system

Abstract

The system according to the embodiment aims to provide a user with an immersive virtual travel experience by suggesting individually tailored routes and activities. [Solution] A system according to an embodiment includes a reception unit, an answering unit, a suggestion unit, and a display unit. The reception unit accepts questions from a user. The answering unit provides an answer based on the question accepted by the reception unit. The suggestion unit proposes individually tailored routes and activities based on the answers obtained by the answering unit. The display unit displays the routes and activities proposed by the suggestion unit using 360-degree photos and VR technology.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing a sufficient sense of realism by proposing individually tailored routes and activities when users are enjoying a virtual trip.

[0005] The system according to the embodiment aims to provide a user with an immersive virtual travel experience by suggesting individually tailored routes and activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an answering unit, a suggestion unit, and a display unit. The reception unit receives questions from a user. The answering unit provides an answer based on the question received by the reception unit. The suggestion unit suggests individually tailored routes and activities based on the answers obtained by the answering unit. The display unit displays the routes and activities suggested by the suggestion unit using 360-degree photos or VR technology. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with an immersive virtual travel experience by suggesting individually tailored routes and activities. [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 virtual travel system according to an embodiment of the present invention allows users to explore destinations around the world and enjoy virtual travel while learning about culture and history. The virtual travel system accepts questions from users, uses AI to answer the questions, proposes customized routes and activities, and displays them using 360-degree photos and VR technology, providing a sense of realism as if the user were actually in the location. For example, a user may input, "I want to go from ____ to ____." For example, a user may input, "I want to go from my home to the station." This information is then input into the AI. The AI ​​then analyzes the input information and proposes customized routes and activities. For example, it proposes optimal routes and activities based on places the user has previously visited or activities they are interested in. Furthermore, the virtual travel system uses 360-degree photos and VR technology to provide a sense of realism as if the user were actually in the location. For example, it displays 360-degree photos and VR footage of a place the user wants to visit, providing an experience that makes the user feel as if they are actually in that location. This allows the virtual travel system to provide options based on the user's preferences and interests, allowing the user to enjoy an experience similar to real travel. This allows the virtual travel system to enjoy a virtual trip tailored to the user's interests and preferences. For example, if a user is interested in history, the system will suggest historical places and events, allowing the user to experience the experience as if they were in those places. This allows the user to enjoy a virtual trip tailored to their interests and preferences.

[0029] A virtual travel system according to an embodiment includes a reception unit, an answering unit, a suggestion unit, and a display unit. The reception unit accepts questions from a user. The questions from the user may be in, for example, text, audio, or image format, but are not limited to these examples. For example, the reception unit receives a user input such as "I want to go from XX to XX." This information is input to an AI. The answering unit provides an answer based on the question accepted by the reception unit. The answer may be in, for example, text, audio, or image format, but are not limited to these examples. For example, the answering unit uses natural language processing technology to answer the user's question. The suggestion unit proposes customized routes and activities based on the answers obtained by the answering unit. The suggestions may be based on, for example, the user's past behavioral data and preferences, but are not limited to these examples. For example, the suggestion unit proposes optimal routes and activities based on places the user has visited in the past and activities in which the user is interested. The display unit displays the routes and activities proposed by the suggestion unit using 360-degree photos or VR technology. The display may, for example, display 360-degree photos or VR video of a place the user wants to visit, providing the user with an experience of being in that place, but is not limited to such examples. For example, the display unit may provide the user with a sense of realism using 360-degree photos or VR technology. As a result, the virtual travel system according to the embodiment allows the user to input questions, and the AI ​​proposes customized routes and activities based on the questions, displaying them using 360-degree photos and VR technology, allowing the user to enjoy a virtual trip.

[0030] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit analyzes the user's past question history and selects the optimal reception method. The question history includes, for example, past question content, question date and time, etc., but is not limited to these examples. For example, the reception unit preferentially receives topics that the user has frequently asked questions about in the past. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. The reception unit can also select a reception method suitable for a specific time period from the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0031] The reception unit can analyze the user's current interests and concerns and perform filtering when receiving a question. The reception unit analyzes the user's current interests and concerns and performs filtering when receiving a question. The analysis of interests and concerns is performed, for example, based on past behavioral history, survey results, etc., but is not limited to these examples. For example, the reception unit preferentially receives questions related to topics in which the user is currently interested. The reception unit can also analyze the user's social media activity and filter related questions. The reception unit can also filter questions that match the user's current interests based on the user's past search history. In this way, by filtering questions based on the user's current interests and concerns, it is possible to preferentially receive highly relevant questions.

[0032] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. The reception unit selects an appropriate reception means according to the user's input method when receiving a question. Input methods include, but are not limited to, voice input, text input, and image input, for example. For example, when the user uses voice input, the reception unit can receive the question using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the question using natural language processing technology. Furthermore, when the user uses images, the reception unit can also receive the question using image recognition technology. In this way, by selecting the optimal reception means according to the user's input method, questions can be received smoothly.

[0033] When accepting a question, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. When accepting a question, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the reception unit prioritizes accepting questions related to the user's current location. The reception unit can also prioritize accepting questions related to the user's travel destinations. The reception unit can also filter related questions based on the user's geographical location information. In this way, highly relevant questions can be prioritized by taking into account the user's geographical location information.

[0034] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit can analyze the user's social media activity when receiving a question and receive related questions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the reception unit can receive questions related to places where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related questions.

