System

Abstract

An object of a system according to an embodiment is to propose and support an optimal travel plan suitable for an individual's health condition and hobbies and tastes.SOLUTION: A system according to an embodiment includes a wellness information collector, a favorite information collector, a travel plan suggester, and a reservation supporter. The wellness information collector collects wellness information. The favorite information collection unit collects favorite information. The travel plan suggestion unit suggests a travel plan based on the information collected by the wellness information collection unit and the favorite information collection unit. The reservation support unit supports reservations for accommodation facilities and transportation on the basis of the travel plan proposed by the travel plan proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to easily propose and support travel plans tailored to individual health conditions and hobbies and preferences.

[0005] The system according to the embodiment aims to propose and support optimal travel plans tailored to individual health conditions and hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a wellness information collection unit, a favorites information collection unit, a travel plan proposal unit, and a reservation support unit. The wellness information collection unit collects wellness information. The favorites information collection unit collects favorite information. The travel plan proposal unit proposes a travel plan based on the information collected by the wellness information collection unit and the favorites information collection unit. The reservation support unit supports reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose and support optimal travel plans tailored to individual health conditions and hobbies and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The private travel concierge system according to an embodiment of the present invention proposes optimal travel plans based on a user's wellness information and hobbies and preferences, and supports reservations for accommodations and transportation. This allows the private travel concierge system to easily provide users with a trip perfectly suited to their preferences.

[0029] A private travel concierge system according to an embodiment includes a wellness information collection unit, a favorites information collection unit, a travel plan proposal unit, and a reservation support unit. The wellness information collection unit collects wellness information about a user. For example, it collects wellness information from HELPO and analyzes the user's health status, exercise habits, and dietary preferences. The wellness information collection unit can also collect data from a wearable device to monitor the user's health status in real time. For example, it can collect heart rate, step count, and sleep data from the wearable device to evaluate the user's health status. The wellness information collection unit can also collect the user's medical records and comprehensively evaluate the user's health status. For example, it can perform a detailed analysis of the user's health status based on data provided by medical institutions. The favorites information collection unit collects the user's favorites. For example, it can collect information registered as favorites on Tabelog, travel sites, hobby blogs, etc., and analyze the user's favorite restaurants, tourist spots, activities, etc. The favorites information collection unit can also collect the user's past travel history and reflect it in the travel plan. For example, the favorite information collection unit collects information about previously visited travel destinations, accommodations, and activities to understand the user's hobbies and preferences. Furthermore, the favorite information collection unit can collect information from the user's social media accounts and analyze the user's hobbies and preferences. For example, the favorite information collection unit can understand the user's interests based on photos and posts shared on social media. The travel plan proposal unit proposes travel plans based on the information collected by the wellness information collection unit and the favorite information collection unit. For example, the travel plan proposal unit provides plans tailored to the user's needs, such as a relaxing hot spring trip that takes health into consideration or an adventure trip packed with activities tailored to the user's hobbies. Furthermore, the travel plan proposal unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an optimal travel plan based on the user's health condition and hobbies and preferences. For example, the generation AI analyzes the user's health condition and hobbies and preferences and proposes an optimal travel plan. Furthermore, the travel plan proposal unit can also generate a more satisfying travel plan based on the user's past travel reviews. For example, the travel plan proposal unit proposes a plan that includes travel destinations and activities that have been highly rated in the past.The reservation support unit supports reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. For example, it proposes hotels and inns that are best suited to the travel plan selected by the user and handles the reservation procedures on the user's behalf. The reservation support unit also arranges Shinkansen and airplane tickets to ensure the user can enjoy their trip smoothly. Furthermore, the reservation support unit can also suggest spa and relaxation facilities at the travel destination based on the user's health condition and stress level and support reservations. For example, it can arrange aromatherapy and hot stone massage. In this way, the private travel concierge system according to the embodiment can easily provide the user with a trip that perfectly suits their preferences. For example, by being proposed a travel plan that takes health condition into consideration, the user can enjoy the trip with peace of mind. Furthermore, by being proposed a travel plan that matches the user's hobbies and preferences, the user can have a more fulfilling travel experience. Furthermore, by supporting reservations for accommodations and transportation, travel preparations can be made hassle-free.

[0030] The wellness information collection unit can customize menus offered at restaurants at travel destinations based on the user's health condition and dietary restrictions. The wellness information collection unit customizes menus offered at restaurants at travel destinations based on the user's health condition and dietary restrictions, for example. For example, it suggests low-carb menus for a diabetic user. The wellness information collection unit also provides allergy-friendly menus at restaurants at travel destinations based on the user's allergy information. For example, it suggests gluten-free and nut-free menus. The wellness information collection unit also customizes meal menus at travel destinations taking into account the user's nutritional balance. For example, it suggests menus rich in vitamins and minerals. In this way, health management is supported by providing menus that suit the user's health condition and dietary restrictions.

[0031] The wellness information collection unit can suggest an exercise program for the travel destination based on the user's exercise habits and fitness level. The wellness information collection unit suggests an exercise program for the travel destination based on the user's exercise habits and fitness level, for example. For example, it suggests yoga classes or gym use. The wellness information collection unit also suggests walking routes and running courses at the travel destination based on the user's health goals. For example, it suggests walking tours around tourist spots. The wellness information collection unit also arranges personal training sessions at the travel destination based on the user's fitness data. For example, it provides training plans in cooperation with local trainers. This supports health management by providing exercise programs tailored to the user's exercise habits and fitness level.

[0032] The reservation support unit can suggest spa and relaxation facilities at travel destinations based on the user's health condition and stress level, and support reservations. The reservation support unit, for example, suggests spa and relaxation facilities at travel destinations based on the user's health condition and stress level, and support reservations. For example, it arranges aromatherapy and hot stone massages. The reservation support unit also suggests relaxation programs at travel destinations based on the user's wellness information. For example, it arranges detox programs and yoga retreats. The reservation support unit also promotes use of relaxation facilities at travel destinations based on the user's health data. For example, it suggests spa packages and relaxation courses. In this way, spa and relaxation facilities based on the user's health condition and stress level are suggested and reservations are supported, thereby enhancing the relaxation effect.

