System

Abstract

An object of a system according to an embodiment is to automate a process of serving and transporting and to improve efficiency.SOLUTION: A system according to an embodiment includes a serving / transport robot, an order reception unit, a serving unit, and a settlement unit. The serving / transport robot receives an order from a customer. The order reception unit receives an order from a customer. The serving unit serves the dish based on the order received by the order receiving unit. The settlement unit performs settlement of the dish served by the serving unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not automate the process of serving and transporting food, resulting in issues of efficiency and labor shortages.

[0005] The system according to the embodiment aims to automate the process of serving and transporting food and improve efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a serving / transport robot, an order receiving unit, a serving unit, and a payment unit. The serving / transport robot receives orders from customers. The order receiving unit receives orders from customers. The serving unit serves food based on the orders received by the order receiving unit. The payment unit makes payments for the food served by the serving unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process of serving and transporting food, thereby improving efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The food delivery and transport robot system according to the embodiment of the present invention is equipped with a generative AI and is a system that accepts orders from customers, delivers food, and handles payments. This allows the food delivery and transport robot system to fully automate everything from customer service to food delivery and payment.

[0029] A food delivery robot system according to an embodiment includes an order receiving unit, a food serving unit, and a payment unit. The order receiving unit receives orders from customers. For example, if a customer says, "I'd like a hamburger and a cola, please," the generation AI understands this order and records it in an appropriate format. The generation AI uses natural language processing technology to analyze and record the customer's order. The food serving unit serves food based on the order received by the order receiving unit. For example, the robot receives food prepared in the kitchen and delivers it to the designated table. The food serving unit is equipped with sensors for smooth movement while avoiding obstacles. The payment unit handles payment for the food delivered by the food serving unit. For example, if a customer says, "I'll pay by credit card," the generation AI processes the payment based on that information. The payment unit uses an electronic payment service to smoothly complete payment without using cash. This allows the food delivery robot system according to an embodiment to consistently automate customer order receipt, food serving, and payment. For example, this enables efficient service provision in restaurants and reduces the burden on staff. In addition, customers can receive high-quality service, which increases their satisfaction.

[0030] The order reception unit can refer to a customer's past order history in real time and make suggestions that take into account the customer's preferences and allergy information. The generation AI, for example, refers to a customer's past order history and makes menu suggestions that take into account the customer's preferences and allergy information. For example, it may prioritize suggestions of dishes that have been ordered in the past. The generation AI also suggests new menu items based on the customer's past order history. For example, it may suggest new menu items that taste similar to dishes ordered in the past. The generation AI also refers to the customer's allergy information and suggests menu items that do not contain allergenic ingredients. For example, it may suggest dishes that do not contain nuts for a customer with a nut allergy. This makes it possible to make suggestions based on the customer's preferences and allergy information.

[0031] The order reception unit supports multiple languages, allowing for smooth acceptance of orders from foreign customers. The generation AI, for example, supports multiple languages, allowing for smooth acceptance of orders from foreign customers. For example, it supports major languages ​​such as English, Chinese, and Spanish. Furthermore, when a foreign customer places an order in their native language, the generation AI automatically recognizes the language and responds appropriately. For example, if an order is placed in English, it responds in English. The generation AI also responds taking into account the culture and customs of foreign customers. For example, it conducts dialogue that respects the etiquette and manners of a particular culture. This makes it possible to smoothly accept orders from foreign customers.

[0032] The order reception unit allows customers to input orders in advance via a smartphone app, and the robot can receive and respond to that information. For example, customers can input orders in advance via a smartphone app, and the generation AI can receive and respond to that information. For example, the order can be completed before the customer arrives. Furthermore, based on the order information input via the smartphone app, the generation AI can respond smoothly when the customer arrives. For example, it can confirm the order details when the customer arrives. Furthermore, based on the order information input in advance via the smartphone app, the generation AI can make suggestions that take into account the customer's preferences and allergy information. For example, it can suggest a customized menu based on the information input in advance. This makes it possible to input orders in advance and respond smoothly.

[0033] The food delivery unit can monitor the congestion situation in the store in real time and optimize the food delivery route. The food delivery and transport robot, for example, monitors the congestion situation in the store in real time and selects the optimal food delivery route. For example, it avoids congested areas. In addition, a system can be built that analyzes the congestion situation in the store and dynamically adjusts the food delivery route. For example, it can give priority to areas where congestion has been ameliorated. The food delivery and transport robot can also calculate the optimal food delivery route in real time based on the congestion situation in the store. For example, it can select the shortest route. This makes it possible to select the optimal food delivery route depending on the congestion situation in the store.

