System

Abstract

An object of a system according to an embodiment is to efficiently make a travel plan and provide information in real time.SOLUTION: A system includes a keyword input part, an information collection part, an itinerary generation part, and a real-time information provision part. The keyword input unit inputs a keyword of a destination that the user wants to visit. The information collection unit collects information based on the keyword input by the keyword input unit. The itinerary generating unit generates an itinerary based on the information collected by the information collecting unit. The real-time information providing unit provides a real operation status and weather information of a tourist spot.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 made it difficult to efficiently collect the information needed to make travel plans and provide it in real time, which can lead to stress when plans change.

[0005] The system according to the embodiment aims to efficiently plan a trip and provide information in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a keyword input unit, an information collection unit, an itinerary generation unit, and a real-time information provision unit. The keyword input unit inputs keywords for destinations that a user would like to visit. The information collection unit collects information based on the keywords input by the keyword input unit. The itinerary generation unit generates an itinerary based on the information collected by the information collection unit. The real-time information provision unit provides real-time operating status and weather information for tourist destinations. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently plan a trip and provide information in real time. [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 assistance application according to an embodiment of the present invention is a system that provides real-time information, suggesting access methods, payment methods, business hours, surrounding tourist attractions, etc. in the form of an itinerary, simply by the user entering keywords for the destination they wish to visit. This allows the travel assistance application to efficiently support the user's travel plans and reduce the time and stress of searching for information due to changes in plans, etc.

[0029] The travel assistance application according to the embodiment includes a keyword input unit, an information collection unit, an itinerary generation unit, and a real-time information provision unit. The keyword input unit inputs keywords for destinations the user wants to visit. For example, the user can input place names, tourist attraction names, activity names, etc. The information collection unit collects information based on the keywords input by the keyword input unit. For example, the information collection unit collects information about tourist attractions using web scraping technology. It can also use an API to obtain information about transportation schedules and payment methods. It can also perform database searches to collect information about tourist attraction business hours and surrounding tourist attractions. The itinerary generation unit generates an itinerary based on the information collected by the information collection unit. For example, the itinerary generates an itinerary including a list of destinations, transportation options, and time allocation. The real-time information provision unit provides real-time operating status and weather information for tourist attractions. For example, it provides information about temporary closures, congestion status, and weather forecasts for tourist attractions. This allows the user to reduce stress caused by changes in plans. The travel assistance application according to the embodiment efficiently supports the user's travel planning and reduces the time and stress required for information search due to changes in plans, etc.

[0030] The information collection unit can learn the user's past travel history and preferences and generate an individually customized itinerary. For example, the information collection unit uses a generation AI to analyze the user's past travel history and learn the user's preferences based on data on visited tourist spots and accommodations. For example, the information collection unit analyzes the characteristics of previously visited tourist spots and the types of accommodations to generate an itinerary optimal for the next trip. Furthermore, the information collection unit uses a generation AI to estimate the user's preferences based on keywords and search history previously entered by the user and propose a customized itinerary. For example, the information collection unit analyzes trends in previously searched tourist spots and activities to create an itinerary that suits the user. Furthermore, the information collection unit uses a generation AI to learn the user's past travel history and preferences and generate an individually customized itinerary. For example, the information collection unit proposes an itinerary that includes the user's favorite restaurants and shopping spots. This allows the generation AI to generate an individually customized itinerary based on the user's past travel history and preferences, thereby improving user satisfaction.

[0031] The information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, the generation AI of the information collection unit uses GPS data from the user's smartphone to calculate the optimal route from the current location to the destination. For example, the generation AI proposes a real-time route that takes traffic congestion and operation conditions into consideration. Furthermore, the information collection unit can use the generation AI to propose the optimal access method from the current location based on keywords entered by the user. For example, the generation AI generates a route that combines transportation methods such as walking, cycling, and public transportation. Furthermore, the information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, the generation AI proposes an efficient itinerary that takes into consideration travel time and distance between tourist destinations. This can improve travel efficiency by using the user's real-time location information to generate an itinerary that includes the optimal route from the current location.

[0032] The information collection unit can generate a multimedia itinerary including related videos and images based on keywords entered by the user. For example, the information collection unit allows the generation AI to collect videos of related tourist attractions and activities based on the keywords entered by the user and generate a multimedia itinerary. For example, the information collection unit proposes an itinerary including promotional videos of tourist attractions and user review videos. Furthermore, the information collection unit allows the generation AI to collect related images based on keywords entered by the user and generate a visually appealing multimedia itinerary. For example, the information collection unit proposes an itinerary including photos and maps of tourist attractions. Furthermore, the information collection unit allows the generation AI to generate a multimedia itinerary including related videos and images based on keywords entered by the user. For example, the information collection unit proposes an itinerary including 360-degree panoramic images and drone footage of tourist attractions. This allows a visually appealing itinerary to be provided by generating a multimedia itinerary including related videos and images based on keywords entered by the user.

[0033] The information collection unit can refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit uses a generation AI to collect reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit analyzes reviews on travel sites and social media to suggest highly rated tourist destinations. Furthermore, the information collection unit uses the generation AI to refer to other users' ratings based on keywords entered by the user and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit suggests highly rated tourist destinations that match the user's preferences. Furthermore, the information collection unit uses the generation AI to refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, if a user enters "family trip," the generation AI suggests highly rated tourist destinations that are popular with families. This can improve user satisfaction by referring to reviews and ratings from other users and generating an itinerary that includes the most highly rated tourist destinations and activities.

[0034] The real-time information providing unit can collect traffic congestion information in real time and propose an optimal travel route. In the real-time information providing unit, for example, the generation AI collects traffic congestion information in real time from a traffic information providing service and proposes an optimal travel route to the user. For example, it presents an alternative route to avoid traffic congestion. In addition, the real-time information providing unit collects traffic congestion information in real time based on the destination entered by the user and proposes an optimal travel route. For example, it suggests using public transportation that is not affected by traffic congestion. In addition, the real-time information providing unit collects traffic congestion information in real time and proposes an optimal travel route. For example, it calculates the shortest route from the user's current location to the destination and presents a route that avoids traffic congestion. In this way, by collecting traffic congestion information in real time and proposing an optimal travel route, it is possible to improve travel efficiency.

