System

Abstract

The system according to the embodiment aims to enable a traveler to interact with local people and find hidden tourist spots and restaurants.SOLUTION: A system according to an embodiment includes an interaction unit, an analysis unit, a proposal unit, and a provision unit. The dialogue unit supports dialogue between the tourist and the local people. The analysis unit analyzes the conversation content collected by the conversation unit. The proposal unit proposes a sightseeing spot or a restaurant based on the information analyzed by the analysis unit. The providing unit provides the traveler with the information proposed by the proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the drawback of making it difficult for travelers to interact with local people and learn about tourist spots and restaurants that are not listed in guidebooks.

[0005] The system according to the embodiment aims to enable travelers to interact with local people and learn about hidden tourist spots and restaurants. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, a suggestion unit, and a provision unit. The dialogue unit supports dialogue between travelers and local people. The analysis unit analyzes the dialogue content collected by the dialogue unit. The suggestion unit suggests tourist spots or restaurants based on the information analyzed by the analysis unit. The provision unit provides the information suggested by the suggestion unit to the travelers. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to interact with local people and learn about hidden tourist spots and restaurants. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel support system according to an embodiment of the present invention allows travelers to interact with locals in real time, and a generation AI analyzes the content of the conversation to suggest nearby popular spots and hidden gems. The travel support system provides an interface for travelers to interact with locals, and the generation AI analyzes the content of the conversation to suggest tourist spots and restaurants that are best suited to the traveler. For example, the travel support system provides an interface that allows travelers to chat and video call with locals in real time using their smartphones. This interface is designed to allow travelers to easily communicate with locals. Next, the travel support system uses a generation AI to analyze the content of the conversation between the traveler and the locals. The generation AI analyzes the text and voice data entered when the traveler interacts with locals to understand the traveler's interests. For example, if a traveler asks, "What local restaurants do you recommend?", the generation AI analyzes the question and suggests the best restaurants for the traveler based on the information provided by locals. Furthermore, the travel support system uses a generation AI to collect the latest information from the internet and information provided by locals and suggest them to the traveler. For example, the generation AI collects information on popular tourist spots and restaurants popular among locals and provides it to the traveler. Finally, the travel support system recommends tourist spots and restaurants that are best suited to travelers based on the information obtained by the generation AI. Based on the travelers' interests, the generation AI recommends tourist spots and restaurants that avoid overtourism. This allows travelers to avoid crowds and visit places beloved by locals, improving their trip satisfaction. The travel support system allows travelers to interact with locals in real time, and the generation AI analyzes the content of the conversation to recommend popular spots and hidden gems, thereby avoiding overtourism and improving trip satisfaction. For example, the system provides an interface for travelers to interact with locals, and the generation AI analyzes the content of the conversation to recommend tourist spots and restaurants that are best suited to travelers. This allows travelers to avoid crowds and visit places beloved by locals, improving their trip satisfaction.

[0029] A travel support system according to an embodiment includes a dialogue unit, an analysis unit, a suggestion unit, and a provision unit. The dialogue unit supports dialogue between travelers and local people. For example, the dialogue unit provides an interface that allows travelers to chat or video call with local people in real time using a smartphone. The dialogue unit is also designed to enable travelers to easily communicate with local people. For example, the dialogue unit provides a user interface for travelers to interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, or the like. The analysis unit uses a generation AI to analyze the dialogue content collected by the dialogue unit. For example, the analysis unit analyzes text and voice data entered when the travelers interact with local people to understand the travelers' interests. For example, the generation AI uses natural language processing technology to analyze the travelers' questions and requests and identify the travelers' interests. The analysis unit can also use voice recognition technology to convert the travelers' voice data into text data and perform analysis. The suggestion unit suggests tourist spots and restaurants based on the information analyzed by the analysis unit. The suggestion unit, for example, uses a generation AI to collect the latest information on the Internet and information provided by local people and suggest it to travelers. For example, the generation AI collects information on recently popular tourist spots and restaurants popular among locals and provides it to travelers. The suggestion unit can also suggest tourist spots and restaurants that can avoid overtourism based on the traveler's interests. The provision unit provides the traveler with the information suggested by the suggestion unit. The provision unit, for example, suggests tourist spots and restaurants that are best suited to the traveler based on the information acquired by the generation AI. For example, the provision unit sends a notification to the traveler's smartphone and displays information on the suggested tourist spots and restaurants. The provision unit can also provide optimal information based on the traveler's location information and time zone. As a result, the travel support system according to the embodiment allows travelers to interact with local people in real time and suggests and provides tourist spots and restaurants based on the analyzed information, thereby increasing travel satisfaction.

[0030] The dialogue unit may provide an interface through which travelers can chat or video call with local people in real time using their smartphones. Examples of interfaces include, but are not limited to, the design and operation of a user interface. For example, the dialogue unit may provide an interface through which travelers can chat with local people in real time using their smartphones. The dialogue unit may also provide an interface through which travelers can video call with local people using their smartphones. For example, the dialogue unit may provide a user interface through which travelers can interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, or the like. This allows travelers to interact with local people in real time using their smartphones. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit may input traveler input data to a generation AI and have the generation AI analyze the content of the dialogue.

