System

Abstract

An object of the system according to the embodiment is to support orders beyond language barriers in consideration of preferences and dietary restrictions when a foreign tourist uses a Japanese food center.SOLUTION: A system according to an embodiment includes a preference understanding unit, a proposal unit, a translation unit, and an order support unit. The preference understanding unit understands preferences and dietary constraints of a tourist. The suggestion unit suggests an optimal diet based on the preferences and constraints understood by the preference understanding unit. The translation unit translates the menu of the food center proposed by the proposal unit. The order support unit supports an order based on the menu translated by the translation 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] With conventional technology, when foreign tourists visit restaurants in Japan, it is difficult to make appropriate suggestions that take into account their preferences and dietary restrictions, and there is also the language barrier, making it difficult for them to order.

[0005] The system according to the embodiment aims to support ordering by foreign tourists when they visit restaurants in Japan, taking into consideration their preferences and dietary restrictions, and overcoming language barriers. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference understanding unit, a suggestion unit, a translation unit, and an order support unit. The preference understanding unit understands the tourist's preferences and dietary constraints. The suggestion unit suggests optimal restaurants based on the preferences and constraints understood by the preference understanding unit. The translation unit translates the menus of the restaurants suggested by the suggestion unit. The order support unit supports ordering based on the menus translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can support ordering by foreign tourists when they visit restaurants in Japan, taking into consideration their preferences and dietary restrictions, and transcending language barriers. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A support system according to an embodiment of the present invention uses AI technology to provide support to foreign tourists when they visit restaurants in Japan. This support system understands the tourists' preferences and dietary restrictions and recommends the most suitable restaurant based on these. It also translates menus and assists with ordering, allowing tourists to enjoy their meals smoothly. As a result, when foreign tourists visit restaurants in Japan, the support system makes optimal suggestions based on their preferences and restrictions, allowing them to understand the menu and order smoothly without having to face language barriers.

[0029] A support system according to an embodiment includes a preference understanding unit, a suggestion unit, a translation unit, and an order support unit. The preference understanding unit understands a tourist's preferences and dietary restrictions. For example, if a tourist inputs information such as "I'm a vegetarian," "I like spicy food," or "I'd like to try Japanese cuisine," the preference understanding unit analyzes this information. The suggestion unit suggests the most suitable restaurant based on the preferences and restrictions understood by the preference understanding unit. For example, if a tourist inputs "I'm a vegetarian," the suggestion unit suggests restaurants that offer vegetarian menus. The translation unit translates the menus of the restaurants suggested by the suggestion unit. For example, it translates Japanese menus into English, Chinese, French, etc. The order support unit supports ordering based on the menus translated by the translation unit. For example, after a tourist selects a menu, the order support unit converts the order details into Japanese and conveys them to a waiter. This allows the support system according to an embodiment to easily find a restaurant that suits their preferences, understand the menu without language barriers, and smoothly place an order.

[0030] The preference understanding unit analyzes tourists' past dining history and reviews, enabling a more accurate understanding of their preferences and constraints. For example, the preference understanding unit collects reviews of restaurants and meals that tourists have visited in the past, and AI analyzes this data. For example, it extracts the characteristics of restaurants that tourists have given high ratings to in the past, and understands their preference trends. This makes it possible to make more accurate suggestions based on tourists' past data.

[0031] The preference understanding unit can monitor the real-time health status of tourists and make meal suggestions based on that. For example, the preference understanding unit uses AI to monitor allergic reactions and physical condition in real time based on the health information entered by tourists. For example, it can make suggestions to avoid certain ingredients based on allergy information. This makes it possible to make optimal meal suggestions based on the tourist's health status.

[0032] The preference understanding unit can use voice input and gesture recognition to understand the preferences and constraints of tourists. The preference understanding unit, for example, provides an interface that allows tourists to input preferences and constraints by voice. For example, a tourist can input "I'm looking for a vegetarian menu." The preference understanding unit also provides an interface that allows tourists to input preferences and constraints by gesture. For example, the preference understanding unit can recognize specific gestures and make suggestions based on them. This allows tourists to input preferences and constraints intuitively.

