System

Abstract

An object of a system according to an embodiment is to enable a user to easily make a travel plan and easily obtain information on highlights and history at a tourist spot.SOLUTION: A system according to an embodiment includes a travel plan generation unit, a tourist spot guidance unit, and a guide information display unit. The travel plan generation unit generates an optimal travel plan based on the user's desire and conditions. The tourist spot guidance unit guides a tourist spot based on the travel plan generated by the travel plan generation unit. The guide information display part displays the explanation of the highlight and history of the sightseeing spot guided by the sightseeing spot guide part.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 has made it difficult for users to find optimal routes and tourist destinations when planning a trip, and has also made it difficult to obtain information about the sights and history of tourist destinations.

[0005] The system according to the embodiment aims to enable users to easily plan their trips and easily obtain information about the sights and history of tourist spots. [Means for solving the problem]

[0006] The system according to the embodiment includes a travel plan generation unit, a tourist attraction guidance unit, and a guide information display unit. The travel plan generation unit generates an optimal travel plan based on the user's wishes and conditions. The tourist attraction guidance unit provides guidance to tourist attractions based on the travel plan generated by the travel plan generation unit. The guide information display unit displays commentary on the highlights and history of the tourist attractions provided by the tourist attraction guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily plan a trip and easily obtain information about the sights and history of tourist spots. [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 travel planning system according to an embodiment of the present invention is a system that guides users to optimal routes and tourist spots, including transportation options, and schedules trips when users plan their trips. This enables users to efficiently plan their trips, visit tourist spots, and obtain detailed guide information.

[0029] A travel planning system according to an embodiment includes a travel plan generation unit, a tourist attraction guidance unit, and a guide information display unit. The travel plan generation unit generates an optimal travel plan based on a user's preferences and conditions. For example, when a user inputs a request such as "I want to travel from Tokyo to Kyoto in three days," the generation AI calculates an optimal route including transportation options (train, bus, plane, etc.) and suggests tourist attractions and accommodations. The tourist attraction guidance unit guides the user to tourist attractions based on the travel plan generated by the travel plan generation unit. For example, the generation AI suggests a specific schedule such as "visit Kiyomizu-dera Temple and Kinkaku-ji Temple in Kyoto on the first day, and tour Arashiyama and Fushimi Inari Taisha Shrine on the second day." The guide information display unit displays commentary on the highlights and history of the tourist attractions guided by the tourist attraction guidance unit. For example, when a user arrives at Kiyomizu-dera Temple, the generation AI displays a comment such as "Kiyomizu-dera Temple was founded in 778, and the current main hall was rebuilt in 1633." This allows the travel planning system according to an embodiment to enable the user to efficiently plan a trip, tour tourist attractions, and obtain detailed guide information.

[0030] The travel plan generation unit can learn the user's past travel history and preferences and reflect them in the next travel plan. For example, the generation AI in the travel plan generation unit analyzes the user's past travel history and suggests the next travel plan based on the ratings of visited tourist spots and accommodations. For example, if a tourist spot visited in the past has a high rating, similar tourist spots will be included in the next travel plan. The generation AI learns the user's preferences and reflects them in the next travel plan. For example, if the user likes historical places, the generation AI will create a plan centered on historical tourist spots. This makes it possible to provide a customized travel plan based on the user's past travel history and preferences.

[0031] The travel plan generation unit can obtain traffic and weather information in real time and dynamically update the optimal route. For example, the generation AI of the travel plan generation unit obtains traffic conditions in real time and proposes the optimal route to avoid congestion and delays. For example, it proposes alternative means of transportation based on train delay information. The generation AI obtains weather information in real time and dynamically updates the optimal route depending on the weather. For example, when it rains, it proposes a plan that prioritizes indoor tourist spots. This makes it possible to provide the optimal route taking into account traffic conditions and weather information in real time.

