system

Abstract

The system according to this embodiment aims to propose travel plans that meet the diverse needs of users. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, and a transportation management unit. The reception unit inputs basic travel information. The generation unit analyzes the information input by the reception unit and generates a travel plan. The proposal unit proposes gourmet information and activities based on the plan generated by the generation unit. The transportation management unit manages information related to transportation.

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 in 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 prior art, there is a problem that the proposals for travel plans are uniform and it is difficult to meet the diverse needs of users.

[0005] The system according to the embodiment aims to propose a travel plan that meets the diverse needs of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, and a transportation management unit. The reception unit receives basic travel information. The generation unit analyzes the information entered by the reception unit and generates a travel plan. The suggestion unit proposes gourmet information and activities based on the plan generated by the generation unit. The transportation management unit manages information related to transportation. [Effects of the Invention]

[0007] The system according to this embodiment can propose travel plans that meet the diverse needs of users. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 suggestion system according to an embodiment of the present invention is a system in which the user inputs basic travel information, and a generating AI analyzes that information to suggest a travel plan. The travel plan suggestion system takes the user's basic travel information (number of people, mode of transportation, dates, destination, budget) as input. Next, the generating AI analyzes this information and suggests five travel plans that match the settings. In addition to general sightseeing plans, it also suggests unique plans to satisfy even experienced travelers. When the user selects one of the suggested plans, the generating AI suggests multiple gourmet information and activities for the destination. It is also possible to set private cars or rental cars as modes of transportation, allowing for flexible plan suggestions. As a result, the user can easily plan their trip, and because sightseeing and gourmet information is abundant, travel satisfaction is improved. Thus, the travel plan suggestion system can efficiently support the user's travel planning.

[0029] The travel plan suggestion system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, and a transportation management unit. The reception unit inputs basic travel information. Basic travel information includes, but is not limited to, the destination, itinerary, budget, and number of participants. The reception unit stores the information entered by the user in a database and uses it for subsequent processing. The generation unit analyzes the information entered by the reception unit and generates a travel plan. The generation unit, for example, uses a generation AI to suggest five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit can also generate unusual plans. For example, the generation unit can generate plans that include unique tourist destinations or special experiences. The suggestion unit suggests gourmet information and activities based on the plans generated by the generation unit. The suggestion unit suggests multiple gourmet information and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. The transportation management unit manages information related to transportation. The transportation management unit manages settings for private cars and rental cars, for example. The transportation management unit manages reservation methods and usage conditions, and can propose the most suitable mode of transportation to the user. This allows the travel plan proposal system according to the embodiment to efficiently support the user's travel planning.

[0030] The reception desk inputs basic travel information. This information includes, but is not limited to, destination, itinerary, budget, and number of participants. For example, the reception desk stores the information entered by the user in a database for use in subsequent processing. Specifically, when a user selects a destination, the reception desk immediately records that information in the database, and similarly stores other information such as itinerary, budget, and number of participants. This ensures that the information entered by the user is centrally managed, allowing subsequent generation and suggestion departments to access it efficiently. Furthermore, the reception desk has a function to check the consistency of the information entered by the user and notify the user and prompt correction if there is any missing or inconsistent information. For example, if the itinerary is duplicated or the budget is unrealistic, the reception desk displays a warning message and asks the user to re-enter the information. This allows the reception desk to collect accurate and complete information, improving the accuracy and reliability of the entire system. The reception desk can also customize the input process by considering the user's past travel history and preferences. For example, it can collect information to make appropriate suggestions to the user based on previously visited destinations and preferred activities. This allows the reception department to build a foundation for providing users with more personalized travel plans.

[0031] The generation unit analyzes the information entered by the reception unit and generates travel plans. For example, using a generation AI, the generation unit proposes five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit can also generate unique plans. For example, it can generate plans that include unique tourist destinations or special experiences. Specifically, the generation AI analyzes information such as travel destinations, dates, budget, and number of participants entered by the user, and generates the optimal plan based on past data and trend information. The generation AI uses natural language processing technology to understand the user's preferences and requests, and creates a customized plan based on that. For example, if the user enters "I want to enjoy nature," the generation AI will propose a plan that includes nature parks and hiking trails in response to that request. Furthermore, the generation AI can also generate more personalized plans by considering the user's past travel history and ratings. For example, it can suggest new travel destinations and experiences based on tourist destinations visited in the past and preferred activities. In this way, the generation unit can provide the user with the optimal travel plan and improve travel satisfaction. Furthermore, the generation unit can update the generated plans in real time and provide optimal suggestions based on the latest information. For example, it can automatically modify plans in response to changes in weather or traffic conditions, providing users with the most up-to-date information. This allows the generation unit to always provide the best travel plan and support users' travel planning.

[0032] The suggestion unit proposes gourmet information and activities based on the plans generated by the generation unit. For example, the suggestion unit proposes multiple gourmet options and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. Specifically, the suggestion unit analyzes the user's preferences and past ratings based on the information entered by the user and the plans generated by the generation unit, and proposes the most suitable gourmet information and activities. For example, if the user enters "I want to enjoy local cuisine," the suggestion unit will provide popular local restaurants and specialty products according to that request. Furthermore, the suggestion unit can make more personalized suggestions by considering the user's past travel history and ratings. For example, it can propose new gourmet information based on restaurants visited in the past and favorite dishes. In this way, the suggestion unit can provide the user with the most suitable gourmet information and activities, improving their travel satisfaction. In addition, the suggestion unit can update the proposed information in real time and make optimal suggestions based on the latest information. For example, it can automatically adjust the suggested content according to the restaurant reservation status and activity availability, providing the user with the latest information. This allows the proposal department to consistently provide optimal gourmet information and activities, supporting users' travel planning.

