System

Abstract

An object of a system according to an embodiment is to generate an optimal tour plan based on a requirement of a user and regenerate the optimal tour plan by reflecting feedback.SOLUTION: A system includes a hearing part, a generation part, a proposal part, a regeneration part, a securing part, and a bookmark creation part. The hearing unit hears requirements of the user. The generation unit generates a tour plan based on the requirements collected by the hearing unit. The proposal unit proposes the tour plan generated by the generation unit to the user and receives feedback. The regeneration unit regenerates the tour plan based on the feedback collected by the proposal unit. The securing unit secures the transportation means and the lodging place based on the tour plan regenerated by the regeneration unit. The bookmark creation unit creates a bookmark of the trip based on the information secured by the securing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of generating an optimal tour plan based on the user's requirements and then regenerating it by reflecting feedback is complicated.

[0005] The system according to the embodiment aims to generate an optimal tour plan based on the user's requirements and regenerate it by reflecting the user's feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a generation unit, a proposal unit, a regeneration unit, a reservation unit, and a bookmark creation unit. The hearing unit hears user requirements. The generation unit generates a tour plan based on the requirements collected by the hearing unit. The proposal unit proposes the tour plan generated by the generation unit to the user and accepts feedback. The regeneration unit regenerates the tour plan based on the feedback collected by the proposal unit. The reservation unit reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit. The bookmark creation unit creates a travel bookmark based on the information reserved by the reservation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal tour plan based on the user's requirements and regenerate it based on the user's feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning support system according to an embodiment of the present invention listens to a user's requirements, and a generation AI creates and proposes a tour plan, receives feedback, regenerates the plan, secures transportation and accommodations, and creates a travel itinerary. The travel planning support system listens to a user's requirements, and a generation AI creates and proposes a tour plan, receives feedback, regenerates the plan, secures transportation and accommodations, and creates a travel itinerary. For example, the user provides basic information such as the travel destination, duration, and budget to the travel support system. For example, the user inputs information such as "I'm planning a one-week family trip." This information is input to the generation AI. The travel planning support system then uses the generation AI to create multiple tour plans based on the user's requirements. The generation AI then generates an optimal tour plan based on the user's requirements. For example, the generation AI proposes a tour plan that includes family-friendly tourist spots and activities. The generation AI then utilizes past data and statistical information to provide the user with the optimal plan. The travel planning support system then allows the user to evaluate the proposed tour plan and request re-proposals if necessary. For example, the user may communicate a request to the AI, such as, "This plan has few beach activities, so I'd like more added." The generation AI re-creates and proposes a tour plan based on the user's feedback. This allows the user to create a tour plan that suits their needs. Next, the travel planning support system secures transportation and accommodations on the user's behalf. For example, the system automatically reserves plane tickets and hotels. This allows the user to prepare for the trip without any hassle. The travel planning support system also creates a travel bookmark and provides it to the user. The bookmark contains information on the travel itinerary and destinations, which the user can refer to during the trip. This allows the travel planning support system to support the user's trip preparations by proposing the optimal tour plan based on the user's requirements and securing transportation and accommodations on the user's behalf.For example, users can significantly reduce the time and effort required to prepare for a trip and efficiently plan their trip. Users can also create tour plans that suit their preferences, resulting in a more satisfying trip.

[0029] A travel planning support system according to an embodiment includes a hearing unit, a generation unit, a proposal unit, a regeneration unit, a reservation unit, and a bookmark creation unit. The hearing unit hears user requirements. The user requirements include, but are not limited to, travel destinations, duration, budget, and desired activities. For example, the user provides basic information such as the travel destination, duration, and budget to the hearing unit. The generation unit uses a generation AI to generate a tour plan based on the requirements collected by the hearing unit. The generation AI generates an optimal tour plan based on the user requirements using, for example, a machine learning algorithm or natural language processing technology. For example, the generation AI proposes a tour plan including family-friendly tourist spots and activities. The proposal unit proposes the tour plan generated by the generation unit to the user and accepts feedback. For example, the proposal unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. The regeneration unit regenerates the tour plan based on the feedback collected by the proposal unit. For example, the regeneration unit regenerates and proposes a tour plan based on the user's feedback. The reservation unit reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit. The reservation unit, for example, automatically reserves airplane tickets and hotels through a system. The bookmark creation unit creates a travel bookmark based on the information reserved by the reservation unit. The bookmark creation unit creates a bookmark including, for example, travel itinerary and information on destinations to be visited, and provides it to the user. As a result, the travel planning support system according to the embodiment can support the user's travel preparations by proposing an optimal tour plan based on the user's requirements and reserving transportation and accommodations on the user's behalf.

[0030] The travel planning support system further includes a monetization unit that directs users to hotel reservation sites and sells the accumulated statistical data. The monetization unit directs users to hotel reservation sites and sells the accumulated statistical data. For example, when a user books a hotel through the system, the monetization unit receives a commission from the reservation site. The monetization unit can also earn additional revenue by selling the user's travel data as statistical information. This allows the monetization unit to earn additional revenue by directing users to hotel reservation sites and selling the statistical data.

[0031] The hearing unit can collect specific information such as the user's travel destination, duration, and budget. Specific information includes, but is not limited to, examples of the travel destination, duration, budget, and desired activities. For example, the hearing unit receives basic information from the user, such as the travel destination, duration, and budget. By collecting the user's basic travel information, the generation AI can provide basic data for generating an optimal tour plan.

[0032] The generation unit can use the generation AI to generate an appropriate tour plan based on the user's requirements. The generation AI generates an optimal tour plan based on the user's requirements using, for example, a machine learning algorithm or natural language processing technology. For example, the generation AI proposes a tour plan that includes tourist spots and activities for families. This makes it possible to generate an optimal tour plan based on the user's requirements using the generation AI.

[0033] The suggestion unit can propose the generated tour plan to the user and collect user feedback. The feedback can include, but is not limited to, for example, the user's satisfaction level, areas for improvement, and additional requests. For example, the suggestion unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. This allows the tour plan to be proposed to the user and collected feedback, thereby enabling the tour plan to be refined.

[0034] The regeneration unit can regenerate the tour plan based on the user's feedback. Regeneration includes, but is not limited to, for example, a method of modifying the plan based on the content of the feedback. For example, the regeneration unit creates and proposes a new tour plan based on the user's feedback. In this way, by regenerating the tour plan while reflecting the user's feedback, it is possible to provide a plan that meets the user's wishes.

[0035] The reservation unit can reserve transportation and accommodation. Examples of transportation include, but are not limited to, airplanes, trains, buses, and taxis. Examples of accommodation include, but are not limited to, hotels, inns, and guesthouses. For example, the reservation unit automatically reserves airplane tickets and hotels through a system. This automatically reserves transportation and accommodation, thereby reducing the user's effort.

[0036] The bookmark creation unit can create a travel bookmark based on the secured information. The travel bookmark may include, for example, an itinerary, information about destinations to visit, and details about transportation, but is not limited to these examples. The bookmark creation unit creates a bookmark that includes, for example, the travel itinerary and information about destinations to visit, and provides it to the user. By creating a travel bookmark based on the secured information, it is possible to provide information that the user can refer to during their trip.

[0037] The hearing unit can analyze the user's past travel history and select the optimal hearing method. The past travel history includes, but is not limited to, destinations, duration, budget, and activities. The hearing unit, for example, asks related questions based on places the user has visited in the past. The hearing unit can also infer the user's preferred travel style from the user's past travel history and customize the questions based on that. The hearing unit can also ask related questions by referring to travel services the user has used in the past. This allows more appropriate questions to be asked by analyzing the user's past travel history.

[0038] The hearing unit can customize the questions based on the user's current living situation and areas of interest. Examples of the current living situation include, but are not limited to, occupation, family structure, hobbies, etc. Examples of areas of interest include, but are not limited to, travel purpose, interesting activities, etc. The hearing unit poses questions based on, for example, topics that the user has recently been interested in. The hearing unit can also pose related questions based on the user's current living situation (work, family, etc.). The hearing unit can also customize the questions based on the user's recent activities and hobbies. In this way, by customizing the questions based on the user's current situation and interests, more relevant information can be collected.

[0039] The hearing unit can select the optimal hearing means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and touch input. For example, if the user prefers voice input, the hearing unit can conduct voice-based hearing. Alternatively, if the user prefers text input, the hearing unit can ask text-based questions. Alternatively, if the user provides information using images, the hearing unit can conduct hearing using image analysis. In this way, by selecting the hearing means depending on the user's input method, it is possible to provide an interface that is easy for the user to use.

