System

Abstract

An object of a system according to an embodiment is to enable a user to easily make a travel plan and arrange a reservation.SOLUTION: A system according to an embodiment includes an interactive suggester, a travel plan generator, and a reservation arranger. The interactive proposer receives an input from a user. The travel plan generation unit generates a travel plan based on the input received by the interactive suggestion unit. The reservation arrangement unit makes a reservation 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 technology requires users to conduct advance research and plan their own itineraries when making travel arrangements, which poses a significant burden, especially for users who are not used to traveling or foreign tourists.

[0005] The system according to the embodiment aims to enable users to easily create travel plans and make reservations. [Means for solving the problem]

[0006] The system according to the embodiment includes an interactive suggestion unit, a travel plan generation unit, and a reservation arrangement unit. The interactive suggestion unit accepts user input. The travel plan generation unit generates a travel plan based on the input accepted by the interactive suggestion unit. The reservation arrangement unit makes a reservation based on the travel plan generated by the travel plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create travel plans and make reservations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The travel arrangement system according to an embodiment of the present invention is a system that uses an application that utilizes generative AI to interactively propose and arrange preparatory tasks that users would otherwise have to perform themselves, such as arranging hotels and planning sightseeing itineraries. As a result, the travel arrangement system reduces the burden of pre-trip preparations for users who are not used to traveling or foreign tourists who have little experience visiting Japan, and aims to expand market share by attracting these users to the OTAs in the company's group.

[0029] A travel arrangement system according to an embodiment includes an interactive suggestion unit, a travel plan generation unit, and a reservation arrangement unit. The interactive suggestion unit accepts user input. For example, if a user inputs, "Please tell me about a recommended hotel in Tokyo," the interactive suggestion unit accepts the input. The interactive suggestion unit can also accept voice input. For example, if a user speaks, "Where should I go on my next vacation?", the interactive suggestion unit accepts the voice input. The travel plan generation unit generates a travel plan based on the input accepted by the interactive suggestion unit. For example, if a user inputs, "I would like to visit tourist spots during a three-day trip to Tokyo," the travel plan generation unit analyzes information about the tourist spots and proposes an optimal sightseeing course. The travel plan generation unit can also estimate the user's emotions and propose tourist spots and activities according to the emotions. For example, if the user is looking for relaxation, the travel plan generation unit proposes quiet tourist spots. The reservation arrangement unit makes a reservation based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "I would like to book this hotel," the reservation arrangement unit cooperates with an online travel agency (OTA) system to complete the reservation. The reservation arrangement unit can also reserve tickets to tourist spots and make restaurant reservations. For example, if a user inputs, "I would like to make a reservation at this restaurant," the reservation arrangement unit completes the reservation. As a result, the travel arrangement system according to the embodiment can generate a travel plan based on user input and make reservations, thereby reducing the burden of travel preparation.

[0030] The interactive suggestion unit analyzes the user's past interaction history and learns the user's preferences and tendencies, allowing it to make more personalized suggestions. For example, the interactive suggestion unit uses a generation AI to analyze the user's past interaction history and learn the user's preferences for tourist destinations and hotels. For example, it prioritizes suggestions of tourist destinations that have received high ratings in the past. The interactive suggestion unit also extracts specific activity and food preferences from the user's past interaction history and makes suggestions based on that. For example, if the user has previously liked Japanese food, it will suggest Japanese restaurants. The interactive suggestion unit also analyzes the user's past travel history and suggests new tourist destinations that the user has not visited. For example, it will suggest new places with similar characteristics to tourist destinations that the user has previously visited. In this way, the interactive suggestion unit can make more personalized suggestions by analyzing the user's past interaction history and learning their preferences and tendencies.

[0031] The interactive suggestion unit can obtain the user's current location and weather information in real time and propose an optimal travel plan based on that information. In the interactive suggestion unit, for example, the generation AI obtains the user's current location using GPS and suggests nearby tourist attractions and hotels based on that location. For example, it suggests tourist attractions within walking distance of the current location. The interactive suggestion unit also obtains weather information in real time and proposes a travel plan based on the weather. For example, it suggests indoor activities if it is raining and outdoor activities if it is sunny. The interactive suggestion unit also combines the user's current location and weather information to propose optimal travel routes and means of transportation. For example, it suggests a route using public transportation if the weather is bad. In this way, the system can provide more appropriate travel plans by obtaining the user's current location and weather information in real time and proposing optimal travel plans based on that information.

[0032] The interactive suggestion unit can analyze the user's voice input and suggest travel plans in a voice dialogue format. For example, when the user asks, "Where should I go on my next vacation?", the generation AI responds by voice and suggests travel plans. For example, it might suggest, "How about a plan to tour tourist spots in Tokyo?" The interactive suggestion unit also uses voice recognition technology to convert the user's voice input into text, and the generation AI suggests travel plans based on that text. For example, when the user says, "Tell me some recommended hotels," the generation AI will suggest hotels by voice. The interactive suggestion unit also analyzes the user's voice tone and speed to make suggestions based on their emotions. For example, if the user is excited, it will suggest plans that include active activities. This allows for more natural conversations by analyzing the user's voice input and suggesting travel plans in a voice dialogue format.

[0033] The interactive suggestion unit can link with the user's social media account and suggest travel plans based on their interests and concerns on social media. For example, the generation AI of the interactive suggestion unit analyzes the user's social media account and suggests travel plans based on the content of their posts and their "like" history. For example, if the user posts a lot of photos, the interactive suggestion unit will suggest photogenic spots. The interactive suggestion unit also analyzes the content of posts by the user's followers and friends on social media and suggests travel plans based on shared interests and concerns. For example, it will suggest tourist spots that friends have visited. The generation AI of the interactive suggestion unit also links with the user's social media account and suggests travel plans based on information about events and festivals. For example, if the user is interested in music festivals, it will suggest festivals taking place around that time. This allows for more personalized suggestions by linking with the user's social media account and suggesting travel plans based on their interests and concerns.

[0034] The travel plan generation unit can analyze the user's past travel history and propose new travel plans based on previously visited places and experiences. For example, the generation AI analyzes the user's past travel history and proposes new tourist spots that the user has not visited. For example, it proposes new places with similar characteristics to tourist spots visited in the past. The travel plan generation unit also extracts specific activity and food preferences from the user's past travel history and proposes new travel plans based on them. For example, if the user has previously liked Japanese food, it proposes a plan that includes Japanese restaurants. The generation AI also learns the user's past travel history and proposes new travel plans based on tourist spots and hotels that have received high ratings in the past. For example, it prioritizes the proposal of tourist spots that have received high ratings in the past. In this way, the generation AI can analyze the user's past travel history and propose new travel plans based on previously visited places and experiences, thereby providing plans that suit the user's preferences.

[0035] The travel plan generation unit can suggest appropriate activities and tourist spots by taking into account the user's health condition and fitness level. For example, the generation AI of the travel plan generation unit acquires the user's health data and suggests activities according to their health condition. For example, if the user is not getting enough exercise, it will suggest a walking tour. The travel plan generation unit also suggests appropriate tourist spots and activities by taking into account the user's fitness level. For example, if the user is elderly, it will suggest tourist spots that are less strenuous. The travel plan generation unit also learns the user's health condition and fitness level and suggests appropriate activities based on past data. For example, it makes suggestions based on data on activities the user has participated in in the past. This makes it possible to support the user's health by taking into account the user's health condition and fitness level and suggesting appropriate activities and tourist spots.