[0033] The Transportation Management Department manages information related to transportation. For example, it manages settings for private cars and rental cars. The Transportation Management Department manages booking methods and usage conditions, and can suggest the most suitable transportation to users. Specifically, the Transportation Management Department suggests the most suitable transportation based on information such as travel destination, dates, and budget entered by the user. For example, if a user enters "I want to use a rental car," the Transportation Management Department will suggest the most suitable rental car plan according to that request. The Transportation Management Department can also make more personalized suggestions by considering the user's past usage history and ratings. For example, it can suggest new transportation options based on rental car companies used in the past and preferred vehicle types. In this way, the Transportation Management Department can provide the most suitable transportation for users and improve their travel satisfaction. Furthermore, the Transportation Management Department can update suggested information in real time and make the best suggestions based on the latest information. For example, it can automatically adjust the suggested content according to changes in rental car availability and prices, providing users with the latest information. In this way, the Transportation Management Department can always provide the most suitable transportation and support users' travel planning.

[0034] The generation unit can generate not only typical sightseeing plans but also unconventional ones. For example, it can generate plans that include unique tourist destinations or special experiences. The generation unit can also use a generation AI to generate unconventional plans based on the user's preferences and past travel history. For example, the generation AI can receive a prompt such as "Please suggest unique tourist destinations," analyze information about the destinations, and generate an unconventional plan. This allows the generation of unconventional plans to satisfy even experienced travelers.

[0035] The suggestion unit can propose multiple gourmet options and activities for a destination. For example, the suggestion unit can propose multiple gourmet options and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. For example, the suggestion unit can use generative AI to propose gourmet options and activities based on the user's preferences and past travel history. For example, the generative AI can receive a prompt such as "Please suggest recommended gourmet options for this travel destination," analyze the gourmet information, and make suggestions. By proposing multiple gourmet options and activities, user satisfaction can be improved.

[0036] The Transportation Management Department can manage the settings for private cars and rental cars. For example, it manages booking methods and usage conditions and suggests the most suitable mode of transportation to the user. The Transportation Management Department can also manage the settings for private cars and rental cars based on user preferences and past usage history, for example, using generative AI. For example, the generative AI can receive a prompt such as "Please suggest the best mode of transportation for this travel destination," analyze the transportation information, and make a suggestion. This allows for flexible plan suggestions by managing the settings for private cars and rental cars.

[0037] The generation unit can suggest five optimal travel plans based on the user's input information. For example, the generation unit can use a generation AI to suggest the optimal travel plan based on the user's preferences and past travel history. For example, the generation unit can receive a prompt such as "Please suggest the optimal travel plan for this destination," analyze the travel plan, and make a suggestion. The generation unit can also suggest the optimal travel plan based on the user's budget and schedule. In this way, by suggesting the optimal travel plan based on the user's input information, it can provide a plan that meets the user's needs.

[0038] The reception desk can analyze the user's past travel history and provide appropriate suggestions during input. For example, the reception desk can suggest potential next travel destinations based on places the user has visited in the past. For example, the reception desk can suggest the most suitable mode of transportation based on modes of transportation the user has used in the past. For example, the reception desk can also suggest a travel plan that fits the user's budget based on their past travel history. By analyzing the user's past travel history, the reception desk can provide more appropriate suggestions and assist the user's input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform the generation of suggestions.

[0039] The reception unit can filter the user's current lifestyle and areas of interest during input. For example, the reception unit can suggest travel destinations based on themes the user has recently become interested in. For example, the reception unit can suggest the most suitable travel plan based on the user's current lifestyle (e.g., family structure, work situation). The reception unit can also filter travel destinations based on the user's areas of interest (e.g., outdoor activities, cultural experiences). This allows for the suggestion of more appropriate travel plans by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location information during input. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. For example, the reception unit can suggest the most suitable mode of transportation based on the user's geographical location information. For example, the reception unit can automatically set the optimal departure point based on the user's current location. This allows for the provision of more relevant information by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0041] The reception desk can analyze the user's social media activity and obtain relevant information during data entry. For example, the reception desk can suggest the next travel destination based on the travel destination the user has shared on social media. For example, the reception desk can suggest the optimal travel plan based on the user's interests on social media. For example, the reception desk can also suggest a travel plan that fits the user's budget based on their social media activity. By analyzing the user's social media activity, it is possible to provide more relevant information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0042] The generation unit can adjust the level of detail in a plan based on the importance of the trip when generating the plan. For example, for an important trip, the generation unit generates a plan that includes a detailed schedule and activities. For example, for a short trip, the generation unit generates a concise plan. For example, for a trip that includes a special event, the generation unit generates a plan that focuses on that event. This allows the system to provide the user with the most suitable plan by adjusting the level of detail based on the importance of the trip. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input trip importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0043] The generation unit can apply different generation algorithms depending on the travel category when generating a plan. For example, in the case of a family trip, the generation unit generates a plan that includes family-friendly activities. For example, in the case of a business trip, the generation unit generates a plan that includes efficient travel and meeting schedules. For example, in the case of an adventure trip, the generation unit generates a plan that includes adventurous activities. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input travel category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0044] The generation unit can determine the priority of travel plans based on the travel submission date when generating plans. For example, the generation unit will prioritize generating plans for upcoming trips. For example, the generation unit will generate plans with detailed schedules for long-term trips. For example, the generation unit will generate plans earlier for trips that are submitted early. By prioritizing plans based on the travel submission date, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input travel submission date data into a generation AI and have the generation AI perform the determination of plan priorities.