[0040] The hearing unit can prioritize relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. The hearing unit can, for example, ask questions about tourist spots related to the user's current location. The hearing unit can also ask questions about places that are easily accessible from the user's current location. The hearing unit can also ask questions about related events or activities based on the user's geographical location information. This allows for more relevant questions to be asked by taking into account the user's geographical location information.

[0041] The hearing unit can analyze the user's social media activity and ask relevant questions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the hearing unit can ask questions about travel destinations shared by the user on social media. The hearing unit can also ask relevant questions based on the content of the user's social media posts. The hearing unit can also ask relevant questions based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant questions can be asked.

[0042] The hearing unit can customize the hearing method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, additional requests, etc., but is not limited to these examples. The hearing unit improves the hearing method, for example, based on the user's past feedback. The hearing unit can also select a preferred question format from the user's past feedback. The hearing unit can also adjust the hearing progress method by referring to the user's past feedback. In this way, a more effective hearing method can be provided by reflecting the user's past feedback.

[0043] The generation unit can adjust the level of detail of the plan based on the importance of the trip. The importance of the trip includes, but is not limited to, the purpose of the trip, the budget, and the duration of the trip. For example, in the case of an important trip, the generation unit generates a tour plan including a detailed itinerary and activities. Furthermore, in the case of a short trip, the generation unit can generate a concise plan. Furthermore, in the case of a trip including a special event, the generation unit can generate a plan that focuses on the event. By adjusting the level of detail of the plan according to the importance of the trip, it is possible to provide an optimal plan for the user.

[0044] The generation unit can apply different generation algorithms depending on the travel category. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, in the case of a family travel, the generation unit can generate a plan that includes family activities. In addition, in the case of a business travel, the generation unit can also generate a plan that takes into account conferences and business meetings. In addition, in the case of an adventure travel, the generation unit can also generate a plan that includes adventurous activities. In this way, by applying a generation algorithm depending on the travel category, it is possible to provide the user with an optimal tour plan.

[0045] The generation unit can improve the accuracy of generation by referring to the user's past travel results. Past travel results include, but are not limited to, destinations, duration, budget, and activities. For example, the generation unit generates a similar plan based on a travel plan that the user was satisfied with in the past. The generation unit can also generate a plan that includes preferred activities from the user's past travel results. The generation unit can also analyze the user's past travel results and generate an optimal plan. In this way, the accuracy of generation can be improved by referring to the user's past travel results.

[0046] The generation unit can determine the priority of plans based on the time of travel submission. The submission time includes, for example, counting backward from the start date of the travel, the reservation deadline, etc., but is not limited to these examples. For example, the generation unit generates plans with priority when the travel submission time is approaching. Furthermore, the generation unit can also generate detailed plans when the travel submission time is far away. Furthermore, the generation unit can generate optimal plans based on the time of travel submission. In this way, by determining the priority of plans based on the time of travel submission, it is possible to provide plans at the optimal time for the user.

[0047] The generation unit can adjust the order of the plan based on the travel relevance. Travel relevance includes, but is not limited to, for example, proximity of destinations to be visited, commonality of activities, and the like. For example, the generation unit prioritizes incorporating activities with high travel relevance into the plan. The generation unit can also postpone activities with low travel relevance. The generation unit can also adjust the order of the plan based on the travel relevance. In this way, by adjusting the order of the plan based on the travel relevance, it is possible to provide an optimal plan for the user.

[0048] The generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the generation unit can generate a plan that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the generation unit can also generate a plan that explains things in simple terms. Furthermore, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. By adjusting the use of technical terms in the plan according to the user's level of expertise, a plan that is easy for the user to understand can be provided.

[0049] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the tour plan. Examples of the importance of the tour plan include, but are not limited to, the purpose of the trip, the budget, and the duration of the trip. For example, the suggestion unit can make a detailed suggestion for an important tour plan. Furthermore, the suggestion unit can make a concise suggestion for a short-term tour plan. Furthermore, the suggestion unit can make a suggestion that focuses on a special event for a tour plan that includes the special event. In this way, by adjusting the level of detail of the suggestion based on the importance of the tour plan, it is possible to make an optimal suggestion for the user.

[0050] The suggestion unit can apply different suggestion algorithms depending on the category of the tour plan. Examples of tour plan categories include, but are not limited to, business trips, leisure trips, and family trips. For example, in the case of a family trip, the suggestion unit can make suggestions that include family activities. In addition, in the case of a business trip, the suggestion unit can make suggestions that take into account conferences and business meetings. In addition, in the case of an adventure trip, the suggestion unit can make suggestions that include adventurous activities. In this way, by applying the suggestion algorithm depending on the category of the tour plan, it is possible to make optimal suggestions for the user.

[0051] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, the user's satisfaction level, areas for improvement, and additional requests. For example, the suggestion unit makes similar suggestions based on suggestions that the user was satisfied with in the past. The suggestion unit can also make suggestions that include preferred activities from the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results.

[0052] The suggestion unit can determine the priority of proposals based on the time of submission of the tour plan. The submission time includes, for example, counting backward from the start date of the trip, the reservation deadline, etc., but is not limited to these examples. For example, the suggestion unit can give priority to proposing tour plans that are to be submitted soon. In addition, the suggestion unit can also make detailed proposals for tour plans that are to be submitted further away. In addition, the suggestion unit can make optimal proposals based on the time of submission. Thus, by determining the priority of proposals based on the time of submission of the tour plan, proposals can be made at the optimal time for the user.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the tour plan. The relevance of the tour plan includes, but is not limited to, for example, proximity of destinations to be visited, commonality of activities, and the like. For example, the suggestion unit can prioritize suggesting highly relevant activities. The suggestion unit can also postpone less relevant activities. The suggestion unit can also adjust the order of suggestions based on the relevance. In this way, by adjusting the order of suggestions based on the relevance of the tour plan, it is possible to make optimal suggestions for the user.

[0054] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the suggestion unit can make suggestions that use a lot of technical terms. Furthermore, if the user does not have technical knowledge, the suggestion unit can make suggestions that are explained in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestions depending on the user's level of expertise, suggestions that are easy for the user to understand can be made.

[0055] The regeneration unit can select an optimal regeneration method by analyzing the user's past feedback. Past feedback includes, but is not limited to, for example, the user's satisfaction level, areas for improvement, and additional requests. The regeneration unit can improve the regeneration method based on, for example, feedback provided by the user in the past. The regeneration unit can also select a preferred regeneration method from the user's past feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user's past feedback. In this way, a more effective regeneration method can be provided by analyzing the user's past feedback.

[0056] The regeneration unit can customize the regeneration means based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family structure, hobbies, etc. The regeneration unit adjusts the regeneration method based on the user's current living situation (work, family, etc.). The regeneration unit can also customize the regeneration method based on the user's recent activities and hobbies. The regeneration unit can also select the regeneration means taking the user's current living situation into consideration. This enables more effective regeneration by customizing the regeneration means based on the user's current living situation.

[0057] The regeneration unit can improve the regeneration method by reflecting user feedback. Examples of the feedback include, but are not limited to, user satisfaction, improvements, and additional requests. The regeneration unit can improve the regeneration method based on, for example, the feedback provided by the user. The regeneration unit can also select a preferred regeneration method based on the user feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user feedback. In this way, a more effective regeneration method can be provided by reflecting the user feedback.

[0058] The regeneration unit can select the optimal regeneration method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. The regeneration unit performs regeneration, for example, based on information related to the user's current location. The regeneration unit can also select the optimal regeneration method based on the user's geographical location information. The regeneration unit can also perform regeneration by taking into account locations that are easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, it is possible to provide a more relevant regeneration method.

[0059] The regeneration unit can analyze the user's social media activity and suggest a regeneration method. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The regeneration unit performs regeneration based on, for example, information shared by the user on social media. The regeneration unit can also analyze the content of the user's posts on social media and suggest a regeneration method. The regeneration unit can also perform regeneration based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a more relevant regeneration method.

[0060] The regeneration unit can customize the regeneration method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, and additional requests, but is not limited to these examples. The regeneration unit customizes the regeneration method based on, for example, feedback provided by the user in the past. The regeneration unit can also select a preferred regeneration method from the user's past feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user's past feedback. In this way, a more effective regeneration method can be provided by reflecting the user's past feedback.

