System

Abstract

An object of a system according to an embodiment is to provide an optimal travel plan based on a traveler's desire or interest.SOLUTION: A system includes a reception unit, a generation unit, and an adjustment unit. The reception unit inputs a desire or an interest of a user for a trip. The generation unit analyzes the information input by the reception unit and generates a travel plan suitable for the user. The adjustment unit monitors the behavior during the travel based on the plan generated by the generation unit, and adjusts the plan as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to provide optimal travel plans based on travelers' wishes and interests, making travel planning particularly difficult for foreign travelers.

[0005] The system according to the embodiment aims to provide an optimal travel plan based on the traveler's wishes and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an adjustment unit. The reception unit inputs a user's travel wishes or interests. The generation unit analyzes the information input by the reception unit and generates a travel plan suitable for the user. The adjustment unit monitors the user's behavior during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal travel plan based on the traveler's wishes and interests. [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 allows a user to input their travel preferences and interests, and a generation AI analyzes the information to propose an optimal travel plan. The system also monitors the user's travel behavior in real time and adjusts the plan. The user inputs their travel preferences and interests, and a generation AI analyzes the information to propose an optimal travel plan. This plan includes tourist attractions, accommodations, transportation options, and dining options. Furthermore, the travel planning support system monitors the user's travel behavior in real time and adjusts the plan as needed. For example, the user inputs preferences such as "I want to visit a hot spring," "I want to learn about Japanese history," and "I want to eat delicious sushi." This information is input into the generation AI. The generation AI then analyzes the input information and proposes an optimal travel plan for the user. The generation AI generates a plan that matches the user's preferences based on data such as tourist attractions, accommodations, transportation options, and dining options. For example, if the user requests "I want to visit a hot spring," the system provides information on hot spring resorts. If the user requests "I want to eat delicious sushi," the system introduces famous sushi restaurants. Furthermore, the travel planning support system monitors the user's travel behavior in real time and adjusts the plan as needed. For example, if the user finishes touring a tourist spot earlier than planned, the system will suggest moving on to the next tourist spot. The system can also flexibly change plans depending on changes in weather or traffic conditions. This allows the travel planning support system to enable foreign travelers to enjoy their trip with peace of mind, even if they have no knowledge of Japan. For example, a foreign traveler visiting Japan for the first time can plan a trip according to the suggestions made by the generation AI and actually enjoy their trip. Furthermore, if they encounter any problems during their trip, the generation AI will provide support in real time, allowing them to continue their trip with peace of mind.

[0029] A travel planning support system according to an embodiment includes a reception unit, a generation unit, and an adjustment unit. The reception unit inputs a user's travel preferences and interests. The user's travel preferences and interests include, but are not limited to, desired places to visit, a budget, and the purpose of the trip. The reception unit inputs, for example, user preferences such as "I want to go to a hot spring," "I want to learn about Japanese history," and "I want to eat delicious sushi." The generation unit uses a generation AI to analyze the information input by the reception unit and generate a travel plan suitable for the user. The generation AI uses, for example, natural language processing or a machine learning algorithm to select tourist spots based on the user's preferences and plan within the user's budget. For example, the generation AI generates a plan that meets the user's preferences based on data such as tourist spots, accommodations, transportation, and dining locations. The adjustment unit monitors the user's activities during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. Monitoring is performed, for example, by GPS tracking or collecting activity logs. For example, if the user finishes visiting tourist spots earlier than planned, the adjustment unit suggests moving on to the next tourist spot. The adjustment unit can also flexibly change the plan depending on changes in weather and traffic conditions. As a result, the travel planning support system according to the embodiment generates an optimal travel plan based on the user's travel wishes and interests, and adjusts the plan by monitoring the user's behavior during the trip, allowing foreign tourists to enjoy their trip with peace of mind.

[0030] The travel planning support system includes a collection unit that collects information on tourist destinations or accommodations. The collection unit collects information on tourist destinations or accommodations. The information on tourist destinations or accommodations includes, but is not limited to, for example, locations, fees, ratings, and facilities. The collection unit, for example, collects the locations and ratings of tourist destinations and provides them to the user. The collection unit can also collect information on fees and facilities of accommodations and provide them to the user. For example, the collection unit collects the locations and ratings of tourist destinations and provides them to the user. The collection unit can also collect information on fees and facilities of accommodations and provide them to the user. In this way, by collecting information on tourist destinations and accommodations, the accuracy of the travel plans provided to the user is improved.

[0031] The travel planning support system includes an improvement unit that collects user feedback and improves the plan. The improvement unit collects user feedback and improves the plan. Feedback includes, but is not limited to, for example, surveys, reviews, and ratings. For example, the improvement unit collects survey results provided by the user after the trip and uses them to improve the plan. The improvement unit can also collect reviews and ratings provided by the user during the trip and use them to improve the plan. For example, the improvement unit collects survey results provided by the user after the trip and uses them to improve the plan. The improvement unit can also collect reviews and ratings provided by the user during the trip and use them to improve the plan. In this way, by collecting user feedback and improving the plan, it is possible to provide a more satisfying travel plan.

[0032] The travel planning support system includes an information acquisition unit that acquires weather information or traffic information. The information acquisition unit acquires the weather information or traffic information. Weather information or traffic information includes, but is not limited to, weather forecasts, traffic congestion information, and public transportation operation status, for example. The information acquisition unit, for example, acquires a weather forecast and provides it to the user. The information acquisition unit can also acquire traffic congestion information and provide it to the user. For example, the information acquisition unit acquires a weather forecast and provides it to the user. The information acquisition unit can also acquire traffic congestion information and provide it to the user. In this way, acquiring weather information and traffic information improves the accuracy of travel plans, allowing the user to enjoy their trip with peace of mind.

[0033] The travel planning assistance system includes a privacy protection unit that protects the user's privacy. The privacy protection unit protects the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access restrictions, and anonymization. For example, the privacy protection unit encrypts the user's data to prevent unauthorized access from third parties. The privacy protection unit can also set access restrictions on the user's data so that only those with specific authorizations can access it. For example, the privacy protection unit encrypts the user's data to prevent unauthorized access from third parties. The privacy protection unit can also set access restrictions on the user's data so that only those with specific authorizations can access it. This protects the user's privacy, allowing the service to be used with peace of mind.

[0034] The travel planning support system includes a data integration unit that integrates multiple data sources. The data integration unit integrates the multiple data sources. The multiple data sources include, but are not limited to, travel sites, social media, and the user's past travel history. For example, the data integration unit integrates information from travel sites and provides it to the user. The data integration unit can also integrate information from social media and provide it to the user. For example, the data integration unit integrates information from travel sites and provides it to the user. The data integration unit can also integrate information from social media and provide it to the user. In this way, by integrating the multiple data sources, it is possible to provide a more accurate travel plan.

[0035] The reception unit can analyze the user's past travel history and suggest an appropriate input method. For example, the reception unit automatically displays tourist spots and accommodations that the user has visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest preferences related to specific seasons or events based on the user's past travel history. For example, the reception unit automatically displays tourist spots and accommodations that the user has visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the optimal input method can be suggested to the user by analyzing the past travel history.

[0036] The reception unit can perform filtering based on the user's current living situation or areas of interest when the user inputs his / her travel wishes and interests. For example, the reception unit can suggest related travel plans when the user inputs his / her current living situation. The reception unit can also suggest related tourist spots and activities based on the user's areas of interest (history, food culture, etc.). The reception unit can also suggest optimal travel plans based on the user's current living situation (family composition, health status, etc.). For example, the reception unit can suggest related travel plans when the user inputs his / her current living situation. The reception unit can also suggest related tourist spots and activities based on the user's areas of interest (history, food culture, etc.). The reception unit can also suggest optimal travel plans based on the user's current living situation (family composition, health status, etc.). In this way, filtering based on the user's current living situation and areas of interest can suggest more appropriate travel plans.

[0037] The reception unit can select an appropriate input means according to the user's input method when the user inputs his or her travel wishes and interests. For example, the reception unit can automatically display related information when the user simply inputs "I want to go to a hot spring" by voice. The reception unit can also suggest related tourist spots and activities when the user inputs "I want to learn Japanese history" by text. The reception unit can also suggest tourist spots and activities related to the image when the user uploads an image. For example, the reception unit can automatically display related information when the user simply inputs "I want to go to a hot spring" by voice. The reception unit can also suggest related tourist spots and activities when the user inputs "I want to learn Japanese history" by text. The reception unit can also suggest tourist spots and activities related to the image when the user uploads an image. This allows the user to select an optimal input means according to the user's input method, thereby providing a more comfortable input experience.

[0038] When inputting travel wishes and interests, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, when the user inputs their current location, the reception unit prioritizes suggesting nearby tourist attractions and accommodations. Furthermore, when the user specifies a specific area, the reception unit can prioritize displaying information related to that area. Furthermore, the reception unit can also suggest optimal tourist attractions and accommodations in consideration of the distance from the user's current location. For example, when the user inputs their current location, the reception unit prioritizes suggesting nearby tourist attractions and accommodations. Furthermore, when the user specifies a specific area, the reception unit can prioritize displaying information related to that area. Furthermore, the reception unit can also suggest optimal tourist attractions and accommodations in consideration of the distance from the user's current location. In this way, more relevant information can be provided by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity when the user inputs their travel preferences and interests and input related information. For example, the reception unit can suggest related tourist spots and accommodations based on the location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related activities and events. The reception unit can also suggest related information by taking into account the activities of the user's friends on social media. For example, the reception unit can suggest related tourist spots and accommodations based on the location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related activities and events. The reception unit can also suggest related information by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when the user inputs his or her travel preferences and interests. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback.

[0041] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of tourist attractions and accommodations. For example, the generation unit can provide detailed descriptions of important tourist attractions and accommodations, and a brief introduction of other places. The generation unit can also prioritize incorporating highly important tourist attractions and accommodations into the plan. The generation unit can also provide detailed information about tourist attractions and accommodations according to their importance. For example, the generation unit can provide detailed descriptions of important tourist attractions and accommodations, and a brief introduction of other places. The generation unit can also prioritize incorporating highly important tourist attractions and accommodations into the plan. The generation unit can also provide detailed information about tourist attractions and accommodations according to their importance. In this way, by adjusting the level of detail of the plan based on the importance of tourist attractions and accommodations, a more appropriate plan can be provided.

