System

Abstract

An object of a system according to an embodiment is to generate an optimal travel plan on the basis of an input of a user and provide support during a travel.SOLUTION: A system according to an embodiment includes a user input analysis unit a travel plan generation unit a tourist attraction suggestion unit an accommodation facility suggestion unit a travel route optimization unit and a travel support unit. The user input analysis unit analyzes a user input. The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. The sightseeing spot suggestion section suggests a sightseeing spot based on the travel plan generated by the travel plan generation section. The accommodation facility suggestion unit suggests an accommodation facility based on the travel plan generated by the travel plan generation unit. The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. The travel support unit provides support during travel based on the travel plan generated by the travel plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to generate optimal travel plans based on user input and providing sufficient support during the trip.

[0005] The system according to the embodiment aims to generate an optimal travel plan based on user input and provide support during the trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input analysis unit, a travel plan generation unit, a tourist spot suggestion unit, an accommodation suggestion unit, a travel route optimization unit, and an in-travel support unit. The user input analysis unit analyzes user input. The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. The tourist spot suggestion unit suggests tourist spots based on the travel plan generated by the travel plan generation unit. The accommodation suggestion unit suggests accommodations based on the travel plan generated by the travel plan generation unit. The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. The in-travel support unit provides support during the travel based on the travel plan generated by the travel plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal travel plan based on user input and provide support during the trip. [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) The generative AI system according to an embodiment of the present invention is a system that generates travel plans based on input from a user and suggests optimal travel routes, tourist spots, accommodations, etc. This allows the generative AI system to easily create travel plans that match the user's preferences and conditions, enabling the user to enjoy their trip.

[0029] The generation AI system according to the embodiment includes a user input analysis unit, a travel plan generation unit, a tourist spot suggestion unit, an accommodation suggestion unit, a travel route optimization unit, and an in-travel support unit. The user input analysis unit analyzes user input. For example, it can receive user preferences in the form of text input, voice input, or option input. The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. For example, if a user inputs "I want to go to a seaside resort with my family during summer vacation," the generation AI analyzes this information and proposes an optimal travel plan. The tourist spot suggestion unit proposes tourist spots based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to visit historical places," the AI ​​lists historical tourist spots in the area and suggests them to the user. The accommodation suggestion unit proposes accommodations based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to stay in a hotel with an ocean view within a budget of 10,000 yen per night," the AI ​​searches for accommodations that meet those conditions and suggests them to the user. The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "I want to travel from Tokyo to Kyoto and Osaka in three days," the unit optimizes the route and suggests efficient ways to travel and the order in which to visit tourist spots. The travel support unit provides support during the trip based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "Tell me about restaurants near where I am now," the unit searches for nearby restaurants based on the user's current location and suggests them to the user. In this way, the generative AI system supports the user in planning their trip and allows them to enjoy their trip.

[0030] The user input analysis unit can learn the user's past travel history or preferences and generate a more personalized travel plan. For example, the user input analysis unit stores the user's past travel history in a database, and the generation AI learns the user's preferences based on that data. For example, it analyzes the ratings of places and accommodations visited in the past and reflects them in the next travel plan. In addition, to learn the user's preferences, the unit records activities and dining options during the trip, and the generation AI proposes a personalized plan based on that data. For example, it prioritizes suggestions of restaurants and tourist spots that the user has previously liked. In addition, the generation AI learns the user's past travel history and preferences and generates a new travel plan based on that data. For example, it proposes new tourist spots with an atmosphere similar to places the user has previously visited. In this way, by learning the user's past travel history and preferences, it is possible to provide a more personalized travel plan.

[0031] The travel plan generation unit can generate multiple scenarios based on user input and allow the user to select from them. In the travel plan generation unit, for example, the generation AI generates multiple travel plans based on user input and allows the user to select from them. For example, it presents multiple plans based on budget and schedule. The generation AI also analyzes the user's input and generates multiple scenarios based on different themes and activities. For example, it proposes a plan that emphasizes relaxation and a plan that emphasizes activity. The generation AI also generates multiple travel scenarios based on user input and allows the user to select their preferred plan. For example, it presents scenarios that reflect different combinations of tourist spots and means of transportation. This allows the user to choose from multiple scenarios, providing more flexible travel plans.

[0032] The travel plan generation unit can suggest not only a travel plan but also activities and events during the trip based on user input. The travel plan generation unit, for example, suggests activities and events during the trip along with the travel plan based on user input. For example, it generates a plan that includes local festivals and special events. The generation AI also analyzes the user's input and suggests activities and events that can be enjoyed locally in addition to the travel plan. For example, it presents a plan that includes adventure tours and cultural experience options. The generation AI also suggests activities and events during the trip along with the travel plan based on user input. For example, it generates a plan that includes local sporting events and concerts. This enriches the user's travel experience by suggesting activities and events during the trip in addition to the travel plan.

[0033] The travel plan generation unit can analyze the user's input and provide information about local culture or history in addition to the travel plan. The travel plan generation unit, for example, provides information about local culture or history along with the travel plan based on the user's input. For example, it explains the historical background and cultural significance of tourist spots. The generation AI also analyzes the user's input and provides information about local culture and history along with the travel plan. For example, it presents a plan that includes information about traditional festivals and cultural heritage. It also provides information about local culture and history along with the travel plan based on the user's input. For example, it explains the highlights of museums and historical buildings. This deepens the user's travel experience by providing information about local culture and history in addition to the travel plan.

[0034] The tourist spot suggestion unit can analyze the user's past visit history or reviews and suggest more suitable tourist spots. For example, the tourist spot suggestion unit analyzes the user's past visit history and the generation AI suggests suitable tourist spots based on that data. For example, it suggests places similar to spots that have been highly rated in the past. It also analyzes the user's reviews and the generation AI suggests suitable tourist spots based on that data. For example, it selects spots based on activities or themes that the user has previously liked. It also analyzes the user's past visit history and reviews and the generation AI suggests suitable tourist spots based on that data. For example, it suggests new tourist spots with an atmosphere similar to places the user has previously visited. In this way, it is possible to suggest more suitable tourist spots by analyzing the user's past visit history and reviews.