[0033] The reservation support unit can automatically generate a health checklist for during travel based on the health data, thereby supporting health management. The reservation support unit automatically generates a health checklist necessary during travel, for example, based on the user's health data. For example, it provides a medication schedule or health checkup reminders. The reservation support unit also suggests a list of items necessary for health management during travel, based on the user's health condition. For example, it provides a list of supplements and health products. The reservation support unit also provides advice useful for health management during travel, based on the user's health data. For example, it suggests appropriate hydration and rest timing. In this way, a health checklist is automatically generated based on the user's health data, supporting health management during travel.

[0034] The travel plan proposal unit can propose a travel plan that includes dietary restrictions and allergy accommodations according to the user's health condition. The travel plan proposal unit includes appropriate meal options in the travel plan based on, for example, the user's health condition and dietary restrictions. For example, it proposes low-carb menus and gluten-free menus. The travel plan proposal unit also includes allergy-friendly meal options in the travel plan based on the user's allergy information. For example, it proposes nut-free and dairy-free menus. The travel plan proposal unit also takes into account the user's nutritional balance and includes healthy meal options in the travel plan. For example, it proposes menus rich in vitamins and minerals. This supports health management by providing a travel plan that includes dietary restrictions and allergy accommodations according to the user's health condition.

[0035] The travel plan suggestion unit can generate a more satisfying travel plan based on past travel ratings. The travel plan suggestion unit, for example, analyzes the user's past travel ratings and generates a travel plan that incorporates elements that generated high satisfaction. For example, it proposes a plan that includes tourist spots and activities that were highly rated in the past. The travel plan suggestion unit also proposes new travel destinations similar to travel destinations that generated high satisfaction in the past based on the user's travel rating data. For example, it proposes travel destinations with the same theme or atmosphere. The travel plan suggestion unit also analyzes the user's past travel ratings and generates a travel plan that enhances elements that generated high satisfaction. For example, it proposes a plan that enhances specific activities and services. This improves the travel experience by providing a more satisfying travel plan based on the user's past travel ratings.

[0036] The travel plan proposal unit can propose a travel plan that incorporates special experiences tailored to the user's hobbies. The travel plan proposal unit incorporates special experiences into the travel plan based on the user's hobbies and preferences, for example. For example, a local cooking class is proposed for a user who loves cooking. The travel plan proposal unit also incorporates customized special experiences into the travel plan based on the user's favorite information. For example, a local art tour is proposed for a user who loves art. The travel plan proposal unit also incorporates unique experiences into the travel plan that match the user's hobbies and preferences. For example, a local hiking tour is proposed for a user who loves the outdoors. In this way, the travel experience is improved by providing a travel plan that incorporates special experiences tailored to the user's hobbies.

[0037] The travel plan suggestion unit can suggest travel plans that provide cultural experiences including events for interacting with local people. The travel plan suggestion unit, for example, includes events for interacting with local people in the user's travel plan. For example, it suggests homestays or dinner events where users can enjoy local home cooking. The travel plan suggestion unit also customizes events for interacting with local people in the travel plan based on the user's hobbies and preferences. For example, it suggests workshops with local artists. The travel plan suggestion unit also includes events in which users can experience local culture and traditions in the user's travel plan. For example, it suggests local festivals or events where users can experience traditional crafts. This improves the travel experience by providing cultural experiences that include events for interacting with local people.

[0038] The reservation support unit can give priority to suggesting accommodations that take health into consideration. For example, the reservation support unit gives priority to suggesting non-smoking rooms or allergy-friendly rooms based on the user's health condition. For example, it suggests allergy-friendly rooms to users with allergies. The reservation support unit also suggests health-friendly accommodations based on the user's health data. For example, it suggests facilities that offer non-smoking rooms or hypoallergenic bedding. The reservation support unit also suggests accommodations that are suitable for health management according to the user's health condition. For example, it suggests hotels that offer fitness facilities or healthy food menus. In this way, health management is supported by giving priority to suggesting accommodations that take the user's health condition into consideration.

[0039] The reservation support unit can analyze reviews of accommodations and suggest facilities that best match the user's tastes and preferences. For example, the reservation support unit analyzes reviews of accommodations and suggests facilities that best match the user's tastes and preferences. For example, a highly rated resort hotel is suggested for a user who loves resorts. The reservation support unit also customizes and suggests accommodation reviews based on the user's tastes and preferences. For example, a hotel with an art gallery is suggested for a user who loves art. The reservation support unit also analyzes reviews of accommodations and suggests facilities that match the user's tastes and preferences. For example, a hotel with a highly rated restaurant is suggested for a user who loves food. In this way, the travel experience is improved by suggesting accommodations that best match the user's tastes and preferences.

[0040] The reservation support unit can take into account past accommodation history and utilize repeat customer benefits. For example, the reservation support unit suggests accommodations utilizing repeat customer benefits based on the user's past accommodation history. For example, it suggests repeat customer benefits for hotels where the user has stayed in the past. The reservation support unit also takes into account the user's accommodation history and suggests accommodations where repeat customer benefits can be used. For example, it suggests repeat customer benefits for hotels that have been highly rated in the past. The reservation support unit also analyzes the user's past accommodation history and suggests accommodations utilizing repeat customer benefits. For example, it suggests hotels that offer extensive repeat customer benefits. In this way, the travel experience is improved by taking into account the user's past accommodation history and utilizing repeat customer benefits.

