System

Abstract

An object of a system according to an embodiment is to propose an optimal travel plan based on a travel preference, a budget, and a schedule of a user and to consistently make a reservation.SOLUTION: A system according to an embodiment includes a user information analysis section a travel plan suggestion section a reservation coordination section and a generation section. The user information analysis unit analyzes a user's travel preference, budget, and schedule. The travel plan suggestion section suggests an optimum travel plan based on the information analyzed by the user information analysis section. The reservation cooperation unit makes a reservation in cooperation with a cooperative reservation site. The generating unit generates a personalized sightseeing guidebook or travel report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to propose optimal travel plans based on a user's travel preferences, budget, and schedule, and to complete the entire process, including booking.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on the user's travel preferences, budget, and schedule, and to handle the entire process up to booking. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information analysis unit, a travel plan proposal unit, a reservation linkage unit, and a generation unit. The user information analysis unit analyzes the user's travel preferences, budget, and schedule. The travel plan proposal unit proposes an optimal travel plan based on the information analyzed by the user information analysis unit. The reservation linkage unit makes reservations in cooperation with affiliated reservation sites. The generation unit generates a personalized tourist guidebook or travel journal. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal travel plans based on the user's travel preferences, budget, and schedule, and can handle the entire process, right up to booking. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention provides a personalized travel plan based on a user's travel preferences, budget, and schedule. This system uses AI to analyze user information and propose optimal travel plans. It also works with affiliated reservation sites to enable bulk reservations for hotels, trains, rental cars, and other accommodations. Furthermore, it uses generation AI to generate personalized tourist guidebooks and travel journals, enabling a deep understanding of the culture, history, and regional characteristics of the travel destination. This allows the travel planning system to provide users with optimal travel plans and make bulk reservations. It also allows users to gain a deep understanding of the culture and history of the travel destination and record their travel experiences.

[0029] A travel planning system according to an embodiment includes a user information analysis unit, a travel plan proposal unit, a reservation linkage unit, and a generation unit. The user information analysis unit analyzes a user's travel preferences, budget, and schedule. For example, if a user inputs information such as "I love nature, my budget is within 100,000 yen, and I would like a three-day trip," the user information analysis unit performs analysis based on the information. The travel plan proposal unit proposes an optimal travel plan based on the information analyzed by the user information analysis unit. For example, if the user loves nature, the unit proposes tourist spots rich in nature. The reservation linkage unit cooperates with an affiliated reservation site to make reservations. For example, if the user likes a proposed hotel, the reservation linkage unit automatically completes the reservation procedure for that hotel. The generation unit generates a personalized tourist guidebook and travel journal. For example, the generation AI generates a tourist guidebook based on the user's travel plan, detailing the culture, history, and local characteristics of the travel destination. This allows the travel planning system to provide the user with an optimal travel plan and make reservations all at once. The system also allows users to gain a deep understanding of the culture and history of the travel destination and record their travel experiences.

[0030] The user information analysis unit analyzes the user's past travel history and social media posts to identify travel preferences with greater accuracy. The user information analysis unit, for example, analyzes the user's past travel history and collects data such as places visited, length of stay, and means of transportation used. This allows the user's travel preferences to be understood in detail and reflected in the user's next travel plan. The user information analysis unit also analyzes social media posts to identify the user's interests. For example, it analyzes travel photos and comments posted by the user to identify travel preferences. This allows the user's past travel history and social media posts to identify travel preferences with greater accuracy.

[0031] The user information analysis unit can propose a health-conscious travel plan by taking into account the user's health condition and allergy information. The user information analysis unit, for example, collects the user's health condition and allergy information as input data and reflects it in the travel plan. For example, it can propose restaurants that do not contain allergens to a user with allergies. The user information analysis unit also proposes a reasonable schedule based on the user's health condition. For example, it can propose a relaxed sightseeing plan to a user who is not confident in their physical strength. In this way, it is possible to propose a health-conscious travel plan by taking into account the user's health condition and allergy information.

[0032] The travel plan suggestion unit can suggest special events and workshops at travel destinations based on the user's hobbies and interests. The travel plan suggestion unit, for example, collects the user's hobbies and interests as input data and suggests special events and workshops at travel destinations based on that. For example, a local art festival can be introduced to a user who loves art. The travel plan suggestion unit can also suggest special experiences at travel destinations based on the user's interests. For example, a local cooking class can be introduced to a user who loves cooking. In this way, special events and workshops can be suggested based on the user's hobbies and interests.

