system

Abstract

The system according to this embodiment aims to allow users to easily plan their trips. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a guide unit. The reception unit receives input from the user regarding the desired destination and the duration of the trip. The generation unit creates a package tour based on the information received by the reception unit. The guide unit provides real-time information based on the package tour created by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it took a lot of effort and time for a user to make a travel plan.

[0005] The system according to the embodiment aims to enable a user to easily make a travel plan.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a guide unit. The reception unit receives a location the user wants to go to and a travel period. The generation unit composes a package tour based on the information received by the reception unit. The guide unit provides real-time information based on the package tour composed by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment allows users to easily plan their trips. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The travel plan generation system according to an embodiment of the present invention is a system that automatically creates a package tour simply by the user inputting the desired destination and travel duration. This system utilizes data and search functions to create a package tour that is differentiated from other services, based on the user's input. It also provides comfortable travel information in real time by utilizing weather and traffic forecast data. Furthermore, it plans local tours tailored to the user's preferences based on event schedule information in various locations and arranges various tickets at the lowest possible prices. Travelers can pay for everything in one lump sum, and the system also suggests the use of hometown tax return gifts (travel vouchers, travel points, etc.). This allows users to easily create a travel plan tailored to their preferences and enjoy a comfortable and efficient trip. As a result, the travel plan generation system can propose package tours based on the user's wishes at the optimal price and provide guidance that takes into account weather and traffic forecasts and real-time information.

[0029] The travel plan generation system according to this embodiment comprises a reception unit, a generation unit, and a guide unit. The reception unit receives input from the user regarding the places they wish to visit and the duration of their trip. The places the user wishes to visit include, for example, city names, tourist destination names, country names, etc., but are not limited to such examples. The duration of the trip includes, for example, the number of days, start date, and end date, etc., but are not limited to such examples. The reception unit provides, for example, an interface for the user to input the places they wish to visit and the duration of their trip. The generation unit assembles a package tour based on the information received by the reception unit. The generation unit proposes a package tour based on the user's wishes at the optimal price, for example, using a generation AI. The generation unit utilizes data and search functions to assemble a package tour that is differentiated from other companies' services. The generation unit, for example, searches for relevant information from a database and generates the optimal package tour. The generation unit can also generate a package tour based on the user's wishes using a generation AI. The guide unit provides real-time information based on the package tour assembled by the generation unit. The guide unit provides comfortable travel information in real time, for example, by utilizing weather and traffic congestion forecast data. The guide unit provides, for example, an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using a generation AI. As a result, the travel plan generation system according to the embodiment automatically creates a package tour and provides real-time information simply by the user inputting the places they want to go and the duration of their trip.

[0030] The reception desk allows users to input their desired destinations and travel duration. Destinations may include, but are not limited to, city names, tourist attractions, or country names. Travel duration may include, but are not limited to, the number of days, start date, and end date. The reception desk provides an interface for users to input their desired destinations and travel duration. Specifically, it features an intuitive user interface, such as a function to select destinations on a map or a function to select travel duration in a calendar format. Furthermore, the reception desk verifies user input in real time and provides feedback to prevent input errors and inaccuracies. For example, if a city name entered by the user does not exist or the travel duration is inappropriate, a warning message is immediately displayed, prompting the user to enter correct information. The reception desk can also suggest destinations and travel durations based on the user's past travel history and preferences. For example, it can suggest relevant travel destinations based on cities the user has visited in the past or tourist attractions they are interested in. This allows the reception desk to support users in smoothly and accurately entering their desired destinations and travel duration, enabling them to take the first step in creating their travel plan efficiently.

[0031] The generation unit assembles package tours based on information received by the reception unit. For example, the generation unit uses generation AI to propose package tours based on user preferences at the optimal price. The generation unit utilizes data and search functions to create package tours that differentiate themselves from competitors' services. Specifically, the generation unit searches a vast travel database and combines the most suitable accommodations, transportation, sightseeing spots, and meal plans to meet the user's preferences. The generation AI analyzes user input information and generates the optimal travel plan based on past travel data and trend information. For example, based on the city or tourist destination name entered by the user, it searches for popular tourist spots and event information in that region and incorporates them into the travel plan. The generation AI also optimizes pricing, proposing the best plan to fit the user's budget. For example, it compares prices for multiple accommodations and transportation options and presents the most cost-effective choice. Furthermore, the generation unit can provide personalized travel plans considering the user's preferences and past travel history. For example, if a user has previously visited certain tourist destinations or is interested in a specific theme, it generates a customized plan based on that information. This allows the production unit to provide high-quality package tours that fully meet the user's needs and are differentiated from other companies' services.