[0033] The preference understanding unit can compare data of other tourists to understand the tourist's preferences and constraints and find common patterns. The preference understanding unit, for example, analyzes data of other tourists to find common preferences and constraints. For example, it identifies patterns of food preferred by tourists from the same country. By finding common patterns, more accurate suggestions can be made.

[0034] The suggestion unit can analyze the congestion status and reservation status of restaurants in real time and suggest the optimal time to visit. For example, the suggestion unit can analyze the congestion status of the proposed restaurant in real time and suggest the optimal time to visit for tourists. For example, it can recommend a time period when it is least crowded. The suggestion unit can also analyze the reservation status of the proposed restaurant in real time and suggest the optimal reservation time for tourists. For example, it can recommend a time period when it is easiest to make a reservation. This allows tourists to visit the restaurant at the optimal time.

[0035] The suggestion unit can analyze past reviews and ratings of restaurants and identify restaurants that best match the tourist's preferences. The suggestion unit, for example, analyzes past reviews and ratings of restaurants to be suggested and identifies restaurants that best match the tourist's preferences. For example, it prioritizes suggesting highly rated restaurants. This makes it possible to identify restaurants that best match the tourist's preferences.

[0036] The suggestion unit can simultaneously provide information about tourist spots and events near the restaurant and propose an overall sightseeing plan. For example, the suggestion unit simultaneously provides information about tourist spots and events near the restaurant to be proposed and proposes an overall sightseeing plan. For example, it suggests tourist spots to visit after a meal. This allows tourists to enjoy a plan that combines dining and sightseeing.

[0037] The suggestion unit can provide photos and videos of the food at the restaurant to visually convey its appeal. For example, the suggestion unit can provide photos and videos of the food at the restaurant to suggest to visually convey its appeal. For example, it can show photos of the food to stimulate the appetite. This allows tourists to visually feel the appeal of the food.

[0038] The translation department can not only translate the menu, but also provide the background and history of the dishes and detailed information about the ingredients. For example, in addition to translating the menu, the translation department can provide the background and history of the dishes. For example, the translation department can explain the history and origin of sushi. The translation department can also provide detailed information about the ingredients. For example, the translation department can provide nutritional information and information about the origin of the ingredients. This allows tourists to understand the background and detailed information about the dishes and enjoy a deeper dining experience.

[0039] The translation department can collect tourists' feedback on translated menus and continuously improve the accuracy of the translations. For example, the translation department can collect tourists' feedback on translated menus and improve the accuracy of the translations based on that data. For example, if a tourist points out a mistranslation, that information can be reflected. This continuously improves the accuracy of the translations and increases tourist satisfaction.

[0040] In addition to translating the menu, the translation department can also provide cooking instructions and recipes for the dishes so that tourists can recreate them at home. For example, in addition to the translated menu, the translation department can provide cooking instructions and recipes for the dishes. For example, detailed instructions on how to make sushi can be provided so that tourists can recreate the dishes at home.

[0041] The translation unit can add a function to read out the translated menu aloud, making it possible to accommodate visually impaired people.The translation unit can add a function to read out the translated menu aloud, making it possible to accommodate visually impaired people.For example, the translation unit can read out the menu in English or Chinese.This makes it possible to accommodate visually impaired people.

[0042] The order support unit analyzes the order details, compares them with dishes that tourists have ordered in the past, and encourages repeat orders. For example, the order support unit analyzes data on dishes that tourists have ordered in the past and makes suggestions to encourage repeat orders. For example, it re-suggests dishes that tourists have given high ratings to in the past. This allows tourists to re-order dishes that they have ordered in the past.