[0035] The tourist spot suggestion unit can dynamically suggest optimal tourist spots depending on the season or weather. For example, the tourist spot suggestion unit obtains seasonal and weather information in real time, and the generation AI suggests optimal tourist spots based on that data. For example, indoor tourist spots are suggested on rainy days. The generation AI also takes into account seasonal events and activities and suggests optimal tourist spots based on that data. For example, cherry blossom viewing spots are suggested in spring, and ski resorts are suggested in winter. Weather information is also analyzed in real time, and the generation AI suggests optimal tourist spots based on that data. For example, spots that include outdoor activities are suggested on sunny days. This makes it possible to dynamically suggest optimal tourist spots depending on the season and weather.

[0036] The tourist spot suggestion unit can also provide information on local specialties or gourmet food in addition to suggesting tourist spots. The tourist spot suggestion unit, for example, provides information on local specialties and gourmet food in addition to suggesting tourist spots. For example, it generates a plan that includes introductions to popular restaurants and specialty products around tourist spots. The generation AI also provides information on local specialties and gourmet food along with suggesting tourist spots. For example, it suggests spots where you can enjoy local specialties and specialty products. In addition to suggesting tourist spots, it also provides information on local specialties and gourmet food. For example, it generates a plan that includes information on markets and food festivals around tourist spots. This enriches the user's travel experience by providing information on local specialties and gourmet food in addition to suggesting tourist spots.

[0037] The tourist spot suggestion unit can suggest not only tourist spots but also local activities or events based on the user's interests. The tourist spot suggestion unit, for example, suggests local activities and events along with tourist spots based on the user's interests. For example, it generates a plan that includes events and activities related to a theme that interests the user. The generation AI also analyzes the user's interests and suggests local activities and events along with tourist spots. For example, it presents a plan that includes sports and cultural experiences that the user likes. The tourist spot suggestion unit also suggests local activities and events along with tourist spots based on the user's interests. For example, it generates a plan that includes workshops and tours related to a theme that interests the user. This enriches the user's travel experience by suggesting local activities and events in addition to tourist spots.

[0038] The accommodation suggestion unit can analyze the user's past accommodation history or reviews and suggest more suitable accommodations. For example, the accommodation suggestion unit analyzes the user's past accommodation history and the generation AI suggests suitable accommodations based on that data. For example, it suggests facilities similar to hotels that have been highly rated in the past. It also analyzes the user's reviews and the generation AI suggests suitable accommodations based on that data. For example, it selects facilities that offer amenities and services that the user has previously preferred. It also analyzes the user's past accommodation history and reviews and the generation AI suggests suitable accommodations based on that data. For example, it suggests new hotels with an atmosphere similar to places the user has stayed in in the past. In this way, it is possible to suggest more suitable accommodations by analyzing the user's past accommodation history and reviews.

[0039] The accommodation suggestion unit can also provide surrounding security information or access information when suggesting accommodation. The accommodation suggestion unit, for example, provides surrounding security information when suggesting accommodation. For example, it generates a plan including crime rates and location information of police stations. It also provides access information when suggesting accommodation. For example, it presents a plan including how to access the nearest public transportation and major tourist attractions. It also provides surrounding security information and access information when suggesting accommodation. For example, it suggests facilities located in areas with good security or facilities with easy access. In this way, providing surrounding security information and access information when suggesting accommodation improves the safety and convenience of the user.

[0040] The accommodation suggestion unit can provide information on local restaurants or cafes in addition to suggesting accommodation. The accommodation suggestion unit, for example, provides information on local restaurants and cafes in addition to suggesting accommodation. For example, it generates a plan that includes introductions to popular restaurants and cafes around the hotel. The generation AI also provides information on local restaurants and cafes along with suggesting accommodation. For example, it suggests restaurants where you can enjoy local specialties and local products. In addition to suggesting accommodation, it also provides information on local restaurants and cafes. For example, it generates a plan that includes information on markets and food festivals around the hotel. In this way, by providing information on local restaurants and cafes in addition to suggesting accommodation, the user's travel experience is enriched.

[0041] The accommodation suggestion unit can suggest not only accommodations but also local transportation options based on the user's budget and preferences. The accommodation suggestion unit, for example, suggests local transportation options along with accommodations based on the user's budget and preferences. For example, it generates a plan including information on taxis and rental cars that can be used within the budget. The generation AI also analyzes the user's budget and preferences and suggests local transportation options along with accommodations. For example, it presents a plan including information on how to use public transportation and fare information. The unit also suggests local transportation options along with accommodations based on the user's budget and preferences. For example, it generates a plan including information on shuttle buses and bicycle rentals that can be used within the budget. This enhances the user's travel experience by suggesting local transportation options in addition to accommodations.

[0042] The travel route optimization unit can analyze the user's past travel routes or movement history and propose a more efficient route. The travel route optimization unit, for example, analyzes the user's past travel routes, and the generation AI proposes an efficient route based on that data. For example, the unit generates a route that takes into account the means of transportation and travel time used in the past. The unit also analyzes the user's movement history, and the generation AI proposes an efficient route based on that data. For example, the unit optimizes the order of places the user has visited in the past and the means of transportation. The unit also analyzes the user's past travel routes and movement history, and the generation AI proposes an efficient route based on that data. For example, the unit proposes a new efficient route similar to a route the user has used in the past. In this way, more efficient routes can be proposed by analyzing the user's past travel routes and movement history.

[0043] The travel route optimization unit can reflect traffic conditions or weather information in real time and dynamically suggest the optimal route. The travel route optimization unit, for example, acquires traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it generates a route that avoids traffic congestion and bad weather. It also analyzes traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it presents alternative routes to avoid traffic congestion and means of transportation depending on the weather. It also reflects traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it proposes the optimal travel time and route to avoid traffic congestion and bad weather. In this way, the optimal route can be dynamically suggested by reflecting traffic conditions and weather information in real time.