[0032] The guide unit provides real-time information based on package tours created by the generation unit. For example, the guide unit utilizes weather and traffic forecast data to provide comfortable travel in real time. Specifically, the guide unit has an interface to provide users with real-time weather information and traffic conditions at their current location during their trip. For example, if the weather suddenly changes while a user is on their way to a tourist destination, the guide unit immediately notifies them and suggests alternative sightseeing plans and modes of transportation. In the event of traffic congestion, it suggests the optimal detour route to support the user's smooth travel. Furthermore, the guide unit uses generational AI to analyze the user's current situation and behavioral patterns to provide optimal real-time information. For example, if a user is staying at a particular tourist spot for an extended period, it provides information on recommended restaurants and cafes in the surrounding area. It can also suggest additional tourist spots and activities if a user finishes sightseeing earlier than planned. In this way, the guide unit supports users in traveling comfortably and efficiently, and enjoying a fulfilling travel experience. Additionally, the guide unit can collect user feedback to continuously improve the accuracy and quality of the information it provides. For example, by allowing users to leave ratings and comments on the information provided, the guide team can use that feedback to improve their service and provide a better travel experience.

[0033] The generation unit can create package tours by utilizing data and search functions. For example, the generation unit can search for relevant information from the database and generate the optimal package tour. The generation unit can also generate package tours based on user preferences using generation AI. For example, the generation unit can search for information on accommodations and transportation from the database and suggest the optimal combination. The generation unit can also generate package tours based on user preferences using generation AI. This allows us to provide package tours that are differentiated from other companies' services by utilizing data and search functions.

[0034] The guide unit can provide comfortable travel in real time by utilizing weather and traffic congestion forecast data. For example, the guide unit provides an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using generative AI. For example, the guide unit can acquire weather forecast data and propose the optimal travel route to the user. The guide unit can also analyze weather forecast data using generative AI and propose the optimal travel route. For example, the guide unit can acquire traffic congestion forecast data and propose the optimal travel route to the user. The guide unit can also analyze traffic congestion forecast data using generative AI and propose the optimal travel route. This allows for comfortable travel in real time by utilizing weather and traffic congestion forecast data.

[0035] The generation unit can collect event schedule information from various locations and plan local tours tailored to the user's preferences. For example, the generation unit collects event schedule information from data from local tourism associations and online event calendars. The generation unit can also use generation AI to analyze event schedule information and plan local tours tailored to the user's preferences. For example, the generation unit can suggest local tours that include specific events or activities based on the user's preferences. The generation unit can also use generation AI to plan local tours based on the user's preferences. This allows for the planning of local tours tailored to the user's preferences by collecting event schedule information from various locations.

[0036] The generation unit can arrange various tickets at the lowest possible prices. For example, the generation unit collects price information from multiple travel agencies and online booking sites and arranges the cheapest tickets. The generation unit can also use generation AI to analyze price information and arrange the cheapest tickets. For example, the generation unit compares prices for airfare, accommodation, and entrance tickets to tourist attractions and proposes the cheapest tickets. The generation unit can also use generation AI to analyze price information and arrange the cheapest tickets. By arranging various tickets at the lowest possible prices, it can provide users with economical travel plans.

[0037] The generation unit can suggest ways to use Furusato Nozei (hometown tax donation) return gifts. For example, the generation unit collects information on travel vouchers, travel points, and accommodation coupons offered as Furusato Nozei return gifts. The generation unit can also use generation AI to analyze the information on return gifts and make optimal usage suggestions to users. For example, the generation unit can suggest ways to use Furusato Nozei return gifts in accordance with the user's travel plan. The generation unit can also use generation AI to suggest ways to use return gifts based on the user's travel plan. In this way, by suggesting ways to use Furusato Nozei return gifts, it is possible to provide users with even more advantageous travel plans.

[0038] The reception desk can analyze the user's past travel history and suggest the optimal travel duration and method of inputting desired destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest places related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can suggest the optimal travel duration and method of inputting desired destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The reception desk can filter travel durations and destinations based on the user's current living situation and areas of interest. For example, when a user enters their current living situation, the reception desk can suggest suitable travel durations and destinations. The reception desk can also filter and suggest relevant travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception desk can also suggest optimal travel durations and destinations by considering the user's current living situation (work workload, family structure, etc.). This allows for the suggestion of more appropriate travel plans by filtering travel durations and destinations based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0040] The reception desk can automatically suggest candidate destinations based on the user's past travel history when the user enters their departure and destination locations. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as candidate destinations. The reception desk can also analyze the user's past travel patterns and suggest the most suitable candidate destinations. This allows the reception desk to automatically suggest more appropriate candidate destinations by referring to the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without using AI.

