System

Abstract

The system according to the embodiment aims to provide a personal travel guide service that can be immediately used by a traveler on site.SOLUTION: A system according to an embodiment includes a reception unit, a customization unit, a support unit, and a language support unit. The reception unit receives a request from a user. The customization unit performs customization based on the request received by the reception unit. The support unit provides support in real time based on the content customized by the customization unit. The language support unit supports a plurality of languages.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide a personal travel guide service that travelers can use immediately when they are in the area.

[0005] The system according to the embodiment aims to provide a personal travel guide service that can be used immediately by travelers on-site. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a customization unit, a support unit, and a language support unit. The reception unit receives requests from users. The customization unit performs customization based on the requests received by the reception unit. The support unit provides support in real time based on the content customized by the customization unit. The language support unit supports multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personal travel guide service that can be used immediately by travelers on-site. [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) An on-demand personal travel guide system according to an embodiment of the present invention is a system that travelers can use instantly while they are in the area. This system accepts user requests, customizes them, provides real-time support, and supports multiple languages. For example, travelers can request a guide through an app and input their interests, desired tourist destinations, and local spots. Next, the system customizes the guide to meet the travelers' needs and allows for instant and flexible booking. Furthermore, the guide provides not only expert knowledge but also local information and multilingual support. Finally, real-time support is provided. This allows travelers to learn more about the attractions of the local area and enjoy their trip with peace of mind. As a result, the on-demand personal travel guide system can achieve customization to meet the travelers' needs, instant and flexible booking, expertise and local information, multilingual support, and real-time support. For example, by quickly and accurately accepting and customizing requests written by travelers and providing real-time support, traveler satisfaction can be improved.

[0029] The on-demand personal travel guide system according to the embodiment includes a reception unit, a customization unit, a support unit, and a language support unit. The reception unit receives requests from users. Requests include, but are not limited to, text, audio, and images. The reception unit receives requests entered by users through an app, for example. The reception unit can also receive audio requests using voice recognition technology. The reception unit can also receive image requests using image recognition technology. For example, the reception unit analyzes text requests entered by users into the app to understand the request. For audio requests, the reception unit converts the audio into text using voice recognition technology and analyzes the request. For image requests, the reception unit analyzes the image using image recognition technology and understands the request. The customization unit performs customization based on the request received by the reception unit. Customization is performed based on, for example, user preferences and past history, but is not limited to, examples. For example, the customization unit analyzes the user's request and selects the most suitable guide. The customization unit can also reserve a guide based on the user's schedule. Furthermore, the customization unit can also perform customization based on the user's interests, desired tourist destinations, local spots, etc. For example, the customization unit analyzes the request content entered by the user and selects the most suitable guide. When reserving a guide to match the user's schedule, the customization unit analyzes the user's schedule and reserves a guide for the most suitable time slot. When customization is performed based on the user's interests, desired tourist destinations, local spots, etc., the customization unit analyzes the user's interests, desired tourist destinations, local spots, etc. and selects the most suitable guide. The support unit provides support in real time based on the content customized by the customization unit. Support is provided, for example, when there is a sudden change during the trip or when there is an urgent question, but is not limited to such examples. For example, the support unit changes the guide in real time when there is a sudden change during the trip. The support unit can also provide answers in real time when there is an urgent question.Furthermore, the support unit can also handle troubles during the trip and provide emergency contact information. For example, if there is a sudden change during the trip, the support unit can change the guide in real time. If there is an urgent question, the support unit can provide an answer in real time. In the case where troubles during the trip and emergency contact information are provided, the support unit handles troubles during the trip and provides emergency contact information in real time. The language support unit supports multiple languages. Supported languages ​​include, but are not limited to, English, Japanese, and Chinese. For example, the language support unit provides guide explanations in a language selected by the user. The language support unit may also have a translation function to support multiple languages. Furthermore, the language support unit can customize language support means according to the user's language situation. For example, the language support unit provides guide explanations in a language selected by the user. If the language support unit has a translation function to support multiple languages, the language support unit translates in real time and provides it to the user. In the case where language support means is customized according to the user's language situation, the language support unit analyzes the user's language situation and provides optimal language support. As a result, the on-demand personal travel guide system according to the embodiment can efficiently accept user requests, and can provide customization, real-time support, and multilingual support.

[0030] The support unit can perform customization based on the needs of the traveler. For example, if a traveler is interested in a particular theme, the support unit will provide a guide with detailed information on that theme. The support unit can also make reservations for guides to suit the traveler's schedule. The support unit can also perform customization based on the traveler's interests, desired tourist destinations, local spots, etc. For example, if a traveler is interested in historical tourist destinations, the support unit will provide a guide with detailed information on history. When booking a guide to suit the traveler's schedule, the support unit analyzes the traveler's schedule and reserves a guide for the optimal time slot. When customization is performed based on the traveler's interests, desired tourist destinations, local spots, etc., the support unit analyzes the traveler's interests, desired tourist destinations, local spots, etc., and provides the optimal guide. This makes it possible to perform customization according to the traveler's needs.

[0031] The customization unit can make a guide reservation according to the traveler's schedule. For example, if a traveler needs a guide during a specific time period, the customization unit reserves a guide who is available during that time period. The customization unit can also flexibly adjust a guide reservation according to the traveler's schedule. Furthermore, the customization unit can also suggest an optimal sightseeing route based on the traveler's schedule. For example, if a traveler wishes to sightsee in the morning, the customization unit reserves a guide who is available in the morning. When flexibly adjusting a guide reservation according to the traveler's schedule, the customization unit analyzes the traveler's schedule and reserves a guide for the optimal time period. When suggesting an optimal sightseeing route based on the traveler's schedule, the customization unit analyzes the traveler's schedule and suggests an efficient sightseeing route. This makes it possible to make flexible reservations according to the traveler's schedule.

[0032] The support department can provide support in real time if there are any sudden changes during the trip or if there are any urgent questions. For example, if a traveler wants to change their plans, the support department changes the guide in real time. The support department can also provide an answer in real time if a traveler has an urgent question. Furthermore, the support department can also handle troubles during the trip and provide emergency contact information. For example, if a traveler wants to change their plans, the support department changes the guide in real time. If a traveler has an urgent question, the support department provides an answer in real time. If there are any troubles during the trip or if there are any emergency contact information provided, the support department can handle troubles during the trip or provide emergency contact information in real time. This allows for real-time support for sudden changes during the trip or urgent questions.

[0033] The language support unit supports multiple languages, allowing travelers to receive guided explanations in their own language. The language support unit supports multiple languages, for example, English, Japanese, and Chinese. The language support unit can also provide guided explanations in a language selected by the user. Furthermore, the language support unit can have a translation function to support multiple languages. For example, the language support unit provides guided explanations in a language selected by the user. If the language support unit has a translation function to support multiple languages, the language support unit translates in real time and provides the translation to the user. This allows travelers to receive guided explanations in their own language.

[0034] The customization unit can perform customization based on at least one of the traveler's interests or desired tourist spots and local spots. For example, if a traveler is interested in historical tourist spots, the customization unit can provide a detailed guide to those tourist spots. Also, if a traveler wants to know about delicious local restaurants, the customization unit can provide a detailed guide to those restaurants. Furthermore, if a traveler wants to visit a hidden spot, the customization unit can provide a detailed guide to that spot. For example, if a traveler is interested in historical tourist spots, the customization unit can provide a detailed guide to those tourist spots. If a traveler wants to know about delicious local restaurants, the customization unit can provide a detailed guide to those restaurants. If a traveler wants to visit a hidden spot, the customization unit can provide a detailed guide to that spot. This makes it possible to customize according to the traveler's interests and wishes.