[0044] In addition to optimizing the travel route, the travel route optimization unit can also suggest tourist spots or rest points along the way. For example, in addition to optimizing the travel route, the travel route optimization unit suggests tourist spots and rest points along the way. For example, it generates a plan that includes tourist spots and rest facilities that can be stopped at along the way. In addition, the generation AI optimizes the travel route and suggests tourist spots and rest points along the way. For example, it presents activities and relaxation spots that can be enjoyed while traveling. In addition to optimizing the travel route, it also suggests tourist spots and rest points along the way. For example, it generates a plan that includes information on cafes and restaurants that can be stopped at along the way. In this way, by suggesting tourist spots and rest points along the way in addition to optimizing the travel route, the user's travel experience is enriched.

[0045] The travel route optimization unit can suggest not only travel routes but also local activities or events based on the user's interests. The travel route optimization unit, for example, suggests local activities and events along with travel routes based on the user's interests. For example, it generates a plan that includes events and activities related to themes that interest the user. In addition, the generation AI analyzes the user's interests and suggests local activities and events along with travel routes. For example, it presents a plan that includes sports and cultural experiences that the user prefers. In addition, it suggests local activities and events along with travel routes based on the user's interests. For example, it generates a plan that includes workshops and tours related to themes that interest the user. In this way, the user's travel experience is enriched by suggesting local activities and events in addition to travel routes.

[0046] The travel support unit can analyze the user's current location or movement history and provide optimal support in real time. The travel support unit, for example, obtains the user's current location in real time, and the generation AI provides optimal support based on that data. For example, it provides information on tourist spots and restaurants around the current location. The unit also analyzes the user's movement history, and the generation AI provides optimal support based on that data. For example, it provides support that takes into account places visited in the past and means of transportation. The unit also analyzes the user's current location and movement history in real time, and the generation AI provides optimal support based on that data. For example, it provides information on events and activities around the current location. In this way, the unit can provide optimal support in real time by analyzing the user's current location and movement history.

[0047] The travel support unit can also provide information on local emergency contacts or medical institutions when providing support during the trip. The travel support unit, for example, provides local emergency contacts when providing support during the trip. For example, it generates a plan including contact information for police stations and embassies. Also, it provides information on local medical institutions when providing support during the trip. For example, it presents a plan including contact information and opening hours for the nearest hospitals and clinics. Also, it provides information on local emergency contacts and medical institutions when providing support during the trip. For example, it generates a plan including information on contacts and medical institutions that are useful in an emergency. In this way, by providing information on local emergency contacts and medical institutions when providing support during the trip, the safety of the user is ensured.

[0048] The in-travel support unit can provide information about local culture or etiquette in addition to support during the trip. For example, the in-travel support unit provides information about local culture and etiquette in addition to support during the trip. For example, it generates a plan that includes local greetings and dining etiquette. The generation AI also provides information about local culture and etiquette along with support during the trip. For example, it presents a plan that includes information about local customs and traditions. The generation AI also provides information about local culture and etiquette in addition to support during the trip. For example, it generates a plan that includes information about local cultural events and traditional ceremonies. This deepens the user's travel experience by providing information about local culture and etiquette in addition to support during the trip.

[0049] The in-travel support unit can suggest local activities or events as well as support during the trip based on the user's interests. The in-travel support unit, for example, suggests local activities and events along with support during the trip based on the user's interests. For example, it generates a plan that includes events and activities related to a theme that interests the user. The generation AI also analyzes the user's interests and suggests local activities and events along with support during the trip. For example, it presents a plan that includes sports and cultural experiences that the user prefers. Also, based on the user's interests, it suggests local activities and events along with support during the trip. For example, it generates a plan that includes workshops and tours related to a theme that interests the user. This enriches the user's travel experience by suggesting local activities and events in addition to support during the trip.

[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 generative AI system can also be equipped with a health management unit that monitors the user's health condition. For example, if a user becomes ill while traveling, the health management unit can monitor the user's health condition in real time and provide information on medical institutions and first aid advice as needed. It can also suggest meals and activities during the trip based on the user's health data. For example, if a user has a specific allergy, it can suggest restaurants that do not contain allergens. Furthermore, the health management unit can provide relaxing activities and stress-reducing plans based on the user's health condition. This allows the system to provide travel plans that take the user's health into consideration.

[0052] The generative AI system can also be equipped with a hobby analysis unit that learns the user's hobbies and interests. For example, based on data on art galleries and museums the user has visited in the past, it can suggest new art galleries and museums to visit on the user's next trip. If the user is interested in a particular sport or activity, it can also suggest related events and activities based on that information. For example, if the user is interested in surfing, it can suggest beaches where surfing is possible and surfing schools. Furthermore, the hobby analysis unit can suggest workshops and experiential events that the user can participate in during the trip based on the user's interests. This makes it possible to provide travel plans based on the user's hobbies and interests.

[0053] The generative AI system can also be equipped with a safety management module to ensure the user's safety during their trip. For example, it can obtain real-time security information about the area the user is visiting and suggest safe routes and accommodations. In an emergency, it can also provide information about the nearest police station or hospital. Furthermore, the safety management module can monitor the user's current location in real time and issue a warning if the user approaches a dangerous area. This helps ensure the user's safety during their trip.

[0054] The generative AI system can also include an entertainment unit that provides entertainment for the user during their trip. For example, it can provide information about local cinemas and theaters the user will be visiting and book tickets for movies and performances currently showing. It can also provide information about local live music and concerts and suggest events the user can enjoy. The entertainment unit can also suggest entertainment options the user can enjoy during their trip based on their interests, further enhancing the user's travel experience.

[0055] The generative AI system can also include a communication unit that supports communication during the user's trip. For example, it can provide a real-time translation function for users who are unfamiliar with the local language, facilitating communication with local people. It can also provide information about local culture and manners to help the user communicate appropriately. Furthermore, the communication unit can provide support for the user to contact local guides and tour conductors. This can support communication during the user's trip and improve the travel experience.