[0042] When generating a travel plan, the generation unit can apply different generation algorithms depending on the categories of tourist destinations and accommodations. For example, the generation unit applies a generation algorithm that emphasizes historical background and cultural information to historical tourist destinations. The generation unit can also apply a generation algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The generation unit can also apply a generation algorithm that emphasizes shopping and entertainment information to urban tourist destinations. For example, the generation unit applies a generation algorithm that emphasizes historical background and cultural information to historical tourist destinations. The generation unit can also apply a generation algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The generation unit can also apply a generation algorithm that emphasizes shopping and entertainment information to urban tourist destinations. In this way, by applying different generation algorithms depending on the categories of tourist destinations and accommodations, a more appropriate plan can be provided.

[0043] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel plan results. The generation unit, for example, proposes an optimal plan based on travel plans used by the user in the past. The generation unit can also generate a new plan by referring to successful plans from the user's past travel plan results. The generation unit can also analyze the user's past travel plan results to improve the accuracy of the plan. For example, the generation unit proposes an optimal plan based on travel plans used by the user in the past. The generation unit can also generate a new plan by referring to successful plans from the user's past travel plan results. The generation unit can also analyze the user's past travel plan results to improve the accuracy of the plan. In this way, the accuracy of the plan can be improved by referring to the user's past travel plan results.

[0044] When generating a travel plan, the generation unit can determine the priority of the plan based on the timing of when tourist attractions and accommodations are available. For example, the generation unit preferentially incorporates seasonal tourist attractions and accommodations into the plan. The generation unit can also adjust the plan to match the timing of when a particular event or festival is held. The generation unit can also propose an optimal plan based on the timing of when tourist attractions and accommodations are available. For example, the generation unit preferentially incorporates seasonal tourist attractions and accommodations into the plan. The generation unit can also adjust the plan to match the timing of when a particular event or festival is held. The generation unit can also propose an optimal plan based on the timing of when tourist attractions and accommodations are available. In this way, by determining the priority of the plan based on the timing of when tourist attractions and accommodations are available, a more appropriate plan can be provided.

[0045] When generating a travel plan, the generation unit can adjust the order of the plan based on the relevance of tourist destinations and accommodations. For example, the generation unit incorporates highly relevant tourist destinations and accommodations into the plan consecutively. The generation unit can also propose a plan in an optimal order based on the relevance of tourist destinations and accommodations. The generation unit can also prioritize incorporating highly relevant tourist destinations and accommodations into the plan. For example, the generation unit incorporates highly relevant tourist destinations and accommodations into the plan consecutively. The generation unit can also propose a plan in an optimal order based on the relevance of tourist destinations and accommodations. The generation unit can also prioritize incorporating highly relevant tourist destinations and accommodations into the plan. In this way, by adjusting the order of the plan based on the relevance of tourist destinations and accommodations, a more efficient plan can be provided.

[0046] When generating a travel plan, the generation unit may adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a beginner, the generation unit may explain the plan in simple terms, avoiding technical terminology. Also, if the user is an intermediate user, the generation unit may explain the plan using appropriate technical terminology. Also, if the user is an advanced user, the generation unit may provide a detailed plan using a lot of technical terminology. For example, if the user is a beginner, the generation unit may explain the plan in simple terms, avoiding technical terminology. Also, if the user is an intermediate user, the generation unit may explain the plan using appropriate technical terminology. Also, if the user is an advanced user, the generation unit may provide a detailed plan using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the plan according to the user's level of expertise, it is possible to provide a plan that is easier to understand.

[0047] During behavior monitoring, the adjustment unit can analyze the user's past travel behavior and select the optimal monitoring method. The adjustment unit, for example, selects the optimal method based on monitoring methods used by the user in the past. The adjustment unit can also suggest the optimal monitoring method based on the user's past travel behavior. The adjustment unit can also analyze the user's past travel behavior and select the most efficient monitoring method. For example, the adjustment unit selects the optimal method based on monitoring methods used by the user in the past. The adjustment unit can also suggest the optimal monitoring method based on the user's past travel behavior. The adjustment unit can also analyze the user's past travel behavior and select the most efficient monitoring method. In this way, the optimal monitoring method can be selected by analyzing the user's past travel behavior.

[0048] The adjustment unit can customize the monitoring means based on the user's current living situation during behavior monitoring. The adjustment unit, for example, suggests the most appropriate monitoring means based on the user's current living situation (family composition, health condition, etc.). The adjustment unit can also adjust the monitoring frequency and method in consideration of the user's current living situation. The adjustment unit can also customize the level of detail of monitoring in accordance with the user's current living situation. For example, the adjustment unit suggests the most appropriate monitoring means based on the user's current living situation (family composition, health condition, etc.). The adjustment unit can also adjust the monitoring frequency and method in consideration of the user's current living situation. The adjustment unit can also customize the level of detail of monitoring in accordance with the user's current living situation. In this way, more appropriate monitoring can be performed by customizing the monitoring means based on the user's current living situation.

[0049] The adjustment unit can improve the monitoring method by reflecting user feedback during behavior monitoring. The adjustment unit improves the monitoring method, for example, based on feedback provided by the user. The adjustment unit can also preferentially adopt a specific monitoring method based on the user feedback. The adjustment unit can also analyze the user feedback and customize the monitoring method. For example, the adjustment unit improves the monitoring method based on feedback provided by the user. The adjustment unit can also preferentially adopt a specific monitoring method based on the user feedback. The adjustment unit can also analyze the user feedback and customize the monitoring method. In this way, the monitoring method can be improved by reflecting the user feedback.

[0050] The adjustment unit can select an optimal monitoring method by taking into account the user's geographical location information when monitoring behavior. The adjustment unit, for example, suggests an optimal monitoring method based on the user's current location. The adjustment unit can also adjust the monitoring frequency and method by taking into account the user's geographical location information. The adjustment unit can also customize the level of detail of monitoring according to the user's geographical location information. For example, the adjustment unit suggests an optimal monitoring method based on the user's current location. The adjustment unit can also adjust the monitoring frequency and method by taking into account the user's geographical location information. The adjustment unit can also customize the level of detail of monitoring according to the user's geographical location information. In this way, the optimal monitoring method can be selected by taking into account the user's geographical location information.

[0051] During behavior monitoring, the adjustment unit can analyze the user's social media activities and suggest monitoring means. The adjustment unit, for example, analyzes the content of the user's posts on social media and monitors related behavior. The adjustment unit can also monitor related behavior with reference to the activities of the user's friends on social media. The adjustment unit can also suggest an optimal monitoring method based on the user's social media check-in information. For example, the adjustment unit analyzes the content of the user's posts on social media and monitors related behavior. The adjustment unit can also monitor related behavior with reference to the activities of the user's friends on social media. The adjustment unit can also suggest an optimal monitoring method based on the user's social media check-in information. In this way, the optimal monitoring means can be suggested by analyzing the user's social media activities.

[0052] The adjustment unit can customize the monitoring method by reflecting the user's past feedback during behavior monitoring. The adjustment unit, for example, suggests an optimal monitoring method based on feedback provided by the user in the past. The adjustment unit can also preferentially adopt a specific monitoring method based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the monitoring method. For example, the adjustment unit suggests an optimal monitoring method based on feedback provided by the user in the past. The adjustment unit can also preferentially adopt a specific monitoring method based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the monitoring method. In this way, the monitoring method can be customized by reflecting the user's past feedback.

[0053] When collecting information, the collection unit can adjust the level of detail of collection based on the importance of tourist destinations and accommodations. For example, the collection unit collects detailed information about important tourist destinations and accommodations, and briefly collects information about other places. The collection unit can also prioritize collecting information about tourist destinations and accommodations with high importance. The collection unit can also collect detailed information about tourist destinations and accommodations according to their importance. For example, the collection unit collects detailed information about important tourist destinations and accommodations, and briefly collects information about other places. The collection unit can also prioritize collecting information about tourist destinations and accommodations with high importance. The collection unit can also collect detailed information about tourist destinations and accommodations according to their importance. In this way, by adjusting the level of detail of collection based on the importance of tourist destinations and accommodations, more appropriate information can be collected.

[0054] When collecting information, the collection unit can apply different collection algorithms depending on the category of tourist destination or accommodation facility. For example, the collection unit applies a collection algorithm that emphasizes historical background and cultural information to historical tourist destinations. The collection unit can also apply a collection algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The collection unit can also apply a collection algorithm that emphasizes shopping and entertainment information to urban tourist destinations. For example, the collection unit applies a collection algorithm that emphasizes historical background and cultural information to historical tourist destinations. The collection unit can also apply a collection algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The collection unit can also apply a collection algorithm that emphasizes shopping and entertainment information to urban tourist destinations. In this way, by applying different collection algorithms depending on the category of tourist destination or accommodation facility, more appropriate information can be collected.

[0055] When collecting information, the collection unit can determine a collection priority based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. In this way, by determining a collection priority based on the timing of when tourist destinations and accommodation facilities are offered, more appropriate information can be collected.

[0056] When collecting information, the collection unit can determine a collection priority based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. In this way, by determining a collection priority based on the timing of when tourist destinations and accommodation facilities are offered, more appropriate information can be collected.

[0057] When collecting information, the collection unit can adjust the order of collection based on the relevance of tourist destinations and accommodations. The collection unit, for example, collects information on highly relevant tourist destinations and accommodations in succession. The collection unit can also collect information in an optimal order based on the relevance of tourist destinations and accommodations. The collection unit can also prioritize collecting information on highly relevant tourist destinations and accommodations. For example, the collection unit collects information on highly relevant tourist destinations and accommodations in succession. The collection unit can also collect information in an optimal order based on the relevance of tourist destinations and accommodations. The collection unit can also prioritize collecting information on highly relevant tourist destinations and accommodations. In this way, by adjusting the order of collection based on the relevance of tourist destinations and accommodations, more efficient information collection is possible.

[0058] When collecting information, the collection unit can adjust the level of detail of the information to be collected according to the user's level of expertise. For example, if the user is a beginner, the collection unit prioritizes collecting basic information. Furthermore, if the user is an intermediate user, the collection unit can also collect information with an appropriate level of detail. Furthermore, if the user is an advanced user, the collection unit can also prioritize collecting detailed information. For example, if the user is a beginner, the collection unit prioritizes collecting basic information. Furthermore, if the user is an intermediate user, the collection unit can also collect information with an appropriate level of detail. Furthermore, if the user is an advanced user, the collection unit can also prioritize collecting detailed information. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided.