[0041] The reservation support unit can request special amenities tailored to the user's hobbies. For example, the reservation support unit requests special amenities when reserving accommodation based on the user's hobbies and preferences. For example, a user who likes sports may request sports equipment. The reservation support unit also requests customized amenities when reserving accommodation based on the user's favorite information. For example, a user who likes music may request musical instruments. The reservation support unit also requests special amenities when reserving accommodation based on the user's hobbies and preferences. For example, a user who likes art may request an art set. In this way, the travel experience is improved by requesting special amenities tailored to the user's hobbies.

[0042] The reservation support unit can suggest seats that take health into consideration. For example, the reservation support unit prioritizes suggestions of seats with ample legroom or quiet seats based on the user's health condition. For example, it suggests seats with ample legroom to prevent swelling of the legs on long flights. The reservation support unit also suggests health-conscious seats based on the user's health data. For example, it suggests seats where the user can relax in a quiet environment. The reservation support unit also suggests seats that are suitable for health management according to the user's health condition. For example, it suggests seats that offer reclining seats or extra legroom. In this way, the reservation support unit supports a comfortable journey by suggesting seats that take the user's health condition into consideration.

[0043] The reservation support unit can analyze reviews of transportation means and suggest transportation means that best match the user's preferences. For example, the reservation support unit analyzes reviews of transportation means and suggests transportation means that best match the user's preferences. For example, it suggests airlines that offer comfortable seats and excellent service. The reservation support unit also customizes and suggests reviews of transportation means based on the user's preferences. For example, it suggests transportation means with excellent entertainment options. The reservation support unit also analyzes reviews of transportation means and suggests transportation means that match the user's preferences. For example, it suggests transportation means with highly rated meal services. In this way, the reservation support unit supports comfortable travel by suggesting transportation means that best match the user's preferences.

[0044] The reservation support unit can propose an optimal route by taking into account past travel history. The reservation support unit proposes an optimal route, for example, based on the user's past travel history. For example, it proposes an efficient travel plan by taking into account the means of transportation and routes used in the past. The reservation support unit also analyzes the user's travel history and proposes an optimal route based on routes that have generated high satisfaction in the past. For example, it re-proposes a route that provided a comfortable trip. The reservation support unit also builds a system that proposes an optimal route by taking into account the user's past travel history. For example, it proposes the shortest or cheapest route based on past travel data. In this way, efficient travel is supported by proposing an optimal route by taking into account the user's past travel history.

[0045] The reservation support unit can request special services tailored to the user's hobbies and preferences from the means of transportation. For example, the reservation support unit requests special services when reserving a means of transportation based on the user's hobbies and preferences. For example, a user who loves movies may request enhanced in-flight entertainment. The reservation support unit also requests customized services when reserving a means of transportation based on the user's favorite information. For example, a user who loves gourmet food may request a special meal. The reservation support unit also requests special services tailored to the user's hobbies and preferences when reserving a means of transportation. For example, a user who loves music may request in-flight music service. In this way, requesting special services tailored to the user's hobbies supports a comfortable journey.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The private travel concierge system can further include a health monitoring unit to support the user's health management during the trip. For example, the health monitoring unit monitors the user's health condition in real time and issues an alert if an abnormality is detected. The health monitoring unit can also provide advice necessary for health management during the trip based on the user's health data. For example, it can suggest appropriate hydration and rest times. Furthermore, the health monitoring unit can adjust the user's diet and exercise plan during the trip according to the user's health condition. For example, it can suggest a lighter exercise plan if the user is not feeling well. In this way, the system can support health management during the trip by monitoring the user's health condition in real time and providing appropriate advice.

[0048] The private travel concierge system may further include a safety information provider to ensure the user's safety during their trip. The safety information provider, for example, collects the latest safety information for the travel destination and provides it to the user. The safety information provider can also provide emergency contact information and evacuation shelters based on the user's current location. For example, in the event of a natural disaster or a deterioration in public safety, the safety information provider can quickly provide evacuation shelters. Furthermore, the safety information provider can provide safety advice based on the user's travel plan. For example, the safety information provider can suggest that the user refrain from going out at night. This ensures the user's safety during their trip and allows them to enjoy their trip with peace of mind.

[0049] The private travel concierge system can further include a translation support unit to support communication during the user's trip. The translation support unit, for example, translates the language of the travel destination in real time and provides it to the user. The translation support unit can also learn necessary phrases and words in advance based on the user's travel plan. For example, it can provide phrases for ordering at a restaurant or for giving directions. Furthermore, the translation support unit can provide a speech translation function to facilitate communication between the user. For example, it can translate conversations with local people in real time. This supports communication during the user's trip and allows them to enjoy their trip without language barriers.

[0050] The private travel concierge system can further include an entertainment provider to enhance the user's entertainment during their trip. The entertainment provider provides, for example, information on events and tourist spots at the travel destination. The entertainment provider can also propose customized entertainment plans based on the user's hobbies and preferences. For example, it can provide local concert information to a user who loves music. Furthermore, the entertainment provider can also suggest local cultural experiences and activities based on the user's travel plan. For example, it can suggest local festivals and traditional craft experiences. This can enhance the user's entertainment during their trip and provide a richer travel experience.

[0051] The private travel concierge system can further include an eco-plan suggestion unit to reduce the environmental impact of the user's travel. The eco-plan suggestion unit, for example, suggests environmentally friendly travel plans. The eco-plan suggestion unit can also suggest eco-friendly accommodations and transportation methods based on the user's travel plans. For example, it can suggest hotels that use renewable energy or electric vehicles. Furthermore, the eco-plan suggestion unit can provide advice to reduce the environmental impact of the user's travel. For example, it can suggest reducing the use of plastic. This reduces the environmental impact of the user's travel and supports sustainable travel.