[0035] The reception unit can customize the reception method based on the user's past feedback when receiving a question. The reception unit customizes the reception method based on the user's past feedback when receiving a question. Feedback includes, for example, the user's evaluation comments and survey results, but is not limited to these examples. For example, the reception unit suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially receive specific question formats 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.

[0036] The answering unit can adjust the level of detail of the answer by analyzing the importance of the question when answering. The answering unit can adjust the level of detail of the answer by analyzing the importance of the question when answering. The importance of a question includes, for example, the user's urgency and the content of the question, but is not limited to these examples. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a concise answer to a general question. The answering unit can also adjust the level of detail of the answer depending on the user's level of interest. In this way, by adjusting the level of detail of the answer based on the importance of the question, a detailed answer can be provided to an important question.

[0037] The answering unit can apply different algorithms depending on the category of the question when answering. The answering unit applies different algorithms depending on the category of the question when answering. Categories include, but are not limited to, technology categories and business categories. For example, the answering unit can answer a question about history by referring to a historical database. The answering unit can also answer a question about culture by referring to a cultural database. The answering unit can also answer a question about tourism by referring to tourism information. In this way, by applying different answering algorithms depending on the category of the question, more appropriate answers can be provided.

[0038] The answering unit can improve the accuracy of the answer based on the user's past answer results when providing an answer. The answering unit can improve the accuracy of the answer based on the user's past answer results when providing an answer. Past answer results include, for example, answer content and answer date and time, but are not limited to these examples. For example, the answering unit provides the optimal answer based on answers the user has received in the past. The answering unit can also improve the accuracy of the answer based on the user's past answer results. The answering unit can also improve the quality of the answer by referring to the user's past feedback. In this way, the accuracy of the answer can be improved by referring to the user's past answer results.

[0039] The answering unit can analyze the time when a question was submitted and determine the priority of the answer when answering. The answering unit can analyze the time when a question was submitted and determine the priority of the answer when answering. The submission time includes, but is not limited to, for example, the submission date and time or a timestamp of the question. For example, the answering unit prioritizes answers to questions submitted recently. The answering unit can also prioritize answers to questions submitted by users during a specific time period. The answering unit can also adjust the priority of the answers depending on the time when the question was submitted. In this way, answers can be provided quickly by determining the priority of the answers based on the time when the question was submitted.

[0040] The answering unit can analyze the relevance of the questions and adjust the order of the answers when answering. The answering unit analyzes the relevance of the questions and adjusts the order of the answers when answering. Relevance includes, but is not limited to, for example, similarity of the question content and related topics. For example, the answering unit prioritizes answers to questions with high relevance. The answering unit can also adjust the order of the answers according to the user's level of interest. The answering unit can also adjust the order of the answers based on the category of the question. In this way, by adjusting the order of the answers based on the relevance of the questions, answers can be provided preferentially to questions with high relevance.

[0041] The answering unit can adjust the use of technical terms in the answer by analyzing the user's level of expertise when answering. The answering unit can adjust the use of technical terms in the answer by analyzing the user's level of expertise when answering. Expertise levels include, but are not limited to, the user's occupation and past question content. For example, if the user has specialized knowledge, the answering unit can provide an answer that uses a lot of technical terms. Also, if the user has general knowledge, the answering unit can provide an answer in simple language. Also, the answering unit can adjust the use of technical terms in the answer depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the answer depending on the user's level of expertise, a more appropriate answer can be provided.

[0042] The suggestion unit can adjust the level of detail of the suggestion by analyzing the importance of the route or activity when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion by analyzing the importance of the route or activity when making a suggestion. The importance includes, but is not limited to, for example, the user's priority and the importance of the suggestion content. For example, the suggestion unit can provide detailed suggestions for important routes or activities. The suggestion unit can also provide concise suggestions for general routes or activities. The suggestion unit can also adjust the level of detail of the suggestion according to the user's level of interest. In this way, by adjusting the level of detail of the suggestion based on the importance of the route or activity, detailed information can be provided for important suggestions.

[0043] The suggestion unit can apply different algorithms depending on the category of the route or activity when making a suggestion. The suggestion unit applies different algorithms depending on the category of the route or activity when making a suggestion. Examples of categories include, but are not limited to, a tourism category and a sports category. For example, the suggestion unit can make a suggestion for a historical route or activity by referring to a historical database. Furthermore, the suggestion unit can make a suggestion for a cultural route or activity by referring to a cultural database. Furthermore, the suggestion unit can make a suggestion for a tourist route or activity by referring to tourist information. In this way, by applying different suggestion algorithms depending on the category of the route or activity, more appropriate suggestions can be provided.

[0044] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. The suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. Past suggestion results include, for example, proposal content and the date and time of the suggestion, but are not limited to these examples. For example, the suggestion unit provides an optimal suggestion based on suggestions the user has received in the past. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also improve the quality of the suggestion by referring to the user's past feedback. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0045] The suggestion unit can analyze the time of submission of a route or activity to determine the priority of the proposal when making a proposal. The suggestion unit can analyze the time of submission of a route or activity to determine the priority of the proposal when making a proposal. The submission time includes, but is not limited to, for example, the date and time of submission of the proposal or a timestamp. For example, the suggestion unit can prioritize proposing recently submitted routes or activities. The suggestion unit can also prioritize proposing routes and activities submitted by a user during a specific time period. The suggestion unit can also adjust the priority of the proposal depending on the time of submission of the proposal. This allows for rapid provision of proposals by determining the priority of the proposal based on the time of submission of the route or activity.

