System

Abstract

An object of a system according to an embodiment is to automatically create an optimal visit destination and route based on information of a traveler and reflect real-time information.SOLUTION: A system includes an input unit, an analysis unit, a route creation unit, a real-time information acquisition unit, and a translation unit. The input unit inputs information on a traveler. The analysis unit analyzes the information input by the input unit. The route creation unit creates a visit destination and a route based on the information analyzed by the analysis unit. The real-time information acquisition unit acquires real-time information based on the route created by the route creation unit. The translation unit translates the information acquired by the real-time information acquisition unit into the language of the traveler.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 technologies do not adequately automatically create optimal destinations and routes based on traveler information and reflect real-time information, leaving room for improvement.

[0005] The system according to the embodiment aims to automatically create optimal destinations and routes based on traveler information and to reflect real-time information. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a route creation unit, a real-time information acquisition unit, and a translation unit. The input unit inputs information about the traveler. The analysis unit analyzes the information input by the input unit. The route creation unit creates destinations and a route based on the information analyzed by the analysis unit. The real-time information acquisition unit acquires real-time information based on the route created by the route creation unit. The translation unit translates the information acquired by the real-time information acquisition unit into the traveler's language. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create optimal destinations and routes based on traveler information and reflect real-time information. [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) A travel planning system according to an embodiment of the present invention automatically creates optimal destinations and routes based on traveler information and provides a feasible itinerary that takes real-time information into account. The traveler's information is input into the travel planning system, and a generation AI analyzes the information to automatically create optimal destinations and routes. The generation AI then takes into account and analyzes real-time information, such as the business status and transportation status of destinations, to provide a feasible itinerary. The generation AI then translates the information into the traveler's language and provides it in the form of an application. For example, a traveler inputs their own information into the travel planning system. For example, the traveler inputs information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, and travel period. This information is then input into the generation AI. The travel planning system then uses the generation AI to analyze the input information and automatically create optimal destinations and routes for the traveler. The generation AI selects destinations based on the traveler's interests and calculates the optimal route based on the budget and travel period. For example, if a traveler is interested in historical buildings, the system selects destinations centered around those historical buildings in the area and creates an efficient route. Next, the travel planning system uses the generation AI to take into account and analyze real-time information such as the business status and operation status of destinations and provide a feasible itinerary. For example, the system adjusts the itinerary to allow travelers to travel smoothly, taking into account the business days and business hours of destinations and the operation status of public transportation. Next, the travel planning system uses the generation AI to translate the information into the traveler's language and provide it in the form of an application. For example, if the traveler speaks Japanese, the generation AI translates the created information about destinations and routes into Japanese and provides it through an application. This allows travelers to check their travel plans in their own language and enjoy their trip with peace of mind. As a result, the travel planning system automatically creates optimal destinations and routes by simply entering the traveler's information, and provides a feasible itinerary that takes real-time information into account. Furthermore, by providing information translated into the traveler's language through an application, travelers can enjoy their trip with peace of mind. This allows the travel planning system to automatically create optimal destinations and routes based on the traveler's information and provide a feasible itinerary that takes real-time information into account.For example, by simply inputting their information, the system automatically creates optimal destinations and routes, provides a feasible itinerary that takes real-time information into account, and provides information translated into the traveler's language through the application, allowing the traveler to enjoy their trip with peace of mind.

[0029] A travel planning system according to an embodiment includes an input unit, an analysis unit, a route creation unit, a real-time information acquisition unit, and a translation unit. The input unit inputs traveler information. The traveler information includes, but is not limited to, the name, age, gender, interests, budget, and travel period. The input unit allows the traveler to input information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, and travel period. The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit selects destinations based on the traveler's interests and calculates an optimal route based on the budget and travel period. The route creation unit uses a generation AI to create destinations and routes based on the information analyzed by the analysis unit. The route creation is performed based on, but is not limited to, criteria such as the shortest distance, optimal time, and mode of transportation. For example, the route creation unit considers and analyzes real-time information such as the business status and operation status of destinations to provide a feasible itinerary. The real-time information acquisition unit acquires real-time information such as business days and hours of destinations to be visited and the operation status of public transportation. The real-time information may be acquired, for example, through API integration, sensor data, or other methods, but is not limited to these examples. For example, the real-time information acquisition unit acquires real-time information such as business days and hours of destinations to be visited and the operation status of public transportation. The translation unit uses a generation AI to translate the information acquired by the real-time information acquisition unit into the traveler's language. The translation is performed based on criteria such as, for example, machine translation, handling of technical terms, and translation accuracy, but is not limited to these examples. For example, the translation unit translates into the traveler's language and provides it in the form of an application. As a result, the travel planning system according to the embodiment can automatically create optimal destinations and routes based on the traveler's information and provide a feasible itinerary that takes real-time information into account. For example, by simply inputting the traveler's information, the system can automatically create optimal destinations and routes and provide a feasible itinerary that takes real-time information into account.In addition, by providing information translated into travelers' languages ​​through the application, travelers can enjoy their trip with peace of mind.

[0030] The input unit can input information about the traveler's attributes, interests, budget, and duration. Examples of traveler attributes include, but are not limited to, age, gender, and occupation. The input unit can allow the traveler to input information such as age, gender, and occupation. Examples of interests include, but are not limited to, history, nature, and gourmet food. The input unit can allow the traveler to input interests such as history, nature, and gourmet food. Examples of budgets include, but are not limited to, a daily budget and a total budget. The input unit can allow the traveler to input information such as a daily budget and a total budget. Examples of duration include, but are not limited to, the start and end dates of the trip and the number of days of stay. The input unit can allow the traveler to input information such as the start and end dates of the trip and the number of days of stay. By inputting detailed information about the traveler, more accurate analysis is possible. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input traveler information into the generation AI and have the generation AI analyze the information.

[0031] The analysis unit can select destinations based on the traveler's interests and calculate routes that suit the traveler's budget and time frame. The analysis unit, for example, selects destinations based on the traveler's interests. For example, if the traveler is interested in historical buildings, the analysis unit selects destinations that focus on historical buildings in the area. The analysis unit can also select destinations based on the traveler's interests and concerns. For example, if the traveler is interested in nature, the analysis unit selects destinations that focus on the natural scenery in the area. The analysis unit can also select destinations based on the traveler's interests and concerns. For example, if the traveler is interested in gourmet food, the analysis unit selects destinations that focus on gourmet spots in the area. The analysis unit can, for example, calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the optimal route based on the traveler's budget and time frame. The analysis unit can also calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the shortest route based on the traveler's budget and time frame. The analysis unit can also calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the optimal route based on the traveler's budget and time frame. This makes it possible to provide optimal destinations and routes based on the traveler's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may perform analysis using a generation AI model that selects destinations based on the traveler's interests and calculates routes according to budget and time period.

[0032] The route creation unit can take into account and analyze real-time information on the business status and operation status of destinations to provide a feasible schedule. The route creation unit, for example, takes into account and analyzes real-time information on the business status and operation status of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information on the business status and operation status of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information such as the business days and business hours of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information such as the business days and business hours of destinations to provide a feasible schedule. For example, the route creation unit can take into account and analyze .... For example, the route creation unit can create routes using a generative AI model that takes into account and analyzes real-time information on the business and operational status of destinations and provides feasible itineraries.

[0033] The real-time information acquisition unit can acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit acquires, for example, real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit can also acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit can also acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. In this way, by acquiring real-time information on the destinations, it is possible to provide the traveler with the latest information. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input real-time information such as the opening days and business hours of the destination and the operation status of public transportation into the generation AI, and have the generation AI acquire the information.

