes it possible to improve the accuracy of proposals based on past feedback. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past customer feedback data into a generating AI and have the generating AI perform the task of improving the accuracy of proposals.

[0085] The suggestion unit can customize menus based on the customer's current health condition and dietary restrictions when making suggestions. For example, if a customer has a specific health condition (e.g., high blood pressure), the suggestion unit can suggest a menu suitable for that condition. It can also suggest a menu suitable for a customer with specific dietary restrictions (e.g., vegan). Furthermore, if a customer has a specific allergy (e.g., dairy products), the suggestion unit can suggest a menu suitable for that allergy. This allows for menu customization according to health conditions and dietary restrictions. 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 data on the customer's health condition and dietary restrictions into a generating AI and have the generating AI perform menu customization.

[0086] The suggestion unit can estimate the customer's emotions and determine the priority of the menu to suggest based on those emotions. For example, if the customer is tired, the suggestion unit can prioritize suggesting relaxing menu items. If the customer is relaxed, the suggestion unit can prioritize suggesting adventurous menu items. Furthermore, if the customer is in a hurry, the suggestion unit can prioritize suggesting menu items that can be served quickly. This makes it possible to prioritize menu items according to 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI determine the menu priorities.

[0087] The suggestion unit can suggest regionally specific dishes while considering the customer's geographical location. For example, if the customer is in a specific region, the suggestion unit can suggest regionally specific dishes. Furthermore, if the customer is traveling, the suggestion unit can suggest regionally specific dishes of their travel destination. Additionally, if the customer is participating in a specific event (e.g., a local festival), the suggestion unit can suggest dishes related to that event. This enables the suggestion of regionally specific dishes that take geographical location into account. 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 the customer's geographical location into a generating AI and have the generating AI suggest regionally specific dishes.

[0088] The suggestion department can analyze the customer's social media activity and suggest relevant menu items when making a suggestion. For example, it can analyze photos and comments of meals shared by the customer on social media and suggest relevant menu items. It can also suggest relevant menu items based on information about dishes and ingredients that the customer follows on social media. Furthermore, it can analyze food-related groups and events that the customer participates in on social media and suggest relevant menu items. This makes it possible to suggest menu items based on social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the customer's social media activity data into a generating AI and have the generating AI generate menu suggestions.

[0089] The management department can estimate customer emotions and adjust inventory management methods based on the estimated emotions. For example, if a customer is relaxed, the management department can use normal inventory management methods. If a customer is in a hurry, the management department can quickly check inventory and prioritize ordering necessary ingredients. Furthermore, if a customer is excited, the management department can provide a visually appealing inventory management interface. This enables the adjustment of inventory management methods according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 management department may be performed using AI or not. For example, the management department can input customer emotion data into a generative AI and have the generative AI adjust inventory management methods.

[0090] The management department can improve the accuracy of inventory management by analyzing past sales data during management. For example, the management department can predict the demand for a specific dish based on past sales data and adjust inventory accordingly. Furthermore, the management department can predict seasonal demand based on past sales data and adjust inventory accordingly. In addition, the management department can predict demand for a specific event (e.g., Christmas) based on past sales data and adjust inventory accordingly. This enables improved accuracy of inventory management based on past sales data. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past sales data into a generating AI and have the generating AI perform the task of improving the accuracy of inventory management.

[0091] The management department can adjust inventory levels according to the season and events during management. For example, the management department can forecast seasonal demand and adjust inventory levels accordingly. It can also forecast demand for specific events (e.g., Christmas) and adjust inventory levels accordingly. Furthermore, the management department can adjust the inventory levels of specific ingredients according to the season and events. This makes it possible to adjust inventory levels according to the season and events. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input seasonal and event data into a generating AI and have the generating AI perform inventory level adjustments.

[0092] The management department can estimate customer emotions and determine order priorities based on those estimated emotions. For example, if a customer is relaxed, the management department can use the normal ordering procedure. If a customer is in a hurry, the management department can quickly order the necessary ingredients. Furthermore, if a customer is excited, the management department can provide a visually appealing ordering interface. This enables the determination of order priorities according to customer emotions. Emotion estimation is achieved using emotion estimation functions, 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 management department may be performed using AI or not. For example, the management department can input customer emotion data into a generative AI and have the generative AI determine order priorities.

