system

Abstract

The system according to the embodiment aims to propose sightseeing plans based on the user's emotions and increase the revenue of tourist destinations. [Solution] The system according to the embodiment comprises a reception unit, an information provision unit, an empathy unit, a proposal unit, and an income expansion unit. The reception unit accepts user input. The information provision unit provides information about tourist destinations based on the information accepted by the reception unit. The empathy unit understands and empathizes with the user's emotions based on the information provided by the information provision unit. The proposal unit proposes a sightseeing plan based on the user's preferences based on the emotions understood by the empathy unit. The income expansion unit increases the tourist destination's income based on the sightseeing plan proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide information about tourist destinations or suggest sightseeing plans based on the user's emotions, so there is room for improvement.

[0005] The system according to the embodiment aims to propose sightseeing plans based on the user's emotions and increase the revenue of tourist destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an information provision unit, an empathy unit, a proposal unit, and an income expansion unit. The reception unit accepts user input. The information provision unit provides information about tourist destinations based on the information accepted by the reception unit. The empathy unit understands and empathizes with the user's emotions based on the information provided by the information provision unit. The proposal unit proposes a sightseeing plan based on the user's preferences, based on the emotions understood by the empathy unit. The income expansion unit increases the tourist destination's income based on the sightseeing plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose sightseeing plans based on the user's emotions and increase the revenue of tourist destinations. [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 tourism experience improvement system according to an embodiment of the present invention accepts user input, provides information about tourist destinations, understands and empathizes with the user's feelings, proposes sightseeing plans tailored to the user's preferences, and contributes to increasing the tourist destination's revenue. When a user arrives at a tourist destination, an AI character provides detailed information about the destination. The AI ​​character then empathizes with the user's experience and shares the user's feelings through dialogue. Furthermore, the AI ​​character proposes sightseeing plans tailored to the user's preferences. The AI ​​character also contributes to increasing the tourist destination's revenue. For example, by providing information about the tourist destination's local specialties and souvenirs and encouraging their purchases, the AI ​​character contributes to economic revitalization of the tourist destination. This allows the tourism experience improvement system to provide users with new entertainment and provide tourist destinations with opportunities to increase revenue. For example, the AI ​​character allows users to enjoy the attractions of the tourist destination to the fullest, contributing to economic revitalization of the tourist destination.

[0029] A tourism experience improvement system according to an embodiment includes a reception unit, an information provision unit, an empathy unit, a suggestion unit, and an income expansion unit. The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and image input. For example, the reception unit allows a user to perform voice input using a smartphone. The reception unit also allows a user to perform text input using a keyboard. The reception unit also allows a user to perform image input using a camera. The information provision unit provides information about tourist destinations based on the information accepted by the reception unit. Examples of tourist destination information include, but are not limited to, tourist spots, accommodations, and transportation information. For example, the information provision unit provides information about the historical background of the tourist destination, recommended tourist spots, and local gourmet food. The information provision unit can also provide information about local specialties and souvenirs of the tourist destination. The information provision unit can also provide information about events at the tourist destination. The empathy unit understands and empathizes with the user's emotions based on the information provided by the information provision unit. For example, the empathy unit understands the user's emotions using voice recognition technology. The empathy unit can also understand the user's emotions using facial expression recognition technology. The empathy unit can also express empathy based on the user's emotions. The suggestion unit proposes a sightseeing plan tailored to the user's preferences based on the emotions understood by the empathy unit. The sightseeing plan may include, but is not limited to, an itinerary, destinations, and activities. For example, the suggestion unit can propose a plan touring historical sites to a history-loving user. The suggestion unit can also propose a plan to enjoy delicious local cuisine to a gourmet user. The suggestion unit can also propose a plan touring natural landscapes to a user who wants to enjoy natural scenery. The income expansion unit contributes to increasing the income of the tourist destination based on the sightseeing plan proposed by the suggestion unit. For example, the income expansion unit provides information on local specialties and souvenirs, issues coupons, and provides event information. The income expansion unit can also promote the purchase of local specialties and souvenirs of the tourist destination. The income expansion unit can also promote participation in events at the tourist destination.As a result, the tourism experience improvement system according to the embodiment can accept user input and contribute to providing information about tourist destinations, empathizing with emotions, proposing tourism plans, and increasing revenue.

[0030] The empathy unit can understand the user's emotions using voice recognition or facial expression recognition technology. The empathy unit can understand the user's emotions using, for example, voice recognition technology. For example, the empathy unit can analyze the tone and speed of the user's voice to estimate the emotions. The empathy unit can also understand the user's emotions using facial expression recognition technology. For example, the empathy unit can analyze changes in the user's facial expression to estimate the emotions. The empathy unit can also understand the user's emotions by combining voice recognition technology and facial expression recognition technology. For example, the empathy unit can simultaneously analyze the tone of the user's voice and changes in facial expression to estimate the emotions. In this way, the user's emotions can be understood more accurately by using voice recognition or facial expression recognition technology.

[0031] The revenue expansion unit can provide information on local products or souvenirs of the tourist destination, issue coupons, and provide event information. The revenue expansion unit, for example, provides information on local products or souvenirs of the tourist destination. For example, the revenue expansion unit provides detailed information on local products and souvenirs. The revenue expansion unit can also provide methods for purchasing local products and souvenirs. The revenue expansion unit can also issue coupons. For example, the revenue expansion unit issues discount coupons for specific products. The revenue expansion unit can also provide event information. For example, the revenue expansion unit provides information such as the date, time, location, and participation methods of events held at the tourist destination. In this way, by providing information on local products and souvenirs of the tourist destination, issuing coupons, and providing event information, the revenue of the tourist destination can be increased.

[0032] The suggestion unit can suggest a sightseeing plan based on the user's preferences. The suggestion unit, for example, suggests a sightseeing plan based on the user's preferences. For example, the suggestion unit can suggest a plan touring historical sites to a user who loves history. The suggestion unit can also suggest a plan to enjoy delicious local cuisine to a user who loves gourmet food. The suggestion unit can also suggest a plan touring natural scenery to a user who wants to enjoy natural scenery. In this way, suggesting a sightseeing plan that suits the user's preferences improves user satisfaction.

[0033] The information providing unit can provide information on the historical background of the tourist destination, recommended tourist spots, and local gourmet food. The information providing unit, for example, provides the historical background of the tourist destination. For example, the information providing unit provides information on historical events and cultural heritage of the tourist destination. The information providing unit can also provide recommended tourist spots. For example, the information providing unit provides information on popular spots and easily accessible spots in the tourist destination. The information providing unit can also provide local gourmet food information. For example, the information providing unit provides information on the characteristics of local restaurants and cuisine. In this way, the historical background of the tourist destination, recommended tourist spots, and local gourmet food information are provided, thereby improving the user's sightseeing experience.