[0035] The real-time information providing unit can collect event information in real time and suggest events that match the user's interests. In the real-time information providing unit, for example, the generation AI collects event information from an event information providing service in real time and suggests events that match the user's interests. For example, information on music concerts and sporting events is provided. In addition, the real-time information providing unit collects event information in real time based on keywords entered by the user and suggests events that match the user's interests. For example, if the user enters "art," information on art exhibitions is provided. In addition, the real-time information providing unit collects event information in real time based on the user's past event participation history and suggests events that the user is likely to be interested in. In this way, by collecting event information in real time and suggesting events that match the user's interests, it is possible to improve travel satisfaction.

[0036] The real-time information providing unit can collect exchange rate information in real time and suggest the optimal payment method when traveling abroad. In the real-time information providing unit, for example, the generation AI collects exchange rate information in real time from an exchange rate information providing service and suggests the optimal payment method to the user. For example, it suggests whether credit card or cash is more advantageous. In addition, the real-time information providing unit collects exchange rate information in real time based on the destination entered by the user and suggests the optimal payment method. For example, it suggests the optimal payment method based on the local currency exchange rate. In addition, the real-time information providing unit collects exchange rate information in real time based on the generation AI and suggests the optimal payment method when traveling abroad. For example, it suggests the most cost-effective payment method based on the exchange rate of the country the user is visiting. In this way, by collecting exchange rate information in real time and suggesting the optimal payment method when traveling abroad, payment efficiency can be improved.

[0037] The real-time information providing unit can collect health information in real time and propose a travel plan based on the user's health condition. For example, the generation AI in the real-time information providing unit collects health information in real time from a health information providing service and proposes a travel plan based on the user's health condition. For example, if the user is tired, the generation AI suggests tourist spots where the user can relax. The real-time information providing unit also collects health information in real time based on the health condition entered by the user and proposes an optimal travel plan. For example, if the user has an allergy, the generation AI suggests restaurants that cater to allergies. The real-time information providing unit also collects health information in real time and proposes a travel plan based on the user's health condition. For example, if the user is not getting enough exercise, the generation AI suggests walking tours and hiking courses. In this way, by collecting health information in real time and proposing a travel plan based on the user's health condition, travel satisfaction can be improved.

[0038] The information gathering unit can learn the user's transportation preferences and suggest the optimal access method. For example, the generation AI of the information gathering unit learns the user's past transportation selection history and suggests an access method that suits the user's preferences. For example, the generation AI will preferentially suggest taxis to a user who has used taxis frequently in the past. The information gathering unit also learns the user's transportation preferences based on keywords entered by the user and suggests the optimal access method. For example, if the user enters "eco-friendly," the generation AI will suggest public transportation or bicycles. The information gathering unit also learns the user's transportation preferences and suggests the optimal access method. For example, if the user places importance on "comfort," the generation AI will suggest a comfortable transportation method. In this way, the generation AI can learn the user's transportation preferences and suggest the optimal access method, thereby improving travel satisfaction.

[0039] The information gathering unit can suggest the optimal access method according to the user's budget. In the information gathering unit, for example, the generation AI collects the user's budget information and suggests the optimal access method within that budget. For example, if the budget is low, public transportation is suggested, and if the budget is high, a taxi or rental car is suggested. In addition, the information gathering unit allows the generation AI to suggest the optimal access method based on the budget entered by the user. For example, if the user enters "savings," the generation AI will suggest a cost-effective means of transportation. In addition, the information gathering unit allows the generation AI to suggest the optimal access method according to the user's budget. For example, if the user enters "luxury," the generation AI will suggest a comfortable and high-end means of transportation. In this way, by suggesting the optimal access method according to the user's budget, it is possible to improve the cost efficiency of travel.

[0040] The information collection unit can compare the environmental impact of different means of transportation and suggest eco-friendly access methods. For example, the generation AI in the information collection unit compares the environmental impact of different means of transportation and suggests eco-friendly access methods. For example, it may preferentially suggest public transportation or bicycles. The information collection unit also allows the generation AI to suggest means of transportation with a low environmental impact based on keywords entered by the user. For example, if the user enters "environmental protection," it may suggest electric vehicles or walking. The information collection unit also allows the generation AI to compare the environmental impact of different means of transportation and suggest eco-friendly access methods. For example, it may suggest means of travel with a low carbon footprint. This allows the generation AI to compare the environmental impact of different means of transportation and suggest eco-friendly access methods, thereby promoting environmental consideration.

[0041] The information collection unit can suggest the optimal access method to minimize the user's travel time. For example, the information collection unit allows the generation AI to suggest the optimal access method to minimize the user's travel time. For example, it may suggest the shortest route or a route using an expressway. The information collection unit also allows the generation AI to suggest the optimal access method to minimize travel time based on the destination input by the user. For example, it may suggest a direct flight or an express train. The information collection unit also allows the generation AI to suggest the optimal access method to minimize the user's travel time. For example, it may suggest an alternative route to avoid traffic congestion. This allows the generation AI to suggest the optimal access method to minimize the user's travel time, thereby improving travel efficiency.

[0042] The information collection unit can learn the user's past payment history and suggest the optimal payment method. For example, the information collection unit uses a generation AI to analyze the user's past payment history and suggest the optimal payment method. For example, the information collection unit preferentially suggests credit cards to a user who has used credit cards frequently in the past. The information collection unit also uses a generation AI to learn the user's past payment history based on keywords entered by the user and suggest the optimal payment method. For example, if a user enters "point redemption," the information collection unit suggests a payment method that offers a high point redemption rate. The information collection unit also uses a generation AI to learn the user's past payment history and suggest the optimal payment method. For example, if a user uses "cash" frequently, the information collection unit suggests places where cash payments are available. This allows the system to learn the user's past payment history and suggest the optimal payment method, thereby improving payment efficiency.

[0043] The information collection unit can refer to the user's credit score and suggest the optimal payment method. In the information collection unit, for example, the generation AI refers to the user's credit score and suggests the optimal payment method. For example, it suggests a credit card for a user with a high credit score and a debit card or cash for a user with a low credit score. In addition, the information collection unit refers to the credit score and suggests the optimal payment method based on keywords entered by the user. For example, if a user enters "credit", it suggests a payment method according to the credit score. In addition, the information collection unit refers to the user's credit score and suggests the optimal payment method. For example, for a user with a low credit score, it suggests a payment method to improve the credit score. In this way, by referring to the user's credit score and suggesting the optimal payment method, payment efficiency can be improved.