[0059] When collecting feedback, the improvement unit can analyze the user's past feedback history and select the optimal collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback history. The improvement unit can also analyze the user's past feedback history and customize the collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback history. The improvement unit can also analyze the user's past feedback history and customize the collection method. In this way, the optimal collection method can be selected by analyzing the user's past feedback history.

[0060] When collecting feedback, the improvement unit can customize the means of collection based on the user's current living situation. The improvement unit, for example, suggests the optimal means of collection based on the user's current living situation (family composition, health condition, etc.). The improvement unit can also adjust the frequency and method of collection in consideration of the user's current living situation. The improvement unit can also customize the level of detail of collection in accordance with the user's current living situation. For example, the improvement unit suggests the optimal means of collection based on the user's current living situation (family composition, health condition, etc.). The improvement unit can also adjust the frequency and method of collection in consideration of the user's current living situation. The improvement unit can also customize the level of detail of collection in accordance with the user's current living situation. In this way, by customizing the means of collection based on the user's current living situation, more appropriate feedback can be collected.

[0061] The improvement unit can improve the collection method by reflecting the user's feedback when collecting feedback. The improvement unit improves the collection method, for example, based on the feedback provided by the user. The improvement unit can also preferentially adopt a specific collection method based on the user's feedback. The improvement unit can also analyze the user's feedback and customize the collection method. For example, the improvement unit improves the collection method based on the feedback provided by the user. The improvement unit can also preferentially adopt a specific collection method based on the user's feedback. The improvement unit can also analyze the user's feedback and customize the collection method. In this way, the collection method can be improved by reflecting the user's feedback.

[0062] When collecting feedback, the improvement unit can select the optimal collection method by taking into account the user's geographical location information. The improvement unit, for example, suggests the optimal feedback collection method based on the user's current location. The improvement unit can also adjust the frequency and method of collection by taking into account the user's geographical location information. The improvement unit can also customize the level of detail of collection according to the user's geographical location information. For example, the improvement unit suggests the optimal feedback collection method based on the user's current location. The improvement unit can also adjust the frequency and method of collection by taking into account the user's geographical location information. The improvement unit can also customize the level of detail of collection according to the user's geographical location information. In this way, the optimal collection method can be selected by taking into account the user's geographical location information.

[0063] When collecting feedback, the improvement unit can analyze the user's social media activity and suggest a means of collection. For example, the improvement unit analyzes the content of the user's posts on social media and collects related feedback. The improvement unit can also collect related feedback by referring to the activities of the user's friends on social media. The improvement unit can also suggest an optimal feedback collection method based on the user's social media check-in information. For example, the improvement unit analyzes the content of the user's posts on social media and collects related feedback. The improvement unit can also collect related feedback by referring to the activities of the user's friends on social media. The improvement unit can also suggest an optimal feedback collection method based on the user's social media check-in information. In this way, the optimal collection method can be suggested by analyzing the user's social media activity.

[0064] When collecting feedback, the improvement unit can customize the collection method by reflecting the user's past feedback. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback. The improvement unit can also analyze the user's past feedback and customize the collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback. The improvement unit can also analyze the user's past feedback and customize the collection method. In this way, the collection method can be customized by reflecting the user's past feedback.

[0065] When acquiring information, the information acquisition unit can adjust the level of detail of the acquired information based on the importance of weather information and traffic information. For example, the information acquisition unit acquires important weather information and traffic information in detail, and acquires other information in a concise manner. The information acquisition unit can also prioritize acquiring weather information and traffic information with high importance. The information acquisition unit can also adjust the level of detail of the weather information and traffic information according to the importance. For example, the information acquisition unit acquires important weather information and traffic information in detail, and acquires other information in a concise manner. The information acquisition unit can also prioritize acquiring weather information and traffic information with high importance. The information acquisition unit can also adjust the level of detail of the weather information and traffic information according to the importance. In this way, by adjusting the level of detail of the acquired weather information and traffic information based on the importance of the weather information and traffic information, more appropriate information can be provided.

[0066] When acquiring information, the information acquisition unit can apply different acquisition algorithms depending on the category of weather information or traffic information. For example, the information acquisition unit applies an acquisition algorithm that prioritizes weather forecasts and temperature information to weather information. The information acquisition unit can also apply an acquisition algorithm that prioritizes traffic congestion information and the operation status of public transportation to traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the period when a specific event or festival is held. For example, the information acquisition unit applies an acquisition algorithm that prioritizes weather forecasts and temperature information to weather information. The information acquisition unit can also apply an acquisition algorithm that prioritizes traffic congestion information and the operation status of public transportation to traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the period when a specific event or festival is held. In this way, by applying different acquisition algorithms depending on the category of weather information and traffic information, more appropriate information can be provided.

[0067] When acquiring information, the information acquisition unit can determine the priority of acquisition based on the time when weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. In this way, by determining the priority of acquisition based on the time when the weather information and traffic information are provided, more appropriate information can be provided.

[0068] When acquiring information, the information acquisition unit can determine the priority of acquisition based on the time when weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. In this way, by determining the priority of acquisition based on the time when the weather information and traffic information are provided, more appropriate information can be provided.

[0069] When acquiring information, the information acquisition unit can adjust the acquisition order based on the relevance of weather information and traffic information. For example, the information acquisition unit continuously acquires highly relevant weather information and traffic information. The information acquisition unit can also acquire information in an optimal order based on the relevance of the weather information and traffic information. The information acquisition unit can also prioritize acquiring highly relevant weather information and traffic information. For example, the information acquisition unit continuously acquires highly relevant weather information and traffic information. The information acquisition unit can also acquire information in an optimal order based on the relevance of the weather information and traffic information. The information acquisition unit can also prioritize acquiring highly relevant weather information and traffic information. In this way, by adjusting the acquisition order based on the relevance of the weather information and traffic information, more efficient information acquisition is possible.

[0070] When acquiring information, the information acquisition unit can adjust the level of detail of the information to be acquired according to the user's level of expertise. For example, if the user is a beginner, the information acquisition unit prioritizes acquiring basic weather information and traffic information. Furthermore, if the user is an intermediate user, the information acquisition unit can also acquire weather information and traffic information with an appropriate level of detail. Furthermore, if the user is an advanced user, the information acquisition unit can also prioritize acquiring detailed weather information and traffic information. For example, if the user is a beginner, the information acquisition unit prioritizes acquiring basic weather information and traffic information. Furthermore, if the user is an intermediate user, the information acquisition unit can also acquire weather information and traffic information with an appropriate level of detail. Furthermore, if the user is an advanced user, the information acquisition unit can also prioritize acquiring detailed weather information and traffic information. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided.

[0071] During privacy protection, the privacy protection unit can select an optimal protection method by analyzing the user's past privacy setting history. For example, the privacy protection unit suggests an optimal protection method based on the privacy protection settings previously set by the user. The privacy protection unit can also preferentially adopt a specific protection method based on the user's past privacy setting history. The privacy protection unit can also analyze the user's past privacy setting history and customize the protection method. For example, the privacy protection unit suggests an optimal protection method based on the privacy protection settings previously set by the user. The privacy protection unit can also preferentially adopt a specific protection method based on the user's past privacy setting history. The privacy protection unit can also analyze the user's past privacy setting history and customize the protection method. In this way, the optimal protection method can be selected by analyzing the user's past privacy setting history.

[0072] The privacy protection unit can select an optimal protection method during privacy protection by taking into account the user's geographical location information. The privacy protection unit, for example, suggests an optimal privacy protection method based on the user's current location. The privacy protection unit can also adjust the frequency and method of protection by taking into account the user's geographical location information. The privacy protection unit can also customize the level of detail of protection according to the user's geographical location information. For example, the privacy protection unit suggests an optimal privacy protection method based on the user's current location. The privacy protection unit can also adjust the frequency and method of protection by taking into account the user's geographical location information. The privacy protection unit can also customize the level of detail of protection according to the user's geographical location information. In this way, the optimal protection method can be selected by taking into account the user's geographical location information.

[0073] When integrating data, the data integration unit can adjust the level of detail of integration based on the importance of multiple data sources. For example, the data integration unit integrates information from important data sources in detail and integrates other information briefly. The data integration unit can also prioritize integration of data sources with high importance. The data integration unit can also integrate information from data sources in detail according to their importance. For example, the data integration unit integrates information from important data sources in detail and integrates other information briefly. The data integration unit can also prioritize integration of data sources with high importance. The data integration unit can also integrate information from data sources in detail according to their importance. In this way, by adjusting the level of detail of integration based on the importance of multiple data sources, more appropriate data integration can be provided.

[0074] When integrating data, the data integration unit can apply different integration algorithms depending on the category of the data source. For example, the data integration unit applies an integration algorithm that emphasizes weather forecasts and temperature information to weather information. The data integration unit can also apply an integration algorithm that emphasizes congestion information and the operation status of public transportation to traffic information. The data integration unit can also apply an integration algorithm that emphasizes tourist spot and event information to tourist destination information. For example, the data integration unit applies an integration algorithm that emphasizes weather forecasts and temperature information to weather information. The data integration unit can also apply an integration algorithm that emphasizes congestion information and the operation status of public transportation to traffic information. The data integration unit can also apply an integration algorithm that emphasizes tourist spot and event information to tourist destination information. In this way, by applying different integration algorithms depending on the category of the data source, more appropriate data integration can be provided.

[0075] When integrating data, the data integration unit can determine the priority of integration based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. In this way, by determining the priority of integration based on the time when the data sources are provided, more appropriate data integration can be provided.

[0076] When integrating data, the data integration unit can determine the priority of integration based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. In this way, by determining the priority of integration based on the time when the data sources are provided, more appropriate data integration can be provided.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] When the user inputs their travel preferences and interests, the reception unit can analyze the user's past travel history and suggest similar travel plans. For example, the reception unit can suggest travel plans with similar themes or regions based on tourist spots and accommodations the user has visited in the past. The reception unit can also suggest related plans by taking into account the activities and types of meals the user has previously preferred. Furthermore, the reception unit can suggest plans related to specific seasons or events based on the user's past travel history. This makes it possible to provide more personalized travel plans by utilizing the user's past travel history.

[0079] When collecting information about tourist attractions and accommodations, the collection unit can analyze the user's social media activities and prioritize collection of related information. For example, the collection unit can collect information about related tourist attractions and accommodations based on the places the user has checked in to and photos posted on social media. The collection unit can also collect information about places visited by the user's friends and facilities rated by the user, and provide this information to the user. Furthermore, the collection unit can analyze the user's interests and concerns on social media and collect information based on this. This makes it possible to utilize the user's social media activities to provide more relevant information.