[0056] The generative AI system can also be equipped with an eco-support section that considers the environment during the user's trip. For example, it can suggest eco-friendly transportation and accommodation options to help the user enjoy an eco-friendly trip. It can also provide information on local environmental conservation activities and eco-tours so that the user can participate. Furthermore, the eco-support section can provide advice on minimizing the user's environmental impact during the user's trip. This allows the user to enjoy an eco-friendly trip.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user input analysis unit analyzes the user input. For example, the user's preference can be received in the form of text input, voice input, or selection input. Step 2: The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. For example, if the user inputs, "I want to go to a seaside resort with my family during summer vacation," the generation AI analyzes this information and proposes the optimal travel plan. Step 3: The tourist spot suggestion unit suggests tourist spots based on the travel plan generated by the travel plan generation unit. For example, if the user inputs "I want to visit historical places," the unit will list historical tourist spots in the area and suggest them to the user. Step 4: The accommodation suggestion unit suggests accommodations based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to stay in a hotel with an ocean view within a budget of 10,000 yen per night," the unit searches for accommodations that meet those conditions and suggests them to the user. Step 5: The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to travel from Tokyo to Kyoto and Osaka in three days," the unit optimizes the route and suggests efficient ways to travel and the order in which to visit tourist spots. Step 6: The travel support unit provides support during the trip based on the trip plan generated by the trip plan generation unit. For example, if the user inputs "Tell me about restaurants near where I am now," the unit searches for nearby restaurants based on the current location and suggests them to the user.

[0059] (Example 2) The generative AI system according to an embodiment of the present invention is a system that generates travel plans based on input from a user and suggests optimal travel routes, tourist spots, accommodations, etc. This allows the generative AI system to easily create travel plans that match the user's preferences and conditions, enabling the user to enjoy their trip.

[0060] The generation AI system according to the embodiment includes a user input analysis unit, a travel plan generation unit, a tourist spot suggestion unit, an accommodation suggestion unit, a travel route optimization unit, and an in-travel support unit. The user input analysis unit analyzes user input. For example, it can receive user preferences in the form of text input, voice input, or option input. The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. For example, if a user inputs "I want to go to a seaside resort with my family during summer vacation," the generation AI analyzes this information and proposes an optimal travel plan. The tourist spot suggestion unit proposes tourist spots based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to visit historical places," the AI ​​lists historical tourist spots in the area and suggests them to the user. The accommodation suggestion unit proposes accommodations based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to stay in a hotel with an ocean view within a budget of 10,000 yen per night," the AI ​​searches for accommodations that meet those conditions and suggests them to the user. The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "I want to travel from Tokyo to Kyoto and Osaka in three days," the unit optimizes the route and suggests efficient ways to travel and the order in which to visit tourist spots. The travel support unit provides support during the trip based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "Tell me about restaurants near where I am now," the unit searches for nearby restaurants based on the user's current location and suggests them to the user. In this way, the generative AI system supports the user in planning their trip and allows them to enjoy their trip.

[0061] The user input analysis unit can learn the user's past travel history or preferences and generate a more personalized travel plan. For example, the user input analysis unit stores the user's past travel history in a database, and the generation AI learns the user's preferences based on that data. For example, it analyzes the ratings of places and accommodations visited in the past and reflects them in the next travel plan. In addition, to learn the user's preferences, the unit records activities and dining options during the trip, and the generation AI proposes a personalized plan based on that data. For example, it prioritizes suggestions of restaurants and tourist spots that the user has previously liked. In addition, the generation AI learns the user's past travel history and preferences and generates a new travel plan based on that data. For example, it proposes new tourist spots with an atmosphere similar to places the user has previously visited. In this way, by learning the user's past travel history and preferences, it is possible to provide a more personalized travel plan.

[0062] The travel plan generation unit can generate multiple scenarios based on user input and allow the user to select from them. In the travel plan generation unit, for example, the generation AI generates multiple travel plans based on user input and allows the user to select from them. For example, it presents multiple plans based on budget and schedule. The generation AI also analyzes the user's input and generates multiple scenarios based on different themes and activities. For example, it proposes a plan that emphasizes relaxation and a plan that emphasizes activity. The generation AI also generates multiple travel scenarios based on user input and allows the user to select their preferred plan. For example, it presents scenarios that reflect different combinations of tourist spots and means of transportation. This allows the user to choose from multiple scenarios, providing more flexible travel plans.

[0063] The travel plan generation unit can use the emotion estimation function to analyze the emotion of the user when entering data and propose a travel plan that reduces stress. The travel plan generation unit, for example, analyzes the emotion of the user when entering data and generates a travel plan that reduces stress. For example, it prioritizes the suggestion of relaxing tourist spots and accommodations. The emotion estimation function also analyzes the emotion of the user when entering data in real time and proposes activities and events that reduce stress. For example, it presents a plan that includes relaxation and spa options. The emotion of the user when entering data is also analyzed and a travel plan that reduces stress is generated. For example, it proposes quiet places surrounded by nature and relaxing environments. In this way, a travel plan that reduces stress can be provided based on the user's emotions.

[0064] The travel plan generation unit can suggest not only a travel plan but also activities and events during the trip based on user input. The travel plan generation unit, for example, suggests activities and events during the trip along with the travel plan based on user input. For example, it generates a plan that includes local festivals and special events. The generation AI also analyzes the user's input and suggests activities and events that can be enjoyed locally in addition to the travel plan. For example, it presents a plan that includes adventure tours and cultural experience options. The generation AI also suggests activities and events during the trip along with the travel plan based on user input. For example, it generates a plan that includes local sporting events and concerts. This enriches the user's travel experience by suggesting activities and events during the trip in addition to the travel plan.

[0065] The travel plan generation unit can analyze the user's input and provide information about local culture or history in addition to the travel plan. The travel plan generation unit, for example, provides information about local culture or history along with the travel plan based on the user's input. For example, it explains the historical background and cultural significance of tourist spots. The generation AI also analyzes the user's input and provides information about local culture and history along with the travel plan. For example, it presents a plan that includes information about traditional festivals and cultural heritage. It also provides information about local culture and history along with the travel plan based on the user's input. For example, it explains the highlights of museums and historical buildings. This deepens the user's travel experience by providing information about local culture and history in addition to the travel plan.