[0032] The travel plan generation unit can propose a reasonable travel plan by taking into account the user's health condition and physical strength. For example, the generation AI of the travel plan generation unit proposes a reasonable travel plan by taking into account the user's health condition. For example, if the user is elderly, it will create a plan that shortens travel distances and includes more rest time. The generation AI will set an appropriate amount of activity by taking into account the user's physical strength. For example, it will adjust the number of tourist spots to visit depending on physical strength. This makes it possible to provide a travel plan that takes into account the user's health condition and physical strength.

[0033] The travel plan generation unit can recommend popular tourist destinations and accommodations based on the reviews and ratings of other users. In the travel plan generation unit, for example, the generation AI analyzes the reviews and ratings of other users and recommends popular tourist destinations and accommodations. For example, it prioritizes the suggestion of highly rated tourist destinations. The generation AI selects reliable tourist destinations and accommodations based on user reviews. For example, it makes recommendations based on the number of reviews and rating scores. This makes it possible to recommend reliable tourist destinations and accommodations based on the reviews and ratings of other users.

[0034] The tourist attraction information section can predict how crowded a tourist attraction will be and suggest a schedule to avoid the crowds. For example, the generation AI in the tourist attraction information section predicts how crowded a tourist attraction will be in real time and suggests a schedule to avoid the crowds. For example, it creates a visit plan that avoids times when crowds are expected. The generation AI predicts how crowded a tourist attraction will be based on past data and suggests the optimal time to visit. For example, it suggests a plan to visit on a weekday morning. This makes it possible to provide a schedule to avoid the crowds at tourist attractions.

[0035] The tourist attraction information section can provide detailed information and behind-the-scenes stories about tourist spots based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides detailed information about tourist spots based on that. For example, it introduces historical background and cultural episodes. The generation AI provides behind-the-scenes stories about tourist spots based on the user's interests. For example, it provides information that is not found in guidebooks. This makes it possible to provide detailed information and behind-the-scenes stories based on the user's interests and concerns.

[0036] The tourist attraction guidance unit can recommend restaurants and cafes around tourist attractions, taking into account the user's food preferences. For example, the generation AI analyzes the user's food preferences and recommends restaurants and cafes around tourist attractions based on that. For example, it may suggest restaurants for vegetarians. The generation AI selects the most suitable restaurant or cafe according to the user's food preferences. For example, it may make recommendations based on word of mouth and rating scores. This makes it possible to recommend the most suitable restaurant or cafe based on the user's food preferences.

[0037] The tourist attraction guidance unit can suggest eco-friendly methods of transportation such as walking or cycling depending on the user's means of transportation. For example, the generation AI in the tourist attraction guidance unit analyzes the user's means of transportation and suggests eco-friendly methods of transportation such as walking or cycling. For example, it creates a plan that uses bicycles to travel between tourist attractions. The generation AI suggests environmentally friendly methods of transportation depending on the user's means of transportation. For example, it suggests a plan that uses public transportation. This makes it possible to suggest eco-friendly methods of transportation.

[0038] The guide information display unit can provide guide information customized according to the user's language and cultural background. In the guide information display unit, for example, the generation AI provides guide information for tourist spots according to the user's language. For example, the generation AI displays guide information in multiple languages ​​such as English, French, and Chinese. The generation AI provides guide information customized according to the user's cultural background. For example, it prioritizes displaying information related to the user's culture. This makes it possible to provide guide information customized according to the user's language and cultural background.

[0039] The guide information display unit can suggest photo spots at tourist spots and notify the user of the optimal timing to take a photo. For example, the generation AI in the guide information display unit suggests photo spots at tourist spots and notifies the user of the optimal timing to take a photo. For example, the generation AI suggests the optimal timing to take a photo based on sunrise and sunset times. The generation AI takes into account the congestion status of the tourist spot and notifies the user of the optimal timing to take a photo spot. For example, it suggests a time period when it is least crowded. This makes it possible to provide the user with the optimal photo spots and timing at tourist spots.