[0061] The reservation unit can analyze the user's past travel history to select the optimal reservation method. The past travel history includes, but is not limited to, destinations visited, duration, budget, and activities. The reservation unit can, for example, suggest the optimal reservation method based on accommodations used by the user in the past. The reservation unit can also select a preferred accommodation from the user's past travel history. The reservation unit can also select the optimal reservation method by referring to the user's past travel history. In this way, by analyzing the user's past travel history, it is possible to provide a more effective reservation method.

[0062] The securing unit can customize the securing method based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family structure, hobbies, etc. The securing unit adjusts the securing method based on the user's current living situation (work, family, etc.). The securing unit can also customize the securing method based on the user's recent activities and hobbies. The securing unit can also select the securing method taking the user's current living situation into consideration. This allows for more effective securing by customizing the securing method based on the user's current living situation.

[0063] The reservation unit can improve the reservation method by reflecting user feedback. Feedback includes, but is not limited to, user satisfaction, areas for improvement, and additional requests, for example. The reservation unit improves the reservation method based on the feedback provided by the user. The reservation unit can also select a preferred reservation method based on the user feedback. The reservation unit can also adjust the reservation progress method by referring to the user feedback. In this way, a more effective reservation method can be provided by reflecting the user feedback.

[0064] The securing unit can select the optimal securing method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The securing unit performs securing based on, for example, information related to the user's current location. The securing unit can also select the optimal securing method based on the user's geographical location information. The securing unit can also perform securing by taking into account a location that is easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, a more effective securing method can be provided.

[0065] The securing unit can analyze the user's social media activity and suggest a means of securing the user. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The securing unit can secure the user based on, for example, information shared by the user on social media. The securing unit can also analyze the content of the user's posts on social media and suggest a means of securing the user. The securing unit can also secure the user based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more effective means of securing the user can be provided.

[0066] The reservation unit can customize the reservation method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, and additional requests, but is not limited to these examples. The reservation unit customizes the reservation method based on, for example, feedback provided by the user in the past. The reservation unit can also select a preferred reservation method from the user's past feedback. The reservation unit can also adjust the reservation progress method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, a more effective reservation method can be provided.

[0067] The bookmark creation unit can adjust the level of detail of the bookmark based on the importance of the trip. The importance of the trip includes, but is not limited to, the purpose, budget, and duration of the trip. For example, in the case of an important trip, the bookmark creation unit creates a bookmark that includes a detailed itinerary and activities. Furthermore, in the case of a short trip, the bookmark creation unit can also create a simple bookmark. Furthermore, in the case of a trip that includes a special event, the bookmark creation unit can also create a bookmark that focuses on that event. By adjusting the level of detail of the bookmark according to the importance of the trip, it is possible to provide the user with the optimal bookmark.

[0068] The bookmark creation unit can apply different bookmark creation algorithms depending on the travel category. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, in the case of a family travel, the bookmark creation unit can create a bookmark that includes family activities. In addition, in the case of a business travel, the bookmark creation unit can also create a bookmark that takes into account conferences and business meetings. In addition, in the case of an adventure travel, the bookmark creation unit can also create a bookmark that includes adventurous activities. In this way, by applying a bookmark creation algorithm depending on the travel category, it is possible to provide the user with the optimal bookmark.

[0069] The bookmark creation unit can improve the accuracy of the bookmark by referring to the user's past travel results. Past travel results include, but are not limited to, destinations, duration, budget, and activities. For example, the bookmark creation unit creates a similar bookmark based on a travel plan that the user was satisfied with in the past. The bookmark creation unit can also create a bookmark that includes preferred activities from the user's past travel results. The bookmark creation unit can also analyze the user's past travel results and create an optimal bookmark. In this way, the accuracy of the bookmark can be improved by referring to the user's past travel results.

[0070] The bookmark creation unit can determine the priority of bookmarks based on the time of trip submission. The submission time includes, for example, counting backward from the start date of the trip, the reservation deadline, etc., but is not limited to these examples. For example, the bookmark creation unit creates bookmarks with priority when the trip submission time is approaching. Furthermore, the bookmark creation unit can also create a detailed bookmark when the trip submission time is far away. Furthermore, the bookmark creation unit can create an optimal bookmark based on the time of trip submission. In this way, by determining the priority of bookmarks based on the time of trip submission, it is possible to provide bookmarks at the optimal time for the user.

[0071] The bookmark creation unit can adjust the order of bookmarks based on travel relevance. Travel relevance includes, but is not limited to, proximity of destinations and commonality of activities. For example, the bookmark creation unit prioritizes incorporating activities with high travel relevance into the bookmarks. The bookmark creation unit can also postpone activities with low travel relevance. The bookmark creation unit can also adjust the order of bookmarks based on travel relevance. In this way, by adjusting the order of bookmarks based on travel relevance, it is possible to provide the user with the most optimal bookmarks.

[0072] The bookmark creation unit can adjust the use of technical terms in the bookmark according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the bookmark creation unit can create a bookmark that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the bookmark creation unit can also create a bookmark that explains things in simple terms. Furthermore, the bookmark creation unit can adjust the use of technical terms in the bookmark according to the user's level of expertise. By adjusting the use of technical terms in the bookmark according to the user's level of expertise, it is possible to provide a bookmark that is easy for the user to understand.

[0073] The monetization unit can analyze the user's past travel history to select the optimal monetization method. The past travel history includes, but is not limited to, destinations visited, duration, budget, and activities. The monetization unit can, for example, propose the optimal monetization method based on the user's past accommodations. The monetization unit can also select a preferred accommodation from the user's past travel history. The monetization unit can also select the optimal monetization method by referring to the user's past travel history. In this way, a more effective monetization method can be provided by analyzing the user's past travel history.

[0074] The monetization unit can customize the monetization means based on the user's current living situation. Examples of the current living situation include, but are not limited to, occupation, family structure, and hobbies. The monetization unit can adjust the monetization method based on the user's current living situation (work, family, etc.). The monetization unit can also customize the monetization method based on the user's recent activities and hobbies. The monetization unit can also select the monetization means taking the user's current living situation into consideration. Customizing the monetization means based on the user's current living situation enables more effective monetization.

[0075] The monetization unit can improve the monetization method by reflecting user feedback. Feedback includes, but is not limited to, user satisfaction, areas for improvement, and additional requests. The monetization unit improves the monetization method, for example, based on the feedback provided by the user. The monetization unit can also select a preferred monetization method based on the user feedback. The monetization unit can also adjust the monetization progress method by referring to the user feedback. In this way, a more effective monetization method can be provided by reflecting user feedback.

[0076] The monetization unit can select an optimal monetization method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. The monetization unit performs monetization, for example, based on information related to the user's current location. The monetization unit can also select an optimal monetization method based on the user's geographical location information. The monetization unit can also perform monetization by taking into account locations that are easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, a more effective monetization method can be provided.

[0077] The monetization unit can analyze a user's social media activity and suggest monetization methods. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The monetization unit can, for example, monetize based on information shared by the user on social media. The monetization unit can also analyze the content of the user's social media posts and suggest monetization methods. The monetization unit can also monetize based on the activities of the user's friends on social media. In this way, by analyzing a user's social media activity, more effective monetization methods can be provided.

[0078] The monetization unit can customize the monetization method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's satisfaction, areas for improvement, and additional requests. The monetization unit customizes the monetization method, for example, based on the user's past feedback. The monetization unit can also select a preferred monetization method from the user's past feedback. The monetization unit can also adjust the monetization progress method by referring to the user's past feedback. In this way, a more effective monetization method can be provided by reflecting the user's past feedback.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The travel planning support system can also include a health management unit that monitors the user's health condition. The health management unit collects the user's health data and provides advice to minimize health risks during travel. For example, if the user has a chronic illness, the health management unit can use that information to secure accommodation near appropriate medical facilities. The health management unit can also adjust the travel plan according to the user's physical condition and suggest a reasonable schedule. Furthermore, the health management unit can provide reminders for medications needed during the trip, helping the user enjoy their trip while maintaining their health.

[0081] The travel planning support system may further include a hobby suggestion unit that suggests customized activities based on the user's hobbies and interests. The hobby suggestion unit analyzes the user's past travel history and social media activity to suggest activities that the user may be interested in. For example, if the user likes outdoor activities, the hobby suggestion unit may suggest plans that include activities such as hiking and camping. If the user likes cultural experiences, the hobby suggestion unit may also suggest plans to visit museums and historical sites. Furthermore, the hobby suggestion unit may suggest local events and festivals based on the user's interests to increase the enjoyment of the trip.