[0044] The information collection unit can compare fees for different payment methods and suggest the most cost-effective payment method. For example, the information collection unit allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method. For example, it compares fees for credit cards, debit cards, and cash and suggests the optimal method. The information collection unit also allows the generation AI to suggest payment methods with low fees based on keywords entered by the user. For example, if the user enters "save," it suggests payment methods with low fees. The information collection unit also allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method. For example, it suggests payment methods with low fees when traveling abroad. This allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method, thereby improving payment efficiency.

[0045] The information collection unit can provide cashback or point redemption information according to the user's payment method. For example, the information collection unit allows the generation AI to provide cashback or point redemption information according to the user's payment method. For example, the information collection unit presents credit card cashback or point redemption rates. The information collection unit also allows the generation AI to provide cashback or point redemption information based on keywords entered by the user. For example, if the user enters "bargain," the information collection unit suggests the most advantageous payment method. The information collection unit also allows the generation AI to provide cashback or point redemption information according to the user's payment method. For example, the information collection unit provides cashback or point redemption information for specific stores or services. This makes it possible to improve payment satisfaction by providing cashback or point redemption information according to the user's payment method.

[0046] The information collection unit can customize and provide information about surrounding tourist attractions based on the user's interests and preferences. For example, the generation AI analyzes the user's past search history and visit history, and customizes and provides information about surrounding tourist attractions based on the user's interests and preferences. For example, if the user is interested in historical places, the generation AI suggests nearby historical tourist attractions. The information collection unit also customizes and provides information about tourist attractions that matches the user's interests and preferences based on keywords entered by the user. For example, if the user enters "nature," the generation AI suggests tourist attractions with beautiful surrounding natural scenery. The information collection unit also customizes and provides information about surrounding tourist attractions based on the user's interests and preferences. For example, if the user is interested in "food," the generation AI suggests popular restaurants and cafes in the area. This allows the user to customize and provide information about surrounding tourist attractions based on their interests and preferences, thereby improving travel satisfaction.

[0047] The information collection unit can suggest the optimal order to visit tourist spots according to the user's stay time. In the information collection unit, for example, the generation AI takes into account the user's stay time and suggests the optimal order to visit tourist spots. For example, it suggests a route that allows for efficient sightseeing in a short amount of time. In addition, the information collection unit suggests the optimal order to visit tourist spots according to the stay time input by the user, with the generation AI. For example, if the user inputs "half day," it suggests an order of tourist spots that can be visited in half a day. In addition, the information collection unit suggests the optimal order to visit tourist spots according to the user's stay time. For example, if the user inputs "one day," it suggests a route that allows for efficient sightseeing in one day. In this way, the efficiency of travel can be improved by suggesting the optimal order to visit tourist spots according to the user's stay time.

[0048] The information collection unit can collect congestion information at surrounding tourist attractions in real time and suggest a visit time to avoid crowds. For example, the information collection unit uses a generation AI to collect congestion information at tourist attractions in real time and suggest a visit time to the user to avoid crowds. For example, the information collection unit suggests a time period with less crowds. Furthermore, the information collection unit uses a generation AI to collect congestion information in real time based on the tourist attractions entered by the user and suggest an optimal visit time. For example, if a user enters "Tokyo Tower," the information collection unit suggests a time period with less crowds. Furthermore, the information collection unit uses a generation AI to collect congestion information at surrounding tourist attractions in real time and suggest a visit time to avoid crowds. For example, if a user enters "weekend," the information collection unit suggests visiting on a weekday when it is less crowded. In this way, travel satisfaction can be improved by collecting congestion information at surrounding tourist attractions in real time and suggesting a visit time to avoid crowds.

[0049] The information collection unit can provide the user with historical and cultural information about surrounding tourist destinations to deepen their understanding. For example, the generation AI collects historical and cultural information about surrounding tourist destinations and provides it to the user. For example, it explains the historical background and cultural significance of the tourist destination. Furthermore, the information collection unit can provide the generation AI with historical and cultural information about the tourist destination based on the tourist destination entered by the user. For example, if the user enters "Kyoto," it can provide information about the history and culture of Kyoto. Furthermore, the information collection unit can provide the generation AI with historical and cultural information about surrounding tourist destinations to deepen the user's understanding. For example, it can provide information about tourist destination guidebooks and museums. This can provide historical and cultural information about surrounding tourist destinations to deepen the user's understanding, thereby improving travel satisfaction.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The information collection unit can also monitor the user's health condition and generate a health-conscious itinerary. For example, if the user has allergies, it can suggest restaurants and accommodations that cater to allergies. If the user feels they are lacking in exercise, it can generate an itinerary that includes walking tours and hiking trails. Furthermore, if the user is tired, it can suggest an itinerary that includes relaxing spas and hot spring facilities. This can improve travel satisfaction by providing an itinerary that is customized according to the user's health condition.

[0052] The information collection unit can learn the user's past travel history and preferences and generate an individually customized itinerary. For example, it can analyze the characteristics of tourist spots visited in the past and the types of accommodations to generate an itinerary that is optimal for the next trip. The generation AI can also estimate the user's preferences based on keywords and search history entered in the past by the user and suggest a customized itinerary. It can also suggest an itinerary that includes the user's favorite restaurants and shopping spots. This can improve user satisfaction by generating an individually customized itinerary based on the user's past travel history and preferences.

[0053] The information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, it can propose a real-time route that takes traffic congestion and operation conditions into consideration. It can also generate a route that combines transportation methods such as walking, cycling, and public transportation. It can also propose an efficient itinerary that takes into consideration travel time and distance between tourist spots. In this way, it is possible to improve travel efficiency by using the user's real-time location information to generate an itinerary that includes the optimal route from the current location.

[0054] The information collection unit can generate a multimedia itinerary that includes related videos and images based on keywords entered by the user. For example, it can propose an itinerary that includes promotional videos of tourist attractions and user review videos. It can also generate a visually appealing multimedia itinerary that includes photos and maps of tourist attractions. It can also propose an itinerary that includes 360-degree panoramic images and drone footage of tourist attractions. In this way, by generating a multimedia itinerary that includes related videos and images based on keywords entered by the user, it is possible to provide a visually appealing itinerary.

[0055] The information collection unit can refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist spots and activities. For example, it can analyze reviews on travel sites and social media to suggest highly rated tourist spots. It can also suggest highly rated tourist spots that match the user's preferences. It can also suggest highly rated tourist spots that are popular with families. This makes it possible to improve user satisfaction by generating an itinerary that includes the most highly rated tourist spots and activities by referring to reviews and ratings from other users.