[0033] The travel plan suggestion unit can propose the optimal travel plan for the entire group, taking into account the travel preferences of family and friends. The travel plan suggestion unit, for example, collects the travel preferences of family and friends as input data and proposes the optimal travel plan for the entire group. For example, it selects tourist spots and activities that everyone can enjoy. The travel plan suggestion unit also considers the schedule of the entire group and proposes a reasonable travel plan. For example, for a family with children, it proposes activities that the children can enjoy. In this way, by taking into account the travel preferences of family and friends, it is possible to propose the optimal travel plan for the entire group.

[0034] The reservation linking unit can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying option to the user. For example, the reservation linking unit analyzes reviews and ratings on affiliated reservation sites and suggests the most satisfying hotel or transportation option to the user. For example, hotels with high ratings are displayed preferentially. The reservation linking unit also suggests the most suitable option to the user based on the user's past rating history. For example, it suggests a hotel similar to a hotel that has received high ratings in the past. In this way, by analyzing reviews and ratings on affiliated reservation sites, it is possible to suggest the most satisfying option to the user.

[0035] The reservation linking unit can automatically apply special offers and discounts for repeat customers based on the user's past reservation history. The reservation linking unit, for example, analyzes the user's past reservation history and builds a system that automatically applies special offers and discounts for repeat customers. For example, it provides discount coupons for hotels that have been used in the past. The reservation linking unit also customizes special offers based on the user's past usage history. For example, it provides special offers based on services that have been used in the past. This makes it possible to automatically apply special offers and discounts for repeat customers based on the user's past reservation history.

[0036] The reservation linking unit can link with region-specific reservation sites other than the affiliated reservation sites to provide a wider variety of options to the user. The reservation linking unit can link with region-specific reservation sites other than the affiliated reservation sites to provide a wider variety of options to the user. For example, it can suggest accommodations and transportation options that are only available in the region. The reservation linking unit also collects information from region-specific reservation sites to suggest the best options to the user. For example, it can provide information on tourist spots and events in the region. In this way, by linking with region-specific reservation sites other than the affiliated reservation sites, a wider variety of options can be provided to the user.

[0037] The reservation linking unit also works with local guides and tour operators at the travel destination, allowing local activity reservations to be made in one go. The reservation linking unit, for example, works with local guides and tour operators at the travel destination to build a system that also allows local activity reservations to be made in one go. For example, it suggests local guided tours and activities. The reservation linking unit also collects local information and suggests the most suitable activities for the user. For example, it provides information on local events and activities. This allows local activity reservations to be made in one go by working with local guides and tour operators at the travel destination.

[0038] The generation unit can generate a tourist guidebook specialized in a particular theme based on the user's interests and concerns. For example, the generation unit collects the user's interests and concerns as input data and generates a tourist guidebook specialized in a particular theme based on the input data. For example, historical tourist spots can be introduced to a user who loves history. The generation unit also customizes a guidebook specialized in a theme based on the user's interests. For example, nature lovers can be introduced to nature reserves and national parks. In this way, a tourist guidebook specialized in a particular theme can be generated based on the user's interests and concerns.

[0039] The generation unit can generate a tourist guidebook that reflects the latest local information and event information in real time. The generation unit, for example, builds a system that collects the latest local information and event information in real time and generates a tourist guidebook that reflects that information. For example, it introduces local festivals and special events. The generation unit also updates the content of the guidebook based on local information. For example, it provides the latest restaurant information and opening hours of tourist spots. This makes it possible to generate a tourist guidebook that reflects the latest local information and event information in real time.

[0040] The generation unit can create guidebooks in multiple languages ​​to accommodate international users. The generation unit, for example, builds a system that generates tourist guidebooks in multiple languages ​​based on the user's language settings. For example, guidebooks are provided in languages ​​such as English, French, and Chinese. The generation unit also provides the contents of the guidebook in multiple languages ​​to accommodate international users. For example, descriptions of tourist spots and maps are displayed in multiple languages. In this way, by generating guidebooks in multiple languages, international users can be accommodated.

[0041] The generation unit can add interactive elements to the guidebook to increase user engagement. The generation unit, for example, builds a system that adds interactive elements to a tourist guidebook to increase user engagement. For example, it provides quizzes and surveys related to tourist spots. The generation unit also customizes the interactive elements based on the user's response. For example, it provides quizzes related to content in which the user has shown interest. In this way, adding interactive elements to the guidebook can increase user engagement.

