System

Abstract

An object of a system according to an embodiment is to generate a travel plan according to an individual's preference and distribute travel dates and travel destinations according to a reservation status.SOLUTION: A system includes an analysis part, a generation part, a reservation analysis part, and a navigation part. The analysis unit analyzes a user's preference. The generation unit generates a travel plan based on the user's preference analyzed by the analysis unit. The reservation analyzer analyzes the reservation status. The navigation unit distributes travel dates and travel destinations based on the reservation status analyzed by the reservation analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to provide integrated travel plans tailored to individual preferences, and travel dates and destinations are not adequately distributed according to reservation status.

[0005] The system according to the embodiment aims to generate travel plans according to individual preferences and distribute travel dates and destinations according to the reservation status. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a reservation analysis unit, and a navigation unit. The analysis unit analyzes user preferences. The generation unit generates a travel plan based on the user preferences analyzed by the analysis unit. The reservation analysis unit analyzes reservation status. The navigation unit distributes travel dates and travel destinations based on the reservation status analyzed by the reservation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate travel plans according to individual preferences and distribute travel dates and destinations according to the reservation status. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The travel plan providing system according to an embodiment of the present invention is a system that provides all elements of a trip that are tailored to individual preferences in an integrated manner. This system uses a generation AI to analyze the user's preferences and wishes, and then generates and provides the optimal travel plan based on that. As a result, the travel plan providing system can provide plans at lower prices than those of traditional travel agencies, while also expanding the range of products it handles, which is expected to increase sales. It also contributes to solving social issues such as diversifying travel dates and destinations depending on reservation status.

[0029] The travel plan providing system according to the embodiment includes an analysis unit, a generation unit, a reservation analysis unit, and a navigation unit. The analysis unit analyzes a user's preferences. For example, the analysis unit can analyze the user's past behavioral history, survey results, and social media posts. The analysis unit can also consider the user's health condition and allergy information. The generation unit generates a travel plan based on the user's preferences analyzed by the analysis unit. For example, if a user inputs a preference such as "I want to relax in a place rich in nature," the generation unit can propose a travel plan that integrates transportation, accommodations, tourist attractions, etc. based on the preference. The generation unit can also generate a group travel plan taking into account the preferences of the user's family and friends. The reservation analysis unit analyzes reservation status. For example, the reservation analysis unit can analyze the availability of accommodations and the reservation status of transportation. The reservation analysis unit can also analyze past reservation data and predict optimal travel dates to avoid crowds. The navigation unit distributes travel dates and travel destinations based on the reservation status analyzed by the reservation analysis unit. For example, if a particular tourist spot or accommodation is crowded, the navigation unit will suggest other possible locations or travel dates. The navigation unit can also provide VR guidance for station transfers and walking routes. As a result, the travel plan providing system according to the embodiment can provide an integrated travel plan tailored to individual preferences and distribute travel dates and destinations according to reservation status.

[0030] The analysis unit analyzes the user's past travel history and social media posts to propose more personalized travel plans. For example, the generation AI analyzes the user's past travel history and proposes travel plans tailored to the user's preferences based on data such as places visited, length of stay, and means of transportation used. For example, the analysis unit analyzes trends in tourist spots visited in the past and proposes new similar places. The generation AI also analyzes the user's social media posts to extract the user's interests from the posted photos and comments. For example, for a user who posts many photos of natural scenery, the generation AI proposes tourist spots rich in nature. The analysis unit also generates more detailed personalized travel plans by integrating the user's past travel history and social media posts. For example, the generation AI customizes the next travel plan based on preferences and impressions from past trips. This allows the analysis of the user's past travel history and social media posts to propose more personalized travel plans.

[0031] The analysis unit can generate a health-conscious travel plan by taking into account the user's health condition and allergy information. For example, the analysis unit uses a generation AI to analyze the user's health condition and propose an appropriate travel plan. For example, for a user with heart disease, the analysis unit proposes a plan that avoids excessive exercise. The analysis unit also uses a generation AI to consider the user's allergy information and propose restaurants and accommodations that avoid ingredients that may cause allergies. For example, for a user with a nut allergy, the analysis unit selects restaurants that offer nut-free menus. The analysis unit also uses a generation AI to comprehensively analyze the user's health condition and allergy information and generate a health-conscious travel plan. For example, based on the results of a health check, the analysis unit proposes a plan that takes into account the appropriate amount of exercise and meal content. In this way, a health-conscious travel plan can be generated by taking into account the user's health condition and allergy information.

[0032] The generation unit can generate a group travel plan that also takes into account the preferences of the user's family and friends. For example, the generation AI analyzes the preferences of the user's family and friends and proposes a group travel plan that everyone can enjoy. For example, for a family with children, it proposes travel destinations with plenty of activities for children. The generation unit also analyzes the past travel history of the user's family and friends and generates a group travel plan based on their common interests. For example, if everyone loves nature, it will propose tourist spots rich in nature. The generation unit also considers the health status and allergy information of the user's family and friends and proposes a travel plan that everyone can enjoy without worry. For example, if there is a member with an allergy, it will select a restaurant that is allergy-friendly. In this way, a group travel plan can be generated by taking into account the preferences of the user's family and friends.