[0034] The translation unit can translate into the traveler's language and provide it in the form of an application. For example, the translation unit can translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks Japanese, the translation unit can translate the created information about the destinations and route into Japanese and provide it through an application. The translation unit can also translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks English, the translation unit can translate the created information about the destinations and route into English and provide it through an application. The translation unit can also translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks French, the translation unit can translate the created information about the destinations and route into French and provide it through an application. This allows the traveler to check their travel plan in their own language by translating it into their own language. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the created information about the destinations and route into a generation AI and have the generation AI translate the information.

[0035] The input unit can analyze the traveler's past travel history and suggest an input method. The input unit, for example, analyzes the traveler's past travel history and suggests the optimal input method. For example, the input unit automatically displays destinations that the traveler has frequently input in the past as candidates. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. The input unit can also predict and suggest destinations to be used during a specific time period based on the traveler's past travel history. This makes input work more efficient by suggesting the optimal input method based on the past travel history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's past travel history data into a generation AI and have the generation AI suggest the optimal input method.

[0036] The input unit can automatically complete related information by taking into account the traveler's current location information when inputting information. For example, when the traveler opens the app, the input unit automatically obtains the traveler's current location and sets it as the departure point. Furthermore, when the traveler inputs a destination, the input unit can also suggest optimal candidate locations by taking into account the distance from the current location. Furthermore, when the traveler uses the app while traveling, the input unit can also update the current location in real time and reflect it as the departure point. This simplifies the input work by taking into account the current location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's current location information to the generation AI and cause the generation AI to automatically complete related information.

[0037] The input unit can select the optimal input means depending on the traveler's input method (voice, text, image, etc.) at the time of input. For example, the input unit can automatically set related destinations by simply having the traveler input "I want to see historical buildings" by voice. The input unit can also allow the traveler to easily set destinations by performing specific gestures on the smartphone screen. The input unit can also allow the traveler to set destinations more intuitively by combining voice input and gesture input. This selects the optimal means depending on the traveler's input method, thereby making input work more efficient. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's input method data into the generation AI and have the generation AI select the optimal input means.

[0038] The input unit can analyze the traveler's social media activity at the time of input and automatically input related information. For example, the input unit can automatically display places where the traveler has checked in on social media as candidates. The input unit can also analyze the content of the traveler's social media posts and automatically input related destinations. The input unit can also automatically input related destinations by referring to the activity of the traveler's friends on social media. In this way, the input of related information is made more efficient by analyzing social media activity. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's social media data into a generation AI and have the generation AI automatically input related information.

[0039] The input unit can customize the input method by reflecting the traveler's past feedback when inputting data. For example, the input unit preferentially suggests input methods that the traveler has previously preferred. The input unit can also customize the input interface based on the traveler's past feedback. The input unit can also analyze the traveler's past feedback and suggest the optimal input means. In this way, the input method is optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's past feedback data into the generation AI and have the generation AI customize the input method.

[0040] The input unit can provide an optimal input interface by taking into account the traveler's device information at the time of input. For example, if the traveler is using a smartphone, the input unit can provide an input interface that matches the screen size. Furthermore, if the traveler is using a tablet, the input unit can also provide an input interface that is optimized for a large screen. Furthermore, if the traveler is using a smartwatch, the input unit can also provide a simple and highly visible input interface. This makes it possible to provide an optimal input interface by taking into account the device information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's device information to the generation AI and cause the generation AI to provide an optimal input interface.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the traveler's past travel history. For example, the analysis unit can suggest optimal destinations based on places the traveler has visited in the past. The analysis unit can also suggest destinations that avoid crowds based on the traveler's past travel history. The analysis unit can also analyze the traveler's past travel history and suggest the most efficient route. In this way, by referring to the past travel history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the traveler's past travel history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0042] The analysis unit can reflect changes in the traveler's interests in real time during analysis. For example, the analysis unit reflects new places that the traveler has become interested in in the analysis in real time. The analysis unit can also immediately update the analysis results if the traveler's interests change. The analysis unit can also adjust the analysis results based on the traveler's real-time feedback. This allows for reflecting changes in interests in real time, making it possible to provide more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the traveler's interest data into the generation AI and have the generation AI reflect the data in real time.

[0043] During analysis, the analysis unit can customize the analysis results by taking into account the traveler's attribute information. The analysis unit can suggest optimal destinations based on the traveler's age and gender, for example. The analysis unit can also prioritize analysis of places that are likely to be of interest to the traveler based on the traveler's attribute information. The analysis unit can also customize the analysis results by taking into account the traveler's attribute information. In this way, the analysis results are customized by taking into account the attribute information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the traveler's attribute information into the generation AI and have the generation AI customize the analysis results.

[0044] The analysis unit can perform the analysis while taking into account the geographic distribution of travelers. For example, if travelers are concentrated in a specific area, the analysis unit can prioritize the analysis of destinations in that area. The analysis unit can also suggest optimal destinations based on the geographic distribution of travelers. The analysis unit can also customize the analysis results by taking into account the geographic distribution of travelers. In this way, more appropriate analysis results can be provided by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the geographic distribution data of travelers into the generation AI and have the generation AI perform the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the traveler. For example, the analysis unit performs analysis by referring to literature related to fields in which the traveler is interested. The analysis unit can also improve the accuracy of the analysis based on literature related to the traveler's past travels. The analysis unit can also suggest optimal destinations by referring to literature related to the traveler's interests. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the traveler's related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0046] The analysis unit can perform the analysis while taking into account the market value of the traveler. For example, the analysis unit can suggest optimal destinations to visit based on the market value of the traveler. The analysis unit can also customize the analysis results by taking into account the market value of the traveler. The analysis unit can also prioritize the analysis of places that are likely to be of interest to the traveler based on the market value of the traveler. In this way, by taking market value into consideration, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the market value data of the traveler into the generation AI and have the generation AI perform the analysis.

[0047] When creating a route, the route creation unit can improve the accuracy of the route by taking into account the interrelationships between the destinations. The route creation unit creates an efficient route, for example, by taking into account the geographical proximity of the destinations. The route creation unit can also create a route that visits the destinations in an optimal order based on the interrelationships between the destinations. The route creation unit can also create a route that minimizes travel time by taking into account the interrelationships between the destinations. In this way, a more efficient route can be provided by taking into account the interrelationships between the destinations. Some or all of the above-described processing in the route creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the route creation unit can input interrelationship data between the destinations into the generation AI and cause the generation AI to improve the accuracy of the route.

[0048] When creating a route, the route creation unit can customize the route by taking into account attribute information of the destinations. The route creation unit, for example, takes into account the business hours of the destinations to create an optimal visiting order. The route creation unit can also create a route that avoids congestion based on the congestion status of the destinations. The route creation unit can also create a route that suits the interests of the traveler by taking into account attribute information of the destinations. In this way, by taking into account the attribute information of the destinations, it is possible to provide a route that suits the interests of the traveler. Some or all of the above-mentioned processing in the route creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the route creation unit can input attribute information data of the destinations to the generation AI and have the generation AI customize the route.

[0049] The route creation unit can reflect the business status and operation status of destinations in real time when creating a route. For example, the route creation unit creates a route by reflecting the business days and business hours of destinations in real time. The route creation unit can also create a route by reflecting the operation status of public transportation in real time. The route creation unit can also create a route by reflecting the real-time congestion status of destinations. In this way, by reflecting real-time information, it is possible to provide a feasible route. Some or all of the above-mentioned processing in the route creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the route creation unit can input real-time information on the business status and operation status of destinations into the generation AI and have the generation AI reflect the information in real time.

[0050] When creating a route, the route creation unit can create a route taking into account the geographic distribution of the destinations. For example, if the destinations are concentrated in a specific area, the route creation unit creates a route that efficiently travels around that area. The route creation unit can also create an optimal visiting order based on the geographic distribution of the destinations. The route creation unit can also create a route that minimizes travel time by taking into account the geographic distribution of the destinations. In this way, by taking the geographic distribution into account, a more efficient route can be provided. Some or all of the above-described processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input geographic distribution data of the destinations into the generation AI and have the generation AI create a route.