[0056] The real-time information provision unit can collect traffic congestion information in real time and suggest the optimal travel route. For example, the generation AI can collect traffic congestion information in real time from a traffic information service and suggest the optimal travel route to the user. It can also suggest alternative routes to avoid traffic congestion. It can also suggest the use of public transportation that is not affected by traffic congestion. In this way, by collecting traffic congestion information in real time and suggesting the optimal travel route, it is possible to improve travel efficiency.

[0057] The real-time information provision unit can collect event information in real time and suggest events that match the user's interests. For example, the generation AI can collect event information in real time from an event information provision service and suggest events that match the user's interests. It can also provide information on music concerts and sporting events. Furthermore, it can suggest events that the user may be interested in based on the user's past event participation history. In this way, by collecting event information in real time and suggesting events that match the user's interests, it is possible to improve travel satisfaction.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: In the keyword input section, the user inputs keywords for the destination they want to visit. For example, they can input place names, tourist spot names, activity names, etc. Step 2: The information collection unit collects information based on the keywords entered by the keyword input unit. For example, it uses web scraping technology to collect information about tourist spots. It can also use APIs to obtain information about transportation schedules and payment methods. It can also perform database searches to collect information about tourist spot opening hours and surrounding tourist spots. Step 3: The itinerary generation unit generates an itinerary based on the information collected by the information collection unit, for example, an itinerary including a list of destinations, means of transportation, and time allocation. Step 4: The real-time information provider provides real-time information about the operating status of tourist attractions and weather information. For example, it provides information about temporary closures, congestion, and weather forecasts. This helps users reduce stress caused by changes to their plans.

[0060] (Example 2) The travel assistance application according to an embodiment of the present invention is a system that provides real-time information, suggesting access methods, payment methods, business hours, surrounding tourist attractions, etc. in the form of an itinerary, simply by the user entering keywords for the destination they wish to visit. This allows the travel assistance application to efficiently support the user's travel plans and reduce the time and stress of searching for information due to changes in plans, etc.

[0061] The travel assistance application according to the embodiment includes a keyword input unit, an information collection unit, an itinerary generation unit, and a real-time information provision unit. The keyword input unit inputs keywords for destinations the user wants to visit. For example, the user can input place names, tourist attraction names, activity names, etc. The information collection unit collects information based on the keywords input by the keyword input unit. For example, the information collection unit collects information about tourist attractions using web scraping technology. It can also use an API to obtain information about transportation schedules and payment methods. It can also perform database searches to collect information about tourist attraction business hours and surrounding tourist attractions. The itinerary generation unit generates an itinerary based on the information collected by the information collection unit. For example, the itinerary generates an itinerary including a list of destinations, transportation options, and time allocation. The real-time information provision unit provides real-time operating status and weather information for tourist attractions. For example, it provides information about temporary closures, congestion status, and weather forecasts for tourist attractions. This allows the user to reduce stress caused by changes in plans. The travel assistance application according to the embodiment efficiently supports the user's travel planning and reduces the time and stress required for information search due to changes in plans, etc.

[0062] The information collection unit can learn the user's past travel history and preferences and generate an individually customized itinerary. For example, the information collection unit uses a generation AI to analyze the user's past travel history and learn the user's preferences based on data on visited tourist spots and accommodations. For example, the information collection unit analyzes the characteristics of previously visited tourist spots and the types of accommodations to generate an itinerary optimal for the next trip. Furthermore, the information collection unit uses a generation AI to estimate the user's preferences based on keywords and search history previously entered by the user and propose a customized itinerary. For example, the information collection unit analyzes trends in previously searched tourist spots and activities to create an itinerary that suits the user. Furthermore, the information collection unit uses a generation AI to learn the user's past travel history and preferences and generate an individually customized itinerary. For example, the information collection unit proposes an itinerary that includes the user's favorite restaurants and shopping spots. This allows the generation AI to generate an individually customized itinerary based on the user's past travel history and preferences, thereby improving user satisfaction.

[0063] The information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, the generation AI of the information collection unit uses GPS data from the user's smartphone to calculate the optimal route from the current location to the destination. For example, the generation AI proposes a real-time route that takes traffic congestion and operation conditions into consideration. Furthermore, the information collection unit can use the generation AI to propose the optimal access method from the current location based on keywords entered by the user. For example, the generation AI generates a route that combines transportation methods such as walking, cycling, and public transportation. Furthermore, the information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, the generation AI proposes an efficient itinerary that takes into consideration travel time and distance between tourist destinations. This can improve travel efficiency by using the user's real-time location information to generate an itinerary that includes the optimal route from the current location.

[0064] The information collection unit can use the emotion estimation function to analyze emotions associated with keywords entered by the user and generate an itinerary that elicits positive emotions. For example, the information collection unit uses a generation AI to analyze emotions associated with keywords entered by the user and suggest tourist spots and activities that elicit positive emotions. For example, if a user enters "relaxation," the information collection unit generates an itinerary that elicits positive emotions. Furthermore, the information collection unit uses the emotion estimation function to analyze emotions associated with keywords entered by the user and generate an itinerary that elicits positive emotions. For example, if a user enters "adventure," the information collection unit uses a generation AI to analyze emotions associated with keywords entered by the user and generate an itinerary that elicits positive emotions. For example, if a user enters "family trip," the information collection unit uses a generation AI to analyze emotions associated with keywords entered by the user and generate an itinerary that elicits positive emotions. For example, if a user enters "family trip," the information collection unit uses a generation AI to suggest an itinerary that elicits positive emotions. This allows the user's emotions to be analyzed and an itinerary that elicits positive emotions to be generated, thereby improving travel satisfaction.

[0065] The information collection unit can generate a multimedia itinerary including related videos and images based on keywords entered by the user. For example, the information collection unit allows the generation AI to collect videos of related tourist attractions and activities based on the keywords entered by the user and generate a multimedia itinerary. For example, the information collection unit proposes an itinerary including promotional videos of tourist attractions and user review videos. Furthermore, the information collection unit allows the generation AI to collect related images based on keywords entered by the user and generate a visually appealing multimedia itinerary. For example, the information collection unit proposes an itinerary including photos and maps of tourist attractions. Furthermore, the information collection unit allows the generation AI to generate a multimedia itinerary including related videos and images based on keywords entered by the user. For example, the information collection unit proposes an itinerary including 360-degree panoramic images and drone footage of tourist attractions. This allows a visually appealing itinerary to be provided by generating a multimedia itinerary including related videos and images based on keywords entered by the user.