[0035] The support section allows the guide to provide not only specialized knowledge but also local information. For example, the support section allows the guide to provide detailed explanations about the history and culture of the tourist destination. The support section also allows the guide to provide information about recommended local spots and events. The support section also allows the guide to explain local customs and food culture. For example, the support section allows the guide to provide detailed explanations about the history and culture of the tourist destination. If the guide also provides information about recommended local spots and events, the support section allows the guide to provide information about recommended local spots and events. If the guide also explains local customs and food culture, the support section allows the guide to explain about local customs and food culture. In this way, the guide's specialized knowledge and local information allow travelers to learn more about the attractions of the local area.

[0036] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays as candidates guides that the user has frequently requested in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest a guide to be used in a specific time period from the user's past request history. For example, the reception unit automatically displays as candidates guides that the user has frequently requested in the past. When preferentially suggesting input methods (voice, text, etc.) that the user has used in the past, the reception unit analyzes the user's past request history and suggests the optimal input method. When predicting and suggesting a guide to be used in a specific time period from the user's past request history, the reception unit analyzes the user's past request history and suggests a guide to be used in the optimal time period. This makes it possible to provide the optimal reception method based on the user's past request history.

[0037] When receiving a request, the reception unit can perform filtering based on the user's current travel situation and areas of interest. The reception unit, for example, suggests a related guide based on tourist attractions currently visited by the user. The reception unit can also suggest an optimal guide based on the user's areas of interest (history, food, nature, etc.). The reception unit can also suggest a guide that is available at an appropriate time based on the user's travel schedule. For example, the reception unit suggests a related guide based on tourist attractions currently visited by the user. When suggesting an optimal guide based on the user's areas of interest (history, food, nature, etc.), the reception unit analyzes the user's areas of interest and suggests an optimal guide. When suggesting a guide that is available at an appropriate time based on the user's travel schedule, the reception unit analyzes the user's travel schedule and suggests a guide that is available at an optimal time. This makes it possible to filter requests according to the user's current travel situation and areas of interest.

[0038] When accepting a request, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when the user makes a request by voice, the acceptance unit accepts the request using voice recognition technology. Furthermore, when the user makes a request by text, the acceptance unit can also accept the request using text analysis technology. Furthermore, when the user uploads an image, the acceptance unit can also accept the request using image recognition technology. For example, when the user makes a request by voice, the acceptance unit accepts the request using voice recognition technology. When the user makes a request by text, the acceptance unit accepts the request using text analysis technology. When the user uploads an image, the acceptance unit accepts the request using image recognition technology. This makes it possible to provide the optimal acceptance means according to the user's input method.

[0039] When receiving a request, the reception unit can prioritize receiving highly relevant requests in consideration of the user's geographical location information. For example, the reception unit prioritizes receiving requests for tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the reception unit can also prioritize receiving requests related to the area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving requests related to the destination. For example, the reception unit prioritizes receiving requests for tourist spots close to the user's current location. If the user is staying in a specific area, the reception unit prioritizes receiving requests related to the area. If the user is traveling, the reception unit prioritizes receiving requests related to the destination. This makes it possible to provide a priority order for requests based on the user's geographical location information.

[0040] The reception unit can analyze the user's social media activity when receiving a request and receive a related request. The reception unit, for example, receives a request related to a place where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and receive a related request. Furthermore, the reception unit can also receive a related request by referring to the activities of the user's friends on social media. For example, the reception unit receives a request related to a place where the user has checked in on social media. When analyzing the content of the user's posts on social media and receiving a related request, the reception unit analyzes the content of the user's posts on social media and receives a related request. When receiving a related request by referring to the activities of the user's friends on social media, the reception unit receives a related request by referring to the activities of the user's friends on social media. This makes it possible to receive requests based on the user's social media activity.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific guide based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit suggests the optimal reception method based on the user's past feedback. When preferentially suggesting a specific guide based on the user's past feedback, the reception unit analyzes the user's past feedback and preferentially suggests the specific guide. When analyzing the user's past feedback and improving the reception method, the reception unit analyzes the user's past feedback and improves the reception method. This makes it possible to customize the reception method based on the user's past feedback.

[0042] During customization, the customization unit can adjust the level of detail of the customization based on the traveler's interests and desired tourist spots. For example, if the traveler is interested in historical tourist spots, the customization unit can provide detailed historical information. Also, if the traveler is interested in food, the customization unit can provide information on recommended local restaurants. Furthermore, if the traveler is interested in nature, the customization unit can provide detailed information on nature spots. For example, if the traveler is interested in historical tourist spots, the customization unit can provide detailed historical information. If the traveler is interested in food, the customization unit can provide information on recommended local restaurants. If the traveler is interested in nature, the customization unit can provide detailed information on nature spots. This makes it possible to provide the level of detail of the customization according to the traveler's interests and wishes.

[0043] During customization, the customization unit can apply different customization algorithms depending on the traveler's schedule. For example, if the traveler can only stay for a short time, the customization unit can suggest an efficient sightseeing route. Also, if the traveler can stay for a long time, the customization unit can also suggest a detailed sightseeing plan. Furthermore, the customization unit can suggest the most suitable tourist spots to suit the traveler's schedule. For example, if the traveler can only stay for a short time, the customization unit suggests an efficient sightseeing route. If the traveler can stay for a long time, the customization unit suggests a detailed sightseeing plan. When suggesting the most suitable tourist spots to suit the traveler's schedule, the customization unit analyzes the traveler's schedule and suggests the most suitable tourist spots. This makes it possible to provide a customization algorithm that suits the traveler's schedule.

[0044] During customization, the customization unit can improve the accuracy of the customization by referring to the user's past customization results. The customization unit, for example, suggests optimal customization based on tourist destinations that the user has previously favored. The customization unit can also suggest customization based on a specific theme from the user's past customization results. The customization unit can also analyze the user's past customization results to improve the accuracy of the customization. For example, the customization unit suggests optimal customization based on tourist destinations that the user has previously favored. When suggesting customization based on a specific theme from the user's past customization results, the customization unit analyzes the user's past customization results and suggests customization based on the specific theme. When analyzing the user's past customization results to improve the accuracy of the customization, the customization unit analyzes the user's past customization results and improves the accuracy of the customization. This makes it possible to improve the accuracy of the customization based on the user's past customization results.

[0045] During customization, the customization unit can determine the priority of customization based on the time of submission by the traveler. For example, the customization unit prioritizes customization of the request most recently submitted by the traveler. The customization unit can also perform planned customization based on requests submitted in advance by the traveler. Furthermore, the customization unit can provide optimal customization depending on the time of submission by the traveler. For example, the customization unit prioritizes customization of the request most recently submitted by the traveler. When performing planned customization based on requests submitted in advance by the traveler, the customization unit analyzes the time of submission by the traveler and provides optimal customization. When providing optimal customization depending on the time of submission by the traveler, the customization unit analyzes the time of submission by the traveler and provides optimal customization. This makes it possible to provide a priority of customization depending on the time of submission by the traveler.

[0046] During customization, the customization unit can adjust the order of customization based on the traveler's relevance. For example, if the traveler is interested in a particular theme, the customization unit prioritizes customization related to that theme. Also, if the traveler is staying in a particular region, the customization unit can prioritize customization related to that region. Furthermore, the customization unit can propose an optimal customization order based on the traveler's interests and concerns. For example, if the traveler is interested in a particular theme, the customization unit prioritizes customization related to that theme. If the traveler is staying in a particular region, the customization unit prioritizes customization related to that region. When proposing an optimal customization order based on the traveler's interests and concerns, the customization unit analyzes the traveler's interests and concerns and proposes an optimal customization order. This makes it possible to provide a customization order according to the traveler's relevance.