[0033] The generation unit can suggest optimal travel destinations and activities by taking into account the season and weather. For example, the generation AI analyzes seasonal and weather data to suggest optimal travel destinations. For example, it will suggest cool highland areas in the summer and hot spring resorts in the winter. The generation unit also uses the generation AI to suggest activities based on the weather during the trip, based on the weather forecast. For example, it will suggest indoor activities on rainy days and outdoor activities on sunny days. The generation unit also analyzes information on seasonal events and festivals to suggest special experiences at the travel destination. For example, it will suggest cherry blossom viewing spots during cherry blossom season and autumn foliage spots in the fall. This allows the generation unit to suggest optimal travel destinations and activities by taking into account the season and weather.

[0034] The reservation analysis unit analyzes reservation status in real time and can suggest the optimal reservation timing. For example, the generation AI in the reservation analysis unit analyzes the reservation status of accommodation facilities and transportation facilities in real time and suggests the optimal reservation timing. For example, it suggests a time to make a reservation when there are fewer reservations and receive a discount. The reservation analysis unit also analyzes past reservation data and predicts the optimal reservation timing to avoid congestion. For example, it suggests a reservation timing that avoids busy periods based on past data. The reservation analysis unit also monitors reservation status in real time and immediately notifies if there is a cancellation and suggests the optimal reservation timing. For example, it immediately suggests a reservation if there is a cancellation at a popular accommodation facility. This allows the reservation status to be analyzed in real time and the optimal reservation timing to be suggested.

[0035] The reservation analysis unit can analyze past reservation data and predict the optimal travel date to avoid crowds. In the reservation analysis unit, for example, the generation AI analyzes past reservation data and predicts the optimal travel date to avoid crowds. For example, it suggests travel dates that avoid crowded times based on past data. In addition, the reservation analysis unit predicts the congestion status of specific tourist destinations and accommodation facilities based on past reservation data and suggests travel dates that avoid crowds. For example, it suggests times when popular tourist destinations are less crowded. In addition, the reservation analysis unit develops an algorithm that analyzes past reservation data and predicts travel dates that avoid crowds. For example, it predicts peak congestion times from past data and suggests travel dates that avoid those peaks. In this way, it is possible to predict the optimal travel dates to avoid crowds by analyzing past reservation data.

[0036] The reservation analysis unit can compare reservation status in different regions and suggest the optimal travel destination. For example, the generation AI analyzes the reservation status of accommodations and tourist attractions in different regions in real time and suggests the optimal travel destination. For example, it prioritizes suggestions of less crowded regions. The generation AI also compares reservation status in different regions and suggests the optimal travel destination that suits the user's preferences. For example, it suggests a region where you can stay more comfortably within the same budget. The reservation analysis unit also suggests travel destinations that avoid crowds based on the reservation status in different regions. For example, if a popular tourist destination is crowded, it will suggest another region with similar appeal. In this way, the generation AI can suggest the optimal travel destination by comparing the reservation status of different regions.

[0037] The reservation analysis unit can analyze the user's schedule and suggest the optimal travel date. In the reservation analysis unit, for example, the generation AI analyzes the user's schedule and suggests the optimal travel date. For example, it may suggest travel dates taking into account work or school holiday periods. In addition, the reservation analysis unit can comprehensively analyze the user's schedule and reservation status and suggest the optimal travel date. For example, it may suggest travel dates that avoid crowds in line with the user's schedule. In addition, the reservation analysis unit can develop an algorithm that allows the generation AI to suggest the optimal travel date based on the user's schedule. For example, it may analyze the user's schedule and reservation status in real time and suggest the optimal travel date. In this way, it is possible to suggest the optimal travel date by analyzing the user's schedule.

[0038] The navigation unit can analyze the user's movement history and suggest optimal transfer routes and walking routes. In the navigation unit, for example, the generation AI analyzes the user's past movement history and suggests the optimal transfer route. For example, it may prioritize suggestions of stations and lines that have been used in the past. In addition, the navigation unit can suggest the optimal walking route based on the user's movement history. For example, it may suggest routes that have been walked in the past or routes that suit the user's preferences. In addition, the navigation unit can analyze the user's movement history and suggest the optimal transfer route or walking route to avoid congestion. For example, it may suggest a route that avoids busy times based on past data. In this way, the optimal transfer route or walking route can be suggested by analyzing the user's movement history.

[0039] The navigation unit can provide customized VR guidance by taking into account the user's visual and auditory characteristics. For example, the generation AI in the navigation unit analyzes the user's visual characteristics and provides customized VR guidance that is easy on the eyes. For example, for a user with color blindness, it provides guidance with adjusted color contrast. The generation AI in the navigation unit also analyzes the user's auditory characteristics and provides customized VR guidance that is easy on the ears. For example, for a user with hearing impairments, it converts audio guidance into text and provides it. The generation AI in the navigation unit also analyzes the user's visual and auditory characteristics in an integrated manner and provides optimal VR guidance. For example, it provides guidance that takes both visual and auditory characteristics into consideration. This makes it possible to provide customized VR guidance by taking into account the user's visual and auditory characteristics.

[0040] The navigation unit can propose optimal travel routes that combine different means of transportation. For example, the generation AI analyzes different means of transportation and proposes optimal travel routes. For example, it proposes a route that combines trains and buses. The generation AI also proposes optimal routes that combine different means of transportation based on the user's travel history. For example, it prioritizes proposals for means of transportation that have been used in the past. The generation AI also analyzes the operating conditions of different means of transportation in real time and proposes optimal travel routes. For example, it proposes optimal transfer routes depending on the operating conditions. This makes it possible to propose optimal travel routes that combine different means of transportation.