[0051] When creating a route, the route creation unit can improve the accuracy of the route by referring to literature related to the destination. The route creation unit, for example, creates an optimal route by referring to literature related to the destination. The route creation unit can also create an interesting route based on literature related to the history and culture of the destination. The route creation unit can also create a route that matches the traveler's interests by referring to literature related to the destination. In this way, the accuracy of the route is improved by referring to related literature. Some or all of the above-mentioned processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input literature data related to the destination into the generation AI and cause the generation AI to improve the accuracy of the route.

[0052] When creating a route, the route creation unit can create the route taking into account the market value of the destinations. For example, the route creation unit creates an optimal visiting order based on the market value of the destinations. The route creation unit can also create a route that suits the traveler's interests by taking into account the market value of the destinations. The route creation unit can also prioritize incorporating places that are likely to be of interest into the route based on the market value of the destinations. This makes it possible to provide a more appropriate route by taking market value into consideration. Some or all of the above-mentioned processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input market value data of the destinations into the generation AI and have the generation AI create the route.

[0053] When acquiring real-time information, the real-time information acquisition unit can improve the accuracy of acquisition by referring to the past business status of the destination. The real-time information acquisition unit improves the accuracy of acquisition of real-time information based on, for example, the past business days and business hours of the destination. The real-time information acquisition unit can also improve the accuracy of acquisition of real-time information by referring to the past congestion status of the destination. The real-time information acquisition unit can also analyze the past business status of the destination and provide optimal real-time information. In this way, by referring to the past business status, the accuracy of information acquisition is improved. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input past business status data of the destination into the generation AI and cause the generation AI to improve the accuracy of acquisition.

[0054] The real-time information acquisition unit can customize the information by taking into account attribute information of the destinations when acquiring the real-time information. The real-time information acquisition unit, for example, provides optimal real-time information based on the attribute information of the destinations. The real-time information acquisition unit can also provide real-time information that matches the interests of the traveler by taking into account the attribute information of the destinations. The real-time information acquisition unit can also customize the real-time information based on the attribute information of the destinations. This makes it possible to provide more appropriate real-time information by taking the attribute information into account. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input attribute information data of the destinations to the generation AI and cause the generation AI to customize the information.

[0055] The real-time information acquisition unit can reflect the business status and operation status of the destination in real time when acquiring the real-time information. The real-time information acquisition unit provides information that reflects, for example, the business days and business hours of the destination in real time. The real-time information acquisition unit can also provide information that reflects the operation status of public transportation in real time. The real-time information acquisition unit can also provide information that reflects the real-time congestion status of the destination. By reflecting real-time information, more appropriate information can be provided. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input real-time information on the business status and operation status of the destination to the generation AI and cause the generation AI to reflect the information in real time.

[0056] When acquiring real-time information, the real-time information acquisition unit can acquire information taking into account the geographic distribution of destinations. For example, if destinations are concentrated in a specific area, the real-time information acquisition unit prioritizes acquiring real-time information for that area. The real-time information acquisition unit can also acquire optimal real-time information based on the geographic distribution of destinations. The real-time information acquisition unit can also customize the real-time information taking into account the geographic distribution of destinations. This makes it possible to provide more appropriate real-time information by taking the geographic distribution into consideration. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input geographic distribution data of destinations to a generation AI and cause the generation AI to acquire information.

[0057] The real-time information acquisition unit can improve the accuracy of the information by referring to literature related to the destination when acquiring real-time information. The real-time information acquisition unit, for example, refers to literature related to the destination to acquire optimal real-time information. The real-time information acquisition unit can also provide interesting real-time information based on literature related to the history and culture of the destination. The real-time information acquisition unit can also provide real-time information that matches the traveler's interests by referring to literature related to the destination. In this way, by referring to related literature, the accuracy of the information is improved. Some or all of the above-mentioned processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input literature data related to the destination into the generation AI and cause the generation AI to improve the accuracy of the information.

[0058] The real-time information acquisition unit can acquire information taking into account the market value of the destination when acquiring real-time information. The real-time information acquisition unit provides optimal real-time information based on, for example, the market value of the destination. The real-time information acquisition unit can also provide real-time information that matches the traveler's interests by taking into account the market value of the destination. The real-time information acquisition unit can also preferentially acquire real-time information of places that are likely to be of interest based on the market value of the destination. This makes it possible to provide more appropriate real-time information by taking market value into account. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input market value data of the destination to the generation AI and cause the generation AI to acquire information.

[0059] The translation unit can improve the accuracy of the translation by referring to the traveler's past translation history when translating. The translation unit provides the optimal translation based on translation expressions used by the traveler in the past, for example. The translation unit can also improve the accuracy of the translation by referring to the traveler's past translation history. The translation unit can also analyze the traveler's past translation history and provide the most appropriate translation expression. In this way, by referring to the past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the traveler's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0060] The translation unit can customize the translation results by taking into account the traveler's attribute information during translation. The translation unit provides the most appropriate translation expression based on, for example, the traveler's age and gender. The translation unit can also prioritize translating expressions that are likely to be of interest to the traveler based on the traveler's attribute information. The translation unit can also customize the translation results by taking into account the traveler's attribute information. In this way, by taking the attribute information into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the traveler's attribute information data into the generation AI and have the generation AI customize the translation results.

[0061] The translation unit can reflect changes in the language used by the traveler in real time during translation. For example, if the language used by the traveler changes, the translation unit updates the translation results in real time. The translation unit can also provide a language switching function if the traveler uses multiple languages. The translation unit can also reflect changes in the language used by the traveler in real time to provide optimal translation results. By reflecting changes in the language used in real time, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the language used by the traveler into a generation AI and have the generation AI update the data in real time.

[0062] The translation unit can take into account the geographical distribution of travelers when translating. For example, if travelers are concentrated in a specific region, the translation unit provides a translation optimized for the language of that region. The translation unit can also provide the optimal translation expression based on the geographical distribution of travelers. The translation unit can also customize the translation results by taking into account the geographical distribution of travelers. In this way, by taking the geographical distribution into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input geographical distribution data of travelers into a generation AI and have the generation AI perform the translation.

[0063] The translation unit can improve the accuracy of the translation by referring to literature related to the traveler during translation. For example, the translation unit performs translation by referring to literature related to the field in which the traveler is interested. The translation unit can also improve the accuracy of the translation based on literature related to the traveler's past travels. The translation unit can also provide optimal translation expressions by referring to literature related to the traveler's interests. In this way, the accuracy of the translation is improved by referring to related literature. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on literature related to the traveler into a generation AI and have the generation AI improve the accuracy of the translation.

[0064] The translation unit can take into account the market value of the traveler when translating. The translation unit provides the most appropriate translation expression based on the market value of the traveler, for example. The translation unit can also customize the translation result by taking into account the market value of the traveler. The translation unit can also prioritize translating expressions that are likely to be of interest to the traveler based on the market value of the traveler. In this way, by taking market value into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input the market value data of the traveler into a generation AI and have the generation AI perform the translation.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The travel planning system can further include a health management unit that monitors the traveler's health condition. The health management unit monitors the traveler's vital signs, such as heart rate, blood pressure, and body temperature, in real time, and can automatically adjust the travel plan if an abnormality is detected. For example, if the traveler's heart rate suddenly rises, the health management unit can suggest the traveler take a break and change the itinerary of the destinations they visit. Also, if the traveler's blood pressure is high, the health management unit can suggest a less stressful route. Furthermore, if the traveler's body temperature rises, the health management unit can provide information on medical institutions and make reservations as necessary. This allows for flexible travel planning that adapts to the traveler's health condition.