[0042] The generation unit can automatically organize photos and videos taken by the user and incorporate them into a travelogue. The generation unit, for example, builds a system that automatically organizes photos and videos taken by the user and incorporates them into a travelogue. For example, the generation unit organizes photos based on the date and time of shooting and the location. The generation unit also incorporates photos and videos into the travelogue based on the user's travel experience. For example, the generation unit organizes events that occurred during the trip in chronological order and inserts photos and videos. This allows the generation unit to automatically organize photos and videos taken by the user and incorporate them into a travelogue.

[0043] The generation unit can connect the travelogue to social media, allowing users to easily share it. For example, the generation unit can link the travelogue to social media and build a system that allows users to easily share it. For example, the travelogue can be posted to Facebook or Instagram with one click. The generation unit can also link with the user's social media account and automatically share the travelogue. For example, the travelogue can be automatically posted every time it is updated. In this way, by linking the travelogue to social media, users can easily share it.

[0044] The generation unit can provide a function for adding comments from the user's friends and family to the travel diary and for collaborative creation. The generation unit, for example, builds a system that provides a function for adding comments from the user's friends and family to the travel diary and for collaborative creation. For example, it provides an interface that allows comments to be added to the travel diary. The generation unit also collaborates with the user's friends and family to collaboratively create the travel diary. For example, they share events that occurred during the trip and add comments. This makes it possible to provide a function for adding comments from the user's friends and family to the travel diary and for collaborative creation.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The travel plan suggestion unit can suggest special experiences at travel destinations based on the user's travel preferences. For example, if the user is an adventure lover, local activities such as skydiving or rafting can be suggested. Also, if the user is interested in culture, local traditional festivals and cultural events can be introduced. Furthermore, the travel plan suggestion unit can suggest special workshops and seminars based on the user's interests. For example, a local cooking class can be introduced to a user who loves cooking. In this way, special experiences can be suggested based on the user's travel preferences.

[0047] The user information analysis unit analyzes the user's past travel history and social media posts to identify travel preferences with greater accuracy. For example, it collects data such as the places the user has visited in the past, the length of stay, and the means of transportation used, and reflects this data in the user's next travel plans. It can also analyze travel photos and comments posted by the user on social media to identify the user's interests. Furthermore, by analyzing the user's past travel history and social media posts, the user's travel preferences can be understood in detail and reflected in the user's next travel plans. This allows for more accurate identification of travel preferences.

[0048] The user information analysis unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. For example, restaurants that do not contain allergens can be proposed to a user with allergies. It can also propose a reasonable schedule based on the user's health condition. For example, a relaxed sightseeing plan can be proposed to a user who is not confident in their physical strength. Furthermore, by taking into account the user's health condition and allergy information, it is possible to propose health-conscious travel plans. This makes it possible to propose travel plans that take into account the user's health condition and allergy information.

[0049] The travel plan suggestion unit can propose the optimal travel plan for the entire group, taking into account the travel preferences of family and friends. For example, it can collect the travel preferences of family and friends as input data and propose the optimal travel plan for the entire group. For example, it can select tourist spots and activities that everyone can enjoy. It can also propose a reasonable travel plan taking into account the schedule of the entire group. For example, it can suggest activities that children can enjoy for families with children. In this way, it is possible to propose the optimal travel plan for the entire group by taking into account the travel preferences of family and friends.

[0050] The reservation linkage unit can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying options to the user. For example, it can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying hotels and transportation options to the user. For example, it can display hotels with high ratings preferentially. It can also suggest the most suitable options to the user based on the user's past rating history. For example, it can suggest hotels similar to hotels that have received high ratings in the past. In this way, by analyzing reviews and ratings on affiliated reservation sites, it can suggest the most satisfying options to the user.

[0051] The generation unit can generate a tourist guidebook specialized in a particular theme based on the user's interests and concerns. For example, the generation unit can collect the user's interests and concerns as input data and generate a tourist guidebook specialized in a particular theme based on the collected data. For example, historical tourist spots can be introduced to a user who loves history. The guidebook specialized in a theme can also be customized based on the user's interests. For example, nature enthusiasts can be introduced to nature reserves and national parks. In this way, a tourist guidebook specialized in a particular theme can be generated based on the user's interests and concerns.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The user information analysis unit analyzes the user's travel preferences, budget, and schedule. For example, if a user enters information such as "I like nature, my budget is within 100,000 yen, and I would like to travel for three days," the user information analysis unit will perform an analysis based on that information. Step 2: The travel plan suggestion unit suggests an optimal travel plan based on the information analyzed by the user information analysis unit. For example, if the user likes nature, it will suggest tourist spots rich in nature. Step 3: The reservation linking unit links with the affiliated reservation site to make the reservation. For example, if the user likes a suggested hotel, the reservation linking unit automatically completes the reservation procedure for that hotel. Step 4: The generator generates a personalized tourist guidebook or travel journal. For example, the generator AI generates a tourist guidebook based on the user's travel plans, detailing the culture, history, and local characteristics of the travel destination.