[0046] The suggestion unit can analyze the relevance of routes and activities when making suggestions and adjust the order of suggestions. The suggestion unit analyzes the relevance of routes and activities when making suggestions and adjust the order of suggestions. Relevance includes, but is not limited to, similarity of proposal content and related topics. For example, the suggestion unit prioritizes suggesting highly relevant routes and activities. The suggestion unit can also adjust the order of suggestions according to the user's level of interest. The suggestion unit can also adjust the order of suggestions based on the category of the route or activity. In this way, by adjusting the order of suggestions based on the relevance of routes and activities, highly relevant suggestions can be provided preferentially.

[0047] The suggestion unit may analyze the user's level of expertise when making a suggestion and adjust the use of technical terms in the suggestion. The suggestion unit may analyze the user's level of expertise when making a suggestion and adjust the use of technical terms in the suggestion. Examples of the level of expertise include, but are not limited to, the user's occupation and past suggestion content. For example, if the user has specialized knowledge, the suggestion unit may provide a suggestion that uses a lot of technical terms. Furthermore, if the user has general knowledge, the suggestion unit may provide a suggestion in simple language. Furthermore, the suggestion unit may adjust the use of technical terms in the suggestion according to the user's level of expertise. In this way, more appropriate suggestions can be provided by adjusting the use of technical terms in the suggestion according to the user's level of expertise.

[0048] The display unit can select the optimal display method by referring to the user's past display history when displaying. The display unit can select the optimal display method by referring to the user's past display history when displaying. The display history includes, for example, past display content and display date and time, but is not limited to these examples. For example, the display unit provides the optimal display method based on the user's preferred display style in the past. The display unit can also select a display method with high visibility from the user's past display history. The display unit can also improve the display method by referring to the user's past feedback. In this way, the optimal display method can be provided by referring to the user's past display history.

[0049] The display unit can analyze the user's current interests and customize the display content when displaying the information. The display unit analyzes the user's current interests and customize the display content when displaying the information. The analysis of interests and concerns is performed, for example, based on past behavioral history, survey results, etc., but is not limited to these examples. For example, the display unit can prioritize displaying information related to topics in which the user is currently interested. The display unit can also analyze the user's social media activity and customize and display related information. The display unit can also display information that matches the user's current interests based on the user's past search history. In this way, highly relevant information can be provided by customizing the display content based on the user's current interests and concerns.

[0050] The display unit can improve the display method based on user feedback during display. The display unit improves the display method based on user feedback during display. Feedback includes, for example, user evaluation comments and survey results, but is not limited to these examples. For example, the display unit can suggest an optimal display method based on feedback previously provided by the user. The display unit can also preferentially provide a specific display style based on the user's past feedback. The display unit can also analyze the user's past feedback and improve the display method. In this way, the display method can be improved by reflecting the user's feedback.

[0051] The display unit can select an appropriate display method in consideration of the user's geographical location information when displaying information. The display unit can select an appropriate display method in consideration of the user's geographical location information when displaying information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the display unit can prioritize displaying information related to the user's current location. The display unit can also prioritize displaying information related to the user's travel destinations. The display unit can also filter and display related information based on the user's geographical location information. This makes it possible to provide highly relevant information by taking the user's geographical location information into consideration.

[0052] The display unit can analyze the user's social media activity and customize the display content when displaying the information. The display unit analyzes the user's social media activity and customize the display content when displaying the information. Social media activity includes, for example, but is not limited to, posted content and the number of likes. For example, the display unit displays information related to places where the user has checked in on social media. The display unit can also analyze the user's social media posts and customize and display related information. The display unit can also display related information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0053] The display unit can customize the display method based on the user's past feedback when displaying. The display unit customizes the display method based on the user's past feedback when displaying. Feedback includes, for example, the user's evaluation comments and survey results, but is not limited to these examples. For example, the display unit suggests an optimal display method based on the user's past feedback. The display unit can also provide a specific display style with priority based on the user's past feedback. The display unit can also analyze the user's past feedback and improve the display method. In this way, the optimal display method can be provided by reflecting the user's past feedback.

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

[0055] The virtual travel system may further include a health management unit that monitors the user's health and adjusts the travel plan based on the user's health. For example, if the user feels tired, the health management unit may suggest relaxing activities. If the user is active, the health management unit may suggest energetic activities. Furthermore, if the user has a specific health problem, the health management unit may provide a travel plan that takes that problem into consideration. This allows the user to have an optimal travel experience based on their health.

[0056] The virtual travel system may further include a history analysis unit that analyzes the user's past travel history and suggests new travel destinations based on the past travel history. For example, similar travel destinations may be suggested based on the places the user has visited or the activities the user has participated in in the past. The system may also analyze the characteristics of travel destinations the user has liked in the past and suggest new travel destinations based on the results. Furthermore, the system may take into account travel destinations and activities that the user has avoided in the past and make suggestions that avoid those destinations. This makes it possible to provide optimal travel destinations based on the user's past travel history.

[0057] The virtual travel system may further include an activity tracker that acquires real-time activity data of the user and adjusts the travel plan based on the data. For example, if the user is currently walking, the activity tracker may suggest a walking tour. If the user is currently sitting, the activity tracker may suggest activities that can be enjoyed while sitting. Furthermore, if the user is exercising, the activity tracker may suggest active activities. This allows the system to provide an optimal travel plan based on the user's real-time activity data.