[0067] The travel planning system can further include a social media analysis unit that analyzes the traveler's social media activity and automatically inputs related information. The social media analysis unit analyzes the places where the traveler has checked in and the content of posts on social media and reflects this in the travel plan. For example, the social media analysis unit can automatically suggest related destinations based on places the traveler has visited in the past. It can also suggest new destinations to the traveler based on places the traveler's friends have visited. Furthermore, information about specific events and festivals can be obtained from the traveler's social media activity and incorporated into the travel plan. This makes it possible to create personalized travel plans based on social media activity.

[0068] The travel planning system can further include a feedback analysis unit that customizes the travel plan by reflecting the traveler's past feedback. The feedback analysis unit analyzes feedback provided by the traveler in the past and reflects it in the travel plan. For example, the feedback analysis unit can propose a new travel plan based on the traveler's past favorite destinations and activities. It can also improve points that the traveler was dissatisfied with in the past to provide a more satisfying travel plan. Furthermore, it can propose the best destinations for a specific time period or season based on the traveler's past feedback. This makes it possible to create a personalized travel plan that reflects past feedback.

[0069] The travel planning system can further include a location information analysis unit that adjusts the travel plan in real time, taking into account the traveler's current location information. The location information analysis unit acquires the traveler's current location in real time and reflects it in the travel plan. For example, if the traveler arrives at a destination earlier than planned, the location information analysis unit can suggest the next destination early. Also, if the traveler gets caught in traffic congestion, the location information analysis unit can suggest an alternative route. Furthermore, if the traveler stops at an unplanned location, the location information analysis unit can suggest a new destination. This enables flexible travel planning that takes into account the current location information.

[0070] The travel planning system can further include a device adaptation unit that provides an optimal travel plan taking into account the traveler's device information. The device adaptation unit optimizes the travel plan taking into account the type and characteristics of the device used by the traveler. For example, if the traveler is using a smartphone, the device adaptation unit can provide a display method that suits the screen size. Also, if the traveler is using a tablet, the device adaptation unit can provide a display method that is optimized for a large screen. Furthermore, if the traveler is using a smartwatch, the device adaptation unit can provide a simple and highly visible display method. This allows for the provision of an optimal travel plan that takes into account the device information.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The input unit inputs traveler information. Traveler information includes name, age, gender, interests, budget, duration, etc. For example, a traveler can input information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, duration, etc. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it selects destinations based on the traveler's interests and calculates the optimal route based on the traveler's budget and travel period. Step 3: The route creation unit uses the generation AI to create destinations and routes based on the information analyzed by the analysis unit. Route creation is based on criteria such as the shortest distance, optimal time, and transportation method. For example, it takes into account and analyzes real-time information such as the business status and operation status of destinations to provide a feasible itinerary. Step 4: The real-time information acquisition unit acquires real-time information such as the business days and business hours of the destination, the operation status of public transport, etc. Real-time information is acquired through API integration, sensor data, etc. Step 5: The translation unit uses the generation AI to translate the information acquired by the real-time information acquisition unit into the traveler's language. The translation is performed based on criteria such as machine translation, handling of technical terms, and translation accuracy. For example, the translation unit translates into the traveler's language and provides it in the form of an application.

[0073] (Example 2) A travel planning system according to an embodiment of the present invention automatically creates optimal destinations and routes based on traveler information and provides a feasible itinerary that takes real-time information into account. The traveler's information is input into the travel planning system, and a generation AI analyzes the information to automatically create optimal destinations and routes. The generation AI then takes into account and analyzes real-time information, such as the business status and transportation status of destinations, to provide a feasible itinerary. The generation AI then translates the information into the traveler's language and provides it in the form of an application. For example, a traveler inputs their own information into the travel planning system. For example, the traveler inputs information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, and travel period. This information is then input into the generation AI. The travel planning system then uses the generation AI to analyze the input information and automatically create optimal destinations and routes for the traveler. The generation AI selects destinations based on the traveler's interests and calculates the optimal route based on the budget and travel period. For example, if a traveler is interested in historical buildings, the system selects destinations centered around those historical buildings in the area and creates an efficient route. Next, the travel planning system uses the generation AI to take into account and analyze real-time information such as the business status and operation status of destinations and provide a feasible itinerary. For example, the system adjusts the itinerary to allow travelers to travel smoothly, taking into account the business days and business hours of destinations and the operation status of public transportation. Next, the travel planning system uses the generation AI to translate the information into the traveler's language and provide it in the form of an application. For example, if the traveler speaks Japanese, the generation AI translates the created information about destinations and routes into Japanese and provides it through an application. This allows travelers to check their travel plans in their own language and enjoy their trip with peace of mind. As a result, the travel planning system automatically creates optimal destinations and routes by simply entering the traveler's information, and provides a feasible itinerary that takes real-time information into account. Furthermore, by providing information translated into the traveler's language through an application, travelers can enjoy their trip with peace of mind. This allows the travel planning system to automatically create optimal destinations and routes based on the traveler's information and provide a feasible itinerary that takes real-time information into account.For example, by simply inputting their information, the system automatically creates optimal destinations and routes, provides a feasible itinerary that takes real-time information into account, and provides information translated into the traveler's language through the application, allowing the traveler to enjoy their trip with peace of mind.

[0074] A travel planning system according to an embodiment includes an input unit, an analysis unit, a route creation unit, a real-time information acquisition unit, and a translation unit. The input unit inputs traveler information. The traveler information includes, but is not limited to, the name, age, gender, interests, budget, and travel period. The input unit allows the traveler to input information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, and travel period. The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit selects destinations based on the traveler's interests and calculates an optimal route based on the budget and travel period. The route creation unit uses a generation AI to create destinations and routes based on the information analyzed by the analysis unit. The route creation is performed based on, but is not limited to, criteria such as the shortest distance, optimal time, and mode of transportation. For example, the route creation unit considers and analyzes real-time information such as the business status and operation status of destinations to provide a feasible itinerary. The real-time information acquisition unit acquires real-time information such as business days and hours of destinations to be visited and the operation status of public transportation. The real-time information may be acquired, for example, through API integration, sensor data, or other methods, but is not limited to these examples. For example, the real-time information acquisition unit acquires real-time information such as business days and hours of destinations to be visited and the operation status of public transportation. The translation unit uses a generation AI to translate the information acquired by the real-time information acquisition unit into the traveler's language. The translation is performed based on criteria such as, for example, machine translation, handling of technical terms, and translation accuracy, but is not limited to these examples. For example, the translation unit translates into the traveler's language and provides it in the form of an application. As a result, the travel planning system according to the embodiment can automatically create optimal destinations and routes based on the traveler's information and provide a feasible itinerary that takes real-time information into account. For example, by simply inputting the traveler's information, the system can automatically create optimal destinations and routes and provide a feasible itinerary that takes real-time information into account.In addition, by providing information translated into travelers' languages ​​through the application, travelers can enjoy their trip with peace of mind.

[0075] The input unit can input information about the traveler's attributes, interests, budget, and duration. Examples of traveler attributes include, but are not limited to, age, gender, and occupation. The input unit can allow the traveler to input information such as age, gender, and occupation. Examples of interests include, but are not limited to, history, nature, and gourmet food. The input unit can allow the traveler to input interests such as history, nature, and gourmet food. Examples of budgets include, but are not limited to, a daily budget and a total budget. The input unit can allow the traveler to input information such as a daily budget and a total budget. Examples of duration include, but are not limited to, the start and end dates of the trip and the number of days of stay. The input unit can allow the traveler to input information such as the start and end dates of the trip and the number of days of stay. By inputting detailed information about the traveler, more accurate analysis is possible. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input traveler information into the generation AI and have the generation AI analyze the information.