[0045] The generation unit can adjust the order of plans based on the relevance of the trips when generating plans. For example, the generation unit may display the most relevant plans first based on the user's interests. For example, the generation unit may prioritize displaying highly relevant plans based on the user's past travel history. For example, the generation unit may prioritize displaying the most suitable plans based on the user's current living situation. In this way, by adjusting the order of plans based on the relevance of the trips, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input travel relevance data into a generation AI and have the generation AI perform the adjustment of the order of the plans.

[0046] The suggestion function can adjust the level of detail in suggestions based on the importance of the gourmet information and activities. For example, if the gourmet information is important, the suggestion function will provide a suggestion with a detailed explanation and photos. For example, if the activity can be enjoyed in a short time, the suggestion function will provide a concise suggestion. For example, if a special event is included, the suggestion function will provide a suggestion that focuses on that event. In this way, by adjusting the level of detail in suggestions based on the importance of the gourmet information and activities, the suggestion function can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input importance data for gourmet information and activities into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of gourmet information or activity when making suggestions. For example, in the case of family-friendly gourmet information, the suggestion unit will make suggestions that the whole family can enjoy. For example, in the case of business-oriented activities, the suggestion unit will make suggestions that include efficient travel and meeting schedules. For example, in the case of adventure-oriented activities, the suggestion unit will make adventurous suggestions. In this way, by applying different suggestion algorithms depending on the category of gourmet information or activity, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input gourmet information and activity category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0048] The proposal department can prioritize proposals based on the submission timing of gourmet information and activities. For example, it will prioritize proposals for upcoming events. For example, it will submit proposals with detailed schedules for long-term plans. For example, it will submit proposals early for information that is due soon. By prioritizing proposals based on submission timing, the department can provide users with the most suitable suggestions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the submission timing of gourmet information and activities into a generating AI and have the generating AI determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of gourmet information and activities when making suggestions. For example, the suggestion unit may display the most relevant suggestions first based on the user's interests. For example, the suggestion unit may prioritize displaying relevant suggestions based on the user's past travel history. For example, the suggestion unit may prioritize displaying the most suitable suggestions based on the user's current living situation. In this way, by adjusting the order of suggestions based on relevance, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input relevance data of gourmet information and activities into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0050] The transportation management unit can analyze a user's past transportation usage history to select the optimal mode of transport during transportation management. For example, the transportation management unit can suggest the optimal mode of transport based on the user's past usage history. For example, the transportation management unit can suggest a mode of transport that avoids congestion based on the user's past transportation usage history. For example, the transportation management unit can analyze a user's past transportation usage history and suggest the most efficient mode of transport. In this way, by analyzing past transportation usage history, the system can provide the user with the optimal mode of transport. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's past transportation usage history data into a generating AI and have the generating AI select the optimal mode of transport.

[0051] The transportation management unit can customize the selection of transportation methods based on the user's current living situation when managing transportation. For example, if the user is traveling with family, the transportation management unit will suggest family-friendly transportation methods. For example, if the user is traveling for business, the transportation management unit will suggest efficient transportation methods. For example, if the user is on an adventure trip, the transportation management unit will suggest adventurous transportation methods. In this way, by customizing the selection of transportation methods based on the user's current living situation, the optimal transportation method can be provided to the user. Some or all of the above processing in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's current living situation data into a generating AI and have the generating AI perform the selection of transportation methods.

[0052] The transportation management unit can select the optimal mode of transportation by considering the user's geographical location information when managing transportation. For example, the transportation management unit can suggest the mode of transportation closest to the user's current location. For example, the transportation management unit can automatically set the optimal departure point based on the user's geographical location information. The transportation management unit can also suggest the optimal mode of transportation based on the user's current location. In this way, by considering geographical location information, the system can provide the user with the most suitable mode of transportation. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal mode of transportation.

[0053] The transportation management unit can analyze a user's social media activity to select the most suitable mode of transportation during the transportation management process. For example, the transportation management unit can suggest the next mode of transportation based on the mode of transportation shared by the user on social media. For example, the transportation management unit can suggest the most suitable mode of transportation based on the user's interests on social media. For example, the transportation management unit can suggest a mode of transportation that fits the user's budget based on their social media activity. In this way, by analyzing social media activity, the system can provide the user with the most suitable mode of transportation. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input user social media activity data into a generating AI and have the generating AI select the mode of transportation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can propose travel plans that take the user's health condition into consideration. For example, if a user has a pre-existing medical condition, the reception desk will suggest destinations and activities that take that condition into consideration. For example, if a user is elderly, the reception desk will prioritize suggesting barrier-free facilities and transportation options. For example, if a user has allergies, the reception desk can also suggest allergy-friendly restaurants and accommodations. By proposing travel plans that are tailored to the user's health condition, they can enjoy their trip with peace of mind.

[0056] The suggestion department can propose customized travel plans based on the user's hobbies and interests. For example, if the user enjoys outdoor activities, the suggestion department can propose a plan that includes hiking and camping. If the user is interested in history and culture, the suggestion department can propose a plan that visits historical sites and museums. If the user enjoys gourmet food, the suggestion department can propose a plan that explores local specialties and famous restaurants. In this way, by proposing travel plans that match the user's hobbies and interests, a more fulfilling travel experience can be provided.