[0034] The serving unit can provide customers with simple quizzes and games while serving food, making their waiting time more enjoyable. The serving and transporting robot can, for example, ask customers simple quizzes to make their waiting time more enjoyable. For example, it can ask questions about ingredients. The robot can also play simple games while serving food to make their waiting time more enjoyable. For example, it can display a mini-game. The serving and transporting robot can also provide customers with interactive entertainment to make their waiting time more enjoyable. For example, it can play audio quizzes and games. This makes it possible for customers to enjoy their waiting time.

[0035] The food delivery unit can work in cooperation with other robots in the store to achieve efficient food delivery. The food delivery and transport robot can work in cooperation with other robots in the store to achieve efficient food delivery. For example, multiple robots can work together to deliver food. In addition, a system will be built in which robots in the store can communicate with each other and adjust food delivery routes and timing. For example, they can share routes to avoid congestion. In addition, the food delivery and transport robot will work in cooperation with other robots to develop algorithms for efficient food delivery. For example, it will calculate the optimal food delivery order. This will enable the food delivery and transport robot to work in cooperation with other robots in the store to achieve efficient food delivery.

[0036] The payment department can analyze a customer's payment history and suggest the most suitable payment method. For example, the generation AI can analyze a customer's past payment history and suggest the most suitable payment method. For example, it can suggest credit card payment to a customer who has used credit cards in the past. The generation AI can also suggest new payment methods based on the customer's payment history. For example, it can suggest electronic payment to a customer who has previously paid in cash. The generation AI can also analyze a customer's payment history and build a system that suggests the most suitable payment method in real time. For example, it can suggest a payment method based on the customer's preferences. This makes it possible to suggest the most suitable payment method for the customer.

[0037] The payment department can perform facial recognition of customers at the time of payment to strengthen security. For example, the generation AI can perform facial recognition of customers at the time of payment to strengthen security. For example, it can prevent payments from being completed unless the customer passes facial recognition. It can also use facial recognition technology to recognize customers' faces in real time to strengthen payment security. For example, it can perform double authentication using facial recognition and a PIN code. The generation AI can also perform facial recognition of customers at the time of payment to build a system that prevents fraudulent payments. For example, it can verify the customer's identity by comparing it with a facial recognition database. This makes it possible to strengthen security at the time of payment.

[0038] The payment department allows customers to make payments using smartwatches and wearable devices. The generation AI, for example, supports payments using smartwatches and wearable devices. For example, a customer can complete the payment by simply holding their smartwatch over the device. When a customer makes a payment using a wearable device, the generation AI recognizes the device and ensures a smooth payment. For example, it uses NFC technology. The generation AI also builds a system that supports payments using smartwatches and wearable devices. For example, it links device authentication with payment processing. This makes it possible to make payments using smartwatches and wearable devices.

[0039] The payment unit can automatically provide a coupon that can be used on the customer's next visit at the time of payment. For example, the generation AI automatically provides a coupon that can be used on the customer's next visit at the time of payment. For example, it displays a coupon code after payment is completed. Also, when a customer makes a payment, the generation AI automatically issues a coupon that can be used on the customer's next visit. For example, it sends the coupon by email or SMS. The generation AI also builds a system that provides a coupon that can be used on the customer's next visit at the time of payment. For example, it customizes the coupon based on the customer's payment history. This makes it possible to automatically provide a coupon that can be used on the customer's next visit.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The order reception unit can refer to a customer's past order history in real time and make suggestions that take into account the customer's preferences and allergy information. For example, it can prioritize suggestions of dishes that have been ordered in the past. The generation AI can also suggest new menu items based on the customer's past order history. For example, it can suggest new menu items that taste similar to dishes previously ordered. The generation AI can also refer to the customer's allergy information and suggest menu items that do not contain allergenic ingredients. For example, it can suggest dishes that do not contain nuts to a customer with a nut allergy. This makes it possible to make suggestions based on the customer's preferences and allergy information.