[0040] The guide information display unit can recommend activities and events at tourist spots based on the user's preferences. For example, the generation AI in the guide information display unit recommends activities and events at tourist spots based on the user's preferences. For example, it might suggest hiking or rafting to an adventure-loving user. The generation AI selects activities and events that match the user's preferences based on the user's past behavioral data. For example, it might make recommendations based on the evaluations of events that the user has participated in in the past. This makes it possible to provide activities and events that match the user's preferences.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The travel plan generation unit can also propose travel plans based on specific themes based on the user's hobbies and special skills. For example, for a user whose hobby is photography, a plan including scenic spots and suitable times for photography can be proposed. For a user who enjoys cooking, a plan to visit local cooking classes or markets can be proposed. Furthermore, for history buffs, a plan to visit historical ruins and museums can be offered. This makes it possible to provide travel plans customized to the user's hobbies and special skills.

[0043] The travel plan generation unit can also propose cost-effective travel plans taking into account the user's travel budget. For example, it can propose inexpensive accommodations and transportation options to a user with a low budget. It can select cost-effective tourist spots and restaurants for a user with a medium budget. It can provide plans that include luxury accommodations and high-end restaurants to a user with a high budget. This makes it possible to provide the optimal travel plan according to the user's budget.

[0044] The travel plan generation unit can also propose plans optimized for specific purposes depending on the user's travel purpose. For example, a plan including hot springs and spas can be proposed to a user who wants to relax. A plan including activities such as hiking and rafting can be offered to an adventure-seeking user. Furthermore, a plan to visit local festivals and traditional events can be proposed to a user who values ​​cultural experiences. This makes it possible to provide the optimal travel plan according to the user's travel purpose.

[0045] The travel plan generation unit can also be equipped with a health management function to support the user's health management during the trip. For example, it can monitor the user's number of steps and calorie consumption and suggest appropriate times for rest and meals. Furthermore, if the user has a specific health condition (e.g., diabetes or high blood pressure), it can suggest meals and activities that take the user's health condition into consideration. This allows the system to provide a travel plan that takes the user's health condition into consideration.

[0046] The travel plan generation unit can also be equipped with a safety information provision function to ensure the user's safety during their trip. For example, it can provide information on the security of the destination and emergency contact information. Furthermore, if there is a risk of a natural disaster or traffic accident, the information can be notified in real time and alternative plans can be suggested. This allows the travel plan to be provided while ensuring the user's safety.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The travel plan generation unit generates an optimal travel plan based on the user's wishes and conditions. For example, if the user inputs a wish to "travel from Tokyo to Kyoto in three days," the unit calculates the optimal route, including transportation options (train, bus, plane, etc.), and suggests tourist spots and accommodations. Step 2: The tourist attraction guidance unit guides the user to tourist attractions based on the travel plan generated by the travel plan generation unit. For example, it suggests a specific schedule such as "visit Kiyomizu-dera Temple and Kinkaku-ji Temple in Kyoto on the first day, and tour Arashiyama and Fushimi Inari Taisha Shrine on the second day." Step 3: The guide information display unit displays an explanation of the highlights and history of the tourist spot guided by the tourist spot guide unit. For example, when the user arrives at Kiyomizu-dera Temple, an explanation such as "Kiyomizu-dera Temple was founded in 778, and the current main hall was rebuilt in 1633" is displayed.

[0049] (Example 2) A travel planning system according to an embodiment of the present invention is a system that guides users to optimal routes and tourist spots, including transportation options, and schedules trips when users plan their trips. This enables users to efficiently plan their trips, visit tourist spots, and obtain detailed guide information.