[0066] The travel plan generation unit can use the emotion estimation function to analyze the emotion of the user at the time of input and propose a relaxing travel plan. The travel plan generation unit, for example, uses the emotion estimation function to analyze the emotion of the user at the time of input and propose a relaxing travel plan. For example, a plan including quiet beaches and hot springs is generated. The emotion of the user at the time of input is also analyzed in real time to propose a relaxing travel plan. For example, a plan including relaxation and spa options is presented. The emotion estimation function is also used to analyze the emotion of the user at the time of input and propose a relaxing travel plan. For example, a quiet place surrounded by nature or a relaxing environment is proposed. In this way, a relaxing travel plan can be provided based on the user's emotions.

[0067] The tourist spot suggestion unit can analyze the user's past visit history or reviews and suggest more suitable tourist spots. For example, the tourist spot suggestion unit analyzes the user's past visit history and the generation AI suggests suitable tourist spots based on that data. For example, it suggests places similar to spots that have been highly rated in the past. It also analyzes the user's reviews and the generation AI suggests suitable tourist spots based on that data. For example, it selects spots based on activities or themes that the user has previously liked. It also analyzes the user's past visit history and reviews and the generation AI suggests suitable tourist spots based on that data. For example, it suggests new tourist spots with an atmosphere similar to places the user has previously visited. In this way, it is possible to suggest more suitable tourist spots by analyzing the user's past visit history and reviews.

[0068] The tourist spot suggestion unit can dynamically suggest optimal tourist spots depending on the season or weather. For example, the tourist spot suggestion unit obtains seasonal and weather information in real time, and the generation AI suggests optimal tourist spots based on that data. For example, indoor tourist spots are suggested on rainy days. The generation AI also takes into account seasonal events and activities and suggests optimal tourist spots based on that data. For example, cherry blossom viewing spots are suggested in spring, and ski resorts are suggested in winter. Weather information is also analyzed in real time, and the generation AI suggests optimal tourist spots based on that data. For example, spots that include outdoor activities are suggested on sunny days. This makes it possible to dynamically suggest optimal tourist spots depending on the season and weather.

[0069] The tourist spot suggestion unit can use the emotion estimation function to suggest inspiring tourist spots based on the user's emotions. The tourist spot suggestion unit, for example, uses the emotion estimation function to suggest inspiring tourist spots based on the user's emotions. For example, it selects spots that include themes or scenery that are likely to inspire the user. It also analyzes the user's emotions in real time to suggest inspiring tourist spots. For example, it presents plans that include historical landmarks and scenic spots. It also uses the emotion estimation function to suggest inspiring tourist spots based on the user's emotions. For example, it suggests new tourist spots with an atmosphere similar to places that impressed the user in the past. In this way, it is possible to suggest inspiring tourist spots based on the user's emotions.

[0070] The tourist spot suggestion unit can also provide information on local specialties or gourmet food in addition to suggesting tourist spots. The tourist spot suggestion unit, for example, provides information on local specialties and gourmet food in addition to suggesting tourist spots. For example, it generates a plan that includes introductions to popular restaurants and specialty products around tourist spots. The generation AI also provides information on local specialties and gourmet food along with suggesting tourist spots. For example, it suggests spots where you can enjoy local specialties and specialty products. In addition to suggesting tourist spots, it also provides information on local specialties and gourmet food. For example, it generates a plan that includes information on markets and food festivals around tourist spots. This enriches the user's travel experience by providing information on local specialties and gourmet food in addition to suggesting tourist spots.

[0071] The tourist spot suggestion unit can suggest not only tourist spots but also local activities or events based on the user's interests. The tourist spot suggestion unit, for example, suggests local activities and events along with tourist spots based on the user's interests. For example, it generates a plan that includes events and activities related to a theme that interests the user. The generation AI also analyzes the user's interests and suggests local activities and events along with tourist spots. For example, it presents a plan that includes sports and cultural experiences that the user likes. The tourist spot suggestion unit also suggests local activities and events along with tourist spots based on the user's interests. For example, it generates a plan that includes workshops and tours related to a theme that interests the user. This enriches the user's travel experience by suggesting local activities and events in addition to tourist spots.

[0072] The tourist spot suggestion unit can use the emotion estimation function to suggest relaxing tourist spots based on the user's emotions. The tourist spot suggestion unit, for example, uses the emotion estimation function to suggest relaxing tourist spots based on the user's emotions. For example, it generates a plan including quiet beaches and hot spring resorts. It also analyzes the user's emotions in real time to suggest relaxing tourist spots. For example, it presents a plan including relaxation and spa options. It also uses the emotion estimation function to suggest relaxing tourist spots based on the user's emotions. For example, it suggests quiet places surrounded by nature and relaxing environments. This makes it possible to suggest relaxing tourist spots based on the user's emotions.

[0073] The accommodation suggestion unit can analyze the user's past accommodation history or reviews and suggest more suitable accommodations. For example, the accommodation suggestion unit analyzes the user's past accommodation history and the generation AI suggests suitable accommodations based on that data. For example, it suggests facilities similar to hotels that have been highly rated in the past. It also analyzes the user's reviews and the generation AI suggests suitable accommodations based on that data. For example, it selects facilities that offer amenities and services that the user has previously preferred. It also analyzes the user's past accommodation history and reviews and the generation AI suggests suitable accommodations based on that data. For example, it suggests new hotels with an atmosphere similar to places the user has stayed in in the past. In this way, it is possible to suggest more suitable accommodations by analyzing the user's past accommodation history and reviews.

[0074] The accommodation suggestion unit can also provide surrounding security information or access information when suggesting accommodation. The accommodation suggestion unit, for example, provides surrounding security information when suggesting accommodation. For example, it generates a plan including crime rates and location information of police stations. It also provides access information when suggesting accommodation. For example, it presents a plan including how to access the nearest public transportation and major tourist attractions. It also provides surrounding security information and access information when suggesting accommodation. For example, it suggests facilities located in areas with good security or facilities with easy access. In this way, providing surrounding security information and access information when suggesting accommodation improves the safety and convenience of the user.