[0080] When acquiring weather information or traffic information, the information acquisition unit can adjust the method of acquiring information taking into account the user's current living situation. For example, if the user is planning a family trip, weather information and traffic information for the family can be acquired preferentially. Also, if the user is concerned about their health, health-conscious information can be provided. Furthermore, if the user plans to participate in a specific event, information related to that event can be acquired preferentially. In this way, by adjusting the method of acquiring information based on the user's current living situation, more appropriate information can be provided.

[0081] When integrating multiple data sources, the data integration unit can determine the priority of integration based on the time of data source provision. For example, seasonal data sources can be integrated with priority. Data can also be integrated according to the time of a particular event or festival. Furthermore, optimal data can be integrated based on the time of data source provision. Thus, by determining the priority of integration based on the time of data source provision, more appropriate data integration can be provided.

[0082] When generating a travel plan, the generation unit can apply different generation algorithms depending on the category of tourist destinations and accommodations. For example, a generation algorithm that emphasizes historical background and cultural information can be applied to historical tourist destinations. Also, a generation algorithm that emphasizes scenery and the natural environment can be applied to tourist destinations with natural landscapes. Furthermore, a generation algorithm that emphasizes shopping and entertainment information can be applied to urban tourist destinations. In this way, by applying different generation algorithms depending on the category of tourist destinations and accommodations, it is possible to provide more appropriate plans.

[0083] When collecting information, the collection unit can apply different collection algorithms depending on the category of tourist destination or accommodation. For example, a collection algorithm that emphasizes historical background and cultural information can be applied to a historical tourist destination. Also, a collection algorithm that emphasizes scenery and the natural environment can be applied to a tourist destination with natural scenery. Furthermore, a collection algorithm that emphasizes shopping and entertainment information can be applied to an urban tourist destination. In this way, by applying different collection algorithms depending on the category of tourist destination or accommodation, more appropriate information can be collected.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The reception unit inputs the user's travel wishes and interests. The user's travel wishes and interests include, for example, the places they want to visit, their budget, and the purpose of their trip. The user inputs wishes such as "I want to go to a hot spring," "I want to learn about Japanese history," or "I want to eat delicious sushi." Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a travel plan suitable for the user. The generation AI uses natural language processing and machine learning algorithms to select tourist spots based on the user's preferences and plan within the user's budget. It generates a plan that suits the user's preferences based on data such as tourist spots, accommodation, transportation, and places to eat. Step 3: The adjustment unit monitors the user's behavior during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. Monitoring is performed using methods such as GPS tracking and collecting behavior logs. If the user finishes visiting tourist spots earlier than planned, the system will suggest moving on to the next tourist spot. The plan can also be flexibly changed depending on changes in weather and traffic conditions.

[0086] (Example 2) A travel planning support system according to an embodiment of the present invention allows a user to input their travel preferences and interests, and a generation AI analyzes the information to propose an optimal travel plan. The system also monitors the user's travel behavior in real time and adjusts the plan. The user inputs their travel preferences and interests, and a generation AI analyzes the information to propose an optimal travel plan. This plan includes tourist attractions, accommodations, transportation options, and dining options. Furthermore, the travel planning support system monitors the user's travel behavior in real time and adjusts the plan as needed. For example, the user inputs preferences such as "I want to visit a hot spring," "I want to learn about Japanese history," and "I want to eat delicious sushi." This information is input into the generation AI. The generation AI then analyzes the input information and proposes an optimal travel plan for the user. The generation AI generates a plan that matches the user's preferences based on data such as tourist attractions, accommodations, transportation options, and dining options. For example, if the user requests "I want to visit a hot spring," the system provides information on hot spring resorts. If the user requests "I want to eat delicious sushi," the system introduces famous sushi restaurants. Furthermore, the travel planning support system monitors the user's travel behavior in real time and adjusts the plan as needed. For example, if the user finishes touring a tourist spot earlier than planned, the system will suggest moving on to the next tourist spot. The system can also flexibly change plans depending on changes in weather or traffic conditions. This allows the travel planning support system to enable foreign travelers to enjoy their trip with peace of mind, even if they have no knowledge of Japan. For example, a foreign traveler visiting Japan for the first time can plan a trip according to the suggestions made by the generation AI and actually enjoy their trip. Furthermore, if they encounter any problems during their trip, the generation AI will provide support in real time, allowing them to continue their trip with peace of mind.

[0087] A travel planning support system according to an embodiment includes a reception unit, a generation unit, and an adjustment unit. The reception unit inputs a user's travel preferences and interests. The user's travel preferences and interests include, but are not limited to, desired places to visit, a budget, and the purpose of the trip. The reception unit inputs, for example, user preferences such as "I want to go to a hot spring," "I want to learn about Japanese history," and "I want to eat delicious sushi." The generation unit uses a generation AI to analyze the information input by the reception unit and generate a travel plan suitable for the user. The generation AI uses, for example, natural language processing or a machine learning algorithm to select tourist spots based on the user's preferences and plan within the user's budget. For example, the generation AI generates a plan that meets the user's preferences based on data such as tourist spots, accommodations, transportation, and dining locations. The adjustment unit monitors the user's activities during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. Monitoring is performed, for example, by GPS tracking or collecting activity logs. For example, if the user finishes visiting tourist spots earlier than planned, the adjustment unit suggests moving on to the next tourist spot. The adjustment unit can also flexibly change the plan depending on changes in weather and traffic conditions. As a result, the travel planning support system according to the embodiment generates an optimal travel plan based on the user's travel wishes and interests, and adjusts the plan by monitoring the user's behavior during the trip, allowing foreign tourists to enjoy their trip with peace of mind.

[0088] The travel planning support system includes a collection unit that collects information on tourist destinations or accommodations. The collection unit collects information on tourist destinations or accommodations. The information on tourist destinations or accommodations includes, but is not limited to, for example, locations, fees, ratings, and facilities. The collection unit, for example, collects the locations and ratings of tourist destinations and provides them to the user. The collection unit can also collect information on fees and facilities of accommodations and provide them to the user. For example, the collection unit collects the locations and ratings of tourist destinations and provides them to the user. The collection unit can also collect information on fees and facilities of accommodations and provide them to the user. In this way, by collecting information on tourist destinations and accommodations, the accuracy of the travel plans provided to the user is improved.

[0089] The travel planning support system includes an improvement unit that collects user feedback and improves the plan. The improvement unit collects user feedback and improves the plan. Feedback includes, but is not limited to, for example, surveys, reviews, and ratings. For example, the improvement unit collects survey results provided by the user after the trip and uses them to improve the plan. The improvement unit can also collect reviews and ratings provided by the user during the trip and use them to improve the plan. For example, the improvement unit collects survey results provided by the user after the trip and uses them to improve the plan. The improvement unit can also collect reviews and ratings provided by the user during the trip and use them to improve the plan. In this way, by collecting user feedback and improving the plan, it is possible to provide a more satisfying travel plan.

[0090] The travel planning support system includes an information acquisition unit that acquires weather information or traffic information. The information acquisition unit acquires the weather information or traffic information. Weather information or traffic information includes, but is not limited to, weather forecasts, traffic congestion information, and public transportation operation status, for example. The information acquisition unit, for example, acquires a weather forecast and provides it to the user. The information acquisition unit can also acquire traffic congestion information and provide it to the user. For example, the information acquisition unit acquires a weather forecast and provides it to the user. The information acquisition unit can also acquire traffic congestion information and provide it to the user. In this way, acquiring weather information and traffic information improves the accuracy of travel plans, allowing the user to enjoy their trip with peace of mind.

[0091] The travel planning assistance system includes a privacy protection unit that protects the user's privacy. The privacy protection unit protects the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access restrictions, and anonymization. For example, the privacy protection unit encrypts the user's data to prevent unauthorized access from third parties. The privacy protection unit can also set access restrictions on the user's data so that only those with specific authorizations can access it. For example, the privacy protection unit encrypts the user's data to prevent unauthorized access from third parties. The privacy protection unit can also set access restrictions on the user's data so that only those with specific authorizations can access it. This protects the user's privacy, allowing the service to be used with peace of mind.

[0092] The travel planning support system includes a data integration unit that integrates multiple data sources. The data integration unit integrates the multiple data sources. The multiple data sources include, but are not limited to, travel sites, social media, and the user's past travel history. For example, the data integration unit integrates information from travel sites and provides it to the user. The data integration unit can also integrate information from social media and provide it to the user. For example, the data integration unit integrates information from travel sites and provides it to the user. The data integration unit can also integrate information from social media and provide it to the user. In this way, by integrating the multiple data sources, it is possible to provide a more accurate travel plan.

[0093] The reception unit can estimate the user's emotions and adjust the input method for the user's travel preferences and interests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input their travel preferences and interests. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input their travel preferences and interests. In this way, by adjusting the input method according to the user's emotions, a more comfortable input experience can be provided.

[0094] The reception unit can analyze the user's past travel history and suggest an appropriate input method. For example, the reception unit automatically displays tourist spots and accommodations that the user has visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest preferences related to specific seasons or events based on the user's past travel history. For example, the reception unit automatically displays tourist spots and accommodations that the user has visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the optimal input method can be suggested to the user by analyzing the past travel history.

[0095] The reception unit can perform filtering based on the user's current living situation or areas of interest when the user inputs his / her travel wishes and interests. For example, the reception unit can suggest related travel plans when the user inputs his / her current living situation. The reception unit can also suggest related tourist spots and activities based on the user's areas of interest (history, food culture, etc.). The reception unit can also suggest optimal travel plans based on the user's current living situation (family composition, health status, etc.). For example, the reception unit can suggest related travel plans when the user inputs his / her current living situation. The reception unit can also suggest related tourist spots and activities based on the user's areas of interest (history, food culture, etc.). The reception unit can also suggest optimal travel plans based on the user's current living situation (family composition, health status, etc.). In this way, filtering based on the user's current living situation and areas of interest can suggest more appropriate travel plans.