[0050] A travel planning system according to an embodiment includes a travel plan generation unit, a tourist attraction guidance unit, and a guide information display unit. The travel plan generation unit generates an optimal travel plan based on a user's preferences and conditions. For example, when a user inputs a request such as "I want to travel from Tokyo to Kyoto in three days," the generation AI calculates an optimal route including transportation options (train, bus, plane, etc.) and suggests tourist attractions and accommodations. The tourist attraction guidance unit guides the user to tourist attractions based on the travel plan generated by the travel plan generation unit. For example, the generation AI suggests a specific schedule such as "visit Kiyomizu-dera Temple and Kinkaku-ji Temple in Kyoto on the first day, and tour Arashiyama and Fushimi Inari Taisha Shrine on the second day." The guide information display unit displays commentary on the highlights and history of the tourist attractions guided by the tourist attraction guidance unit. For example, when a user arrives at Kiyomizu-dera Temple, the generation AI displays a comment such as "Kiyomizu-dera Temple was founded in 778, and the current main hall was rebuilt in 1633." This allows the travel planning system according to an embodiment to enable the user to efficiently plan a trip, tour tourist attractions, and obtain detailed guide information.

[0051] The travel plan generation unit can learn the user's past travel history and preferences and reflect them in the next travel plan. For example, the generation AI in the travel plan generation unit analyzes the user's past travel history and suggests the next travel plan based on the ratings of visited tourist spots and accommodations. For example, if a tourist spot visited in the past has a high rating, similar tourist spots will be included in the next travel plan. The generation AI learns the user's preferences and reflects them in the next travel plan. For example, if the user likes historical places, the generation AI will create a plan centered on historical tourist spots. This makes it possible to provide a customized travel plan based on the user's past travel history and preferences.

[0052] The travel plan generation unit can obtain traffic and weather information in real time and dynamically update the optimal route. For example, the generation AI of the travel plan generation unit obtains traffic conditions in real time and proposes the optimal route to avoid congestion and delays. For example, it proposes alternative means of transportation based on train delay information. The generation AI obtains weather information in real time and dynamically updates the optimal route depending on the weather. For example, when it rains, it proposes a plan that prioritizes indoor tourist spots. This makes it possible to provide the optimal route taking into account traffic conditions and weather information in real time.

[0053] The travel plan generation unit can use the emotion estimation function to analyze the user's expectations and anxieties regarding travel plans and propose plans that elicit positive emotions. The travel plan generation unit, for example, uses the emotion estimation function to analyze the user's expectations and anxieties regarding travel plans. For example, it identifies elements that make the user feel anxious and proposes plans that alleviate them. The generation AI analyzes elements that increase the user's expectations and proposes plans that reflect these. For example, it prioritizes the inclusion of tourist spots that the user is looking forward to in the schedule. This makes it possible to provide travel plans that take the user's emotions into consideration.

[0054] The travel plan generation unit can propose a reasonable travel plan by taking into account the user's health condition and physical strength. For example, the generation AI of the travel plan generation unit proposes a reasonable travel plan by taking into account the user's health condition. For example, if the user is elderly, it will create a plan that shortens travel distances and includes more rest time. The generation AI will set an appropriate amount of activity by taking into account the user's physical strength. For example, it will adjust the number of tourist spots to visit depending on physical strength. This makes it possible to provide a travel plan that takes into account the user's health condition and physical strength.

[0055] The travel plan generation unit can recommend popular tourist destinations and accommodations based on the reviews and ratings of other users. In the travel plan generation unit, for example, the generation AI analyzes the reviews and ratings of other users and recommends popular tourist destinations and accommodations. For example, it prioritizes the suggestion of highly rated tourist destinations. The generation AI selects reliable tourist destinations and accommodations based on user reviews. For example, it makes recommendations based on the number of reviews and rating scores. This makes it possible to recommend reliable tourist destinations and accommodations based on the reviews and ratings of other users.

[0056] The travel plan generation unit uses the emotion estimation function to monitor the user's emotions in real time when making travel plans and can make suggestions to reduce stress. The travel plan generation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when making travel plans. For example, it can suggest tourist spots where you can relax if you are feeling stressed. The generation AI analyzes the user's emotions and makes suggestions to reduce stress. For example, it can incorporate relaxing activities into the schedule. This makes it possible to provide a travel plan that takes the user's emotions into consideration and reduces stress.