[0041] The reception desk can refer to the user's calendar information when the user enters their departure and destination locations and make suggestions based on their schedule. For example, the reception desk can refer to appointments registered in the user's calendar and automatically set the departure and destination locations. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Based on the user's calendar information, the reception desk can also suggest the optimal route to match the schedule. In this way, by referring to the user's calendar information, it is possible to make appropriate suggestions based on the schedule. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0042] The reception desk can prioritize suggesting highly relevant travel plans by taking into account the user's geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. The reception desk can also suggest easily accessible travel destinations based on the user's geographical location. The reception desk can also suggest travel destinations with convenient transportation options by taking into account the user's geographical location. In this way, by taking into account the user's geographical location, the reception desk can prioritize suggesting highly relevant travel plans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0043] The generation unit can adjust the level of detail in package tours based on the importance of the trip. For example, for an important business trip, the generation unit's AI might suggest a tour that includes a detailed schedule and luxury hotels. For a family trip, the generation unit's AI might suggest a tour that includes activities for children and family-friendly accommodations. For a short weekend trip, the generation unit's AI might suggest a tour that efficiently covers the main tourist attractions. By adjusting the level of detail in package tours based on the importance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0044] The generation unit can apply different generation algorithms depending on the travel category. For example, in the case of an adventure tour, the generation unit can apply a generation algorithm that emphasizes outdoor activities. In the case of a cultural tour, the generation unit can also apply a generation algorithm that emphasizes historical sites and museums. In the case of a relaxation tour, the generation unit can also apply a generation algorithm that emphasizes spas and resorts. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI.

[0045] The generation unit can prioritize package tours based on when the travel request is submitted. For example, the generation unit's AI can propose a tour that responds quickly to a travel request submitted at the last minute. The generation unit's AI can also propose a tour with detailed planning for a travel request submitted early. The generation unit's AI can also propose a tour that prioritizes travel requests tailored to specific events or seasons. By prioritizing package tours based on when the travel request is submitted, the system can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0046] The generation unit can adjust the order of package tours based on the relevance of the trip. For example, the generation unit's generating AI can suggest tours that prioritize visits to highly relevant tourist spots based on the user's areas of interest. The generation unit's generating AI can also suggest tours that include highly relevant tourist spots based on the user's past travel history. The generation unit's generating AI can also suggest tours that include highly relevant tourist spots based on the user's current living situation. By adjusting the order of package tours based on the relevance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generating AI, or without using the generating AI.

[0047] The guide unit can optimize the current guide content by referring to past guide data. For example, the guide unit can provide optimal guide content based on guide data of places the user has visited in the past. The guide unit can also provide guide content tailored to the user's preferences from past guide data. The guide unit can also analyze past guide data and provide the most efficient guide content. In this way, the current guide content can be optimized by referring to past guide data. Some or all of the above processing in the guide unit may be performed using AI, for example, or without using AI.

[0048] The guiding function can apply different guiding methods depending on the travel category. For example, in the case of an adventure tour, the guiding function can apply a guiding method that emphasizes outdoor activities. In the case of a cultural tour, the guiding function can also apply a guiding method that emphasizes historical sites and museums. In the case of a relaxation tour, the guiding function can also apply a guiding method that emphasizes spas and resorts. By applying different guiding methods for each travel category, the guiding function can provide the user with the most suitable guide. Some or all of the above processing in the guiding function may be performed using AI, for example, or not using AI.

[0049] The guide department can analyze real-time changes in information based on when travel requests are submitted. For example, the guide department can provide guide information that responds quickly to travel requests submitted at the last minute. The guide department can also provide guide information that allows for detailed planning for travel requests submitted early. The guide department can also provide guide information that prioritizes travel requests tailored to specific events or seasons. This allows for the provision of optimal information to users by analyzing real-time changes in information based on when travel requests are submitted. Some or all of the above processing in the guide department may be performed using AI, for example, or not using AI.

[0050] The guide unit can analyze real-time information by referring to travel-related market data. For example, the guide unit can suggest optimal tourist spots based on market data of the travel destination. The guide unit can also suggest routes to avoid crowds based on market data of the travel destination. The guide unit can also suggest restaurants and shops tailored to the user's preferences based on market data of the travel destination. In this way, by referring to travel-related market data, the guide unit can provide the user with the most optimal information. Some or all of the above processing in the guide unit may be performed using AI, for example, or not using AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the user's past travel history and suggest the optimal travel duration and method of inputting desired destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest places related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can suggest the optimal travel duration and method of inputting desired destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0053] The reception desk can filter travel durations and destinations based on the user's current living situation and areas of interest. For example, when a user enters their current living situation, the reception desk can suggest suitable travel durations and destinations. The reception desk can also filter and suggest relevant travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception desk can also suggest optimal travel durations and destinations by considering the user's current living situation (work workload, family structure, etc.). This allows for the suggestion of more appropriate travel plans by filtering travel durations and destinations based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0054] The generation unit can adjust the level of detail in package tours based on the importance of the trip. For example, for an important business trip, the generation unit's AI might suggest a tour that includes a detailed schedule and luxury hotels. For a family trip, the generation unit's AI might suggest a tour that includes activities for children and family-friendly accommodations. For a short weekend trip, the generation unit's AI might suggest a tour that efficiently covers the main tourist attractions. By adjusting the level of detail in package tours based on the importance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0055] The generation unit can apply different generation algorithms depending on the travel category. For example, in the case of an adventure tour, the generation unit can apply a generation algorithm that emphasizes outdoor activities. In the case of a cultural tour, the generation unit can also apply a generation algorithm that emphasizes historical sites and museums. In the case of a relaxation tour, the generation unit can also apply a generation algorithm that emphasizes spas and resorts. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI.