[0043] The order support unit can reconfirm the tourist's preferences and restrictions when placing an order and suggest the most suitable order contents. For example, the order support unit can reconfirm the tourist's preferences and restrictions when placing an order and suggest the most suitable order contents. For example, it can suggest a vegetarian menu. This makes it possible to suggest the most suitable order contents according to the tourist's preferences and restrictions.

[0044] The order support unit can analyze the order details and suggest drinks and desserts that go well with the dishes ordered by the tourist. The order support unit can, for example, analyze the order details and suggest drinks and desserts that go well with the dishes ordered by the tourist. For example, it can suggest sake that goes well with sushi. This makes it possible to suggest drinks and desserts that go well with the dishes ordered by the tourist.

[0045] The order support unit can also accommodate cases where tourists want to avoid certain ingredients or have special requests when ordering. For example, the order support unit can make suggestions based on allergy information. This allows the special requests of tourists to be accommodated.

[0046] The support system includes a review provider, which collects reviews and ratings from other tourists and locals and provides restaurant information based on the collected information. For example, if a tourist enters "I'm looking for a highly rated Japanese restaurant," the review provider will suggest highly rated Japanese restaurants. This allows tourists to select restaurants based on reliable information.

[0047] The review providing unit can analyze the reliability of reviews and ratings and provide highly reliable information preferentially. The review providing unit can, for example, analyze the reliability of reviews and ratings and provide highly reliable information preferentially. For example, highly reliable reviews can be displayed preferentially. This makes it possible to provide highly reliable information.

[0048] The review providing unit analyzes reviews and ratings and can monitor the satisfaction of restaurants visited by tourists in real time. The review providing unit, for example, analyzes reviews and ratings and builds a system that monitors the satisfaction of restaurants visited by tourists in real time. For example, it immediately analyzes reviews after a visit. This makes it possible to monitor the satisfaction of tourists in real time.

[0049] The review providing unit can analyze the reviews and ratings, identify areas for improvement in restaurants visited by tourists, and provide feedback. The review providing unit, for example, analyzes the reviews and ratings, and identifies areas for improvement in restaurants visited by tourists. For example, it provides feedback on the quality of service and the taste of food. This makes it possible to identify areas for improvement in restaurants and provide feedback.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The support system can also include a cultural understanding unit that understands the tourist's cultural background and suggests restaurants based on that. For example, if a tourist has specific religious restrictions, the system can suggest restaurants that meet those restrictions. Also, if a tourist is interested in a particular cultural event or festival, the system can suggest restaurants related to that event. This allows tourists to enjoy a dining experience that is tailored to their cultural background.

[0052] The support system can also be equipped with a social media analysis unit that analyzes social media data to understand tourists' food preferences. For example, it can analyze food photos and comments posted by tourists on Instagram and Twitter to understand their taste trends. It can also suggest restaurants that tourists like based on the food-related accounts and hashtags they follow. This allows for more accurate recommendations based on tourists' social media activity.

[0053] The support system can also include a tourist destination analysis unit that analyzes data on tourist destinations that tourists have visited in the past to understand the tourist's food preferences. For example, the support system can suggest popular restaurants in the area based on the data on tourist destinations that tourists have visited in the past. It can also suggest similar restaurants based on the types of food that tourists preferred at a particular tourist destination. This allows for more accurate suggestions based on the data on tourist destinations that tourists have visited in the past.

[0054] The support system can also be equipped with an event analysis unit that analyzes data on cooking classes and dining events that tourists have attended in the past in order to understand the tourists' food preferences. For example, based on data on cooking classes and dining events that tourists have attended in the past, the system can suggest restaurants that serve the dishes and ingredients that tourists learned at those events. Also, based on the skills that tourists learned in a particular cooking class, the system can suggest restaurants that serve dishes that utilize those skills. This allows for more accurate suggestions based on data on tourists' past cooking classes and dining events.