[0047] During customization, the customization unit can adjust the use of specialized terminology in the customization according to the expertise level of the traveler. For example, if the traveler is a beginner, the customization unit provides an easy-to-understand explanation avoiding specialized terminology. Furthermore, if the traveler is an intermediate traveler, the customization unit can provide an explanation using appropriate specialized terminology. Furthermore, if the traveler is an advanced traveler, the customization unit can provide an explanation using detailed specialized terminology. For example, if the traveler is a beginner, the customization unit provides an easy-to-understand explanation avoiding specialized terminology. If the traveler is an intermediate traveler, the customization unit provides an explanation using appropriate specialized terminology. If the traveler is an advanced traveler, the customization unit provides an explanation using detailed specialized terminology. This makes it possible to provide the use of specialized terminology in the customization according to the expertise level of the traveler.

[0048] When providing support, the support unit can analyze the traveler's past support history and select the optimal support method. The support unit provides the optimal support, for example, based on the support methods that the traveler preferred in the past. The support unit can also provide support based on a specific theme from the traveler's past support history. Furthermore, the support unit can analyze the traveler's past support history and improve the accuracy of the support. For example, the support unit provides the optimal support based on the support methods that the traveler preferred in the past. When providing support based on a specific theme from the traveler's past support history, the support unit analyzes the traveler's past support history and provides support based on the specific theme. When analyzing the traveler's past support history and improving the accuracy of support, the support unit analyzes the traveler's past support history and improves the accuracy of support. This makes it possible to provide the optimal support method based on the traveler's past support history.

[0049] When providing support, the support unit can customize the means of support based on the traveler's current travel situation. For example, when the traveler is at a tourist spot, the support unit provides information related to the tourist spot. Also, when the traveler is traveling, the support unit can provide support related to the travel. Furthermore, when the traveler is taking a break, the support unit can provide information that will help the traveler relax. For example, when the traveler is at a tourist spot, the support unit provides information related to the tourist spot. When the traveler is traveling, the support unit provides support related to the travel. When the traveler is taking a break, the support unit provides information that will help the traveler relax. This makes it possible to provide means of support that are appropriate for the traveler's current travel situation.

[0050] The support unit can improve the support method by reflecting the user's feedback when providing support. The support unit improves the support method, for example, based on the feedback provided by the user. The support unit can also preferentially provide a specific support method based on the user's feedback. Furthermore, the support unit can analyze the user's feedback and improve the accuracy of the support. For example, the support unit improves the support method based on the feedback provided by the user. When preferentially providing a specific support method based on the user's feedback, the support unit analyzes the user's feedback and preferentially provides the specific support method. When analyzing the user's feedback and improving the accuracy of the support, the support unit analyzes the user's feedback and improves the accuracy of the support. This makes it possible to improve the support method based on the user's feedback.

[0051] When providing support, the support unit can select the optimal support method by taking into consideration the traveler's geographical location information. For example, the support unit can prioritize providing support related to tourist attractions close to the traveler's current location. Furthermore, if the traveler is staying in a specific area, the support unit can also prioritize providing support related to that area. Furthermore, if the traveler is traveling, the support unit can also prioritize providing support related to the travel destination. For example, the support unit can prioritize providing support related to tourist attractions close to the traveler's current location. If the traveler is staying in a specific area, the support unit can prioritize providing support related to that area. If the traveler is traveling, the support unit can prioritize providing support related to the travel destination. This makes it possible to provide the optimal support method based on the traveler's geographical location information.

[0052] When providing support, the support unit can analyze the traveler's social media activity and suggest support measures. For example, the support unit provides support related to places where the traveler has checked in on social media. The support unit can also analyze the content of the traveler's posts on social media and provide related support. Furthermore, the support unit can provide related support by referring to the activities of the traveler's friends on social media. For example, the support unit provides support related to places where the traveler has checked in on social media. When analyzing the content of the traveler's posts on social media and providing related support, the support unit analyzes the content of the traveler's posts on social media and provides related support. When providing related support by referring to the activities of the traveler's friends on social media, the support unit provides related support by referring to the activities of the traveler's friends on social media. This makes it possible to provide support measures based on the traveler's social media activity.

[0053] When providing support, the support unit can customize the support method by reflecting the traveler's past feedback. For example, the support unit suggests the optimal support method based on feedback provided by the traveler in the past. The support unit can also prioritize providing a specific support method based on the traveler's past feedback. Furthermore, the support unit can analyze the traveler's past feedback and improve the accuracy of support. For example, the support unit suggests the optimal support method based on feedback provided by the traveler in the past. When prioritizing providing a specific support method based on the traveler's past feedback, the support unit analyzes the traveler's past feedback and prioritizes providing the specific support method. When analyzing the traveler's past feedback and improving the accuracy of support, the support unit analyzes the traveler's past feedback and improves the accuracy of support. This makes it possible to customize the support method based on the traveler's past feedback.

[0054] When providing language support, the language support unit can analyze the traveler's past language support history to select the optimal language support method. The language support unit, for example, provides optimal language support based on the language support method that the traveler has previously preferred. The language support unit can also provide language support based on a specific theme from the traveler's past language support history. The language support unit can also analyze the traveler's past language support history to improve the accuracy of language support. For example, the language support unit provides optimal language support based on the language support method that the traveler has previously preferred. When providing language support based on a specific theme from the traveler's past language support history, the language support unit analyzes the traveler's past language support history to provide language support based on the specific theme. When analyzing the traveler's past language support history to improve the accuracy of language support, the language support unit analyzes the traveler's past language support history to improve the accuracy of language support. This makes it possible to provide the optimal language support method based on the traveler's past language support history.

[0055] When providing language support, the language support unit can customize the language support means based on the traveler's current language situation. For example, if the traveler uses a specific language, the language support unit provides support corresponding to that language. Furthermore, if the traveler uses multiple languages, the language support unit can also provide a language switching function. Furthermore, the language support unit can also provide optimal language support according to the traveler's language situation. For example, if the traveler uses a specific language, the language support unit provides support corresponding to that language. If the traveler uses multiple languages, the language support unit provides a language switching function. When providing optimal language support according to the traveler's language situation, the language support unit analyzes the traveler's language situation and provides optimal language support. This makes it possible to provide language support means according to the traveler's current language situation.

[0056] The language support unit can improve the language support method by reflecting user feedback during language support. For example, the language support unit improves the language support method based on feedback provided by the user. The language support unit can also prioritize providing a specific language support method based on the user feedback. Furthermore, the language support unit can analyze the user feedback and improve the accuracy of language support. For example, the language support unit improves the language support method based on feedback provided by the user. When prioritizing providing a specific language support method based on the user feedback, the language support unit analyzes the user feedback and prioritizes providing the specific language support method. When analyzing the user feedback and improving the accuracy of language support, the language support unit analyzes the user feedback and improves the accuracy of language support. This makes it possible to improve the language support method based on user feedback.

[0057] When providing language support, the language support unit can select the optimal language support method by taking into consideration the traveler's geographical location information. For example, the language support unit can prioritize providing language support related to tourist attractions close to the traveler's current location. Furthermore, if the traveler is staying in a specific area, the language support unit can also prioritize providing language support related to the area. Furthermore, if the traveler is traveling, the language support unit can also prioritize providing language support related to the travel destination. For example, the language support unit prioritizes providing language support related to tourist attractions close to the traveler's current location. If the traveler is staying in a specific area, the language support unit prioritizes providing language support related to the area. If the traveler is traveling, the language support unit prioritizes providing language support related to the travel destination. This makes it possible to provide the optimal language support method based on the traveler's geographical location information.