[0076] The analysis unit can select destinations based on the traveler's interests and calculate routes that suit the traveler's budget and time frame. The analysis unit, for example, selects destinations based on the traveler's interests. For example, if the traveler is interested in historical buildings, the analysis unit selects destinations that focus on historical buildings in the area. The analysis unit can also select destinations based on the traveler's interests and concerns. For example, if the traveler is interested in nature, the analysis unit selects destinations that focus on the natural scenery in the area. The analysis unit can also select destinations based on the traveler's interests and concerns. For example, if the traveler is interested in gourmet food, the analysis unit selects destinations that focus on gourmet spots in the area. The analysis unit can, for example, calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the optimal route based on the traveler's budget and time frame. The analysis unit can also calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the shortest route based on the traveler's budget and time frame. The analysis unit can also calculate routes that suit the traveler's budget and time frame. For example, the analysis unit calculates the optimal route based on the traveler's budget and time frame. This makes it possible to provide optimal destinations and routes based on the traveler's interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may perform analysis using a generation AI model that selects destinations based on the traveler's interests and calculates routes according to budget and time period.

[0077] The route creation unit can take into account and analyze real-time information on the business status and operation status of destinations to provide a feasible schedule. The route creation unit, for example, takes into account and analyzes real-time information on the business status and operation status of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information on the business status and operation status of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information such as the business days and business hours of destinations to provide a feasible schedule. For example, the route creation unit takes into account and analyzes real-time information such as the business days and business hours of destinations to provide a feasible schedule, and the route creation unit can also take into account and analyze real-time information such as the business days and business hours of destinations to provide a feasible schedule. For example, the route creation unit can take into account and analyze .... For example, the route creation unit can create routes using a generative AI model that takes into account and analyzes real-time information on the business and operational status of destinations and provides feasible itineraries.

[0078] The real-time information acquisition unit can acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit acquires, for example, real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit can also acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. The real-time information acquisition unit can also acquire real-time information on the business days and business hours of the destinations and the operation status of public transportation. For example, the real-time information acquisition unit acquires real-time information on the business days and business hours of the destinations and the operation status of public transportation. In this way, by acquiring real-time information on the destinations, it is possible to provide the traveler with the latest information. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input real-time information such as the opening days and business hours of the destination and the operation status of public transportation into the generation AI, and have the generation AI acquire the information.

[0079] The translation unit can translate into the traveler's language and provide it in the form of an application. For example, the translation unit can translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks Japanese, the translation unit can translate the created information about the destinations and route into Japanese and provide it through an application. The translation unit can also translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks English, the translation unit can translate the created information about the destinations and route into English and provide it through an application. The translation unit can also translate into the traveler's language and provide it in the form of an application. For example, if the traveler speaks French, the translation unit can translate the created information about the destinations and route into French and provide it through an application. This allows the traveler to check their travel plan in their own language by translating it into their own language. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the created information about the destinations and route into a generation AI and have the generation AI translate the information.

[0080] The input unit can estimate the traveler's emotions and adjust the design of the input interface based on the estimated traveler's emotions. For example, the input unit can estimate the traveler's emotions and adjust the design of the input interface based on the estimated traveler's emotions. For example, if the traveler is nervous, the input unit can provide a calm interface to reduce visual stress. If the traveler is having fun, the input unit can provide a bright interface to make input work more enjoyable. If the traveler is tired, the input unit can provide a simple, highly visible interface to make input work easier. This allows the input interface to be adjusted according to the traveler's emotions, providing a more comfortable input environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit can be performed using AI, for example, or without AI. For example, the input unit can input the traveler's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The input unit can analyze the traveler's past travel history and suggest an input method. The input unit, for example, analyzes the traveler's past travel history and suggests the optimal input method. For example, the input unit automatically displays destinations that the traveler has frequently input in the past as candidates. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. The input unit can also predict and suggest destinations to be used during a specific time period based on the traveler's past travel history. This makes input work more efficient by suggesting the optimal input method based on the past travel history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's past travel history data into a generation AI and have the generation AI suggest the optimal input method.

[0082] The input unit can automatically complete related information by taking into account the traveler's current location information when inputting information. For example, when the traveler opens the app, the input unit automatically obtains the traveler's current location and sets it as the departure point. Furthermore, when the traveler inputs a destination, the input unit can also suggest optimal candidate locations by taking into account the distance from the current location. Furthermore, when the traveler uses the app while traveling, the input unit can also update the current location in real time and reflect it as the departure point. This simplifies the input work by taking into account the current location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's current location information to the generation AI and cause the generation AI to automatically complete related information.

[0083] The input unit can select the optimal input means depending on the traveler's input method (voice, text, image, etc.) at the time of input. For example, the input unit can automatically set related destinations by simply having the traveler input "I want to see historical buildings" by voice. The input unit can also allow the traveler to easily set destinations by performing specific gestures on the smartphone screen. The input unit can also allow the traveler to set destinations more intuitively by combining voice input and gesture input. This selects the optimal means depending on the traveler's input method, thereby making input work more efficient. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's input method data into the generation AI and have the generation AI select the optimal input means.

[0084] The input unit can estimate the traveler's emotions and determine the priority of information to be input based on the estimated traveler's emotions. For example, if the traveler is feeling stressed, the input unit can prioritize input of only the most important information. Furthermore, if the traveler is relaxed, the input unit can also prompt the traveler to input detailed information. Furthermore, if the traveler is in a hurry, the input unit can also prompt the traveler to input only the minimum necessary information. This allows the input work to be made more efficient by determining the priority of input information according to the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the traveler's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The input unit can analyze the traveler's social media activity at the time of input and automatically input related information. For example, the input unit can automatically display places where the traveler has checked in on social media as candidates. The input unit can also analyze the content of the traveler's social media posts and automatically input related destinations. The input unit can also automatically input related destinations by referring to the activity of the traveler's friends on social media. In this way, the input of related information is made more efficient by analyzing social media activity. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's social media data into a generation AI and have the generation AI automatically input related information.

[0086] The input unit can customize the input method by reflecting the traveler's past feedback when inputting data. For example, the input unit preferentially suggests input methods that the traveler has previously preferred. The input unit can also customize the input interface based on the traveler's past feedback. The input unit can also analyze the traveler's past feedback and suggest the optimal input means. In this way, the input method is optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's past feedback data into the generation AI and have the generation AI customize the input method.

[0087] The input unit can provide an optimal input interface by taking into account the traveler's device information at the time of input. For example, if the traveler is using a smartphone, the input unit can provide an input interface that matches the screen size. Furthermore, if the traveler is using a tablet, the input unit can also provide an input interface that is optimized for a large screen. Furthermore, if the traveler is using a smartwatch, the input unit can also provide a simple and highly visible input interface. This makes it possible to provide an optimal input interface by taking into account the device information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the traveler's device information to the generation AI and cause the generation AI to provide an optimal input interface.

[0088] The analysis unit can estimate the traveler's emotions and adjust the analysis algorithm based on the estimated traveler's emotions. For example, if the traveler is relaxed, the analysis unit can perform a detailed analysis and suggest more destinations. Furthermore, if the traveler is in a hurry, the analysis unit can prioritize analyzing the most efficient route. Furthermore, if the traveler is excited, the analysis unit can prioritize analyzing visually stimulating destinations. This allows for adjusting the analysis algorithm according to the traveler's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the traveler's past travel history. For example, the analysis unit can suggest optimal destinations based on places the traveler has visited in the past. The analysis unit can also suggest destinations that avoid crowds based on the traveler's past travel history. The analysis unit can also analyze the traveler's past travel history and suggest the most efficient route. In this way, by referring to the past travel history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the traveler's past travel history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0090] The analysis unit can reflect changes in the traveler's interests in real time during analysis. For example, the analysis unit reflects new places that the traveler has become interested in in the analysis in real time. The analysis unit can also immediately update the analysis results if the traveler's interests change. The analysis unit can also adjust the analysis results based on the traveler's real-time feedback. This allows for reflecting changes in interests in real time, making it possible to provide more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the traveler's interest data into the generation AI and have the generation AI reflect the data in real time.