[0075] The accommodation suggestion unit can use the emotion estimation function to suggest comfortable accommodation based on the user's emotions. The accommodation suggestion unit, for example, uses the emotion estimation function to suggest comfortable accommodation based on the user's emotions. For example, it selects facilities that offer a relaxing environment and highly rated services. It also analyzes the user's emotions in real time to suggest comfortable accommodation. For example, it presents facilities that offer amenities and services that allow the user to relax. It also uses the emotion estimation function to suggest comfortable accommodation based on the user's emotions. For example, it suggests a new hotel with an atmosphere similar to a facility that the user found comfortable in the past. In this way, it is possible to suggest comfortable accommodation based on the user's emotions.

[0076] The accommodation suggestion unit can provide information on local restaurants or cafes in addition to suggesting accommodation. The accommodation suggestion unit, for example, provides information on local restaurants and cafes in addition to suggesting accommodation. For example, it generates a plan that includes introductions to popular restaurants and cafes around the hotel. The generation AI also provides information on local restaurants and cafes along with suggesting accommodation. For example, it suggests restaurants where you can enjoy local specialties and local products. In addition to suggesting accommodation, it also provides information on local restaurants and cafes. For example, it generates a plan that includes information on markets and food festivals around the hotel. In this way, by providing information on local restaurants and cafes in addition to suggesting accommodation, the user's travel experience is enriched.

[0077] The accommodation suggestion unit can suggest not only accommodations but also local transportation options based on the user's budget and preferences. The accommodation suggestion unit, for example, suggests local transportation options along with accommodations based on the user's budget and preferences. For example, it generates a plan including information on taxis and rental cars that can be used within the budget. The generation AI also analyzes the user's budget and preferences and suggests local transportation options along with accommodations. For example, it presents a plan including information on how to use public transportation and fare information. The unit also suggests local transportation options along with accommodations based on the user's budget and preferences. For example, it generates a plan including information on shuttle buses and bicycle rentals that can be used within the budget. This enhances the user's travel experience by suggesting local transportation options in addition to accommodations.

[0078] The accommodation suggestion unit can use the emotion estimation function to suggest relaxing accommodations based on the user's emotions. The accommodation suggestion unit, for example, uses the emotion estimation function to suggest relaxing accommodations based on the user's emotions. For example, it selects hotels that offer a quiet environment and relaxation facilities. It also analyzes the user's emotions in real time and suggests relaxing accommodations. For example, it presents facilities that offer amenities and services that allow the user to relax. It also uses the emotion estimation function to suggest relaxing accommodations based on the user's emotions. For example, it suggests new hotels with an atmosphere similar to facilities where the user has relaxed in the past. In this way, it is possible to suggest relaxing accommodations based on the user's emotions.

[0079] The travel route optimization unit can analyze the user's past travel routes or movement history and propose a more efficient route. The travel route optimization unit, for example, analyzes the user's past travel routes, and the generation AI proposes an efficient route based on that data. For example, the unit generates a route that takes into account the means of transportation and travel time used in the past. The unit also analyzes the user's movement history, and the generation AI proposes an efficient route based on that data. For example, the unit optimizes the order of places the user has visited in the past and the means of transportation. The unit also analyzes the user's past travel routes and movement history, and the generation AI proposes an efficient route based on that data. For example, the unit proposes a new efficient route similar to a route the user has used in the past. In this way, more efficient routes can be proposed by analyzing the user's past travel routes and movement history.

[0080] The travel route optimization unit can reflect traffic conditions or weather information in real time and dynamically suggest the optimal route. The travel route optimization unit, for example, acquires traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it generates a route that avoids traffic congestion and bad weather. It also analyzes traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it presents alternative routes to avoid traffic congestion and means of transportation depending on the weather. It also reflects traffic conditions and weather information in real time, and the generation AI proposes the optimal route based on that data. For example, it proposes the optimal travel time and route to avoid traffic congestion and bad weather. In this way, the optimal route can be dynamically suggested by reflecting traffic conditions and weather information in real time.

[0081] The travel route optimization unit can use the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. The travel route optimization unit, for example, uses the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. For example, it selects a route that avoids crowds or a means of transportation that allows the user to relax. It also analyzes the user's emotions in real time to propose a travel route that reduces stress. For example, it presents a route that includes means of transportation and rest points that allow the user to relax. It also uses the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. For example, it proposes a new route that avoids routes that the user has previously found stressful. In this way, it is possible to propose a travel route that reduces stress based on the user's emotions.

[0082] The travel route optimization unit can use the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. The travel route optimization unit, for example, uses the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. For example, it selects a route that avoids crowds or a means of transportation that allows the user to relax. It also analyzes the user's emotions in real time to propose a travel route that reduces stress. For example, it presents a route that includes means of transportation and rest points that allow the user to relax. It also uses the emotion estimation function to propose a travel route that reduces stress based on the user's emotions. For example, it proposes a new route that avoids routes that the user has previously found stressful. In this way, it is possible to propose a travel route that reduces stress based on the user's emotions.

[0083] In addition to optimizing the travel route, the travel route optimization unit can also suggest tourist spots or rest points along the way. For example, in addition to optimizing the travel route, the travel route optimization unit suggests tourist spots and rest points along the way. For example, it generates a plan that includes tourist spots and rest facilities that can be stopped at along the way. In addition, the generation AI optimizes the travel route and suggests tourist spots and rest points along the way. For example, it presents activities and relaxation spots that can be enjoyed while traveling. In addition to optimizing the travel route, it also suggests tourist spots and rest points along the way. For example, it generates a plan that includes information on cafes and restaurants that can be stopped at along the way. In this way, by suggesting tourist spots and rest points along the way in addition to optimizing the travel route, the user's travel experience is enriched.