[0042] The order reception unit supports multiple languages, allowing for smooth orders from foreign customers. For example, it supports major languages ​​such as English, Chinese, and Spanish. Furthermore, when a foreign customer places an order in their native language, the generation AI automatically recognizes the language and responds appropriately. For example, if an order is placed in English, it will respond in English. The generation AI also responds taking into account the culture and customs of foreign customers. For example, it will converse in a way that respects the etiquette and manners of a particular culture. This makes it possible to smoothly accept orders from foreign customers.

[0043] The order reception unit allows customers to input orders in advance via a smartphone app, and the robot can receive and respond to the orders. For example, the order can be completed before the customer arrives at the store. Furthermore, based on the order information input via the smartphone app, the generation AI can respond smoothly when the customer arrives. For example, it can confirm the order details when the customer arrives. Furthermore, the generation AI can make suggestions that take into account the customer's preferences and allergy information based on the order information input in advance via the smartphone app. For example, it can suggest a customized menu based on the information input in advance. This makes it possible to input orders in advance and respond smoothly.

[0044] The food delivery unit can monitor the congestion situation in the store in real time and optimize the food delivery route. For example, it can avoid congested areas. In addition, a system can be built that analyzes the congestion situation in the store and dynamically adjusts the food delivery route. For example, it can give priority to areas where congestion has been mitigated. In addition, the food delivery and transportation robot calculates the optimal food delivery route in real time based on the congestion situation in the store. For example, it can select the shortest route. This makes it possible to select the optimal food delivery route depending on the congestion situation in the store.

[0045] The serving unit can provide simple quizzes and games to customers while serving food, making their waiting time more enjoyable. For example, it can ask customers simple quizzes to make their waiting time more enjoyable. For example, it can ask questions about ingredients. The robot can also provide customers with simple games while serving food, making their waiting time more enjoyable. For example, it can display a mini-game. The serving and transporting robot can also provide interactive entertainment to customers, making their waiting time more enjoyable. For example, it can play audio quizzes and games. This makes it possible for customers to enjoy their waiting time.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The order reception unit accepts an order from a customer. For example, if a customer says, "I'd like a hamburger and a Coke, please," the generation AI understands this order and records it in an appropriate format. The generation AI uses natural language processing technology to analyze and record the customer's order. Step 2: The serving unit serves the food based on the order received by the order receiving unit. For example, the robot receives the food prepared in the kitchen and delivers it to the designated table. The serving unit is equipped with sensors that enable it to move smoothly while avoiding obstacles. Step 3: The payment department processes the payment for the food served by the serving department. For example, if a customer says, "I'll pay by credit card," the generation AI processes the payment based on that information. The payment department uses an electronic payment service to complete the payment smoothly without using cash.

[0048] (Example 2) The food delivery and transport robot system according to the embodiment of the present invention is equipped with a generative AI and is a system that accepts orders from customers, delivers food, and handles payments. This allows the food delivery and transport robot system to fully automate everything from customer service to food delivery and payment.

[0049] A food delivery robot system according to an embodiment includes an order receiving unit, a food serving unit, and a payment unit. The order receiving unit receives orders from customers. For example, if a customer says, "I'd like a hamburger and a cola, please," the generation AI understands this order and records it in an appropriate format. The generation AI uses natural language processing technology to analyze and record the customer's order. The food serving unit serves food based on the order received by the order receiving unit. For example, the robot receives food prepared in the kitchen and delivers it to the designated table. The food serving unit is equipped with sensors for smooth movement while avoiding obstacles. The payment unit handles payment for the food delivered by the food serving unit. For example, if a customer says, "I'll pay by credit card," the generation AI processes the payment based on that information. The payment unit uses an electronic payment service to smoothly complete payment without using cash. This allows the food delivery robot system according to an embodiment to consistently automate customer order receipt, food serving, and payment. For example, this enables efficient service provision in restaurants and reduces the burden on staff. In addition, customers can receive high-quality service, which increases their satisfaction.

[0050] The order reception unit can analyze the tone and speed of a customer's voice, estimate the customer's emotional state, and respond appropriately. For example, the generation AI analyzes the tone and speed of a customer's voice in real time to estimate the customer's emotional state, such as whether they are relaxed or in a hurry. For example, if the customer is in a hurry, it responds quickly. It also analyzes the tone and speed of a customer's voice, and if the customer is feeling stressed, it engages in dialogue to relax the customer. For example, it speaks in a calm voice. It also analyzes the tone and speed of a customer's voice, and if the customer is enjoying themselves, it engages in dialogue to further enhance their enjoyment. For example, it engages in humorous conversation. This makes it possible to respond appropriately according to the customer's emotional state.