[0091] During analysis, the analysis unit can customize the analysis results by taking into account the traveler's attribute information. The analysis unit can suggest optimal destinations based on the traveler's age and gender, for example. The analysis unit can also prioritize analysis of places that are likely to be of interest to the traveler based on the traveler's attribute information. The analysis unit can also customize the analysis results by taking into account the traveler's attribute information. In this way, the analysis results are customized by taking into account the attribute information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the traveler's attribute information into the generation AI and have the generation AI customize the analysis results.

[0092] The analysis unit can estimate the traveler's emotions and adjust the display method of the analysis results based on the estimated traveler's emotions. For example, if the traveler is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the traveler is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the traveler is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The analysis unit can perform the analysis while taking into account the geographic distribution of travelers. For example, if travelers are concentrated in a specific area, the analysis unit can prioritize the analysis of destinations in that area. The analysis unit can also suggest optimal destinations based on the geographic distribution of travelers. The analysis unit can also customize the analysis results by taking into account the geographic distribution of travelers. In this way, more appropriate analysis results can be provided by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the geographic distribution data of travelers into the generation AI and have the generation AI perform the analysis.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the traveler. For example, the analysis unit performs analysis by referring to literature related to fields in which the traveler is interested. The analysis unit can also improve the accuracy of the analysis based on literature related to the traveler's past travels. The analysis unit can also suggest optimal destinations by referring to literature related to the traveler's interests. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the traveler's related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0095] The analysis unit can perform the analysis while taking into account the market value of the traveler. For example, the analysis unit can suggest optimal destinations to visit based on the market value of the traveler. The analysis unit can also customize the analysis results by taking into account the market value of the traveler. The analysis unit can also prioritize the analysis of places that are likely to be of interest to the traveler based on the market value of the traveler. In this way, by taking market value into consideration, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the market value data of the traveler into the generation AI and have the generation AI perform the analysis.

[0096] The route creation unit can estimate the traveler's emotions and adjust the route creation criteria based on the estimated traveler's emotions. For example, if the traveler is relaxed, the route creation unit can create a route that proceeds at a leisurely pace. Furthermore, if the traveler is in a hurry, the route creation unit can also prioritize creating the shortest route. Furthermore, if the traveler is excited, the route creation unit can create a visually stimulating route. By adjusting the route creation criteria according to the traveler's emotions, a more appropriate route can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the route creation unit can be performed using, for example, an AI, or without an AI. For example, the route creation unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] When creating a route, the route creation unit can improve the accuracy of the route by taking into account the interrelationships between the destinations. The route creation unit creates an efficient route, for example, by taking into account the geographical proximity of the destinations. The route creation unit can also create a route that visits the destinations in an optimal order based on the interrelationships between the destinations. The route creation unit can also create a route that minimizes travel time by taking into account the interrelationships between the destinations. In this way, a more efficient route can be provided by taking into account the interrelationships between the destinations. Some or all of the above-described processing in the route creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the route creation unit can input interrelationship data between the destinations into the generation AI and cause the generation AI to improve the accuracy of the route.

[0098] When creating a route, the route creation unit can customize the route by taking into account attribute information of the destinations. The route creation unit, for example, takes into account the business hours of the destinations to create an optimal visiting order. The route creation unit can also create a route that avoids congestion based on the congestion status of the destinations. The route creation unit can also create a route that suits the interests of the traveler by taking into account attribute information of the destinations. In this way, by taking into account the attribute information of the destinations, it is possible to provide a route that suits the interests of the traveler. Some or all of the above-mentioned processing in the route creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the route creation unit can input attribute information data of the destinations to the generation AI and have the generation AI customize the route.

[0099] The route creation unit can reflect the business status and operation status of destinations in real time when creating a route. For example, the route creation unit creates a route by reflecting the business days and business hours of destinations in real time. The route creation unit can also create a route by reflecting the operation status of public transportation in real time. The route creation unit can also create a route by reflecting the real-time congestion status of destinations. In this way, by reflecting real-time information, it is possible to provide a feasible route. Some or all of the above-mentioned processing in the route creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the route creation unit can input real-time information on the business status and operation status of destinations into the generation AI and have the generation AI reflect the information in real time.

[0100] The route creation unit can estimate the traveler's emotions and adjust the route display method based on the estimated traveler's emotions. For example, if the traveler is nervous, the route creation unit can provide a simple, highly visible display method. Furthermore, if the traveler is relaxed, the route creation unit can provide a display method that includes detailed information. Furthermore, if the traveler is in a hurry, the route creation unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the route creation unit can be performed using, for example, an AI, or without an AI. For example, the route creation unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] When creating a route, the route creation unit can create a route taking into account the geographic distribution of the destinations. For example, if the destinations are concentrated in a specific area, the route creation unit creates a route that efficiently travels around that area. The route creation unit can also create an optimal visiting order based on the geographic distribution of the destinations. The route creation unit can also create a route that minimizes travel time by taking into account the geographic distribution of the destinations. In this way, by taking the geographic distribution into account, a more efficient route can be provided. Some or all of the above-described processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input geographic distribution data of the destinations into the generation AI and have the generation AI create a route.

[0102] When creating a route, the route creation unit can improve the accuracy of the route by referring to literature related to the destination. The route creation unit, for example, creates an optimal route by referring to literature related to the destination. The route creation unit can also create an interesting route based on literature related to the history and culture of the destination. The route creation unit can also create a route that matches the traveler's interests by referring to literature related to the destination. In this way, the accuracy of the route is improved by referring to related literature. Some or all of the above-mentioned processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input literature data related to the destination into the generation AI and cause the generation AI to improve the accuracy of the route.

[0103] When creating a route, the route creation unit can create the route taking into account the market value of the destinations. For example, the route creation unit creates an optimal visiting order based on the market value of the destinations. The route creation unit can also create a route that suits the traveler's interests by taking into account the market value of the destinations. The route creation unit can also prioritize incorporating places that are likely to be of interest into the route based on the market value of the destinations. This makes it possible to provide a more appropriate route by taking market value into consideration. Some or all of the above-mentioned processing in the route creation unit may be performed using, or without, a generation AI. For example, the route creation unit can input market value data of the destinations into the generation AI and have the generation AI create the route.

[0104] The real-time information acquisition unit can estimate the traveler's emotions and adjust the real-time information acquisition method based on the estimated traveler's emotions. For example, if the traveler is relaxed, the real-time information acquisition unit can provide detailed real-time information. Furthermore, if the traveler is in a hurry, the real-time information acquisition unit can provide only the most important real-time information. Furthermore, if the traveler is excited, the real-time information acquisition unit can provide visually stimulating real-time information. This allows for more appropriate information provision by adjusting the information acquisition method according to the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the real-time information acquisition unit can be performed using AI, or without AI. For example, the real-time information acquisition unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] When acquiring real-time information, the real-time information acquisition unit can improve the accuracy of acquisition by referring to the past business status of the destination. The real-time information acquisition unit improves the accuracy of acquisition of real-time information based on, for example, the past business days and business hours of the destination. The real-time information acquisition unit can also improve the accuracy of acquisition of real-time information by referring to the past congestion status of the destination. The real-time information acquisition unit can also analyze the past business status of the destination and provide optimal real-time information. In this way, by referring to the past business status, the accuracy of information acquisition is improved. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input past business status data of the destination into the generation AI and cause the generation AI to improve the accuracy of acquisition.

[0106] The real-time information acquisition unit can customize the information by taking into account attribute information of the destinations when acquiring the real-time information. The real-time information acquisition unit, for example, provides optimal real-time information based on the attribute information of the destinations. The real-time information acquisition unit can also provide real-time information that matches the interests of the traveler by taking into account the attribute information of the destinations. The real-time information acquisition unit can also customize the real-time information based on the attribute information of the destinations. This makes it possible to provide more appropriate real-time information by taking the attribute information into account. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input attribute information data of the destinations to the generation AI and cause the generation AI to customize the information.