[0036] The travel plan generation unit can propose a travel plan that everyone can enjoy, taking into account the user's family composition and information about accompanying persons. For example, the generation AI of the travel plan generation unit analyzes the user's family composition and proposes tourist spots and activities that everyone can enjoy. For example, for a family with children, it proposes a plan that includes activities for children. The travel plan generation unit also considers information about accompanying persons and proposes a travel plan that everyone can enjoy. For example, if an elderly person is accompanying, it proposes tourist spots that are less stressful. The travel plan generation unit also learns information about the user's family composition and accompanying persons and proposes a travel plan that everyone can enjoy based on past data. For example, it proposes a new plan based on tourist spots that the family has visited in the past. In this way, by considering the user's family composition and information about accompanying persons and proposing a travel plan that everyone can enjoy, it is possible to improve the satisfaction of family and group travel.

[0037] The travel plan generation unit can propose travel plans along specific themes based on the user's hobbies and special skills. For example, the generation AI of the travel plan generation unit analyzes the user's hobbies and special skills and proposes travel plans along specific themes based on that. For example, a plan to visit photogenic spots is proposed for a user whose hobby is photography. The travel plan generation unit also proposes travel plans that include related events and activities based on the user's hobbies and special skills. For example, a plan including music festivals is proposed for a user whose hobby is music. The travel plan generation unit also learns the user's hobbies and special skills and proposes travel plans along specific themes based on past data. For example, a new plan is proposed based on events and activities that the user has participated in in the past. In this way, travel plans along specific themes are proposed based on the user's hobbies and special skills, making it possible to provide trips that match the user's interests.

[0038] The reservation arrangement unit can analyze the user's past accommodation history and make new suggestions based on hotels and tourist attractions that the user was satisfied with in the past. For example, the reservation arrangement unit uses a generation AI to analyze the user's past accommodation history and make new suggestions based on hotels that were highly rated in the past. For example, it can suggest hotels from the same chain as a hotel that the user was satisfied with in the past. The reservation arrangement unit also extracts the features and services of specific hotels from the user's past accommodation history and suggests new hotels based on them. For example, it can suggest a new hotel that has the features of a hotel that the user has highly rated in the past. The reservation arrangement unit also uses a generation AI to learn the user's past accommodation history and make new suggestions based on tourist attractions that the user was satisfied with in the past. For example, it can suggest new tourist attractions that have similar features to tourist attractions that the user has visited in the past. In this way, by analyzing the user's past accommodation history and making new suggestions based on hotels and tourist attractions that the user was satisfied with in the past, it becomes possible to make suggestions that match the user's preferences.

[0039] The reservation arrangement unit can search for and suggest optimal hotels and tourist attractions in real time based on the user's budget and preferences. In the reservation arrangement unit, for example, the generation AI receives the user's budget as input and searches for and suggests optimal hotels within that budget in real time. For example, when a user sets a budget, the unit suggests optimal hotels within that range. The reservation arrangement unit also receives the user's preferences as input and searches for and suggests optimal tourist attractions in real time based on those preferences. For example, if the user likes nature, the unit suggests tourist attractions rich in nature. In addition, the generation AI in the reservation arrangement unit learns the user's budget and preferences and searches for and suggests optimal hotels and tourist attractions in real time based on past data. For example, new suggestions are made based on hotels and tourist attractions that have received high ratings in the past. This allows the unit to search for and suggest optimal hotels and tourist attractions in real time based on the user's budget and preferences, making it possible to make suggestions that meet the user's needs.

[0040] The reservation arrangement unit can propose appropriate restaurants and meal plans by taking into account the user's dietary preferences and allergy information. For example, the generation AI in the reservation arrangement unit receives the user's dietary preferences as input and proposes appropriate restaurants based on them. For example, if the user prefers Japanese food, it proposes Japanese restaurants. The reservation arrangement unit also receives the user's allergy information as input and proposes safe meal plans based on that information. For example, if the user has a nut allergy, it proposes restaurants that offer nut-free menus. The generation AI in the reservation arrangement unit also learns the user's dietary preferences and allergy information and proposes appropriate restaurants and meal plans based on past data. For example, it makes new proposals based on restaurants that have received high ratings in the past. This allows the user's dietary preferences and allergy information to be taken into account and appropriate restaurants and meal plans to be proposed, thereby improving the user's health and satisfaction.

[0041] The reservation arrangement unit can propose the optimal travel route and means of transportation by taking into account the user's means of transportation and traffic conditions. In the reservation arrangement unit, for example, the generation AI receives the user's means of transportation as input and proposes the optimal travel route based on that. For example, if the user uses public transportation, the optimal route is proposed. The reservation arrangement unit also obtains the user's traffic conditions in real time and proposes the optimal means of transportation based on that. For example, if traffic congestion occurs, an alternative route is proposed. In addition, the generation AI in the reservation arrangement unit learns the user's means of transportation and traffic conditions and proposes the optimal travel route and means of transportation based on past data. For example, new proposals are made based on means of transportation used in the past. In this way, the user's travel can be made smoother by considering the user's means of transportation and traffic conditions and proposing the optimal travel route and means of transportation.

[0042] The interactive suggestion unit can learn the user's past travel history and preferences and make customized suggestions based on them. For example, the interactive suggestion unit uses a generation AI to analyze the user's past travel history and suggest new tourist spots that the user has not visited. For example, it can suggest new places with similar characteristics to tourist spots visited in the past. The interactive suggestion unit can also extract specific activity and food preferences from the user's past travel history and make customized suggestions based on them. For example, if the user has previously preferred Japanese food, it can suggest plans that include Japanese restaurants. The interactive suggestion unit can also learn the user's past travel history and make customized suggestions based on tourist spots and hotels that have received high ratings in the past. For example, it can prioritize suggestions of tourist spots that have received high ratings in the past. This allows the interactive suggestion unit to learn the user's past travel history and preferences and make customized suggestions based on them, making it possible to make suggestions that meet the user's needs.

[0043] The interactive suggestion unit can consider the user's lifestyle and daily activities and propose travel plans that suit them. For example, the generation AI of the interactive suggestion unit receives the user's lifestyle as input and proposes travel plans based on that. For example, if the user has an active lifestyle, it proposes plans that include active activities. The interactive suggestion unit also considers the user's daily activities and proposes travel plans that suit them. For example, if the user is looking to relax, it proposes quiet tourist spots. The interactive suggestion unit also learns the user's lifestyle and daily activities and proposes travel plans based on past data. For example, it proposes new plans based on data on activities participated in in the past. This makes it possible to propose travel plans that suit the user's needs by considering the user's lifestyle and daily activities and proposing plans that suit them.