[0096] The reception unit can select an appropriate input means according to the user's input method when the user inputs his or her travel wishes and interests. For example, the reception unit can automatically display related information when the user simply inputs "I want to go to a hot spring" by voice. The reception unit can also suggest related tourist spots and activities when the user inputs "I want to learn Japanese history" by text. The reception unit can also suggest tourist spots and activities related to the image when the user uploads an image. For example, the reception unit can automatically display related information when the user simply inputs "I want to go to a hot spring" by voice. The reception unit can also suggest related tourist spots and activities when the user inputs "I want to learn Japanese history" by text. The reception unit can also suggest tourist spots and activities related to the image when the user uploads an image. This allows the user to select an optimal input means according to the user's input method, thereby providing a more comfortable input experience.

[0097] The reception unit can estimate the user's emotions and determine the priority of the travel wishes and interests input based on the estimated user emotions. For example, if the user is excited, the reception unit can preferentially suggest active activities. Furthermore, if the user is relaxed, the reception unit can preferentially suggest relaxing tourist spots and accommodations. Furthermore, if the user is stressed, the reception unit can preferentially suggest activities that are useful for relieving stress. For example, if the user is excited, the reception unit preferentially suggest active activities. Furthermore, if the user is relaxed, the reception unit can preferentially suggest relaxing tourist spots and accommodations. Furthermore, if the user is stressed, the reception unit can preferentially suggest activities that are useful for relieving stress. In this way, by determining the priority based on the user's emotions, a more appropriate travel plan can be proposed.

[0098] When inputting travel wishes and interests, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, when the user inputs their current location, the reception unit prioritizes suggesting nearby tourist attractions and accommodations. Furthermore, when the user specifies a specific area, the reception unit can prioritize displaying information related to that area. Furthermore, the reception unit can also suggest optimal tourist attractions and accommodations in consideration of the distance from the user's current location. For example, when the user inputs their current location, the reception unit prioritizes suggesting nearby tourist attractions and accommodations. Furthermore, when the user specifies a specific area, the reception unit can prioritize displaying information related to that area. Furthermore, the reception unit can also suggest optimal tourist attractions and accommodations in consideration of the distance from the user's current location. In this way, more relevant information can be provided by taking the user's geographical location information into consideration.

[0099] The reception unit can analyze the user's social media activity when the user inputs their travel preferences and interests and input related information. For example, the reception unit can suggest related tourist spots and accommodations based on the location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related activities and events. The reception unit can also suggest related information by taking into account the activities of the user's friends on social media. For example, the reception unit can suggest related tourist spots and accommodations based on the location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related activities and events. The reception unit can also suggest related information by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided.

[0100] The reception unit can customize the input method by reflecting the user's past feedback when the user inputs his or her travel preferences and interests. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback.

[0101] The generation unit can estimate the user's emotions and adjust the representation of the travel plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a travel plan that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan that adds visually stimulating effects. For example, if the user is relaxed, the generation unit generates a travel plan that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan that adds visually stimulating effects. In this way, by adjusting the representation of the travel plan based on the user's emotions, a more appropriate plan can be provided.

[0102] When generating a travel plan, the generation unit can adjust the level of detail of the plan based on the importance of tourist attractions and accommodations. For example, the generation unit can provide detailed descriptions of important tourist attractions and accommodations, and a brief introduction of other places. The generation unit can also prioritize incorporating highly important tourist attractions and accommodations into the plan. The generation unit can also provide detailed information about tourist attractions and accommodations according to their importance. For example, the generation unit can provide detailed descriptions of important tourist attractions and accommodations, and a brief introduction of other places. The generation unit can also prioritize incorporating highly important tourist attractions and accommodations into the plan. The generation unit can also provide detailed information about tourist attractions and accommodations according to their importance. In this way, by adjusting the level of detail of the plan based on the importance of tourist attractions and accommodations, a more appropriate plan can be provided.

[0103] When generating a travel plan, the generation unit can apply different generation algorithms depending on the categories of tourist destinations and accommodations. For example, the generation unit applies a generation algorithm that emphasizes historical background and cultural information to historical tourist destinations. The generation unit can also apply a generation algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The generation unit can also apply a generation algorithm that emphasizes shopping and entertainment information to urban tourist destinations. For example, the generation unit applies a generation algorithm that emphasizes historical background and cultural information to historical tourist destinations. The generation unit can also apply a generation algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The generation unit can also apply a generation algorithm that emphasizes shopping and entertainment information to urban tourist destinations. In this way, by applying different generation algorithms depending on the categories of tourist destinations and accommodations, a more appropriate plan can be provided.

[0104] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel plan results. The generation unit, for example, proposes an optimal plan based on travel plans used by the user in the past. The generation unit can also generate a new plan by referring to successful plans from the user's past travel plan results. The generation unit can also analyze the user's past travel plan results to improve the accuracy of the plan. For example, the generation unit proposes an optimal plan based on travel plans used by the user in the past. The generation unit can also generate a new plan by referring to successful plans from the user's past travel plan results. The generation unit can also analyze the user's past travel plan results to improve the accuracy of the plan. In this way, the accuracy of the plan can be improved by referring to the user's past travel plan results.

[0105] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point travel plan. Furthermore, if the user is relaxed, the generation unit can generate a longer travel plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point travel plan. Furthermore, if the user is relaxed, the generation unit can generate a longer travel plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. By adjusting the length of the travel plan based on the user's emotions, a more appropriate plan can be provided.

[0106] When generating a travel plan, the generation unit can determine the priority of the plan based on the timing of when tourist attractions and accommodations are available. For example, the generation unit preferentially incorporates seasonal tourist attractions and accommodations into the plan. The generation unit can also adjust the plan to match the timing of when a particular event or festival is held. The generation unit can also propose an optimal plan based on the timing of when tourist attractions and accommodations are available. For example, the generation unit preferentially incorporates seasonal tourist attractions and accommodations into the plan. The generation unit can also adjust the plan to match the timing of when a particular event or festival is held. The generation unit can also propose an optimal plan based on the timing of when tourist attractions and accommodations are available. In this way, by determining the priority of the plan based on the timing of when tourist attractions and accommodations are available, a more appropriate plan can be provided.

[0107] When generating a travel plan, the generation unit can adjust the order of the plan based on the relevance of tourist destinations and accommodations. For example, the generation unit incorporates highly relevant tourist destinations and accommodations into the plan consecutively. The generation unit can also propose a plan in an optimal order based on the relevance of tourist destinations and accommodations. The generation unit can also prioritize incorporating highly relevant tourist destinations and accommodations into the plan. For example, the generation unit incorporates highly relevant tourist destinations and accommodations into the plan consecutively. The generation unit can also propose a plan in an optimal order based on the relevance of tourist destinations and accommodations. The generation unit can also prioritize incorporating highly relevant tourist destinations and accommodations into the plan. In this way, by adjusting the order of the plan based on the relevance of tourist destinations and accommodations, a more efficient plan can be provided.

[0108] When generating a travel plan, the generation unit may adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a beginner, the generation unit may explain the plan in simple terms, avoiding technical terminology. Also, if the user is an intermediate user, the generation unit may explain the plan using appropriate technical terminology. Also, if the user is an advanced user, the generation unit may provide a detailed plan using a lot of technical terminology. For example, if the user is a beginner, the generation unit may explain the plan in simple terms, avoiding technical terminology. Also, if the user is an intermediate user, the generation unit may explain the plan using appropriate technical terminology. Also, if the user is an advanced user, the generation unit may provide a detailed plan using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the plan according to the user's level of expertise, it is possible to provide a plan that is easier to understand.

[0109] The adjustment unit can estimate the user's emotions and adjust the criteria for behavior monitoring during the trip based on the estimated user's emotions. For example, when the user is relaxed, the adjustment unit performs behavior monitoring at a leisurely pace. Furthermore, when the user is in a hurry, the adjustment unit can also perform behavior monitoring quickly. Furthermore, when the user is excited, the adjustment unit can also perform detailed behavior monitoring. For example, when the user is relaxed, the adjustment unit performs behavior monitoring at a leisurely pace. Furthermore, when the user is in a hurry, the adjustment unit can also perform behavior monitoring quickly. Furthermore, when the user is excited, the adjustment unit can also perform detailed behavior monitoring. In this way, by adjusting the criteria for behavior monitoring based on the user's emotions, more appropriate monitoring can be performed.

[0110] During behavior monitoring, the adjustment unit can analyze the user's past travel behavior and select the optimal monitoring method. The adjustment unit, for example, selects the optimal method based on monitoring methods used by the user in the past. The adjustment unit can also suggest the optimal monitoring method based on the user's past travel behavior. The adjustment unit can also analyze the user's past travel behavior and select the most efficient monitoring method. For example, the adjustment unit selects the optimal method based on monitoring methods used by the user in the past. The adjustment unit can also suggest the optimal monitoring method based on the user's past travel behavior. The adjustment unit can also analyze the user's past travel behavior and select the most efficient monitoring method. In this way, the optimal monitoring method can be selected by analyzing the user's past travel behavior.

[0111] The adjustment unit can customize the monitoring means based on the user's current living situation during behavior monitoring. The adjustment unit, for example, suggests the most appropriate monitoring means based on the user's current living situation (family composition, health condition, etc.). The adjustment unit can also adjust the monitoring frequency and method in consideration of the user's current living situation. The adjustment unit can also customize the level of detail of monitoring in accordance with the user's current living situation. For example, the adjustment unit suggests the most appropriate monitoring means based on the user's current living situation (family composition, health condition, etc.). The adjustment unit can also adjust the monitoring frequency and method in consideration of the user's current living situation. The adjustment unit can also customize the level of detail of monitoring in accordance with the user's current living situation. In this way, more appropriate monitoring can be performed by customizing the monitoring means based on the user's current living situation.

[0112] The adjustment unit can improve the monitoring method by reflecting user feedback during behavior monitoring. The adjustment unit improves the monitoring method, for example, based on feedback provided by the user. The adjustment unit can also preferentially adopt a specific monitoring method based on the user feedback. The adjustment unit can also analyze the user feedback and customize the monitoring method. For example, the adjustment unit improves the monitoring method based on feedback provided by the user. The adjustment unit can also preferentially adopt a specific monitoring method based on the user feedback. The adjustment unit can also analyze the user feedback and customize the monitoring method. In this way, the monitoring method can be improved by reflecting the user feedback.