[0057] The generation unit can analyze a user's past travel history and suggest new travel plans based on places and experiences they have visited in the past. For example, the generation unit can suggest nearby tourist attractions to places the user has visited in the past. For example, the generation unit can suggest plans that include activities the user has enjoyed in the past. For example, the generation unit can suggest new recommended accommodations and restaurants based on accommodations and restaurants the user has used in the past. This allows for the provision of more personalized travel plans by utilizing the user's past travel history.

[0058] The reception desk can suggest optimal travel destinations and activities by considering the user's geographical location. For example, the reception desk can suggest travel destinations close to the user's current location. For example, the reception desk can automatically set the optimal departure point based on the user's geographical location. For example, the reception desk can also suggest the optimal mode of transportation based on the user's current location. In this way, by considering the user's geographical location, it is possible to provide more relevant information.

[0059] The suggestion department can analyze users' social media activity, obtain relevant information, and make suggestions. For example, it can suggest the next travel destination based on the travel destinations the user has shared on social media. For example, it can suggest the optimal travel plan based on the user's interests on social media. For example, it can also suggest a travel plan that fits the user's budget based on their social media activity. In this way, by analyzing users' social media activity, it is possible to provide more relevant information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk enters the basic travel information. This information includes the destination, itinerary, budget, and number of participants. The reception desk saves the information entered by the user to a database and uses it for subsequent processing. Step 2: The generation unit analyzes the information entered by the reception unit and generates travel plans. Using generation AI, the generation unit proposes five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit also generates plans that include unique tourist destinations and special experiences. Step 3: The suggestion unit proposes gourmet information and activities based on the plan generated by the generation unit. The suggestion unit can propose multiple gourmet information and activities based on the user's preferences and can also make the most suitable suggestions for the user based on past data. Step 4: The Transportation Management Department manages information related to transportation. The Transportation Management Department manages the settings for private cars and rental cars, manages reservation methods and usage conditions, and proposes the most suitable transportation method to users.

[0062] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system in which the user inputs basic travel information, and a generating AI analyzes that information to suggest a travel plan. The travel plan suggestion system takes the user's basic travel information (number of people, mode of transportation, dates, destination, budget) as input. Next, the generating AI analyzes this information and suggests five travel plans that match the settings. In addition to general sightseeing plans, it also suggests unique plans to satisfy even experienced travelers. When the user selects one of the suggested plans, the generating AI suggests multiple gourmet information and activities for the destination. It is also possible to set private cars or rental cars as modes of transportation, allowing for flexible plan suggestions. As a result, the user can easily plan their trip, and because sightseeing and gourmet information is abundant, travel satisfaction is improved. Thus, the travel plan suggestion system can efficiently support the user's travel planning.

[0063] The travel plan suggestion system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, and a transportation management unit. The reception unit inputs basic travel information. Basic travel information includes, but is not limited to, the destination, itinerary, budget, and number of participants. The reception unit stores the information entered by the user in a database and uses it for subsequent processing. The generation unit analyzes the information entered by the reception unit and generates a travel plan. The generation unit, for example, uses a generation AI to suggest five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit can also generate unusual plans. For example, the generation unit can generate plans that include unique tourist destinations or special experiences. The suggestion unit suggests gourmet information and activities based on the plans generated by the generation unit. The suggestion unit suggests multiple gourmet information and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. The transportation management unit manages information related to transportation. The transportation management unit manages settings for private cars and rental cars, for example. The transportation management unit manages reservation methods and usage conditions, and can propose the most suitable mode of transportation to the user. This allows the travel plan proposal system according to the embodiment to efficiently support the user's travel planning.

[0064] The reception desk inputs basic travel information. This information includes, but is not limited to, destination, itinerary, budget, and number of participants. For example, the reception desk stores the information entered by the user in a database for use in subsequent processing. Specifically, when a user selects a destination, the reception desk immediately records that information in the database, and similarly stores other information such as itinerary, budget, and number of participants. This ensures that the information entered by the user is centrally managed, allowing subsequent generation and suggestion departments to access it efficiently. Furthermore, the reception desk has a function to check the consistency of the information entered by the user and notify the user and prompt correction if there is any missing or inconsistent information. For example, if the itinerary is duplicated or the budget is unrealistic, the reception desk displays a warning message and asks the user to re-enter the information. This allows the reception desk to collect accurate and complete information, improving the accuracy and reliability of the entire system. The reception desk can also customize the input process by considering the user's past travel history and preferences. For example, it can collect information to make appropriate suggestions to the user based on previously visited destinations and preferred activities. This allows the reception department to build a foundation for providing users with more personalized travel plans.

[0065] The generation unit analyzes the information entered by the reception unit and generates travel plans. For example, using a generation AI, the generation unit proposes five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit can also generate unique plans. For example, it can generate plans that include unique tourist destinations or special experiences. Specifically, the generation AI analyzes information such as travel destinations, dates, budget, and number of participants entered by the user, and generates the optimal plan based on past data and trend information. The generation AI uses natural language processing technology to understand the user's preferences and requests, and creates a customized plan based on that. For example, if the user enters "I want to enjoy nature," the generation AI will propose a plan that includes nature parks and hiking trails in response to that request. Furthermore, the generation AI can also generate more personalized plans by considering the user's past travel history and ratings. For example, it can suggest new travel destinations and experiences based on tourist destinations visited in the past and preferred activities. In this way, the generation unit can provide the user with the optimal travel plan and improve travel satisfaction. Furthermore, the generation unit can update the generated plans in real time and provide optimal suggestions based on the latest information. For example, it can automatically modify plans in response to changes in weather or traffic conditions, providing users with the most up-to-date information. This allows the generation unit to always provide the best travel plan and support users' travel planning.