[0093] The management department can prioritize the management of regionally specific ingredients by considering geographical location information during management. For example, the management department can predict the demand for regionally specific ingredients and prioritize inventory management. The management department can also prioritize inventory management by considering the supply situation of regionally specific ingredients. Furthermore, the management department can prioritize inventory management by considering the seasonal supply situation of regionally specific ingredients. This makes it possible to manage the inventory of regionally specific ingredients while considering geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location information into a generating AI and have the generating AI perform inventory management of regionally specific ingredients.

[0094] The management department can analyze social media activity and manage the inventory of related ingredients during management. For example, the management department can analyze trends on social media and manage the inventory of related ingredients. It can also analyze customer posts on social media and manage the inventory of related ingredients. Furthermore, the management department can analyze event information on social media and manage the inventory of related ingredients. This makes it possible to manage ingredient inventory based on social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input social media activity data into a generating AI and have the generating AI perform inventory management of related ingredients.

[0095] The communications department can estimate the customer's emotions and adjust its communication methods based on those emotions. For example, if the customer is relaxed, the communications department can communicate in a friendly and casual tone. If the customer is in a hurry, it can communicate quickly and concisely. Furthermore, if the customer is excited, it can communicate in an energetic and positive tone. This allows for adjustment of communication methods according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communications department may be performed using AI or not. For example, the communications department can input customer emotion data into a generative AI and have the generative AI adjust the communication method.

[0096] The communications department can analyze past interaction history to provide the optimal response during communication. For example, the communications department can provide the optimal response based on the customer's preferred response methods in the past. It can also provide the optimal response by avoiding response methods that the customer was dissatisfied with in the past. Furthermore, the communications department can analyze the customer's past interaction history and provide the optimal response for similar situations. This enables the provision of the optimal response based on past interaction history. Some or all of the above processes in the communications department may be performed using AI, for example, or not. For example, the communications department can input past interaction history data into a generating AI and have the generating AI execute the optimal response.

[0097] The communications department can customize its responses based on the customer's current situation and requests during communication. For example, if a customer has a specific request (e.g., making a reservation), the communications department can provide a response that meets that request. Furthermore, if a customer is in a specific situation (e.g., participating in an event), the communications department can provide a response that is appropriate to that situation. In addition, the communications department can analyze the customer's current situation and requests to provide the optimal response. This allows for the customization of responses based on the customer's current situation and requests. Some or all of the above processes in the communications department may be performed using AI, or not. For example, the communications department can input customer current situation and request data into a generating AI and have the generating AI perform the customization of the response.

[0098] The communications department can estimate customer emotions and determine communication priorities based on those estimated emotions. For example, if a customer has an urgent request, the communications department can prioritize that request. Alternatively, if the customer is relaxed, the communications department can use standard procedures. Furthermore, if the customer is agitated, the communications department can respond quickly and provide a positive experience. This enables the determination of communication priorities according to customer emotions. Emotion estimation is achieved using emotion estimation functions, 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 communications department may be performed using AI or not. For example, the communications department can input customer emotion data into a generative AI and have the generative AI determine communication priorities.

[0099] The communications department can provide region-specific information while considering geographical location information during communication. For example, if a customer is in a specific region, the communications department can provide region-specific information. Furthermore, if a customer is traveling, the communications department can provide region-specific information for their travel destination. Additionally, if a customer is participating in a specific event (e.g., a local festival), the communications department can provide information related to that event. This enables the provision of region-specific information that takes geographical location information into account. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department can input geographical location information into a generating AI and have the generating AI perform the provision of region-specific information.

[0100] The communications department can analyze social media activity during communication and provide relevant information. For example, the communications department can provide relevant information based on information shared by customers on social media. It can also provide relevant information based on accounts and groups that customers follow on social media. Furthermore, the communications department can analyze events and activities that customers participate in on social media and provide relevant information. This makes it possible to provide relevant information based on social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can input social media activity data into a generating AI and have the generating AI perform the task of providing relevant information.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The suggestion unit can estimate the customer's emotions and adjust the menu suggestions based on those emotions. For example, if a customer inputs "I'm tired today," it can suggest relaxing dishes. Similarly, if a customer inputs "I'm not feeling well today," it can suggest dishes that will lift their spirits. Furthermore, the suggestion unit can estimate customer emotions in real time and adjust menu suggestions based on those emotions. For example, it can estimate emotions from the customer's facial expressions and voice and suggest menu items based on those emotions. This enables menu suggestions tailored to the customer's emotions.