[0056] The guide unit can optimize the current guide content by referring to past guide data. For example, the guide unit can provide optimal guide content based on guide data of places the user has visited in the past. The guide unit can also provide guide content tailored to the user's preferences from past guide data. The guide unit can also analyze past guide data and provide the most efficient guide content. In this way, the current guide content can be optimized by referring to past guide data. Some or all of the above processing in the guide unit may be performed using AI, for example, or without using AI.

[0057] The guiding function can apply different guiding methods depending on the travel category. For example, in the case of an adventure tour, the guiding function can apply a guiding method that emphasizes outdoor activities. In the case of a cultural tour, the guiding function can also apply a guiding method that emphasizes historical sites and museums. In the case of a relaxation tour, the guiding function can also apply a guiding method that emphasizes spas and resorts. By applying different guiding methods for each travel category, the guiding function can provide the user with the most suitable guide. Some or all of the above processing in the guiding function may be performed using AI, for example, or not using AI.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives the user's desired destination and travel duration. The destination includes city names, tourist attractions, country names, etc., and the travel duration includes the number of days, start date, and end date. The reception desk provides an interface for entering this information. Step 2: The generation unit creates package tours based on the information received by the reception unit. The generation unit uses generation AI to propose package tours based on user preferences at the optimal price, and utilizes data and search functions to generate package tours that are differentiated from other companies' services. The generation unit searches the database for relevant information and generates the optimal package tour. Step 3: The guide unit provides real-time information based on the package tours created by the generation unit. The guide unit utilizes weather and traffic forecast data to provide comfortable travel in real time and provides an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using generation AI.

[0060] (Example of form 2) The travel plan generation system according to an embodiment of the present invention is a system that automatically creates a package tour simply by the user inputting the desired destination and travel duration. This system utilizes data and search functions to create a package tour that is differentiated from other services, based on the user's input. It also provides comfortable travel information in real time by utilizing weather and traffic forecast data. Furthermore, it plans local tours tailored to the user's preferences based on event schedule information in various locations and arranges various tickets at the lowest possible prices. Travelers can pay for everything in one lump sum, and the system also suggests the use of hometown tax return gifts (travel vouchers, travel points, etc.). This allows users to easily create a travel plan tailored to their preferences and enjoy a comfortable and efficient trip. As a result, the travel plan generation system can propose package tours based on the user's wishes at the optimal price and provide guidance that takes into account weather and traffic forecasts and real-time information.

[0061] The travel plan generation system according to this embodiment comprises a reception unit, a generation unit, and a guide unit. The reception unit receives input from the user regarding the places they wish to visit and the duration of their trip. The places the user wishes to visit include, for example, city names, tourist destination names, country names, etc., but are not limited to such examples. The duration of the trip includes, for example, the number of days, start date, and end date, etc., but are not limited to such examples. The reception unit provides, for example, an interface for the user to input the places they wish to visit and the duration of their trip. The generation unit assembles a package tour based on the information received by the reception unit. The generation unit proposes a package tour based on the user's wishes at the optimal price, for example, using a generation AI. The generation unit utilizes data and search functions to assemble a package tour that is differentiated from other companies' services. The generation unit, for example, searches for relevant information from a database and generates the optimal package tour. The generation unit can also generate a package tour based on the user's wishes using a generation AI. The guide unit provides real-time information based on the package tour assembled by the generation unit. The guide unit provides comfortable travel information in real time, for example, by utilizing weather and traffic congestion forecast data. The guide unit provides, for example, an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using a generation AI. As a result, the travel plan generation system according to the embodiment automatically creates a package tour and provides real-time information simply by the user inputting the places they want to go and the duration of their trip.

[0062] The reception desk allows users to input their desired destinations and travel duration. Destinations may include, but are not limited to, city names, tourist attractions, or country names. Travel duration may include, but are not limited to, the number of days, start date, and end date. The reception desk provides an interface for users to input their desired destinations and travel duration. Specifically, it features an intuitive user interface, such as a function to select destinations on a map or a function to select travel duration in a calendar format. Furthermore, the reception desk verifies user input in real time and provides feedback to prevent input errors and inaccuracies. For example, if a city name entered by the user does not exist or the travel duration is inappropriate, a warning message is immediately displayed, prompting the user to enter correct information. The reception desk can also suggest destinations and travel durations based on the user's past travel history and preferences. For example, it can suggest relevant travel destinations based on cities the user has visited in the past or tourist attractions they are interested in. This allows the reception desk to support users in smoothly and accurately entering their desired destinations and travel duration, enabling them to take the first step in creating their travel plan efficiently.