[0051] The order reception unit can refer to a customer's past order history in real time and make suggestions that take into account the customer's preferences and allergy information. The generation AI, for example, refers to a customer's past order history and makes menu suggestions that take into account the customer's preferences and allergy information. For example, it may prioritize suggestions of dishes that have been ordered in the past. The generation AI also suggests new menu items based on the customer's past order history. For example, it may suggest new menu items that taste similar to dishes ordered in the past. The generation AI also refers to the customer's allergy information and suggests menu items that do not contain allergenic ingredients. For example, it may suggest dishes that do not contain nuts for a customer with a nut allergy. This makes it possible to make suggestions based on the customer's preferences and allergy information.

[0052] The order reception unit can use the emotion estimation function to analyze the emotions felt by the customer when placing an order and generate dialogue to elicit positive emotions. The generation AI, for example, analyzes the customer's emotions in real time and generates dialogue to elicit positive emotions. For example, if the customer is nervous, it will use dialogue to relax them. It can also analyze the customer's emotions and generate humorous dialogue to elicit positive emotions. For example, it can tell jokes to make the customer smile. The generation AI can also analyze the customer's emotions and offer words of encouragement to elicit positive emotions. For example, if the customer is feeling down, it will use words of encouragement. This makes it possible to have dialogue that elicits positive emotions in the customer.

[0053] The order reception unit supports multiple languages, allowing for smooth acceptance of orders from foreign customers. The generation AI, for example, supports multiple languages, allowing for smooth acceptance of orders from foreign customers. For example, it supports major languages ​​such as English, Chinese, and Spanish. Furthermore, when a foreign customer places an order in their native language, the generation AI automatically recognizes the language and responds appropriately. For example, if an order is placed in English, it responds in English. The generation AI also responds taking into account the culture and customs of foreign customers. For example, it conducts dialogue that respects the etiquette and manners of a particular culture. This makes it possible to smoothly accept orders from foreign customers.

[0054] The order reception unit allows customers to input orders in advance via a smartphone app, and the robot can receive and respond to that information. For example, customers can input orders in advance via a smartphone app, and the generation AI can receive and respond to that information. For example, the order can be completed before the customer arrives. Furthermore, based on the order information input via the smartphone app, the generation AI can respond smoothly when the customer arrives. For example, it can confirm the order details when the customer arrives. Furthermore, based on the order information input in advance via the smartphone app, the generation AI can make suggestions that take into account the customer's preferences and allergy information. For example, it can suggest a customized menu based on the information input in advance. This makes it possible to input orders in advance and respond smoothly.

[0055] The order reception unit can use the emotion estimation function to generate dialogue to reduce the stress customers feel when ordering and provide a relaxing atmosphere. The generation AI, for example, analyzes the customer's emotions in real time and generates dialogue to reduce stress. For example, it may speak to the customer in a calm voice. In addition, to reduce the stress customers feel when ordering, the generation AI may play relaxing music or sound effects. For example, it may play calm background music. The generation AI may also analyze the customer's emotions and generate dialogue to provide a relaxing atmosphere. For example, it may provide topics that will help the customer relax. This makes it possible to reduce stress for customers and provide a relaxing atmosphere.

[0056] The serving unit can analyze the customer's facial expressions while serving food and evaluate their satisfaction in real time. For example, a serving and transporting robot can analyze the customer's facial expressions in real time and evaluate their satisfaction. For example, it can determine whether the customer is smiling. In addition, by analyzing the customer's facial expressions, if their satisfaction is low, the generative AI can take appropriate action. For example, it can suggest additional services. In addition, a system can be built that analyzes the customer's facial expressions while serving food and evaluates their satisfaction in real time. For example, it can calculate a satisfaction score based on the customer's facial expression data. This makes it possible to evaluate customer satisfaction in real time.

[0057] The food delivery unit can monitor the congestion situation in the store in real time and optimize the food delivery route. The food delivery and transport robot, for example, monitors the congestion situation in the store in real time and selects the optimal food delivery route. For example, it avoids congested areas. In addition, a system can be built that analyzes the congestion situation in the store and dynamically adjusts the food delivery route. For example, it can give priority to areas where congestion has been ameliorated. The food delivery and transport robot can also calculate the optimal food delivery route in real time based on the congestion situation in the store. For example, it can select the shortest route. This makes it possible to select the optimal food delivery route depending on the congestion situation in the store.