[0041] The navigation unit monitors the user's physical condition while traveling and can suggest a route that suits their physical condition. For example, the generation AI in the navigation unit monitors the user's physical condition in real time and suggests a route that suits their physical condition. For example, it will suggest a route that includes breaks when the user is tired. The generation AI in the navigation unit also analyzes the user's physical condition data and suggests a route that takes their physical condition into consideration. For example, it will suggest a shorter route when they are not feeling well. The navigation unit also develops an algorithm that suggests the optimal route based on the user's physical condition. For example, it will adjust the route in real time depending on their physical condition. This makes it possible to monitor the user's physical condition and suggest a route that suits their physical condition.

[0042] The generation unit can analyze the user's transportation preferences and suggest the most suitable transportation method. For example, the generation AI in the generation unit analyzes the user's past transportation history and suggests transportation methods that suit their preferences. For example, it prioritizes suggestions of transportation methods that have been used in the past. The generation unit also suggests the most suitable transportation method based on the user's transportation preferences. For example, it suggests first class or business class for a user who prioritizes comfort. The generation AI in the generation unit also analyzes the user's transportation preferences and suggests environmentally friendly transportation methods. For example, it suggests eco-friendly transportation methods. In this way, the generation unit can analyze the user's transportation preferences and suggest the most suitable transportation method.

[0043] The generation unit suggests activities for the user while traveling, enabling the user to make effective use of their travel time. For example, the generation AI of the generation unit suggests activities for the user while traveling, enabling the user to make effective use of their travel time. For example, the generation AI suggests audiobooks and podcasts that can be enjoyed while traveling. The generation unit also suggests a plan for making effective use of travel time based on the user's activities while traveling. For example, it suggests exercises and stretches that can be done while traveling. The generation unit also suggests activities for making effective use of travel time by analyzing the user's activities while traveling. For example, it suggests online courses and workshops that can be learned while traveling. This allows the generation AI to suggest activities for the user while traveling, enabling the user to make effective use of their travel time.

[0044] The generation unit can propose an optimal travel plan that combines different means of transportation. For example, the generation AI analyzes different means of transportation and proposes an optimal travel plan. For example, it proposes a route that combines trains and buses. The generation unit also proposes an optimal plan that combines different means of transportation based on the user's travel history. For example, it prioritizes proposals for means of transportation that have been used in the past. The generation unit also analyzes the operating status of different means of transportation in real time and proposes an optimal travel plan. For example, it proposes an optimal transfer route depending on the operating status. This makes it possible to propose an optimal travel plan that combines different means of transportation.

[0045] The generation unit can analyze the user's needs while traveling and suggest services that can be used while traveling. For example, the generation AI in the generation unit analyzes the user's needs while traveling and suggest services that can be used while traveling. For example, it suggests Wi-Fi services and charging spots that can be used while traveling. The generation unit also uses the generation AI to suggest entertainment services that can be used while traveling based on the user's needs while traveling. For example, it suggests movies and music that can be enjoyed while traveling. The generation unit also uses the generation AI to analyze the user's needs while traveling and suggest comfortable services that can be used while traveling. For example, it suggests relaxation services and massages that can be used while traveling. In this way, the generation unit can analyze the user's needs while traveling and suggest services that can be used while traveling.

[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 analysis unit can also analyze a user's hobbies and interests and suggest travel plans based on specific themes. For example, a plan to visit historical sites and museums can be suggested to a user who loves history. A gourmet tour to enjoy local specialties can also be suggested to a user who is interested in food. Furthermore, an active travel plan including hiking and camping can be suggested to a user who loves the outdoors. This makes it possible to provide themed trips that match the user's hobbies and interests.

[0048] The analysis unit not only analyzes a user's past travel history and social media posts, but also the websites the user has visited and search history. For example, it can suggest travel destinations that the user might be interested in based on data from travel blogs and tourist information sites that the user frequently visits. It can also analyze the keywords the user has searched for to suggest related tourist spots and activities. It can also analyze the content of videos and images the user has viewed to suggest visually appealing travel plans. This allows for a comprehensive analysis of the user's online behavior and provides more accurate travel plans.

[0049] The analysis unit not only takes into account the user's health condition and allergy information, but can also analyze the user's fitness level and exercise habits. For example, an active travel plan can be suggested to a user who exercises regularly. It can also suggest a healthy travel plan that incorporates light exercise to a user who is not getting enough exercise. Furthermore, it can analyze the user's sleep patterns and stress levels to suggest a relaxing travel plan. This makes it possible to provide travel plans that take into account the user's overall health.

[0050] The generation unit can generate a travel plan that takes into account not only the preferences of the user's family and friends, but also the preferences and needs of the user's pet. For example, it can suggest pet-friendly accommodations and restaurants. It can also suggest activities and tourist spots that can be enjoyed with pets. It can also suggest pet-friendly travel plans that take into account the pet's health condition and allergy information. This makes it possible to provide a travel plan that the whole family, including the pet, can enjoy.

[0051] The generation unit can propose travel plans that take into account not only the season and weather, but also special occasions such as the user's birthday or anniversary. For example, for a user's birthday, a travel plan including a special dinner or a surprise event can be proposed. For a wedding anniversary, it is also possible to propose romantic travel destinations and activities. Furthermore, for a user's child's birthday, a travel plan including children's activities and theme parks can be proposed. In this way, it is possible to provide travel plans for celebrating special occasions.