[0063] The generation unit assembles package tours based on information received by the reception unit. For example, the generation unit uses generation AI to propose package tours based on user preferences at the optimal price. The generation unit utilizes data and search functions to create package tours that differentiate themselves from competitors' services. Specifically, the generation unit searches a vast travel database and combines the most suitable accommodations, transportation, sightseeing spots, and meal plans to meet the user's preferences. The generation AI analyzes user input information and generates the optimal travel plan based on past travel data and trend information. For example, based on the city or tourist destination name entered by the user, it searches for popular tourist spots and event information in that region and incorporates them into the travel plan. The generation AI also optimizes pricing, proposing the best plan to fit the user's budget. For example, it compares prices for multiple accommodations and transportation options and presents the most cost-effective choice. Furthermore, the generation unit can provide personalized travel plans considering the user's preferences and past travel history. For example, if a user has previously visited certain tourist destinations or is interested in a specific theme, it generates a customized plan based on that information. This allows the production unit to provide high-quality package tours that fully meet the user's needs and are differentiated from other companies' services.

[0064] The guide unit provides real-time information based on package tours created by the generation unit. For example, the guide unit utilizes weather and traffic forecast data to provide comfortable travel in real time. Specifically, the guide unit has an interface to provide users with real-time weather information and traffic conditions at their current location during their trip. For example, if the weather suddenly changes while a user is on their way to a tourist destination, the guide unit immediately notifies them and suggests alternative sightseeing plans and modes of transportation. In the event of traffic congestion, it suggests the optimal detour route to support the user's smooth travel. Furthermore, the guide unit uses generational AI to analyze the user's current situation and behavioral patterns to provide optimal real-time information. For example, if a user is staying at a particular tourist spot for an extended period, it provides information on recommended restaurants and cafes in the surrounding area. It can also suggest additional tourist spots and activities if a user finishes sightseeing earlier than planned. In this way, the guide unit supports users in traveling comfortably and efficiently, and enjoying a fulfilling travel experience. Additionally, the guide unit can collect user feedback to continuously improve the accuracy and quality of the information it provides. For example, by allowing users to leave ratings and comments on the information provided, the guide team can use that feedback to improve their service and provide a better travel experience.

[0065] The generation unit can create package tours by utilizing data and search functions. For example, the generation unit can search for relevant information from the database and generate the optimal package tour. The generation unit can also generate package tours based on user preferences using generation AI. For example, the generation unit can search for information on accommodations and transportation from the database and suggest the optimal combination. The generation unit can also generate package tours based on user preferences using generation AI. This allows us to provide package tours that are differentiated from other companies' services by utilizing data and search functions.

[0066] The guide unit can provide comfortable travel in real time by utilizing weather and traffic congestion forecast data. For example, the guide unit provides an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using generative AI. For example, the guide unit can acquire weather forecast data and propose the optimal travel route to the user. The guide unit can also analyze weather forecast data using generative AI and propose the optimal travel route. For example, the guide unit can acquire traffic congestion forecast data and propose the optimal travel route to the user. The guide unit can also analyze traffic congestion forecast data using generative AI and propose the optimal travel route. This allows for comfortable travel in real time by utilizing weather and traffic congestion forecast data.

[0067] The generation unit can collect event schedule information from various locations and plan local tours tailored to the user's preferences. For example, the generation unit collects event schedule information from data from local tourism associations and online event calendars. The generation unit can also use generation AI to analyze event schedule information and plan local tours tailored to the user's preferences. For example, the generation unit can suggest local tours that include specific events or activities based on the user's preferences. The generation unit can also use generation AI to plan local tours based on the user's preferences. This allows for the planning of local tours tailored to the user's preferences by collecting event schedule information from various locations.

[0068] The generation unit can arrange various tickets at the lowest possible prices. For example, the generation unit collects price information from multiple travel agencies and online booking sites and arranges the cheapest tickets. The generation unit can also use generation AI to analyze price information and arrange the cheapest tickets. For example, the generation unit compares prices for airfare, accommodation, and entrance tickets to tourist attractions and proposes the cheapest tickets. The generation unit can also use generation AI to analyze price information and arrange the cheapest tickets. By arranging various tickets at the lowest possible prices, it can provide users with economical travel plans.

[0069] The generation unit can suggest ways to use Furusato Nozei (hometown tax donation) return gifts. For example, the generation unit collects information on travel vouchers, travel points, and accommodation coupons offered as Furusato Nozei return gifts. The generation unit can also use generation AI to analyze the information on return gifts and make optimal usage suggestions to users. For example, the generation unit can suggest ways to use Furusato Nozei return gifts in accordance with the user's travel plan. The generation unit can also use generation AI to suggest ways to use return gifts based on the user's travel plan. In this way, by suggesting ways to use Furusato Nozei return gifts, it is possible to provide users with even more advantageous travel plans.