[0066] The information collection unit can refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit uses a generation AI to collect reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit analyzes reviews on travel sites and social media to suggest highly rated tourist destinations. Furthermore, the information collection unit uses the generation AI to refer to other users' ratings based on keywords entered by the user and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, the information collection unit suggests highly rated tourist destinations that match the user's preferences. Furthermore, the information collection unit uses the generation AI to refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist destinations and activities. For example, if a user enters "family trip," the generation AI suggests highly rated tourist destinations that are popular with families. This can improve user satisfaction by referring to reviews and ratings from other users and generating an itinerary that includes the most highly rated tourist destinations and activities.

[0067] The information collection unit can use the emotion estimation function to analyze other users' emotional responses to keywords entered by the user and generate an itinerary that resonates with the user. For example, the information collection unit uses a generation AI to analyze other users' emotional responses and generate an itinerary that includes tourist spots and activities that resonate with the user. For example, if a user enters "relaxation," the AI ​​suggests tourist spots that other users found relaxing. Furthermore, the information collection unit uses the emotion estimation function to analyze other users' emotional responses to keywords entered by the user and generate an itinerary that resonates with the user. For example, if a user enters "adventure," the AI ​​suggests tourist spots that other users found adventurous. Furthermore, the information collection unit uses the generation AI to analyze other users' emotional responses and generate an itinerary that includes tourist spots and activities that resonate with the user. For example, if a user enters "family trip," the AI ​​suggests tourist spots that other users found enjoyable for their families. This allows the AI ​​to analyze other users' emotional responses to keywords entered by the user and generate an itinerary that resonates with the user, thereby improving user satisfaction.

[0068] The real-time information providing unit can collect traffic congestion information in real time and propose an optimal travel route. In the real-time information providing unit, for example, the generation AI collects traffic congestion information in real time from a traffic information providing service and proposes an optimal travel route to the user. For example, it presents an alternative route to avoid traffic congestion. In addition, the real-time information providing unit collects traffic congestion information in real time based on the destination entered by the user and proposes an optimal travel route. For example, it suggests using public transportation that is not affected by traffic congestion. In addition, the real-time information providing unit collects traffic congestion information in real time and proposes an optimal travel route. For example, it calculates the shortest route from the user's current location to the destination and presents a route that avoids traffic congestion. In this way, by collecting traffic congestion information in real time and proposing an optimal travel route, it is possible to improve travel efficiency.

[0069] The real-time information providing unit can collect event information in real time and suggest events that match the user's interests. In the real-time information providing unit, for example, the generation AI collects event information from an event information providing service in real time and suggests events that match the user's interests. For example, information on music concerts and sporting events is provided. In addition, the real-time information providing unit collects event information in real time based on keywords entered by the user and suggests events that match the user's interests. For example, if the user enters "art," information on art exhibitions is provided. In addition, the real-time information providing unit collects event information in real time based on the user's past event participation history and suggests events that the user is likely to be interested in. In this way, by collecting event information in real time and suggesting events that match the user's interests, it is possible to improve travel satisfaction.

[0070] The real-time information providing unit can use the emotion estimation function to analyze the user's current emotional state and provide real-time information for reducing stress. For example, the generation AI in the real-time information providing unit analyzes the user's current emotional state and provides real-time information for reducing stress. For example, the unit suggests tourist spots and activities that will help them relax. The real-time information providing unit also uses the emotion estimation function to analyze the user's current emotional state and provide real-time information for reducing stress. For example, if the user is feeling stressed, the unit suggests relaxing music or a meditation app. The real-time information providing unit also uses the generation AI to analyze the user's current emotional state and provide real-time information for reducing stress. For example, if the user is tired, the unit suggests nearby cafes or rest spots. This allows the user's current emotional state to be analyzed and real-time information for reducing stress to be provided, thereby improving travel satisfaction.

[0071] The real-time information providing unit can collect exchange rate information in real time and suggest the optimal payment method when traveling abroad. In the real-time information providing unit, for example, the generation AI collects exchange rate information in real time from an exchange rate information providing service and suggests the optimal payment method to the user. For example, it suggests whether credit card or cash is more advantageous. In addition, the real-time information providing unit collects exchange rate information in real time based on the destination entered by the user and suggests the optimal payment method. For example, it suggests the optimal payment method based on the local currency exchange rate. In addition, the real-time information providing unit collects exchange rate information in real time based on the generation AI and suggests the optimal payment method when traveling abroad. For example, it suggests the most cost-effective payment method based on the exchange rate of the country the user is visiting. In this way, by collecting exchange rate information in real time and suggesting the optimal payment method when traveling abroad, payment efficiency can be improved.

[0072] The real-time information providing unit can collect health information in real time and propose a travel plan based on the user's health condition. For example, the generation AI in the real-time information providing unit collects health information in real time from a health information providing service and proposes a travel plan based on the user's health condition. For example, if the user is tired, the generation AI suggests tourist spots where the user can relax. The real-time information providing unit also collects health information in real time based on the health condition entered by the user and proposes an optimal travel plan. For example, if the user has an allergy, the generation AI suggests restaurants that cater to allergies. The real-time information providing unit also collects health information in real time and proposes a travel plan based on the user's health condition. For example, if the user is not getting enough exercise, the generation AI suggests walking tours and hiking courses. In this way, by collecting health information in real time and proposing a travel plan based on the user's health condition, travel satisfaction can be improved.

[0073] The real-time information providing unit can use the emotion estimation function to provide real-time entertainment information according to the user's emotions. For example, the generation AI in the real-time information providing unit analyzes the user's emotional state and provides entertainment information in real time. For example, if the user wants to relax, relaxing movies and music are suggested. The real-time information providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide entertainment information based on the results. For example, if the user is excited, action movies and sporting events are suggested. The real-time information providing unit also monitors the user's emotional state in real time using the generation AI and provides entertainment information according to the emotions. For example, if the user is feeling sad, comedy movies and music that will brighten the mood are suggested. This makes it possible to improve travel satisfaction by providing real-time entertainment information according to the user's emotions.

[0074] The information gathering unit can learn the user's transportation preferences and suggest the optimal access method. For example, the generation AI of the information gathering unit learns the user's past transportation selection history and suggests an access method that suits the user's preferences. For example, the generation AI will preferentially suggest taxis to a user who has used taxis frequently in the past. The information gathering unit also learns the user's transportation preferences based on keywords entered by the user and suggests the optimal access method. For example, if the user enters "eco-friendly," the generation AI will suggest public transportation or bicycles. The information gathering unit also learns the user's transportation preferences and suggests the optimal access method. For example, if the user places importance on "comfort," the generation AI will suggest a comfortable transportation method. In this way, the generation AI can learn the user's transportation preferences and suggest the optimal access method, thereby improving travel satisfaction.