[0058] When providing language support, the language support unit can analyze the traveler's social media activity and suggest language support means. For example, the language support unit provides language support related to places where the traveler has checked in on social media. The language support unit can also analyze the traveler's social media posts and provide related language support. Furthermore, the language support unit can provide related language support by referring to the activities of the traveler's friends on social media. For example, the language support unit provides language support related to places where the traveler has checked in on social media. When analyzing the traveler's social media posts and providing related language support, the language support unit analyzes the traveler's social media posts and provides related language support. When providing related language support by referring to the activities of the traveler's friends on social media, the language support unit provides related language support by referring to the activities of the traveler's friends on social media. In this way, language support means based on the traveler's social media activity can be provided.

[0059] The language support unit can customize the language support method by reflecting the traveler's past feedback when providing language support. For example, the language support unit suggests the optimal language support method based on the traveler's past feedback. The language support unit can also preferentially provide a specific language support method based on the traveler's past feedback. Furthermore, the language support unit can analyze the traveler's past feedback to improve the accuracy of language support. For example, the language support unit suggests the optimal language support method based on the traveler's past feedback. When preferentially providing a specific language support method based on the traveler's past feedback, the language support unit analyzes the traveler's past feedback and preferentially provides the specific language support method. When analyzing the traveler's past feedback to improve the accuracy of language support, the language support unit analyzes the traveler's past feedback and improves the accuracy of language support. This makes it possible to customize the language support method based on the traveler's past feedback.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can monitor the user's current health condition and adjust the request reception method based on the health condition. For example, if the user is tired, a simple interface can be provided to minimize input steps. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is not feeling well, voice input can be prioritized to allow requests to be received quickly. This makes it possible to provide a request reception method that suits the user's health condition.

[0062] The customization unit can analyze the user's past travel history and perform customization based on past travel destinations. For example, it can suggest similar tourist spots based on tourist spots the user has visited in the past. It can also suggest related activities based on activities the user has liked in the past. It can also suggest customization based on specific themes from the user's past travel history. This makes it possible to customize based on the user's past travel history.

[0063] The language support unit can adjust the language support method based on the user's learning style. For example, if the user is a visual learner, support that makes extensive use of visual aids can be provided. If the user is an auditory learner, support centered on audio guides can be provided. Furthermore, if the user is an experiential learner, interactive support can be provided. This makes it possible to provide a language support method that suits the user's learning style.

[0064] The reception unit can monitor the user's social media activity in real time and automatically generate related requests. For example, if the user checks in at a specific tourist attraction, the reception unit can suggest a guide related to that tourist attraction. Also, if the user is participating in a specific event, the reception unit can provide information related to that event. Furthermore, the reception unit can suggest related requests based on places visited by the user's friends. This makes it possible to automatically generate requests based on the user's social media activity.

[0065] The support unit can adjust the content of support taking into account the user's current weather information. For example, if it is raining, indoor tourist spots can be suggested. On the other hand, if it is sunny, outdoor tourist spots can be suggested. Furthermore, in the case of extreme weather (for example, extreme heat or cold waves), appropriate countermeasure information can be provided. This allows support content to be provided based on the user's current weather information.

[0066] The customization unit can acquire the user's current location information in real time and perform customization based on the location information. For example, when the user approaches a specific tourist spot, information related to that tourist spot can be provided. Also, when the user is staying in a specific area, tourist spots related to that area can be suggested. Furthermore, when the user is traveling, tourist information related to the destination can be provided. This makes it possible to customize based on the user's current location information.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives a request from a user. The request may be text, voice, or image. For example, the reception unit receives a request entered by the user through the app. It can also receive voice requests using voice recognition technology or image requests using image recognition technology. This allows the content of the user's request to be analyzed and understood. Step 2: The customization unit performs customization based on the request received by the reception unit. Customization is performed based on the user's preferences and past history. For example, the customization unit analyzes the user's request, selects the most suitable guide, and makes reservations for the guide based on the user's schedule. Customization is also performed based on the user's interests, desired tourist destinations, local spots, etc. Step 3: The Support Department provides real-time support based on the content customized by the Customization Department. For example, if there are sudden changes during the trip or urgent questions, they will respond in real time. They will also handle any troubles during the trip and provide emergency contact information. Step 4: The language support unit supports multiple languages. For example, it provides guide instructions in the language selected by the user, or translates in real time using a translation function. It also customizes language support methods according to the user's language situation.

[0069] (Example 2) An on-demand personal travel guide system according to an embodiment of the present invention is a system that travelers can use instantly while they are in the area. This system accepts user requests, customizes them, provides real-time support, and supports multiple languages. For example, travelers can request a guide through an app and input their interests, desired tourist destinations, and local spots. Next, the system customizes the guide to meet the travelers' needs and allows for instant and flexible booking. Furthermore, the guide provides not only expert knowledge but also local information and multilingual support. Finally, real-time support is provided. This allows travelers to learn more about the attractions of the local area and enjoy their trip with peace of mind. As a result, the on-demand personal travel guide system can achieve customization to meet the travelers' needs, instant and flexible booking, expertise and local information, multilingual support, and real-time support. For example, by quickly and accurately accepting and customizing requests written by travelers and providing real-time support, traveler satisfaction can be improved.

[0070] The on-demand personal travel guide system according to the embodiment includes a reception unit, a customization unit, a support unit, and a language support unit. The reception unit receives requests from users. Requests include, but are not limited to, text, audio, and images. The reception unit receives requests entered by users through an app, for example. The reception unit can also receive audio requests using voice recognition technology. The reception unit can also receive image requests using image recognition technology. For example, the reception unit analyzes text requests entered by users into the app to understand the request. For audio requests, the reception unit converts the audio into text using voice recognition technology and analyzes the request. For image requests, the reception unit analyzes the image using image recognition technology and understands the request. The customization unit performs customization based on the request received by the reception unit. Customization is performed based on, for example, user preferences and past history, but is not limited to, examples. For example, the customization unit analyzes the user's request and selects the most suitable guide. The customization unit can also reserve a guide based on the user's schedule. Furthermore, the customization unit can also perform customization based on the user's interests, desired tourist destinations, local spots, etc. For example, the customization unit analyzes the request content entered by the user and selects the most suitable guide. When reserving a guide to match the user's schedule, the customization unit analyzes the user's schedule and reserves a guide for the most suitable time slot. When customization is performed based on the user's interests, desired tourist destinations, local spots, etc., the customization unit analyzes the user's interests, desired tourist destinations, local spots, etc. and selects the most suitable guide. The support unit provides support in real time based on the content customized by the customization unit. Support is provided, for example, when there is a sudden change during the trip or when there is an urgent question, but is not limited to such examples. For example, the support unit changes the guide in real time when there is a sudden change during the trip. The support unit can also provide answers in real time when there is an urgent question.Furthermore, the support unit can also handle troubles during the trip and provide emergency contact information. For example, if there is a sudden change during the trip, the support unit can change the guide in real time. If there is an urgent question, the support unit can provide an answer in real time. In the case where troubles during the trip and emergency contact information are provided, the support unit handles troubles during the trip and provides emergency contact information in real time. The language support unit supports multiple languages. Supported languages ​​include, but are not limited to, English, Japanese, and Chinese. For example, the language support unit provides guide explanations in a language selected by the user. The language support unit may also have a translation function to support multiple languages. Furthermore, the language support unit can customize language support means according to the user's language situation. For example, the language support unit provides guide explanations in a language selected by the user. If the language support unit has a translation function to support multiple languages, the language support unit translates in real time and provides it to the user. In the case where language support means is customized according to the user's language situation, the language support unit analyzes the user's language situation and provides optimal language support. As a result, the on-demand personal travel guide system according to the embodiment can efficiently accept user requests, and can provide customization, real-time support, and multilingual support.