[0052] The reservation analysis unit not only analyzes reservation status in real time, but also suggests the optimal reservation timing taking into account the user's budget. For example, it can suggest the best time to make a reservation at the most advantageous time within the budget. It can also suggest accommodations and transportation methods that suit the user's budget. It can also provide discount and campaign information taking into account the user's budget. This makes it possible to provide the optimal reservation timing and plan that suits the user's budget.

[0053] The reservation analysis unit not only analyzes past reservation data, but also predicts the optimal travel date taking into account the user's preferences and interests. For example, if the user is interested in a particular event or festival, it can suggest a travel date that coincides with that event. Also, if the user wants to enjoy a particular season or scenery, it can suggest a travel date that coincides with that time of year. It can also suggest the optimal travel date taking into account the user's work or school schedule. This makes it possible to provide the optimal travel date that matches the user's preferences and schedule.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The analysis unit analyzes the user's preferences. For example, the analysis unit can analyze the user's past behavioral history, survey results, and social media posts. The analysis unit can also take into account the user's health condition and allergy information. Step 2: The generation unit generates a travel plan based on the user's preferences analyzed by the analysis unit. For example, if the user inputs a preference such as "I want to relax in a place rich in nature," the generation unit will propose a travel plan that integrates transportation, accommodations, tourist spots, etc. based on that preference. The generation unit can also generate a group travel plan that takes into account the preferences of the user's family and friends. Step 3: The reservation analysis unit analyzes the reservation status. For example, the reservation analysis unit can analyze the availability of accommodations and the reservation status of transportation. The reservation analysis unit can also analyze past reservation data and predict the optimal travel date to avoid crowds. Step 4: The navigation unit distributes travel dates and destinations based on the reservation status analyzed by the reservation analysis unit. For example, if a particular tourist spot or accommodation is crowded, the navigation unit suggests other possible destinations or dates. The navigation unit can also provide VR guidance for station transfers and walking routes.

[0056] (Example 2) The travel plan providing system according to an embodiment of the present invention is a system that provides all elements of a trip that are tailored to individual preferences in an integrated manner. This system uses a generation AI to analyze the user's preferences and wishes, and then generates and provides the optimal travel plan based on that. As a result, the travel plan providing system can provide plans at lower prices than those of traditional travel agencies, while also expanding the range of products it handles, which is expected to increase sales. It also contributes to solving social issues such as diversifying travel dates and destinations depending on reservation status.

[0057] The travel plan providing system according to the embodiment includes an analysis unit, a generation unit, a reservation analysis unit, and a navigation unit. The analysis unit analyzes a user's preferences. For example, the analysis unit can analyze the user's past behavioral history, survey results, and social media posts. The analysis unit can also consider the user's health condition and allergy information. The generation unit generates a travel plan based on the user's preferences analyzed by the analysis unit. For example, if a user inputs a preference such as "I want to relax in a place rich in nature," the generation unit can propose a travel plan that integrates transportation, accommodations, tourist attractions, etc. based on the preference. The generation unit can also generate a group travel plan taking into account the preferences of the user's family and friends. The reservation analysis unit analyzes reservation status. For example, the reservation analysis unit can analyze the availability of accommodations and the reservation status of transportation. The reservation analysis unit can also analyze past reservation data and predict optimal travel dates to avoid crowds. The navigation unit distributes travel dates and travel destinations based on the reservation status analyzed by the reservation analysis unit. For example, if a particular tourist spot or accommodation is crowded, the navigation unit will suggest other possible locations or travel dates. The navigation unit can also provide VR guidance for station transfers and walking routes. As a result, the travel plan providing system according to the embodiment can provide an integrated travel plan tailored to individual preferences and distribute travel dates and destinations according to reservation status.

[0058] The analysis unit analyzes the user's past travel history and social media posts to propose more personalized travel plans. For example, the generation AI analyzes the user's past travel history and proposes travel plans tailored to the user's preferences based on data such as places visited, length of stay, and means of transportation used. For example, the analysis unit analyzes trends in tourist spots visited in the past and proposes new similar places. The generation AI also analyzes the user's social media posts to extract the user's interests from the posted photos and comments. For example, for a user who posts many photos of natural scenery, the generation AI proposes tourist spots rich in nature. The analysis unit also generates more detailed personalized travel plans by integrating the user's past travel history and social media posts. For example, the generation AI customizes the next travel plan based on preferences and impressions from past trips. This allows the analysis of the user's past travel history and social media posts to propose more personalized travel plans.

[0059] The analysis unit can generate a health-conscious travel plan by taking into account the user's health condition and allergy information. For example, the analysis unit uses a generation AI to analyze the user's health condition and propose an appropriate travel plan. For example, for a user with heart disease, the analysis unit proposes a plan that avoids excessive exercise. The analysis unit also uses a generation AI to consider the user's allergy information and propose restaurants and accommodations that avoid ingredients that may cause allergies. For example, for a user with a nut allergy, the analysis unit selects restaurants that offer nut-free menus. The analysis unit also uses a generation AI to comprehensively analyze the user's health condition and allergy information and generate a health-conscious travel plan. For example, based on the results of a health check, the analysis unit proposes a plan that takes into account the appropriate amount of exercise and meal content. In this way, a health-conscious travel plan can be generated by taking into account the user's health condition and allergy information.