[0084] The travel route optimization unit can suggest not only travel routes but also local activities or events based on the user's interests. The travel route optimization unit, for example, suggests local activities and events along with travel routes based on the user's interests. For example, it generates a plan that includes events and activities related to themes that interest the user. In addition, the generation AI analyzes the user's interests and suggests local activities and events along with travel routes. For example, it presents a plan that includes sports and cultural experiences that the user prefers. In addition, it suggests local activities and events along with travel routes based on the user's interests. For example, it generates a plan that includes workshops and tours related to themes that interest the user. In this way, the user's travel experience is enriched by suggesting local activities and events in addition to travel routes.

[0085] The travel route optimization unit can use the emotion estimation function to suggest a relaxing travel route based on the user's emotions. The travel route optimization unit, for example, uses the emotion estimation function to suggest a relaxing travel route based on the user's emotions. For example, it selects a route that avoids crowds or a means of transportation that allows relaxation. It also analyzes the user's emotions in real time to suggest a relaxing travel route. For example, it presents a route that includes means of transportation and rest points that allow the user to relax. It also uses the emotion estimation function to suggest a relaxing travel route based on the user's emotions. For example, it suggests a new route that is similar to a route that the user found relaxing in the past. In this way, it is possible to suggest a relaxing travel route based on the user's emotions.

[0086] The travel support unit can analyze the user's current location or movement history and provide optimal support in real time. The travel support unit, for example, obtains the user's current location in real time, and the generation AI provides optimal support based on that data. For example, it provides information on tourist spots and restaurants around the current location. The unit also analyzes the user's movement history, and the generation AI provides optimal support based on that data. For example, it provides support that takes into account places visited in the past and means of transportation. The unit also analyzes the user's current location and movement history in real time, and the generation AI provides optimal support based on that data. For example, it provides information on events and activities around the current location. In this way, the unit can provide optimal support in real time by analyzing the user's current location and movement history.

[0087] The travel support unit can also provide information on local emergency contacts or medical institutions when providing support during the trip. The travel support unit, for example, provides local emergency contacts when providing support during the trip. For example, it generates a plan including contact information for police stations and embassies. Also, it provides information on local medical institutions when providing support during the trip. For example, it presents a plan including contact information and opening hours for the nearest hospitals and clinics. Also, it provides information on local emergency contacts and medical institutions when providing support during the trip. For example, it generates a plan including information on contacts and medical institutions that are useful in an emergency. In this way, by providing information on local emergency contacts and medical institutions when providing support during the trip, the safety of the user is ensured.

[0088] The during-travel support unit can use the emotion estimation function to provide support that gives a sense of security based on the user's emotions. The during-travel support unit, for example, uses the emotion estimation function to provide support that gives a sense of security based on the user's emotions. For example, it suggests activities or spots where the user can relax when feeling anxious. It also analyzes the user's emotions in real time to provide support that gives a sense of security. For example, it provides information on accommodations and restaurants where the user can feel safe. It also uses the emotion estimation function to provide support that gives a sense of security based on the user's emotions. For example, it suggests places or services that the user has felt safe in the past. In this way, it is possible to provide support that gives a sense of security based on the user's emotions.

[0089] The in-travel support unit can provide information about local culture or etiquette in addition to support during the trip. For example, the in-travel support unit provides information about local culture and etiquette in addition to support during the trip. For example, it generates a plan that includes local greetings and dining etiquette. The generation AI also provides information about local culture and etiquette along with support during the trip. For example, it presents a plan that includes information about local customs and traditions. The generation AI also provides information about local culture and etiquette in addition to support during the trip. For example, it generates a plan that includes information about local cultural events and traditional ceremonies. This deepens the user's travel experience by providing information about local culture and etiquette in addition to support during the trip.

[0090] The in-travel support unit can suggest local activities or events as well as support during the trip based on the user's interests. The in-travel support unit, for example, suggests local activities and events along with support during the trip based on the user's interests. For example, it generates a plan that includes events and activities related to a theme that interests the user. The generation AI also analyzes the user's interests and suggests local activities and events along with support during the trip. For example, it presents a plan that includes sports and cultural experiences that the user prefers. Also, based on the user's interests, it suggests local activities and events along with support during the trip. For example, it generates a plan that includes workshops and tours related to a theme that interests the user. This enriches the user's travel experience by suggesting local activities and events in addition to support during the trip.

[0091] The during-travel support unit can use the emotion estimation function to provide support to help the user relax based on the user's emotions. The during-travel support unit, for example, uses the emotion estimation function to provide support to help the user relax based on the user's emotions. For example, it may suggest activities or spots where the user can relax. It may also analyze the user's emotions in real time to provide support to help the user relax. For example, it may provide information on accommodations and restaurants where the user can relax. It may also use the emotion estimation function to provide support to help the user relax based on the user's emotions. For example, it may suggest places or services where the user has been able to relax in the past. In this way, it may be possible to provide support to help the user relax based on the user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The generative AI system can also be equipped with a health management unit that monitors the user's health condition. For example, if a user becomes ill while traveling, the health management unit can monitor the user's health condition in real time and provide information on medical institutions and first aid advice as needed. It can also suggest meals and activities during the trip based on the user's health data. For example, if a user has a specific allergy, it can suggest restaurants that do not contain allergens. Furthermore, the health management unit can provide relaxing activities and stress-reducing plans based on the user's health condition. This allows the system to provide travel plans that take the user's health into consideration.

[0094] The generative AI system can also be equipped with a hobby analysis unit that learns the user's hobbies and interests. For example, based on data on art galleries and museums the user has visited in the past, it can suggest new art galleries and museums to visit on the user's next trip. If the user is interested in a particular sport or activity, it can also suggest related events and activities based on that information. For example, if the user is interested in surfing, it can suggest beaches where surfing is possible and surfing schools. Furthermore, the hobby analysis unit can suggest workshops and experiential events that the user can participate in during the trip based on the user's interests. This makes it possible to provide travel plans based on the user's hobbies and interests.

[0095] The generative AI system can also include an emotion adjustment unit that estimates the user's emotions and adjusts the travel plan based on the estimated emotions. For example, if the user is feeling stressed, the emotion adjustment unit will prioritize suggesting relaxing tourist spots and accommodations. If the user is excited, it can also suggest active activities and events. Furthermore, the emotion adjustment unit can analyze the user's emotions in real time and flexibly adjust the travel plan during the trip. For example, if the user is tired, it will add rest points and relaxation facilities. This makes it possible to provide the optimal travel plan based on the user's emotions.