[0071] The support unit can perform customization based on the needs of the traveler. For example, if a traveler is interested in a particular theme, the support unit will provide a guide with detailed information on that theme. The support unit can also make reservations for guides to suit the traveler's schedule. The support unit can also perform customization based on the traveler's interests, desired tourist destinations, local spots, etc. For example, if a traveler is interested in historical tourist destinations, the support unit will provide a guide with detailed information on history. When booking a guide to suit the traveler's schedule, the support unit analyzes the traveler's schedule and reserves a guide for the optimal time slot. When customization is performed based on the traveler's interests, desired tourist destinations, local spots, etc., the support unit analyzes the traveler's interests, desired tourist destinations, local spots, etc., and provides the optimal guide. This makes it possible to perform customization according to the traveler's needs.

[0072] The customization unit can make a guide reservation according to the traveler's schedule. For example, if a traveler needs a guide during a specific time period, the customization unit reserves a guide who is available during that time period. The customization unit can also flexibly adjust a guide reservation according to the traveler's schedule. Furthermore, the customization unit can also suggest an optimal sightseeing route based on the traveler's schedule. For example, if a traveler wishes to sightsee in the morning, the customization unit reserves a guide who is available in the morning. When flexibly adjusting a guide reservation according to the traveler's schedule, the customization unit analyzes the traveler's schedule and reserves a guide for the optimal time period. When suggesting an optimal sightseeing route based on the traveler's schedule, the customization unit analyzes the traveler's schedule and suggests an efficient sightseeing route. This makes it possible to make flexible reservations according to the traveler's schedule.

[0073] The support department can provide support in real time if there are any sudden changes during the trip or if there are any urgent questions. For example, if a traveler wants to change their plans, the support department changes the guide in real time. The support department can also provide an answer in real time if a traveler has an urgent question. Furthermore, the support department can also handle troubles during the trip and provide emergency contact information. For example, if a traveler wants to change their plans, the support department changes the guide in real time. If a traveler has an urgent question, the support department provides an answer in real time. If there are any troubles during the trip or if there are any emergency contact information provided, the support department can handle troubles during the trip or provide emergency contact information in real time. This allows for real-time support for sudden changes during the trip or urgent questions.

[0074] The language support unit supports multiple languages, allowing travelers to receive guided explanations in their own language. The language support unit supports multiple languages, for example, English, Japanese, and Chinese. The language support unit can also provide guided explanations in a language selected by the user. Furthermore, the language support unit can have a translation function to support multiple languages. For example, the language support unit provides guided explanations in a language selected by the user. If the language support unit has a translation function to support multiple languages, the language support unit translates in real time and provides the translation to the user. This allows travelers to receive guided explanations in their own language.

[0075] The customization unit can perform customization based on at least one of the traveler's interests or desired tourist spots and local spots. For example, if a traveler is interested in historical tourist spots, the customization unit can provide a detailed guide to those tourist spots. Also, if a traveler wants to know about delicious local restaurants, the customization unit can provide a detailed guide to those restaurants. Furthermore, if a traveler wants to visit a hidden spot, the customization unit can provide a detailed guide to that spot. For example, if a traveler is interested in historical tourist spots, the customization unit can provide a detailed guide to those tourist spots. If a traveler wants to know about delicious local restaurants, the customization unit can provide a detailed guide to those restaurants. If a traveler wants to visit a hidden spot, the customization unit can provide a detailed guide to that spot. This makes it possible to customize according to the traveler's interests and wishes.

[0076] The support section allows the guide to provide not only specialized knowledge but also local information. For example, the support section allows the guide to provide detailed explanations about the history and culture of the tourist destination. The support section also allows the guide to provide information about recommended local spots and events. The support section also allows the guide to explain local customs and food culture. For example, the support section allows the guide to provide detailed explanations about the history and culture of the tourist destination. If the guide also provides information about recommended local spots and events, the support section allows the guide to provide information about recommended local spots and events. If the guide also explains local customs and food culture, the support section allows the guide to explain about local customs and food culture. In this way, the guide's specialized knowledge and local information allow travelers to learn more about the attractions of the local area.

[0077] The reception unit can estimate the user's emotions and adjust the request reception method based on the estimated user emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept requests. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input and quickly accept requests. This makes it possible to provide a request reception method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays as candidates guides that the user has frequently requested in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest a guide to be used in a specific time period from the user's past request history. For example, the reception unit automatically displays as candidates guides that the user has frequently requested in the past. When preferentially suggesting input methods (voice, text, etc.) that the user has used in the past, the reception unit analyzes the user's past request history and suggests the optimal input method. When predicting and suggesting a guide to be used in a specific time period from the user's past request history, the reception unit analyzes the user's past request history and suggests a guide to be used in the optimal time period. This makes it possible to provide the optimal reception method based on the user's past request history.

[0079] When receiving a request, the reception unit can perform filtering based on the user's current travel situation and areas of interest. The reception unit, for example, suggests a related guide based on tourist attractions currently visited by the user. The reception unit can also suggest an optimal guide based on the user's areas of interest (history, food, nature, etc.). The reception unit can also suggest a guide that is available at an appropriate time based on the user's travel schedule. For example, the reception unit suggests a related guide based on tourist attractions currently visited by the user. When suggesting an optimal guide based on the user's areas of interest (history, food, nature, etc.), the reception unit analyzes the user's areas of interest and suggests an optimal guide. When suggesting a guide that is available at an appropriate time based on the user's travel schedule, the reception unit analyzes the user's travel schedule and suggests a guide that is available at an optimal time. This makes it possible to filter requests according to the user's current travel situation and areas of interest.

[0080] When accepting a request, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when the user makes a request by voice, the acceptance unit accepts the request using voice recognition technology. Furthermore, when the user makes a request by text, the acceptance unit can also accept the request using text analysis technology. Furthermore, when the user uploads an image, the acceptance unit can also accept the request using image recognition technology. For example, when the user makes a request by voice, the acceptance unit accepts the request using voice recognition technology. When the user makes a request by text, the acceptance unit accepts the request using text analysis technology. When the user uploads an image, the acceptance unit accepts the request using image recognition technology. This makes it possible to provide the optimal acceptance means according to the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user emotions. For example, if the user is making an urgent request, the reception unit can prioritize the request. Furthermore, if the user is relaxed, the reception unit can also accept the request with normal priority. Furthermore, if the user is feeling stressed, the reception unit can raise the priority to respond quickly. For example, if the user is making an urgent request, the reception unit can prioritize the request. If the user is relaxed, the reception unit can accept the request with normal priority. If the user is feeling stressed, the reception unit can raise the priority to respond quickly. This makes it possible to provide a priority of requests according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When receiving a request, the reception unit can prioritize receiving highly relevant requests in consideration of the user's geographical location information. For example, the reception unit prioritizes receiving requests for tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the reception unit can also prioritize receiving requests related to the area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving requests related to the destination. For example, the reception unit prioritizes receiving requests for tourist spots close to the user's current location. If the user is staying in a specific area, the reception unit prioritizes receiving requests related to the area. If the user is traveling, the reception unit prioritizes receiving requests related to the destination. This makes it possible to provide a priority order for requests based on the user's geographical location information.