[0075] The information gathering unit can suggest the optimal access method according to the user's budget. In the information gathering unit, for example, the generation AI collects the user's budget information and suggests the optimal access method within that budget. For example, if the budget is low, public transportation is suggested, and if the budget is high, a taxi or rental car is suggested. In addition, the information gathering unit allows the generation AI to suggest the optimal access method based on the budget entered by the user. For example, if the user enters "savings," the generation AI will suggest a cost-effective means of transportation. In addition, the information gathering unit allows the generation AI to suggest the optimal access method according to the user's budget. For example, if the user enters "luxury," the generation AI will suggest a comfortable and high-end means of transportation. In this way, by suggesting the optimal access method according to the user's budget, it is possible to improve the cost efficiency of travel.

[0076] The information collection unit can use the emotion estimation function to analyze the user's emotions while traveling and suggest comfortable means of transportation. For example, the generation AI in the information collection unit analyzes the user's emotions while traveling and suggests comfortable means of transportation. For example, if the user is feeling stressed, it suggests means of transportation that allow them to relax. The information collection unit also uses the emotion estimation function to analyze the user's emotions while traveling in real time and suggest comfortable means of transportation based on the results. For example, if the user is tired, it suggests means of transportation with comfortable seats. The information collection unit also monitors the user's emotions while traveling in real time and suggests comfortable means of transportation according to the emotions. For example, if the user wants to relax, it suggests means of transportation with a quiet environment. In this way, by analyzing the user's emotions while traveling and suggesting comfortable means of transportation, it is possible to improve travel satisfaction.

[0077] The information collection unit can compare the environmental impact of different means of transportation and suggest eco-friendly access methods. For example, the generation AI in the information collection unit compares the environmental impact of different means of transportation and suggests eco-friendly access methods. For example, it may preferentially suggest public transportation or bicycles. The information collection unit also allows the generation AI to suggest means of transportation with a low environmental impact based on keywords entered by the user. For example, if the user enters "environmental protection," it may suggest electric vehicles or walking. The information collection unit also allows the generation AI to compare the environmental impact of different means of transportation and suggest eco-friendly access methods. For example, it may suggest means of travel with a low carbon footprint. This allows the generation AI to compare the environmental impact of different means of transportation and suggest eco-friendly access methods, thereby promoting environmental consideration.

[0078] The information collection unit can suggest the optimal access method to minimize the user's travel time. For example, the information collection unit allows the generation AI to suggest the optimal access method to minimize the user's travel time. For example, it may suggest the shortest route or a route using an expressway. The information collection unit also allows the generation AI to suggest the optimal access method to minimize travel time based on the destination input by the user. For example, it may suggest a direct flight or an express train. The information collection unit also allows the generation AI to suggest the optimal access method to minimize the user's travel time. For example, it may suggest an alternative route to avoid traffic congestion. This allows the generation AI to suggest the optimal access method to minimize the user's travel time, thereby improving travel efficiency.

[0079] The information collection unit can use the emotion estimation function to suggest a relaxing mode of transportation that corresponds to the user's emotions. For example, the generation AI in the information collection unit analyzes the user's emotional state and suggests a relaxing mode of transportation. For example, if the user is feeling stressed, it suggests a mode of transportation with a relaxing seat. The information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggests a relaxing mode of transportation based on the results. For example, if the user is tired, it suggests a mode of transportation with a comfortable seat. The information collection unit also monitors the user's emotional state in real time and suggests a relaxing mode of transportation that corresponds to the user's emotions. For example, if the user wants to relax, it suggests a mode of transportation with a quiet environment. This makes it possible to improve travel satisfaction by suggesting a relaxing mode of transportation that corresponds to the user's emotions.

[0080] The information collection unit can learn the user's past payment history and suggest the optimal payment method. For example, the information collection unit uses a generation AI to analyze the user's past payment history and suggest the optimal payment method. For example, the information collection unit preferentially suggests credit cards to a user who has used credit cards frequently in the past. The information collection unit also uses a generation AI to learn the user's past payment history based on keywords entered by the user and suggest the optimal payment method. For example, if a user enters "point redemption," the information collection unit suggests a payment method that offers a high point redemption rate. The information collection unit also uses a generation AI to learn the user's past payment history and suggest the optimal payment method. For example, if a user uses "cash" frequently, the information collection unit suggests places where cash payments are available. This allows the system to learn the user's past payment history and suggest the optimal payment method, thereby improving payment efficiency.

[0081] The information collection unit can refer to the user's credit score and suggest the optimal payment method. In the information collection unit, for example, the generation AI refers to the user's credit score and suggests the optimal payment method. For example, it suggests a credit card for a user with a high credit score and a debit card or cash for a user with a low credit score. In addition, the information collection unit refers to the credit score and suggests the optimal payment method based on keywords entered by the user. For example, if a user enters "credit", it suggests a payment method according to the credit score. In addition, the information collection unit refers to the user's credit score and suggests the optimal payment method. For example, for a user with a low credit score, it suggests a payment method to improve the credit score. In this way, by referring to the user's credit score and suggesting the optimal payment method, payment efficiency can be improved.

[0082] The information collection unit can use the emotion estimation function to analyze the user's emotions when making a payment and suggest a payment method that reduces stress. For example, the generation AI in the information collection unit analyzes the user's emotions when making a payment and suggests a payment method that reduces stress. For example, if the user is feeling stressed, it suggests an easy and smooth payment method. The information collection unit also uses the emotion estimation function to analyze the user's emotions when making a payment in real time and suggests a payment method that reduces stress based on the results. For example, if the user is nervous, it suggests an easy payment method. The information collection unit also monitors the user's emotions when making a payment in real time and suggests a payment method that reduces stress according to the emotion. For example, if the user wants to relax, it suggests a smooth payment method. In this way, by analyzing the user's emotions when making a payment and suggesting a payment method that reduces stress, it is possible to improve payment satisfaction.

[0083] The information collection unit can compare fees for different payment methods and suggest the most cost-effective payment method. For example, the information collection unit allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method. For example, it compares fees for credit cards, debit cards, and cash and suggests the optimal method. The information collection unit also allows the generation AI to suggest payment methods with low fees based on keywords entered by the user. For example, if the user enters "save," it suggests payment methods with low fees. The information collection unit also allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method. For example, it suggests payment methods with low fees when traveling abroad. This allows the generation AI to compare fees for different payment methods and suggest the most cost-effective payment method, thereby improving payment efficiency.