[0055] The support system can also be equipped with a purchase history analysis unit that analyzes data on ingredients and dishes purchased by tourists in the past in order to understand the tourist's food preferences. For example, based on the data on ingredients and dishes purchased by tourists in the past, restaurants that serve those ingredients and dishes can be suggested. Also, if a tourist has a preference for a particular ingredient, restaurants that serve dishes using that ingredient can be suggested. This allows for more accurate suggestions based on the tourist's past purchase history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The preference understanding unit understands the tourist's preferences and dietary restrictions. For example, if a tourist enters information such as "I'm a vegetarian," "I like spicy food," or "I'd like to try Japanese food," the preference understanding unit will perform analysis based on this information. Step 2: The suggestion unit suggests optimal dining options based on the preferences and constraints understood by the preference understanding unit. For example, if a tourist inputs "I'm a vegetarian," the suggestion unit suggests restaurants that offer vegetarian menus. Step 3: The translation department translates the restaurant menu proposed by the proposal department. For example, the Japanese menu is translated into English, Chinese, French, etc. Step 4: The order support department supports ordering based on the menu translated by the translation department. For example, after a tourist selects a menu item, the order support department translates the order into Japanese and conveys it to the waiter.

[0058] (Example 2) A support system according to an embodiment of the present invention uses AI technology to provide support to foreign tourists when they visit restaurants in Japan. This support system understands the tourists' preferences and dietary restrictions and recommends the most suitable restaurant based on these. It also translates menus and assists with ordering, allowing tourists to enjoy their meals smoothly. As a result, when foreign tourists visit restaurants in Japan, the support system makes optimal suggestions based on their preferences and restrictions, allowing them to understand the menu and order smoothly without having to face language barriers.

[0059] A support system according to an embodiment includes a preference understanding unit, a suggestion unit, a translation unit, and an order support unit. The preference understanding unit understands a tourist's preferences and dietary restrictions. For example, if a tourist inputs information such as "I'm a vegetarian," "I like spicy food," or "I'd like to try Japanese cuisine," the preference understanding unit analyzes this information. The suggestion unit suggests the most suitable restaurant based on the preferences and restrictions understood by the preference understanding unit. For example, if a tourist inputs "I'm a vegetarian," the suggestion unit suggests restaurants that offer vegetarian menus. The translation unit translates the menus of the restaurants suggested by the suggestion unit. For example, it translates Japanese menus into English, Chinese, French, etc. The order support unit supports ordering based on the menus translated by the translation unit. For example, after a tourist selects a menu, the order support unit converts the order details into Japanese and conveys them to a waiter. This allows the support system according to an embodiment to easily find a restaurant that suits their preferences, understand the menu without language barriers, and smoothly place an order.

[0060] The preference understanding unit analyzes tourists' past dining history and reviews, enabling a more accurate understanding of their preferences and constraints. For example, the preference understanding unit collects reviews of restaurants and meals that tourists have visited in the past, and AI analyzes this data. For example, it extracts the characteristics of restaurants that tourists have given high ratings to in the past, and understands their preference trends. This makes it possible to make more accurate suggestions based on tourists' past data.

[0061] The preference understanding unit can monitor the real-time health status of tourists and make meal suggestions based on that. For example, the preference understanding unit uses AI to monitor allergic reactions and physical condition in real time based on the health information entered by tourists. For example, it can make suggestions to avoid certain ingredients based on allergy information. This makes it possible to make optimal meal suggestions based on the tourist's health status.

[0062] The preference understanding unit can estimate the tourist's emotions and make suggestions that elicit positive feelings. For example, the preference understanding unit analyzes the facial expressions and voice of the tourist when entering information to estimate the tourist's emotions. For example, if it detects a smile or an excited voice, it will make suggestions that match those emotions. This makes it possible to make suggestions that are in line with the tourist's emotions, improving satisfaction.

[0063] The preference understanding unit can use voice input and gesture recognition to understand the preferences and constraints of tourists. The preference understanding unit, for example, provides an interface that allows tourists to input preferences and constraints by voice. For example, a tourist can input "I'm looking for a vegetarian menu." The preference understanding unit also provides an interface that allows tourists to input preferences and constraints by gesture. For example, the preference understanding unit can recognize specific gestures and make suggestions based on them. This allows tourists to input preferences and constraints intuitively.