[0083] The reception unit can analyze the user's social media activity when receiving a request and receive a related request. The reception unit, for example, receives a request related to a place where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and receive a related request. Furthermore, the reception unit can also receive a related request by referring to the activities of the user's friends on social media. For example, the reception unit receives a request related to a place where the user has checked in on social media. When analyzing the content of the user's posts on social media and receiving a related request, the reception unit analyzes the content of the user's posts on social media and receives a related request. When receiving a related request by referring to the activities of the user's friends on social media, the reception unit receives a related request by referring to the activities of the user's friends on social media. This makes it possible to receive requests based on the user's social media activity.

[0084] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific guide based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit suggests the optimal reception method based on the user's past feedback. When preferentially suggesting a specific guide based on the user's past feedback, the reception unit analyzes the user's past feedback and preferentially suggests the specific guide. When analyzing the user's past feedback and improving the reception method, the reception unit analyzes the user's past feedback and improves the reception method. This makes it possible to customize the reception method based on the user's past feedback.

[0085] The customization unit can estimate the user's emotion and adjust the customization expression method based on the estimated user's emotion. For example, the customization unit provides detailed customization options when the user is relaxed. The customization unit can also provide simple and quick customization options when the user is in a hurry. Furthermore, the customization unit can provide visually appealing customization options when the user is excited. For example, the customization unit provides detailed customization options when the user is relaxed. The customization unit provides simple and quick customization options when the user is in a hurry. The customization unit provides visually appealing customization options when the user is excited. This makes it possible to provide a customization expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During customization, the customization unit can adjust the level of detail of the customization based on the traveler's interests and desired tourist spots. For example, if the traveler is interested in historical tourist spots, the customization unit can provide detailed historical information. Also, if the traveler is interested in food, the customization unit can provide information on recommended local restaurants. Furthermore, if the traveler is interested in nature, the customization unit can provide detailed information on nature spots. For example, if the traveler is interested in historical tourist spots, the customization unit can provide detailed historical information. If the traveler is interested in food, the customization unit can provide information on recommended local restaurants. If the traveler is interested in nature, the customization unit can provide detailed information on nature spots. This makes it possible to provide the level of detail of the customization according to the traveler's interests and wishes.

[0087] During customization, the customization unit can apply different customization algorithms depending on the traveler's schedule. For example, if the traveler can only stay for a short time, the customization unit can suggest an efficient sightseeing route. Also, if the traveler can stay for a long time, the customization unit can also suggest a detailed sightseeing plan. Furthermore, the customization unit can suggest the most suitable tourist spots to suit the traveler's schedule. For example, if the traveler can only stay for a short time, the customization unit suggests an efficient sightseeing route. If the traveler can stay for a long time, the customization unit suggests a detailed sightseeing plan. When suggesting the most suitable tourist spots to suit the traveler's schedule, the customization unit analyzes the traveler's schedule and suggests the most suitable tourist spots. This makes it possible to provide a customization algorithm that suits the traveler's schedule.

[0088] During customization, the customization unit can improve the accuracy of the customization by referring to the user's past customization results. The customization unit, for example, suggests optimal customization based on tourist destinations that the user has previously favored. The customization unit can also suggest customization based on a specific theme from the user's past customization results. The customization unit can also analyze the user's past customization results to improve the accuracy of the customization. For example, the customization unit suggests optimal customization based on tourist destinations that the user has previously favored. When suggesting customization based on a specific theme from the user's past customization results, the customization unit analyzes the user's past customization results and suggests customization based on the specific theme. When analyzing the user's past customization results to improve the accuracy of the customization, the customization unit analyzes the user's past customization results and improves the accuracy of the customization. This makes it possible to improve the accuracy of the customization based on the user's past customization results.

[0089] The customization unit can estimate the user's emotions and adjust the length of the customization based on the estimated user's emotions. For example, if the user is in a hurry, the customization unit provides a short and to-the-point customization. The customization unit can also provide a detailed customization if the user is relaxed. The customization unit can also provide a visually appealing customization if the user is excited. For example, if the user is in a hurry, the customization unit provides a short and to-the-point customization. If the user is relaxed, the customization unit provides a detailed customization. If the user is excited, the customization unit provides a visually appealing customization. This makes it possible to provide a length of customization according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] During customization, the customization unit can determine the priority of customization based on the time of submission by the traveler. For example, the customization unit prioritizes customization of the request most recently submitted by the traveler. The customization unit can also perform planned customization based on requests submitted in advance by the traveler. Furthermore, the customization unit can provide optimal customization depending on the time of submission by the traveler. For example, the customization unit prioritizes customization of the request most recently submitted by the traveler. When performing planned customization based on requests submitted in advance by the traveler, the customization unit analyzes the time of submission by the traveler and provides optimal customization. When providing optimal customization depending on the time of submission by the traveler, the customization unit analyzes the time of submission by the traveler and provides optimal customization. This makes it possible to provide a priority of customization depending on the time of submission by the traveler.

[0091] During customization, the customization unit can adjust the order of customization based on the traveler's relevance. For example, if the traveler is interested in a particular theme, the customization unit prioritizes customization related to that theme. Also, if the traveler is staying in a particular region, the customization unit can prioritize customization related to that region. Furthermore, the customization unit can propose an optimal customization order based on the traveler's interests and concerns. For example, if the traveler is interested in a particular theme, the customization unit prioritizes customization related to that theme. If the traveler is staying in a particular region, the customization unit prioritizes customization related to that region. When proposing an optimal customization order based on the traveler's interests and concerns, the customization unit analyzes the traveler's interests and concerns and proposes an optimal customization order. This makes it possible to provide a customization order according to the traveler's relevance.

[0092] During customization, the customization unit can adjust the use of specialized terminology in the customization according to the expertise level of the traveler. For example, if the traveler is a beginner, the customization unit provides an easy-to-understand explanation avoiding specialized terminology. Furthermore, if the traveler is an intermediate traveler, the customization unit can provide an explanation using appropriate specialized terminology. Furthermore, if the traveler is an advanced traveler, the customization unit can provide an explanation using detailed specialized terminology. For example, if the traveler is a beginner, the customization unit provides an easy-to-understand explanation avoiding specialized terminology. If the traveler is an intermediate traveler, the customization unit provides an explanation using appropriate specialized terminology. If the traveler is an advanced traveler, the customization unit provides an explanation using detailed specialized terminology. This makes it possible to provide the use of specialized terminology in the customization according to the expertise level of the traveler.

[0093] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is nervous, the support unit provides support in a calm voice. Also, if the user is relaxed, the support unit can provide support in a cheerful voice. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. For example, if the user is nervous, the support unit provides support in a calm voice. If the user is relaxed, the support unit provides support in a cheerful voice. If the user is in a hurry, the support unit provides quick and concise support. This makes it possible to provide a support method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] When providing support, the support unit can analyze the traveler's past support history and select the optimal support method. The support unit provides the optimal support, for example, based on the support methods that the traveler preferred in the past. The support unit can also provide support based on a specific theme from the traveler's past support history. Furthermore, the support unit can analyze the traveler's past support history and improve the accuracy of the support. For example, the support unit provides the optimal support based on the support methods that the traveler preferred in the past. When providing support based on a specific theme from the traveler's past support history, the support unit analyzes the traveler's past support history and provides support based on the specific theme. When analyzing the traveler's past support history and improving the accuracy of support, the support unit analyzes the traveler's past support history and improves the accuracy of support. This makes it possible to provide the optimal support method based on the traveler's past support history.