[0044] The interactive suggestion unit can propose customized travel plans along specific themes based on the user's hobbies and special skills. In the interactive suggestion unit, for example, the generation AI analyzes the user's hobbies and special skills and proposes travel plans along specific themes based on that. For example, a user whose hobby is photography can be proposed a plan to visit photogenic spots. The interactive suggestion unit also proposes travel plans that include related events and activities based on the user's hobbies and special skills. For example, a user whose hobby is music can be proposed a plan that includes music festivals. The interactive suggestion unit also learns the user's hobbies and special skills and proposes travel plans along specific themes based on past data. For example, it can propose new plans based on events and activities that the user has participated in in the past. In this way, customized travel plans along specific themes can be proposed based on the user's hobbies and special skills, thereby providing a trip that matches the user's interests.

[0045] The interactive suggestion unit can propose a customized travel plan that everyone can enjoy, taking into account the user's family composition and information about accompanying persons. For example, the interactive suggestion unit uses a generation AI to analyze the user's family composition and propose tourist spots and activities that everyone can enjoy. For example, for a family with children, it proposes a plan that includes activities for children. The interactive suggestion unit also considers information about accompanying persons to propose a travel plan that everyone can enjoy. For example, if an elderly person is accompanying, it proposes tourist spots that are less stressful. The interactive suggestion unit also uses a generation AI to learn information about the user's family composition and accompanying persons, and proposes a travel plan that everyone can enjoy based on past data. For example, it proposes a new plan based on tourist spots that the family has visited in the past. In this way, by considering the user's family composition and information about accompanying persons and proposing a customized travel plan that everyone can enjoy, it is possible to improve the satisfaction of family and group travel.

[0046] The interactive suggestion unit can make suggestions not only in the user's native language but also in multiple languages ​​that the user can understand. For example, the generation AI of the interactive suggestion unit receives the user's native language as input and makes suggestions based on that. For example, if the user speaks English, suggestions are made in English. The interactive suggestion unit also takes into account multiple languages ​​that the user can understand and makes suggestions based on that. For example, if the user can understand English and Chinese, suggestions are made in both languages. The generation AI of the interactive suggestion unit also learns the user's language data and makes suggestions in multiple languages ​​based on past data. For example, if the user has used English and Japanese in the past, suggestions are made in both languages. This allows the system to accommodate a wider range of users by making suggestions not only in the user's native language but also in multiple languages ​​that the user can understand.

[0047] The interactive suggestion unit can take into account the user's cultural background and customs and make suggestions in language that suits them. In the interactive suggestion unit, for example, the generation AI receives the user's cultural background as input and makes suggestions based on that. For example, if the user is familiar with Japanese culture, the interactive suggestion unit will make suggestions that suit Japanese culture. The interactive suggestion unit also takes into account the user's customs and makes suggestions in language that suits them. For example, if the user has specific cultural customs, the interactive suggestion unit will make suggestions in language that suits those customs. The interactive suggestion unit also learns the user's cultural background and customs and makes suggestions in appropriate language based on past data. For example, if the user has had a specific cultural background in the past, the interactive suggestion unit will make suggestions in language that suits that background. This allows the user's cultural background and customs to be taken into account and suggestions made in language that suits them, providing a more personalized travel experience.

[0048] The interactive suggestion unit can support communication at the destination by making suggestions not only in the user's native language but also in the local language of the travel destination. For example, the generation AI of the interactive suggestion unit takes into account the user's native language and the local language of the travel destination and makes suggestions in both languages. For example, if the user speaks English and the travel destination is Japan, suggestions will be made in English and Japanese. The interactive suggestion unit also makes suggestions in the local language of the travel destination so that the user can understand the local language of the travel destination. For example, if the user is traveling to Japan, suggestions will be made in Japanese. The generation AI of the interactive suggestion unit also learns the user's native language and the local language and makes suggestions in both languages ​​based on past data. For example, if the user has used English and Japanese in the past, suggestions will be made in both languages. This makes it possible to support communication at the destination and improve the travel experience by making suggestions in not only the user's native language but also the local language of the travel destination.

[0049] The interactive suggestion unit can take into account the user's language learning progress and make suggestions in the language being learned. For example, the generation AI of the interactive suggestion unit receives the user's language learning progress as input and makes suggestions based on that. For example, if the user is learning Japanese, the interactive suggestion unit makes suggestions in Japanese. The interactive suggestion unit also takes into account the user's language learning progress and makes suggestions in the language being learned. For example, if the user is learning beginner-level Japanese, the interactive suggestion unit makes suggestions in simple Japanese. The interactive suggestion unit also learns the user's language learning progress and makes suggestions in the language being learned based on past data. For example, if the user has previously learned intermediate-level Japanese, the interactive suggestion unit makes suggestions in Japanese appropriate for that level. In this way, by taking into account the user's language learning progress and making suggestions in the language being learned, the user's learning can be supported and the travel experience can be improved.

[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 interactive suggestion unit can suggest appropriate activities and tourist spots by taking into account the user's health condition and fitness level. For example, the generation AI can obtain the user's health data and suggest activities according to their health condition. If the user is not getting enough exercise, it can suggest a walking tour. It can also suggest appropriate tourist spots and activities by taking into account the user's fitness level. For elderly users, it can suggest tourist spots that are less strenuous. Furthermore, the generation AI can learn the user's health condition and fitness level and suggest appropriate activities based on past data. Suggestions can be made based on data from activities participated in in the past.

[0052] The interactive suggestion unit can link with the user's social media account and suggest travel plans based on their interests and concerns on social media. The generation AI analyzes the user's social media account and suggests travel plans based on the content of their posts and their "like" history. If the user posts a lot of photos, it can suggest photogenic spots. It can also analyze the content posted by the user's followers and friends on social media and suggest travel plans based on shared interests and concerns. It can suggest tourist spots that friends have visited. Furthermore, the generation AI can link with the user's social media account and suggest travel plans based on information about events and festivals. If the user is interested in music festivals, it can suggest festivals taking place around that time.

[0053] The interactive suggestion unit can obtain the user's current location and weather information in real time, and based on that, suggest optimal travel plans. The generation AI obtains the user's current location using GPS and suggests nearby tourist attractions and hotels based on that location. It can suggest tourist attractions within walking distance of the current location. It can also obtain weather information in real time and suggest travel plans based on the weather. It can suggest indoor activities if it rains, and outdoor activities if it is sunny. Furthermore, the generation AI can combine the user's current location and weather information to suggest optimal travel routes and means of transportation. It can suggest routes using public transportation if the weather is bad.

[0054] The interactive suggestion unit can analyze the user's voice input and suggest travel plans in a voice dialogue format. When the user asks, "Where should I go on my next vacation?", the generation AI will respond in voice and suggest a travel plan. For example, it could suggest, "How about a plan to tour tourist spots in Tokyo?". In addition, using voice recognition technology, the user's voice input can be converted into text, and the generation AI can suggest travel plans based on that text. When the user says, "Recommend a hotel," the generation AI can suggest a hotel by voice. Furthermore, the generation AI can analyze the user's voice tone and speed to make suggestions based on their emotions. If the user is excited, it can suggest a plan that includes active activities.

[0055] The interactive suggestion unit analyzes the user's past interaction history, learning their preferences and tendencies to make more personalized suggestions. The generation AI analyzes the user's past interaction history and learns the user's preferences for tourist destinations and hotels. It can prioritize suggestions of tourist destinations that have received high ratings in the past. It can also extract specific activity and food preferences from the user's past interaction history and make suggestions based on that. If the user has previously liked Japanese food, it can suggest Japanese restaurants. Furthermore, the generation AI can analyze the user's past travel history to suggest new tourist destinations that the user has not visited. It can suggest new places with similar characteristics to tourist destinations that have been visited in the past.