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

[0053] Step 1: The wellness information collection unit collects the user's wellness information. For example, it collects wellness information from HELPO and analyzes the user's health status, exercise habits, and dietary preferences. It can also collect data from wearable devices to understand the user's health status in real time. For example, it can collect heart rate, step count, and sleep data from wearable devices to evaluate the user's health status. It can also collect the user's medical records and comprehensively evaluate the user's health status. For example, it can analyze the user's health status in detail based on data provided by medical institutions. Step 2: The favorite information collection unit collects the user's favorite information. For example, it collects information registered as favorites on Tabelog, travel sites, hobby blogs, etc., and analyzes the user's favorite restaurants, tourist spots, activities, etc. It can also collect the user's past travel history and reflect it in travel plans. For example, it collects information on past travel destinations, accommodations, and activities to understand the user's hobbies and preferences. It can also collect information from the user's social media account and analyze the hobbies and preferences. For example, it can understand the user's interests based on photos and posts shared on social media. Step 3: The travel plan suggestion unit proposes travel plans based on the information collected by the wellness information collection unit and the favorite information collection unit. For example, it provides plans tailored to the user's needs, such as a relaxing hot spring trip that takes health into consideration or an adventure trip packed with activities tailored to the user's hobbies. It also uses generation AI (e.g., text generation AI or multimodal generation AI) to generate optimal travel plans based on the user's health condition and hobbies and preferences. For example, the generation AI analyzes the user's health condition and hobbies and preferences and proposes optimal travel plans. It can also generate more satisfying travel plans based on the user's past travel ratings. For example, it proposes plans that include travel destinations and activities that have been highly rated in the past. Step 4: The reservation support unit helps users make reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. For example, it can suggest hotels and inns that best fit the user's selected travel plan and handle the reservation process. It can also arrange bullet train and airplane tickets to ensure the user can enjoy their trip smoothly. It can also suggest spa and relaxation facilities at the travel destination based on the user's health condition and stress level and support reservations. For example, it can arrange aromatherapy or hot stone massage.

[0054] (Example 2) The private travel concierge system according to an embodiment of the present invention proposes optimal travel plans based on a user's wellness information and hobbies and preferences, and supports reservations for accommodations and transportation. This allows the private travel concierge system to easily provide users with a trip perfectly suited to their preferences.

[0055] A private travel concierge system according to an embodiment includes a wellness information collection unit, a favorites information collection unit, a travel plan proposal unit, and a reservation support unit. The wellness information collection unit collects wellness information about a user. For example, it collects wellness information from HELPO and analyzes the user's health status, exercise habits, and dietary preferences. The wellness information collection unit can also collect data from a wearable device to monitor the user's health status in real time. For example, it can collect heart rate, step count, and sleep data from the wearable device to evaluate the user's health status. The wellness information collection unit can also collect the user's medical records and comprehensively evaluate the user's health status. For example, it can perform a detailed analysis of the user's health status based on data provided by medical institutions. The favorites information collection unit collects the user's favorites. For example, it can collect information registered as favorites on Tabelog, travel sites, hobby blogs, etc., and analyze the user's favorite restaurants, tourist spots, activities, etc. The favorites information collection unit can also collect the user's past travel history and reflect it in the travel plan. For example, the favorite information collection unit collects information about previously visited travel destinations, accommodations, and activities to understand the user's hobbies and preferences. Furthermore, the favorite information collection unit can collect information from the user's social media accounts and analyze the user's hobbies and preferences. For example, the favorite information collection unit can understand the user's interests based on photos and posts shared on social media. The travel plan proposal unit proposes travel plans based on the information collected by the wellness information collection unit and the favorite information collection unit. For example, the travel plan proposal unit provides plans tailored to the user's needs, such as a relaxing hot spring trip that takes health into consideration or an adventure trip packed with activities tailored to the user's hobbies. Furthermore, the travel plan proposal unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an optimal travel plan based on the user's health condition and hobbies and preferences. For example, the generation AI analyzes the user's health condition and hobbies and preferences and proposes an optimal travel plan. Furthermore, the travel plan proposal unit can also generate a more satisfying travel plan based on the user's past travel reviews. For example, the travel plan proposal unit proposes a plan that includes travel destinations and activities that have been highly rated in the past.The reservation support unit supports reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. For example, it proposes hotels and inns that are best suited to the travel plan selected by the user and handles the reservation procedures on the user's behalf. The reservation support unit also arranges Shinkansen and airplane tickets to ensure the user can enjoy their trip smoothly. Furthermore, the reservation support unit can also suggest spa and relaxation facilities at the travel destination based on the user's health condition and stress level and support reservations. For example, it can arrange aromatherapy and hot stone massage. In this way, the private travel concierge system according to the embodiment can easily provide the user with a trip that perfectly suits their preferences. For example, by being proposed a travel plan that takes health condition into consideration, the user can enjoy the trip with peace of mind. Furthermore, by being proposed a travel plan that matches the user's hobbies and preferences, the user can have a more fulfilling travel experience. Furthermore, by supporting reservations for accommodations and transportation, travel preparations can be made hassle-free.

[0056] The wellness information collection unit can customize menus offered at restaurants at travel destinations based on the user's health condition and dietary restrictions. The wellness information collection unit customizes menus offered at restaurants at travel destinations based on the user's health condition and dietary restrictions, for example. For example, it suggests low-carb menus for a diabetic user. The wellness information collection unit also provides allergy-friendly menus at restaurants at travel destinations based on the user's allergy information. For example, it suggests gluten-free and nut-free menus. The wellness information collection unit also customizes meal menus at travel destinations taking into account the user's nutritional balance. For example, it suggests menus rich in vitamins and minerals. In this way, health management is supported by providing menus that suit the user's health condition and dietary restrictions.

[0057] The wellness information collection unit can suggest an exercise program for the travel destination based on the user's exercise habits and fitness level. The wellness information collection unit suggests an exercise program for the travel destination based on the user's exercise habits and fitness level, for example. For example, it suggests yoga classes or gym use. The wellness information collection unit also suggests walking routes and running courses at the travel destination based on the user's health goals. For example, it suggests walking tours around tourist spots. The wellness information collection unit also arranges personal training sessions at the travel destination based on the user's fitness data. For example, it provides training plans in cooperation with local trainers. This supports health management by providing exercise programs tailored to the user's exercise habits and fitness level.