[0058] The serving unit can use the emotion estimation function to generate actions and sounds that heighten the sense of anticipation and satisfaction felt by customers when serving food. The serving and transporting robot, for example, analyzes customers' emotions in real time and performs actions that heighten their sense of anticipation and satisfaction. For example, it may bow politely. It also analyzes customers' emotions and generates sounds that heighten their sense of anticipation and satisfaction. For example, it may speak in a warm voice. Furthermore, a system can be built that uses the emotion estimation function to generate actions and sounds that heighten the sense of anticipation and satisfaction felt by customers when serving food. For example, it can select actions and sounds that correspond to the customer's emotions. This makes it possible to generate actions and sounds that heighten customers' sense of anticipation and satisfaction.

[0059] The serving unit can provide customers with simple quizzes and games while serving food, making their waiting time more enjoyable. The serving and transporting robot can, for example, ask customers simple quizzes to make their waiting time more enjoyable. For example, it can ask questions about ingredients. The robot can also play simple games while serving food to make their waiting time more enjoyable. For example, it can display a mini-game. The serving and transporting robot can also provide customers with interactive entertainment to make their waiting time more enjoyable. For example, it can play audio quizzes and games. This makes it possible for customers to enjoy their waiting time.

[0060] The food delivery unit can work in cooperation with other robots in the store to achieve efficient food delivery. The food delivery and transport robot can work in cooperation with other robots in the store to achieve efficient food delivery. For example, multiple robots can work together to deliver food. In addition, a system will be built in which robots in the store can communicate with each other and adjust food delivery routes and timing. For example, they can share routes to avoid congestion. In addition, the food delivery and transport robot will work in cooperation with other robots to develop algorithms for efficient food delivery. For example, it will calculate the optimal food delivery order. This will enable the food delivery and transport robot to work in cooperation with other robots in the store to achieve efficient food delivery.

[0061] The serving unit can use the emotion estimation function to generate dialogue and actions to alleviate the anxiety and dissatisfaction customers feel when serving food. The serving and transporting robot, for example, analyzes customers' emotions in real time and engages in dialogue to alleviate their anxiety and dissatisfaction. For example, it uses reassuring words. It also analyzes customers' emotions and takes actions to alleviate their anxiety and dissatisfaction. For example, it serves food with careful movements. In addition, a system can be built that uses the emotion estimation function to generate dialogue and actions to alleviate customers' anxiety and dissatisfaction when serving food. For example, it responds according to the customer's emotions. This makes it possible to generate dialogue and actions that alleviate customers' anxiety and dissatisfaction.

[0062] The payment department can analyze a customer's payment history and suggest the most suitable payment method. For example, the generation AI can analyze a customer's past payment history and suggest the most suitable payment method. For example, it can suggest credit card payment to a customer who has used credit cards in the past. The generation AI can also suggest new payment methods based on the customer's payment history. For example, it can suggest electronic payment to a customer who has previously paid in cash. The generation AI can also analyze a customer's payment history and build a system that suggests the most suitable payment method in real time. For example, it can suggest a payment method based on the customer's preferences. This makes it possible to suggest the most suitable payment method for the customer.

[0063] The payment department can perform facial recognition of customers at the time of payment to strengthen security. For example, the generation AI can perform facial recognition of customers at the time of payment to strengthen security. For example, it can prevent payments from being completed unless the customer passes facial recognition. It can also use facial recognition technology to recognize customers' faces in real time to strengthen payment security. For example, it can perform double authentication using facial recognition and a PIN code. The generation AI can also perform facial recognition of customers at the time of payment to build a system that prevents fraudulent payments. For example, it can verify the customer's identity by comparing it with a facial recognition database. This makes it possible to strengthen security at the time of payment.

[0064] The payment unit can use the emotion estimation function to generate dialogue to reduce the stress felt by customers when making a payment. For example, the generation AI analyzes the customer's emotions in real time when making a payment and generates dialogue to reduce stress. For example, it may speak to the customer in a calm voice. In addition, to reduce the stress felt by customers when making a payment, the generation AI may play relaxing music or sound effects. For example, it may play calming background music. The generation AI may also analyze the customer's emotions and generate dialogue to reduce stress. For example, it may provide topics that will help the customer relax. This makes it possible to generate dialogue that reduces stress for customers when making a payment.