[0058] The virtual travel system may further include a social analysis unit that analyzes the user's social media activity and suggests travel destinations based on the analyzed social media activity. For example, the system may suggest travel destinations related to places the user has checked in on social media. The system may also suggest the same places based on places the user's friends have visited. Furthermore, the system may suggest travel destinations related to topics the user has shown interest in on social media. This allows the system to provide optimal travel destinations based on the user's social media activity.

[0059] The virtual travel system may further include a location information analysis unit that analyzes the user's geographical location information and suggests travel destinations based on the analyzed geographical location information. For example, the system may suggest travel destinations close to the user's current location. It may also suggest travel destinations related to places the user has visited in the past. Furthermore, if the user is interested in a particular region, it may suggest travel destinations related to that region. This makes it possible to provide optimal travel destinations based on the user's geographical location information.

[0060] The virtual travel system may further include a feedback analysis unit that analyzes the user's past feedback and improves the travel plan based on the feedback. For example, the content of the travel plan may be adjusted based on the user's past feedback. New travel destinations or activities may also be suggested based on the information obtained from the user's feedback. Furthermore, the user's feedback may be analyzed to identify areas for improvement to improve the quality of the travel plan. This allows the system to provide an optimal travel plan based on the user's feedback.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts questions from users. Questions from users can be in text, audio, or image format. For example, a user might enter, "I want to go from ____ to ____." This information is input into the AI. Step 2: The answering unit provides an answer based on the question received by the receiving unit. The answer is provided in text format, audio format, image format, etc. For example, the answering unit uses natural language processing technology to answer the user's question. Step 3: The suggestion unit proposes customized routes and activities based on the answers obtained by the answering unit. The suggestions are based on the user's past behavioral data and preferences. For example, the suggestion unit proposes optimal routes and activities based on the places the user has visited in the past and the activities in which the user is interested. Step 4: The display unit displays the route or activity suggested by the suggestion unit using 360-degree photos or VR technology. The display unit displays 360-degree photos or VR images of the place the user wants to visit, providing the user with an experience that makes them feel as if they are in that place.

[0063] (Example 2) A virtual travel system according to an embodiment of the present invention allows users to explore destinations around the world and enjoy virtual travel while learning about culture and history. The virtual travel system accepts questions from users, uses AI to answer the questions, proposes customized routes and activities, and displays them using 360-degree photos and VR technology, providing a sense of realism as if the user were actually in the location. For example, a user may input, "I want to go from ____ to ____." For example, a user may input, "I want to go from my home to the station." This information is then input into the AI. The AI ​​then analyzes the input information and proposes customized routes and activities. For example, it proposes optimal routes and activities based on places the user has previously visited or activities they are interested in. Furthermore, the virtual travel system uses 360-degree photos and VR technology to provide a sense of realism as if the user were actually in the location. For example, it displays 360-degree photos and VR footage of a place the user wants to visit, providing an experience that makes the user feel as if they are actually in that location. This allows the virtual travel system to provide options based on the user's preferences and interests, allowing the user to enjoy an experience similar to real travel. This allows the virtual travel system to enjoy a virtual trip tailored to the user's interests and preferences. For example, if a user is interested in history, the system will suggest historical places and events, allowing the user to experience the experience as if they were in those places. This allows the user to enjoy a virtual trip tailored to their interests and preferences.

[0064] A virtual travel system according to an embodiment includes a reception unit, an answering unit, a suggestion unit, and a display unit. The reception unit accepts questions from a user. The questions from the user may be in, for example, text, audio, or image format, but are not limited to these examples. For example, the reception unit receives a user input such as "I want to go from XX to XX." This information is input to an AI. The answering unit provides an answer based on the question accepted by the reception unit. The answer may be in, for example, text, audio, or image format, but are not limited to these examples. For example, the answering unit uses natural language processing technology to answer the user's question. The suggestion unit proposes customized routes and activities based on the answers obtained by the answering unit. The suggestions may be based on, for example, the user's past behavioral data and preferences, but are not limited to these examples. For example, the suggestion unit proposes optimal routes and activities based on places the user has visited in the past and activities in which the user is interested. The display unit displays the routes and activities proposed by the suggestion unit using 360-degree photos or VR technology. The display may, for example, display 360-degree photos or VR video of a place the user wants to visit, providing the user with an experience of being in that place, but is not limited to such examples. For example, the display unit may provide the user with a sense of realism using 360-degree photos or VR technology. As a result, the virtual travel system according to the embodiment allows the user to input questions, and the AI ​​proposes customized routes and activities based on the questions, displaying them using 360-degree photos and VR technology, allowing the user to enjoy a virtual trip.