[0054] (Example 2) A travel planning system according to an embodiment of the present invention provides a personalized travel plan based on a user's travel preferences, budget, and schedule. This system uses AI to analyze user information and propose optimal travel plans. It also works with affiliated reservation sites to enable bulk reservations for hotels, trains, rental cars, and other accommodations. Furthermore, it uses generation AI to generate personalized tourist guidebooks and travel journals, enabling a deep understanding of the culture, history, and regional characteristics of the travel destination. This allows the travel planning system to provide users with optimal travel plans and make bulk reservations. It also allows users to gain a deep understanding of the culture and history of the travel destination and record their travel experiences.

[0055] A travel planning system according to an embodiment includes a user information analysis unit, a travel plan proposal unit, a reservation linkage unit, and a generation unit. The user information analysis unit analyzes a user's travel preferences, budget, and schedule. For example, if a user inputs information such as "I love nature, my budget is within 100,000 yen, and I would like a three-day trip," the user information analysis unit performs analysis based on the information. The travel plan proposal unit proposes an optimal travel plan based on the information analyzed by the user information analysis unit. For example, if the user loves nature, the unit proposes tourist spots rich in nature. The reservation linkage unit cooperates with an affiliated reservation site to make reservations. For example, if the user likes a proposed hotel, the reservation linkage unit automatically completes the reservation procedure for that hotel. The generation unit generates a personalized tourist guidebook and travel journal. For example, the generation AI generates a tourist guidebook based on the user's travel plan, detailing the culture, history, and local characteristics of the travel destination. This allows the travel planning system to provide the user with an optimal travel plan and make reservations all at once. The system also allows users to gain a deep understanding of the culture and history of the travel destination and record their travel experiences.

[0056] The user information analysis unit analyzes the user's past travel history and social media posts to identify travel preferences with greater accuracy. The user information analysis unit, for example, analyzes the user's past travel history and collects data such as places visited, length of stay, and means of transportation used. This allows the user's travel preferences to be understood in detail and reflected in the user's next travel plan. The user information analysis unit also analyzes social media posts to identify the user's interests. For example, it analyzes travel photos and comments posted by the user to identify travel preferences. This allows the user's past travel history and social media posts to identify travel preferences with greater accuracy.

[0057] The user information analysis unit can propose a health-conscious travel plan by taking into account the user's health condition and allergy information. The user information analysis unit, for example, collects the user's health condition and allergy information as input data and reflects it in the travel plan. For example, it can propose restaurants that do not contain allergens to a user with allergies. The user information analysis unit also proposes a reasonable schedule based on the user's health condition. For example, it can propose a relaxed sightseeing plan to a user who is not confident in their physical strength. In this way, it is possible to propose a health-conscious travel plan by taking into account the user's health condition and allergy information.

[0058] The user information analysis unit uses the emotion estimation function to analyze the emotions of the user when entering their travel plans and can suggest travel destinations that will elicit positive emotions. For example, the user information analysis unit analyzes facial expressions and voice tone when the user enters their travel plans to estimate their emotions. For example, if it detects a smile or an excited voice, it suggests travel destinations that match those emotions. The user information analysis unit also uses the emotion estimation function to monitor the user's emotions in real time and suggests travel destinations that will elicit positive emotions. For example, if the user is feeling stressed, it suggests travel destinations that will help the user relax. In this way, the emotion estimation function can suggest travel destinations that will evoke positive emotions in the user.

[0059] The travel plan suggestion unit can suggest special events and workshops at travel destinations based on the user's hobbies and interests. The travel plan suggestion unit, for example, collects the user's hobbies and interests as input data and suggests special events and workshops at travel destinations based on that. For example, a local art festival can be introduced to a user who loves art. The travel plan suggestion unit can also suggest special experiences at travel destinations based on the user's interests. For example, a local cooking class can be introduced to a user who loves cooking. In this way, special events and workshops can be suggested based on the user's hobbies and interests.