[0060] The analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest a travel plan that is optimal for that emotion. For example, the analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest a quiet natural environment if the user wants to relax. For example, a hot spring or resort area can be suggested for a user who is highly stressed. The analysis unit can also use the emotion estimation function to suggest activities that correspond to the user's emotional state. For example, adventure sports or theme parks can be suggested for an excited user. The analysis unit can also use the emotion estimation function to monitor the user's emotional state in real time and adjust the plan according to emotional changes during the trip. For example, if the user feels tired during the trip, a relaxing activity can be added. In this way, by analyzing the user's current emotional state, a travel plan that is optimal for that emotion can be suggested.

[0061] The generation unit can generate a group travel plan that also takes into account the preferences of the user's family and friends. For example, the generation AI analyzes the preferences of the user's family and friends and proposes a group travel plan that everyone can enjoy. For example, for a family with children, it proposes travel destinations with plenty of activities for children. The generation unit also analyzes the past travel history of the user's family and friends and generates a group travel plan based on their common interests. For example, if everyone loves nature, it will propose tourist spots rich in nature. The generation unit also considers the health status and allergy information of the user's family and friends and proposes a travel plan that everyone can enjoy without worry. For example, if there is a member with an allergy, it will select a restaurant that is allergy-friendly. In this way, a group travel plan can be generated by taking into account the preferences of the user's family and friends.

[0062] The generation unit can suggest optimal travel destinations and activities by taking into account the season and weather. For example, the generation AI analyzes seasonal and weather data to suggest optimal travel destinations. For example, it will suggest cool highland areas in the summer and hot spring resorts in the winter. The generation unit also uses the generation AI to suggest activities based on the weather during the trip, based on the weather forecast. For example, it will suggest indoor activities on rainy days and outdoor activities on sunny days. The generation unit also analyzes information on seasonal events and festivals to suggest special experiences at the travel destination. For example, it will suggest cherry blossom viewing spots during cherry blossom season and autumn foliage spots in the fall. This allows the generation unit to suggest optimal travel destinations and activities by taking into account the season and weather.

[0063] The generation unit can use the emotion estimation function to monitor the user's emotions during the trip in real time and adjust the plan as needed. For example, the generation unit can use the emotion estimation function to monitor the user's emotions during the trip in real time and add relaxing activities when stress levels rise, such as suggesting a spa or massage. The generation unit can also use the emotion estimation function to adjust the meal plan according to changes in the user's emotions during the trip, such as suggesting nutritious meals when the user is tired. The generation unit can also use the emotion estimation function to monitor the user's emotions during the trip and add active activities when emotions are positive, such as suggesting hiking or cycling. This allows the generation unit to monitor the user's emotions during the trip in real time and adjust the plan as needed.

[0064] The reservation analysis unit analyzes reservation status in real time and can suggest the optimal reservation timing. For example, the generation AI in the reservation analysis unit analyzes the reservation status of accommodation facilities and transportation facilities in real time and suggests the optimal reservation timing. For example, it suggests a time to make a reservation when there are fewer reservations and receive a discount. The reservation analysis unit also analyzes past reservation data and predicts the optimal reservation timing to avoid congestion. For example, it suggests a reservation timing that avoids busy periods based on past data. The reservation analysis unit also monitors reservation status in real time and immediately notifies if there is a cancellation and suggests the optimal reservation timing. For example, it immediately suggests a reservation if there is a cancellation at a popular accommodation facility. This allows the reservation status to be analyzed in real time and the optimal reservation timing to be suggested.

[0065] The reservation analysis unit can analyze past reservation data and predict the optimal travel date to avoid crowds. In the reservation analysis unit, for example, the generation AI analyzes past reservation data and predicts the optimal travel date to avoid crowds. For example, it suggests travel dates that avoid crowded times based on past data. In addition, the reservation analysis unit predicts the congestion status of specific tourist destinations and accommodation facilities based on past reservation data and suggests travel dates that avoid crowds. For example, it suggests times when popular tourist destinations are less crowded. In addition, the reservation analysis unit develops an algorithm that analyzes past reservation data and predicts travel dates that avoid crowds. For example, it predicts peak congestion times from past data and suggests travel dates that avoid those peaks. In this way, it is possible to predict the optimal travel dates to avoid crowds by analyzing past reservation data.

[0066] The reservation analysis unit can use the emotion estimation function to analyze the user's stress level and suggest travel dates and destinations that will help reduce stress. The reservation analysis unit, for example, uses the emotion estimation function to analyze the user's stress level and suggest travel dates that will help reduce stress. For example, it can suggest travel destinations that will help the user relax during times of high stress. The reservation analysis unit can also use the emotion estimation function to analyze the user's stress level and suggest travel destinations that will help reduce stress. For example, it can suggest places rich in nature or quiet resort areas. The reservation analysis unit can also use the emotion estimation function to monitor the user's stress level in real time and suggest travel plans that will help the user relax immediately when stress levels rise. For example, it can suggest travel plans for taking a sudden vacation. In this way, the reservation analysis unit can analyze the user's stress level and suggest travel dates and destinations that will help the user reduce stress.

[0067] The reservation analysis unit can compare reservation status in different regions and suggest the optimal travel destination. For example, the generation AI analyzes the reservation status of accommodations and tourist attractions in different regions in real time and suggests the optimal travel destination. For example, it prioritizes suggestions of less crowded regions. The generation AI also compares reservation status in different regions and suggests the optimal travel destination that suits the user's preferences. For example, it suggests a region where you can stay more comfortably within the same budget. The reservation analysis unit also suggests travel destinations that avoid crowds based on the reservation status in different regions. For example, if a popular tourist destination is crowded, it will suggest another region with similar appeal. In this way, the generation AI can suggest the optimal travel destination by comparing the reservation status of different regions.