[0056] The interactive suggestion unit can make suggestions not only in the user's native language, but also in multiple languages ​​that the user understands. The generation AI receives the user's native language as input and makes suggestions based on it. If the user speaks English, suggestions can be made in English. It can also take into account multiple languages ​​that the user understands and make suggestions based on that. If the user understands English and Chinese, suggestions can be made in both languages. Furthermore, the generation AI can learn the user's language data and make suggestions in multiple languages ​​based on past data. If the user has used English and Japanese in the past, suggestions can be made in both languages.

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

[0058] Step 1: The interactive suggestion unit accepts user input. For example, if the user inputs "Please tell me about recommended hotels in Tokyo," the interactive suggestion unit accepts the input. The interactive suggestion unit can also accept voice input. For example, if the user speaks, "Where should I go on my next vacation?", the interactive suggestion unit accepts the voice input. Step 2: The travel plan generation unit generates a travel plan based on the input received by the interactive suggestion unit. For example, if a user inputs, "I would like to visit tourist spots on a three-day trip to Tokyo," the travel plan generation unit analyzes information about the tourist spots and suggests the optimal sightseeing course. The travel plan generation unit can also estimate the user's emotions and suggest tourist spots and activities according to their emotions. For example, if the user is looking for relaxation, it will suggest quiet tourist spots. Step 3: The reservation arrangement unit makes a reservation based on the travel plan generated by the travel plan generation unit. For example, if the user inputs "I would like to reserve this hotel," the reservation arrangement unit will complete the reservation in cooperation with the OTA's system. The reservation arrangement unit can also reserve tickets to tourist attractions and reservations at restaurants. For example, if the user inputs "I would like to reserve this restaurant," the reservation arrangement unit will complete the reservation.

[0059] (Example 2) The travel arrangement system according to an embodiment of the present invention is a system that uses an application that utilizes generative AI to interactively propose and arrange preparatory tasks that users would otherwise have to perform themselves, such as arranging hotels and planning sightseeing itineraries. As a result, the travel arrangement system reduces the burden of pre-trip preparations for users who are not used to traveling or foreign tourists who have little experience visiting Japan, and aims to expand market share by attracting these users to the OTAs in the company's group.

[0060] A travel arrangement system according to an embodiment includes an interactive suggestion unit, a travel plan generation unit, and a reservation arrangement unit. The interactive suggestion unit accepts user input. For example, if a user inputs, "Please tell me about a recommended hotel in Tokyo," the interactive suggestion unit accepts the input. The interactive suggestion unit can also accept voice input. For example, if a user speaks, "Where should I go on my next vacation?", the interactive suggestion unit accepts the voice input. The travel plan generation unit generates a travel plan based on the input accepted by the interactive suggestion unit. For example, if a user inputs, "I would like to visit tourist spots during a three-day trip to Tokyo," the travel plan generation unit analyzes information about the tourist spots and proposes an optimal sightseeing course. The travel plan generation unit can also estimate the user's emotions and propose tourist spots and activities according to the emotions. For example, if the user is looking for relaxation, the travel plan generation unit proposes quiet tourist spots. The reservation arrangement unit makes a reservation based on the travel plan generated by the travel plan generation unit. For example, if a user inputs, "I would like to book this hotel," the reservation arrangement unit cooperates with an online travel agency (OTA) system to complete the reservation. The reservation arrangement unit can also reserve tickets to tourist spots and make restaurant reservations. For example, if a user inputs, "I would like to make a reservation at this restaurant," the reservation arrangement unit completes the reservation. As a result, the travel arrangement system according to the embodiment can generate a travel plan based on user input and make reservations, thereby reducing the burden of travel preparation.

[0061] The interactive suggestion unit can estimate a user's emotions in real time and make suggestions based on those emotions. For example, the generation AI of the interactive suggestion unit analyzes the user's input content and voice tone to estimate emotions in real time. For example, if the user is feeling stressed, the generation AI will suggest a travel plan that allows them to relax. The interactive suggestion unit also captures the user's facial expressions and voice with a camera or microphone, and the generation AI analyzes their emotions. For example, if the user is excited, the generation AI will suggest a plan that includes active activities. The interactive suggestion unit also learns the user's past emotional data and makes suggestions based on their current emotional state. For example, if a user has previously expressed a desire to relax, the generation AI will suggest a relaxing travel plan. This makes it possible to provide more personalized travel plans by making suggestions based on the user's emotions.

[0062] The interactive suggestion unit analyzes the user's past interaction history and learns the user's preferences and tendencies, allowing it to make more personalized suggestions. For example, the interactive suggestion unit uses a generation AI to analyze the user's past interaction history and learn the user's preferences for tourist destinations and hotels. For example, it prioritizes suggestions of tourist destinations that have received high ratings in the past. The interactive suggestion unit also extracts specific activity and food preferences from the user's past interaction history and makes suggestions based on that. For example, if the user has previously liked Japanese food, it will suggest Japanese restaurants. The interactive suggestion unit also analyzes the user's past travel history and suggests new tourist destinations that the user has not visited. For example, it will suggest new places with similar characteristics to tourist destinations that the user has previously visited. In this way, the interactive suggestion unit can make more personalized suggestions by analyzing the user's past interaction history and learning their preferences and tendencies.

[0063] The interactive suggestion unit can obtain the user's current location and weather information in real time and propose an optimal travel plan based on that information. In the interactive suggestion unit, for example, the generation AI obtains the user's current location using GPS and suggests nearby tourist attractions and hotels based on that location. For example, it suggests tourist attractions within walking distance of the current location. The interactive suggestion unit also obtains weather information in real time and proposes a travel plan based on the weather. For example, it suggests indoor activities if it is raining and outdoor activities if it is sunny. The interactive suggestion unit also combines the user's current location and weather information to propose optimal travel routes and means of transportation. For example, it suggests a route using public transportation if the weather is bad. In this way, the system can provide more appropriate travel plans by obtaining the user's current location and weather information in real time and proposing optimal travel plans based on that information.

[0064] The interactive suggestion unit can analyze the user's voice input and suggest travel plans in a voice dialogue format. For example, when the user asks, "Where should I go on my next vacation?", the generation AI responds by voice and suggests travel plans. For example, it might suggest, "How about a plan to tour tourist spots in Tokyo?" The interactive suggestion unit also uses voice recognition technology to convert the user's voice input into text, and the generation AI suggests travel plans based on that text. For example, when the user says, "Tell me some recommended hotels," the generation AI will suggest hotels by voice. The interactive suggestion unit also analyzes the user's voice tone and speed to make suggestions based on their emotions. For example, if the user is excited, it will suggest plans that include active activities. This allows for more natural conversations by analyzing the user's voice input and suggesting travel plans in a voice dialogue format.