[0058] The wellness information collection unit can analyze the user's stress level using the emotion estimation function and suggest a relaxing travel plan. The wellness information collection unit, for example, uses the emotion estimation function to analyze the user's stress level and suggest a relaxing travel destination. For example, it may suggest a hot spring resort or a resort area rich in nature. The wellness information collection unit may also suggest a relaxation activity based on the user's stress level. For example, it may arrange a massage or a meditation session. The wellness information collection unit may also suggest a travel plan that is effective for reducing stress based on the user's emotion data. For example, it may suggest a retreat or spa experience in a tranquil environment. In this way, stress reduction is supported by providing a relaxing travel plan that matches the user's stress level.

[0059] The reservation support unit can suggest spa and relaxation facilities at travel destinations based on the user's health condition and stress level, and support reservations. The reservation support unit, for example, suggests spa and relaxation facilities at travel destinations based on the user's health condition and stress level, and support reservations. For example, it arranges aromatherapy and hot stone massages. The reservation support unit also suggests relaxation programs at travel destinations based on the user's wellness information. For example, it arranges detox programs and yoga retreats. The reservation support unit also promotes use of relaxation facilities at travel destinations based on the user's health data. For example, it suggests spa packages and relaxation courses. In this way, spa and relaxation facilities based on the user's health condition and stress level are suggested and reservations are supported, thereby enhancing the relaxation effect.

[0060] The reservation support unit can automatically generate a health checklist for during travel based on the health data, thereby supporting health management. The reservation support unit automatically generates a health checklist necessary during travel, for example, based on the user's health data. For example, it provides a medication schedule or health checkup reminders. The reservation support unit also suggests a list of items necessary for health management during travel, based on the user's health condition. For example, it provides a list of supplements and health products. The reservation support unit also provides advice useful for health management during travel, based on the user's health data. For example, it suggests appropriate hydration and rest timing. In this way, a health checklist is automatically generated based on the user's health data, supporting health management during travel.

[0061] The reservation support unit can use the emotion estimation function to monitor mood fluctuations during the trip in real time and suggest appropriate activities. The reservation support unit, for example, monitors the user's emotion data in real time and suggests activities that correspond to mood fluctuations. For example, it suggests relaxing activities or energetic activities. The reservation support unit also uses the emotion estimation function to analyze the user's mood fluctuations and suggest activities that are effective in reducing stress during the trip. For example, it suggests nature walks or art therapy. The reservation support unit also suggests activities in real time that correspond to mood fluctuations during the trip based on the user's emotion data. For example, it suggests relaxation activities when the user is feeling depressed. In this way, by suggesting activities in real time that correspond to the user's mood fluctuations, satisfaction during the trip is improved.

[0062] The travel plan proposal unit can propose a travel plan that includes dietary restrictions and allergy accommodations according to the user's health condition. The travel plan proposal unit includes appropriate meal options in the travel plan based on, for example, the user's health condition and dietary restrictions. For example, it proposes low-carb menus and gluten-free menus. The travel plan proposal unit also includes allergy-friendly meal options in the travel plan based on the user's allergy information. For example, it proposes nut-free and dairy-free menus. The travel plan proposal unit also takes into account the user's nutritional balance and includes healthy meal options in the travel plan. For example, it proposes menus rich in vitamins and minerals. This supports health management by providing a travel plan that includes dietary restrictions and allergy accommodations according to the user's health condition.

[0063] The travel plan suggestion unit can generate a more satisfying travel plan based on past travel ratings. The travel plan suggestion unit, for example, analyzes the user's past travel ratings and generates a travel plan that incorporates elements that generated high satisfaction. For example, it proposes a plan that includes tourist spots and activities that were highly rated in the past. The travel plan suggestion unit also proposes new travel destinations similar to travel destinations that generated high satisfaction in the past based on the user's travel rating data. For example, it proposes travel destinations with the same theme or atmosphere. The travel plan suggestion unit also analyzes the user's past travel ratings and generates a travel plan that enhances elements that generated high satisfaction. For example, it proposes a plan that enhances specific activities and services. This improves the travel experience by providing a more satisfying travel plan based on the user's past travel ratings.

[0064] The travel plan proposal unit can use the emotion estimation function to predict emotions during the trip and suggest activities at the optimal time. For example, the travel plan proposal unit uses the emotion estimation function to predict the user's emotions during the trip and suggest activities at the optimal time. For example, when the user wants to relax, it suggests a spa or massage. The travel plan proposal unit also predicts emotional changes during the trip based on the user's emotion data and suggests appropriate activities. For example, it suggests an adventure activity when the user is feeling energetic. The travel plan proposal unit also uses the emotion estimation function to monitor the user's emotions during the trip in real time and suggest activities at the optimal time. For example, it suggests a refreshing activity when the emotion score is low. In this way, the travel experience is improved by predicting the user's emotions during the trip and suggesting activities at the optimal time.

[0065] The travel plan proposal unit can propose a travel plan that incorporates special experiences tailored to the user's hobbies. The travel plan proposal unit incorporates special experiences into the travel plan based on the user's hobbies and preferences, for example. For example, a local cooking class is proposed for a user who loves cooking. The travel plan proposal unit also incorporates customized special experiences into the travel plan based on the user's favorite information. For example, a local art tour is proposed for a user who loves art. The travel plan proposal unit also incorporates unique experiences into the travel plan that match the user's hobbies and preferences. For example, a local hiking tour is proposed for a user who loves the outdoors. In this way, the travel experience is improved by providing a travel plan that incorporates special experiences tailored to the user's hobbies.