[0066] The suggestion unit proposes gourmet information and activities based on the plans generated by the generation unit. For example, the suggestion unit proposes multiple gourmet options and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. Specifically, the suggestion unit analyzes the user's preferences and past ratings based on the information entered by the user and the plans generated by the generation unit, and proposes the most suitable gourmet information and activities. For example, if the user enters "I want to enjoy local cuisine," the suggestion unit will provide popular local restaurants and specialty products according to that request. Furthermore, the suggestion unit can make more personalized suggestions by considering the user's past travel history and ratings. For example, it can propose new gourmet information based on restaurants visited in the past and favorite dishes. In this way, the suggestion unit can provide the user with the most suitable gourmet information and activities, improving their travel satisfaction. In addition, the suggestion unit can update the proposed information in real time and make optimal suggestions based on the latest information. For example, it can automatically adjust the suggested content according to the restaurant reservation status and activity availability, providing the user with the latest information. This allows the proposal department to consistently provide optimal gourmet information and activities, supporting users' travel planning.

[0067] The Transportation Management Department manages information related to transportation. For example, it manages settings for private cars and rental cars. The Transportation Management Department manages booking methods and usage conditions, and can suggest the most suitable transportation to users. Specifically, the Transportation Management Department suggests the most suitable transportation based on information such as travel destination, dates, and budget entered by the user. For example, if a user enters "I want to use a rental car," the Transportation Management Department will suggest the most suitable rental car plan according to that request. The Transportation Management Department can also make more personalized suggestions by considering the user's past usage history and ratings. For example, it can suggest new transportation options based on rental car companies used in the past and preferred vehicle types. In this way, the Transportation Management Department can provide the most suitable transportation for users and improve their travel satisfaction. Furthermore, the Transportation Management Department can update suggested information in real time and make the best suggestions based on the latest information. For example, it can automatically adjust the suggested content according to changes in rental car availability and prices, providing users with the latest information. In this way, the Transportation Management Department can always provide the most suitable transportation and support users' travel planning.

[0068] The generation unit can generate not only typical sightseeing plans but also unconventional ones. For example, it can generate plans that include unique tourist destinations or special experiences. The generation unit can also use a generation AI to generate unconventional plans based on the user's preferences and past travel history. For example, the generation AI can receive a prompt such as "Please suggest unique tourist destinations," analyze information about the destinations, and generate an unconventional plan. This allows the generation of unconventional plans to satisfy even experienced travelers.

[0069] The suggestion unit can propose multiple gourmet options and activities for a destination. For example, the suggestion unit can propose multiple gourmet options and activities based on the user's preferences. The suggestion unit can also make optimal suggestions to the user based on past data. For example, the suggestion unit can use generative AI to propose gourmet options and activities based on the user's preferences and past travel history. For example, the generative AI can receive a prompt such as "Please suggest recommended gourmet options for this travel destination," analyze the gourmet information, and make suggestions. By proposing multiple gourmet options and activities, user satisfaction can be improved.

[0070] The Transportation Management Department can manage the settings for private cars and rental cars. For example, it manages booking methods and usage conditions and suggests the most suitable mode of transportation to the user. The Transportation Management Department can also manage the settings for private cars and rental cars based on user preferences and past usage history, for example, using generative AI. For example, the generative AI can receive a prompt such as "Please suggest the best mode of transportation for this travel destination," analyze the transportation information, and make a suggestion. This allows for flexible plan suggestions by managing the settings for private cars and rental cars.

[0071] The generation unit can suggest five optimal travel plans based on the user's input information. For example, the generation unit can use a generation AI to suggest the optimal travel plan based on the user's preferences and past travel history. For example, the generation unit can receive a prompt such as "Please suggest the optimal travel plan for this destination," analyze the travel plan, and make a suggestion. The generation unit can also suggest the optimal travel plan based on the user's budget and schedule. In this way, by suggesting the optimal travel plan based on the user's input information, it can provide a plan that meets the user's needs.

[0072] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk may provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk may prioritize voice input to allow for quick input of basic travel information. By adjusting the input interface according to the user's emotions, it is possible to reduce user stress and improve input efficiency. 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. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past travel history and provide appropriate suggestions during input. For example, the reception desk can suggest potential next travel destinations based on places the user has visited in the past. For example, the reception desk can suggest the most suitable mode of transportation based on modes of transportation the user has used in the past. For example, the reception desk can also suggest a travel plan that fits the user's budget based on their past travel history. By analyzing the user's past travel history, the reception desk can provide more appropriate suggestions and assist the user's input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform the generation of suggestions.