[0084] The information collection unit can provide cashback or point redemption information according to the user's payment method. For example, the information collection unit allows the generation AI to provide cashback or point redemption information according to the user's payment method. For example, the information collection unit presents credit card cashback or point redemption rates. The information collection unit also allows the generation AI to provide cashback or point redemption information based on keywords entered by the user. For example, if the user enters "bargain," the information collection unit suggests the most advantageous payment method. The information collection unit also allows the generation AI to provide cashback or point redemption information according to the user's payment method. For example, the information collection unit provides cashback or point redemption information for specific stores or services. This makes it possible to improve payment satisfaction by providing cashback or point redemption information according to the user's payment method.

[0085] The information collection unit uses the emotion estimation function to suggest a payment method that corresponds to the user's emotions, thereby improving satisfaction at the time of payment. In the information collection unit, for example, the generation AI analyzes the user's emotional state and suggests a payment method that corresponds to the emotion. For example, if the user is feeling stressed, it suggests an easy and smooth payment method. In addition, the information collection unit uses the emotion estimation function to analyze the user's emotional state in real time and suggests a payment method based on the results. For example, if the user wants to relax, it suggests a smooth payment method. In addition, the information collection unit monitors the user's emotional state in real time and suggests a payment method that corresponds to the emotion. For example, it suggests a payment method that gives the user a sense of satisfaction. In this way, it is possible to suggest a payment method that corresponds to the user's emotions, thereby improving satisfaction at the time of payment and improving payment satisfaction.

[0086] The information collection unit can customize and provide information about surrounding tourist attractions based on the user's interests and preferences. For example, the generation AI analyzes the user's past search history and visit history, and customizes and provides information about surrounding tourist attractions based on the user's interests and preferences. For example, if the user is interested in historical places, the generation AI suggests nearby historical tourist attractions. The information collection unit also customizes and provides information about tourist attractions that matches the user's interests and preferences based on keywords entered by the user. For example, if the user enters "nature," the generation AI suggests tourist attractions with beautiful surrounding natural scenery. The information collection unit also customizes and provides information about surrounding tourist attractions based on the user's interests and preferences. For example, if the user is interested in "food," the generation AI suggests popular restaurants and cafes in the area. This allows the user to customize and provide information about surrounding tourist attractions based on their interests and preferences, thereby improving travel satisfaction.

[0087] The information collection unit can suggest the optimal order to visit tourist spots according to the user's stay time. In the information collection unit, for example, the generation AI takes into account the user's stay time and suggests the optimal order to visit tourist spots. For example, it suggests a route that allows for efficient sightseeing in a short amount of time. In addition, the information collection unit suggests the optimal order to visit tourist spots according to the stay time input by the user, with the generation AI. For example, if the user inputs "half day," it suggests an order of tourist spots that can be visited in half a day. In addition, the information collection unit suggests the optimal order to visit tourist spots according to the user's stay time. For example, if the user inputs "one day," it suggests a route that allows for efficient sightseeing in one day. In this way, the efficiency of travel can be improved by suggesting the optimal order to visit tourist spots according to the user's stay time.

[0088] The information collection unit uses the emotion estimation function to provide tourist destination information according to the user's emotions, thereby improving satisfaction with the destinations. For example, the generation AI in the information collection unit analyzes the user's emotional state and provides tourist destination information according to the emotions. For example, if the user wants to relax, it suggests tourist destinations where the user can relax. The information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide tourist destination information based on the results. For example, if the user is excited, it suggests active activities. The information collection unit also monitors the user's emotional state in real time and provides tourist destination information according to the emotions. For example, if the user wants to relax, it suggests quiet tourist destinations. This provides tourist destination information according to the user's emotions, thereby improving satisfaction with the destinations and improving travel satisfaction.

[0089] The information collection unit can collect congestion information at surrounding tourist attractions in real time and suggest a visit time to avoid crowds. For example, the information collection unit uses a generation AI to collect congestion information at tourist attractions in real time and suggest a visit time to the user to avoid crowds. For example, the information collection unit suggests a time period with less crowds. Furthermore, the information collection unit uses a generation AI to collect congestion information in real time based on the tourist attractions entered by the user and suggest an optimal visit time. For example, if a user enters "Tokyo Tower," the information collection unit suggests a time period with less crowds. Furthermore, the information collection unit uses a generation AI to collect congestion information at surrounding tourist attractions in real time and suggest a visit time to avoid crowds. For example, if a user enters "weekend," the information collection unit suggests visiting on a weekday when it is less crowded. In this way, travel satisfaction can be improved by collecting congestion information at surrounding tourist attractions in real time and suggesting a visit time to avoid crowds.

[0090] The information collection unit can provide the user with historical and cultural information about surrounding tourist destinations to deepen their understanding. For example, the generation AI collects historical and cultural information about surrounding tourist destinations and provides it to the user. For example, it explains the historical background and cultural significance of the tourist destination. Furthermore, the information collection unit can provide the generation AI with historical and cultural information about the tourist destination based on the tourist destination entered by the user. For example, if the user enters "Kyoto," it can provide information about the history and culture of Kyoto. Furthermore, the information collection unit can provide the generation AI with historical and cultural information about surrounding tourist destinations to deepen the user's understanding. For example, it can provide information about tourist destination guidebooks and museums. This can provide historical and cultural information about surrounding tourist destinations to deepen the user's understanding, thereby improving travel satisfaction.

[0091] The information collection unit can use the emotion estimation function to provide information on relaxing tourist spots that correspond to the user's emotions. For example, the generation AI in the information collection unit analyzes the user's emotional state and provides information on relaxing tourist spots. For example, if the user is feeling stressed, it suggests tourist spots where they can relax. The information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide information on relaxing tourist spots based on the results. For example, if the user is tired, it suggests quiet tourist spots. The information collection unit also monitors the user's emotional state in real time and provides information on relaxing tourist spots that correspond to the user's emotions. For example, if the user wants to relax, it suggests tourist spots rich in nature. This makes it possible to improve travel satisfaction by providing information on relaxing tourist spots that correspond to the user's emotions.

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

[0093] The information collection unit can also monitor the user's health condition and generate a health-conscious itinerary. For example, if the user has allergies, it can suggest restaurants and accommodations that cater to allergies. If the user feels they are lacking in exercise, it can generate an itinerary that includes walking tours and hiking trails. Furthermore, if the user is tired, it can suggest an itinerary that includes relaxing spas and hot spring facilities. This can improve travel satisfaction by providing an itinerary that is customized according to the user's health condition.