[0034] The reception unit can anonymize data and implement security measures when receiving user input. For example, the reception unit anonymizes data when receiving user input. For example, the reception unit deletes the user's personal information and anonymizes the data. The reception unit can also implement security measures for the data. For example, the reception unit encrypts the data to prevent unauthorized access. The reception unit can also perform access control to ensure data security. In this way, the user's privacy is protected by anonymizing the data and implementing security measures.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also automatically complement related information by referring to content that the user has input in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving input. For example, the reception unit preferentially displays information about related tourist spots based on the user's current location. The reception unit can also filter and provide related information based on the user's areas of interest (history, gourmet food, etc.). The reception unit can also select optimal information based on the user's current situation (time of day, weather, etc.). In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0037] The reception unit can select the optimal reception means based on the user's input method when receiving input. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving input. For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, the reception unit can accept the input using a keyboard or touch panel. Also, if the user selects image input, the reception unit can accept the input using image recognition technology. In this way, convenience for the user is improved by selecting the optimal reception means according to the user's input method.

[0038] The reception unit can preferentially receive highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit preferentially receives highly relevant information taking into account the user's geographical location information when receiving input. For example, the reception unit preferentially receives information about nearby tourist spots based on the user's current location. The reception unit can also preferentially receive related event information based on the user's geographical location information. The reception unit can also preferentially receive optimal information taking into account the distance from the user's current location. In this way, highly relevant information can be provided preferentially by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving input. For example, the reception unit analyzes the user's social media activity and receives related information when receiving input. For example, the reception unit preferentially receives information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0040] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit adjusts the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] The information providing unit can determine the level of detail of the information based on the importance of the tourist destination when providing the information. For example, the information providing unit adjusts the level of detail of the information based on the importance of the tourist destination when providing the information. For example, the information providing unit provides detailed information about important tourist destinations. The information providing unit can also provide concise information about less important tourist destinations. The information providing unit can also adjust the level of detail of the information in stages depending on the importance of the tourist destination. In this way, by adjusting the level of detail of the information based on the importance of the tourist destination, it is possible to provide information that is important to the user.

[0042] The information providing unit can apply different information provision algorithms based on the category of the tourist destination when providing information. For example, the information providing unit applies different information provision algorithms based on the category of the tourist destination when providing information. For example, the information providing unit can provide information about historical tourist destinations using an algorithm that emphasizes historical background. Furthermore, the information providing unit can provide information about gourmet food using an algorithm that emphasizes the characteristics of cuisine and recommended menus. Furthermore, the information providing unit can provide information about natural landscapes using an algorithm that emphasizes the beauty of the scenery and the highlights. In this way, by applying different information provision algorithms depending on the category of the tourist destination, more appropriate information can be provided.

[0043] The information providing unit can improve the accuracy of information when providing information based on the user's past information provision results. For example, when providing information, the information providing unit improves the accuracy of information by referring to the user's past information provision results. For example, the information providing unit improves the accuracy of information based on feedback provided by the user on information provided in the past. The information providing unit can also analyze the user's past information provision results and provide optimal information. The information providing unit can also improve the information provision algorithm by referring to the user's past information provision results. In this way, the accuracy of information is improved by referring to the user's past information provision results.

[0044] The information providing unit can set the priority of information based on the time of visiting a tourist destination when providing information. For example, the information providing unit determines the priority of information based on the time of visiting a tourist destination when providing information. For example, the information providing unit can provide optimal information preferentially depending on the time of visiting a tourist destination. The information providing unit can also provide information about seasonal attractions and events preferentially. The information providing unit can also provide related information preferentially based on the time of visiting a tourist destination. In this way, optimal information can be provided by determining the priority of information based on the time of visiting a tourist destination.

[0045] The information providing unit can set the order of information based on the relevance of tourist destinations when providing information. For example, the information providing unit adjusts the order of information based on the relevance of tourist destinations when providing information. For example, the information providing unit prioritizes providing the most relevant information based on the relevance of tourist destinations. The information providing unit can also adjust the order of information based on the relevance of tourist destinations. The information providing unit can also group and provide related information based on the relevance of tourist destinations. In this way, by adjusting the order of information based on the relevance of tourist destinations, highly relevant information can be provided.

[0046] The information providing unit can adjust the use of technical terms in the information based on the user's level of expertise when providing information. For example, the information providing unit adjusts the use of technical terms in the information based on the user's level of expertise when providing information. For example, if the user has technical knowledge, the information providing unit can provide information that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the information providing unit can also provide information that avoids technical terms. Furthermore, the information providing unit can adjust the use of technical terms in the information based on the user's level of expertise. In this way, by adjusting the use of technical terms in the information based on the user's level of expertise, it is possible to provide information that is easier to understand.

[0047] The empathy unit can improve the accuracy of empathy based on the user's past experiences when empathizing. For example, the empathy unit improves the accuracy of empathy by referring to the user's past experiences when empathizing. For example, the empathy unit selects words of empathy based on events the user experienced in the past. The empathy unit can also improve the accuracy of empathy by referring to the user's past experiences. The empathy unit can also analyze the user's past experiences and provide optimal words of empathy. In this way, the accuracy of empathy is improved by referring to the user's past experiences.

[0048] The empathy unit can adjust the means of empathy based on the user's current situation when empathizing. For example, the empathy unit customizes the means of empathy based on the user's current situation when empathizing. For example, the empathy unit selects the optimal means of empathy based on the user's current location. The empathy unit can also customize the means of empathy based on the user's current situation (time of day, weather, etc.). The empathy unit can also select the means of empathy taking the user's current situation into consideration. In this way, by customizing the means of empathy based on the user's current situation, more appropriate empathy can be provided.

[0049] The empathy unit can select an optimal empathy method based on the user's geographical location information when empathizing. For example, the empathy unit selects an optimal empathy method by taking into account the user's geographical location information when empathizing. For example, if the user is at a tourist spot, the empathy unit can provide empathy words related to the location. Also, if the user is at home, the empathy unit can provide empathy words that will help the user relax. Also, if the user is on the move, the empathy unit can provide concise and to-the-point empathy words. In this way, the optimal empathy method can be provided by taking into account the user's geographical location information.

[0050] The empathy unit can provide a means of empathy by analyzing the user's social media activity when the user empathizes. For example, the empathy unit can analyze the user's social media activity when the user empathizes and suggest a means of empathy. For example, the empathy unit can provide words of empathy based on photos the user shared on social media. The empathy unit can also analyze the content of the user's social media posts and provide related words of empathy. The empathy unit can also suggest a means of empathy by referring to the activities of the user's friends on social media. In this way, related means of empathy can be provided by analyzing the user's social media activity.

[0051] The empathy unit can adjust the method of empathy by reflecting the user's past feedback when empathizing. For example, the empathy unit customizes the method of empathy by reflecting the user's past feedback when empathizing. For example, the empathy unit customizes the method of empathy based on feedback provided by the user in the past. The empathy unit can also improve the accuracy of empathy by reflecting the user's past feedback. The empathy unit can also analyze the user's past feedback and improve the method of empathy. In this way, the accuracy of empathy is improved by reflecting the user's past feedback.