[0066] The travel plan suggestion unit can suggest travel plans that provide cultural experiences including events for interacting with local people. The travel plan suggestion unit, for example, includes events for interacting with local people in the user's travel plan. For example, it suggests homestays or dinner events where users can enjoy local home cooking. The travel plan suggestion unit also customizes events for interacting with local people in the travel plan based on the user's hobbies and preferences. For example, it suggests workshops with local artists. The travel plan suggestion unit also includes events in which users can experience local culture and traditions in the user's travel plan. For example, it suggests local festivals or events where users can experience traditional crafts. This improves the travel experience by providing cultural experiences that include events for interacting with local people.

[0067] The travel plan proposal unit can use the emotion estimation function to make real-time adjustments to the plan in response to emotional changes during the trip. For example, the travel plan proposal unit uses the emotion estimation function to monitor emotional changes of the user during the trip in real time and adjust the plan. For example, when the user is feeling depressed, a relaxation activity is added. The travel plan proposal unit also makes real-time adjustments to the plan in response to emotional changes during the trip based on the user's emotion data. For example, when the user is feeling energetic, an adventure activity is added. The travel plan proposal unit also uses the emotion estimation function to monitor emotional changes during the trip and make real-time adjustments to the plan to match the user's mood. For example, when the emotion score is low, a refreshing activity is added. In this way, real-time adjustments to the plan in response to emotional changes during the trip improve the travel experience.

[0068] The reservation support unit can give priority to suggesting accommodations that take health into consideration. For example, the reservation support unit gives priority to suggesting non-smoking rooms or allergy-friendly rooms based on the user's health condition. For example, it suggests allergy-friendly rooms to users with allergies. The reservation support unit also suggests health-friendly accommodations based on the user's health data. For example, it suggests facilities that offer non-smoking rooms or hypoallergenic bedding. The reservation support unit also suggests accommodations that are suitable for health management according to the user's health condition. For example, it suggests hotels that offer fitness facilities or healthy food menus. In this way, health management is supported by giving priority to suggesting accommodations that take the user's health condition into consideration.

[0069] The reservation support unit can analyze reviews of accommodations and suggest facilities that best match the user's tastes and preferences. For example, the reservation support unit analyzes reviews of accommodations and suggests facilities that best match the user's tastes and preferences. For example, a highly rated resort hotel is suggested for a user who loves resorts. The reservation support unit also customizes and suggests accommodation reviews based on the user's tastes and preferences. For example, a hotel with an art gallery is suggested for a user who loves art. The reservation support unit also analyzes reviews of accommodations and suggests facilities that match the user's tastes and preferences. For example, a hotel with a highly rated restaurant is suggested for a user who loves food. In this way, the travel experience is improved by suggesting accommodations that best match the user's tastes and preferences.

[0070] The reservation support unit can use the emotion estimation function to identify and recommend the accommodation where the user can be most relaxed. The reservation support unit, for example, uses the emotion estimation function to identify and recommend the accommodation where the user can be most relaxed. For example, it may suggest facilities with a high relaxing effect based on past emotion data. The reservation support unit also identifies and recommends the accommodation where the user can be most relaxed based on the user's emotion data. For example, it may suggest hotels with excellent spa and relaxation facilities. The reservation support unit also uses the emotion estimation function to identify and recommend the accommodation where the user can be most relaxed in real time. For example, it may preferentially recommend facilities with a high emotion score. This allows the travel experience to be improved by identifying and recommending the accommodation where the user can be most relaxed.

[0071] The reservation support unit can take into account past accommodation history and utilize repeat customer benefits. For example, the reservation support unit suggests accommodations utilizing repeat customer benefits based on the user's past accommodation history. For example, it suggests repeat customer benefits for hotels where the user has stayed in the past. The reservation support unit also takes into account the user's accommodation history and suggests accommodations where repeat customer benefits can be used. For example, it suggests repeat customer benefits for hotels that have been highly rated in the past. The reservation support unit also analyzes the user's past accommodation history and suggests accommodations utilizing repeat customer benefits. For example, it suggests hotels that offer extensive repeat customer benefits. In this way, the travel experience is improved by taking into account the user's past accommodation history and utilizing repeat customer benefits.

[0072] The reservation support unit can request special amenities tailored to the user's hobbies. For example, the reservation support unit requests special amenities when reserving accommodation based on the user's hobbies and preferences. For example, a user who likes sports may request sports equipment. The reservation support unit also requests customized amenities when reserving accommodation based on the user's favorite information. For example, a user who likes music may request musical instruments. The reservation support unit also requests special amenities when reserving accommodation based on the user's hobbies and preferences. For example, a user who likes art may request an art set. In this way, the travel experience is improved by requesting special amenities tailored to the user's hobbies.

[0073] The reservation support unit can use the emotion estimation function to make suggestions that take emotional changes into consideration when selecting accommodations. The reservation support unit, for example, uses the emotion estimation function to suggest accommodations that take the user's emotional changes into consideration. For example, it suggests facilities that have a high relaxing effect based on past emotional data. The reservation support unit also suggests accommodations that respond to emotional changes based on the user's emotional data. For example, it suggests relaxation facilities that are effective in reducing stress. The reservation support unit also uses the emotion estimation function to monitor the user's emotional changes in real time and suggest the most suitable accommodations. For example, it prioritizes suggesting facilities with a high emotional score. This improves the travel experience by suggesting accommodations that take the user's emotional changes into consideration.

[0074] The reservation support unit can suggest seats that take health into consideration. For example, the reservation support unit prioritizes suggestions of seats with ample legroom or quiet seats based on the user's health condition. For example, it suggests seats with ample legroom to prevent swelling of the legs on long flights. The reservation support unit also suggests health-conscious seats based on the user's health data. For example, it suggests seats where the user can relax in a quiet environment. The reservation support unit also suggests seats that are suitable for health management according to the user's health condition. For example, it suggests seats that offer reclining seats or extra legroom. In this way, the reservation support unit supports a comfortable journey by suggesting seats that take the user's health condition into consideration.