[0070] The reception desk can estimate the user's emotions and adjust the input method for travel duration and desired destinations based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel duration and desired destinations. This provides a user-friendly interface by adjusting the input method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The reception desk can analyze the user's past travel history and suggest the optimal travel duration and method of inputting desired destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest places related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can suggest the optimal travel duration and method of inputting desired destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0072] The reception desk can filter travel durations and destinations based on the user's current living situation and areas of interest. For example, when a user enters their current living situation, the reception desk can suggest suitable travel durations and destinations. The reception desk can also filter and suggest relevant travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception desk can also suggest optimal travel durations and destinations by considering the user's current living situation (work workload, family structure, etc.). This allows for the suggestion of more appropriate travel plans by filtering travel durations and destinations based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0073] The reception desk can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is having fun, the reception desk can provide an interface with bright colors to make the input process more enjoyable. If the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface based on the user's emotions, a user-friendly interface can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The reception desk can automatically suggest candidate destinations based on the user's past travel history when the user enters their departure and destination locations. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as candidate destinations. The reception desk can also analyze the user's past travel patterns and suggest the most suitable candidate destinations. This allows the reception desk to automatically suggest more appropriate candidate destinations by referring to the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without using AI.

[0075] The reception desk can refer to the user's calendar information when the user enters their departure and destination locations and make suggestions based on their schedule. For example, the reception desk can refer to appointments registered in the user's calendar and automatically set the departure and destination locations. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Based on the user's calendar information, the reception desk can also suggest the optimal route to match the schedule. In this way, by referring to the user's calendar information, it is possible to make appropriate suggestions based on the schedule. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0076] The reception desk can prioritize suggesting highly relevant travel plans by taking into account the user's geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. The reception desk can also suggest easily accessible travel destinations based on the user's geographical location. The reception desk can also suggest travel destinations with convenient transportation options by taking into account the user's geographical location. In this way, by taking into account the user's geographical location, the reception desk can prioritize suggesting highly relevant travel plans. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0077] The generation unit can estimate the user's emotions and adjust the content of the package tour based on those emotions. For example, if the user is relaxed, the generation AI can suggest a tour that proceeds at a leisurely pace. If the user is in a hurry, the generation AI can suggest a tour that emphasizes the shortest route. If the user is excited, the generation AI can suggest a tour with visually stimulating effects. In this way, by adjusting the content of the package tour based on the user's emotions, the optimal tour can be provided to the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The generation unit can adjust the level of detail in package tours based on the importance of the trip. For example, for an important business trip, the generation unit's AI might suggest a tour that includes a detailed schedule and luxury hotels. For a family trip, the generation unit's AI might suggest a tour that includes activities for children and family-friendly accommodations. For a short weekend trip, the generation unit's AI might suggest a tour that efficiently covers the main tourist attractions. By adjusting the level of detail in package tours based on the importance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0079] The generation unit can apply different generation algorithms depending on the travel category. For example, in the case of an adventure tour, the generation unit can apply a generation algorithm that emphasizes outdoor activities. In the case of a cultural tour, the generation unit can also apply a generation algorithm that emphasizes historical sites and museums. In the case of a relaxation tour, the generation unit can also apply a generation algorithm that emphasizes spas and resorts. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI.

[0080] The generation unit can estimate the user's emotions and adjust the length of the package tour based on those emotions. For example, if the user is in a hurry, the generation AI can suggest a short tour that covers the main sights. If the user is relaxed, the generation AI can suggest a longer tour that proceeds at a leisurely pace. If the user is excited, the generation AI can suggest a tour with more activities. By adjusting the length of the package tour based on the user's emotions, the system can provide the user with the most suitable tour. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit can prioritize package tours based on when the travel request is submitted. For example, the generation unit's AI can propose a tour that responds quickly to a travel request submitted at the last minute. The generation unit's AI can also propose a tour with detailed planning for a travel request submitted early. The generation unit's AI can also propose a tour that prioritizes travel requests tailored to specific events or seasons. By prioritizing package tours based on when the travel request is submitted, the system can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0082] The generation unit can adjust the order of package tours based on the relevance of the trip. For example, the generation unit's generating AI can suggest tours that prioritize visits to highly relevant tourist spots based on the user's areas of interest. The generation unit's generating AI can also suggest tours that include highly relevant tourist spots based on the user's past travel history. The generation unit's generating AI can also suggest tours that include highly relevant tourist spots based on the user's current living situation. By adjusting the order of package tours based on the relevance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generating AI, or without using the generating AI.

[0083] The guide unit can estimate the user's emotions and adjust the display method of real-time information based on the estimated emotions. For example, if the user is tense, the guide unit can provide a simple and highly visible display method. If the user is relaxed, the guide unit can also provide a display method that includes detailed information. If the user is in a hurry, the guide unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of real-time information based on the user's emotions, the system can provide the user with the most optimal information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The guide unit can optimize the current guide content by referring to past guide data. For example, the guide unit can provide optimal guide content based on guide data of places the user has visited in the past. The guide unit can also provide guide content tailored to the user's preferences from past guide data. The guide unit can also analyze past guide data and provide the most efficient guide content. In this way, the current guide content can be optimized by referring to past guide data. Some or all of the above processing in the guide unit may be performed using AI, for example, or without using AI.