[0052] The suggestion unit can determine the level of detail of the proposal based on the importance of the sightseeing plan when making the suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the sightseeing plan when making the suggestion. For example, the suggestion unit can make detailed suggestions for important sightseeing plans. The suggestion unit can also make brief suggestions for less important sightseeing plans. The suggestion unit can also gradually adjust the level of detail of the proposal depending on the importance of the sightseeing plan. In this way, by adjusting the level of detail of the proposal based on the importance of the sightseeing plan, it is possible to provide suggestions that are important to the user.

[0053] The suggestion unit can apply different suggestion algorithms based on the category of the sightseeing plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms based on the category of the sightseeing plan when making a suggestion. For example, the suggestion unit can make suggestions related to historical sightseeing plans using an algorithm that emphasizes historical background. Furthermore, the suggestion unit can make suggestions related to gourmet food using an algorithm that emphasizes the characteristics of cuisine and recommended menus. Furthermore, the suggestion unit can make suggestions related to natural landscapes using an algorithm that emphasizes the beauty of the scenery and the highlights. In this way, by applying different suggestion algorithms depending on the category of the sightseeing plan, more appropriate suggestions can be provided.

[0054] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion based on feedback the user has given on suggestions received in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. The suggestion unit can also improve the suggestion algorithm by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0055] The suggestion unit can set the priority of the proposals based on the time of submission of the sightseeing plan when making the proposal. For example, the suggestion unit determines the priority of the proposals based on the time of submission of the sightseeing plan when making the proposal. For example, the suggestion unit can prioritize the most suitable proposal depending on the time of submission of the sightseeing plan. The suggestion unit can also prioritize the proposal of seasonal attractions and event information. The suggestion unit can also prioritize the related proposals based on the time of submission of the sightseeing plan. In this way, by determining the priority of the proposals based on the time of submission of the sightseeing plan, the most suitable proposals can be provided.

[0056] The suggestion unit can set the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit prioritizes the most relevant suggestions based on the relevance of the sightseeing plans. The suggestion unit can also adjust the order of suggestions based on the relevance of the sightseeing plans. The suggestion unit can also group related suggestions based on the relevance of the sightseeing plans. In this way, by adjusting the order of suggestions based on the relevance of the sightseeing plans, highly relevant suggestions can be provided.

[0057] The suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a proposal that avoids technical terminology. Also, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal based on the user's level of expertise, it is possible to provide a proposal that is easier to understand.

[0058] The income expansion unit can improve the accuracy of providing information about local specialties and souvenirs of tourist destinations when income is expanded. For example, the income expansion unit improves the accuracy of providing information about local specialties and souvenirs of tourist destinations when income is expanded. For example, the income expansion unit provides detailed information about local specialties and souvenirs of tourist destinations. The income expansion unit can also provide detailed instructions on how to purchase local specialties and souvenirs of tourist destinations. The income expansion unit can also reflect user feedback to improve the accuracy of providing information about local specialties and souvenirs of tourist destinations. This improves the accuracy of providing information about local specialties and souvenirs of tourist destinations, thereby enhancing the effect of income expansion.

[0059] The income expansion unit can analyze the event information of the tourist destination to determine the optimal income expansion method when expanding income. For example, the income expansion unit analyzes the event information of the tourist destination to select the optimal income expansion method when expanding income. For example, the income expansion unit proposes the optimal income expansion method based on the event information of the tourist destination. The income expansion unit can also analyze the event information of the tourist destination to select the income expansion method. The income expansion unit can also improve the income expansion method by referring to the event information of the tourist destination. In this way, the optimal income expansion method can be selected by analyzing the event information of the tourist destination.

[0060] The income expansion unit can adjust the income expansion method by reflecting user feedback when expanding income. For example, the income expansion unit improves the income expansion method by reflecting user feedback when expanding income. For example, the income expansion unit improves the income expansion method based on feedback previously provided by the user. The income expansion unit can also improve the accuracy of income expansion by reflecting user feedback. The income expansion unit can also analyze user feedback and improve the income expansion method. In this way, the income expansion method can be improved by reflecting user feedback.

[0061] The income expansion unit can select an optimal income expansion method based on the user's geographical location information when expanding income. For example, the income expansion unit selects an optimal income expansion method by taking into account the user's geographical location information when expanding income. For example, when the user is in a tourist spot, the income expansion unit can suggest income expansion methods related to the location. Furthermore, when the user is at home, the income expansion unit can provide information on local products and souvenirs that can be purchased online. Furthermore, when the user is on the move, the income expansion unit can suggest a concise and to-the-point income expansion method. In this way, the optimal income expansion method can be provided by taking into account the user's geographical location information.

[0062] The income expansion unit can provide means for increasing income by analyzing the user's social media activity during income expansion. For example, the income expansion unit can analyze the user's social media activity during income expansion to suggest means for increasing income. For example, the income expansion unit can suggest means for increasing income based on photos shared by the user on social media. The income expansion unit can also analyze the content of the user's social media posts to suggest related means for increasing income. The income expansion unit can also suggest means for increasing income by referring to the activities of the user's friends on social media. In this way, related means for increasing income can be provided by analyzing the user's social media activity.

[0063] The income expansion unit can adjust the income expansion method by reflecting the user's past feedback when expanding income. For example, the income expansion unit customizes the income expansion method by reflecting the user's past feedback when expanding income. For example, the income expansion unit customizes the income expansion method based on feedback provided by the user in the past. The income expansion unit can also improve the accuracy of income expansion by reflecting the user's past feedback. The income expansion unit can also analyze the user's past feedback and improve the income expansion method. In this way, the accuracy of income expansion is improved by reflecting the user's past feedback.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The tourism experience improvement system can further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and body temperature, and monitors the user's health condition in real time. For example, if the user has been walking for a long time, it can send a notification urging the user to take a break. Also, if the user complains of feeling unwell, it can provide information about nearby medical facilities. Furthermore, the health management unit can suggest an appropriate sightseeing plan based on the user's health data. This makes it possible to provide a tourism experience that takes the user's health condition into consideration.

[0066] The tourism experience improvement system may further include a history analysis unit that analyzes the user's past travel history. The history analysis unit collects data on tourist spots visited and activities participated in by the user in the past to understand the user's preferences and interests. For example, if the user has visited many historical tourist spots in the past, the history analysis unit may suggest a plan that includes historical tourist spots. The history analysis unit may also suggest similar activities based on the user's evaluations of activities they have participated in in the past. Furthermore, the history analysis unit may suggest optimal travel times and routes based on the user's travel history. This makes it possible to provide a personalized tourism experience that utilizes the user's past travel history.