[0095] When providing support, the support unit can customize the means of support based on the traveler's current travel situation. For example, when the traveler is at a tourist spot, the support unit provides information related to the tourist spot. Also, when the traveler is traveling, the support unit can provide support related to the travel. Furthermore, when the traveler is taking a break, the support unit can provide information that will help the traveler relax. For example, when the traveler is at a tourist spot, the support unit provides information related to the tourist spot. When the traveler is traveling, the support unit provides support related to the travel. When the traveler is taking a break, the support unit provides information that will help the traveler relax. This makes it possible to provide means of support that are appropriate for the traveler's current travel situation.

[0096] The support unit can improve the support method by reflecting the user's feedback when providing support. The support unit improves the support method, for example, based on the feedback provided by the user. The support unit can also preferentially provide a specific support method based on the user's feedback. Furthermore, the support unit can analyze the user's feedback and improve the accuracy of the support. For example, the support unit improves the support method based on the feedback provided by the user. When preferentially providing a specific support method based on the user's feedback, the support unit analyzes the user's feedback and preferentially provides the specific support method. When analyzing the user's feedback and improving the accuracy of the support, the support unit analyzes the user's feedback and improves the accuracy of the support. This makes it possible to improve the support method based on the user's feedback.

[0097] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user needs urgent support, the support unit responds with priority. Furthermore, if the user is relaxed, the support unit can respond with normal priority. Furthermore, if the user is stressed, the support unit can raise the priority to respond quickly. For example, if the user needs urgent support, the support unit responds with priority. If the user is relaxed, the support unit responds with normal priority. If the user is stressed, the support unit raises the priority to respond quickly. This makes it possible to provide support priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] When providing support, the support unit can select the optimal support method by taking into consideration the traveler's geographical location information. For example, the support unit can prioritize providing support related to tourist attractions close to the traveler's current location. Furthermore, if the traveler is staying in a specific area, the support unit can also prioritize providing support related to that area. Furthermore, if the traveler is traveling, the support unit can also prioritize providing support related to the travel destination. For example, the support unit can prioritize providing support related to tourist attractions close to the traveler's current location. If the traveler is staying in a specific area, the support unit can prioritize providing support related to that area. If the traveler is traveling, the support unit can prioritize providing support related to the travel destination. This makes it possible to provide the optimal support method based on the traveler's geographical location information.

[0099] When providing support, the support unit can analyze the traveler's social media activity and suggest support measures. For example, the support unit provides support related to places where the traveler has checked in on social media. The support unit can also analyze the content of the traveler's posts on social media and provide related support. Furthermore, the support unit can provide related support by referring to the activities of the traveler's friends on social media. For example, the support unit provides support related to places where the traveler has checked in on social media. When analyzing the content of the traveler's posts on social media and providing related support, the support unit analyzes the content of the traveler's posts on social media and provides related support. When providing related support by referring to the activities of the traveler's friends on social media, the support unit provides related support by referring to the activities of the traveler's friends on social media. This makes it possible to provide support measures based on the traveler's social media activity.

[0100] When providing support, the support unit can customize the support method by reflecting the traveler's past feedback. For example, the support unit suggests the optimal support method based on feedback provided by the traveler in the past. The support unit can also prioritize providing a specific support method based on the traveler's past feedback. Furthermore, the support unit can analyze the traveler's past feedback and improve the accuracy of support. For example, the support unit suggests the optimal support method based on feedback provided by the traveler in the past. When prioritizing providing a specific support method based on the traveler's past feedback, the support unit analyzes the traveler's past feedback and prioritizes providing the specific support method. When analyzing the traveler's past feedback and improving the accuracy of support, the support unit analyzes the traveler's past feedback and improves the accuracy of support. This makes it possible to customize the support method based on the traveler's past feedback.

[0101] The language support unit can estimate the user's emotions and adjust the language support method based on the estimated user's emotions. For example, if the user is nervous, the language support unit can provide language support in a calm voice. Also, if the user is relaxed, the language support unit can provide language support in a cheerful voice. Furthermore, if the user is in a hurry, the language support unit can provide quick and concise language support. For example, if the user is nervous, the language support unit can provide language support in a calm voice. If the user is relaxed, the language support unit can provide language support in a cheerful voice. If the user is in a hurry, the language support unit can provide quick and concise language support. This makes it possible to provide a language support method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When providing language support, the language support unit can analyze the traveler's past language support history to select the optimal language support method. The language support unit, for example, provides optimal language support based on the language support method that the traveler has previously preferred. The language support unit can also provide language support based on a specific theme from the traveler's past language support history. The language support unit can also analyze the traveler's past language support history to improve the accuracy of language support. For example, the language support unit provides optimal language support based on the language support method that the traveler has previously preferred. When providing language support based on a specific theme from the traveler's past language support history, the language support unit analyzes the traveler's past language support history to provide language support based on the specific theme. When analyzing the traveler's past language support history to improve the accuracy of language support, the language support unit analyzes the traveler's past language support history to improve the accuracy of language support. This makes it possible to provide the optimal language support method based on the traveler's past language support history.

[0103] When providing language support, the language support unit can customize the language support means based on the traveler's current language situation. For example, if the traveler uses a specific language, the language support unit provides support corresponding to that language. Furthermore, if the traveler uses multiple languages, the language support unit can also provide a language switching function. Furthermore, the language support unit can also provide optimal language support according to the traveler's language situation. For example, if the traveler uses a specific language, the language support unit provides support corresponding to that language. If the traveler uses multiple languages, the language support unit provides a language switching function. When providing optimal language support according to the traveler's language situation, the language support unit analyzes the traveler's language situation and provides optimal language support. This makes it possible to provide language support means according to the traveler's current language situation.

[0104] The language support unit can improve the language support method by reflecting user feedback during language support. For example, the language support unit improves the language support method based on feedback provided by the user. The language support unit can also prioritize providing a specific language support method based on the user feedback. Furthermore, the language support unit can analyze the user feedback and improve the accuracy of language support. For example, the language support unit improves the language support method based on feedback provided by the user. When prioritizing providing a specific language support method based on the user feedback, the language support unit analyzes the user feedback and prioritizes providing the specific language support method. When analyzing the user feedback and improving the accuracy of language support, the language support unit analyzes the user feedback and improves the accuracy of language support. This makes it possible to improve the language support method based on user feedback.

[0105] The language support unit can estimate the user's emotions and determine the priority of language support based on the estimated user emotions. For example, the language support unit prioritizes language support when the user needs urgent language support. Furthermore, the language support unit can also respond with normal priority when the user is relaxed. Furthermore, the language support unit can raise the priority to respond quickly when the user is stressed. For example, the language support unit prioritizes language support when the user needs urgent language support. When the user is relaxed, the language support unit responds with normal priority. When the user is stressed, the language support unit raises the priority to respond quickly. This makes it possible to provide language support priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] When providing language support, the language support unit can select the optimal language support method by taking into consideration the traveler's geographical location information. For example, the language support unit can prioritize providing language support related to tourist attractions close to the traveler's current location. Furthermore, if the traveler is staying in a specific area, the language support unit can also prioritize providing language support related to the area. Furthermore, if the traveler is traveling, the language support unit can also prioritize providing language support related to the travel destination. For example, the language support unit prioritizes providing language support related to tourist attractions close to the traveler's current location. If the traveler is staying in a specific area, the language support unit prioritizes providing language support related to the area. If the traveler is traveling, the language support unit prioritizes providing language support related to the travel destination. This makes it possible to provide the optimal language support method based on the traveler's geographical location information.