[0107] The real-time information acquisition unit can reflect the business status and operation status of the destination in real time when acquiring the real-time information. The real-time information acquisition unit provides information that reflects, for example, the business days and business hours of the destination in real time. The real-time information acquisition unit can also provide information that reflects the operation status of public transportation in real time. The real-time information acquisition unit can also provide information that reflects the real-time congestion status of the destination. By reflecting real-time information, more appropriate information can be provided. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input real-time information on the business status and operation status of the destination to the generation AI and cause the generation AI to reflect the information in real time.

[0108] The real-time information acquisition unit can estimate the traveler's emotions and adjust the display method of real-time information based on the estimated traveler's emotions. For example, if the traveler is nervous, the real-time information acquisition unit can provide a simple, highly visible display method. Furthermore, if the traveler is relaxed, the real-time information acquisition unit can provide a display method that includes detailed information. Furthermore, if the traveler is in a hurry, the real-time information acquisition unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the real-time information acquisition unit may be performed using AI, or may be performed without AI. For example, the real-time information acquisition unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0109] When acquiring real-time information, the real-time information acquisition unit can acquire information taking into account the geographic distribution of destinations. For example, if destinations are concentrated in a specific area, the real-time information acquisition unit prioritizes acquiring real-time information for that area. The real-time information acquisition unit can also acquire optimal real-time information based on the geographic distribution of destinations. The real-time information acquisition unit can also customize the real-time information taking into account the geographic distribution of destinations. This makes it possible to provide more appropriate real-time information by taking the geographic distribution into consideration. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input geographic distribution data of destinations to a generation AI and cause the generation AI to acquire information.

[0110] The real-time information acquisition unit can improve the accuracy of the information by referring to literature related to the destination when acquiring real-time information. The real-time information acquisition unit, for example, refers to literature related to the destination to acquire optimal real-time information. The real-time information acquisition unit can also provide interesting real-time information based on literature related to the history and culture of the destination. The real-time information acquisition unit can also provide real-time information that matches the traveler's interests by referring to literature related to the destination. In this way, by referring to related literature, the accuracy of the information is improved. Some or all of the above-mentioned processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input literature data related to the destination into the generation AI and cause the generation AI to improve the accuracy of the information.

[0111] The real-time information acquisition unit can acquire information taking into account the market value of the destination when acquiring real-time information. The real-time information acquisition unit provides optimal real-time information based on, for example, the market value of the destination. The real-time information acquisition unit can also provide real-time information that matches the traveler's interests by taking into account the market value of the destination. The real-time information acquisition unit can also preferentially acquire real-time information of places that are likely to be of interest based on the market value of the destination. This makes it possible to provide more appropriate real-time information by taking market value into account. Some or all of the above-described processing in the real-time information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time information acquisition unit can input market value data of the destination to the generation AI and cause the generation AI to acquire information.

[0112] The translation unit can estimate the traveler's emotions and adjust the translation expression method based on the estimated traveler's emotions. For example, if the traveler is nervous, the translation unit can translate using a calm expression method. Furthermore, if the traveler is relaxed, the translation unit can translate using a cheerful expression method. Furthermore, if the traveler is in a hurry, the translation unit can translate using a quick and concise expression method. This allows for adjusting the translation expression method according to the traveler's emotions, thereby providing a more appropriate translation result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] The translation unit can improve the accuracy of the translation by referring to the traveler's past translation history when translating. The translation unit provides the optimal translation based on translation expressions used by the traveler in the past, for example. The translation unit can also improve the accuracy of the translation by referring to the traveler's past translation history. The translation unit can also analyze the traveler's past translation history and provide the most appropriate translation expression. In this way, by referring to the past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the traveler's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0114] The translation unit can customize the translation results by taking into account the traveler's attribute information during translation. The translation unit provides the most appropriate translation expression based on, for example, the traveler's age and gender. The translation unit can also prioritize translating expressions that are likely to be of interest to the traveler based on the traveler's attribute information. The translation unit can also customize the translation results by taking into account the traveler's attribute information. In this way, by taking the attribute information into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the traveler's attribute information data into the generation AI and have the generation AI customize the translation results.

[0115] The translation unit can reflect changes in the language used by the traveler in real time during translation. For example, if the language used by the traveler changes, the translation unit updates the translation results in real time. The translation unit can also provide a language switching function if the traveler uses multiple languages. The translation unit can also reflect changes in the language used by the traveler in real time to provide optimal translation results. By reflecting changes in the language used in real time, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the language used by the traveler into a generation AI and have the generation AI update the data in real time.

[0116] The translation unit can estimate the traveler's emotions and adjust the display method of the translation results based on the estimated traveler's emotions. For example, if the traveler is nervous, the translation unit can provide a simple, highly visible display method. Furthermore, if the traveler is relaxed, the translation unit can provide a display method that includes detailed information. Furthermore, if the traveler is in a hurry, the translation unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0117] The translation unit can take into account the geographical distribution of travelers when translating. For example, if travelers are concentrated in a specific region, the translation unit provides a translation optimized for the language of that region. The translation unit can also provide the optimal translation expression based on the geographical distribution of travelers. The translation unit can also customize the translation results by taking into account the geographical distribution of travelers. In this way, by taking the geographical distribution into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input geographical distribution data of travelers into a generation AI and have the generation AI perform the translation.

[0118] The translation unit can improve the accuracy of the translation by referring to literature related to the traveler during translation. For example, the translation unit performs translation by referring to literature related to the field in which the traveler is interested. The translation unit can also improve the accuracy of the translation based on literature related to the traveler's past travels. The translation unit can also provide optimal translation expressions by referring to literature related to the traveler's interests. In this way, the accuracy of the translation is improved by referring to related literature. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on literature related to the traveler into a generation AI and have the generation AI improve the accuracy of the translation.

[0119] The translation unit can take into account the market value of the traveler when translating. The translation unit provides the most appropriate translation expression based on the market value of the traveler, for example. The translation unit can also customize the translation result by taking into account the market value of the traveler. The translation unit can also prioritize translating expressions that are likely to be of interest to the traveler based on the market value of the traveler. In this way, by taking market value into consideration, more appropriate translation results can be provided. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input the market value data of the traveler into a generation AI and have the generation AI perform the translation. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, route creation unit, real-time information acquisition unit, and translation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input traveler information using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The route creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates destinations and routes based on the analyzed information. The real-time information acquisition unit acquires real-time information, such as opening days and business hours of destinations and the operation status of public transportation, via the communication I / F 26 of the data processing device 12. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the acquired information into the traveler's language and provides it via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, route creation unit, real-time information acquisition unit, and translation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input traveler information using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The route creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates destinations and routes based on the analyzed information. The real-time information acquisition unit acquires real-time information, such as opening days and business hours of destinations and the operation status of public transportation facilities, via the communication I / F 26 of the data processing device 12. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the acquired information into the traveler's language and provides it through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, route creation unit, real-time information acquisition unit, and translation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can input traveler information using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The route creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates destinations and routes based on the analyzed information. The real-time information acquisition unit acquires real-time information, such as opening days and business hours of destinations and the operation status of public transportation, via the communication I / F 26 of the data processing device 12. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the acquired information into the traveler's language and provides it through the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, route creation unit, real-time information acquisition unit, and translation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input traveler information using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The route creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates destinations and routes based on the analyzed information. The real-time information acquisition unit acquires real-time information, such as opening days and business hours of destinations and the operation status of public transportation, via the communication I / F 26 of the data processing device 12. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the acquired information into the traveler's language and provides it through the speaker 240 of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The travel planning system can further include a health management unit that monitors the traveler's health condition. The health management unit monitors the traveler's vital signs, such as heart rate, blood pressure, and body temperature, in real time, and can automatically adjust the travel plan if an abnormality is detected. For example, if the traveler's heart rate suddenly rises, the health management unit can suggest the traveler take a break and change the itinerary of the destinations they visit. Also, if the traveler's blood pressure is high, the health management unit can suggest a less stressful route. Furthermore, if the traveler's body temperature rises, the health management unit can provide information on medical institutions and make reservations as necessary. This allows for flexible travel planning that adapts to the traveler's health condition.