[0082] The travel planning support system can also be equipped with a navigation unit that utilizes the user's geographical location information to propose the optimal route in real time. The navigation unit proposes the optimal travel route and means of transportation based on the user's current location. For example, when the user is touring tourist spots, the navigation unit can propose a route that avoids crowds. The navigation unit can also quickly provide the optimal route if the user gets lost, helping the user reach their destination smoothly. Furthermore, the navigation unit can provide traffic information in real time while the user is traveling and give advice on how to avoid congestion and delays.

[0083] The travel planning support system may further include a meal suggestion unit that analyzes the user's past travel history and proposes meal plans for the trip. The meal suggestion unit proposes optimal restaurants and meal plans based on the user's preferences and allergy information. For example, if the user is vegetarian, it may propose vegetarian restaurants. Also, if the user has a preference for a particular dish, it may propose restaurants that serve that dish. Furthermore, the meal suggestion unit may introduce local cuisine of the user's travel destination, helping the user enjoy new dining experiences during the trip.

[0084] The travel planning support system may further include a cultural information unit that provides information about the culture and customs of the user's travel destination. The cultural information unit provides advice to help the user understand cultural differences at the travel destination and take appropriate actions. For example, the cultural information unit may provide information about etiquette and customs of the country the user will visit. The cultural information unit may also provide information for the user to participate in cultural events and festivals at the travel destination. Furthermore, the cultural information unit may suggest activities that will deepen the user's cultural experience at the travel destination, thereby increasing the user's satisfaction with the trip.

[0085] The travel planning support system may further include a shopping unit that provides shopping information for the user's travel destination. The shopping unit suggests optimal shopping spots based on the user's preferences and budget. For example, if the user prefers luxury brands, it may suggest luxury shopping areas. If the user is looking for local specialties, it may also suggest local markets and souvenir shops. Furthermore, the shopping unit may provide sale information and discount coupons for the user's travel destination, helping the user to enjoy shopping more during their trip.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The hearing section hears the user's requirements. The user's requirements include, for example, the travel destination, duration, budget, desired activities, etc. The hearing section has the user provide basic information such as the travel destination, duration, and budget. Step 2: The generation unit uses the generation AI to generate a tour plan based on the requirements collected by the hearing unit. The generation AI uses machine learning algorithms and natural language processing techniques to generate an optimal tour plan based on the user's requirements. For example, it proposes a tour plan that includes tourist spots and activities for families. Step 3: The suggestion unit proposes the tour plan generated by the generation unit to the user and receives feedback. The suggestion unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. Step 4: The regeneration unit regenerates the tour plan based on the feedback collected by the suggestion unit. The regeneration unit regenerates and proposes the tour plan based on the user's feedback. Step 5: The reservation unit reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit. The reservation unit automatically reserves plane tickets and hotels through the system. Step 6: The bookmark creation unit creates a travel bookmark based on the information secured by the securing unit. The bookmark creation unit creates a bookmark including information on the travel itinerary and destinations to be visited, and provides it to the user.

[0088] (Example 2) A travel planning support system according to an embodiment of the present invention listens to a user's requirements, and a generation AI creates and proposes a tour plan, receives feedback, regenerates the plan, secures transportation and accommodations, and creates a travel itinerary. The travel planning support system listens to a user's requirements, and a generation AI creates and proposes a tour plan, receives feedback, regenerates the plan, secures transportation and accommodations, and creates a travel itinerary. For example, the user provides basic information such as the travel destination, duration, and budget to the travel support system. For example, the user inputs information such as "I'm planning a one-week family trip." This information is input to the generation AI. The travel planning support system then uses the generation AI to create multiple tour plans based on the user's requirements. The generation AI then generates an optimal tour plan based on the user's requirements. For example, the generation AI proposes a tour plan that includes family-friendly tourist spots and activities. The generation AI then utilizes past data and statistical information to provide the user with the optimal plan. The travel planning support system then allows the user to evaluate the proposed tour plan and request re-proposals if necessary. For example, the user may communicate a request to the AI, such as, "This plan has few beach activities, so I'd like more added." The generation AI re-creates and proposes a tour plan based on the user's feedback. This allows the user to create a tour plan that suits their needs. Next, the travel planning support system secures transportation and accommodations on the user's behalf. For example, the system automatically reserves plane tickets and hotels. This allows the user to prepare for the trip without any hassle. The travel planning support system also creates a travel bookmark and provides it to the user. The bookmark contains information on the travel itinerary and destinations, which the user can refer to during the trip. This allows the travel planning support system to support the user's trip preparations by proposing the optimal tour plan based on the user's requirements and securing transportation and accommodations on the user's behalf.For example, users can significantly reduce the time and effort required to prepare for a trip and efficiently plan their trip. Users can also create tour plans that suit their preferences, resulting in a more satisfying trip.

[0089] A travel planning support system according to an embodiment includes a hearing unit, a generation unit, a proposal unit, a regeneration unit, a reservation unit, and a bookmark creation unit. The hearing unit hears user requirements. The user requirements include, but are not limited to, travel destinations, duration, budget, and desired activities. For example, the user provides basic information such as the travel destination, duration, and budget to the hearing unit. The generation unit uses a generation AI to generate a tour plan based on the requirements collected by the hearing unit. The generation AI generates an optimal tour plan based on the user requirements using, for example, a machine learning algorithm or natural language processing technology. For example, the generation AI proposes a tour plan including family-friendly tourist spots and activities. The proposal unit proposes the tour plan generated by the generation unit to the user and accepts feedback. For example, the proposal unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. The regeneration unit regenerates the tour plan based on the feedback collected by the proposal unit. For example, the regeneration unit regenerates and proposes a tour plan based on the user's feedback. The reservation unit reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit. The reservation unit, for example, automatically reserves airplane tickets and hotels through a system. The bookmark creation unit creates a travel bookmark based on the information reserved by the reservation unit. The bookmark creation unit creates a bookmark including, for example, travel itinerary and information on destinations to be visited, and provides it to the user. As a result, the travel planning support system according to the embodiment can support the user's travel preparations by proposing an optimal tour plan based on the user's requirements and reserving transportation and accommodations on the user's behalf.

[0090] The travel planning support system further includes a monetization unit that directs users to hotel reservation sites and sells the accumulated statistical data. The monetization unit directs users to hotel reservation sites and sells the accumulated statistical data. For example, when a user books a hotel through the system, the monetization unit receives a commission from the reservation site. The monetization unit can also earn additional revenue by selling the user's travel data as statistical information. This allows the monetization unit to earn additional revenue by directing users to hotel reservation sites and selling the statistical data.

[0091] The hearing unit can collect specific information such as the user's travel destination, duration, and budget. Specific information includes, but is not limited to, examples of the travel destination, duration, budget, and desired activities. For example, the hearing unit receives basic information from the user, such as the travel destination, duration, and budget. By collecting the user's basic travel information, the generation AI can provide basic data for generating an optimal tour plan.

[0092] The generation unit can use the generation AI to generate an appropriate tour plan based on the user's requirements. The generation AI generates an optimal tour plan based on the user's requirements using, for example, a machine learning algorithm or natural language processing technology. For example, the generation AI proposes a tour plan that includes tourist spots and activities for families. This makes it possible to generate an optimal tour plan based on the user's requirements using the generation AI.

[0093] The suggestion unit can propose the generated tour plan to the user and collect user feedback. The feedback can include, but is not limited to, for example, the user's satisfaction level, areas for improvement, and additional requests. For example, the suggestion unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. This allows the tour plan to be proposed to the user and collected feedback, thereby enabling the tour plan to be refined.

[0094] The regeneration unit can regenerate the tour plan based on the user's feedback. Regeneration includes, but is not limited to, for example, a method of modifying the plan based on the content of the feedback. For example, the regeneration unit creates and proposes a new tour plan based on the user's feedback. In this way, by regenerating the tour plan while reflecting the user's feedback, it is possible to provide a plan that meets the user's wishes.

[0095] The reservation unit can reserve transportation and accommodation. Examples of transportation include, but are not limited to, airplanes, trains, buses, and taxis. Examples of accommodation include, but are not limited to, hotels, inns, and guesthouses. For example, the reservation unit automatically reserves airplane tickets and hotels through a system. This automatically reserves transportation and accommodation, thereby reducing the user's effort.

[0096] The bookmark creation unit can create a travel bookmark based on the secured information. The travel bookmark may include, for example, an itinerary, information about destinations to visit, and details about transportation, but is not limited to these examples. The bookmark creation unit creates a bookmark that includes, for example, the travel itinerary and information about destinations to visit, and provides it to the user. By creating a travel bookmark based on the secured information, it is possible to provide information that the user can refer to during their trip.