[0103] The proposal department can analyze customers' past order history and suggest menus tailored to the season and events. For example, it can suggest seasonal or event-specific menus based on dishes customers have ordered in the past. Furthermore, the proposal department can analyze customers' past order history and suggest menus that match the characteristics of each season and the type of event. For example, it can suggest cold dishes in the summer and hot dishes in the winter. It can also suggest menus suitable for specific events (e.g., Christmas). This enables the suggestion of menus tailored to the season and events.

[0104] The management department can monitor inventory levels in real time. For example, they can check the inventory status of ingredients needed for a proposed menu in real time and order the necessary ingredients appropriately. They can also analyze the times or days of the week when specific dishes sell well and maintain appropriate inventory levels based on that information. For example, if a particular dish sells well on Friday nights, they can secure a larger inventory of that dish. Furthermore, they can monitor inventory levels in real time and set alerts to prevent stockouts. For example, they can issue an alert when inventory falls below a certain level, allowing for a quick response. This enables proper inventory management by providing real-time information on inventory levels.

[0105] The communications department can communicate with customers via voice and make reservations at stores in response to their requests. For example, if a customer requests a reservation for tomorrow night, the department can make the reservation accordingly. It can also confirm and modify reservations based on customer requests. For instance, if a customer requests to change a reservation, the department can do so. Furthermore, it can collect customer feedback to improve services. For example, if a customer provides feedback stating that they appreciated the service, this information can be collected and used to improve the service. This allows for reservations tailored to customer needs through voice communication.

[0106] The suggestion department can estimate the customer's emotions and adjust the menu suggestion method based on those estimates. For example, if the customer is relaxed, it can suggest a menu with detailed explanations. If the customer is in a hurry, it can suggest a concise and to-the-point menu. Furthermore, if the customer is excited, it can suggest a visually appealing menu. This allows for adjustments to the menu suggestion method according to the customer's emotions.

[0107] The proposal department can improve the accuracy of its proposals by analyzing past customer feedback. For example, it can prioritize proposing menu items that customers have previously given high ratings to. It can also exclude menu items that customers have previously given low ratings to. Furthermore, it can suggest similar menu items based on past customer feedback. This makes it possible to improve the accuracy of proposals based on past feedback.

[0108] The proposal department can customize menus based on the customer's current health condition and dietary restrictions when making a proposal. For example, if a customer has a specific health condition (e.g., high blood pressure), a menu suitable for that condition can be proposed. Similarly, if a customer has specific dietary restrictions (e.g., vegan), a menu suitable for those restrictions can be proposed. Furthermore, if a customer has specific allergies (e.g., dairy products), a menu suitable for those allergies can be proposed. This allows for menu customization according to health conditions and dietary restrictions.

[0109] The suggestion department can estimate the customer's emotions and determine the priority of menu suggestions based on those emotions. For example, if the customer is tired, it can prioritize suggesting relaxing menu items. If the customer is relaxed, it can prioritize suggesting adventurous menu items. Furthermore, if the customer is in a hurry, it can prioritize suggesting menu items that can be served quickly. This makes it possible to prioritize menu items according to the customer's emotions.

[0110] The proposal department can suggest regionally specific dishes by considering the customer's geographical location when making a proposal. For example, if a customer is in a specific region, it can suggest dishes unique to that region. If a customer is traveling, it can suggest dishes unique to their destination. Furthermore, if a customer is participating in a specific event (e.g., a local festival), it can suggest dishes related to that event. This makes it possible to suggest regionally specific dishes that take geographical location into consideration.