[0065] The virtual travel system further includes a reception unit that analyzes the user's emotions and adjusts the timing of question acceptance based on the analyzed user emotions. The reception unit analyzes the user's emotions and adjusts the timing of question acceptance based on the analyzed user emotions. Emotion analysis is performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is excited, the reception unit can immediately accept questions and respond quickly. If the user is relaxed, the reception unit can also accept questions at a slower pace. If the user is stressed, the reception unit can start with simple questions and gradually accept more detailed questions. This allows questions to be accepted at a more appropriate time by adjusting the timing of question acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0066] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit analyzes the user's past question history and selects the optimal reception method. The question history includes, for example, past question content, question date and time, etc., but is not limited to these examples. For example, the reception unit preferentially receives topics that the user has frequently asked questions about in the past. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. The reception unit can also select a reception method suitable for a specific time period from the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0067] The reception unit can analyze the user's current interests and concerns and perform filtering when receiving a question. The reception unit analyzes the user's current interests and concerns and performs filtering when receiving a question. The analysis of interests and concerns is performed, for example, based on past behavioral history, survey results, etc., but is not limited to these examples. For example, the reception unit preferentially receives questions related to topics in which the user is currently interested. The reception unit can also analyze the user's social media activity and filter related questions. The reception unit can also filter questions that match the user's current interests based on the user's past search history. In this way, by filtering questions based on the user's current interests and concerns, it is possible to preferentially receive highly relevant questions.

[0068] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. The reception unit selects an appropriate reception means according to the user's input method when receiving a question. Input methods include, but are not limited to, voice input, text input, and image input, for example. For example, when the user uses voice input, the reception unit can receive the question using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the question using natural language processing technology. Furthermore, when the user uses images, the reception unit can also receive the question using image recognition technology. In this way, by selecting the optimal reception means according to the user's input method, questions can be received smoothly.

[0069] The reception unit can analyze the user's emotions and determine the priority of questions to be received based on the analyzed user emotions. The reception unit can analyze the user's emotions and determine the priority of questions to be received based on the analyzed user emotions. Emotion analysis is performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is excited, the reception unit can prioritize receiving important questions. Also, if the user is relaxed, the reception unit can prioritize receiving general questions. Also, if the user is stressed, the reception unit can prioritize receiving simple questions. In this way, by determining the priority of questions according to the user's emotions, important questions can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] When accepting a question, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. When accepting a question, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the reception unit prioritizes accepting questions related to the user's current location. The reception unit can also prioritize accepting questions related to the user's travel destinations. The reception unit can also filter related questions based on the user's geographical location information. In this way, highly relevant questions can be prioritized by taking into account the user's geographical location information.

[0071] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit can analyze the user's social media activity when receiving a question and receive related questions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the reception unit can receive questions related to places where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related questions.

[0072] The reception unit can customize the reception method based on the user's past feedback when receiving a question. The reception unit customizes the reception method based on the user's past feedback when receiving a question. Feedback includes, for example, the user's evaluation comments and survey results, but is not limited to these examples. For example, the reception unit suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially receive specific question formats 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.

[0073] The answering unit can analyze the user's emotions and adjust the way the answer is expressed based on the analyzed user's emotions. The answering unit can analyze the user's emotions and adjust the way the answer is expressed based on the analyzed user's emotions. Emotion analysis can be performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is nervous, the answering unit can provide a simple, highly visible answer. Also, if the user is relaxed, the answering unit can provide an answer that includes detailed information. Also, if the user is in a hurry, the answering unit can provide a concise answer that focuses on the main points. This allows the answering unit to adjust the way the answer is expressed based on the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The answering unit can adjust the level of detail of the answer by analyzing the importance of the question when answering. The answering unit can adjust the level of detail of the answer by analyzing the importance of the question when answering. The importance of a question includes, for example, the user's urgency and the content of the question, but is not limited to these examples. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a concise answer to a general question. The answering unit can also adjust the level of detail of the answer depending on the user's level of interest. In this way, by adjusting the level of detail of the answer based on the importance of the question, a detailed answer can be provided to an important question.

[0075] The answering unit can apply different algorithms depending on the category of the question when answering. The answering unit applies different algorithms depending on the category of the question when answering. Categories include, but are not limited to, technology categories and business categories. For example, the answering unit can answer a question about history by referring to a historical database. The answering unit can also answer a question about culture by referring to a cultural database. The answering unit can also answer a question about tourism by referring to tourism information. In this way, by applying different answering algorithms depending on the category of the question, more appropriate answers can be provided.

[0076] The answering unit can improve the accuracy of the answer based on the user's past answer results when providing an answer. The answering unit can improve the accuracy of the answer based on the user's past answer results when providing an answer. Past answer results include, for example, answer content and answer date and time, but are not limited to these examples. For example, the answering unit provides the optimal answer based on answers the user has received in the past. The answering unit can also improve the accuracy of the answer based on the user's past answer results. The answering unit can also improve the quality of the answer by referring to the user's past feedback. In this way, the accuracy of the answer can be improved by referring to the user's past answer results.

[0077] The answering unit can analyze the user's emotions and adjust the length of the answer based on the analyzed user's emotions. The answering unit can analyze the user's emotions and adjust the length of the answer based on the analyzed user's emotions. Emotion analysis can be performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is in a hurry, the answering unit can provide a short, to-the-point answer. If the user is relaxed, the answering unit can provide a longer answer with detailed explanations. If the user is excited, the answering unit can provide an answer with visually stimulating effects. This allows the length of the answer to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The answering unit can analyze the time when a question was submitted and determine the priority of the answer when answering. The answering unit can analyze the time when a question was submitted and determine the priority of the answer when answering. The submission time includes, but is not limited to, for example, the submission date and time or a timestamp of the question. For example, the answering unit prioritizes answers to questions submitted recently. The answering unit can also prioritize answers to questions submitted by users during a specific time period. The answering unit can also adjust the priority of the answers depending on the time when the question was submitted. In this way, answers can be provided quickly by determining the priority of the answers based on the time when the question was submitted.