[0085] The guiding function can apply different guiding methods depending on the travel category. For example, in the case of an adventure tour, the guiding function can apply a guiding method that emphasizes outdoor activities. In the case of a cultural tour, the guiding function can also apply a guiding method that emphasizes historical sites and museums. In the case of a relaxation tour, the guiding function can also apply a guiding method that emphasizes spas and resorts. By applying different guiding methods for each travel category, the guiding function can provide the user with the most suitable guide. Some or all of the above processing in the guiding function may be performed using AI, for example, or not using AI.

[0086] The guide unit can estimate the user's emotions and adjust the importance of real-time information based on the estimated emotions. For example, if the user is stressed, the guide unit will prioritize displaying important information. If the user is relaxed, the guide unit can also provide a display method that includes detailed information. If the user is in a hurry, the guide unit can also provide a display method that gets straight to the point. This allows for optimal information delivery to the user by adjusting the importance of real-time information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The guide department can analyze real-time changes in information based on when travel requests are submitted. For example, the guide department can provide guide information that responds quickly to travel requests submitted at the last minute. The guide department can also provide guide information that allows for detailed planning for travel requests submitted early. The guide department can also provide guide information that prioritizes travel requests tailored to specific events or seasons. This allows for the provision of optimal information to users by analyzing real-time changes in information based on when travel requests are submitted. Some or all of the above processing in the guide department may be performed using AI, for example, or not using AI.

[0088] The guide unit can analyze real-time information by referring to travel-related market data. For example, the guide unit can suggest optimal tourist spots based on market data of the travel destination. The guide unit can also suggest routes to avoid crowds based on market data of the travel destination. The guide unit can also suggest restaurants and shops tailored to the user's preferences based on market data of the travel destination. In this way, by referring to travel-related market data, the guide unit can provide the user with the most optimal information. Some or all of the above processing in the guide unit may be performed using AI, for example, or not using AI.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The reception desk can estimate the user's emotions and adjust the input method for travel duration and desired destinations based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel duration and desired destinations. This provides a user-friendly interface by adjusting the input method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit can estimate the user's emotions and adjust the content of the package tour based on those emotions. For example, if the user is relaxed, the generation AI can suggest a tour that proceeds at a leisurely pace. If the user is in a hurry, the generation AI can suggest a tour that emphasizes the shortest route. If the user is excited, the generation AI can suggest a tour with visually stimulating effects. In this way, by adjusting the content of the package tour based on the user's emotions, the optimal tour can be provided to the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The guide unit can estimate the user's emotions and adjust the display method of real-time information based on the estimated emotions. For example, if the user is tense, the guide unit can provide a simple and highly visible display method. If the user is relaxed, the guide unit can also provide a display method that includes detailed information. If the user is in a hurry, the guide unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of real-time information based on the user's emotions, the system can provide the user with the most optimal information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The reception desk can analyze the user's past travel history and suggest the optimal travel duration and method of inputting desired destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest places related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can suggest the optimal travel duration and method of inputting desired destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0094] The reception desk can filter travel durations and destinations based on the user's current living situation and areas of interest. For example, when a user enters their current living situation, the reception desk can suggest suitable travel durations and destinations. The reception desk can also filter and suggest relevant travel destinations based on the user's areas of interest (history, nature, art, etc.). The reception desk can also suggest optimal travel durations and destinations by considering the user's current living situation (work workload, family structure, etc.). This allows for the suggestion of more appropriate travel plans by filtering travel durations and destinations based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0095] The generation unit can adjust the level of detail in package tours based on the importance of the trip. For example, for an important business trip, the generation unit's AI might suggest a tour that includes a detailed schedule and luxury hotels. For a family trip, the generation unit's AI might suggest a tour that includes activities for children and family-friendly accommodations. For a short weekend trip, the generation unit's AI might suggest a tour that efficiently covers the main tourist attractions. By adjusting the level of detail in package tours based on the importance of the trip, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without using the generation AI.

[0096] The generation unit can apply different generation algorithms depending on the travel category. For example, in the case of an adventure tour, the generation unit can apply a generation algorithm that emphasizes outdoor activities. In the case of a cultural tour, the generation unit can also apply a generation algorithm that emphasizes historical sites and museums. In the case of a relaxation tour, the generation unit can also apply a generation algorithm that emphasizes spas and resorts. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable tour. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI.

[0097] The guide unit can optimize the current guide content by referring to past guide data. For example, the guide unit can provide optimal guide content based on guide data of places the user has visited in the past. The guide unit can also provide guide content tailored to the user's preferences from past guide data. The guide unit can also analyze past guide data and provide the most efficient guide content. In this way, the current guide content can be optimized by referring to past guide data. Some or all of the above processing in the guide unit may be performed using AI, for example, or without using AI.