[0097] The hearing unit can estimate the user's emotions and adjust the timing of the hearing based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the hearing unit can conduct the hearing during a time when the user is able to relax. Furthermore, if the user is relaxed, the hearing unit can extend the hearing time to ask more detailed questions. Furthermore, if the user is in a hurry, the hearing unit can conduct the hearing in a short period of time while focusing on the main points. This allows for more effective hearing by adjusting the timing of the hearing according to the user's emotions.

[0098] The hearing unit can analyze the user's past travel history and select the optimal hearing method. The past travel history includes, but is not limited to, destinations, duration, budget, and activities. The hearing unit, for example, asks related questions based on places the user has visited in the past. The hearing unit can also infer the user's preferred travel style from the user's past travel history and customize the questions based on that. The hearing unit can also ask related questions by referring to travel services the user has used in the past. This allows more appropriate questions to be asked by analyzing the user's past travel history.

[0099] The hearing unit can customize the questions based on the user's current living situation and areas of interest. Examples of the current living situation include, but are not limited to, occupation, family structure, hobbies, etc. Examples of areas of interest include, but are not limited to, travel purpose, interesting activities, etc. The hearing unit poses questions based on, for example, topics that the user has recently been interested in. The hearing unit can also pose related questions based on the user's current living situation (work, family, etc.). The hearing unit can also customize the questions based on the user's recent activities and hobbies. In this way, by customizing the questions based on the user's current situation and interests, more relevant information can be collected.

[0100] The hearing unit can select the optimal hearing means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and touch input. For example, if the user prefers voice input, the hearing unit can conduct voice-based hearing. Alternatively, if the user prefers text input, the hearing unit can ask text-based questions. Alternatively, if the user provides information using images, the hearing unit can conduct hearing using image analysis. In this way, by selecting the hearing means depending on the user's input method, it is possible to provide an interface that is easy for the user to use.

[0101] The hearing unit can estimate the user's emotions and determine the priority of questions to be asked based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the hearing unit can start with questions that will relax the user. Furthermore, if the user is relaxed, the hearing unit can also prioritize detailed questions. Furthermore, if the user is in a hurry, the hearing unit can also prioritize important questions. This allows for more effective hearing by determining the priority of questions according to the user's emotions.

[0102] The hearing unit can prioritize relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. The hearing unit can, for example, ask questions about tourist spots related to the user's current location. The hearing unit can also ask questions about places that are easily accessible from the user's current location. The hearing unit can also ask questions about related events or activities based on the user's geographical location information. This allows for more relevant questions to be asked by taking into account the user's geographical location information.

[0103] The hearing unit can analyze the user's social media activity and ask relevant questions. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the hearing unit can ask questions about travel destinations shared by the user on social media. The hearing unit can also ask relevant questions based on the content of the user's social media posts. The hearing unit can also ask relevant questions based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant questions can be asked.

[0104] The hearing unit can customize the hearing method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, additional requests, etc., but is not limited to these examples. The hearing unit improves the hearing method, for example, based on the user's past feedback. The hearing unit can also select a preferred question format from the user's past feedback. The hearing unit can also adjust the hearing progress method by referring to the user's past feedback. In this way, a more effective hearing method can be provided by reflecting the user's past feedback.

[0105] The generation unit can estimate the user's emotions and adjust the presentation method of the tour plan based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the generation unit can generate a tour plan that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a tour plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a tour plan that adds visually stimulating effects. Thus, by adjusting the presentation method of the tour plan according to the user's emotions, a more effective plan can be provided.

[0106] The generation unit can adjust the level of detail of the plan based on the importance of the trip. The importance of the trip includes, but is not limited to, the purpose of the trip, the budget, and the duration of the trip. For example, in the case of an important trip, the generation unit generates a tour plan including a detailed itinerary and activities. Furthermore, in the case of a short trip, the generation unit can generate a concise plan. Furthermore, in the case of a trip including a special event, the generation unit can generate a plan that focuses on the event. By adjusting the level of detail of the plan according to the importance of the trip, it is possible to provide an optimal plan for the user.

[0107] The generation unit can apply different generation algorithms depending on the travel category. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, in the case of a family travel, the generation unit can generate a plan that includes family activities. In addition, in the case of a business travel, the generation unit can also generate a plan that takes into account conferences and business meetings. In addition, in the case of an adventure travel, the generation unit can also generate a plan that includes adventurous activities. In this way, by applying a generation algorithm depending on the travel category, it is possible to provide the user with an optimal tour plan.

[0108] The generation unit can improve the accuracy of generation by referring to the user's past travel results. Past travel results include, but are not limited to, destinations, duration, budget, and activities. For example, the generation unit generates a similar plan based on a travel plan that the user was satisfied with in the past. The generation unit can also generate a plan that includes preferred activities from the user's past travel results. The generation unit can also analyze the user's past travel results and generate an optimal plan. In this way, the accuracy of generation can be improved by referring to the user's past travel results.

[0109] The generation unit can estimate the user's emotion and adjust the length of the tour plan based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the generation unit can generate a short-term tour plan when the user is in a hurry. The generation unit can also generate a long-term tour plan when the user is relaxed. The generation unit can also generate a plan with many activities when the user is excited. By adjusting the length of the tour plan according to the user's emotion, the optimal plan can be provided to the user.

[0110] The generation unit can determine the priority of plans based on the time of travel submission. The submission time includes, for example, counting backward from the start date of the travel, the reservation deadline, etc., but is not limited to these examples. For example, the generation unit generates plans with priority when the travel submission time is approaching. Furthermore, the generation unit can also generate detailed plans when the travel submission time is far away. Furthermore, the generation unit can generate optimal plans based on the time of travel submission. In this way, by determining the priority of plans based on the time of travel submission, it is possible to provide plans at the optimal time for the user.

[0111] The generation unit can adjust the order of the plan based on the travel relevance. Travel relevance includes, but is not limited to, for example, proximity of destinations to be visited, commonality of activities, and the like. For example, the generation unit prioritizes incorporating activities with high travel relevance into the plan. The generation unit can also postpone activities with low travel relevance. The generation unit can also adjust the order of the plan based on the travel relevance. In this way, by adjusting the order of the plan based on the travel relevance, it is possible to provide an optimal plan for the user.

[0112] The generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the generation unit can generate a plan that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the generation unit can also generate a plan that explains things in simple terms. Furthermore, the generation unit can adjust the use of technical terms in the plan according to the user's level of expertise. By adjusting the use of technical terms in the plan according to the user's level of expertise, a plan that is easy for the user to understand can be provided.

[0113] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the user is excited. In this way, by adjusting the way in which suggestions are expressed according to the user's emotion, more effective suggestions can be made.

[0114] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the tour plan. Examples of the importance of the tour plan include, but are not limited to, the purpose of the trip, the budget, and the duration of the trip. For example, the suggestion unit can make a detailed suggestion for an important tour plan. Furthermore, the suggestion unit can make a concise suggestion for a short-term tour plan. Furthermore, the suggestion unit can make a suggestion that focuses on a special event for a tour plan that includes the special event. In this way, by adjusting the level of detail of the suggestion based on the importance of the tour plan, it is possible to make an optimal suggestion for the user.

[0115] The suggestion unit can apply different suggestion algorithms depending on the category of the tour plan. Examples of tour plan categories include, but are not limited to, business trips, leisure trips, and family trips. For example, in the case of a family trip, the suggestion unit can make suggestions that include family activities. In addition, in the case of a business trip, the suggestion unit can make suggestions that take into account conferences and business meetings. In addition, in the case of an adventure trip, the suggestion unit can make suggestions that include adventurous activities. In this way, by applying the suggestion algorithm depending on the category of the tour plan, it is possible to make optimal suggestions for the user.

[0116] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, the user's satisfaction level, areas for improvement, and additional requests. For example, the suggestion unit makes similar suggestions based on suggestions that the user was satisfied with in the past. The suggestion unit can also make suggestions that include preferred activities from the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results.

[0117] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can provide a short suggestion when the user is in a hurry. The suggestion unit can also provide a detailed suggestion when the user is relaxed. The suggestion unit can also provide a visually stimulating suggestion when the user is excited. In this way, by adjusting the length of the suggestion according to the user's emotion, it is possible to provide the most suitable suggestion for the user.