[0079] The answering unit can analyze the relevance of the questions and adjust the order of the answers when answering. The answering unit analyzes the relevance of the questions and adjusts the order of the answers when answering. Relevance includes, but is not limited to, for example, similarity of the question content and related topics. For example, the answering unit prioritizes answers to questions with high relevance. The answering unit can also adjust the order of the answers according to the user's level of interest. The answering unit can also adjust the order of the answers based on the category of the question. In this way, by adjusting the order of the answers based on the relevance of the questions, answers can be provided preferentially to questions with high relevance.

[0080] The answering unit can adjust the use of technical terms in the answer by analyzing the user's level of expertise when answering. The answering unit can adjust the use of technical terms in the answer by analyzing the user's level of expertise when answering. Expertise levels include, but are not limited to, the user's occupation and past question content. For example, if the user has specialized knowledge, the answering unit can provide an answer that uses a lot of technical terms. Also, if the user has general knowledge, the answering unit can provide an answer in simple language. Also, the answering unit can adjust the use of technical terms in the answer depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the answer depending on the user's level of expertise, a more appropriate answer can be provided.

[0081] The suggestion unit can analyze the user's emotions and adjust the way the suggestions are expressed based on the analyzed user emotions. The suggestion unit can analyze the user's emotions and adjust the way the suggestions are expressed based on the analyzed user emotions. Emotion analysis is performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise suggestion that focuses on the main points. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The suggestion unit can adjust the level of detail of the suggestion by analyzing the importance of the route or activity when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion by analyzing the importance of the route or activity when making a suggestion. The importance includes, but is not limited to, for example, the user's priority and the importance of the suggestion content. For example, the suggestion unit can provide detailed suggestions for important routes or activities. The suggestion unit can also provide concise suggestions for general routes or activities. The suggestion unit can also adjust the level of detail of the suggestion according to the user's level of interest. In this way, by adjusting the level of detail of the suggestion based on the importance of the route or activity, detailed information can be provided for important suggestions.

[0083] The suggestion unit can apply different algorithms depending on the category of the route or activity when making a suggestion. The suggestion unit applies different algorithms depending on the category of the route or activity when making a suggestion. Examples of categories include, but are not limited to, a tourism category and a sports category. For example, the suggestion unit can make a suggestion for a historical route or activity by referring to a historical database. Furthermore, the suggestion unit can make a suggestion for a cultural route or activity by referring to a cultural database. Furthermore, the suggestion unit can make a suggestion for a tourist route or activity by referring to tourist information. In this way, by applying different suggestion algorithms depending on the category of the route or activity, more appropriate suggestions can be provided.

[0084] The suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. The suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results when making a suggestion. Past suggestion results include, for example, proposal content and the date and time of the suggestion, but are not limited to these examples. For example, the suggestion unit provides an optimal suggestion based on suggestions the user has received in the past. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit can also improve the quality of the suggestion by referring to the user's past feedback. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0085] The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user emotions. The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user emotions. Emotion analysis can be performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The suggestion unit can analyze the time of submission of a route or activity to determine the priority of the proposal when making a proposal. The suggestion unit can analyze the time of submission of a route or activity to determine the priority of the proposal when making a proposal. The submission time includes, but is not limited to, for example, the date and time of submission of the proposal or a timestamp. For example, the suggestion unit can prioritize proposing recently submitted routes or activities. The suggestion unit can also prioritize proposing routes and activities submitted by a user during a specific time period. The suggestion unit can also adjust the priority of the proposal depending on the time of submission of the proposal. This allows for rapid provision of proposals by determining the priority of the proposal based on the time of submission of the route or activity.

[0087] The suggestion unit can analyze the relevance of routes and activities when making suggestions and adjust the order of suggestions. The suggestion unit analyzes the relevance of routes and activities when making suggestions and adjust the order of suggestions. Relevance includes, but is not limited to, similarity of proposal content and related topics. For example, the suggestion unit prioritizes suggesting highly relevant routes and activities. The suggestion unit can also adjust the order of suggestions according to the user's level of interest. The suggestion unit can also adjust the order of suggestions based on the category of the route or activity. In this way, by adjusting the order of suggestions based on the relevance of routes and activities, highly relevant suggestions can be provided preferentially.

[0088] The suggestion unit may analyze the user's level of expertise when making a suggestion and adjust the use of technical terms in the suggestion. The suggestion unit may analyze the user's level of expertise when making a suggestion and adjust the use of technical terms in the suggestion. Examples of the level of expertise include, but are not limited to, the user's occupation and past suggestion content. For example, if the user has specialized knowledge, the suggestion unit may provide a suggestion that uses a lot of technical terms. Furthermore, if the user has general knowledge, the suggestion unit may provide a suggestion in simple language. Furthermore, the suggestion unit may adjust the use of technical terms in the suggestion according to the user's level of expertise. In this way, more appropriate suggestions can be provided by adjusting the use of technical terms in the suggestion according to the user's level of expertise.