[0064] The preference understanding unit can compare data of other tourists to understand the tourist's preferences and constraints and find common patterns. The preference understanding unit, for example, analyzes data of other tourists to find common preferences and constraints. For example, it identifies patterns of food preferred by tourists from the same country. By finding common patterns, more accurate suggestions can be made.

[0065] The preference understanding unit can analyze the emotions of tourists in real time and dynamically adjust the input content. For example, the preference understanding unit can analyze the emotions of tourists when they input information in real time and dynamically adjust the input content. For example, if a tourist is excited, the unit can make suggestions that match that emotion. This makes it possible to adjust the input content according to the tourist's emotions.

[0066] The suggestion unit can analyze the congestion status and reservation status of restaurants in real time and suggest the optimal time to visit. For example, the suggestion unit can analyze the congestion status of the proposed restaurant in real time and suggest the optimal time to visit for tourists. For example, it can recommend a time period when it is least crowded. The suggestion unit can also analyze the reservation status of the proposed restaurant in real time and suggest the optimal reservation time for tourists. For example, it can recommend a time period when it is easiest to make a reservation. This allows tourists to visit the restaurant at the optimal time.

[0067] The suggestion unit can analyze past reviews and ratings of restaurants and identify restaurants that best match the tourist's preferences. The suggestion unit, for example, analyzes past reviews and ratings of restaurants to be suggested and identifies restaurants that best match the tourist's preferences. For example, it prioritizes suggesting highly rated restaurants. This makes it possible to identify restaurants that best match the tourist's preferences.

[0068] The suggestion unit can use the emotion estimation function to analyze the emotions of tourists when they receive suggestions and make suggestions that will elicit a positive response. For example, the suggestion unit analyzes the emotions of tourists when they receive suggestions and makes suggestions that will elicit a positive response. For example, if a tourist is happy, the suggestion unit makes suggestions that match those emotions. This makes it possible to make suggestions that are in line with the tourist's emotions, improving satisfaction.

[0069] The suggestion unit can simultaneously provide information about tourist spots and events near the restaurant and propose an overall sightseeing plan. For example, the suggestion unit simultaneously provides information about tourist spots and events near the restaurant to be proposed and proposes an overall sightseeing plan. For example, it suggests tourist spots to visit after a meal. This allows tourists to enjoy a plan that combines dining and sightseeing.

[0070] The suggestion unit can provide photos and videos of the food at the restaurant to visually convey its appeal. For example, the suggestion unit can provide photos and videos of the food at the restaurant to suggest to visually convey its appeal. For example, it can show photos of the food to stimulate the appetite. This allows tourists to visually feel the appeal of the food.

[0071] The suggestion unit can use the emotion estimation function to analyze in real time the emotions of tourists when they receive suggestions and dynamically adjust the content of the suggestions. For example, the suggestion unit analyzes in real time the emotions of tourists when they receive suggestions and dynamically adjusts the content of the suggestions. For example, if a tourist is happy, the suggestion unit makes suggestions that match that emotion. This makes it possible to adjust the content of the suggestions according to the tourist's emotions.

[0072] The translation department can not only translate the menu, but also provide the background and history of the dishes and detailed information about the ingredients. For example, in addition to translating the menu, the translation department can provide the background and history of the dishes. For example, the translation department can explain the history and origin of sushi. The translation department can also provide detailed information about the ingredients. For example, the translation department can provide nutritional information and information about the origin of the ingredients. This allows tourists to understand the background and detailed information about the dishes and enjoy a deeper dining experience.

[0073] The translation department can collect tourists' feedback on translated menus and continuously improve the accuracy of the translations. For example, the translation department can collect tourists' feedback on translated menus and improve the accuracy of the translations based on that data. For example, if a tourist points out a mistranslation, that information can be reflected. This continuously improves the accuracy of the translations and increases tourist satisfaction.