[0074] The reception unit can filter the user's current lifestyle and areas of interest during input. For example, the reception unit can suggest travel destinations based on themes the user has recently become interested in. For example, the reception unit can suggest the most suitable travel plan based on the user's current lifestyle (e.g., family structure, work situation). The reception unit can also filter travel destinations based on the user's areas of interest (e.g., outdoor activities, cultural experiences). This allows for the suggestion of more appropriate travel plans by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of input fields based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize displaying the most important input fields. If the user is relaxed, the reception unit will sequentially display detailed input fields. If the user is in a hurry, the reception unit will display only the minimum necessary input fields. This reduces the user's input burden by prioritizing input fields according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location information during input. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. For example, the reception unit can suggest the most suitable mode of transportation based on the user's geographical location information. For example, the reception unit can automatically set the optimal departure point based on the user's current location. This allows for the provision of more relevant information by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0077] The reception desk can analyze the user's social media activity and obtain relevant information during data entry. For example, the reception desk can suggest the next travel destination based on the travel destination the user has shared on social media. For example, the reception desk can suggest the optimal travel plan based on the user's interests on social media. For example, the reception desk can also suggest a travel plan that fits the user's budget based on their social media activity. By analyzing the user's social media activity, it is possible to provide more relevant information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0078] The generation unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a plan with detailed explanations. If the user is in a hurry, the generation unit will generate a concise plan that gets straight to the point. If the user is excited, the generation unit will generate a visually appealing plan. By adjusting the way the plan is presented according to the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the plan is presented.

[0079] The generation unit can adjust the level of detail in a plan based on the importance of the trip when generating the plan. For example, for an important trip, the generation unit generates a plan that includes a detailed schedule and activities. For example, for a short trip, the generation unit generates a concise plan. For example, for a trip that includes a special event, the generation unit generates a plan that focuses on that event. This allows the system to provide the user with the most suitable plan by adjusting the level of detail based on the importance of the trip. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input trip importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0080] The generation unit can apply different generation algorithms depending on the travel category when generating a plan. For example, in the case of a family trip, the generation unit generates a plan that includes family-friendly activities. For example, in the case of a business trip, the generation unit generates a plan that includes efficient travel and meeting schedules. For example, in the case of an adventure trip, the generation unit generates a plan that includes adventurous activities. By applying different generation algorithms depending on the travel category, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input travel category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer plan with detailed explanations. If the user is in a hurry, the generation unit will generate a short, concise plan. If the user is excited, the generation unit will generate a plan with visually stimulating effects. By adjusting the length of the plan according to the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the plan.

[0082] The generation unit can determine the priority of travel plans based on the travel submission date when generating plans. For example, the generation unit will prioritize generating plans for upcoming trips. For example, the generation unit will generate plans with detailed schedules for long-term trips. For example, the generation unit will generate plans earlier for trips that are submitted early. By prioritizing plans based on the travel submission date, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input travel submission date data into a generation AI and have the generation AI perform the determination of plan priorities.

[0083] The generation unit can adjust the order of plans based on the relevance of the trips when generating plans. For example, the generation unit may display the most relevant plans first based on the user's interests. For example, the generation unit may prioritize displaying highly relevant plans based on the user's past travel history. For example, the generation unit may prioritize displaying the most suitable plans based on the user's current living situation. In this way, by adjusting the order of plans based on the relevance of the trips, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input travel relevance data into a generation AI and have the generation AI perform the adjustment of the order of the plans.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit will provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit will provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it can provide the user with the most suitable suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0085] The suggestion function can adjust the level of detail in suggestions based on the importance of the gourmet information and activities. For example, if the gourmet information is important, the suggestion function will provide a suggestion with a detailed explanation and photos. For example, if the activity can be enjoyed in a short time, the suggestion function will provide a concise suggestion. For example, if a special event is included, the suggestion function will provide a suggestion that focuses on that event. In this way, by adjusting the level of detail in suggestions based on the importance of the gourmet information and activities, the suggestion function can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input importance data for gourmet information and activities into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of gourmet information or activity when making suggestions. For example, in the case of family-friendly gourmet information, the suggestion unit will make suggestions that the whole family can enjoy. For example, in the case of business-oriented activities, the suggestion unit will make suggestions that include efficient travel and meeting schedules. For example, in the case of adventure-oriented activities, the suggestion unit will make adventurous suggestions. In this way, by applying different suggestion algorithms depending on the category of gourmet information or activity, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input gourmet information and activity category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the user is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the user is excited, the suggestion unit will provide a suggestion with visually stimulating effects. By adjusting the length of the suggestions according to the user's emotions, the system can provide the user with the most suitable suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0088] The proposal department can prioritize proposals based on the submission timing of gourmet information and activities. For example, it will prioritize proposals for upcoming events. For example, it will submit proposals with detailed schedules for long-term plans. For example, it will submit proposals early for information that is due soon. By prioritizing proposals based on submission timing, the department can provide users with the most suitable suggestions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the submission timing of gourmet information and activities into a generating AI and have the generating AI determine the priority of proposals.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of gourmet information and activities when making suggestions. For example, the suggestion unit may display the most relevant suggestions first based on the user's interests. For example, the suggestion unit may prioritize displaying relevant suggestions based on the user's past travel history. For example, the suggestion unit may prioritize displaying the most suitable suggestions based on the user's current living situation. In this way, by adjusting the order of suggestions based on relevance, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input relevance data of gourmet information and activities into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0090] The transportation management unit can estimate the user's emotions and adjust the method of selecting transportation based on the estimated emotions. For example, if the user is relaxed, the transportation management unit will suggest a comfortable mode of transportation. For example, if the user is in a hurry, the transportation management unit will suggest the fastest mode of transportation. For example, if the user is excited, the transportation management unit will suggest an adventurous mode of transportation. In this way, by adjusting the method of selecting transportation according to the user's emotions, the optimal mode of transportation can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transportation management unit may be performed using AI, for example, or not using AI. For example, the transportation management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of selecting transportation.