[0089] The display unit can analyze the user's emotions and adjust the display method based on the analyzed user's emotions. The display unit can analyze the user's emotions and adjust the display method based on the analyzed user's emotions. Emotion analysis is performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, the display unit can provide an interface with subdued colors when the user is nervous. The display unit can also provide an interface with bright colors when the user is having fun. The display unit can also provide a simple, highly visible interface when the user is tired. This allows for a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The display unit can select the optimal display method by referring to the user's past display history when displaying. The display unit can select the optimal display method by referring to the user's past display history when displaying. The display history includes, for example, past display content and display date and time, but is not limited to these examples. For example, the display unit provides the optimal display method based on the user's preferred display style in the past. The display unit can also select a display method with high visibility from the user's past display history. The display unit can also improve the display method by referring to the user's past feedback. In this way, the optimal display method can be provided by referring to the user's past display history.

[0091] The display unit can analyze the user's current interests and customize the display content when displaying the information. The display unit analyzes the user's current interests and customize the display content when displaying the information. The analysis of interests and concerns is performed, for example, based on past behavioral history, survey results, etc., but is not limited to these examples. For example, the display unit can prioritize displaying information related to topics in which the user is currently interested. The display unit can also analyze the user's social media activity and customize and display related information. The display unit can also display information that matches the user's current interests based on the user's past search history. In this way, highly relevant information can be provided by customizing the display content based on the user's current interests and concerns.

[0092] The display unit can improve the display method based on user feedback during display. The display unit improves the display method based on user feedback during display. Feedback includes, for example, user evaluation comments and survey results, but is not limited to these examples. For example, the display unit can suggest an optimal display method based on feedback previously provided by the user. The display unit can also preferentially provide a specific display style based on the user's past feedback. The display unit can also analyze the user's past feedback and improve the display method. In this way, the display method can be improved by reflecting the user's feedback.

[0093] The display unit can analyze the user's emotions and determine display priorities based on the analyzed user emotions. The display unit can analyze the user's emotions and determine display priorities based on the analyzed user emotions. Emotion analysis is performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. For example, when the user is excited, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying general information. Furthermore, when the user is stressed, the display unit can prioritize displaying simple information. In this way, by determining display priorities according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The display unit can select an appropriate display method in consideration of the user's geographical location information when displaying information. The display unit can select an appropriate display method in consideration of the user's geographical location information when displaying information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the display unit can prioritize displaying information related to the user's current location. The display unit can also prioritize displaying information related to the user's travel destinations. The display unit can also filter and display related information based on the user's geographical location information. This makes it possible to provide highly relevant information by taking the user's geographical location information into consideration.

[0095] The display unit can analyze the user's social media activity and customize the display content when displaying the information. The display unit analyzes the user's social media activity and customize the display content when displaying the information. Social media activity includes, for example, but is not limited to, posted content and the number of likes. For example, the display unit displays information related to places where the user has checked in on social media. The display unit can also analyze the user's social media posts and customize and display related information. The display unit can also display related information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0096] The display unit can customize the display method based on the user's past feedback when displaying. The display unit customizes the display method based on the user's past feedback when displaying. Feedback includes, for example, the user's evaluation comments and survey results, but is not limited to these examples. For example, the display unit suggests an optimal display method based on the user's past feedback. The display unit can also provide a specific display style with priority based on the user's past feedback. The display unit can also analyze the user's past feedback and improve the display method. In this way, the optimal display method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, response unit, suggestion unit, and display unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from the user. The answer unit is implemented by the specific processing unit 290 of the data processing device 12 and answers the user's questions. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests customized routes and activities. The display unit is implemented by the output device 40 of the smart device 14 and provides a sense of realism to the user using 360-degree photography and VR technology. Furthermore, the reception unit with an emotion analysis function analyzes the user's emotions using, for example, the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of question reception by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, response unit, suggestion unit, and display unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions from the user. The answer unit is realized by the specific processing unit 290 of the data processing device 12 and provides answers to the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests customized routes and activities. The display unit provides a sense of realism to the user using, for example, the speaker 240 and camera 42 of the smart glasses 214. Furthermore, the reception unit with an emotion analysis function analyzes the user's emotions using, for example, the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of receiving the question using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, response unit, suggestion unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives questions from the user. The answer unit is realized by the specific processing unit 290 of the data processing device 12 and provides answers to the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests customized routes and activities. The display unit provides the user with a sense of realism using, for example, the display 343 and speaker 240 of the headset-type terminal 314. Furthermore, the reception unit with an emotion analysis function analyzes the user's emotions using, for example, the camera 42 and microphone 238 of the headset-type terminal 314, and adjusts the timing of receiving the question using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, response unit, suggestion unit, and display unit 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 control unit 46A of the robot 414 and receives questions from the user. The answer unit is realized by the specific processing unit 290 of the data processing device 12 and answers the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests customized routes and activities. The display unit provides the user with a sense of realism using, for example, the speaker 240 and camera 42 of the robot 414. Furthermore, the reception unit with an emotion analysis function analyzes the user's emotions using, for example, the camera 42 and microphone 238 of the robot 414, and adjusts the timing of receiving the question using the specific processing unit 290 of the data processing device 12.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The virtual travel system may further include a health management unit that monitors the user's health and adjusts the travel plan based on the user's health. For example, if the user feels tired, the health management unit may suggest relaxing activities. If the user is active, the health management unit may suggest energetic activities. Furthermore, if the user has a specific health problem, the health management unit may provide a travel plan that takes that problem into consideration. This allows the user to have an optimal travel experience based on their health.

[0099] The virtual travel system may further include an emotion analysis unit that analyzes the user's emotions and selects travel destinations based on the analyzed emotions. For example, if the user is feeling stressed, the emotion analysis unit may suggest a relaxing travel destination. If the user is excited, the emotion analysis unit may also suggest an active travel destination. Furthermore, if the user is sad, the emotion analysis unit may also suggest a travel destination that will brighten the mood. This makes it possible to provide the optimal travel destination according to the user's emotions.