[0113] The adjustment unit can estimate the user's emotions and determine a priority order for behavior monitoring based on the estimated user's emotions. For example, when the user is excited, the adjustment unit prioritizes monitoring active behavior. Furthermore, when the user is relaxed, the adjustment unit can also prioritize monitoring relaxing behavior. Furthermore, when the user is stressed, the adjustment unit can also prioritize monitoring behavior that helps relieve stress. For example, when the user is excited, the adjustment unit prioritizes monitoring active behavior. Furthermore, when the user is relaxed, the adjustment unit can also prioritize monitoring relaxing behavior. Furthermore, when the user is stressed, the adjustment unit can also prioritize monitoring behavior that helps relieve stress. In this way, by determining the priority order for behavior monitoring based on the user's emotions, more appropriate monitoring can be performed.

[0114] The adjustment unit can select an optimal monitoring method by taking into account the user's geographical location information when monitoring behavior. The adjustment unit, for example, suggests an optimal monitoring method based on the user's current location. The adjustment unit can also adjust the monitoring frequency and method by taking into account the user's geographical location information. The adjustment unit can also customize the level of detail of monitoring according to the user's geographical location information. For example, the adjustment unit suggests an optimal monitoring method based on the user's current location. The adjustment unit can also adjust the monitoring frequency and method by taking into account the user's geographical location information. The adjustment unit can also customize the level of detail of monitoring according to the user's geographical location information. In this way, the optimal monitoring method can be selected by taking into account the user's geographical location information.

[0115] During behavior monitoring, the adjustment unit can analyze the user's social media activities and suggest monitoring means. The adjustment unit, for example, analyzes the content of the user's posts on social media and monitors related behavior. The adjustment unit can also monitor related behavior with reference to the activities of the user's friends on social media. The adjustment unit can also suggest an optimal monitoring method based on the user's social media check-in information. For example, the adjustment unit analyzes the content of the user's posts on social media and monitors related behavior. The adjustment unit can also monitor related behavior with reference to the activities of the user's friends on social media. The adjustment unit can also suggest an optimal monitoring method based on the user's social media check-in information. In this way, the optimal monitoring means can be suggested by analyzing the user's social media activities.

[0116] The adjustment unit can customize the monitoring method by reflecting the user's past feedback during behavior monitoring. The adjustment unit, for example, suggests an optimal monitoring method based on feedback provided by the user in the past. The adjustment unit can also preferentially adopt a specific monitoring method based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the monitoring method. For example, the adjustment unit suggests an optimal monitoring method based on feedback provided by the user in the past. The adjustment unit can also preferentially adopt a specific monitoring method based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the monitoring method. In this way, the monitoring method can be customized by reflecting the user's past feedback.

[0117] The collection unit can estimate the user's emotions and adjust a method for collecting information about tourist destinations and accommodations based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting information about relaxing tourist destinations and accommodations. Furthermore, when the user is excited, the collection unit can prioritize collecting information about active tourist destinations and accommodations. Furthermore, when the user is stressed, the collection unit can prioritize collecting information about tourist destinations and accommodations that are useful for stress relief. For example, when the user is relaxed, the collection unit prioritizes collecting information about relaxing tourist destinations and accommodations. Furthermore, when the user is excited, the collection unit can prioritize collecting information about active tourist destinations and accommodations. Furthermore, when the user is stressed, the collection unit can prioritize collecting information about tourist destinations and accommodations that are useful for stress relief. In this way, by adjusting the information collection method based on the user's emotions, more appropriate information can be collected.

[0118] When collecting information, the collection unit can adjust the level of detail of collection based on the importance of tourist destinations and accommodations. For example, the collection unit collects detailed information about important tourist destinations and accommodations, and briefly collects information about other places. The collection unit can also prioritize collecting information about tourist destinations and accommodations with high importance. The collection unit can also collect detailed information about tourist destinations and accommodations according to their importance. For example, the collection unit collects detailed information about important tourist destinations and accommodations, and briefly collects information about other places. The collection unit can also prioritize collecting information about tourist destinations and accommodations with high importance. The collection unit can also collect detailed information about tourist destinations and accommodations according to their importance. In this way, by adjusting the level of detail of collection based on the importance of tourist destinations and accommodations, more appropriate information can be collected.

[0119] When collecting information, the collection unit can apply different collection algorithms depending on the category of tourist destination or accommodation facility. For example, the collection unit applies a collection algorithm that emphasizes historical background and cultural information to historical tourist destinations. The collection unit can also apply a collection algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The collection unit can also apply a collection algorithm that emphasizes shopping and entertainment information to urban tourist destinations. For example, the collection unit applies a collection algorithm that emphasizes historical background and cultural information to historical tourist destinations. The collection unit can also apply a collection algorithm that emphasizes scenery and the natural environment to tourist destinations with natural landscapes. The collection unit can also apply a collection algorithm that emphasizes shopping and entertainment information to urban tourist destinations. In this way, by applying different collection algorithms depending on the category of tourist destination or accommodation facility, more appropriate information can be collected.

[0120] When collecting information, the collection unit can determine a collection priority based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. In this way, by determining a collection priority based on the timing of when tourist destinations and accommodation facilities are offered, more appropriate information can be collected.

[0121] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is excited, the collection unit prioritizes collecting information about active tourist spots and accommodations. Furthermore, when the user is relaxed, the collection unit can prioritize collecting information about relaxing tourist spots and accommodations. Furthermore, when the user is stressed, the collection unit can prioritize collecting information about tourist spots and accommodations that are useful for stress relief. For example, when the user is excited, the collection unit prioritizes collecting information about active tourist spots and accommodations. Furthermore, when the user is relaxed, the collection unit can prioritize collecting information about relaxing tourist spots and accommodations. Furthermore, when the user is stressed, the collection unit can prioritize collecting information about tourist spots and accommodations that are useful for stress relief. In this way, by determining the priority of information to be collected based on the user's emotions, more appropriate information can be collected.

[0122] When collecting information, the collection unit can determine a collection priority based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. For example, the collection unit prioritizes collecting information about seasonal tourist destinations and accommodation facilities. The collection unit can also collect information in accordance with the timing of when specific events or festivals are held. The collection unit can also collect optimal information based on the timing of when tourist destinations and accommodation facilities are offered. In this way, by determining a collection priority based on the timing of when tourist destinations and accommodation facilities are offered, more appropriate information can be collected.

[0123] When collecting information, the collection unit can adjust the order of collection based on the relevance of tourist destinations and accommodations. The collection unit, for example, collects information on highly relevant tourist destinations and accommodations in succession. The collection unit can also collect information in an optimal order based on the relevance of tourist destinations and accommodations. The collection unit can also prioritize collecting information on highly relevant tourist destinations and accommodations. For example, the collection unit collects information on highly relevant tourist destinations and accommodations in succession. The collection unit can also collect information in an optimal order based on the relevance of tourist destinations and accommodations. The collection unit can also prioritize collecting information on highly relevant tourist destinations and accommodations. In this way, by adjusting the order of collection based on the relevance of tourist destinations and accommodations, more efficient information collection is possible.

[0124] When collecting information, the collection unit can adjust the level of detail of the information to be collected according to the user's level of expertise. For example, if the user is a beginner, the collection unit prioritizes collecting basic information. Furthermore, if the user is an intermediate user, the collection unit can also collect information with an appropriate level of detail. Furthermore, if the user is an advanced user, the collection unit can also prioritize collecting detailed information. For example, if the user is a beginner, the collection unit prioritizes collecting basic information. Furthermore, if the user is an intermediate user, the collection unit can also collect information with an appropriate level of detail. Furthermore, if the user is an advanced user, the collection unit can also prioritize collecting detailed information. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided.

[0125] The improvement unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the improvement unit requests detailed feedback when the user is relaxed. Furthermore, the improvement unit can request brief feedback when the user is in a hurry. Furthermore, the improvement unit can provide a visually stimulating feedback form when the user is excited. For example, the improvement unit requests detailed feedback when the user is relaxed. Furthermore, the improvement unit can request brief feedback when the user is in a hurry. Furthermore, the improvement unit can provide a visually stimulating feedback form when the user is excited. In this way, by adjusting the feedback collection method based on the user's emotions, more appropriate feedback can be collected.

[0126] When collecting feedback, the improvement unit can analyze the user's past feedback history and select the optimal collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback history. The improvement unit can also analyze the user's past feedback history and customize the collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback history. The improvement unit can also analyze the user's past feedback history and customize the collection method. In this way, the optimal collection method can be selected by analyzing the user's past feedback history.

[0127] When collecting feedback, the improvement unit can customize the means of collection based on the user's current living situation. The improvement unit, for example, suggests the optimal means of collection based on the user's current living situation (family composition, health condition, etc.). The improvement unit can also adjust the frequency and method of collection in consideration of the user's current living situation. The improvement unit can also customize the level of detail of collection in accordance with the user's current living situation. For example, the improvement unit suggests the optimal means of collection based on the user's current living situation (family composition, health condition, etc.). The improvement unit can also adjust the frequency and method of collection in consideration of the user's current living situation. The improvement unit can also customize the level of detail of collection in accordance with the user's current living situation. In this way, by customizing the means of collection based on the user's current living situation, more appropriate feedback can be collected.

[0128] The improvement unit can improve the collection method by reflecting the user's feedback when collecting feedback. The improvement unit improves the collection method, for example, based on the feedback provided by the user. The improvement unit can also preferentially adopt a specific collection method based on the user's feedback. The improvement unit can also analyze the user's feedback and customize the collection method. For example, the improvement unit improves the collection method based on the feedback provided by the user. The improvement unit can also preferentially adopt a specific collection method based on the user's feedback. The improvement unit can also analyze the user's feedback and customize the collection method. In this way, the collection method can be improved by reflecting the user's feedback.

[0129] The improvement unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, when the user is excited, the improvement unit prioritizes collecting active feedback. Furthermore, when the user is relaxed, the improvement unit can also prioritize collecting feedback that helps the user relax. Furthermore, when the user is feeling stressed, the improvement unit can also prioritize collecting feedback that helps the user relieve stress. For example, when the user is excited, the improvement unit prioritizes collecting active feedback. Furthermore, when the user is relaxed, the improvement unit can also prioritize collecting feedback that helps the user relax. Furthermore, when the user is feeling stressed, the improvement unit can also prioritize collecting feedback that helps the user relieve stress. In this way, by determining the priority of feedback based on the user's emotions, more appropriate feedback can be collected.