[0067] The tourism experience improvement system may further include a location information tracking unit that utilizes the user's real-time location information. The location information tracking unit tracks the user's current location in real time and suggests an optimal sightseeing route. For example, if the user gets lost in a tourist attraction, the location information tracking unit can guide the user to the shortest route from their current location to their destination. The location information tracking unit can also provide information on nearby tourist attractions and restaurants based on the user's current location. Furthermore, the location information tracking unit can record the user's movement history so that it can be reviewed later. This makes it possible to provide an efficient sightseeing experience that utilizes the user's location information.

[0068] The tourism experience improvement system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit analyzes the photos and posts the user has shared on social media to understand the user's interests. For example, if the user has shared many photos of food on social media, the social media analysis unit may prioritize providing local gourmet information. The social media analysis unit may also suggest related tourist spots based on the locations the user has checked in to on social media. Furthermore, the social media analysis unit may suggest activities that are suitable for the user based on the activities of the user's friends. This makes it possible to provide a personalized tourism experience that utilizes the user's social media activities.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, a user can input voice using a smartphone, or text using a keyboard. Also, a user can input images using a camera. Step 2: The information provider provides tourist destination information based on the information received by the reception unit. The tourist destination information includes tourist attractions, accommodations, transportation information, historical background, recommended tourist spots, local gourmet information, information on local specialties and souvenirs, event information, etc. Step 3: The empathy unit understands and empathizes with the user's emotions based on the information provided by the information providing unit. The empathy unit uses voice recognition technology and facial expression recognition technology to understand the user's emotions and can offer empathetic words. Step 4: The suggestion unit proposes a sightseeing plan that matches the user's preferences based on the emotions understood by the empathy unit. The sightseeing plan includes the itinerary, destinations to visit, activities, etc. For example, a plan touring historical sites is proposed to a user who loves history, a plan to enjoy delicious local cuisine to a user who loves food, and a plan touring natural scenery to a user who wants to enjoy natural scenery. Step 5: The Revenue Expansion Department contributes to increasing the tourist destination's revenue based on the tourism plan proposed by the Proposal Department. The Revenue Expansion Department provides information on the tourist destination's local products and souvenirs, issues coupons, and provides event information to promote the purchase of local products and souvenirs and participation in events.

[0071] (Example 2) A tourism experience improvement system according to an embodiment of the present invention accepts user input, provides information about tourist destinations, understands and empathizes with the user's feelings, proposes sightseeing plans tailored to the user's preferences, and contributes to increasing the tourist destination's revenue. When a user arrives at a tourist destination, an AI character provides detailed information about the destination. The AI ​​character then empathizes with the user's experience and shares the user's feelings through dialogue. Furthermore, the AI ​​character proposes sightseeing plans tailored to the user's preferences. The AI ​​character also contributes to increasing the tourist destination's revenue. For example, by providing information about the tourist destination's local specialties and souvenirs and encouraging their purchases, the AI ​​character contributes to economic revitalization of the tourist destination. This allows the tourism experience improvement system to provide users with new entertainment and provide tourist destinations with opportunities to increase revenue. For example, the AI ​​character allows users to enjoy the attractions of the tourist destination to the fullest, contributing to economic revitalization of the tourist destination.

[0072] A tourism experience improvement system according to an embodiment includes a reception unit, an information provision unit, an empathy unit, a suggestion unit, and an income expansion unit. The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and image input. For example, the reception unit allows a user to perform voice input using a smartphone. The reception unit also allows a user to perform text input using a keyboard. The reception unit also allows a user to perform image input using a camera. The information provision unit provides information about tourist destinations based on the information accepted by the reception unit. Examples of tourist destination information include, but are not limited to, tourist spots, accommodations, and transportation information. For example, the information provision unit provides information about the historical background of the tourist destination, recommended tourist spots, and local gourmet food. The information provision unit can also provide information about local specialties and souvenirs of the tourist destination. The information provision unit can also provide information about events at the tourist destination. The empathy unit understands and empathizes with the user's emotions based on the information provided by the information provision unit. For example, the empathy unit understands the user's emotions using voice recognition technology. The empathy unit can also understand the user's emotions using facial expression recognition technology. The empathy unit can also express empathy based on the user's emotions. The suggestion unit proposes a sightseeing plan tailored to the user's preferences based on the emotions understood by the empathy unit. The sightseeing plan may include, but is not limited to, an itinerary, destinations, and activities. For example, the suggestion unit can propose a plan touring historical sites to a history-loving user. The suggestion unit can also propose a plan to enjoy delicious local cuisine to a gourmet user. The suggestion unit can also propose a plan touring natural landscapes to a user who wants to enjoy natural scenery. The income expansion unit contributes to increasing the income of the tourist destination based on the sightseeing plan proposed by the suggestion unit. For example, the income expansion unit provides information on local specialties and souvenirs, issues coupons, and provides event information. The income expansion unit can also promote the purchase of local specialties and souvenirs of the tourist destination. The income expansion unit can also promote participation in events at the tourist destination.As a result, the tourism experience improvement system according to the embodiment can accept user input and contribute to providing information about tourist destinations, empathizing with emotions, proposing tourism plans, and increasing revenue.

[0073] The empathy unit can understand the user's emotions using voice recognition or facial expression recognition technology. The empathy unit can understand the user's emotions using, for example, voice recognition technology. For example, the empathy unit can analyze the tone and speed of the user's voice to estimate the emotions. The empathy unit can also understand the user's emotions using facial expression recognition technology. For example, the empathy unit can analyze changes in the user's facial expression to estimate the emotions. The empathy unit can also understand the user's emotions by combining voice recognition technology and facial expression recognition technology. For example, the empathy unit can simultaneously analyze the tone of the user's voice and changes in facial expression to estimate the emotions. In this way, the user's emotions can be understood more accurately by using voice recognition or facial expression recognition technology.

[0074] The revenue expansion unit can provide information on local products or souvenirs of the tourist destination, issue coupons, and provide event information. The revenue expansion unit, for example, provides information on local products or souvenirs of the tourist destination. For example, the revenue expansion unit provides detailed information on local products and souvenirs. The revenue expansion unit can also provide methods for purchasing local products and souvenirs. The revenue expansion unit can also issue coupons. For example, the revenue expansion unit issues discount coupons for specific products. The revenue expansion unit can also provide event information. For example, the revenue expansion unit provides information such as the date, time, location, and participation methods of events held at the tourist destination. In this way, by providing information on local products and souvenirs of the tourist destination, issuing coupons, and providing event information, the revenue of the tourist destination can be increased.

[0075] The suggestion unit can suggest a sightseeing plan based on the user's preferences. The suggestion unit, for example, suggests a sightseeing plan based on the user's preferences. For example, the suggestion unit can suggest a plan touring historical sites to a user who loves history. The suggestion unit can also suggest a plan to enjoy delicious local cuisine to a user who loves gourmet food. The suggestion unit can also suggest a plan touring natural scenery to a user who wants to enjoy natural scenery. In this way, suggesting a sightseeing plan that suits the user's preferences improves user satisfaction.