[0068] The reservation analysis unit can analyze the user's schedule and suggest the optimal travel date. In the reservation analysis unit, for example, the generation AI analyzes the user's schedule and suggests the optimal travel date. For example, it may suggest travel dates taking into account work or school holiday periods. In addition, the reservation analysis unit can comprehensively analyze the user's schedule and reservation status and suggest the optimal travel date. For example, it may suggest travel dates that avoid crowds in line with the user's schedule. In addition, the reservation analysis unit can develop an algorithm that allows the generation AI to suggest the optimal travel date based on the user's schedule. For example, it may analyze the user's schedule and reservation status in real time and suggest the optimal travel date. In this way, it is possible to suggest the optimal travel date by analyzing the user's schedule.

[0069] The reservation analysis unit can use the emotion estimation function to predict the user's emotions based on the congestion level at the travel destination and propose a plan to avoid the crowds. For example, the reservation analysis unit can use the emotion estimation function to predict the user's emotions based on the congestion level at the travel destination and propose a plan to avoid the crowds. For example, if congestion is expected, the reservation analysis unit can propose a quiet place. The reservation analysis unit can also use the emotion estimation function to monitor the congestion level at the travel destination in real time, predict the user's emotions, and adjust the plan. For example, the reservation analysis unit can propose a plan to sightsee during less crowded times. The reservation analysis unit can also use the emotion estimation function to predict the user's emotions based on the congestion level at the travel destination and propose an activity to avoid the crowds. For example, the reservation analysis unit can avoid crowded tourist spots and propose a relaxing activity instead. In this way, the reservation analysis unit can predict the user's emotions based on the congestion level at the travel destination and propose a plan to avoid the crowds.

[0070] The navigation unit can analyze the user's movement history and suggest optimal transfer routes and walking routes. In the navigation unit, for example, the generation AI analyzes the user's past movement history and suggests the optimal transfer route. For example, it may prioritize suggestions of stations and lines that have been used in the past. In addition, the navigation unit can suggest the optimal walking route based on the user's movement history. For example, it may suggest routes that have been walked in the past or routes that suit the user's preferences. In addition, the navigation unit can analyze the user's movement history and suggest the optimal transfer route or walking route to avoid congestion. For example, it may suggest a route that avoids busy times based on past data. In this way, the optimal transfer route or walking route can be suggested by analyzing the user's movement history.

[0071] The navigation unit can provide customized VR guidance by taking into account the user's visual and auditory characteristics. For example, the generation AI in the navigation unit analyzes the user's visual characteristics and provides customized VR guidance that is easy on the eyes. For example, for a user with color blindness, it provides guidance with adjusted color contrast. The generation AI in the navigation unit also analyzes the user's auditory characteristics and provides customized VR guidance that is easy on the ears. For example, for a user with hearing impairments, it converts audio guidance into text and provides it. The generation AI in the navigation unit also analyzes the user's visual and auditory characteristics in an integrated manner and provides optimal VR guidance. For example, it provides guidance that takes both visual and auditory characteristics into consideration. This makes it possible to provide customized VR guidance by taking into account the user's visual and auditory characteristics.

[0072] The navigation unit can use the emotion estimation function to provide VR guidance to reduce the user's anxiety and stress. For example, the navigation unit can use the emotion estimation function to analyze the user's anxiety and stress and provide relaxing VR guidance. For example, it can display calming music or scenery. The navigation unit can also use the emotion estimation function to monitor the user's anxiety and stress in real time and adjust the VR guidance as needed. For example, it can provide detailed guidance when the user gets lost. The navigation unit can also use the emotion estimation function to provide customized VR guidance to reduce the user's anxiety and stress. For example, it can provide guidance tailored to the user's preferences. In this way, it can provide VR guidance to reduce the user's anxiety and stress.

[0073] The navigation unit can propose optimal travel routes that combine different means of transportation. For example, the generation AI analyzes different means of transportation and proposes optimal travel routes. For example, it proposes a route that combines trains and buses. The generation AI also proposes optimal routes that combine different means of transportation based on the user's travel history. For example, it prioritizes proposals for means of transportation that have been used in the past. The generation AI also analyzes the operating conditions of different means of transportation in real time and proposes optimal travel routes. For example, it proposes optimal transfer routes depending on the operating conditions. This makes it possible to propose optimal travel routes that combine different means of transportation.

[0074] The navigation unit monitors the user's physical condition while traveling and can suggest a route that suits their physical condition. For example, the generation AI in the navigation unit monitors the user's physical condition in real time and suggests a route that suits their physical condition. For example, it will suggest a route that includes breaks when the user is tired. The generation AI in the navigation unit also analyzes the user's physical condition data and suggests a route that takes their physical condition into consideration. For example, it will suggest a shorter route when they are not feeling well. The navigation unit also develops an algorithm that suggests the optimal route based on the user's physical condition. For example, it will adjust the route in real time depending on their physical condition. This makes it possible to monitor the user's physical condition and suggest a route that suits their physical condition.