[0130] When collecting feedback, the improvement unit can select the optimal collection method by taking into account the user's geographical location information. The improvement unit, for example, suggests the optimal feedback collection method based on the user's current location. The improvement unit can also adjust the frequency and method of collection by taking into account the user's geographical location information. The improvement unit can also customize the level of detail of collection according to the user's geographical location information. For example, the improvement unit suggests the optimal feedback collection method based on the user's current location. The improvement unit can also adjust the frequency and method of collection by taking into account the user's geographical location information. The improvement unit can also customize the level of detail of collection according to the user's geographical location information. In this way, the optimal collection method can be selected by taking into account the user's geographical location information.

[0131] When collecting feedback, the improvement unit can analyze the user's social media activity and suggest a means of collection. For example, the improvement unit analyzes the content of the user's posts on social media and collects related feedback. The improvement unit can also collect related feedback by referring to the activities of the user's friends on social media. The improvement unit can also suggest an optimal feedback collection method based on the user's social media check-in information. For example, the improvement unit analyzes the content of the user's posts on social media and collects related feedback. The improvement unit can also collect related feedback by referring to the activities of the user's friends on social media. The improvement unit can also suggest an optimal feedback collection method based on the user's social media check-in information. In this way, the optimal collection method can be suggested by analyzing the user's social media activity.

[0132] When collecting feedback, the improvement unit can customize the collection method by reflecting the user's past feedback. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback. The improvement unit can also analyze the user's past feedback and customize the collection method. For example, the improvement unit suggests the optimal collection method based on feedback provided by the user in the past. The improvement unit can also preferentially adopt a specific collection method based on the user's past feedback. The improvement unit can also analyze the user's past feedback and customize the collection method. In this way, the collection method can be customized by reflecting the user's past feedback.

[0133] The information acquisition unit can estimate the user's emotions and adjust the method of acquiring weather information and traffic information based on the estimated user's emotions. For example, the information acquisition unit can provide detailed weather information and traffic information when the user is relaxed. Furthermore, the information acquisition unit can provide concise weather information and traffic information when the user is in a hurry. Furthermore, the information acquisition unit can provide weather information and traffic information with visually stimulating effects when the user is excited. For example, the information acquisition unit can provide detailed weather information and traffic information when the user is relaxed. Furthermore, the information acquisition unit can provide concise weather information and traffic information when the user is in a hurry. Furthermore, the information acquisition unit can provide weather information and traffic information with visually stimulating effects when the user is excited. In this way, by adjusting the information acquisition method based on the user's emotions, more appropriate information can be provided.

[0134] When acquiring information, the information acquisition unit can adjust the level of detail of the acquired information based on the importance of weather information and traffic information. For example, the information acquisition unit acquires important weather information and traffic information in detail, and acquires other information in a concise manner. The information acquisition unit can also prioritize acquiring weather information and traffic information with high importance. The information acquisition unit can also adjust the level of detail of the weather information and traffic information according to the importance. For example, the information acquisition unit acquires important weather information and traffic information in detail, and acquires other information in a concise manner. The information acquisition unit can also prioritize acquiring weather information and traffic information with high importance. The information acquisition unit can also adjust the level of detail of the weather information and traffic information according to the importance. In this way, by adjusting the level of detail of the acquired weather information and traffic information based on the importance of the weather information and traffic information, more appropriate information can be provided.

[0135] When acquiring information, the information acquisition unit can apply different acquisition algorithms depending on the category of weather information or traffic information. For example, the information acquisition unit applies an acquisition algorithm that prioritizes weather forecasts and temperature information to weather information. The information acquisition unit can also apply an acquisition algorithm that prioritizes traffic congestion information and the operation status of public transportation to traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the period when a specific event or festival is held. For example, the information acquisition unit applies an acquisition algorithm that prioritizes weather forecasts and temperature information to weather information. The information acquisition unit can also apply an acquisition algorithm that prioritizes traffic congestion information and the operation status of public transportation to traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the period when a specific event or festival is held. In this way, by applying different acquisition algorithms depending on the category of weather information and traffic information, more appropriate information can be provided.

[0136] When acquiring information, the information acquisition unit can determine the priority of acquisition based on the time when weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. In this way, by determining the priority of acquisition based on the time when the weather information and traffic information are provided, more appropriate information can be provided.

[0137] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. For example, when the user is excited, the information acquisition unit prioritizes acquiring active weather information and traffic information. Furthermore, when the user is relaxed, the information acquisition unit can prioritize acquiring relaxing weather information and traffic information. Furthermore, when the user is stressed, the information acquisition unit can prioritize acquiring weather information and traffic information that is useful for stress relief. For example, when the user is excited, the information acquisition unit prioritizes acquiring active weather information and traffic information. Furthermore, when the user is relaxed, the information acquisition unit can prioritize acquiring relaxing weather information and traffic information. Furthermore, when the user is stressed, the information acquisition unit can prioritize acquiring weather information and traffic information that is useful for stress relief. In this way, by determining the priority of information to be acquired based on the user's emotions, more appropriate information can be provided.

[0138] When acquiring information, the information acquisition unit can determine the priority of acquisition based on the time when weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. For example, the information acquisition unit prioritizes acquisition of seasonal weather information and traffic information. The information acquisition unit can also acquire weather information and traffic information in accordance with the time when a specific event or festival is held. The information acquisition unit can also acquire optimal information based on the time when the weather information and traffic information are provided. In this way, by determining the priority of acquisition based on the time when the weather information and traffic information are provided, more appropriate information can be provided.

[0139] When acquiring information, the information acquisition unit can adjust the acquisition order based on the relevance of weather information and traffic information. For example, the information acquisition unit continuously acquires highly relevant weather information and traffic information. The information acquisition unit can also acquire information in an optimal order based on the relevance of the weather information and traffic information. The information acquisition unit can also prioritize acquiring highly relevant weather information and traffic information. For example, the information acquisition unit continuously acquires highly relevant weather information and traffic information. The information acquisition unit can also acquire information in an optimal order based on the relevance of the weather information and traffic information. The information acquisition unit can also prioritize acquiring highly relevant weather information and traffic information. In this way, by adjusting the acquisition order based on the relevance of the weather information and traffic information, more efficient information acquisition is possible.

[0140] When acquiring information, the information acquisition unit can adjust the level of detail of the information to be acquired according to the user's level of expertise. For example, if the user is a beginner, the information acquisition unit prioritizes acquiring basic weather information and traffic information. Furthermore, if the user is an intermediate user, the information acquisition unit can also acquire weather information and traffic information with an appropriate level of detail. Furthermore, if the user is an advanced user, the information acquisition unit can also prioritize acquiring detailed weather information and traffic information. For example, if the user is a beginner, the information acquisition unit prioritizes acquiring basic weather information and traffic information. Furthermore, if the user is an intermediate user, the information acquisition unit can also acquire weather information and traffic information with an appropriate level of detail. Furthermore, if the user is an advanced user, the information acquisition unit can also prioritize acquiring detailed weather information and traffic information. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided.

[0141] The privacy protection unit may estimate a user's emotion and adjust a privacy protection method based on the estimated user's emotion. For example, the privacy protection unit may provide detailed privacy protection settings when the user is relaxed. Also, the privacy protection unit may provide simple privacy protection settings when the user is in a hurry. Also, the privacy protection unit may provide privacy protection settings with visually stimulating effects when the user is excited. For example, the privacy protection unit may provide detailed privacy protection settings when the user is relaxed. Also, the privacy protection unit may provide simple privacy protection settings when the user is in a hurry. Also, the privacy protection unit may provide privacy protection settings with visually stimulating effects when the user is excited. In this way, by adjusting the privacy protection method based on the user's emotion, more appropriate privacy protection can be provided.

[0142] During privacy protection, the privacy protection unit can select an optimal protection method by analyzing the user's past privacy setting history. For example, the privacy protection unit suggests an optimal protection method based on the privacy protection settings previously set by the user. The privacy protection unit can also preferentially adopt a specific protection method based on the user's past privacy setting history. The privacy protection unit can also analyze the user's past privacy setting history and customize the protection method. For example, the privacy protection unit suggests an optimal protection method based on the privacy protection settings previously set by the user. The privacy protection unit can also preferentially adopt a specific protection method based on the user's past privacy setting history. The privacy protection unit can also analyze the user's past privacy setting history and customize the protection method. In this way, the optimal protection method can be selected by analyzing the user's past privacy setting history.

[0143] The privacy protection unit may estimate a user's emotion and determine a priority of privacy protection based on the estimated user's emotion. For example, if the user is excited, the privacy protection unit may preferentially provide an active privacy protection setting. Also, if the user is relaxed, the privacy protection unit may preferentially provide a privacy protection setting that helps to relax. Also, if the user is stressed, the privacy protection unit may preferentially provide a privacy protection setting that helps to relieve stress. For example, if the user is excited, the privacy protection unit may preferentially provide an active privacy protection setting. Also, if the user is relaxed, the privacy protection unit may preferentially provide a privacy protection setting that helps to relax. Also, if the user is stressed, the privacy protection unit may preferentially provide a privacy protection setting that helps to relieve stress. In this way, by determining the priority of privacy protection based on the user's emotion, more appropriate privacy protection can be provided.

[0144] The privacy protection unit can select an optimal protection method during privacy protection by taking into account the user's geographical location information. The privacy protection unit, for example, suggests an optimal privacy protection method based on the user's current location. The privacy protection unit can also adjust the frequency and method of protection by taking into account the user's geographical location information. The privacy protection unit can also customize the level of detail of protection according to the user's geographical location information. For example, the privacy protection unit suggests an optimal privacy protection method based on the user's current location. The privacy protection unit can also adjust the frequency and method of protection by taking into account the user's geographical location information. The privacy protection unit can also customize the level of detail of protection according to the user's geographical location information. In this way, the optimal protection method can be selected by taking into account the user's geographical location information.

[0145] The data integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user's emotions. For example, when the user is relaxed, the data integration unit provides a detailed data integration method. Furthermore, when the user is in a hurry, the data integration unit can provide a simple data integration method. Furthermore, when the user is excited, the data integration unit can provide a data integration method that adds a visually stimulating effect. For example, when the user is relaxed, the data integration unit provides a detailed data integration method. Furthermore, when the user is in a hurry, the data integration unit can provide a simple data integration method. Furthermore, when the user is excited, the data integration unit can provide a data integration method that adds a visually stimulating effect. In this way, by adjusting the data integration method based on the user's emotions, more appropriate data integration can be provided.