[0065] The interactive suggestion unit can link with the user's social media account and suggest travel plans based on their interests and concerns on social media. For example, the generation AI of the interactive suggestion unit analyzes the user's social media account and suggests travel plans based on the content of their posts and their "like" history. For example, if the user posts a lot of photos, the interactive suggestion unit will suggest photogenic spots. The interactive suggestion unit also analyzes the content of posts by the user's followers and friends on social media and suggests travel plans based on shared interests and concerns. For example, it will suggest tourist spots that friends have visited. The generation AI of the interactive suggestion unit also links with the user's social media account and suggests travel plans based on information about events and festivals. For example, if the user is interested in music festivals, it will suggest festivals taking place around that time. This allows for more personalized suggestions by linking with the user's social media account and suggesting travel plans based on their interests and concerns.

[0066] The interactive suggestion unit uses the emotion estimation function to analyze the emotions a user feels when selecting a travel plan and make suggestions that elicit positive emotions. For example, the generation AI of the interactive suggestion unit analyzes the user's facial expressions and voice to estimate the user's emotions in real time when selecting a travel plan. For example, if the user is smiling, the interactive suggestion unit prioritizes suggesting that plan. The interactive suggestion unit also uses the emotion estimation function to analyze the user's emotional response to the plan selected and make suggestions to elicit positive emotions. For example, if the user is excited, the interactive suggestion unit adds an active activity. The generation AI of the interactive suggestion unit also learns the user's emotional data and makes suggestions that elicit positive emotions based on past emotional responses. For example, if a user has previously expressed a desire to relax, the interactive suggestion unit will suggest a relaxing travel plan. In this way, by using the emotion estimation function to analyze the user's emotions and make suggestions that elicit positive emotions, user satisfaction is improved.

[0067] The travel plan generation unit can estimate the user's emotions and suggest tourist spots and activities according to their emotions. For example, the generation AI of the travel plan generation unit analyzes the user's input content and voice tone to estimate emotions in real time. For example, if the user is excited, active activities are suggested. The travel plan generation unit also captures the user's facial expressions and voice with a camera or microphone, and the generation AI analyzes their emotions. For example, if the user is looking to relax, quiet tourist spots are suggested. The generation AI of the travel plan generation unit also learns the user's past emotional data and suggests tourist spots and activities based on the user's current emotional state. For example, if a user has previously wanted to relax, relaxing tourist spots are suggested. This makes it possible to provide more personalized travel plans by suggesting tourist spots and activities according to the user's emotions.

[0068] The travel plan generation unit can analyze the user's past travel history and propose new travel plans based on previously visited places and experiences. For example, the generation AI analyzes the user's past travel history and proposes new tourist spots that the user has not visited. For example, it proposes new places with similar characteristics to tourist spots visited in the past. The travel plan generation unit also extracts specific activity and food preferences from the user's past travel history and proposes new travel plans based on them. For example, if the user has previously liked Japanese food, it proposes a plan that includes Japanese restaurants. The generation AI also learns the user's past travel history and proposes new travel plans based on tourist spots and hotels that have received high ratings in the past. For example, it prioritizes the proposal of tourist spots that have received high ratings in the past. In this way, the generation AI can analyze the user's past travel history and propose new travel plans based on previously visited places and experiences, thereby providing plans that suit the user's preferences.

[0069] The travel plan generation unit can suggest appropriate activities and tourist spots by taking into account the user's health condition and fitness level. For example, the generation AI of the travel plan generation unit acquires the user's health data and suggests activities according to their health condition. For example, if the user is not getting enough exercise, it will suggest a walking tour. The travel plan generation unit also suggests appropriate tourist spots and activities by taking into account the user's fitness level. For example, if the user is elderly, it will suggest tourist spots that are less strenuous. The travel plan generation unit also learns the user's health condition and fitness level and suggests appropriate activities based on past data. For example, it makes suggestions based on data on activities the user has participated in in the past. This makes it possible to support the user's health by taking into account the user's health condition and fitness level and suggesting appropriate activities and tourist spots.

[0070] The travel plan generation unit can propose a travel plan that everyone can enjoy, taking into account the user's family composition and information about accompanying persons. For example, the generation AI of the travel plan generation unit analyzes the user's family composition and proposes tourist spots and activities that everyone can enjoy. For example, for a family with children, it proposes a plan that includes activities for children. The travel plan generation unit also considers information about accompanying persons and proposes a travel plan that everyone can enjoy. For example, if an elderly person is accompanying, it proposes tourist spots that are less stressful. The travel plan generation unit also learns information about the user's family composition and accompanying persons and proposes a travel plan that everyone can enjoy based on past data. For example, it proposes a new plan based on tourist spots that the family has visited in the past. In this way, by considering the user's family composition and information about accompanying persons and proposing a travel plan that everyone can enjoy, it is possible to improve the satisfaction of family and group travel.

[0071] The travel plan generation unit can propose travel plans along specific themes based on the user's hobbies and special skills. For example, the generation AI of the travel plan generation unit analyzes the user's hobbies and special skills and proposes travel plans along specific themes based on that. For example, a plan to visit photogenic spots is proposed for a user whose hobby is photography. The travel plan generation unit also proposes travel plans that include related events and activities based on the user's hobbies and special skills. For example, a plan including music festivals is proposed for a user whose hobby is music. The travel plan generation unit also learns the user's hobbies and special skills and proposes travel plans along specific themes based on past data. For example, a new plan is proposed based on events and activities that the user has participated in in the past. In this way, travel plans along specific themes are proposed based on the user's hobbies and special skills, making it possible to provide trips that match the user's interests.

[0072] The travel plan generation unit uses the emotion estimation function to analyze the emotions of the user when selecting a travel plan and can propose plans that elicit positive emotions. For example, the travel plan generation unit uses the generation AI to analyze the user's facial expressions and voice and estimate the emotion when selecting a travel plan in real time. For example, if the user is smiling, that plan is preferentially proposed. The travel plan generation unit also uses the emotion estimation function to analyze the user's emotional response to the plan selected and proposes plans that elicit positive emotions. For example, if the user is excited, an active activity is added. The travel plan generation unit also uses the generation AI to learn the user's emotional data and propose plans that elicit positive emotions based on past emotional responses. For example, if a user has previously sought relaxation, a relaxing travel plan is proposed. This makes it possible to improve user satisfaction by using the emotion estimation function to analyze the user's emotions and propose plans that elicit positive emotions.

[0073] The reservation arrangement unit can estimate the user's emotions and suggest hotels and tourist attractions according to the emotions. In the reservation arrangement unit, for example, the generation AI analyzes the user's input content and voice tone to estimate emotions in real time. For example, if the user is looking to relax, a quiet hotel will be suggested. The reservation arrangement unit also captures the user's facial expressions and voice with a camera or microphone, and the generation AI analyzes their emotions. For example, if the user is excited, an active tourist attraction will be suggested. In addition, the generation AI learns the user's past emotional data and suggests hotels and tourist attractions based on the user's current emotional state. For example, if a user has previously sought relaxation, a relaxing hotel will be suggested. This makes it possible to provide a more personalized travel experience by suggesting hotels and tourist attractions according to the user's emotions.