[0076] The information providing unit can provide information on the historical background of the tourist destination, recommended tourist spots, and local gourmet food. The information providing unit, for example, provides the historical background of the tourist destination. For example, the information providing unit provides information on historical events and cultural heritage of the tourist destination. The information providing unit can also provide recommended tourist spots. For example, the information providing unit provides information on popular spots and easily accessible spots in the tourist destination. The information providing unit can also provide local gourmet food information. For example, the information providing unit provides information on the characteristics of local restaurants and cuisine. In this way, the historical background of the tourist destination, recommended tourist spots, and local gourmet food information are provided, thereby improving the user's sightseeing experience.

[0077] The reception unit can anonymize data and implement security measures when receiving user input. For example, the reception unit anonymizes data when receiving user input. For example, the reception unit deletes the user's personal information and anonymizes the data. The reception unit can also implement security measures for the data. For example, the reception unit encrypts the data to prevent unauthorized access. The reception unit can also perform access control to ensure data security. In this way, the user's privacy is protected by anonymizing the data and implementing security measures.

[0078] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of input reception based on the estimated user's emotion. For example, if the user is excited, the reception unit can advance the timing of input reception and provide information quickly. Also, if the user is relaxed, the reception unit can delay the timing of input reception and provide information at a leisurely pace. Also, if the user is feeling stressed, the reception unit can adjust the timing of input reception and wait until the user calms down. In this way, by adjusting the timing of input reception according to the user's emotion, information can be provided at a more appropriate timing.

[0079] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also automatically complement related information by referring to content that the user has input in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history.

[0080] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving input. For example, the reception unit preferentially displays information about related tourist spots based on the user's current location. The reception unit can also filter and provide related information based on the user's areas of interest (history, gourmet food, etc.). The reception unit can also select optimal information based on the user's current situation (time of day, weather, etc.). In this way, highly relevant information can be provided by filtering based on the user's current situation and areas of interest.

[0081] The reception unit can select the optimal reception means based on the user's input method when receiving input. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving input. For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, the reception unit can accept the input using a keyboard or touch panel. Also, if the user selects image input, the reception unit can accept the input using image recognition technology. In this way, convenience for the user is improved by selecting the optimal reception means according to the user's input method.

[0082] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of information to be received based on the estimated user's emotion. For example, the reception unit can provide important information preferentially when the user is excited. Furthermore, the reception unit can also provide detailed information preferentially when the user is relaxed. Furthermore, the reception unit can also provide concise information that is to the point preferentially when the user is feeling stressed. In this way, by determining the priority of information based on the user's emotion, important information can be provided preferentially.

[0083] The reception unit can preferentially receive highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit preferentially receives highly relevant information taking into account the user's geographical location information when receiving input. For example, the reception unit preferentially receives information about nearby tourist spots based on the user's current location. The reception unit can also preferentially receive related event information based on the user's geographical location information. The reception unit can also preferentially receive optimal information taking into account the distance from the user's current location. In this way, highly relevant information can be provided preferentially by taking into account the user's geographical location information.

[0084] The reception unit can analyze the user's social media activity and receive related information when receiving input. For example, the reception unit analyzes the user's social media activity and receives related information when receiving input. For example, the reception unit preferentially receives information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0085] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit adjusts the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0086] The information providing unit can estimate the user's emotion and adjust the way information is presented based on the estimated user's emotion. The information providing unit, for example, estimates the user's emotion and adjusts the way information is presented based on the estimated user's emotion. For example, if the user is excited, the information providing unit can provide information using a visually stimulating presentation method. Furthermore, if the user is relaxed, the information providing unit can provide information using a calm presentation method. Furthermore, if the user is feeling stressed, the information providing unit can provide information using a concise and to-the-point presentation method. In this way, by adjusting the way information is presented based on the user's emotion, more appropriate information can be provided.

[0087] The information providing unit can determine the level of detail of the information based on the importance of the tourist destination when providing the information. For example, the information providing unit adjusts the level of detail of the information based on the importance of the tourist destination when providing the information. For example, the information providing unit provides detailed information about important tourist destinations. The information providing unit can also provide concise information about less important tourist destinations. The information providing unit can also adjust the level of detail of the information in stages depending on the importance of the tourist destination. In this way, by adjusting the level of detail of the information based on the importance of the tourist destination, it is possible to provide information that is important to the user.

[0088] The information providing unit can apply different information provision algorithms based on the category of the tourist destination when providing information. For example, the information providing unit applies different information provision algorithms based on the category of the tourist destination when providing information. For example, the information providing unit can provide information about historical tourist destinations using an algorithm that emphasizes historical background. Furthermore, the information providing unit can provide information about gourmet food using an algorithm that emphasizes the characteristics of cuisine and recommended menus. Furthermore, the information providing unit can provide information about natural landscapes using an algorithm that emphasizes the beauty of the scenery and the highlights. In this way, by applying different information provision algorithms depending on the category of the tourist destination, more appropriate information can be provided.

[0089] The information providing unit can improve the accuracy of information when providing information based on the user's past information provision results. For example, when providing information, the information providing unit improves the accuracy of information by referring to the user's past information provision results. For example, the information providing unit improves the accuracy of information based on feedback provided by the user on information provided in the past. The information providing unit can also analyze the user's past information provision results and provide optimal information. The information providing unit can also improve the information provision algorithm by referring to the user's past information provision results. In this way, the accuracy of information is improved by referring to the user's past information provision results.

[0090] The information providing unit can estimate the user's emotion and adjust the length of the information based on the estimated user's emotion. The information providing unit, for example, estimates the user's emotion and adjusts the length of the information based on the estimated user's emotion. For example, when the user is excited, the information providing unit can provide short, to-the-point information. Furthermore, when the user is relaxed, the information providing unit can provide detailed information. Furthermore, when the user is stressed, the information providing unit can provide concise, to-the-point information. In this way, by adjusting the length of the information based on the user's emotion, more appropriate information can be provided.

[0091] The information providing unit can set the priority of information based on the time of visiting a tourist destination when providing information. For example, the information providing unit determines the priority of information based on the time of visiting a tourist destination when providing information. For example, the information providing unit can provide optimal information preferentially depending on the time of visiting a tourist destination. The information providing unit can also provide information about seasonal attractions and events preferentially. The information providing unit can also provide related information preferentially based on the time of visiting a tourist destination. In this way, optimal information can be provided by determining the priority of information based on the time of visiting a tourist destination.

[0092] The information providing unit can set the order of information based on the relevance of tourist destinations when providing information. For example, the information providing unit adjusts the order of information based on the relevance of tourist destinations when providing information. For example, the information providing unit prioritizes providing the most relevant information based on the relevance of tourist destinations. The information providing unit can also adjust the order of information based on the relevance of tourist destinations. The information providing unit can also group and provide related information based on the relevance of tourist destinations. In this way, by adjusting the order of information based on the relevance of tourist destinations, highly relevant information can be provided.