[0098] The guiding function can apply different guiding methods depending on the travel category. For example, in the case of an adventure tour, the guiding function can apply a guiding method that emphasizes outdoor activities. In the case of a cultural tour, the guiding function can also apply a guiding method that emphasizes historical sites and museums. In the case of a relaxation tour, the guiding function can also apply a guiding method that emphasizes spas and resorts. By applying different guiding methods for each travel category, the guiding function can provide the user with the most suitable guide. Some or all of the above processing in the guiding function may be performed using AI, for example, or not using AI.

[0099] The guide unit can estimate the user's emotions and adjust the importance of real-time information based on the estimated emotions. For example, if the user is stressed, the guide unit will prioritize displaying important information. If the user is relaxed, the guide unit can also provide a display method that includes detailed information. If the user is in a hurry, the guide unit can also provide a display method that gets straight to the point. This allows for optimal information delivery to the user by adjusting the importance of real-time information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk receives the user's desired destination and travel duration. The destination includes city names, tourist attractions, country names, etc., and the travel duration includes the number of days, start date, and end date. The reception desk provides an interface for entering this information. Step 2: The generation unit creates package tours based on the information received by the reception unit. The generation unit uses generation AI to propose package tours based on user preferences at the optimal price, and utilizes data and search functions to generate package tours that are differentiated from other companies' services. The generation unit searches the database for relevant information and generates the optimal package tour. Step 3: The guide unit provides real-time information based on the package tours created by the generation unit. The guide unit utilizes weather and traffic forecast data to provide comfortable travel in real time and provides an interface for providing weather and traffic information in real time. The guide unit can also provide real-time information using generation AI.

[0102] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0105] Each of the multiple elements described above, including the reception unit, generation unit, and guide unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input the place they want to go and the duration of their trip. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generation AI to propose a package tour based on the user's wishes at the optimal price. The guide unit is implemented, for example, by the output device 40 of the smart device 14 and provides comfortable travel in real time by utilizing weather and traffic congestion forecast data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the reception unit, generation unit, and guide unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input the place they want to go and the duration of their trip. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generation AI to propose a package tour based on the user's wishes at the optimal price. The guide unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides comfortable travel in real time by utilizing weather and traffic congestion forecast data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, generation unit, and guide unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input the desired destination and the duration of the trip. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generation AI to propose a package tour based on the user's wishes at the optimal price. The guide unit is implemented by, for example, the display 343 of the headset terminal 314 and provides comfortable travel in real time by utilizing weather and traffic congestion forecast data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0154] Each of the multiple elements described above, including the reception unit, generation unit, and guide unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input the place they want to go and the duration of their trip. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses a generation AI to propose a package tour based on the user's wishes at the optimal price. The guide unit is implemented by, for example, the speaker 240 of the robot 414 and provides a comfortable journey in real time by utilizing weather and traffic congestion forecast data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0155] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A reception desk where the user enters the place they want to go and the duration of their trip, A generation unit that creates a package tour based on the information received by the reception unit, The system includes a guide unit that provides real-time information based on the package tour composed by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Create package tours using data and search functions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide portion is We provide a comfortable travel experience in real time by utilizing weather and traffic forecast data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We collect event schedule information from various locations and plan local tours that suit the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Arrange the cheapest tickets available. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Proposing ways to utilize return gifts from the Furusato Nozei (hometown tax) system. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for travel duration and desired destinations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal travel duration and how to input desired destinations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Filter travel duration and destinations based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their departure and destination locations, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When the user enters their departure and destination locations, the system will refer to their calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Prioritize suggesting highly relevant travel plans by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the package tour content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Adjust the level of detail in package tours based on the importance of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Apply different generation algorithms depending on the travel category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the length of the package tour based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Prioritizing package tours based on when travel plans are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Adjust the order of package tours based on the relevance of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide portion is It estimates the user's emotions and adjusts how real-time information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide portion is Optimize current guide content by referring to past guide data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide portion is Apply different guiding methods to each travel category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide portion is It estimates the user's emotions and adjusts the importance of real-time information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide portion is Analyze real-time changes in information based on when travel plans are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide portion is Analyze real-time information by referring to travel-related market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the user enters the place they want to go and the duration of their trip, A generation unit that creates a package tour based on the information received by the reception unit, The system includes a guide unit that provides real-time information based on the package tour composed by the generation unit. A system characterized by the following features.

2. The generating unit is Create package tours using data and search functions. The system according to feature 1.

3. The aforementioned guide portion is We provide a comfortable travel experience in real time by utilizing weather and traffic forecast data. The system according to feature 1.

4. The generating unit is We collect event schedule information from various locations and plan local tours that suit the user's preferences. The system according to feature 1.

5. The generating unit is Arrange the cheapest tickets available. The system according to feature 1.

6. The generating unit is Proposing ways to utilize return gifts from the Furusato Nozei (hometown tax) system. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for travel duration and desired destinations based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal travel duration and how to input desired destinations. The system according to feature 1.

Images