[0065] The payment department allows customers to make payments using smartwatches and wearable devices. The generation AI, for example, supports payments using smartwatches and wearable devices. For example, a customer can complete the payment by simply holding their smartwatch over the device. When a customer makes a payment using a wearable device, the generation AI recognizes the device and ensures a smooth payment. For example, it uses NFC technology. The generation AI also builds a system that supports payments using smartwatches and wearable devices. For example, it links device authentication with payment processing. This makes it possible to make payments using smartwatches and wearable devices.

[0066] The payment unit can automatically provide a coupon that can be used on the customer's next visit at the time of payment. For example, the generation AI automatically provides a coupon that can be used on the customer's next visit at the time of payment. For example, it displays a coupon code after payment is completed. Also, when a customer makes a payment, the generation AI automatically issues a coupon that can be used on the customer's next visit. For example, it sends the coupon by email or SMS. The generation AI also builds a system that provides a coupon that can be used on the customer's next visit at the time of payment. For example, it customizes the coupon based on the customer's payment history. This makes it possible to automatically provide a coupon that can be used on the customer's next visit.

[0067] The payment unit can use the emotion estimation function to generate dialogue and suggestions to increase the satisfaction felt by customers when making a payment. For example, the generation AI analyzes the customer's emotions in real time when making a payment and engages in dialogue to increase satisfaction. For example, it may express gratitude. In addition, to increase the satisfaction felt by customers when making a payment, the generation AI may play relaxing music or sound effects. For example, it may play calming background music. The generation AI may also analyze the customer's emotions and engage in dialogue and suggestions to increase satisfaction. For example, it may make suggestions to encourage the customer to visit the store again. This makes it possible to generate dialogue and suggestions that increase customer satisfaction when making a payment.

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

[0069] The order reception unit can analyze the tone and speed of a customer's voice, estimate the customer's emotional state, and respond appropriately. For example, if the customer is in a hurry, a prompt response will be made. In addition, by analyzing the tone and speed of a customer's voice, if the customer is feeling stressed, the generation AI will engage in dialogue to relax the customer. For example, it will speak in a calm voice. In addition, the generation AI will analyze the tone and speed of a customer's voice, and if the customer is enjoying themselves, it will engage in dialogue to further enhance their enjoyment. For example, it will engage in conversation with a touch of humor. This makes it possible to respond appropriately according to the customer's emotional state.

[0070] The order reception unit can refer to a customer's past order history in real time and make suggestions that take into account the customer's preferences and allergy information. For example, it can prioritize suggestions of dishes that have been ordered in the past. The generation AI can also suggest new menu items based on the customer's past order history. For example, it can suggest new menu items that taste similar to dishes previously ordered. The generation AI can also refer to the customer's allergy information and suggest menu items that do not contain allergenic ingredients. For example, it can suggest dishes that do not contain nuts to a customer with a nut allergy. This makes it possible to make suggestions based on the customer's preferences and allergy information.

[0071] The order reception unit can use the emotion estimation function to analyze the emotions a customer feels when placing an order and generate dialogue to elicit positive emotions. For example, if a customer is nervous, it will generate dialogue to relax them. It can also analyze the customer's emotions and generate humorous dialogue to elicit positive emotions. For example, it might tell a joke to make the customer smile. The generation AI can also analyze the customer's emotions and offer encouraging words to elicit positive emotions. For example, if a customer is feeling down, it might offer encouraging words. This makes it possible to have a dialogue that elicits positive emotions in customers.

[0072] The order reception unit supports multiple languages, allowing for smooth orders from foreign customers. For example, it supports major languages ​​such as English, Chinese, and Spanish. Furthermore, when a foreign customer places an order in their native language, the generation AI automatically recognizes the language and responds appropriately. For example, if an order is placed in English, it will respond in English. The generation AI also responds taking into account the culture and customs of foreign customers. For example, it will converse in a way that respects the etiquette and manners of a particular culture. This makes it possible to smoothly accept orders from foreign customers.

[0073] The order reception unit allows customers to input orders in advance via a smartphone app, and the robot can receive and respond to the orders. For example, the order can be completed before the customer arrives at the store. Furthermore, based on the order information input via the smartphone app, the generation AI can respond smoothly when the customer arrives. For example, it can confirm the order details when the customer arrives. Furthermore, the generation AI can make suggestions that take into account the customer's preferences and allergy information based on the order information input in advance via the smartphone app. For example, it can suggest a customized menu based on the information input in advance. This makes it possible to input orders in advance and respond smoothly.