[0091] The transportation management unit can analyze a user's past transportation usage history to select the optimal mode of transport during transportation management. For example, the transportation management unit can suggest the optimal mode of transport based on the user's past usage history. For example, the transportation management unit can suggest a mode of transport that avoids congestion based on the user's past transportation usage history. For example, the transportation management unit can analyze a user's past transportation usage history and suggest the most efficient mode of transport. In this way, by analyzing past transportation usage history, the system can provide the user with the optimal mode of transport. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's past transportation usage history data into a generating AI and have the generating AI select the optimal mode of transport.

[0092] The transportation management unit can customize the selection of transportation methods based on the user's current living situation when managing transportation. For example, if the user is traveling with family, the transportation management unit will suggest family-friendly transportation methods. For example, if the user is traveling for business, the transportation management unit will suggest efficient transportation methods. For example, if the user is on an adventure trip, the transportation management unit will suggest adventurous transportation methods. In this way, by customizing the selection of transportation methods based on the user's current living situation, the optimal transportation method can be provided to the user. Some or all of the above processing in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's current living situation data into a generating AI and have the generating AI perform the selection of transportation methods.

[0093] The transportation management unit can estimate the user's emotions and determine the priority of transportation options based on those emotions. For example, if the user is relaxed, the transportation management unit will prioritize suggesting comfortable transportation options. If the user is in a hurry, the transportation management unit will prioritize suggesting the fastest transportation options. If the user is excited, the transportation management unit will prioritize suggesting adventurous transportation options. By prioritizing transportation options according to the user's emotions, the system can provide the user with the most suitable transportation option. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transportation management unit may be performed using AI or not. For example, the transportation management unit can input user emotion data into a generative AI and have the generative AI determine the priority of transportation options.

[0094] The transportation management unit can select the optimal mode of transportation by considering the user's geographical location information when managing transportation. For example, the transportation management unit can suggest the mode of transportation closest to the user's current location. For example, the transportation management unit can automatically set the optimal departure point based on the user's geographical location information. The transportation management unit can also suggest the optimal mode of transportation based on the user's current location. In this way, by considering geographical location information, the system can provide the user with the most suitable mode of transportation. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal mode of transportation.

[0095] The transportation management unit can analyze a user's social media activity to select the most suitable mode of transportation during the transportation management process. For example, the transportation management unit can suggest the next mode of transportation based on the mode of transportation shared by the user on social media. For example, the transportation management unit can suggest the most suitable mode of transportation based on the user's interests on social media. For example, the transportation management unit can suggest a mode of transportation that fits the user's budget based on their social media activity. In this way, by analyzing social media activity, the system can provide the user with the most suitable mode of transportation. Some or all of the above processes in the transportation management unit may be performed using AI, for example, or without AI. For example, the transportation management unit can input user social media activity data into a generating AI and have the generating AI select the mode of transportation.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can propose travel plans that take the user's health condition into consideration. For example, if a user has a pre-existing medical condition, the reception desk will suggest destinations and activities that take that condition into consideration. For example, if a user is elderly, the reception desk will prioritize suggesting barrier-free facilities and transportation options. For example, if a user has allergies, the reception desk can also suggest allergy-friendly restaurants and accommodations. By proposing travel plans that are tailored to the user's health condition, they can enjoy their trip with peace of mind.

[0098] The generation unit can estimate the user's emotions and adjust the travel plan options based on those emotions. For example, if the user is feeling stressed, the generation unit will suggest relaxing destinations and activities. If the user is excited, for example, the generation unit will suggest plans that include adventure and exciting activities. If the user is sad, for example, the generation unit can also suggest destinations that are for healing and refreshment. By suggesting travel plans that match the user's emotions, a more satisfying travel experience can be provided.

[0099] The suggestion department can propose customized travel plans based on the user's hobbies and interests. For example, if the user enjoys outdoor activities, the suggestion department can propose a plan that includes hiking and camping. If the user is interested in history and culture, the suggestion department can propose a plan that visits historical sites and museums. If the user enjoys gourmet food, the suggestion department can propose a plan that explores local specialties and famous restaurants. In this way, by proposing travel plans that match the user's hobbies and interests, a more fulfilling travel experience can be provided.

[0100] The transportation management unit can estimate the user's emotions and select a mode of transportation based on those emotions. For example, if the user is relaxed, the transportation management unit will suggest a comfortable mode of transportation. If the user is in a hurry, the transportation management unit will suggest the fastest mode of transportation. If the user is excited, the transportation management unit can also suggest an adventurous mode of transportation. By suggesting modes of transportation that match the user's emotions, it is possible to provide a more comfortable and satisfying travel experience.

[0101] The generation unit can analyze a user's past travel history and suggest new travel plans based on places and experiences they have visited in the past. For example, the generation unit can suggest nearby tourist attractions to places the user has visited in the past. For example, the generation unit can suggest plans that include activities the user has enjoyed in the past. For example, the generation unit can suggest new recommended accommodations and restaurants based on accommodations and restaurants the user has used in the past. This allows for the provision of more personalized travel plans by utilizing the user's past travel history.

[0102] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide suggestions with detailed explanations. If the user is in a hurry, for example, the suggestion function will provide concise suggestions that get straight to the point. If the user is excited, for example, the suggestion function may also provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions can be provided.

[0103] The reception desk can suggest optimal travel destinations and activities by considering the user's geographical location. For example, the reception desk can suggest travel destinations close to the user's current location. For example, the reception desk can automatically set the optimal departure point based on the user's geographical location. For example, the reception desk can also suggest the optimal mode of transportation based on the user's current location. In this way, by considering the user's geographical location, it is possible to provide more relevant information.