[0074] The translation unit uses the emotion estimation function to analyze the emotions of tourists when they see the translated menu and can provide a translation that elicits a positive response. For example, the translation unit analyzes the emotions of tourists when they see the translated menu and provides a translation that elicits a positive response. For example, if a tourist is happy, it provides a translation that matches that emotion. This makes it possible to provide a translation that is in line with the tourist's emotions, improving satisfaction.

[0075] In addition to translating the menu, the translation department can also provide cooking instructions and recipes for the dishes so that tourists can recreate them at home. For example, in addition to the translated menu, the translation department can provide cooking instructions and recipes for the dishes. For example, detailed instructions on how to make sushi can be provided so that tourists can recreate the dishes at home.

[0076] The translation unit can add a function to read out the translated menu aloud, making it possible to accommodate visually impaired people.The translation unit can add a function to read out the translated menu aloud, making it possible to accommodate visually impaired people.For example, the translation unit can read out the menu in English or Chinese.This makes it possible to accommodate visually impaired people.

[0077] The translation unit can use the emotion estimation function to analyze tourists' emotions in real time when they view a translated menu and dynamically adjust the translation content. For example, the translation unit can analyze tourists' emotions in real time when they view a translated menu and dynamically adjust the translation content. For example, if a tourist is happy, the translation unit provides a translation that matches that emotion. This makes it possible to adjust the translation content according to the tourist's emotions.

[0078] The order support unit analyzes the order details, compares them with dishes that tourists have ordered in the past, and encourages repeat orders. For example, the order support unit analyzes data on dishes that tourists have ordered in the past and makes suggestions to encourage repeat orders. For example, it re-suggests dishes that tourists have given high ratings to in the past. This allows tourists to re-order dishes that they have ordered in the past.

[0079] The order support unit can reconfirm the tourist's preferences and restrictions when placing an order and suggest the most suitable order contents. For example, the order support unit can reconfirm the tourist's preferences and restrictions when placing an order and suggest the most suitable order contents. For example, it can suggest a vegetarian menu. This makes it possible to suggest the most suitable order contents according to the tourist's preferences and restrictions.

[0080] The order support unit can use the emotion estimation function to analyze the emotions of tourists when they place an order and provide support that elicits a positive response. For example, the order support unit can analyze the emotions of tourists when they place an order and provide support that elicits a positive response. For example, if a tourist is happy, support that matches that emotion can be provided. This makes it possible to provide support that is in line with the tourist's emotions, improving satisfaction.

[0081] The order support unit can analyze the order details and suggest drinks and desserts that go well with the dishes ordered by the tourist. The order support unit can, for example, analyze the order details and suggest drinks and desserts that go well with the dishes ordered by the tourist. For example, it can suggest sake that goes well with sushi. This makes it possible to suggest drinks and desserts that go well with the dishes ordered by the tourist.

[0082] The order support unit can also accommodate cases where tourists want to avoid certain ingredients or have special requests when ordering. For example, the order support unit can make suggestions based on allergy information. This allows the special requests of tourists to be accommodated.

[0083] The order support unit can use the emotion estimation function to analyze the emotions of tourists when they place an order in real time and dynamically adjust the order contents. For example, the order support unit can analyze the emotions of tourists when they place an order in real time and dynamically adjust the order contents. For example, if a tourist is happy, the order contents that match that emotion can be suggested. This makes it possible to adjust the order contents according to the tourist's emotions.

[0084] The support system includes a review provider, which collects reviews and ratings from other tourists and locals and provides restaurant information based on the collected information. For example, if a tourist enters "I'm looking for a highly rated Japanese restaurant," the review provider will suggest highly rated Japanese restaurants. This allows tourists to select restaurants based on reliable information.

[0085] The review providing unit can analyze the reliability of reviews and ratings and provide highly reliable information preferentially. The review providing unit can, for example, analyze the reliability of reviews and ratings and provide highly reliable information preferentially. For example, highly reliable reviews can be displayed preferentially. This makes it possible to provide highly reliable information.