[0122] The travel planning system can further include an emotion analysis unit that estimates the traveler's emotions and selects travel destinations based on the estimated emotions. The emotion analysis unit estimates emotions based on the traveler's input information, past travel history, and real-time feedback, and suggests places where the traveler can relax or get excited. For example, if the traveler is feeling stressed, the emotion analysis unit can suggest a resort area rich in nature. Also, if the traveler is excited, the emotion analysis unit can suggest a city with plenty of activities. Furthermore, if the traveler is relaxed, the emotion analysis unit can suggest cultural tourist spots. This makes it possible to provide the optimal travel destination according to the traveler's emotions.

[0123] The travel planning system can further include a social media analysis unit that analyzes the traveler's social media activity and automatically inputs related information. The social media analysis unit analyzes the places where the traveler has checked in and the content of posts on social media and reflects this in the travel plan. For example, the social media analysis unit can automatically suggest related destinations based on places the traveler has visited in the past. It can also suggest new destinations to the traveler based on places the traveler's friends have visited. Furthermore, information about specific events and festivals can be obtained from the traveler's social media activity and incorporated into the travel plan. This makes it possible to create personalized travel plans based on social media activity.

[0124] The travel planning system can further include an activity suggestion unit that estimates the traveler's emotions and suggests activities to do during the trip based on the estimated emotions. The activity suggestion unit suggests activities that can be enjoyed during the trip based on the traveler's emotion data. For example, if the traveler is tired, the activity suggestion unit can suggest relaxing spas and hot springs. If the traveler is excited, the activity suggestion unit can also suggest adventure sports and nightlife. Furthermore, if the traveler is relaxed, the activity suggestion unit can also suggest cultural events and workshops. This makes it possible to provide the optimal activities according to the traveler's emotions.

[0125] The travel planning system can further include a feedback analysis unit that customizes the travel plan by reflecting the traveler's past feedback. The feedback analysis unit analyzes feedback provided by the traveler in the past and reflects it in the travel plan. For example, the feedback analysis unit can propose a new travel plan based on the traveler's past favorite destinations and activities. It can also improve points that the traveler was dissatisfied with in the past to provide a more satisfying travel plan. Furthermore, it can propose the best destinations for a specific time period or season based on the traveler's past feedback. This makes it possible to create a personalized travel plan that reflects past feedback.

[0126] The travel planning system can further include a support unit that estimates the traveler's emotions and provides support during the trip based on the estimated emotions. The support unit provides necessary support during the trip based on the traveler's emotion data. For example, if the traveler is feeling anxious, the support unit can provide messages and information that give the traveler a sense of security. Also, if the traveler is excited, the support unit can suggest activities and events to maintain the traveler's excitement. Furthermore, if the traveler is tired, the support unit can suggest places to rest or relax. In this way, by providing support according to the traveler's emotions, a more comfortable travel experience can be achieved.

[0127] The travel planning system can further include a location information analysis unit that adjusts the travel plan in real time, taking into account the traveler's current location information. The location information analysis unit acquires the traveler's current location in real time and reflects it in the travel plan. For example, if the traveler arrives at a destination earlier than planned, the location information analysis unit can suggest the next destination early. Also, if the traveler gets caught in traffic congestion, the location information analysis unit can suggest an alternative route. Furthermore, if the traveler stops at an unplanned location, the location information analysis unit can suggest a new destination. This enables flexible travel planning that takes into account the current location information.

[0128] The travel planning system can further include a communication support unit that estimates the traveler's emotions and supports communication during the trip based on the estimated emotions. The communication support unit provides support to facilitate communication during the trip based on the traveler's emotional data. For example, if the traveler is nervous, the communication support unit can provide simple phrases or a translation function. If the traveler is relaxed, the communication support unit can also provide information to promote interaction with local people. Furthermore, if the traveler is excited, the communication support unit can also provide a social media linkage function to share the excitement. This makes it possible to provide communication support that corresponds to the traveler's emotions.

[0129] The travel planning system can further include a device adaptation unit that provides an optimal travel plan taking into account the traveler's device information. The device adaptation unit optimizes the travel plan taking into account the type and characteristics of the device used by the traveler. For example, if the traveler is using a smartphone, the device adaptation unit can provide a display method that suits the screen size. Also, if the traveler is using a tablet, the device adaptation unit can provide a display method that is optimized for a large screen. Furthermore, if the traveler is using a smartwatch, the device adaptation unit can provide a simple and highly visible display method. This allows for the provision of an optimal travel plan that takes into account the device information.

[0130] The travel planning system can further include a safety measures unit that estimates the traveler's emotions and suggests safety measures during the trip based on the estimated emotions. The safety measures unit suggests safety measures during the trip based on the traveler's emotion data. For example, if the traveler is feeling anxious, the safety measures unit can provide information on safe routes and evacuation sites. Also, if the traveler is excited, the safety measures unit can suggest relaxation methods to reduce the excitement. Furthermore, if the traveler is relaxed, the safety measures unit can also issue safety warnings. In this way, safety measures are provided according to the traveler's emotions, allowing them to enjoy their trip with peace of mind.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The input unit inputs traveler information. Traveler information includes name, age, gender, interests, budget, duration, etc. For example, a traveler can input information such as attributes (age, gender, etc.), interests (tourist destinations, activities, etc.), budget, duration, etc. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it selects destinations based on the traveler's interests and calculates the optimal route based on the traveler's budget and travel period. Step 3: The route creation unit uses the generation AI to create destinations and routes based on the information analyzed by the analysis unit. Route creation is based on criteria such as the shortest distance, optimal time, and transportation method. For example, it takes into account and analyzes real-time information such as the business status and operation status of destinations to provide a feasible itinerary. Step 4: The real-time information acquisition unit acquires real-time information such as the business days and business hours of the destination, the operation status of public transport, etc. Real-time information is acquired through API integration, sensor data, etc. Step 5: The translation unit uses the generation AI to translate the information acquired by the real-time information acquisition unit into the traveler's language. The translation is performed based on criteria such as machine translation, handling of technical terms, and translation accuracy. For example, the translation unit translates into the traveler's language and provides it in the form of an application.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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 AI 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting traveler information; an analysis unit that analyzes the information input by the input unit; a route creation unit that creates destinations and routes based on the information analyzed by the analysis unit; a real-time information acquisition unit that acquires real-time information based on the route created by the route creation unit; a translation unit that translates the information acquired by the real-time information acquisition unit into the traveler's language. A system characterized by:

2. The input unit Enter traveler information, interests, budget, and travel period 2. The system of claim 1.

3. The analysis unit Select destinations based on the traveler's interests and calculate routes based on budget and duration 2. The system of claim 1.

4. The route creation unit Provides feasible schedules by taking into account and analyzing real-time information on the business and operational status of destinations 2. The system of claim 1.

5. The real-time information acquisition unit Get real-time information on the opening days and opening hours of your destination, as well as the status of public transport 2. The system of claim 1.

6. The translation unit Translated into the traveler's language and provided in the form of an application 2. The system of claim 1.

7. The input unit Estimating traveler emotions and adjusting the input interface design based on the estimated traveler emotions 2. The system of claim 1.

8. The input unit Analyzes travellers' past travel history and suggests input methods 2. The system of claim 1.

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