[0146] When integrating data, the data integration unit can adjust the level of detail of integration based on the importance of multiple data sources. For example, the data integration unit integrates information from important data sources in detail and integrates other information briefly. The data integration unit can also prioritize integration of data sources with high importance. The data integration unit can also integrate information from data sources in detail according to their importance. For example, the data integration unit integrates information from important data sources in detail and integrates other information briefly. The data integration unit can also prioritize integration of data sources with high importance. The data integration unit can also integrate information from data sources in detail according to their importance. In this way, by adjusting the level of detail of integration based on the importance of multiple data sources, more appropriate data integration can be provided.

[0147] When integrating data, the data integration unit can apply different integration algorithms depending on the category of the data source. For example, the data integration unit applies an integration algorithm that emphasizes weather forecasts and temperature information to weather information. The data integration unit can also apply an integration algorithm that emphasizes congestion information and the operation status of public transportation to traffic information. The data integration unit can also apply an integration algorithm that emphasizes tourist spot and event information to tourist destination information. For example, the data integration unit applies an integration algorithm that emphasizes weather forecasts and temperature information to weather information. The data integration unit can also apply an integration algorithm that emphasizes congestion information and the operation status of public transportation to traffic information. The data integration unit can also apply an integration algorithm that emphasizes tourist spot and event information to tourist destination information. In this way, by applying different integration algorithms depending on the category of the data source, more appropriate data integration can be provided.

[0148] When integrating data, the data integration unit can determine the priority of integration based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. In this way, by determining the priority of integration based on the time when the data sources are provided, more appropriate data integration can be provided.

[0149] The data integration unit can estimate the user's emotions and determine the priority of data to be integrated based on the estimated user's emotions. For example, when the user is excited, the data integration unit prioritizes integration of active data. Furthermore, when the user is relaxed, the data integration unit can also prioritize integration of data that helps to relax. Furthermore, when the user is stressed, the data integration unit can also prioritize integration of data that helps to relieve stress. For example, when the user is excited, the data integration unit prioritizes integration of active data. Furthermore, when the user is relaxed, the data integration unit can also prioritize integration of data that helps to relax. Furthermore, when the user is stressed, the data integration unit can also prioritize integration of data that helps to relieve stress. In this way, by determining the priority of data to be integrated based on the user's emotions, more appropriate data integration can be provided.

[0150] When integrating data, the data integration unit can determine the priority of integration based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. For example, the data integration unit prioritizes integration of seasonal data sources. The data integration unit can also integrate data according to the time when a specific event or festival is held. The data integration unit can also integrate optimal data based on the time when the data sources are provided. In this way, by determining the priority of integration based on the time when the data sources are provided, more appropriate data integration can be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, adjustment unit, collection unit, improvement unit, information acquisition unit, privacy protection unit, and data integration unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's travel wishes and interests using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan suitable for the user using a generation AI. The adjustment unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and monitors the user's behavior during the trip and adjusts the plan as necessary. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on tourist spots and accommodations. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the plan. The information acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather information and traffic information. The privacy protection unit is implemented by a specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The data integration unit is implemented by a specific processing unit 290 of the data processing device 12 and integrates multiple data sources. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, adjustment unit, collection unit, improvement unit, information acquisition unit, privacy protection unit, and data integration unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's travel wishes and interests using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan suitable for the user using a generation AI. The adjustment unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and monitors behavior during the trip and adjusts the plan as necessary. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on tourist spots and accommodations. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the plan. The information acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather information and traffic information. The privacy protection unit is implemented by a specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The data integration unit is implemented by a specific processing unit 290 of the data processing device 12 and integrates multiple data sources. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, adjustment unit, collection unit, improvement unit, information acquisition unit, privacy protection unit, and data integration unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the user's travel wishes and interests using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan suitable for the user using a generation AI. The adjustment unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and monitors behavior during the trip and adjusts the plan as necessary. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on tourist spots and accommodations. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the plan. The information acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather information and traffic information. The privacy protection unit is implemented by a specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The data integration unit is implemented by a specific processing unit 290 of the data processing device 12 and integrates multiple data sources. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, adjustment unit, collection unit, improvement unit, information acquisition unit, privacy protection unit, and data integration unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's travel wishes and interests using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a travel plan suitable for the user using a generation AI. The adjustment unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and monitors the user's behavior during the trip and adjusts the plan as necessary. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on tourist spots and accommodations. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve the plan. The information acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires weather and traffic information. The privacy protection unit is implemented by a specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The data integration unit is implemented by a specific processing unit 290 of the data processing device 12 and integrates multiple data sources.

[0151] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0152] When the user inputs their travel preferences and interests, the reception unit can analyze the user's past travel history and suggest similar travel plans. For example, the reception unit can suggest travel plans with similar themes or regions based on tourist spots and accommodations the user has visited in the past. The reception unit can also suggest related plans by taking into account the activities and types of meals the user has previously preferred. Furthermore, the reception unit can suggest plans related to specific seasons or events based on the user's past travel history. This makes it possible to provide more personalized travel plans by utilizing the user's past travel history.

[0153] When collecting information about tourist attractions and accommodations, the collection unit can analyze the user's social media activities and prioritize collection of related information. For example, the collection unit can collect information about related tourist attractions and accommodations based on the places the user has checked in to and photos posted on social media. The collection unit can also collect information about places visited by the user's friends and facilities rated by the user, and provide this information to the user. Furthermore, the collection unit can analyze the user's interests and concerns on social media and collect information based on this. This makes it possible to utilize the user's social media activities to provide more relevant information.

[0154] When collecting user feedback and improving the plan, the improvement unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback can be requested. If the user is in a hurry, brief feedback can be requested. Furthermore, if the user is excited, a visually stimulating feedback form can be provided. In this way, by adjusting the feedback collection method according to the user's emotions, more appropriate feedback can be collected and used to improve the plan.

[0155] When acquiring weather information or traffic information, the information acquisition unit can adjust the method of acquiring information taking into account the user's current living situation. For example, if the user is planning a family trip, weather information and traffic information for the family can be acquired preferentially. Also, if the user is concerned about their health, health-conscious information can be provided. Furthermore, if the user plans to participate in a specific event, information related to that event can be acquired preferentially. In this way, by adjusting the method of acquiring information based on the user's current living situation, more appropriate information can be provided.

[0156] When protecting the user's privacy, the privacy protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated emotions. For example, if the user is relaxed, detailed privacy protection settings can be provided. If the user is in a hurry, simple privacy protection settings can be provided. Furthermore, if the user is excited, privacy protection settings with visually stimulating effects can be provided. In this way, by adjusting the privacy protection method based on the user's emotions, more appropriate privacy protection can be provided.

[0157] When integrating multiple data sources, the data integration unit can determine the priority of integration based on the time of data source provision. For example, seasonal data sources can be integrated with priority. Data can also be integrated according to the time of a particular event or festival. Furthermore, optimal data can be integrated based on the time of data source provision. Thus, by determining the priority of integration based on the time of data source provision, more appropriate data integration can be provided.

[0158] The reception unit can estimate the user's emotions and adjust the input method for travel wishes and interests based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input travel wishes and interests. In this way, by adjusting the input method according to the user's emotions, a more comfortable input experience can be provided.

[0159] When generating a travel plan, the generation unit can apply different generation algorithms depending on the category of tourist destinations and accommodations. For example, a generation algorithm that emphasizes historical background and cultural information can be applied to historical tourist destinations. Also, a generation algorithm that emphasizes scenery and the natural environment can be applied to tourist destinations with natural landscapes. Furthermore, a generation algorithm that emphasizes shopping and entertainment information can be applied to urban tourist destinations. In this way, by applying different generation algorithms depending on the category of tourist destinations and accommodations, it is possible to provide more appropriate plans.

[0160] The adjustment unit can estimate the user's emotions and adjust the criteria for behavior monitoring during the trip based on the estimated emotions. For example, if the user is relaxed, behavior monitoring can be performed at a leisurely pace. If the user is in a hurry, behavior monitoring can be performed quickly. Furthermore, if the user is excited, detailed behavior monitoring can be performed. In this way, by adjusting the criteria for behavior monitoring based on the user's emotions, more appropriate monitoring can be performed.

[0161] When collecting information, the collection unit can apply different collection algorithms depending on the category of tourist destination or accommodation. For example, a collection algorithm that emphasizes historical background and cultural information can be applied to a historical tourist destination. Also, a collection algorithm that emphasizes scenery and the natural environment can be applied to a tourist destination with natural scenery. Furthermore, a collection algorithm that emphasizes shopping and entertainment information can be applied to an urban tourist destination. In this way, by applying different collection algorithms depending on the category of tourist destination or accommodation, more appropriate information can be collected.

[0162] The processing flow of the second embodiment will be briefly explained below.

[0163] Step 1: The reception unit inputs the user's travel wishes and interests. The user's travel wishes and interests include, for example, the places they want to visit, their budget, and the purpose of their trip. The user inputs wishes such as "I want to go to a hot spring," "I want to learn about Japanese history," or "I want to eat delicious sushi." Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a travel plan suitable for the user. The generation AI uses natural language processing and machine learning algorithms to select tourist spots based on the user's preferences and plan within the user's budget. It generates a plan that suits the user's preferences based on data such as tourist spots, accommodation, transportation, and places to eat. Step 3: The adjustment unit monitors the user's behavior during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. Monitoring is performed using methods such as GPS tracking and collecting behavior logs. If the user finishes visiting tourist spots earlier than planned, the system will suggest moving on to the next tourist spot. The plan can also be flexibly changed depending on changes in weather and traffic conditions.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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."

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Explanation of symbols]

[0236] 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 reception unit for inputting the user's travel preferences or interests; a generation unit that analyzes the information input by the reception unit and generates a travel plan suitable for the user; an adjustment unit that monitors behavior during the trip based on the plan generated by the generation unit and adjusts the plan as necessary. A system characterized by:

2. Equipped with a collection department that collects information on tourist destinations and accommodations 2. The system of claim 1.

3. Have an improvement department that collects user feedback and improves the plan 2. The system of claim 1.

4. An information acquisition unit that acquires weather information or traffic information is provided.

2. The system of claim 1.

5. Equipped with a privacy protection unit that protects user privacy 2. The system of claim 1.

6. Equipped with a data integration section that integrates multiple data sources 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input method of travel wishes and interests based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and suggests the most appropriate input method 2. The system of claim 1.

9. The reception unit Filtering travel preferences or interests based on the user's current life situation or areas of interest 2. The system of claim 1.

Images