[0111] The proposal department can analyze customers' social media activity and suggest relevant menu items when making proposals. For example, it can analyze photos and comments on meals shared by customers on social media and suggest relevant menu items. It can also suggest relevant menu items based on information about dishes and ingredients that customers follow on social media. Furthermore, it can analyze food-related groups and events that customers participate in on social media and suggest relevant menu items. This makes it possible to propose menu items based on social media activity.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives customer preferences and allergy information. For example, if a customer enters "I have a nut allergy," that information can be received. Similarly, if a customer enters "I like spicy food," that information can also be received. Step 2: The suggestion department proposes meal menus based on the customer's preferences and allergy information. For example, they can suggest menus that do not contain nuts or spicy dishes. They can also suggest similar dishes based on the customer's past ordering history. Step 3: The management department optimizes store inventory management and ingredient ordering based on the menus proposed by the proposal department. For example, they monitor the inventory status of ingredients needed for the proposed menus in real time and order the necessary ingredients appropriately. They also analyze the times and days of the week when specific dishes sell well and use that information to maintain appropriate inventory levels.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] For example, the reception unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, customer preferences and allergy information are entered using the touch panel 38A or microphone 38B of the smart device 14. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the customer's input information. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and performs inventory management and order optimization. The communication unit communicates with the customer by voice using the speaker 40B or display 40A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, customer preferences and allergy information are entered using the microphone 238 of the smart glasses 214. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests menus based on the customer's input information. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and performs inventory management and order optimization. The communication unit communicates with the customer by voice using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, customer preferences and allergy information are entered using the microphone 238 of the headset terminal 314. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests menus based on the customer's input information. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and performs inventory management and order optimization. The communication unit communicates with the customer by voice using the speaker 240 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] For example, the reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. For example, customer preferences and allergy information are entered using the microphone 238 of the robot 414. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests a menu based on the customer's input information. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs inventory management and order optimization. The communication unit communicates with the customer by voice using the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception area where customers can input their preferences and allergy information, Based on the customer's preferences and allergy information, a proposal department suggests a meal menu. Based on the menu proposed by the aforementioned proposal unit, the system includes a management unit that streamlines store inventory management and food ordering. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Based on customer feedback, the system learns what meal menus to suggest for future visits. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest menus based on the customer's past order history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Get real-time inventory status The system described in Appendix 1, characterized by the features described herein. (Note 5) We communicate with customers via voice, The store has a communications department that handles store reservations in response to customer requests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Analyze the times of day or days when specific dishes sell well and adjust inventory levels based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, The system estimates the customer's emotions and adjusts menu suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We analyze customers' past order history and suggest menus tailored to the season and events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates customer emotions and adjusts how preferences and allergy information are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyzes customers' past input history to provide an efficient input interface. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Filter input based on the customer's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the customer's emotions and prioritizes the input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Taking into account the customer's geographical location, we prioritize accepting input related to local ingredients and dishes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is Analyze customers' social media activity and automatically input relevant preferences and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We estimate customer emotions and adjust menu suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we analyze past customer feedback to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, customize the menu based on the customer's current health condition and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates customer emotions and prioritizes menu suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we take the customer's geographical location into consideration and suggest regionally specific dishes. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we analyze the customer's social media activity and suggest relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, Estimate customer sentiment and adjust inventory management methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During management, analyze past sales data to improve the accuracy of inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, adjust inventory levels according to the season and events. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, 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 25) The aforementioned management department, During management, prioritize the inventory management of regionally specific ingredients, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, analyze social media activity and manage the inventory of related ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications department, We estimate customer emotions and adjust our communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications department, During communication, we analyze past interaction history to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications department, During communication, customize the response based on the customer's current situation and requests. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communications department, Estimate customer emotions and prioritize communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications department, When communicating, provide region-specific information while considering geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned communications department, During communication, we analyze social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 reception area where customers can input their preferences and allergy information, Based on the customer's preferences and allergy information, a proposal department suggests a meal menu. Based on the menu proposed by the aforementioned proposal unit, the system includes a management unit that streamlines store inventory management and food ordering. A system characterized by the following features.

2. The aforementioned proposal section is, Based on the customer feedback mentioned above, the system learns what meal menus to suggest for future visits. The system according to feature 1.

3. The aforementioned proposal section is, Based on the customer's past order history, we propose a menu. The system according to feature 1.

4. The aforementioned management department, Get real-time inventory status The system according to feature 1.

5. We communicate with the aforementioned customer by voice, The facility includes a communication department that makes store reservations in response to customer requests. The system according to feature 1.

6. The aforementioned management department, Analyze the times of day or days when specific dishes sell well and adjust inventory levels based on that information. The system according to feature 1.

7. The aforementioned proposal section is, The system estimates the customer's emotions and adjusts the menu suggestions based on those estimated emotions. The system according to feature 1.

8. The aforementioned proposal section is, We analyze the customer's past order history and propose menus tailored to the season and events. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the customer's emotions and adjusts the input method for preferences and allergy information based on the estimated customer emotions. The system according to feature 1.

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