[0118] The suggestion unit can determine the priority of proposals based on the time of submission of the tour plan. The submission time includes, for example, counting backward from the start date of the trip, the reservation deadline, etc., but is not limited to these examples. For example, the suggestion unit can give priority to proposing tour plans that are to be submitted soon. In addition, the suggestion unit can also make detailed proposals for tour plans that are to be submitted further away. In addition, the suggestion unit can make optimal proposals based on the time of submission. Thus, by determining the priority of proposals based on the time of submission of the tour plan, proposals can be made at the optimal time for the user.

[0119] The suggestion unit can adjust the order of suggestions based on the relevance of the tour plan. The relevance of the tour plan includes, but is not limited to, for example, proximity of destinations to be visited, commonality of activities, and the like. For example, the suggestion unit can prioritize suggesting highly relevant activities. The suggestion unit can also postpone less relevant activities. The suggestion unit can also adjust the order of suggestions based on the relevance. In this way, by adjusting the order of suggestions based on the relevance of the tour plan, it is possible to make optimal suggestions for the user.

[0120] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the suggestion unit can make suggestions that use a lot of technical terms. Furthermore, if the user does not have technical knowledge, the suggestion unit can make suggestions that are explained in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestions depending on the user's level of expertise, suggestions that are easy for the user to understand can be made.

[0121] The regeneration unit can estimate the user's emotion and adjust the regeneration method based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the regeneration unit performs detailed regeneration when the user is relaxed. Furthermore, the regeneration unit can also perform concise regeneration when the user is in a hurry. Furthermore, the regeneration unit can also perform visually stimulating regeneration when the user is excited. This allows for more effective regeneration by adjusting the regeneration method according to the user's emotion.

[0122] The regeneration unit can select an optimal regeneration method by analyzing the user's past feedback. Past feedback includes, but is not limited to, for example, the user's satisfaction level, areas for improvement, and additional requests. The regeneration unit can improve the regeneration method based on, for example, feedback provided by the user in the past. The regeneration unit can also select a preferred regeneration method from the user's past feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user's past feedback. In this way, a more effective regeneration method can be provided by analyzing the user's past feedback.

[0123] The regeneration unit can customize the regeneration means based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family structure, hobbies, etc. The regeneration unit adjusts the regeneration method based on the user's current living situation (work, family, etc.). The regeneration unit can also customize the regeneration method based on the user's recent activities and hobbies. The regeneration unit can also select the regeneration means taking the user's current living situation into consideration. This enables more effective regeneration by customizing the regeneration means based on the user's current living situation.

[0124] The regeneration unit can improve the regeneration method by reflecting user feedback. Examples of the feedback include, but are not limited to, user satisfaction, improvements, and additional requests. The regeneration unit can improve the regeneration method based on, for example, the feedback provided by the user. The regeneration unit can also select a preferred regeneration method based on the user feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user feedback. In this way, a more effective regeneration method can be provided by reflecting the user feedback.

[0125] The regeneration unit can estimate the user's emotion and determine a priority of regeneration based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the regeneration unit can increase the priority of regeneration when the user is in a hurry. The regeneration unit can also decrease the priority of regeneration when the user is relaxed. The regeneration unit can also adjust the priority of regeneration when the user is excited. This enables more effective regeneration by determining the priority of regeneration according to the user's emotion.

[0126] The regeneration unit can select the optimal regeneration method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. The regeneration unit performs regeneration, for example, based on information related to the user's current location. The regeneration unit can also select the optimal regeneration method based on the user's geographical location information. The regeneration unit can also perform regeneration by taking into account locations that are easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, it is possible to provide a more relevant regeneration method.

[0127] The regeneration unit can analyze the user's social media activity and suggest a regeneration method. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The regeneration unit performs regeneration based on, for example, information shared by the user on social media. The regeneration unit can also analyze the content of the user's posts on social media and suggest a regeneration method. The regeneration unit can also perform regeneration based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a more relevant regeneration method.

[0128] The regeneration unit can customize the regeneration method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, and additional requests, but is not limited to these examples. The regeneration unit customizes the regeneration method based on, for example, feedback provided by the user in the past. The regeneration unit can also select a preferred regeneration method from the user's past feedback. The regeneration unit can also adjust the regeneration progress method by referring to the user's past feedback. In this way, a more effective regeneration method can be provided by reflecting the user's past feedback.

[0129] The securing unit can estimate the user's emotions and adjust the securing method based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the securing unit can suggest a detailed securing method when the user is relaxed. Furthermore, the securing unit can also suggest a simple securing method when the user is in a hurry. Furthermore, the securing unit can also suggest a visually stimulating securing method when the user is excited. This allows for more effective securing by adjusting the securing method according to the user's emotions.

[0130] The reservation unit can analyze the user's past travel history to select the optimal reservation method. The past travel history includes, but is not limited to, destinations visited, duration, budget, and activities. The reservation unit can, for example, suggest the optimal reservation method based on accommodations used by the user in the past. The reservation unit can also select a preferred accommodation from the user's past travel history. The reservation unit can also select the optimal reservation method by referring to the user's past travel history. In this way, by analyzing the user's past travel history, it is possible to provide a more effective reservation method.

[0131] The securing unit can customize the securing method based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family structure, hobbies, etc. The securing unit adjusts the securing method based on the user's current living situation (work, family, etc.). The securing unit can also customize the securing method based on the user's recent activities and hobbies. The securing unit can also select the securing method taking the user's current living situation into consideration. This allows for more effective securing by customizing the securing method based on the user's current living situation.

[0132] The reservation unit can improve the reservation method by reflecting user feedback. Feedback includes, but is not limited to, user satisfaction, areas for improvement, and additional requests, for example. The reservation unit improves the reservation method based on the feedback provided by the user. The reservation unit can also select a preferred reservation method based on the user feedback. The reservation unit can also adjust the reservation progress method by referring to the user feedback. In this way, a more effective reservation method can be provided by reflecting the user feedback.

[0133] The reservation unit can estimate the user's emotions and determine reservation priorities based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the reservation unit can increase the reservation priority if the user is in a hurry. The reservation unit can also lower the reservation priority if the user is relaxed. The reservation unit can also adjust the reservation priority if the user is excited. This allows for more effective reservation by determining the reservation priority according to the user's emotions.

[0134] The securing unit can select the optimal securing method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The securing unit performs securing based on, for example, information related to the user's current location. The securing unit can also select the optimal securing method based on the user's geographical location information. The securing unit can also perform securing by taking into account a location that is easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, a more effective securing method can be provided.

[0135] The securing unit can analyze the user's social media activity and suggest a means of securing the user. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The securing unit can secure the user based on, for example, information shared by the user on social media. The securing unit can also analyze the content of the user's posts on social media and suggest a means of securing the user. The securing unit can also secure the user based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more effective means of securing the user can be provided.

[0136] The reservation unit can customize the reservation method by reflecting the user's past feedback. Past feedback includes, for example, the user's satisfaction level, areas for improvement, and additional requests, but is not limited to these examples. The reservation unit customizes the reservation method based on, for example, feedback provided by the user in the past. The reservation unit can also select a preferred reservation method from the user's past feedback. The reservation unit can also adjust the reservation progress method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, a more effective reservation method can be provided.

[0137] The bookmark creation unit can estimate the user's emotion and adjust the way the bookmark is displayed based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the bookmark creation unit can create a detailed bookmark when the user is relaxed. Furthermore, the bookmark creation unit can create a simple bookmark when the user is in a hurry. Furthermore, the bookmark creation unit can create a visually stimulating bookmark when the user is excited. This allows for more effective bookmarks to be provided by adjusting the way the bookmark is displayed based on the user's emotion.

[0138] The bookmark creation unit can adjust the level of detail of the bookmark based on the importance of the trip. The importance of the trip includes, but is not limited to, the purpose, budget, and duration of the trip. For example, in the case of an important trip, the bookmark creation unit creates a bookmark that includes a detailed itinerary and activities. Furthermore, in the case of a short trip, the bookmark creation unit can also create a simple bookmark. Furthermore, in the case of a trip that includes a special event, the bookmark creation unit can also create a bookmark that focuses on that event. By adjusting the level of detail of the bookmark according to the importance of the trip, it is possible to provide the user with the optimal bookmark.

[0139] The bookmark creation unit can apply different bookmark creation algorithms depending on the travel category. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, in the case of a family travel, the bookmark creation unit can create a bookmark that includes family activities. In addition, in the case of a business travel, the bookmark creation unit can also create a bookmark that takes into account conferences and business meetings. In addition, in the case of an adventure travel, the bookmark creation unit can also create a bookmark that includes adventurous activities. In this way, by applying a bookmark creation algorithm depending on the travel category, it is possible to provide the user with the optimal bookmark.