[0104] The generation unit can estimate the user's emotions and adjust how the plan is presented based on those emotions. For example, if the user is relaxed, the generation unit will generate a plan with detailed explanations. If the user is in a hurry, for example, the generation unit will generate a concise plan that gets straight to the point. If the user is excited, for example, the generation unit can also generate a visually appealing plan. By adjusting how the plan is presented according to the user's emotions, it is possible to provide a more satisfying travel plan.

[0105] The suggestion department can analyze users' social media activity, obtain relevant information, and make suggestions. For example, it can suggest the next travel destination based on the travel destinations the user has shared on social media. For example, it can suggest the optimal travel plan based on the user's interests on social media. For example, it can also suggest a travel plan that fits the user's budget based on their social media activity. In this way, by analyzing users' social media activity, it is possible to provide more relevant information.

[0106] The transportation management unit can estimate the user's emotions and adjust the method of selecting transportation based on those emotions. For example, if the user is relaxed, the transportation management unit will suggest a comfortable mode of transportation. If the user is in a hurry, the transportation management unit will suggest the fastest mode of transportation. If the user is excited, the transportation management unit can also suggest an adventurous mode of transportation. By suggesting transportation methods that match the user's emotions, it is possible to provide a more comfortable and satisfying travel experience.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk enters the basic travel information. This information includes the destination, itinerary, budget, and number of participants. The reception desk saves the information entered by the user to a database and uses it for subsequent processing. Step 2: The generation unit analyzes the information entered by the reception unit and generates travel plans. Using generation AI, the generation unit proposes five optimal travel plans based on the user's input information. In addition to general sightseeing plans, the generation unit also generates plans that include unique tourist destinations and special experiences. Step 3: The suggestion unit proposes gourmet information and activities based on the plan generated by the generation unit. The suggestion unit can propose multiple gourmet information and activities based on the user's preferences and can also make the most suitable suggestions for the user based on past data. Step 4: The Transportation Management Department manages information related to transportation. The Transportation Management Department manages the settings for private cars and rental cars, manages reservation methods and usage conditions, and proposes the most suitable transportation method to users.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and transportation management unit, is implemented by, for example, 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 stores the basic travel information entered by the user in the database 24. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The proposal unit is implemented by, for example, the output device 40 of the smart device 14 and presents the user with gourmet information and activities. The transportation management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the settings for private cars and rental cars. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and transportation management unit, is implemented, for example, by 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 stores the basic travel information entered by the user by voice in the database 24. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The proposal unit is implemented, for example, by the speaker 240 of the smart glasses 214 and presents gourmet information and activities to the user by voice. The transportation management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages the settings for private cars and rental cars. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and transportation management 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 stores the basic travel information entered by the user by voice in the database 24. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The proposal unit is implemented by, for example, the display 343 of the headset terminal 314 and displays gourmet information and activities to the user. The transportation management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the settings for private cars and rental cars. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and transportation management 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 stores the basic travel information entered by the user by voice in the database 24. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The proposal unit is implemented by, for example, the speaker 240 of the robot 414 and presents gourmet information and activities to the user by voice. The transportation management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the settings for private cars and rental cars. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The reception area where you enter basic travel information, A generation unit analyzes the information entered by the reception unit and generates a travel plan, Based on the plan generated by the aforementioned generation unit, a proposal unit proposes gourmet information and activities, It comprises a transportation management department that manages information related to transportation methods, A system characterized by the following features. (Note 2) The generating unit is In addition to typical sightseeing plans, it also generates unique and unconventional plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Suggest multiple gourmet options and activities for your destination. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Transportation Management Department, Manage settings for private cars and rental cars. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the user's input information, we suggest five optimal travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past travel history and provides appropriate suggestions as they enter their information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting data, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts how the plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a plan, adjust the level of detail based on the importance of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a plan, different generation algorithms are applied depending on the travel category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a plan, prioritize the plan based on when the travel request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a plan, adjust the order of the plan based on the relevance of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the gourmet information and activities. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of gourmet information and activities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on the timing of submissions of gourmet information and activity listings. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of gourmet information and activities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Transportation Management Department, The system estimates the user's emotions and adjusts the method of selecting transportation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Transportation Management Department, When managing transportation options, the system analyzes the user's past transportation usage history to select the most suitable option. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Transportation Management Department, When managing transportation options, the selection of transportation methods is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Transportation Management Department, It estimates the user's emotions and determines the priority of transportation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Transportation Management Department, When managing transportation options, the system selects the most suitable mode of transport by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Transportation Management Department, When managing transportation options, the system analyzes users' social media activity to select the most appropriate mode of transport. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. The reception area where you enter basic travel information, A generation unit analyzes the information entered by the reception unit and generates a travel plan, Based on the plan generated by the aforementioned generation unit, a proposal unit proposes gourmet information and activities, It comprises a transportation management department that manages information related to transportation methods, A system characterized by the following features.

2. The generating unit is In addition to typical sightseeing plans, it also generates unique and unconventional plans. The system according to feature 1.

3. The aforementioned proposal section is, Suggest multiple gourmet options and activities for your destination. The system according to feature 1.

4. The aforementioned Transportation Management Department, Manage settings for private cars and rental cars. The system according to feature 1.

5. The generating unit is Based on the user's input information, we suggest five optimal travel plans. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past travel history and provides appropriate suggestions as they enter their information. The system according to feature 1.

8. The aforementioned reception unit is When inputting data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

10. The aforementioned reception unit is When inputting data, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system according to feature 1.

Images