[0060] The travel plan suggestion unit can propose the optimal travel plan for the entire group, taking into account the travel preferences of family and friends. The travel plan suggestion unit, for example, collects the travel preferences of family and friends as input data and proposes the optimal travel plan for the entire group. For example, it selects tourist spots and activities that everyone can enjoy. The travel plan suggestion unit also considers the schedule of the entire group and proposes a reasonable travel plan. For example, for a family with children, it proposes activities that the children can enjoy. In this way, by taking into account the travel preferences of family and friends, it is possible to propose the optimal travel plan for the entire group.

[0061] The travel plan proposal unit uses the emotion estimation function to monitor the user's emotions in real time when proposing a travel plan, and can continuously make optimal proposals. For example, the travel plan proposal unit monitors the user's emotions in real time when proposing a travel plan, and adjusts the proposal content according to changes in emotions. For example, if negative emotions are detected, a different proposal is made. The travel plan proposal unit also uses the emotion estimation function to continuously make optimal proposals according to the user's emotions. For example, if the user is excited, a proposal is made to maintain that excitement. In this way, by using the emotion estimation function, optimal proposals can continuously be made according to the user's emotions.

[0062] The reservation linking unit can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying option to the user. For example, the reservation linking unit analyzes reviews and ratings on affiliated reservation sites and suggests the most satisfying hotel or transportation option to the user. For example, hotels with high ratings are displayed preferentially. The reservation linking unit also suggests the most suitable option to the user based on the user's past rating history. For example, it suggests a hotel similar to a hotel that has received high ratings in the past. In this way, by analyzing reviews and ratings on affiliated reservation sites, it is possible to suggest the most satisfying option to the user.

[0063] The reservation linking unit can automatically apply special offers and discounts for repeat customers based on the user's past reservation history. The reservation linking unit, for example, analyzes the user's past reservation history and builds a system that automatically applies special offers and discounts for repeat customers. For example, it provides discount coupons for hotels that have been used in the past. The reservation linking unit also customizes special offers based on the user's past usage history. For example, it provides special offers based on services that have been used in the past. This makes it possible to automatically apply special offers and discounts for repeat customers based on the user's past reservation history.

[0064] The reservation linking unit can use the emotion estimation function to analyze the user's emotions during the reservation procedure and provide an interface for reducing stress. The reservation linking unit, for example, analyzes the user's emotions in real time during the reservation procedure and provides an interface for reducing stress. For example, if the user's emotions become negative, it can suggest a simple procedure. The reservation linking unit also uses the emotion estimation function to provide an interface according to the user's emotions. For example, if the user is feeling stressed, it can provide an interface that helps the user relax. In this way, by using the emotion estimation function, it is possible to provide an interface that reduces the user's stress during the reservation procedure.

[0065] The reservation linking unit can link with region-specific reservation sites other than the affiliated reservation sites to provide a wider variety of options to the user. The reservation linking unit can link with region-specific reservation sites other than the affiliated reservation sites to provide a wider variety of options to the user. For example, it can suggest accommodations and transportation options that are only available in the region. The reservation linking unit also collects information from region-specific reservation sites to suggest the best options to the user. For example, it can provide information on tourist spots and events in the region. In this way, by linking with region-specific reservation sites other than the affiliated reservation sites, a wider variety of options can be provided to the user.

[0066] The reservation linking unit also works with local guides and tour operators at the travel destination, allowing local activity reservations to be made in one go. The reservation linking unit, for example, works with local guides and tour operators at the travel destination to build a system that also allows local activity reservations to be made in one go. For example, it suggests local guided tours and activities. The reservation linking unit also collects local information and suggests the most suitable activities for the user. For example, it provides information on local events and activities. This allows local activity reservations to be made in one go by working with local guides and tour operators at the travel destination.

[0067] The reservation linkage unit uses the emotion estimation function to monitor the user's emotions during the reservation procedure in real time and propose optimal reservation options. The reservation linkage unit, for example, builds a system that monitors the user's emotions in real time during the reservation procedure and proposes optimal reservation options. For example, it presents multiple options until the emotion becomes positive. The reservation linkage unit also uses the emotion estimation function to propose reservation options according to the user's emotions. For example, if the user is feeling stressed, it proposes a simple procedure. In this way, by using the emotion estimation function, it is possible to monitor the user's emotions during the reservation procedure in real time and propose optimal reservation options.