[0140] The bookmark creation unit can improve the accuracy of the bookmark by referring to the user's past travel results. Past travel results include, but are not limited to, destinations, duration, budget, and activities. For example, the bookmark creation unit creates a similar bookmark based on a travel plan that the user was satisfied with in the past. The bookmark creation unit can also create a bookmark that includes preferred activities from the user's past travel results. The bookmark creation unit can also analyze the user's past travel results and create an optimal bookmark. In this way, the accuracy of the bookmark can be improved by referring to the user's past travel results.

[0141] The bookmark creation unit can estimate the user's emotion and adjust the length of the bookmark based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the bookmark creation unit can create a short bookmark when the user is in a hurry. Furthermore, the bookmark creation unit can create a detailed bookmark when the user is relaxed. Furthermore, the bookmark creation unit can create a visually stimulating bookmark when the user is excited. By adjusting the length of the bookmark according to the user's emotion, it is possible to provide the optimal bookmark for the user.

[0142] The bookmark creation unit can determine the priority of bookmarks based on the time of trip submission. The submission time includes, for example, counting backward from the start date of the trip, the reservation deadline, etc., but is not limited to these examples. For example, the bookmark creation unit creates bookmarks with priority when the trip submission time is approaching. Furthermore, the bookmark creation unit can also create a detailed bookmark when the trip submission time is far away. Furthermore, the bookmark creation unit can create an optimal bookmark based on the time of trip submission. In this way, by determining the priority of bookmarks based on the time of trip submission, it is possible to provide bookmarks at the optimal time for the user.

[0143] The bookmark creation unit can adjust the order of bookmarks based on travel relevance. Travel relevance includes, but is not limited to, proximity of destinations and commonality of activities. For example, the bookmark creation unit prioritizes incorporating activities with high travel relevance into the bookmarks. The bookmark creation unit can also postpone activities with low travel relevance. The bookmark creation unit can also adjust the order of bookmarks based on travel relevance. In this way, by adjusting the order of bookmarks based on travel relevance, it is possible to provide the user with the most optimal bookmarks.

[0144] The bookmark creation unit can adjust the use of technical terms in the bookmark according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether the user has travel experience and the level of understanding of technical terms. For example, if the user has technical knowledge, the bookmark creation unit can create a bookmark that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the bookmark creation unit can also create a bookmark that explains things in simple terms. Furthermore, the bookmark creation unit can adjust the use of technical terms in the bookmark according to the user's level of expertise. By adjusting the use of technical terms in the bookmark according to the user's level of expertise, it is possible to provide a bookmark that is easy for the user to understand.

[0145] The monetization unit can estimate the user's emotions and adjust the monetization method based on the estimated user's emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the monetization unit can suggest a detailed monetization method when the user is relaxed. Furthermore, the monetization unit can suggest a simple monetization method when the user is in a hurry. Furthermore, the monetization unit can suggest a visually stimulating monetization method when the user is excited. This allows for more effective monetization by adjusting the monetization method according to the user's emotions.

[0146] The monetization unit can analyze the user's past travel history to select the optimal monetization method. The past travel history includes, but is not limited to, destinations visited, duration, budget, and activities. The monetization unit can, for example, propose the optimal monetization method based on the user's past accommodations. The monetization unit can also select a preferred accommodation from the user's past travel history. The monetization unit can also select the optimal monetization method by referring to the user's past travel history. In this way, a more effective monetization method can be provided by analyzing the user's past travel history.

[0147] The monetization unit can customize the monetization means based on the user's current living situation. Examples of the current living situation include, but are not limited to, occupation, family structure, and hobbies. The monetization unit can adjust the monetization method based on the user's current living situation (work, family, etc.). The monetization unit can also customize the monetization method based on the user's recent activities and hobbies. The monetization unit can also select the monetization means taking the user's current living situation into consideration. Customizing the monetization means based on the user's current living situation enables more effective monetization.

[0148] The monetization unit can improve the monetization method by reflecting user feedback. Feedback includes, but is not limited to, user satisfaction, areas for improvement, and additional requests. The monetization unit improves the monetization method, for example, based on the feedback provided by the user. The monetization unit can also select a preferred monetization method based on the user feedback. The monetization unit can also adjust the monetization progress method by referring to the user feedback. In this way, a more effective monetization method can be provided by reflecting user feedback.

[0149] The monetization unit can estimate the user's emotions and determine monetization priorities based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the monetization unit can increase the monetization priority if the user is in a hurry. The monetization unit can also decrease the monetization priority if the user is relaxed. The monetization unit can also adjust the monetization priority if the user is excited. This enables more effective monetization by determining the monetization priority according to the user's emotions.

[0150] The monetization unit can select an optimal monetization method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. The monetization unit performs monetization, for example, based on information related to the user's current location. The monetization unit can also select an optimal monetization method based on the user's geographical location information. The monetization unit can also perform monetization by taking into account locations that are easily accessible from the user's current location. In this way, by taking into account the user's geographical location information, a more effective monetization method can be provided.

[0151] The monetization unit can analyze a user's social media activity and suggest monetization methods. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The monetization unit can, for example, monetize based on information shared by the user on social media. The monetization unit can also analyze the content of the user's social media posts and suggest monetization methods. The monetization unit can also monetize based on the activities of the user's friends on social media. In this way, by analyzing a user's social media activity, more effective monetization methods can be provided.

[0152] The monetization unit can customize the monetization method by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's satisfaction, areas for improvement, and additional requests. The monetization unit customizes the monetization method, for example, based on the user's past feedback. The monetization unit can also select a preferred monetization method from the user's past feedback. The monetization unit can also adjust the monetization progress method by referring to the user's past feedback. In this way, a more effective monetization method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned hearing unit, generation unit, proposal unit, regeneration unit, reservation unit, bookmark creation unit, and monetization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the hearing unit collects user requirements using the reception device 38 and microphone 38B of the smart device 14, and the collected requirements are processed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a tour plan using a generation AI. The proposal unit proposes a tour plan to the user through, for example, the display 40A and speaker 40B of the smart device 14 and accepts feedback. The regeneration unit regenerates the tour plan based on the feedback, for example, by the specific processing unit 290 of the data processing device 12. The reservation unit, for example, reserves transportation and accommodations, for example, by the specific processing unit 290 of the data processing device 12. The bookmark creation unit creates a travel bookmark using, for example, the specific processing unit 290 of the data processing device 12 and provides it to the user through the display 40A of the smart device 14. The monetization unit, for example, uses the specific processing unit 290 of the data processing device 12 to guide users to hotel reservation sites and sell statistical data. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned hearing unit, generation unit, proposal unit, regeneration unit, reservation unit, bookmark creation unit, and monetization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the hearing unit collects user requirements using the microphone 238 of the smart glasses 214, and the collected requirements are processed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour plan using a generation AI. The proposal unit proposes a tour plan to the user through, for example, the speaker 240 of the smart glasses 214 and accepts feedback. The regeneration unit regenerates the tour plan based on the feedback, for example, by the specific processing unit 290 of the data processing device 12. The reservation unit reserves transportation and accommodations, for example, by the specific processing unit 290 of the data processing device 12. The bookmark creation unit creates a travel itinerary using, for example, the specific processing unit 290 of the data processing device 12, and provides it to the user through the display of the smart glasses 214. The profit-making unit, for example, directs users to hotel reservation sites and sells statistical data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned hearing unit, generation unit, proposal unit, regeneration unit, reservation unit, bookmark creation unit, and monetization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the hearing unit collects user requirements using the microphone 238 of the headset-type terminal 314, and the collected requirements are processed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour plan using a generation AI. The proposal unit proposes a tour plan to the user, for example, through the speaker 240 of the headset-type terminal 314, and accepts feedback. The regeneration unit regenerates the tour plan based on the feedback, for example, by the specific processing unit 290 of the data processing device 12. The reservation unit reserves transportation and accommodation, for example, by the specific processing unit 290 of the data processing device 12. The bookmark creation unit creates a travel bookmark, for example, by using the specific processing unit 290 of the data processing device 12, and provides it to the user through the display 343 of the headset terminal 314. The monetization unit, for example, uses the specific processing unit 290 of the data processing device 12 to guide users to hotel reservation sites and sell statistical data. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned hearing unit, generation unit, proposal unit, regeneration unit, reservation unit, bookmark creation unit, and monetization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the hearing unit collects user requirements using the microphone 238 of the robot 414, and the collected requirements are processed by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour plan using a generation AI. The proposal unit proposes a tour plan to the user through, for example, the speaker 240 of the robot 414 and accepts feedback. The regeneration unit regenerates the tour plan based on the feedback, for example, by the specific processing unit 290 of the data processing device 12. The reservation unit reserves transportation and accommodations, for example, by the specific processing unit 290 of the data processing device 12. The bookmark creation unit creates a travel itinerary using, for example, the specific processing unit 290 of the data processing device 12, and provides it to the user through the display of the robot 414. The profit-making unit, for example, directs users to hotel reservation sites and sells statistical data using the specific processing unit 290 of the data processing device 12.