[0096] The generative AI system can also include an emotional support unit that estimates the user's emotions and provides support during the trip based on the estimated emotions. For example, if the user is feeling anxious, the emotional support unit can suggest information and activities to reassure them. Also, if the user is enjoying themselves, the emotional support unit can suggest events and activities to further enhance those emotions. Furthermore, the emotional support unit can analyze the user's emotions in real time and flexibly adjust the support provided during the trip. For example, if the user is tired, the emotional support unit can add relaxation spots and rest points. This allows the system to provide optimal support based on the user's emotions.

[0097] The generative AI system may further include an emotional activity unit that estimates the user's emotions and suggests activities during the trip based on the estimated emotions. For example, if the user feels like relaxing, the emotional activity unit may suggest relaxation or spa options. If the user feels like staying active, the emotional activity unit may suggest adventure tours or sporting events. Furthermore, the emotional activity unit may analyze the user's emotions in real time and flexibly adjust the activities during the trip. For example, if the user feels tired, it may add relaxing activities. This makes it possible to provide optimal activities based on the user's emotions.

[0098] The generative AI system can also include an emotional eating unit that estimates the user's emotions and suggests meals for the trip based on the estimated emotions. For example, if the user feels like relaxing, the emotional eating unit can suggest restaurants or cafes with a relaxing atmosphere. Alternatively, if the user feels like spending an energetic time, the emotional eating unit can suggest lively restaurants or bars. Furthermore, the emotional eating unit can analyze the user's emotions in real time and flexibly adjust the meal plan for the trip. For example, if the user feels tired, it can add meal options that will help them relax. This makes it possible to provide an optimal meal plan based on the user's emotions.

[0099] The generative AI system can also be equipped with a safety management module to ensure the user's safety during their trip. For example, it can obtain real-time security information about the area the user is visiting and suggest safe routes and accommodations. In an emergency, it can also provide information about the nearest police station or hospital. Furthermore, the safety management module can monitor the user's current location in real time and issue a warning if the user approaches a dangerous area. This helps ensure the user's safety during their trip.

[0100] The generative AI system can also include an entertainment unit that provides entertainment for the user during their trip. For example, it can provide information about local cinemas and theaters the user will be visiting and book tickets for movies and performances currently showing. It can also provide information about local live music and concerts and suggest events the user can enjoy. The entertainment unit can also suggest entertainment options the user can enjoy during their trip based on their interests, further enhancing the user's travel experience.

[0101] The generative AI system can also include a communication unit that supports communication during the user's trip. For example, it can provide a real-time translation function for users who are unfamiliar with the local language, facilitating communication with local people. It can also provide information about local culture and manners to help the user communicate appropriately. Furthermore, the communication unit can provide support for the user to contact local guides and tour conductors. This can support communication during the user's trip and improve the travel experience.

[0102] The generative AI system can also be equipped with an eco-support section that considers the environment during the user's trip. For example, it can suggest eco-friendly transportation and accommodation options to help the user enjoy an eco-friendly trip. It can also provide information on local environmental conservation activities and eco-tours so that the user can participate. Furthermore, the eco-support section can provide advice on minimizing the user's environmental impact during the user's trip. This allows the user to enjoy an eco-friendly trip.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The user input analysis unit analyzes the user input. For example, the user's preference can be received in the form of text input, voice input, or selection input. Step 2: The travel plan generation unit generates a travel plan based on the user input analyzed by the user input analysis unit. For example, if the user inputs, "I want to go to a seaside resort with my family during summer vacation," the generation AI analyzes this information and proposes the optimal travel plan. Step 3: The tourist spot suggestion unit suggests tourist spots based on the travel plan generated by the travel plan generation unit. For example, if the user inputs "I want to visit historical places," the unit will list historical tourist spots in the area and suggest them to the user. Step 4: The accommodation suggestion unit suggests accommodations based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to stay in a hotel with an ocean view within a budget of 10,000 yen per night," the unit searches for accommodations that meet those conditions and suggests them to the user. Step 5: The travel route optimization unit optimizes the travel route based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to travel from Tokyo to Kyoto and Osaka in three days," the unit optimizes the route and suggests efficient ways to travel and the order in which to visit tourist spots. Step 6: The travel support unit provides support during the trip based on the trip plan generated by the trip plan generation unit. For example, if the user inputs "Tell me about restaurants near where I am now," the unit searches for nearby restaurants based on the current location and suggests them to the user.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 user input analysis unit that analyzes user input; a travel plan generation unit that generates a travel plan based on the user input analyzed by the user input analysis unit; a tourist spot suggestion unit that suggests tourist spots based on the travel plan generated by the travel plan generation unit; an accommodation suggestion unit that suggests accommodations based on the travel plan generated by the travel plan generation unit; a travel route optimization unit that optimizes a travel route based on the travel plan generated by the travel plan generation unit; and an in-travel support unit that provides support during the trip based on the travel plan generated by the travel plan generation unit.

2. The travel plan generation unit The system according to claim 1, characterized in that it analyzes the user's emotions at the time of input and proposes travel plans that reduce stress.

3. The tourist spot suggestion unit Analyzing the user's past visit history or reviews to suggest more suitable tourist spots 2. The system of claim 1.

4. The accommodation facility suggestion unit Analyzing the user's past accommodation history or reviews to suggest more suitable accommodations 2. The system of claim 1.

5. The travel route optimization unit Analyzing the user's past travel routes or movement history and suggesting more efficient routes 2. The system of claim 1.

6. The travel support unit includes: The system according to claim 1, wherein the system provides support that provides a sense of security based on the user's emotions.

7. The tourist spot suggestion unit The system according to claim 1, wherein the system suggests inspiring tourist spots based on the user's emotions.

8. The accommodation facility suggestion unit The system according to claim 1, characterized in that it suggests comfortable accommodations based on the user's emotions.

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