[0074] The reservation arrangement unit can analyze the user's past accommodation history and make new suggestions based on hotels and tourist attractions that the user was satisfied with in the past. For example, the reservation arrangement unit uses a generation AI to analyze the user's past accommodation history and make new suggestions based on hotels that were highly rated in the past. For example, it can suggest hotels from the same chain as a hotel that the user was satisfied with in the past. The reservation arrangement unit also extracts the features and services of specific hotels from the user's past accommodation history and suggests new hotels based on them. For example, it can suggest a new hotel that has the features of a hotel that the user has highly rated in the past. The reservation arrangement unit also uses a generation AI to learn the user's past accommodation history and make new suggestions based on tourist attractions that the user was satisfied with in the past. For example, it can suggest new tourist attractions that have similar features to tourist attractions that the user has visited in the past. In this way, by analyzing the user's past accommodation history and making new suggestions based on hotels and tourist attractions that the user was satisfied with in the past, it becomes possible to make suggestions that match the user's preferences.

[0075] The reservation arrangement unit can search for and suggest optimal hotels and tourist attractions in real time based on the user's budget and preferences. In the reservation arrangement unit, for example, the generation AI receives the user's budget as input and searches for and suggests optimal hotels within that budget in real time. For example, when a user sets a budget, the unit suggests optimal hotels within that range. The reservation arrangement unit also receives the user's preferences as input and searches for and suggests optimal tourist attractions in real time based on those preferences. For example, if the user likes nature, the unit suggests tourist attractions rich in nature. In addition, the generation AI in the reservation arrangement unit learns the user's budget and preferences and searches for and suggests optimal hotels and tourist attractions in real time based on past data. For example, new suggestions are made based on hotels and tourist attractions that have received high ratings in the past. This allows the unit to search for and suggest optimal hotels and tourist attractions in real time based on the user's budget and preferences, making it possible to make suggestions that meet the user's needs.

[0076] The reservation arrangement unit can propose appropriate restaurants and meal plans by taking into account the user's dietary preferences and allergy information. For example, the generation AI in the reservation arrangement unit receives the user's dietary preferences as input and proposes appropriate restaurants based on them. For example, if the user prefers Japanese food, it proposes Japanese restaurants. The reservation arrangement unit also receives the user's allergy information as input and proposes safe meal plans based on that information. For example, if the user has a nut allergy, it proposes restaurants that offer nut-free menus. The generation AI in the reservation arrangement unit also learns the user's dietary preferences and allergy information and proposes appropriate restaurants and meal plans based on past data. For example, it makes new proposals based on restaurants that have received high ratings in the past. This allows the user's dietary preferences and allergy information to be taken into account and appropriate restaurants and meal plans to be proposed, thereby improving the user's health and satisfaction.

[0077] The reservation arrangement unit can propose the optimal travel route and means of transportation by taking into account the user's means of transportation and traffic conditions. In the reservation arrangement unit, for example, the generation AI receives the user's means of transportation as input and proposes the optimal travel route based on that. For example, if the user uses public transportation, the optimal route is proposed. The reservation arrangement unit also obtains the user's traffic conditions in real time and proposes the optimal means of transportation based on that. For example, if traffic congestion occurs, an alternative route is proposed. In addition, the generation AI in the reservation arrangement unit learns the user's means of transportation and traffic conditions and proposes the optimal travel route and means of transportation based on past data. For example, new proposals are made based on means of transportation used in the past. In this way, the user's travel can be made smoother by considering the user's means of transportation and traffic conditions and proposing the optimal travel route and means of transportation.

[0078] The reservation arrangement unit uses the emotion estimation function to analyze the emotions of the user when selecting a hotel or tourist destination and can make suggestions that elicit positive emotions. For example, the reservation arrangement unit uses the generation AI to analyze the user's facial expressions and voice and estimate the emotion when selecting a hotel or tourist destination in real time. For example, if the user is smiling, the reservation arrangement unit prioritizes suggesting those hotels and tourist destinations. The reservation arrangement unit also uses the emotion estimation function to analyze the user's emotional response to the hotel or tourist destination selected by the user and makes suggestions that elicit positive emotions. For example, if the user is excited, the reservation arrangement unit adds active tourist destinations. The reservation arrangement unit also uses the generation AI to learn the user's emotional data and make suggestions that elicit positive emotions based on past emotional responses. For example, if a user has previously expressed a desire to relax, the reservation arrangement unit can suggest a relaxing hotel. This makes it possible to improve user satisfaction by using the emotion estimation function to analyze the user's emotions and make suggestions that elicit positive emotions.

[0079] The interactive suggestion unit can estimate the user's emotions and make customized suggestions based on those emotions. For example, the interactive suggestion unit uses a generation AI to analyze the user's input and voice tone to estimate emotions in real time. For example, if the user is adventurous, the generation AI can suggest an adventure trip. The interactive suggestion unit also captures the user's facial expressions and voice with a camera or microphone, and the generation AI analyzes their emotions. For example, if the user is looking to relax, the generation AI can suggest quiet tourist spots. The interactive suggestion unit also uses the generation AI to learn the user's past emotional data and make customized suggestions based on the user's current emotional state. For example, if a user has previously expressed a desire to relax, the generation AI can suggest a relaxing travel plan. This makes it possible to provide a more personalized travel experience by making customized suggestions based on the user's emotions.

[0080] The interactive suggestion unit can learn the user's past travel history and preferences and make customized suggestions based on them. For example, the interactive suggestion unit uses a generation AI to analyze the user's past travel history and suggest new tourist spots that the user has not visited. For example, it can suggest new places with similar characteristics to tourist spots visited in the past. The interactive suggestion unit can also extract specific activity and food preferences from the user's past travel history and make customized suggestions based on them. For example, if the user has previously preferred Japanese food, it can suggest plans that include Japanese restaurants. The interactive suggestion unit can also learn the user's past travel history and make customized suggestions based on tourist spots and hotels that have received high ratings in the past. For example, it can prioritize suggestions of tourist spots that have received high ratings in the past. This allows the interactive suggestion unit to learn the user's past travel history and preferences and make customized suggestions based on them, making it possible to make suggestions that meet the user's needs.

[0081] The interactive suggestion unit can consider the user's lifestyle and daily activities and propose travel plans that suit them. For example, the generation AI of the interactive suggestion unit receives the user's lifestyle as input and proposes travel plans based on that. For example, if the user has an active lifestyle, it proposes plans that include active activities. The interactive suggestion unit also considers the user's daily activities and proposes travel plans that suit them. For example, if the user is looking to relax, it proposes quiet tourist spots. The interactive suggestion unit also learns the user's lifestyle and daily activities and proposes travel plans based on past data. For example, it proposes new plans based on data on activities participated in in the past. This makes it possible to propose travel plans that suit the user's needs by considering the user's lifestyle and daily activities and proposing plans that suit them.

[0082] The interactive suggestion unit can propose customized travel plans along specific themes based on the user's hobbies and special skills. In the interactive suggestion unit, for example, the generation AI analyzes the user's hobbies and special skills and proposes travel plans along specific themes based on that. For example, a user whose hobby is photography can be proposed a plan to visit photogenic spots. The interactive suggestion unit also proposes travel plans that include related events and activities based on the user's hobbies and special skills. For example, a user whose hobby is music can be proposed a plan that includes music festivals. The interactive suggestion unit also learns the user's hobbies and special skills and proposes travel plans along specific themes based on past data. For example, it can propose new plans based on events and activities that the user has participated in in the past. In this way, customized travel plans along specific themes can be proposed based on the user's hobbies and special skills, thereby providing a trip that matches the user's interests.