[0107] When providing language support, the language support unit can analyze the traveler's social media activity and suggest language support means. For example, the language support unit provides language support related to places where the traveler has checked in on social media. The language support unit can also analyze the traveler's social media posts and provide related language support. Furthermore, the language support unit can provide related language support by referring to the activities of the traveler's friends on social media. For example, the language support unit provides language support related to places where the traveler has checked in on social media. When analyzing the traveler's social media posts and providing related language support, the language support unit analyzes the traveler's social media posts and provides related language support. When providing related language support by referring to the activities of the traveler's friends on social media, the language support unit provides related language support by referring to the activities of the traveler's friends on social media. In this way, language support means based on the traveler's social media activity can be provided.

[0108] The language support unit can customize the language support method by reflecting the traveler's past feedback when providing language support. For example, the language support unit suggests the optimal language support method based on the traveler's past feedback. The language support unit can also preferentially provide a specific language support method based on the traveler's past feedback. Furthermore, the language support unit can analyze the traveler's past feedback to improve the accuracy of language support. For example, the language support unit suggests the optimal language support method based on the traveler's past feedback. When preferentially providing a specific language support method based on the traveler's past feedback, the language support unit analyzes the traveler's past feedback and preferentially provides the specific language support method. When analyzing the traveler's past feedback to improve the accuracy of language support, the language support unit analyzes the traveler's past feedback and improves the accuracy of language support. This makes it possible to customize the language support method based on the traveler's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, customization unit, support unit, and language support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives requests from a user. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and performs customization based on the user request. The support unit is realized by the control unit 46A of the smart device 14 and provides support in real time. The language support unit is realized by the specific processing unit 290 of the data processing device 12 and supports multiple languages. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, customization unit, support unit, and language support 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 a request from a user. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and performs customization based on the user's request. The support unit is realized by the control unit 46A of the smart glasses 214 and provides support in real time. The language support unit is realized by the specific processing unit 290 of the data processing device 12 and supports multiple languages. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, customization unit, support unit, and language support 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 requests from a user. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and performs customization based on the user request. The support unit is realized by the control unit 46A of the headset type terminal 314 and provides support in real time. The language support unit is realized by the specific processing unit 290 of the data processing device 12 and supports multiple languages. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, customization unit, support unit, and language support 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 requests from a user. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and performs customization based on the user request. The support unit is realized by the control unit 46A of the robot 414 and provides support in real time. The language support unit is realized by the specific processing unit 290 of the data processing device 12 and supports multiple languages.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can monitor the user's current health condition and adjust the request reception method based on the health condition. For example, if the user is tired, a simple interface can be provided to minimize input steps. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is not feeling well, voice input can be prioritized to allow requests to be received quickly. This makes it possible to provide a request reception method that suits the user's health condition.

[0111] The customization unit can analyze the user's past travel history and perform customization based on past travel destinations. For example, it can suggest similar tourist spots based on tourist spots the user has visited in the past. It can also suggest related activities based on activities the user has liked in the past. It can also suggest customization based on specific themes from the user's past travel history. This makes it possible to customize based on the user's past travel history.

[0112] The support unit can estimate the user's emotions and adjust the content of support based on the estimated user's emotions. For example, if the user is feeling anxious, it can provide information that gives a sense of security. If the user is excited, it can also provide interesting information. Furthermore, if the user is tired, it can also provide information that helps the user relax. In this way, it is possible to provide support content that corresponds to the user's emotions.

[0113] The language support unit can adjust the language support method based on the user's learning style. For example, if the user is a visual learner, support that makes extensive use of visual aids can be provided. If the user is an auditory learner, support centered on audio guides can be provided. Furthermore, if the user is an experiential learner, interactive support can be provided. This makes it possible to provide a language support method that suits the user's learning style.

[0114] The customization unit can estimate the user's emotions and adjust the order of customization based on the estimated user's emotions. For example, if the user is excited, the most interesting tourist spots can be suggested first. If the user is relaxed, a leisurely sightseeing route can be suggested. Furthermore, if the user is in a hurry, an efficient sightseeing route can be suggested. In this way, the customization order can be provided according to the user's emotions.

[0115] The reception unit can monitor the user's social media activity in real time and automatically generate related requests. For example, if the user checks in at a specific tourist attraction, the reception unit can suggest a guide related to that tourist attraction. Also, if the user is participating in a specific event, the reception unit can provide information related to that event. Furthermore, the reception unit can suggest related requests based on places visited by the user's friends. This makes it possible to automatically generate requests based on the user's social media activity.

[0116] The customization unit can estimate the user's emotion and adjust the level of customization detail based on the estimated user's emotion. For example, if the user is relaxed, detailed tourist information can be provided. If the user is in a hurry, brief tourist information can be provided. Furthermore, if the user is excited, visually appealing tourist information can be provided. In this way, the level of customization detail can be provided according to the user's emotion.

[0117] The support unit can adjust the content of support taking into account the user's current weather information. For example, if it is raining, indoor tourist spots can be suggested. On the other hand, if it is sunny, outdoor tourist spots can be suggested. Furthermore, in the case of extreme weather (for example, extreme heat or cold waves), appropriate countermeasure information can be provided. This allows support content to be provided based on the user's current weather information.

[0118] The language support unit can estimate the user's emotions and adjust the tone of the language support based on the estimated user's emotions. For example, if the user is nervous, support can be provided in a calm tone. If the user is relaxed, support can be provided in a bright tone. Furthermore, if the user is in a hurry, support can be provided in a quick and concise tone. In this way, the tone of the language support can be provided according to the user's emotions.

[0119] The customization unit can acquire the user's current location information in real time and perform customization based on the location information. For example, when the user approaches a specific tourist spot, information related to that tourist spot can be provided. Also, when the user is staying in a specific area, tourist spots related to that area can be suggested. Furthermore, when the user is traveling, tourist information related to the destination can be provided. This makes it possible to customize based on the user's current location information.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit receives a request from a user. The request may be text, voice, or image. For example, the reception unit receives a request entered by the user through the app. It can also receive voice requests using voice recognition technology or image requests using image recognition technology. This allows the content of the user's request to be analyzed and understood. Step 2: The customization unit performs customization based on the request received by the reception unit. Customization is performed based on the user's preferences and past history. For example, the customization unit analyzes the user's request, selects the most suitable guide, and makes reservations for the guide based on the user's schedule. Customization is also performed based on the user's interests, desired tourist destinations, local spots, etc. Step 3: The Support Department provides real-time support based on the content customized by the Customization Department. For example, if there are sudden changes during the trip or urgent questions, they will respond in real time. They will also handle any troubles during the trip and provide emergency contact information. Step 4: The language support unit supports multiple languages. For example, it provides guide instructions in the language selected by the user, or translates in real time using a translation function. It also customizes language support methods according to the user's language situation.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 requests from users; a customization unit that performs customization based on the request received by the reception unit; a support unit that provides support in real time based on the content customized by the customization unit; a language support unit that supports multiple languages; A system characterized by:

2. The support portion is Customize based on traveler needs 2. The system of claim 1.

3. The customization unit Book a guide to fit your schedule 2. The system of claim 1.

4. The support portion is Providing real-time support if there are any sudden changes or urgent questions during your trip 2. The system of claim 1.

5. The language support unit Supports multiple languages, allowing travelers to receive guided explanations in their own language 2. The system of claim 1.

6. The customization unit Customize based on the traveler's interests or desired tourist destinations and / or local spots 2. The system of claim 1.

7. The support portion is Guides provide local knowledge as well as expert knowledge 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the way requests are accepted based on the estimated user emotions.

2. The system of claim 1.

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