[0075] The reservation support unit can analyze reviews of transportation means and suggest transportation means that best match the user's preferences. For example, the reservation support unit analyzes reviews of transportation means and suggests transportation means that best match the user's preferences. For example, it suggests airlines that offer comfortable seats and excellent service. The reservation support unit also customizes and suggests reviews of transportation means based on the user's preferences. For example, it suggests transportation means with excellent entertainment options. The reservation support unit also analyzes reviews of transportation means and suggests transportation means that match the user's preferences. For example, it suggests transportation means with highly rated meal services. In this way, the reservation support unit supports comfortable travel by suggesting transportation means that best match the user's preferences.

[0076] The reservation support unit can use the emotion estimation function to identify and suggest the transportation means that will allow the user to travel most comfortably. The reservation support unit, for example, uses the emotion estimation function to identify and suggest the transportation means that will allow the user to travel most comfortably. For example, it may suggest a transportation means with high comfort based on past emotion data. The reservation support unit also identifies and suggests a transportation means with high comfort based on the user's emotion data. For example, it may suggest a transportation means that offers relaxing seats and excellent service. The reservation support unit also uses the emotion estimation function to identify and suggest the transportation means that will allow the user to travel most comfortably in real time. For example, it may preferentially suggest transportation means with a high emotion score. In this way, the reservation support unit supports comfortable travel by identifying and suggesting the transportation means that will allow the user to travel most comfortably.

[0077] The reservation support unit can propose an optimal route by taking into account past travel history. The reservation support unit proposes an optimal route, for example, based on the user's past travel history. For example, it proposes an efficient travel plan by taking into account the means of transportation and routes used in the past. The reservation support unit also analyzes the user's travel history and proposes an optimal route based on routes that have generated high satisfaction in the past. For example, it re-proposes a route that provided a comfortable trip. The reservation support unit also builds a system that proposes an optimal route by taking into account the user's past travel history. For example, it proposes the shortest or cheapest route based on past travel data. In this way, efficient travel is supported by proposing an optimal route by taking into account the user's past travel history.

[0078] The reservation support unit can request special services tailored to the user's hobbies and preferences from the means of transportation. For example, the reservation support unit requests special services when reserving a means of transportation based on the user's hobbies and preferences. For example, a user who loves movies may request enhanced in-flight entertainment. The reservation support unit also requests customized services when reserving a means of transportation based on the user's favorite information. For example, a user who loves gourmet food may request a special meal. The reservation support unit also requests special services tailored to the user's hobbies and preferences when reserving a means of transportation. For example, a user who loves music may request in-flight music service. In this way, requesting special services tailored to the user's hobbies supports a comfortable journey.

[0079] The reservation support unit can use the emotion estimation function to make suggestions that take emotional changes into consideration when selecting a means of transportation. The reservation support unit, for example, uses the emotion estimation function to suggest a means of transportation that takes into consideration the user's emotional changes. For example, it suggests a highly comfortable means of transportation based on past emotional data. The reservation support unit also suggests a means of transportation that corresponds to the user's emotional changes based on the user's emotional data. For example, it suggests a means of transportation that offers a relaxation service that is effective in reducing stress. The reservation support unit also uses the emotion estimation function to monitor the user's emotional changes in real time and suggest the optimal means of transportation. For example, it prioritizes suggesting means of transportation with a high emotional score. In this way, the reservation support unit supports comfortable travel by suggesting means of transportation that take into consideration the user's emotional changes.

[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 private travel concierge system can further include a health monitoring unit to support the user's health management during the trip. For example, the health monitoring unit monitors the user's health condition in real time and issues an alert if an abnormality is detected. The health monitoring unit can also provide advice necessary for health management during the trip based on the user's health data. For example, it can suggest appropriate hydration and rest times. Furthermore, the health monitoring unit can adjust the user's diet and exercise plan during the trip according to the user's health condition. For example, it can suggest a lighter exercise plan if the user is not feeling well. In this way, the system can support health management during the trip by monitoring the user's health condition in real time and providing appropriate advice.

[0082] The private travel concierge system may further include a safety information provider to ensure the user's safety during their trip. The safety information provider, for example, collects the latest safety information for the travel destination and provides it to the user. The safety information provider can also provide emergency contact information and evacuation shelters based on the user's current location. For example, in the event of a natural disaster or a deterioration in public safety, the safety information provider can quickly provide evacuation shelters. Furthermore, the safety information provider can provide safety advice based on the user's travel plan. For example, the safety information provider can suggest that the user refrain from going out at night. This ensures the user's safety during their trip and allows them to enjoy their trip with peace of mind.

[0083] The private travel concierge system can further include a translation support unit to support communication during the user's trip. The translation support unit, for example, translates the language of the travel destination in real time and provides it to the user. The translation support unit can also learn necessary phrases and words in advance based on the user's travel plan. For example, it can provide phrases for ordering at a restaurant or for giving directions. Furthermore, the translation support unit can provide a speech translation function to facilitate communication between the user. For example, it can translate conversations with local people in real time. This supports communication during the user's trip and allows them to enjoy their trip without language barriers.

[0084] The private travel concierge system can further include an entertainment provider to enhance the user's entertainment during their trip. The entertainment provider provides, for example, information on events and tourist spots at the travel destination. The entertainment provider can also propose customized entertainment plans based on the user's hobbies and preferences. For example, it can provide local concert information to a user who loves music. Furthermore, the entertainment provider can also suggest local cultural experiences and activities based on the user's travel plan. For example, it can suggest local festivals and traditional craft experiences. This can enhance the user's entertainment during their trip and provide a richer travel experience.

[0085] The private travel concierge system can further include an eco-plan suggestion unit to reduce the environmental impact of the user's travel. The eco-plan suggestion unit, for example, suggests environmentally friendly travel plans. The eco-plan suggestion unit can also suggest eco-friendly accommodations and transportation methods based on the user's travel plans. For example, it can suggest hotels that use renewable energy or electric vehicles. Furthermore, the eco-plan suggestion unit can provide advice to reduce the environmental impact of the user's travel. For example, it can suggest reducing the use of plastic. This reduces the environmental impact of the user's travel and supports sustainable travel.