[0083] The interactive suggestion unit can propose a customized travel plan that everyone can enjoy, taking into account the user's family composition and information about accompanying persons. For example, the interactive suggestion unit uses a generation AI to analyze the user's family composition and propose tourist spots and activities that everyone can enjoy. For example, for a family with children, it proposes a plan that includes activities for children. The interactive suggestion unit also considers information about accompanying persons to propose a travel plan that everyone can enjoy. For example, if an elderly person is accompanying, it proposes tourist spots that are less stressful. The interactive suggestion unit also uses a generation AI to learn information about the user's family composition and accompanying persons, and proposes a travel plan that everyone can enjoy based on past data. For example, it proposes a new plan based on tourist spots that the family has visited in the past. In this way, by considering the user's family composition and information about accompanying persons and proposing a customized travel plan that everyone can enjoy, it is possible to improve the satisfaction of family and group travel.

[0084] The interactive suggestion unit uses an emotion estimation function to analyze the emotions of a user when selecting a customized plan and make suggestions that elicit positive emotions. For example, the generation AI of the interactive suggestion unit analyzes the user's facial expressions and voice to estimate the user's emotions in real time when selecting a customized plan. For example, if the user is smiling, the interactive suggestion unit preferentially suggests that plan. The interactive suggestion unit also uses the emotion estimation function to analyze the user's emotional response to the customized plan selected and make suggestions to elicit positive emotions. For example, if the user is excited, the interactive suggestion unit adds an active activity. The generation AI also learns the user's emotional data and makes suggestions that elicit positive emotions based on past emotional responses. For example, if a user has previously expressed a desire to relax, the interactive suggestion unit suggests a relaxing travel plan. This allows the emotion estimation function to analyze the user's emotions and make suggestions that elicit positive emotions, thereby improving user satisfaction.

[0085] The interactive suggestion unit can estimate the user's emotions and make suggestions in language that corresponds to the emotions. For example, the generation AI of the interactive suggestion unit analyzes the user's input content and voice tone to estimate emotions in real time. For example, if the user is nervous, suggestions are made in relaxing language. The interactive suggestion unit also captures the user's facial expressions and voice with a camera or microphone, and the generation AI analyzes the emotions. For example, if the user is excited, suggestions are made in energetic language. The generation AI of the interactive suggestion unit also learns the user's past emotional data and makes suggestions in appropriate language based on the user's current emotional state. For example, if a user has previously indicated that they want to relax, suggestions are made in relaxing language. This makes it possible to provide a more personalized travel experience by making suggestions in language that corresponds to the user's emotions.

[0086] The interactive suggestion unit can make suggestions not only in the user's native language but also in multiple languages ​​that the user can understand. For example, the generation AI of the interactive suggestion unit receives the user's native language as input and makes suggestions based on that. For example, if the user speaks English, suggestions are made in English. The interactive suggestion unit also takes into account multiple languages ​​that the user can understand and makes suggestions based on that. For example, if the user can understand English and Chinese, suggestions are made in both languages. The generation AI of the interactive suggestion unit also learns the user's language data and makes suggestions in multiple languages ​​based on past data. For example, if the user has used English and Japanese in the past, suggestions are made in both languages. This allows the system to accommodate a wider range of users by making suggestions not only in the user's native language but also in multiple languages ​​that the user can understand.

[0087] The interactive suggestion unit can take into account the user's cultural background and customs and make suggestions in language that suits them. In the interactive suggestion unit, for example, the generation AI receives the user's cultural background as input and makes suggestions based on that. For example, if the user is familiar with Japanese culture, the interactive suggestion unit will make suggestions that suit Japanese culture. The interactive suggestion unit also takes into account the user's customs and makes suggestions in language that suits them. For example, if the user has specific cultural customs, the interactive suggestion unit will make suggestions in language that suits those customs. The interactive suggestion unit also learns the user's cultural background and customs and makes suggestions in appropriate language based on past data. For example, if the user has had a specific cultural background in the past, the interactive suggestion unit will make suggestions in language that suits that background. This allows the user's cultural background and customs to be taken into account and suggestions made in language that suits them, providing a more personalized travel experience.

[0088] The interactive suggestion unit can support communication at the destination by making suggestions not only in the user's native language but also in the local language of the travel destination. For example, the generation AI of the interactive suggestion unit takes into account the user's native language and the local language of the travel destination and makes suggestions in both languages. For example, if the user speaks English and the travel destination is Japan, suggestions will be made in English and Japanese. The interactive suggestion unit also makes suggestions in the local language of the travel destination so that the user can understand the local language of the travel destination. For example, if the user is traveling to Japan, suggestions will be made in Japanese. The generation AI of the interactive suggestion unit also learns the user's native language and the local language and makes suggestions in both languages ​​based on past data. For example, if the user has used English and Japanese in the past, suggestions will be made in both languages. This makes it possible to support communication at the destination and improve the travel experience by making suggestions in not only the user's native language but also the local language of the travel destination.

[0089] The interactive suggestion unit can take into account the user's language learning progress and make suggestions in the language being learned. For example, the generation AI of the interactive suggestion unit receives the user's language learning progress as input and makes suggestions based on that. For example, if the user is learning Japanese, the interactive suggestion unit makes suggestions in Japanese. The interactive suggestion unit also takes into account the user's language learning progress and makes suggestions in the language being learned. For example, if the user is learning beginner-level Japanese, the interactive suggestion unit makes suggestions in simple Japanese. The interactive suggestion unit also learns the user's language learning progress and makes suggestions in the language being learned based on past data. For example, if the user has previously learned intermediate-level Japanese, the interactive suggestion unit makes suggestions in Japanese appropriate for that level. In this way, by taking into account the user's language learning progress and making suggestions in the language being learned, the user's learning can be supported and the travel experience can be improved.

[0090] The interactive suggestion unit uses the emotion estimation function to analyze the emotion a user feels when selecting a language and make suggestions that elicit positive emotions. For example, the generation AI of the interactive suggestion unit analyzes the user's facial expressions and voice to estimate the emotion a user feels when selecting a language in real time. For example, if the user is smiling, that language is preferentially suggested. The interactive suggestion unit also uses the emotion estimation function to analyze the user's emotional response to the language selected and make suggestions that elicit positive emotions. For example, if the user is excited, the system makes suggestions using energetic language. The generation AI of the interactive suggestion unit also learns the user's emotional data and makes suggestions that elicit positive emotions based on past emotional responses. For example, if a user has previously requested relaxation, the system makes suggestions using relaxing language. In this way, by using the emotion estimation function to analyze the user's emotions and make suggestions that elicit positive emotions, user satisfaction can be improved.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The interactive suggestion unit can suggest appropriate activities and tourist spots by taking into account the user's health condition and fitness level. For example, the generation AI can obtain the user's health data and suggest activities according to their health condition. If the user is not getting enough exercise, it can suggest a walking tour. It can also suggest appropriate tourist spots and activities by taking into account the user's fitness level. For elderly users, it can suggest tourist spots that are less strenuous. Furthermore, the generation AI can learn the user's health condition and fitness level and suggest appropriate activities based on past data. Suggestions can be made based on data from activities participated in in the past.