[0068] The generation unit can generate a tourist guidebook specialized in a particular theme based on the user's interests and concerns. For example, the generation unit collects the user's interests and concerns as input data and generates a tourist guidebook specialized in a particular theme based on the input data. For example, historical tourist spots can be introduced to a user who loves history. The generation unit also customizes a guidebook specialized in a theme based on the user's interests. For example, nature lovers can be introduced to nature reserves and national parks. In this way, a tourist guidebook specialized in a particular theme can be generated based on the user's interests and concerns.

[0069] The generation unit can generate a tourist guidebook that reflects the latest local information and event information in real time. The generation unit, for example, builds a system that collects the latest local information and event information in real time and generates a tourist guidebook that reflects that information. For example, it introduces local festivals and special events. The generation unit also updates the content of the guidebook based on local information. For example, it provides the latest restaurant information and opening hours of tourist spots. This makes it possible to generate a tourist guidebook that reflects the latest local information and event information in real time.

[0070] The generation unit uses the emotion estimation function to analyze the emotions of the user when viewing a guidebook and can emphasize content that elicits positive emotions. For example, the generation unit analyzes facial expressions and voice tones when the user views a guidebook to estimate emotions. For example, if a smile or an excited voice is detected, the generation unit emphasizes that content. The generation unit also uses the emotion estimation function to provide guidebook content that corresponds to the user's emotions. For example, if the user is excited, the generation unit emphasizes content that will maintain that excitement. In this way, by using the emotion estimation function, it is possible to emphasize content that elicits positive emotions when the user views a guidebook.

[0071] The generation unit can create guidebooks in multiple languages ​​to accommodate international users. The generation unit, for example, builds a system that generates tourist guidebooks in multiple languages ​​based on the user's language settings. For example, guidebooks are provided in languages ​​such as English, French, and Chinese. The generation unit also provides the contents of the guidebook in multiple languages ​​to accommodate international users. For example, descriptions of tourist spots and maps are displayed in multiple languages. In this way, by generating guidebooks in multiple languages, international users can be accommodated.

[0072] The generation unit can add interactive elements to the guidebook to increase user engagement. The generation unit, for example, builds a system that adds interactive elements to a tourist guidebook to increase user engagement. For example, it provides quizzes and surveys related to tourist spots. The generation unit also customizes the interactive elements based on the user's response. For example, it provides quizzes related to content in which the user has shown interest. In this way, adding interactive elements to the guidebook can increase user engagement.

[0073] The generation unit uses the emotion estimation function to acquire the user's emotional reactions to the guidebook content in real time and continuously improve the content. For example, the generation unit collects the user's emotional reactions to the guidebook content in real time and builds a system that continuously improves the content based on that data. For example, it emphasizes content that receives many positive reactions. The generation unit also uses the emotion estimation function to provide guidebook content that corresponds to the user's emotions. For example, if the user is excited, it emphasizes content that will maintain that excitement. In this way, by using the emotion estimation function, the user's emotional reactions to the guidebook content can be collected in real time and the content can be continuously improved.

[0074] The generation unit can acquire the user's emotions during the trip in real time and generate a travelogue that reflects those emotions. The generation unit, for example, builds a system that records the user's emotions during the trip in real time and generates a travelogue that reflects those emotions based on that data. For example, it displays changes in emotions during the trip in a graph. The generation unit also uses an emotion estimation function to provide a travelogue that corresponds to the user's emotions. For example, if the user is excited, it generates a travelogue that reflects that excitement. This makes it possible to record the user's emotions during the trip in real time and generate a travelogue that reflects those emotions.

[0075] The generation unit can automatically organize photos and videos taken by the user and incorporate them into a travelogue. The generation unit, for example, builds a system that automatically organizes photos and videos taken by the user and incorporates them into a travelogue. For example, the generation unit organizes photos based on the date and time of shooting and the location. The generation unit also incorporates photos and videos into the travelogue based on the user's travel experience. For example, the generation unit organizes events that occurred during the trip in chronological order and inserts photos and videos. This allows the generation unit to automatically organize photos and videos taken by the user and incorporate them into a travelogue.

[0076] The generation unit can use the emotion estimation function to analyze the user's emotions regarding the content of the travelogue and emphasize episodes that elicit positive emotions. The generation unit, for example, builds a system that analyzes the user's emotions regarding the content of the travelogue in real time and emphasizes episodes that elicit positive emotions. For example, it records in detail moments when emotions are heightened. The generation unit also uses the emotion estimation function to provide travelogue content that corresponds to the user's emotions. For example, if the user is excited, it emphasizes episodes that reflect that excitement. In this way, by using the emotion estimation function, the user's emotions regarding the content of the travelogue can be analyzed and episodes that elicit positive emotions can be emphasized.