[0086] The review providing unit can use the emotion estimation function to analyze the emotions of tourists when they see reviews and ratings, and provide information that elicits a positive response. For example, the review providing unit can analyze the emotions of tourists when they see reviews and ratings, and provide information that elicits a positive response. For example, if a tourist is happy, the review providing unit can provide information that matches that emotion. This makes it possible to provide information that matches the tourist's emotions.

[0087] The review providing unit analyzes reviews and ratings and can monitor the satisfaction of restaurants visited by tourists in real time. The review providing unit, for example, analyzes reviews and ratings and builds a system that monitors the satisfaction of restaurants visited by tourists in real time. For example, it immediately analyzes reviews after a visit. This makes it possible to monitor the satisfaction of tourists in real time.

[0088] The review providing unit can analyze the reviews and ratings, identify areas for improvement in restaurants visited by tourists, and provide feedback. The review providing unit, for example, analyzes the reviews and ratings, and identifies areas for improvement in restaurants visited by tourists. For example, it provides feedback on the quality of service and the taste of food. This makes it possible to identify areas for improvement in restaurants and provide feedback.

[0089] The review providing unit can use the emotion estimation function to analyze in real time the emotions of tourists when they view reviews and ratings, and dynamically adjust the information content. For example, the review providing unit analyzes in real time the emotions of tourists when they view reviews and ratings, and dynamically adjusts the information content. For example, if a tourist is happy, information that matches that emotion is provided. This makes it possible to adjust the information content according to the tourist's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The support system can also include a cultural understanding unit that understands the tourist's cultural background and suggests restaurants based on that. For example, if a tourist has specific religious restrictions, the system can suggest restaurants that meet those restrictions. Also, if a tourist is interested in a particular cultural event or festival, the system can suggest restaurants related to that event. This allows tourists to enjoy a dining experience that is tailored to their cultural background.

[0092] The support system can also be equipped with a social media analysis unit that analyzes social media data to understand tourists' food preferences. For example, it can analyze food photos and comments posted by tourists on Instagram and Twitter to understand their taste trends. It can also suggest restaurants that tourists like based on the food-related accounts and hashtags they follow. This allows for more accurate recommendations based on tourists' social media activity.

[0093] The support system can also include a tourist destination analysis unit that analyzes data on tourist destinations that tourists have visited in the past to understand the tourist's food preferences. For example, the support system can suggest popular restaurants in the area based on the data on tourist destinations that tourists have visited in the past. It can also suggest similar restaurants based on the types of food that tourists preferred at a particular tourist destination. This allows for more accurate suggestions based on the data on tourist destinations that tourists have visited in the past.

[0094] The support system can also be equipped with an event analysis unit that analyzes data on cooking classes and dining events that tourists have attended in the past in order to understand the tourists' food preferences. For example, based on data on cooking classes and dining events that tourists have attended in the past, the system can suggest restaurants that serve the dishes and ingredients that tourists learned at those events. Also, based on the skills that tourists learned in a particular cooking class, the system can suggest restaurants that serve dishes that utilize those skills. This allows for more accurate suggestions based on data on tourists' past cooking classes and dining events.

[0095] The support system can also be equipped with a purchase history analysis unit that analyzes data on ingredients and dishes purchased by tourists in the past in order to understand the tourist's food preferences. For example, based on the data on ingredients and dishes purchased by tourists in the past, restaurants that serve those ingredients and dishes can be suggested. Also, if a tourist has a preference for a particular ingredient, restaurants that serve dishes using that ingredient can be suggested. This allows for more accurate suggestions based on the tourist's past purchase history.

[0096] The suggestion unit can estimate the tourist's emotions and determine whether the atmosphere and service of the restaurant to be suggested matches the tourist's emotions. For example, if the tourist wants to relax, the suggestion unit can suggest a restaurant with a quiet and calm atmosphere. On the other hand, if the tourist is excited, the suggestion unit can suggest a restaurant with a lively atmosphere. In this way, it is possible to suggest restaurants that offer an atmosphere and service that matches the tourist's emotions.