[0153] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0154] The travel planning support system can also include a health management unit that monitors the user's health condition. The health management unit collects the user's health data and provides advice to minimize health risks during travel. For example, if the user has a chronic illness, the health management unit can use that information to secure accommodation near appropriate medical facilities. The health management unit can also adjust the travel plan according to the user's physical condition and suggest a reasonable schedule. Furthermore, the health management unit can provide reminders for medications needed during the trip, helping the user enjoy their trip while maintaining their health.

[0155] The travel planning support system may further include a hobby suggestion unit that suggests customized activities based on the user's hobbies and interests. The hobby suggestion unit analyzes the user's past travel history and social media activity to suggest activities that the user may be interested in. For example, if the user likes outdoor activities, the hobby suggestion unit may suggest plans that include activities such as hiking and camping. If the user likes cultural experiences, the hobby suggestion unit may also suggest plans to visit museums and historical sites. Furthermore, the hobby suggestion unit may suggest local events and festivals based on the user's interests to increase the enjoyment of the trip.

[0156] The travel planning support system may further include an emotion support unit that estimates the user's emotions and provides support during the trip based on the estimated emotions. The emotion support unit provides advice to reduce the stress and anxiety the user feels during the trip. For example, if the user is nervous, it may suggest activities and places that will help the user relax. Also, if the user is excited, it may suggest activities that will help the user utilize that energy. Furthermore, the emotion support unit provides encouragement and reminders at appropriate times according to the emotions the user feels during the trip, supporting the user to have a more comfortable trip.

[0157] The travel planning support system can also be equipped with a navigation unit that utilizes the user's geographical location information to propose the optimal route in real time. The navigation unit proposes the optimal travel route and means of transportation based on the user's current location. For example, when the user is touring tourist spots, the navigation unit can propose a route that avoids crowds. The navigation unit can also quickly provide the optimal route if the user gets lost, helping the user reach their destination smoothly. Furthermore, the navigation unit can provide traffic information in real time while the user is traveling and give advice on how to avoid congestion and delays.

[0158] The travel planning support system may further include an entertainment unit that estimates the user's emotions and provides entertainment during the trip based on the estimated emotions. The entertainment unit provides content that the user can enjoy during the trip, thereby increasing the satisfaction of the trip. For example, if the user wants to relax, it may suggest relaxing music or movies. If the user is excited, it may suggest action movies or exciting games. Furthermore, the entertainment unit may provide local entertainment information that can be enjoyed during the trip according to the user's emotions, thereby increasing the enjoyment of the trip.

[0159] The travel planning support system may further include a meal suggestion unit that analyzes the user's past travel history and proposes meal plans for the trip. The meal suggestion unit proposes optimal restaurants and meal plans based on the user's preferences and allergy information. For example, if the user is vegetarian, it may propose vegetarian restaurants. Also, if the user has a preference for a particular dish, it may propose restaurants that serve that dish. Furthermore, the meal suggestion unit may introduce local cuisine of the user's travel destination, helping the user enjoy new dining experiences during the trip.

[0160] The travel planning support system may further include a communication unit that estimates the user's emotions and supports communication during the trip based on the estimated emotions. The communication unit provides advice to help the user communicate smoothly with other travelers and local people during the trip. For example, if the user is nervous, the communication unit may suggest a communication method that will help the user relax. Also, if the user is excited, the communication unit may suggest a communication method that will make use of that energy. Furthermore, the communication unit may provide communication hints and phrases at appropriate times according to the user's emotions, supporting interactions during the trip.

[0161] The travel planning support system may further include a cultural information unit that provides information about the culture and customs of the user's travel destination. The cultural information unit provides advice to help the user understand cultural differences at the travel destination and take appropriate actions. For example, the cultural information unit may provide information about etiquette and customs of the country the user will visit. The cultural information unit may also provide information for the user to participate in cultural events and festivals at the travel destination. Furthermore, the cultural information unit may suggest activities that will deepen the user's cultural experience at the travel destination, thereby increasing the user's satisfaction with the trip.

[0162] The travel planning support system may further include a safety measure unit that estimates the user's emotions and provides safety measures during the trip based on the estimated emotions. The safety measure unit provides advice to help the user stay safe during the trip. For example, if the user feels anxious, the safety measure unit may provide information about safe areas and places to avoid. If the user feels relaxed, the safety measure unit may also suggest activities that the user can enjoy with peace of mind. Furthermore, the safety measure unit may provide safety reminders at appropriate times according to the user's emotions to ensure safety during the trip.

[0163] The travel planning support system may further include a shopping unit that provides shopping information for the user's travel destination. The shopping unit suggests optimal shopping spots based on the user's preferences and budget. For example, if the user prefers luxury brands, it may suggest luxury shopping areas. If the user is looking for local specialties, it may also suggest local markets and souvenir shops. Furthermore, the shopping unit may provide sale information and discount coupons for the user's travel destination, helping the user to enjoy shopping more during their trip.

[0164] The processing flow of the second embodiment will be briefly explained below.

[0165] Step 1: The hearing section hears the user's requirements. The user's requirements include, for example, the travel destination, duration, budget, desired activities, etc. The hearing section has the user provide basic information such as the travel destination, duration, and budget. Step 2: The generation unit uses the generation AI to generate a tour plan based on the requirements collected by the hearing unit. The generation AI uses machine learning algorithms and natural language processing techniques to generate an optimal tour plan based on the user's requirements. For example, it proposes a tour plan that includes tourist spots and activities for families. Step 3: The suggestion unit proposes the tour plan generated by the generation unit to the user and receives feedback. The suggestion unit allows the user to evaluate the proposed tour plan and request a re-proposal if necessary. Step 4: The regeneration unit regenerates the tour plan based on the feedback collected by the suggestion unit. The regeneration unit regenerates and proposes the tour plan based on the user's feedback. Step 5: The reservation unit reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit. The reservation unit automatically reserves plane tickets and hotels through the system. Step 6: The bookmark creation unit creates a travel bookmark based on the information secured by the securing unit. The bookmark creation unit creates a bookmark including information on the travel itinerary and destinations to be visited, and provides it to the user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0186] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0187] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0203] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0204] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0205] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0206] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0208] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0209] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0210] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0211] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0213] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0214] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0215] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0216] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0217] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0218] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0219] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0220] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0221] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0222] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0223] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0224] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0225] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0226] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0227] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0228] 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.

[0229] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0230] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0231] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0232] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0233] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0234] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0235] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0236] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0237] [Explanation of symbols]

[0238] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a hearing department that hears user requirements; a generation unit that generates a tour plan based on the requirements collected by the hearing unit; a suggestion unit that suggests the tour plan generated by the generation unit to a user and receives feedback; a regeneration unit that regenerates a tour plan based on the feedback collected by the suggestion unit; a reservation unit that reserves transportation and accommodations based on the tour plan regenerated by the regeneration unit; a bookmark creation unit that creates a travel guide based on the information secured by the securing unit; Equipped with A system characterized by:

2. The hotel will also have a monetization department that will direct guests to hotel booking sites and sell the accumulated statistical data. The system of claim 1 .

3. The hearing section Collect specific information about the user's travel destination, duration, budget, etc. The system of claim 1 .

4. The generation unit Generative AI generates an appropriate tour plan based on user requirements The system of claim 1 .

5. The proposal unit Propose the generated tour plan to the user and collect user feedback The system of claim 1 .

6. The regeneration unit Regenerate tour plans based on user feedback The system of claim 1 .

7. The securing unit is Secure transportation and accommodation The system of claim 1 .

8. The bookmark creation unit Create a travel itinerary based on the information obtained The system of claim 1 .

9. The hearing section Estimate the user's emotions and adjust the timing of hearings based on the estimated user emotions The system of claim 1 .

Images