[0093] The information providing unit can adjust the use of technical terms in the information based on the user's level of expertise when providing information. For example, the information providing unit adjusts the use of technical terms in the information based on the user's level of expertise when providing information. For example, if the user has technical knowledge, the information providing unit can provide information that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the information providing unit can also provide information that avoids technical terms. Furthermore, the information providing unit can adjust the use of technical terms in the information based on the user's level of expertise. In this way, by adjusting the use of technical terms in the information based on the user's level of expertise, it is possible to provide information that is easier to understand.

[0094] The empathy unit can estimate the user's emotion and adjust the manner of expressing empathy based on the estimated user's emotion. The empathy unit, for example, estimates the user's emotion and adjusts the manner of expressing empathy based on the estimated user's emotion. For example, the empathy unit emphasizes words of empathy when the user is excited. Furthermore, the empathy unit can express gentle words of empathy when the user is relaxed. Furthermore, the empathy unit can express comforting words when the user is stressed. In this way, by adjusting the manner of expressing empathy based on the user's emotion, more appropriate empathy can be provided.

[0095] The empathy unit can improve the accuracy of empathy based on the user's past experiences when empathizing. For example, the empathy unit improves the accuracy of empathy by referring to the user's past experiences when empathizing. For example, the empathy unit selects words of empathy based on events the user experienced in the past. The empathy unit can also improve the accuracy of empathy by referring to the user's past experiences. The empathy unit can also analyze the user's past experiences and provide optimal words of empathy. In this way, the accuracy of empathy is improved by referring to the user's past experiences.

[0096] The empathy unit can adjust the means of empathy based on the user's current situation when empathizing. For example, the empathy unit customizes the means of empathy based on the user's current situation when empathizing. For example, the empathy unit selects the optimal means of empathy based on the user's current location. The empathy unit can also customize the means of empathy based on the user's current situation (time of day, weather, etc.). The empathy unit can also select the means of empathy taking the user's current situation into consideration. In this way, by customizing the means of empathy based on the user's current situation, more appropriate empathy can be provided.

[0097] The empathy unit can estimate the user's emotions and determine the priority of empathy based on the estimated user's emotions. The empathy unit, for example, estimates the user's emotions and determines the priority of empathy based on the estimated user's emotions. For example, if the user is excited, the empathy unit can set the priority of empathy high and provide words of empathy quickly. Also, if the user is relaxed, the empathy unit can set the priority of empathy low and provide words of empathy at a leisurely pace. Also, if the user is feeling stressed, the empathy unit can set the priority of empathy to a medium level and provide words of empathy at an appropriate time. In this way, by determining the priority of empathy based on the user's emotions, empathy can be provided at a more appropriate time.

[0098] The empathy unit can select an optimal empathy method based on the user's geographical location information when empathizing. For example, the empathy unit selects an optimal empathy method by taking into account the user's geographical location information when empathizing. For example, if the user is at a tourist spot, the empathy unit can provide empathy words related to the location. Also, if the user is at home, the empathy unit can provide empathy words that will help the user relax. Also, if the user is on the move, the empathy unit can provide concise and to-the-point empathy words. In this way, the optimal empathy method can be provided by taking into account the user's geographical location information.

[0099] The empathy unit can provide a means of empathy by analyzing the user's social media activity when the user empathizes. For example, the empathy unit can analyze the user's social media activity when the user empathizes and suggest a means of empathy. For example, the empathy unit can provide words of empathy based on photos the user shared on social media. The empathy unit can also analyze the content of the user's social media posts and provide related words of empathy. The empathy unit can also suggest a means of empathy by referring to the activities of the user's friends on social media. In this way, related means of empathy can be provided by analyzing the user's social media activity.

[0100] The empathy unit can adjust the method of empathy by reflecting the user's past feedback when empathizing. For example, the empathy unit customizes the method of empathy by reflecting the user's past feedback when empathizing. For example, the empathy unit customizes the method of empathy based on feedback provided by the user in the past. The empathy unit can also improve the accuracy of empathy by reflecting the user's past feedback. The empathy unit can also analyze the user's past feedback and improve the method of empathy. In this way, the accuracy of empathy is improved by reflecting the user's past feedback.

[0101] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can make suggestions using a visually stimulating way of expression. Furthermore, if the user is relaxed, the suggestion unit can make suggestions using a calming way of expression. Furthermore, if the user is stressed, the suggestion unit can make suggestions using a concise and to-the-point way of expression. In this way, by adjusting the way in which suggestions are expressed based on the user's emotion, more appropriate suggestions can be provided.

[0102] The suggestion unit can determine the level of detail of the proposal based on the importance of the sightseeing plan when making the suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the sightseeing plan when making the suggestion. For example, the suggestion unit can make detailed suggestions for important sightseeing plans. The suggestion unit can also make brief suggestions for less important sightseeing plans. The suggestion unit can also gradually adjust the level of detail of the proposal depending on the importance of the sightseeing plan. In this way, by adjusting the level of detail of the proposal based on the importance of the sightseeing plan, it is possible to provide suggestions that are important to the user.

[0103] The suggestion unit can apply different suggestion algorithms based on the category of the sightseeing plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms based on the category of the sightseeing plan when making a suggestion. For example, the suggestion unit can make suggestions related to historical sightseeing plans using an algorithm that emphasizes historical background. Furthermore, the suggestion unit can make suggestions related to gourmet food using an algorithm that emphasizes the characteristics of cuisine and recommended menus. Furthermore, the suggestion unit can make suggestions related to natural landscapes using an algorithm that emphasizes the beauty of the scenery and the highlights. In this way, by applying different suggestion algorithms depending on the category of the sightseeing plan, more appropriate suggestions can be provided.

[0104] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion based on feedback the user has given on suggestions received in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. The suggestion unit can also improve the suggestion algorithm by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0105] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can make a short and to-the-point suggestion when the user is excited. Also, the suggestion unit can make a detailed suggestion when the user is relaxed. Also, the suggestion unit can make a concise and to-the-point suggestion when the user is stressed. In this way, by adjusting the length of the suggestion based on the user's emotion, more appropriate suggestions can be provided.

[0106] The suggestion unit can set the priority of the proposals based on the time of submission of the sightseeing plan when making the proposal. For example, the suggestion unit determines the priority of the proposals based on the time of submission of the sightseeing plan when making the proposal. For example, the suggestion unit can prioritize the most suitable proposal depending on the time of submission of the sightseeing plan. The suggestion unit can also prioritize the proposal of seasonal attractions and event information. The suggestion unit can also prioritize the related proposals based on the time of submission of the sightseeing plan. In this way, by determining the priority of the proposals based on the time of submission of the sightseeing plan, the most suitable proposals can be provided.

[0107] The suggestion unit can set the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the sightseeing plans when making suggestions. For example, the suggestion unit prioritizes the most relevant suggestions based on the relevance of the sightseeing plans. The suggestion unit can also adjust the order of suggestions based on the relevance of the sightseeing plans. The suggestion unit can also group related suggestions based on the relevance of the sightseeing plans. In this way, by adjusting the order of suggestions based on the relevance of the sightseeing plans, highly relevant suggestions can be provided.