[0075] The navigation unit can use the emotion estimation function to monitor the emotions of the user while traveling in real time and provide guidance according to the emotions. For example, the navigation unit can use the emotion estimation function to monitor the emotions of the user while traveling in real time and provide guidance according to the emotions. For example, detailed guidance can be provided when the user feels anxious. The navigation unit can also use the emotion estimation function to analyze the emotions of the user while traveling and provide relaxing guidance. For example, calm music or scenery can be displayed. The navigation unit can also use the emotion estimation function to monitor the emotions of the user while traveling and provide an encouraging message when the user's emotions become heightened. For example, a reassuring message can be displayed when the user gets lost. In this way, the navigation unit can monitor the emotions of the user while traveling in real time and provide guidance according to the emotions.

[0076] The generation unit can analyze the user's transportation preferences and suggest the most suitable transportation method. For example, the generation AI in the generation unit analyzes the user's past transportation history and suggests transportation methods that suit their preferences. For example, it prioritizes suggestions of transportation methods that have been used in the past. The generation unit also suggests the most suitable transportation method based on the user's transportation preferences. For example, it suggests first class or business class for a user who prioritizes comfort. The generation AI in the generation unit also analyzes the user's transportation preferences and suggests environmentally friendly transportation methods. For example, it suggests eco-friendly transportation methods. In this way, the generation unit can analyze the user's transportation preferences and suggest the most suitable transportation method.

[0077] The generation unit suggests activities for the user while traveling, enabling the user to make effective use of their travel time. For example, the generation AI of the generation unit suggests activities for the user while traveling, enabling the user to make effective use of their travel time. For example, the generation AI suggests audiobooks and podcasts that can be enjoyed while traveling. The generation unit also suggests a plan for making effective use of travel time based on the user's activities while traveling. For example, it suggests exercises and stretches that can be done while traveling. The generation unit also suggests activities for making effective use of travel time by analyzing the user's activities while traveling. For example, it suggests online courses and workshops that can be learned while traveling. This allows the generation AI to suggest activities for the user while traveling, enabling the user to make effective use of their travel time.

[0078] The generation unit can use the emotion estimation function to analyze the emotions of a user on the move and provide entertainment that matches the emotions. For example, the generation unit can use the emotion estimation function to analyze the emotions of a user on the move and provide relaxing entertainment. For example, it can suggest calming music or relaxation videos. The generation unit can also use the emotion estimation function to analyze the emotions of a user on the move and suggest movies or dramas that match the emotions. For example, it can suggest action movies when emotions are high. The generation unit can also use the emotion estimation function to monitor the emotions of a user on the move in real time and provide entertainment that matches the emotions. For example, it can suggest comedy movies when emotions are low. In this way, it is possible to analyze the emotions of a user on the move and provide entertainment that matches the emotions.

[0079] The generation unit can propose an optimal travel plan that combines different means of transportation. For example, the generation AI analyzes different means of transportation and proposes an optimal travel plan. For example, it proposes a route that combines trains and buses. The generation unit also proposes an optimal plan that combines different means of transportation based on the user's travel history. For example, it prioritizes proposals for means of transportation that have been used in the past. The generation unit also analyzes the operating status of different means of transportation in real time and proposes an optimal travel plan. For example, it proposes an optimal transfer route depending on the operating status. This makes it possible to propose an optimal travel plan that combines different means of transportation.

[0080] The generation unit can analyze the user's needs while traveling and suggest services that can be used while traveling. For example, the generation AI in the generation unit analyzes the user's needs while traveling and suggest services that can be used while traveling. For example, it suggests Wi-Fi services and charging spots that can be used while traveling. The generation unit also uses the generation AI to suggest entertainment services that can be used while traveling based on the user's needs while traveling. For example, it suggests movies and music that can be enjoyed while traveling. The generation unit also uses the generation AI to analyze the user's needs while traveling and suggest comfortable services that can be used while traveling. For example, it suggests relaxation services and massages that can be used while traveling. In this way, the generation unit can analyze the user's needs while traveling and suggest services that can be used while traveling.

[0081] The generation unit can use the emotion estimation function to monitor the emotions of a user while traveling in real time and provide a service according to the emotions. For example, the generation unit can use the emotion estimation function to monitor the emotions of a user while traveling in real time and provide a service according to the emotions. For example, when emotions are high, a relaxation service can be provided. The generation unit can also use the emotion estimation function to analyze the emotions of a user while traveling and provide entertainment according to the emotions. For example, when emotions are low, a comedy movie can be provided. The generation unit can also use the emotion estimation function to monitor the emotions of a user while traveling in real time and provide food and drinks according to the emotions. For example, when emotions are high, an energy drink can be provided. In this way, the generation unit can monitor the emotions of a user while traveling in real time and provide a service according to the emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The analysis unit can also analyze a user's hobbies and interests and suggest travel plans based on specific themes. For example, a plan to visit historical sites and museums can be suggested to a user who loves history. A gourmet tour to enjoy local specialties can also be suggested to a user who is interested in food. Furthermore, an active travel plan including hiking and camping can be suggested to a user who loves the outdoors. This makes it possible to provide themed trips that match the user's hobbies and interests.

[0084] The analysis unit not only analyzes a user's past travel history and social media posts, but also the websites the user has visited and search history. For example, it can suggest travel destinations that the user might be interested in based on data from travel blogs and tourist information sites that the user frequently visits. It can also analyze the keywords the user has searched for to suggest related tourist spots and activities. It can also analyze the content of videos and images the user has viewed to suggest visually appealing travel plans. This allows for a comprehensive analysis of the user's online behavior and provides more accurate travel plans.