[0057] The tourist attraction information section can predict how crowded a tourist attraction will be and suggest a schedule to avoid the crowds. For example, the generation AI in the tourist attraction information section predicts how crowded a tourist attraction will be in real time and suggests a schedule to avoid the crowds. For example, it creates a visit plan that avoids times when crowds are expected. The generation AI predicts how crowded a tourist attraction will be based on past data and suggests the optimal time to visit. For example, it suggests a plan to visit on a weekday morning. This makes it possible to provide a schedule to avoid the crowds at tourist attractions.

[0058] The tourist attraction information section can provide detailed information and behind-the-scenes stories about tourist spots based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides detailed information about tourist spots based on that. For example, it introduces historical background and cultural episodes. The generation AI provides behind-the-scenes stories about tourist spots based on the user's interests. For example, it provides information that is not found in guidebooks. This makes it possible to provide detailed information and behind-the-scenes stories based on the user's interests and concerns.

[0059] The tourist attraction guidance unit can use the emotion estimation function to analyze the emotions a user feels when visiting tourist attractions and create the most enjoyable schedule. For example, the tourist attraction guidance unit uses the emotion estimation function to analyze the emotions a user feels when visiting tourist attractions in real time and create the most enjoyable schedule. For example, tourist attractions that increase positive emotions are prioritized in the schedule. The generation AI suggests enjoyable tourist attractions based on the user's emotions. For example, it creates a plan that prioritizes visiting tourist attractions that the user is interested in. This makes it possible to provide the most enjoyable schedule based on the user's emotions.

[0060] The tourist attraction guidance unit can recommend restaurants and cafes around tourist attractions, taking into account the user's food preferences. For example, the generation AI analyzes the user's food preferences and recommends restaurants and cafes around tourist attractions based on that. For example, it may suggest restaurants for vegetarians. The generation AI selects the most suitable restaurant or cafe according to the user's food preferences. For example, it may make recommendations based on word of mouth and rating scores. This makes it possible to recommend the most suitable restaurant or cafe based on the user's food preferences.

[0061] The tourist attraction guidance unit can suggest eco-friendly methods of transportation such as walking or cycling depending on the user's means of transportation. For example, the generation AI in the tourist attraction guidance unit analyzes the user's means of transportation and suggests eco-friendly methods of transportation such as walking or cycling. For example, it creates a plan that uses bicycles to travel between tourist attractions. The generation AI suggests environmentally friendly methods of transportation depending on the user's means of transportation. For example, it suggests a plan that uses public transportation. This makes it possible to suggest eco-friendly methods of transportation.

[0062] The tourist attraction information unit uses the emotion estimation function to monitor in real time the emotions that the user feels about their experience at a tourist attraction and can make suggestions to improve satisfaction. The tourist attraction information unit, for example, uses the emotion estimation function to monitor in real time the emotions that the user feels about their experience at a tourist attraction. For example, if satisfaction is low, the cause is identified and improvements are proposed. The generation AI makes suggestions to improve satisfaction based on the user's emotions. For example, adding an activity that the user can enjoy. This makes it possible to make suggestions to improve satisfaction based on the user's emotions.

[0063] The guide information display unit can provide guide information customized according to the user's language and cultural background. In the guide information display unit, for example, the generation AI provides guide information for tourist spots according to the user's language. For example, the generation AI displays guide information in multiple languages ​​such as English, French, and Chinese. The generation AI provides guide information customized according to the user's cultural background. For example, it prioritizes displaying information related to the user's culture. This makes it possible to provide guide information customized according to the user's language and cultural background.

[0064] The guide information display unit uses the emotion estimation function to analyze the emotions the user feels toward the guide information at tourist spots, and can prioritize displaying information that piques the user's interest. The guide information display unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward the guide information at tourist spots in real time. For example, it prioritizes displaying information that the user is interested in. The generation AI selects and prioritizes information that piques the user's interest based on the user's emotions. For example, it displays historical episodes in which the user has shown interest in detail. This makes it possible to prioritize displaying information that piques the user's interest based on the user's emotions.