[0094] The information collection unit can learn the user's past travel history and preferences and generate an individually customized itinerary. For example, it can analyze the characteristics of tourist spots visited in the past and the types of accommodations to generate an itinerary that is optimal for the next trip. The generation AI can also estimate the user's preferences based on keywords and search history entered in the past by the user and suggest a customized itinerary. It can also suggest an itinerary that includes the user's favorite restaurants and shopping spots. This can improve user satisfaction by generating an individually customized itinerary based on the user's past travel history and preferences.

[0095] The information collection unit can use the user's real-time location information to generate an itinerary that includes the optimal route from the current location. For example, it can propose a real-time route that takes traffic congestion and operation conditions into consideration. It can also generate a route that combines transportation methods such as walking, cycling, and public transportation. It can also propose an efficient itinerary that takes into consideration travel time and distance between tourist spots. In this way, it is possible to improve travel efficiency by using the user's real-time location information to generate an itinerary that includes the optimal route from the current location.

[0096] The information collection unit can use the emotion estimation function to analyze emotions associated with keywords entered by the user and generate an itinerary that elicits positive emotions. For example, if the user enters "relaxation," an itinerary that includes relaxing tourist spots and activities can be generated. If the user enters "adventure," it is also possible to suggest tourist spots and activities that stimulate the sense of adventure. Furthermore, if the user enters "family trip," it is also possible to suggest an itinerary that includes tourist spots and activities that the whole family can enjoy. In this way, by analyzing the user's emotions and generating an itinerary that elicits positive emotions, it is possible to improve travel satisfaction.

[0097] The information collection unit can generate a multimedia itinerary that includes related videos and images based on keywords entered by the user. For example, it can propose an itinerary that includes promotional videos of tourist attractions and user review videos. It can also generate a visually appealing multimedia itinerary that includes photos and maps of tourist attractions. It can also propose an itinerary that includes 360-degree panoramic images and drone footage of tourist attractions. In this way, by generating a multimedia itinerary that includes related videos and images based on keywords entered by the user, it is possible to provide a visually appealing itinerary.

[0098] The information collection unit can refer to reviews and ratings from other users and generate an itinerary that includes the most highly rated tourist spots and activities. For example, it can analyze reviews on travel sites and social media to suggest highly rated tourist spots. It can also suggest highly rated tourist spots that match the user's preferences. It can also suggest highly rated tourist spots that are popular with families. This makes it possible to improve user satisfaction by generating an itinerary that includes the most highly rated tourist spots and activities by referring to reviews and ratings from other users.

[0099] The information collection unit can use the emotion estimation function to analyze other users' emotional reactions to keywords entered by the user and generate an itinerary that resonates with the user. For example, if a user enters "relaxation," it is possible to suggest tourist spots where other users felt relaxed. Also, if a user enters "adventure," it is possible to suggest tourist spots where other users felt adventurous. Furthermore, if a user enters "family trip," it is possible to suggest tourist spots where other users felt they had fun with their families. In this way, by analyzing other users' emotional reactions to keywords entered by the user and generating an itinerary that resonates with the user, it is possible to improve user satisfaction.

[0100] The real-time information provision unit can collect traffic congestion information in real time and suggest the optimal travel route. For example, the generation AI can collect traffic congestion information in real time from a traffic information service and suggest the optimal travel route to the user. It can also suggest alternative routes to avoid traffic congestion. It can also suggest the use of public transportation that is not affected by traffic congestion. In this way, by collecting traffic congestion information in real time and suggesting the optimal travel route, it is possible to improve travel efficiency.

[0101] The real-time information provision unit can collect event information in real time and suggest events that match the user's interests. For example, the generation AI can collect event information in real time from an event information provision service and suggest events that match the user's interests. It can also provide information on music concerts and sporting events. Furthermore, it can suggest events that the user may be interested in based on the user's past event participation history. In this way, by collecting event information in real time and suggesting events that match the user's interests, it is possible to improve travel satisfaction.

[0102] The real-time information provision unit can use the emotion estimation function to analyze the user's current emotional state and provide real-time information to reduce stress. For example, the generation AI can analyze the user's current emotional state and provide real-time information to reduce stress. It can also suggest relaxing tourist spots and activities. Furthermore, if the user is tired, it can suggest nearby cafes and rest spots. This makes it possible to improve travel satisfaction by analyzing the user's current emotional state and providing real-time information to reduce stress.

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

[0104] Step 1: In the keyword input section, the user inputs keywords for the destination they want to visit. For example, they can input place names, tourist spot names, activity names, etc. Step 2: The information collection unit collects information based on the keywords entered by the keyword input unit. For example, it uses web scraping technology to collect information about tourist spots. It can also use APIs to obtain information about transportation schedules and payment methods. It can also perform database searches to collect information about tourist spot opening hours and surrounding tourist spots. Step 3: The itinerary generation unit generates an itinerary based on the information collected by the information collection unit, for example, an itinerary including a list of destinations, means of transportation, and time allocation. Step 4: The real-time information provider provides real-time information about the operating status of tourist attractions and weather information. For example, it provides information about temporary closures, congestion, and weather forecasts. This helps users reduce stress caused by changes to their plans.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0133] 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0139] 7, 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0149] 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a keyword input section for inputting keywords for destinations that the user wishes to visit; an information collection unit that collects information based on the keyword input by the keyword input unit; an itinerary generation unit that generates an itinerary based on the information collected by the information collection unit; A real-time information providing unit that provides real-time operating status and weather information of tourist spots. A system characterized by:

2. The information collecting unit Learns your travel history and preferences to generate personalized itineraries 2. The system of claim 1.

3. The information collecting unit Uses the user's real-time location to generate an itinerary with the best route from their current location 2. The system of claim 1.

4. The information collecting unit Analyzes the sentiment of the keywords entered by the user and generates an itinerary that elicits positive emotions 2. The system of claim 1.

5. The information collecting unit Generate a multimedia itinerary with relevant videos and images based on user-entered keywords 2. The system of claim 1.

6. The information collecting unit See reviews and ratings from other users and generate an itinerary that includes the most highly rated attractions and activities 2. The system of claim 1.

7. The information collecting unit Analyze other users' emotional responses to keywords entered by the user to generate an itinerary that resonates with them 2. The system of claim 1.

Images