[0085] The analysis unit not only takes into account the user's health condition and allergy information, but can also analyze the user's fitness level and exercise habits. For example, an active travel plan can be suggested to a user who exercises regularly. It can also suggest a healthy travel plan that incorporates light exercise to a user who is not getting enough exercise. Furthermore, it can analyze the user's sleep patterns and stress levels to suggest a relaxing travel plan. This makes it possible to provide travel plans that take into account the user's overall health.

[0086] The analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest a travel plan that best suits that emotion. For example, if the user is feeling adventurous, the emotion estimation function can suggest a travel plan that includes exciting activities. If the user wants to relax, the emotion estimation function can also suggest a quiet natural environment or a spa resort. Furthermore, if the user is in a sociable mood, the emotion estimation function can also suggest a travel plan that includes local events and festivals. This makes it possible to provide a travel plan that matches the user's emotional state.

[0087] The generation unit can generate a travel plan that takes into account not only the preferences of the user's family and friends, but also the preferences and needs of the user's pet. For example, it can suggest pet-friendly accommodations and restaurants. It can also suggest activities and tourist spots that can be enjoyed with pets. It can also suggest pet-friendly travel plans that take into account the pet's health condition and allergy information. This makes it possible to provide a travel plan that the whole family, including the pet, can enjoy.

[0088] The generation unit can propose travel plans that take into account not only the season and weather, but also special occasions such as the user's birthday or anniversary. For example, for a user's birthday, a travel plan including a special dinner or a surprise event can be proposed. For a wedding anniversary, it is also possible to propose romantic travel destinations and activities. Furthermore, for a user's child's birthday, a travel plan including children's activities and theme parks can be proposed. In this way, it is possible to provide travel plans for celebrating special occasions.

[0089] The generation unit can use the emotion estimation function to monitor the user's emotions during the trip in real time and adjust the plan as needed. For example, if the user feels tired, the emotion estimation function can be used to add relaxing activities. Also, if the user feels excited, the emotion estimation function can be used to add active activities. Furthermore, if the user feels anxious, the emotion estimation function can be used to provide reassuring support and guidance. This makes it possible to flexibly adjust the plan according to the user's emotions during the trip.

[0090] The reservation analysis unit not only analyzes reservation status in real time, but also suggests the optimal reservation timing taking into account the user's budget. For example, it can suggest the best time to make a reservation at the most advantageous time within the budget. It can also suggest accommodations and transportation methods that suit the user's budget. It can also provide discount and campaign information taking into account the user's budget. This makes it possible to provide the optimal reservation timing and plan that suits the user's budget.

[0091] The reservation analysis unit not only analyzes past reservation data, but also predicts the optimal travel date taking into account the user's preferences and interests. For example, if the user is interested in a particular event or festival, it can suggest a travel date that coincides with that event. Also, if the user wants to enjoy a particular season or scenery, it can suggest a travel date that coincides with that time of year. It can also suggest the optimal travel date taking into account the user's work or school schedule. This makes it possible to provide the optimal travel date that matches the user's preferences and schedule.

[0092] The reservation analysis unit can use the emotion estimation function to analyze the user's stress level and suggest travel dates and destinations that will reduce stress. For example, the emotion estimation function can be used to suggest travel destinations that will help the user relax when they are feeling stressed. The emotion estimation function can also be used to suggest travel dates that suit the user's stress level. Furthermore, the emotion estimation function can be used to monitor the user's stress level in real time and suggest travel plans that will help the user relax immediately when stress levels rise. This makes it possible to provide optimal travel dates and destinations that suit the user's stress level.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The analysis unit analyzes the user's preferences. For example, the analysis unit can analyze the user's past behavioral history, survey results, and social media posts. The analysis unit can also take into account the user's health condition and allergy information. Step 2: The generation unit generates a travel plan based on the user's preferences analyzed by the analysis unit. For example, if the user inputs a preference such as "I want to relax in a place rich in nature," the generation unit will propose a travel plan that integrates transportation, accommodations, tourist spots, etc. based on that preference. The generation unit can also generate a group travel plan that takes into account the preferences of the user's family and friends. Step 3: The reservation analysis unit analyzes the reservation status. For example, the reservation analysis unit can analyze the availability of accommodations and the reservation status of transportation. The reservation analysis unit can also analyze past reservation data and predict the optimal travel date to avoid crowds. Step 4: The navigation unit distributes travel dates and destinations based on the reservation status analyzed by the reservation analysis unit. For example, if a particular tourist spot or accommodation is crowded, the navigation unit suggests other possible destinations or dates. The navigation unit can also provide VR guidance for station transfers and walking routes.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes user preferences; a generation unit that generates a travel plan based on the user's preferences analyzed by the analysis unit; a reservation analysis unit that analyzes reservation status; a navigation unit that distributes travel dates and travel destinations based on the reservation status analyzed by the reservation analysis unit; Equipped with A system characterized by:

2. The analysis unit Analyzing the user's past travel history and the content posted on the SNS, and proposing a more personalized travel plan 2. The system of claim 1.

3. The analysis unit Generate the travel plan that takes health into consideration, taking into account the user's health condition and allergy information 2. The system of claim 1.

4. The analysis unit Analyzing the user's current emotional state and proposing the travel plan that best suits that emotional state 2. The system of claim 1.

5. The generation unit Generate a group travel plan taking into consideration the preferences of the user's family and friends 2. The system of claim 1.

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