[0065] The guide information display unit can suggest photo spots at tourist spots and notify the user of the optimal timing to take a photo. For example, the generation AI in the guide information display unit suggests photo spots at tourist spots and notifies the user of the optimal timing to take a photo. For example, the generation AI suggests the optimal timing to take a photo based on sunrise and sunset times. The generation AI takes into account the congestion status of the tourist spot and notifies the user of the optimal timing to take a photo spot. For example, it suggests a time period when it is least crowded. This makes it possible to provide the user with the optimal photo spots and timing at tourist spots.

[0066] The guide information display unit can recommend activities and events at tourist spots based on the user's preferences. For example, the generation AI in the guide information display unit recommends activities and events at tourist spots based on the user's preferences. For example, it might suggest hiking or rafting to an adventure-loving user. The generation AI selects activities and events that match the user's preferences based on the user's past behavioral data. For example, it might make recommendations based on the evaluations of events that the user has participated in in the past. This makes it possible to provide activities and events that match the user's preferences.

[0067] The guide information display unit can use the emotion estimation function to monitor in real time the emotions that the user feels about their experience at a tourist spot and dynamically update the guide information. The guide information display unit, for example, uses the emotion estimation function to monitor in real time the emotions that the user feels about their experience at a tourist spot. For example, if satisfaction is low, it identifies the cause and suggests improvements. The generation AI dynamically updates the guide information based on the user's emotions. For example, it prioritizes displaying information that the user is interested in. This makes it possible to dynamically update the guide information based on the user's emotions.

[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 travel plan generation unit can also propose travel plans based on specific themes based on the user's hobbies and special skills. For example, for a user whose hobby is photography, a plan including scenic spots and suitable times for photography can be proposed. For a user who enjoys cooking, a plan to visit local cooking classes or markets can be proposed. Furthermore, for history buffs, a plan to visit historical ruins and museums can be offered. This makes it possible to provide travel plans customized to the user's hobbies and special skills.

[0070] The travel plan generation unit can also propose cost-effective travel plans taking into account the user's travel budget. For example, it can propose inexpensive accommodations and transportation options to a user with a low budget. It can select cost-effective tourist spots and restaurants for a user with a medium budget. It can provide plans that include luxury accommodations and high-end restaurants to a user with a high budget. This makes it possible to provide the optimal travel plan according to the user's budget.

[0071] The travel plan generation unit can also propose plans optimized for specific purposes depending on the user's travel purpose. For example, a plan including hot springs and spas can be proposed to a user who wants to relax. A plan including activities such as hiking and rafting can be offered to an adventure-seeking user. Furthermore, a plan to visit local festivals and traditional events can be proposed to a user who values ​​cultural experiences. This makes it possible to provide the optimal travel plan according to the user's travel purpose.

[0072] The travel plan generation unit can also be equipped with a health management function to support the user's health management during the trip. For example, it can monitor the user's number of steps and calorie consumption and suggest appropriate times for rest and meals. Furthermore, if the user has a specific health condition (e.g., diabetes or high blood pressure), it can suggest meals and activities that take the user's health condition into consideration. This allows the system to provide a travel plan that takes the user's health condition into consideration.

[0073] The travel plan generation unit can also be equipped with a safety information provision function to ensure the user's safety during their trip. For example, it can provide information on the security of the destination and emergency contact information. Furthermore, if there is a risk of a natural disaster or traffic accident, the information can be notified in real time and alternative plans can be suggested. This allows the travel plan to be provided while ensuring the user's safety.

[0074] The travel plan generation unit can use the emotion estimation function to monitor the fatigue and stress the user feels during the trip and suggest activities that will refresh them. For example, it can suggest cafes or parks where the user can relax after a long journey. Furthermore, if the user is feeling stressed, it can provide a plan that includes a relaxing massage or spa. This allows the system to provide a travel plan that takes the user's emotions into consideration.