[0074] The order reception unit uses the emotion estimation function to generate dialogue to reduce the stress customers feel when ordering, and to provide a relaxing atmosphere. For example, it might speak to them in a calm voice. In addition, to reduce the stress customers feel when ordering, the generation AI might play relaxing music or sound effects. For example, it might play calming background music. The generation AI might also analyze the customer's emotions and engage in dialogue to provide a relaxing atmosphere. For example, it might offer topics that will help the customer relax. This makes it possible to reduce stress for customers and provide a relaxing atmosphere.

[0075] The serving unit can analyze the customer's facial expressions while serving food and evaluate their satisfaction in real time. For example, it can determine whether the customer is smiling. In addition, by analyzing the customer's facial expressions, if the customer's satisfaction is low, the generative AI will take appropriate action. For example, it can suggest additional services. In addition, a system can be built that analyzes the customer's facial expressions while serving food and evaluates their satisfaction in real time. For example, it can calculate a satisfaction score based on the customer's facial expression data. This makes it possible to evaluate customer satisfaction in real time.

[0076] The food delivery unit can monitor the congestion situation in the store in real time and optimize the food delivery route. For example, it can avoid congested areas. In addition, a system can be built that analyzes the congestion situation in the store and dynamically adjusts the food delivery route. For example, it can give priority to areas where congestion has been mitigated. In addition, the food delivery and transportation robot calculates the optimal food delivery route in real time based on the congestion situation in the store. For example, it can select the shortest route. This makes it possible to select the optimal food delivery route depending on the congestion situation in the store.

[0077] The serving unit can use the emotion estimation function to generate actions and sounds to increase the anticipation and satisfaction felt by customers when serving food. For example, it can analyze the customer's emotions in real time and perform actions to increase anticipation and satisfaction. For example, it can bow politely. It can also analyze the customer's emotions and generate sounds to increase anticipation and satisfaction. For example, it can speak to the customer in a warm voice. It can also use the emotion estimation function to build a system that generates actions and sounds to increase the anticipation and satisfaction felt by customers when serving food. For example, it can select actions and sounds according to the customer's emotions. This makes it possible to generate actions and sounds that increase the customer's anticipation and satisfaction.

[0078] The serving unit can provide simple quizzes and games to customers while serving food, making their waiting time more enjoyable. For example, it can ask customers simple quizzes to make their waiting time more enjoyable. For example, it can ask questions about ingredients. The robot can also provide customers with simple games while serving food, making their waiting time more enjoyable. For example, it can display a mini-game. The serving and transporting robot can also provide interactive entertainment to customers, making their waiting time more enjoyable. For example, it can play audio quizzes and games. This makes it possible for customers to enjoy their waiting time.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The order reception unit accepts an order from a customer. For example, if a customer says, "I'd like a hamburger and a Coke, please," the generation AI understands this order and records it in an appropriate format. The generation AI uses natural language processing technology to analyze and record the customer's order. Step 2: The serving unit serves the food based on the order received by the order receiving unit. For example, the robot receives the food prepared in the kitchen and delivers it to the designated table. The serving unit is equipped with sensors that enable it to move smoothly while avoiding obstacles. Step 3: The payment department processes the payment for the food served by the serving department. For example, if a customer says, "I'll pay by credit card," the generation AI processes the payment based on that information. The payment department uses an electronic payment service to complete the payment smoothly without using cash.

[0081] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A delivery and transport robot equipped with generative AI, The serving and transporting robot is an order reception unit that receives orders from customers; a serving unit that serves food based on the orders received by the order receiving unit; a payment unit that performs payment for the food served by the serving unit. A system characterized by:

2. The order receiving unit Analyzing the tone and speed of the customer's voice, estimating the customer's emotional state, and responding appropriately 2. The system of claim 1.

3. The order receiving unit The system references the customer's past order history in real time and makes suggestions that take into account the customer's preferences and allergy information.

2. The system of claim 1.

4. The order receiving unit Analyze the emotions felt by the customer when placing an order and generate dialogue to elicit positive emotions 2. The system of claim 1.

5. The order receiving unit Supports multiple languages, allowing foreign customers to smoothly accept orders 2. The system of claim 1.

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