[0097] The suggestion unit can estimate the tourist's emotions and determine whether the menu of the restaurant to be suggested matches the tourist's emotions. For example, if the tourist is feeling down, the suggestion unit can suggest a restaurant that offers sweet desserts to lift the tourist's spirits. Also, if the tourist is tired, the suggestion unit can suggest a restaurant that offers refreshing snacks and drinks. In this way, it is possible to suggest a restaurant that offers a menu that matches the tourist's emotions.

[0098] The suggestion unit can estimate the tourist's emotions and determine whether the services of the restaurant to be suggested match the tourist's emotions. For example, if the tourist is nervous, the suggestion unit can suggest a restaurant that offers services that will help the tourist relax. Also, if the tourist wants to have fun, the suggestion unit can suggest a restaurant that offers entertainment. In this way, it is possible to suggest a restaurant that offers services that match the tourist's emotions.

[0099] The suggestion unit can estimate the tourist's emotions and determine whether the location of the restaurant to be suggested matches the tourist's emotions. For example, if the tourist wants to relax, the suggestion unit can suggest a restaurant in a quiet location surrounded by nature. On the other hand, if the tourist is excited, the suggestion unit can suggest a restaurant in a lively location in the center of the city. In this way, it is possible to suggest restaurants that offer locations that match the tourist's emotions.

[0100] The suggestion unit can estimate the tourist's emotions and determine whether the special events and promotions of the proposed restaurant match the tourist's emotions. For example, if the tourist feels like having fun, the suggestion unit can suggest restaurants that offer live music or special dinner shows. Also, if the tourist feels like relaxing, the suggestion unit can suggest restaurants that offer special dinners in a quiet environment. In this way, it is possible to suggest restaurants that offer special events and promotions that match the tourist's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The preference understanding unit understands the tourist's preferences and dietary restrictions. For example, if a tourist enters information such as "I'm a vegetarian," "I like spicy food," or "I'd like to try Japanese food," the preference understanding unit will perform analysis based on this information. Step 2: The suggestion unit suggests optimal dining options based on the preferences and constraints understood by the preference understanding unit. For example, if a tourist inputs "I'm a vegetarian," the suggestion unit suggests restaurants that offer vegetarian menus. Step 3: The translation department translates the restaurant menu proposed by the proposal department. For example, the Japanese menu is translated into English, Chinese, French, etc. Step 4: The order support department supports ordering based on the menu translated by the translation department. For example, after a tourist selects a menu item, the order support department translates the order into Japanese and conveys it to the waiter.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 preference understanding unit that understands tourist preferences and dietary constraints; a suggestion unit that suggests an optimal restaurant based on the preferences and constraints understood by the preference understanding unit; a translation unit that translates the menu of the restaurant proposed by the proposal unit; an order support unit that supports orders based on the menu translated by the translation unit; A system characterized by:

2. The preference understanding unit Monitoring the real-time health status of said tourists and making dietary suggestions accordingly; 2. The system of claim 1.

3. The proposal unit Analyze the congestion and reservation status of the restaurant in real time and suggest the best time to visit 2. The system of claim 1.

4. The translation unit Provide not only the translation of the menu but also the background and history of the dishes and detailed information about the ingredients 2. The system of claim 1.

5. The order support unit Analyzing the order details and comparing them with dishes the tourist has ordered in the past to encourage repeat orders 2. The system of claim 1.

6. The preference understanding unit Estimate the tourists' emotions and make suggestions that will elicit positive emotions.

2. The system of claim 1.

7. The proposal unit Analyzing the emotions of the tourists when they receive the proposal and making proposals that elicit positive reactions 2. The system of claim 1.

8. The translation unit Analyzing the emotions of the tourists when they see the translated menu and performing the translation to elicit a positive response.

2. The system of claim 1.

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