[0086] The private travel concierge system can also estimate the user's emotions and provide music that matches their mood during the trip. For example, if the user wants to relax, it can suggest relaxing music. Or, if the user is feeling energetic, it can suggest upbeat music. Furthermore, it can automatically generate a playlist that matches the user's mood during the trip based on the user's emotional data. For example, if the emotional score is high, it can suggest positive music. This allows the system to provide music that matches the user's emotions, thereby enhancing their mood during the trip.

[0087] The private travel concierge system can also estimate the user's emotions and suggest meals that match their mood during the trip. For example, if the user wants to relax, it can suggest meals that have a relaxing effect. Also, if the user is feeling energetic, it can suggest nutritious meals. Furthermore, it can customize a meal menu based on the user's emotional data to match their mood during the trip. For example, if the emotional score is high, it can suggest meals that will help them maintain a positive mood. In this way, it can improve the user's mood during the trip by providing meals that match their emotions.

[0088] The private travel concierge system can also estimate the user's emotions and suggest activities that match their mood during the trip. For example, if the user wants to relax, it can suggest relaxation activities. Or, if the user is feeling energetic, it can suggest adventure activities. Furthermore, it can customize an activity plan based on the user's emotional data to match their mood during the trip. For example, if the emotional score is high, it can suggest activities to maintain a positive mood. This allows the system to provide activities that match the user's emotions and improve their mood during the trip.

[0089] The private travel concierge system can also estimate the user's emotions and suggest tourist spots that match their mood during the trip. For example, if the user wants to relax, quiet tourist spots can be suggested. On the other hand, if the user is feeling energetic, active tourist spots can be suggested. Furthermore, tourist spots can be customized to match the user's mood during the trip based on the user's emotional data. For example, if the emotional score is high, tourist spots that will help maintain a positive mood can be suggested. In this way, by providing tourist spots that match the user's emotions, the system can improve the user's mood during the trip.

[0090] The private travel concierge system can also estimate the user's emotions and suggest accommodations that match their mood during the trip. For example, when a user wants to relax, it can suggest accommodations with plenty of relaxation facilities. On the other hand, when a user is feeling energetic, it can suggest accommodations with plenty of activities. Furthermore, it can customize accommodations to match the user's mood during the trip based on the user's emotional data. For example, when the emotional score is high, it can suggest accommodations that will help them maintain a positive mood. In this way, by providing accommodations that match the user's emotions, it is possible to improve the user's mood during the trip.

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

[0092] Step 1: The wellness information collection unit collects the user's wellness information. For example, it collects wellness information from HELPO and analyzes the user's health status, exercise habits, and dietary preferences. It can also collect data from wearable devices to understand the user's health status in real time. For example, it can collect heart rate, step count, and sleep data from wearable devices to evaluate the user's health status. It can also collect the user's medical records and comprehensively evaluate the user's health status. For example, it can analyze the user's health status in detail based on data provided by medical institutions. Step 2: The favorite information collection unit collects the user's favorite information. For example, it collects information registered as favorites on Tabelog, travel sites, hobby blogs, etc., and analyzes the user's favorite restaurants, tourist spots, activities, etc. It can also collect the user's past travel history and reflect it in travel plans. For example, it collects information on past travel destinations, accommodations, and activities to understand the user's hobbies and preferences. It can also collect information from the user's social media account and analyze the hobbies and preferences. For example, it can understand the user's interests based on photos and posts shared on social media. Step 3: The travel plan suggestion unit proposes travel plans based on the information collected by the wellness information collection unit and the favorite information collection unit. For example, it provides plans tailored to the user's needs, such as a relaxing hot spring trip that takes health into consideration or an adventure trip packed with activities tailored to the user's hobbies. It also uses generation AI (e.g., text generation AI or multimodal generation AI) to generate optimal travel plans based on the user's health condition and hobbies and preferences. For example, the generation AI analyzes the user's health condition and hobbies and preferences and proposes optimal travel plans. It can also generate more satisfying travel plans based on the user's past travel ratings. For example, it proposes plans that include travel destinations and activities that have been highly rated in the past. Step 4: The reservation support unit helps users make reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. For example, it can suggest hotels and inns that best fit the user's selected travel plan and handle the reservation process. It can also arrange bullet train and airplane tickets to ensure the user can enjoy their trip smoothly. It can also suggest spa and relaxation facilities at the travel destination based on the user's health condition and stress level and support reservations. For example, it can arrange aromatherapy or hot stone massage.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 wellness information collection unit that collects wellness information; a favorite information collection unit that collects favorite information; a travel plan suggestion unit that suggests a travel plan based on the information collected by the wellness information collection unit and the favorite information collection unit; a reservation support unit that supports reservations for accommodations and transportation based on the travel plan proposed by the travel plan proposal unit. A system characterized by:

2. The wellness information collection unit Customize restaurant menus at your destination based on your health and dietary restrictions 2. The system of claim 1.

3. The wellness information collection unit Recommends exercise programs for your trip based on your exercise habits and fitness level 2. The system of claim 1.

4. The wellness information collection unit Analyze your stress levels and suggest relaxing travel plans 2. The system of claim 1.

5. The reservation support unit Recommend and assist with booking spa and relaxation options at your destination based on your health and stress levels 2. The system of claim 1.

6. The reservation support unit Automatically generate a health checklist for travel based on health data to assist with health management 2. The system of claim 1.

7. The reservation support unit Real-time monitoring of mood fluctuations during travel and suggesting appropriate activities 2. The system of claim 1.

8. The travel plan proposal unit Propose the above travel plan, including dietary restrictions and allergies according to health conditions 2. The system of claim 1.

Images