[0075] The travel plan generation unit can use the emotion estimation function to propose a plan that maximizes the excitement and joy the user feels during their trip. For example, if the user shows a strong interest in a particular tourist spot, the schedule can be centered around that tourist spot. Furthermore, events and activities that the user is looking forward to can be prioritized in the schedule. This makes it possible to provide the most enjoyable travel plan based on the user's emotions.

[0076] The travel plan generation unit can use the emotion estimation function to propose plans to reduce the anxiety and worry the user feels during the trip. For example, if the user feels anxious about a place they are visiting for the first time, detailed guide information and maps can be provided. Furthermore, if the user feels anxious about a specific situation (e.g., heights or confined spaces), a plan that avoids that situation can be proposed. This makes it possible to provide a travel plan that takes the user's emotions into consideration.

[0077] The travel plan generation unit can use the emotion estimation function to propose plans to reduce the sense of loneliness the user feels while traveling. For example, if the user is traveling alone, it can suggest events or group tours where the user can interact with local people. Furthermore, it can provide activities that allow the user to participate in communities with specific interests. This allows the system to provide travel plans that take the user's emotions into consideration.

[0078] The travel plan generation unit can use the emotion estimation function to propose a plan that maximizes the user's satisfaction during the trip. For example, if the user expresses high satisfaction with a particular tourist spot or activity, the schedule can be created around that. Furthermore, it can provide a plan to revisit tourist spots and activities that the user has previously rated highly. This allows the system to provide the most satisfying travel plan based on the user's emotions.

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

[0080] Step 1: The travel plan generation unit generates an optimal travel plan based on the user's wishes and conditions. For example, if the user inputs a wish to "travel from Tokyo to Kyoto in three days," the unit calculates the optimal route, including transportation options (train, bus, plane, etc.), and suggests tourist spots and accommodations. Step 2: The tourist attraction guidance unit guides the user to tourist attractions based on the travel plan generated by the travel plan generation unit. For example, it suggests a specific schedule such as "visit Kiyomizu-dera Temple and Kinkaku-ji Temple in Kyoto on the first day, and tour Arashiyama and Fushimi Inari Taisha Shrine on the second day." Step 3: The guide information display unit displays an explanation of the highlights and history of the tourist spot guided by the tourist spot guide unit. For example, when the user arrives at Kiyomizu-dera Temple, an explanation such as "Kiyomizu-dera Temple was founded in 778, and the current main hall was rebuilt in 1633" is displayed.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. 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.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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.

[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, 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] 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.

[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 travel plan generation unit that generates an optimal travel plan based on the user's wishes and conditions; a tourist spot guide unit that provides tourist spot guidance based on the travel plan created by the travel plan creation unit; a guide information display unit that displays an explanation of the highlights and history of the tourist spot guided by the tourist spot guide unit. A system characterized by:

2. The travel plan generation unit Get real-time traffic and weather information and dynamically update the best route 2. The system of claim 1.

3. The travel plan generation unit Taking into consideration the health condition and physical strength of the user, the travel plan is proposed.

2. The system of claim 1.

4. The tourist destination information section Predicting the congestion situation at the tourist destination and proposing a schedule to avoid the congestion 2. The system of claim 1.

5. The guide information display unit Offering interactive quizzes and games related to the history and culture of the tourist destination, providing an educational and entertaining experience 2. The system of claim 1.

6. The travel plan generation unit Analyze the user's expectations and anxieties regarding the travel plan and propose a plan that elicits positive emotions 2. The system of claim 1.

7. The tourist destination information section Analyze the user's feelings when visiting the tourist spot and create the most enjoyable schedule 2. The system of claim 1.

8. The guide information display unit Analyzing the feelings of the user regarding guide information at the tourist spot and displaying information that attracts the user's interest with priority 2. The system of claim 1.

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