[0077] The generation unit can connect the travelogue to social media, allowing users to easily share it. For example, the generation unit can link the travelogue to social media and build a system that allows users to easily share it. For example, the travelogue can be posted to Facebook or Instagram with one click. The generation unit can also link with the user's social media account and automatically share the travelogue. For example, the travelogue can be automatically posted every time it is updated. In this way, by linking the travelogue to social media, users can easily share it.

[0078] The generation unit can provide a function for adding comments from the user's friends and family to the travel diary and for collaborative creation. The generation unit, for example, builds a system that provides a function for adding comments from the user's friends and family to the travel diary and for collaborative creation. For example, it provides an interface that allows comments to be added to the travel diary. The generation unit also collaborates with the user's friends and family to collaboratively create the travel diary. For example, they share events that occurred during the trip and add comments. This makes it possible to provide a function for adding comments from the user's friends and family to the travel diary and for collaborative creation.

[0079] The generation unit uses the emotion estimation function to acquire the user's emotional reactions to the content of the travelogue in real time, and can continuously improve the content. For example, the generation unit collects the user's emotional reactions to the content of the travelogue in real time, and builds a system that continuously improves the content based on that data. For example, it emphasizes content that has many positive reactions. The generation unit also uses the emotion estimation function to provide travelogue content that corresponds to the user's emotions. For example, if the user is excited, it emphasizes content that reflects that excitement. In this way, by using the emotion estimation function, the user's emotional reactions to the content of the travelogue can be collected in real time, and the content can be continuously improved.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The travel plan suggestion unit can suggest special experiences at travel destinations based on the user's travel preferences. For example, if the user is an adventure lover, local activities such as skydiving or rafting can be suggested. Also, if the user is interested in culture, local traditional festivals and cultural events can be introduced. Furthermore, the travel plan suggestion unit can suggest special workshops and seminars based on the user's interests. For example, a local cooking class can be introduced to a user who loves cooking. In this way, special experiences can be suggested based on the user's travel preferences.

[0082] The user information analysis unit analyzes the user's past travel history and social media posts to identify travel preferences with greater accuracy. For example, it collects data such as the places the user has visited in the past, the length of stay, and the means of transportation used, and reflects this data in the user's next travel plans. It can also analyze travel photos and comments posted by the user on social media to identify the user's interests. Furthermore, by analyzing the user's past travel history and social media posts, the user's travel preferences can be understood in detail and reflected in the user's next travel plans. This allows for more accurate identification of travel preferences.

[0083] The user information analysis unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. For example, restaurants that do not contain allergens can be proposed to a user with allergies. It can also propose a reasonable schedule based on the user's health condition. For example, a relaxed sightseeing plan can be proposed to a user who is not confident in their physical strength. Furthermore, by taking into account the user's health condition and allergy information, it is possible to propose health-conscious travel plans. This makes it possible to propose travel plans that take into account the user's health condition and allergy information.

[0084] The user information analysis unit can use the emotion estimation function to analyze the emotions of the user when entering their travel plans and suggest travel destinations that will elicit positive emotions. For example, when a user enters their travel plans, the unit can analyze their facial expressions and voice tone to estimate their emotions. For example, if a smile or an excited voice is detected, it can suggest travel destinations that match those emotions. It can also monitor the user's emotions in real time and suggest travel destinations that will elicit positive emotions. For example, if the user is feeling stressed, it can suggest travel destinations that will help them relax. As a result, the emotion estimation function can be used to suggest travel destinations that will evoke positive emotions in the user.

[0085] The travel plan suggestion unit can propose the optimal travel plan for the entire group, taking into account the travel preferences of family and friends. For example, it can collect the travel preferences of family and friends as input data and propose the optimal travel plan for the entire group. For example, it can select tourist spots and activities that everyone can enjoy. It can also propose a reasonable travel plan taking into account the schedule of the entire group. For example, it can suggest activities that children can enjoy for families with children. In this way, it is possible to propose the optimal travel plan for the entire group by taking into account the travel preferences of family and friends.