[0093] The interactive suggestion unit can link with the user's social media account and suggest travel plans based on their interests and concerns on social media. The generation AI analyzes the user's social media account and suggests travel plans based on the content of their posts and their "like" history. If the user posts a lot of photos, it can suggest photogenic spots. It can also analyze the content posted by the user's followers and friends on social media and suggest travel plans based on shared interests and concerns. It can suggest tourist spots that friends have visited. Furthermore, the generation AI can link with the user's social media account and suggest travel plans based on information about events and festivals. If the user is interested in music festivals, it can suggest festivals taking place around that time.

[0094] The interactive suggestion unit can obtain the user's current location and weather information in real time, and based on that, suggest optimal travel plans. The generation AI obtains the user's current location using GPS and suggests nearby tourist attractions and hotels based on that location. It can suggest tourist attractions within walking distance of the current location. It can also obtain weather information in real time and suggest travel plans based on the weather. It can suggest indoor activities if it rains, and outdoor activities if it is sunny. Furthermore, the generation AI can combine the user's current location and weather information to suggest optimal travel routes and means of transportation. It can suggest routes using public transportation if the weather is bad.

[0095] The interactive suggestion unit can analyze the user's voice input and suggest travel plans in a voice dialogue format. When the user asks, "Where should I go on my next vacation?", the generation AI will respond in voice and suggest a travel plan. For example, it could suggest, "How about a plan to tour tourist spots in Tokyo?". In addition, using voice recognition technology, the user's voice input can be converted into text, and the generation AI can suggest travel plans based on that text. When the user says, "Recommend a hotel," the generation AI can suggest a hotel by voice. Furthermore, the generation AI can analyze the user's voice tone and speed to make suggestions based on their emotions. If the user is excited, it can suggest a plan that includes active activities.

[0096] The interactive suggestion unit analyzes the user's past interaction history, learning their preferences and tendencies to make more personalized suggestions. The generation AI analyzes the user's past interaction history and learns the user's preferences for tourist destinations and hotels. It can prioritize suggestions of tourist destinations that have received high ratings in the past. It can also extract specific activity and food preferences from the user's past interaction history and make suggestions based on that. If the user has previously liked Japanese food, it can suggest Japanese restaurants. Furthermore, the generation AI can analyze the user's past travel history to suggest new tourist destinations that the user has not visited. It can suggest new places with similar characteristics to tourist destinations that have been visited in the past.

[0097] The interactive suggestion unit uses the emotion estimation function to analyze the user's emotions when selecting a travel plan and can make suggestions that elicit positive emotions. The generation AI analyzes the user's facial expressions and voice to estimate the user's emotions in real time when selecting a travel plan. If the user is smiling, that plan can be suggested preferentially. The emotion estimation function can also be used to analyze the user's emotional response to the plan selected and make suggestions to elicit positive emotions. If the user is excited, an active activity can be added. Furthermore, the generation AI can learn the user's emotional data and make suggestions that elicit positive emotions based on past emotional responses. For users who have previously expressed a desire for relaxation, relaxing travel plans can be suggested.

[0098] The interactive suggestion unit can estimate the user's emotions and make customized suggestions based on those emotions. The generation AI analyzes the user's input and voice tone to estimate emotions in real time. If the user has an adventurous spirit, it can suggest an adventure trip. The generation AI can also capture the user's facial expressions and voice with a camera or microphone and analyze their emotions. If the user is looking for relaxation, it can suggest quiet tourist spots. Furthermore, the generation AI can learn the user's past emotional data and make customized suggestions based on their current emotional state. If a user has previously expressed a desire for relaxation, it can suggest a relaxing travel plan.

[0099] The interactive suggestion unit uses the emotion estimation function to analyze the emotions the user feels when selecting a customized plan and can make suggestions that elicit positive emotions. The generation AI analyzes the user's facial expressions and voice to estimate the user's emotions in real time when selecting a customized plan. If the user is smiling, that plan can be preferentially suggested. The emotion estimation function can also be used to analyze the user's emotional response to the customized plan selected and make suggestions to elicit positive emotions. If the user is excited, an active activity can be added. Furthermore, the generation AI can learn the user's emotional data and make suggestions that elicit positive emotions based on past emotional responses. For users who have previously expressed a desire for relaxation, relaxing travel plans can be suggested.

[0100] The interactive suggestion unit can estimate the user's emotions and make suggestions in language that corresponds to those emotions. The generation AI analyzes the user's input and voice tone to estimate emotions in real time. If the user is nervous, suggestions can be made in relaxing language. The generation AI can also capture the user's facial expressions and voice with a camera or microphone and analyze their emotions. If the user is excited, suggestions can be made in energetic language. Furthermore, the generation AI can learn the user's past emotional data and make suggestions in appropriate language based on their current emotional state. For users who have previously indicated they want to relax, suggestions can be made in relaxing language.

[0101] The interactive suggestion unit can make suggestions not only in the user's native language, but also in multiple languages ​​that the user understands. The generation AI receives the user's native language as input and makes suggestions based on it. If the user speaks English, suggestions can be made in English. It can also take into account multiple languages ​​that the user understands and make suggestions based on that. If the user understands English and Chinese, suggestions can be made in both languages. Furthermore, the generation AI can learn the user's language data and make suggestions in multiple languages ​​based on past data. If the user has used English and Japanese in the past, suggestions can be made in both languages.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The interactive suggestion unit accepts user input. For example, if the user inputs "Please tell me about recommended hotels in Tokyo," the interactive suggestion unit accepts the input. The interactive suggestion unit can also accept voice input. For example, if the user speaks, "Where should I go on my next vacation?", the interactive suggestion unit accepts the voice input. Step 2: The travel plan generation unit generates a travel plan based on the input received by the interactive suggestion unit. For example, if a user inputs, "I would like to visit tourist spots on a three-day trip to Tokyo," the travel plan generation unit analyzes information about the tourist spots and suggests the optimal sightseeing course. The travel plan generation unit can also estimate the user's emotions and suggest tourist spots and activities according to their emotions. For example, if the user is looking for relaxation, it will suggest quiet tourist spots. Step 3: The reservation arrangement unit makes a reservation based on the travel plan generated by the travel plan generation unit. For example, if the user inputs "I would like to reserve this hotel," the reservation arrangement unit will complete the reservation in cooperation with the OTA's system. The reservation arrangement unit can also reserve tickets to tourist attractions and reservations at restaurants. For example, if the user inputs "I would like to reserve this restaurant," the reservation arrangement unit will complete the reservation.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. an interactive suggestion unit that accepts user input; an itinerary generation unit that generates an itinerary based on the input received by the interactive suggestion unit; a reservation arrangement unit that makes reservations based on the travel plan generated by the travel plan generation unit. A system characterized by:

2. The interactive suggestion unit Estimating the user's emotions in real time and making suggestions according to the emotions 2. The system of claim 1.

3. The interactive suggestion unit Analyzing the user's past interaction history, learning the user's preferences and tendencies, and making more personalized suggestions 2. The system of claim 1.

4. The interactive suggestion unit Obtaining the user's current location and weather information in real time and proposing the best travel plan based on that information 2. The system of claim 1.

5. The interactive suggestion unit Analyzing the user's voice input and proposing the travel plan in a voice dialogue format 2. The system of claim 1.

Images