[0108] The suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a proposal that avoids technical terminology. Also, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal based on the user's level of expertise, it is possible to provide a proposal that is easier to understand.

[0109] The income expansion unit can estimate the user's emotions and adjust the income expansion method based on the estimated user emotions. The income expansion unit, for example, estimates the user's emotions and adjusts the income expansion method based on the estimated user emotions. For example, if the user is excited, the income expansion unit can suggest a method to encourage the purchase of local products or souvenirs. Furthermore, if the user is relaxed, the income expansion unit can suggest an income expansion method at a leisurely pace. Furthermore, if the user is stressed, the income expansion unit can suggest a concise and to-the-point income expansion method. In this way, by adjusting the income expansion method based on the user's emotions, more effective income expansion is possible.

[0110] The income expansion unit can improve the accuracy of providing information about local specialties and souvenirs of tourist destinations when income is expanded. For example, the income expansion unit improves the accuracy of providing information about local specialties and souvenirs of tourist destinations when income is expanded. For example, the income expansion unit provides detailed information about local specialties and souvenirs of tourist destinations. The income expansion unit can also provide detailed instructions on how to purchase local specialties and souvenirs of tourist destinations. The income expansion unit can also reflect user feedback to improve the accuracy of providing information about local specialties and souvenirs of tourist destinations. This improves the accuracy of providing information about local specialties and souvenirs of tourist destinations, thereby enhancing the effect of income expansion.

[0111] The income expansion unit can analyze the event information of the tourist destination to determine the optimal income expansion method when expanding income. For example, the income expansion unit analyzes the event information of the tourist destination to select the optimal income expansion method when expanding income. For example, the income expansion unit proposes the optimal income expansion method based on the event information of the tourist destination. The income expansion unit can also analyze the event information of the tourist destination to select the income expansion method. The income expansion unit can also improve the income expansion method by referring to the event information of the tourist destination. In this way, the optimal income expansion method can be selected by analyzing the event information of the tourist destination.

[0112] The income expansion unit can adjust the income expansion method by reflecting user feedback when expanding income. For example, the income expansion unit improves the income expansion method by reflecting user feedback when expanding income. For example, the income expansion unit improves the income expansion method based on feedback previously provided by the user. The income expansion unit can also improve the accuracy of income expansion by reflecting user feedback. The income expansion unit can also analyze user feedback and improve the income expansion method. In this way, the income expansion method can be improved by reflecting user feedback.

[0113] The income expansion unit can estimate the user's emotions and determine the priority of income expansion based on the estimated user's emotions. The income expansion unit, for example, estimates the user's emotions and determines the priority of income expansion based on the estimated user's emotions. For example, if the user is excited, the income expansion unit can set the priority of income expansion high and quickly suggest ways to increase income. Also, if the user is relaxed, the income expansion unit can set the priority of income expansion low and suggest ways to increase income at a leisurely pace. Also, if the user is stressed, the income expansion unit can set the priority of income expansion to a medium level and suggest ways to increase income at an appropriate time. In this way, by determining the priority of income expansion based on the user's emotions, more effective income expansion is possible.

[0114] The income expansion unit can select an optimal income expansion method based on the user's geographical location information when expanding income. For example, the income expansion unit selects an optimal income expansion method by taking into account the user's geographical location information when expanding income. For example, when the user is in a tourist spot, the income expansion unit can suggest income expansion methods related to the location. Furthermore, when the user is at home, the income expansion unit can provide information on local products and souvenirs that can be purchased online. Furthermore, when the user is on the move, the income expansion unit can suggest a concise and to-the-point income expansion method. In this way, the optimal income expansion method can be provided by taking into account the user's geographical location information.

[0115] The income expansion unit can provide means for increasing income by analyzing the user's social media activity during income expansion. For example, the income expansion unit can analyze the user's social media activity during income expansion to suggest means for increasing income. For example, the income expansion unit can suggest means for increasing income based on photos shared by the user on social media. The income expansion unit can also analyze the content of the user's social media posts to suggest related means for increasing income. The income expansion unit can also suggest means for increasing income by referring to the activities of the user's friends on social media. In this way, related means for increasing income can be provided by analyzing the user's social media activity.

[0116] The income expansion unit can adjust the income expansion method by reflecting the user's past feedback when expanding income. For example, the income expansion unit customizes the income expansion method by reflecting the user's past feedback when expanding income. For example, the income expansion unit customizes the income expansion method based on feedback provided by the user in the past. The income expansion unit can also improve the accuracy of income expansion by reflecting the user's past feedback. The income expansion unit can also analyze the user's past feedback and improve the income expansion method. In this way, the accuracy of income expansion is improved by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, information provision unit, empathy unit, suggestion unit, and income expansion 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 is realized by the reception device 38 of the smart device 14 and accepts voice input and text input from the user. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on tourist destinations. The empathy unit is realized, for example, by the control unit 46A of the smart device 14 and understands and empathizes with the user's emotions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a sightseeing plan based on the user's preferences. The income expansion unit is realized, for example, by the control unit 46A of the smart device 14 and provides information on local products and souvenirs of tourist destinations to encourage purchases. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, information provision unit, empathy unit, suggestion unit, and income expansion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts the user's voice input. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on tourist destinations. The empathy unit is realized, for example, by the control unit 46A of the smart glasses 214 and understands and empathizes with the user's emotions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a sightseeing plan based on the user's preferences. The income expansion unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides information on local products and souvenirs of tourist destinations to encourage purchases. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, information provision unit, empathy unit, suggestion unit, and income expansion unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and accepts voice input from the user. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on tourist destinations. The empathy unit is realized, for example, by the control unit 46A of the headset terminal 314 and understands and empathizes with the user's emotions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a sightseeing plan based on the user's preferences. The income expansion unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides information on local products and souvenirs of tourist destinations to encourage purchases. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, information provision unit, empathy unit, suggestion unit, and income expansion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts voice input from the user. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on tourist destinations. The empathy unit is realized, for example, by the control unit 46A of the robot 414 and understands and empathizes with the user's emotions. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a sightseeing plan based on the user's preferences. The income expansion unit is realized, for example, by the control unit 46A of the robot 414 and provides information on local products and souvenirs of tourist destinations to encourage purchases.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The tourism experience improvement system can further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and body temperature, and monitors the user's health condition in real time. For example, if the user has been walking for a long time, it can send a notification urging the user to take a break. Also, if the user complains of feeling unwell, it can provide information about nearby medical facilities. Furthermore, the health management unit can suggest an appropriate sightseeing plan based on the user's health data. This makes it possible to provide a tourism experience that takes the user's health condition into consideration.

[0119] The tourism experience improvement system may further include a history analysis unit that analyzes the user's past travel history. The history analysis unit collects data on tourist spots visited and activities participated in by the user in the past to understand the user's preferences and interests. For example, if the user has visited many historical tourist spots in the past, the history analysis unit may suggest a plan that includes historical tourist spots. The history analysis unit may also suggest similar activities based on the user's evaluations of activities they have participated in in the past. Furthermore, the history analysis unit may suggest optimal travel times and routes based on the user's travel history. This makes it possible to provide a personalized tourism experience that utilizes the user's past travel history.