[0100] The virtual travel system may further include a history analysis unit that analyzes the user's past travel history and suggests new travel destinations based on the past travel history. For example, similar travel destinations may be suggested based on the places the user has visited or the activities the user has participated in in the past. The system may also analyze the characteristics of travel destinations the user has liked in the past and suggest new travel destinations based on the results. Furthermore, the system may take into account travel destinations and activities that the user has avoided in the past and make suggestions that avoid those destinations. This makes it possible to provide optimal travel destinations based on the user's past travel history.

[0101] The virtual travel system may further include an activity tracker that acquires real-time activity data of the user and adjusts the travel plan based on the data. For example, if the user is currently walking, the activity tracker may suggest a walking tour. If the user is currently sitting, the activity tracker may suggest activities that can be enjoyed while sitting. Furthermore, if the user is exercising, the activity tracker may suggest active activities. This allows the system to provide an optimal travel plan based on the user's real-time activity data.

[0102] The virtual travel system may further include a social analysis unit that analyzes the user's social media activity and suggests travel destinations based on the analyzed social media activity. For example, the system may suggest travel destinations related to places the user has checked in on social media. The system may also suggest the same places based on places the user's friends have visited. Furthermore, the system may suggest travel destinations related to topics the user has shown interest in on social media. This allows the system to provide optimal travel destinations based on the user's social media activity.

[0103] The virtual travel system may further include an emotion adjustment unit that analyzes the user's emotions and adjusts activities during the trip based on the analyzed emotions. For example, if the user feels tired during the trip, the emotion adjustment unit may suggest a relaxing activity. If the user feels excited, the emotion adjustment unit may suggest an energetic activity. Furthermore, if the user feels stressed, the emotion adjustment unit may suggest an activity that will reduce stress. In this way, the optimal activity can be provided according to the user's emotions.

[0104] The virtual travel system may further include a location information analysis unit that analyzes the user's geographical location information and suggests travel destinations based on the analyzed geographical location information. For example, the system may suggest travel destinations close to the user's current location. It may also suggest travel destinations related to places the user has visited in the past. Furthermore, if the user is interested in a particular region, it may suggest travel destinations related to that region. This makes it possible to provide optimal travel destinations based on the user's geographical location information.

[0105] The virtual travel system may further include an emotional support unit that analyzes the user's emotions and provides support during the trip based on the analyzed emotions. For example, if the user feels anxious during the trip, the emotional support unit may provide information that gives a sense of security. Also, if the user feels excited during the trip, the emotional support unit may provide information that further increases the excitement. Furthermore, if the user feels tired during the trip, the emotional support unit may provide information that helps the user relax. In this way, optimal support can be provided according to the user's emotions.

[0106] The virtual travel system may further include a feedback analysis unit that analyzes the user's past feedback and improves the travel plan based on the feedback. For example, the content of the travel plan may be adjusted based on the user's past feedback. New travel destinations or activities may also be suggested based on the information obtained from the user's feedback. Furthermore, the user's feedback may be analyzed to identify areas for improvement to improve the quality of the travel plan. This allows the system to provide an optimal travel plan based on the user's feedback.

[0107] The virtual travel system may further include an emotion communication unit that analyzes the user's emotions and adjusts communication during the trip based on the analyzed emotions. For example, if the user feels nervous during the trip, the emotion communication unit may provide communication that helps the user relax. Also, if the user feels excited during the trip, the emotion communication unit may provide communication that shares that excitement. Furthermore, if the user feels stressed during the trip, the emotion communication unit may provide communication that helps to alleviate stress. In this way, optimal communication can be provided according to the user's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit accepts questions from users. Questions from users can be in text, audio, or image format. For example, a user might enter, "I want to go from ____ to ____." This information is input into the AI. Step 2: The answering unit provides an answer based on the question received by the receiving unit. The answer is provided in text format, audio format, image format, etc. For example, the answering unit uses natural language processing technology to answer the user's question. Step 3: The suggestion unit proposes customized routes and activities based on the answers obtained by the answering unit. The suggestions are based on the user's past behavioral data and preferences. For example, the suggestion unit proposes optimal routes and activities based on the places the user has visited in the past and the activities in which the user is interested. Step 4: The display unit displays the route or activity suggested by the suggestion unit using 360-degree photos or VR technology. The display unit displays 360-degree photos or VR images of the place the user wants to visit, providing the user with an experience that makes them feel as if they are in that place.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 questions from users; an answering unit that answers the questions received by the receiving unit; a suggestion unit that proposes individually tailored routes and activities based on the answers obtained by the answering unit; a display unit that displays the route and activity proposed by the proposal unit using 360-degree photos or VR technology. A system characterized by:

2. The reception unit Analyze user emotions and adjust the timing of accepting questions based on the analyzed user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting questions, filter them based on the user's current interests and concerns.

2. The system of claim 1.

5. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Analyze user sentiment and prioritize questions based on the sentiment 2. The system of claim 1.

7. The reception unit When accepting questions, the system takes into account the user's geographic location information and prioritizes relevant questions.

2. The system of claim 1.

8. The reception unit When a question is received, the user's social media activity is analyzed and related questions are received.

2. The system of claim 1.

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