[0086] The travel plan suggestion unit uses the emotion estimation function to monitor the user's emotions in real time when proposing a travel plan, and can continuously make optimal suggestions. For example, when proposing a travel plan, the unit can monitor the user's emotions in real time and adjust the suggestion content according to changes in emotions. For example, if a negative emotion is detected, a different suggestion can be made. The unit can also continuously make optimal suggestions according to the user's emotions. For example, if the user is excited, a suggestion can be made to maintain that excitement. In this way, by using the emotion estimation function, optimal suggestions can be continuously made according to the user's emotions.

[0087] The reservation linkage unit can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying options to the user. For example, it can analyze reviews and ratings on affiliated reservation sites and suggest the most satisfying hotels and transportation options to the user. For example, it can display hotels with high ratings preferentially. It can also suggest the most suitable options to the user based on the user's past rating history. For example, it can suggest hotels similar to hotels that have received high ratings in the past. In this way, by analyzing reviews and ratings on affiliated reservation sites, it can suggest the most satisfying options to the user.

[0088] The reservation linkage unit can use the emotion estimation function to analyze the user's emotions during the reservation procedure and provide an interface for reducing stress. For example, it can analyze the user's emotions in real time during the reservation procedure and provide an interface for reducing stress. For example, it can suggest a simple procedure if the emotion becomes negative. It can also provide an interface according to the user's emotions. For example, if the user is feeling stressed, it can provide an interface that helps the user relax. In this way, by using the emotion estimation function, it is possible to provide an interface that reduces the user's stress during the reservation procedure.

[0089] The generation unit can generate a tourist guidebook specialized in a particular theme based on the user's interests and concerns. For example, the generation unit can collect the user's interests and concerns as input data and generate a tourist guidebook specialized in a particular theme based on the collected data. For example, historical tourist spots can be introduced to a user who loves history. The guidebook specialized in a theme can also be customized based on the user's interests. For example, nature enthusiasts can be introduced to nature reserves and national parks. In this way, a tourist guidebook specialized in a particular theme can be generated based on the user's interests and concerns.

[0090] The generation unit can use the emotion estimation function to analyze the emotion of the user when viewing a guidebook and emphasize content that elicits positive emotions. For example, when the user views a guidebook, the generation unit can analyze facial expressions and voice tone to estimate emotions. For example, if a smile or an excited voice is detected, the content can be emphasized. It can also provide guidebook content that corresponds to the user's emotions. For example, if the user is excited, content that will maintain that excitement can be emphasized. In this way, by using the emotion estimation function, it is possible to emphasize content that elicits positive emotions when the user views a guidebook.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The user information analysis unit analyzes the user's travel preferences, budget, and schedule. For example, if a user enters information such as "I like nature, my budget is within 100,000 yen, and I would like to travel for three days," the user information analysis unit will perform an analysis based on that information. Step 2: The travel plan suggestion unit suggests an optimal travel plan based on the information analyzed by the user information analysis unit. For example, if the user likes nature, it will suggest tourist spots rich in nature. Step 3: The reservation linking unit links with the affiliated reservation site to make the reservation. For example, if the user likes a suggested hotel, the reservation linking unit automatically completes the reservation procedure for that hotel. Step 4: The generator generates a personalized tourist guidebook or travel journal. For example, the generator AI generates a tourist guidebook based on the user's travel plans, detailing the culture, history, and local characteristics of the travel destination.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information analysis unit that analyzes the user's travel preferences, budget, and schedule; a travel plan suggestion unit that suggests an optimal travel plan based on the information analyzed by the user information analysis unit; The reservation coordination department handles reservations in cooperation with affiliated reservation sites, A generating unit that generates a personalized tourist guidebook or travelogue. A system characterized by:

2. The user information analysis unit Analyzing a user's past travel history or social media posts to more accurately identify said travel preferences 2. The system of claim 1.

3. The travel plan suggestion unit Suggesting special events and workshops at travel destinations based on the user's hobbies or interests 2. The system of claim 1.

4. The reservation linking unit Analyzing reviews or ratings on the partner booking sites and suggesting the most satisfying options to the user 2. The system of claim 1.

5. The generation unit Generate the tourist guidebook specialized in a particular theme based on the user's interests or concerns.

2. The system of claim 1.

6. The user information analysis unit Analyzes emotions when users enter travel plans and suggests travel destinations that evoke positive emotions 2. The system of claim 1.

7. The reservation linking unit Analyzing the user's emotions during the reservation process and providing an interface to reduce stress 2. The system of claim 1.

8. The generation unit The user's feelings during the trip are acquired in real time, and the travelogue is generated reflecting the feelings.

2. The system of claim 1.

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