[0120] The tourism experience improvement system may further include a location information tracking unit that utilizes the user's real-time location information. The location information tracking unit tracks the user's current location in real time and suggests an optimal sightseeing route. For example, if the user gets lost in a tourist attraction, the location information tracking unit can guide the user to the shortest route from their current location to their destination. The location information tracking unit can also provide information on nearby tourist attractions and restaurants based on the user's current location. Furthermore, the location information tracking unit can record the user's movement history so that it can be reviewed later. This makes it possible to provide an efficient sightseeing experience that utilizes the user's location information.

[0121] The tourism experience improvement system may further include an emotion customization unit that estimates the user's emotion and customizes the experience at the tourist attraction based on the estimated emotion. The emotion customization unit monitors the user's emotion in real time and provides an experience according to the user's emotion. For example, if the user is excited, it may suggest an active activity. Alternatively, if the user is relaxed, it may suggest a relaxing experience in a quiet place. Furthermore, the emotion customization unit may adjust the priority of events and activities at the tourist attraction based on the user's emotion. This makes it possible to provide a personalized tourism experience according to the user's emotion.

[0122] The tourism experience improvement system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit analyzes the photos and posts the user has shared on social media to understand the user's interests. For example, if the user has shared many photos of food on social media, the social media analysis unit may prioritize providing local gourmet information. The social media analysis unit may also suggest related tourist spots based on the locations the user has checked in to on social media. Furthermore, the social media analysis unit may suggest activities that are suitable for the user based on the activities of the user's friends. This makes it possible to provide a personalized tourism experience that utilizes the user's social media activities.

[0123] The tourism experience improvement system may further include an emotion recording unit that estimates the user's emotions and records the experience at a tourist spot based on the estimated emotions. The emotion recording unit monitors the user's emotions in real time and records the experience according to the user's emotions. For example, if the user is excited, it can automatically take and record photos and videos of that moment. Alternatively, if the user is relaxed, it can record an experience in a quiet place. Furthermore, the emotion recording unit can automatically edit highlights of the experience based on the user's emotions so that the highlights can be reviewed later. This allows memories according to the user's emotions to be recorded and enjoyed later.

[0124] The tourism experience improvement system may further include an emotion sharing unit that estimates the user's emotions and shares experiences at tourist spots based on the estimated emotions. The emotion sharing unit monitors the user's emotions in real time and shares experiences on social media that correspond to the user's emotions. For example, if the user is excited, photos and videos of that moment can be automatically posted on social media. Alternatively, if the user is relaxed, an experience of a quiet place can be shared. Furthermore, the emotion sharing unit can automatically edit highlights of the experience based on the user's emotions and share them with friends and family. This allows experiences that correspond to the user's emotions to be shared in real time and enjoyed by others.

[0125] The tourism experience improvement system may further include an emotion evaluation unit that estimates the user's emotions and evaluates the experience at the tourist destination based on the estimated emotions. The emotion evaluation unit monitors the user's emotions in real time and evaluates the experience according to the user's emotions. For example, if the user is excited, the experience can be highly rated. Alternatively, if the user is relaxed, the experience can be evaluated based on the degree of relaxation. Furthermore, the emotion evaluation unit can automatically collect feedback on the experience based on the user's emotions and reflect it in the next sightseeing plan. This allows the experience to be evaluated according to the user's emotions and improve the next sightseeing experience.

[0126] The tourism experience improvement system may further include an emotion guide unit that estimates the user's emotion and guides the user through experiences at tourist attractions based on the estimated emotion. The emotion guide unit monitors the user's emotion in real time and provides guidance according to the user's emotion. For example, if the user is excited, the emotion guide unit may guide the user through active activities. Alternatively, if the user is relaxed, the emotion guide unit may guide the user through relaxing experiences in quiet places. Furthermore, the emotion guide unit may adjust the priority of events and activities at tourist attractions based on the user's emotion. This makes it possible to provide a personalized guide according to the user's emotion.

[0127] The tourism experience improvement system may further include an emotion optimization unit that estimates the user's emotions and optimizes the experience at tourist attractions based on the estimated emotions. The emotion optimization unit monitors the user's emotions in real time and optimizes the experience according to the user's emotions. For example, if the user is excited, it may preferentially suggest active activities. Alternatively, if the user is relaxed, it may preferentially suggest relaxing experiences in quiet places. Furthermore, the emotion optimization unit may adjust the priority of events and activities at tourist attractions based on the user's emotions. This makes it possible to provide a personalized experience according to the user's emotions.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, a user can input voice using a smartphone, or text using a keyboard. Also, a user can input images using a camera. Step 2: The information provider provides tourist destination information based on the information received by the reception unit. The tourist destination information includes tourist attractions, accommodations, transportation information, historical background, recommended tourist spots, local gourmet information, information on local specialties and souvenirs, event information, etc. Step 3: The empathy unit understands and empathizes with the user's emotions based on the information provided by the information providing unit. The empathy unit uses voice recognition technology and facial expression recognition technology to understand the user's emotions and can offer empathetic words. Step 4: The suggestion unit proposes a sightseeing plan that matches the user's preferences based on the emotions understood by the empathy unit. The sightseeing plan includes the itinerary, destinations to visit, activities, etc. For example, a plan touring historical sites is proposed to a user who loves history, a plan to enjoy delicious local cuisine to a user who loves food, and a plan touring natural scenery to a user who wants to enjoy natural scenery. Step 5: The Revenue Expansion Department contributes to increasing the tourist destination's revenue based on the tourism plan proposed by the Proposal Department. The Revenue Expansion Department provides information on the tourist destination's local products and souvenirs, issues coupons, and provides event information to promote the purchase of local products and souvenirs and participation in events.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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 that receives input from a user; an information providing unit that provides information about tourist spots based on the information received by the reception unit; an empathy unit that understands and empathizes with the user's feelings based on the information provided by the information providing unit; a suggestion unit that suggests a sightseeing plan based on the user's preferences based on the emotions understood by the empathy unit; and a revenue increasing unit that increases the revenue of the tourist destination based on the sightseeing plan proposed by the proposal unit. A system characterized by:

2. The sympathetic part is Understand user emotions using voice or facial expression recognition technology 2. The system of claim 1.

3. The revenue generation department: Providing information on local specialties or souvenirs, issuing coupons, and providing event information 2. The system of claim 1.

4. The proposal unit Suggest sightseeing plans based on user preferences 2. The system of claim 1.

5. The information providing unit Providing historical background of tourist destinations, recommended tourist spots, and local gourmet information 2. The system of claim 1.

6. The reception unit When accepting user input, data is anonymized and security measures are taken.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and determine the optimal reception method 2. The system of claim 1.

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