[0031] The analysis unit can analyze text or voice data input when a traveler converses with local people to understand the traveler's interests. The analysis unit can, for example, use natural language processing technology to analyze a traveler's questions or requests and identify the traveler's interests. For example, if a traveler asks, "Please tell me some recommended local restaurants," the analysis unit analyzes the question and understands the traveler's interests. The analysis unit can also use voice recognition technology to convert the traveler's voice data into text data and analyze it. For example, the analysis unit can analyze voice data input when a traveler converses with local people to understand the traveler's interests. The analysis unit can also use a generation AI to analyze the content of the traveler's conversation and understand the traveler's interests. For example, the generation AI can analyze the content of the traveler's conversation and identify the traveler's interests. This allows for understanding the traveler's interests and makes more appropriate suggestions. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the traveler's dialogue data into the generation AI and have the generation AI understand the traveler's interests and concerns.

[0032] The suggestion unit allows the generation AI to collect the latest information on the internet or information provided by local people and suggest it to travelers. The generation AI, for example, collects the latest information on the internet and suggests it to travelers. For example, the generation AI collects information on popular tourist spots and restaurants popular among locals and provides it to travelers. The generation AI can also collect information provided by local people and suggest it to travelers. For example, the generation AI collects information on tourist spots and restaurants provided by local people and provides it to travelers. Furthermore, the generation AI can suggest the best tourist spots and restaurants for travelers based on the latest information on the internet and information provided by local people. For example, the generation AI suggests tourist spots and restaurants that can avoid overtourism based on the traveler's interests. This makes it possible to make the best suggestions for travelers based on the latest information and information from local people. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit suggests the best tourist spots and restaurants for travelers based on the information collected by the generation AI.

[0033] The providing unit can suggest tourist spots or restaurants to the traveler based on the information acquired by the generation AI. The providing unit, for example, suggests tourist spots or restaurants that are optimal for the traveler based on the information acquired by the generation AI. For example, the providing unit sends a notification to the traveler's smartphone and displays information about the suggested tourist spots or restaurants. The providing unit can also provide optimal information based on the traveler's location information and time of day. For example, the providing unit provides information about nearby tourist spots and restaurants based on the traveler's current location. Furthermore, if the traveler is having a conversation at night, the providing unit can also provide information about stores and spots that are open at night. Furthermore, if the traveler is in a location where a specific event is being held, the providing unit can provide information related to the event. This can increase travel satisfaction by suggesting tourist spots and restaurants that are optimal for the traveler. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit suggests tourist spots and restaurants that are optimal for the traveler based on the information acquired by the generation AI.

[0034] The dialogue unit can analyze the traveler's past dialogue history and select the optimal dialogue method. For example, the dialogue unit selects a similar dialogue method based on the traveler's preferred dialogue style in the past. For example, if the traveler has previously said, "I like casual dialogue," the dialogue unit selects a casual dialogue method based on that dialogue style. The dialogue unit can also prioritize providing related information based on questions the traveler has frequently asked in the past. For example, if the traveler has frequently asked, "What are some recommended tourist spots?" in the past, the dialogue unit prioritizes providing information related to that question. The dialogue unit can also identify specific interests and concerns from the traveler's past dialogue history and select a dialogue method based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the dialogue unit selects a dialogue method based on that interest. This enables more effective dialogue by selecting the optimal dialogue method based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's past dialogue data into a generation AI and have the generation AI select the optimal dialogue method.

[0035] During the dialogue, the dialogue unit can customize the dialogue content based on the traveler's current location information and time of day. For example, the dialogue unit provides information on nearby tourist spots and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in XX," the dialogue unit provides information on nearby tourist spots and restaurants based on the traveler's location. Furthermore, if the traveler is having a dialogue at night, the dialogue unit can provide information on stores and spots that are open at night. For example, if the traveler says, "I want to go to XX at night," the dialogue unit can provide information on stores and spots that are open at night. Furthermore, if the traveler is in a location where a specific event is being held, the dialogue unit can provide information related to that event. For example, if the traveler says, "I'm participating in the XX event," the dialogue unit provides information related to the event. This allows for customizing the dialogue content based on the traveler's current location information and time of day, thereby providing more appropriate information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the traveler's location information and time of day data into a generation AI and have the generation AI customize the dialogue content.

[0036] The dialogue unit can select the optimal dialogue means depending on the traveler's input method during dialogue. For example, if the traveler uses voice input, the dialogue unit prioritizes voice dialogue. For example, if the traveler says, "I want to talk by voice," the dialogue unit prioritizes voice dialogue based on the input method. The dialogue unit can also prioritize text dialogue if the traveler uses text input. For example, if the traveler says, "I want to talk by text," the dialogue unit prioritizes text dialogue based on the input method. The dialogue unit can also provide related information based on the image if the traveler sends an image. For example, if the traveler says, "Tell me about this image," the dialogue unit provides related information based on the image. This enables more effective dialogue by selecting the optimal dialogue means depending on the traveler's input method. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's input data into a generation AI and have the generation AI select the optimal dialogue means.

[0037] During the dialogue, the dialogue unit can prioritize providing relevant information by taking into account the traveler's geographical location information. For example, the dialogue unit prioritizes providing information about nearby tourist attractions and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in X," the dialogue unit prioritizes providing information about nearby tourist attractions and restaurants based on the traveler's location. Furthermore, if the traveler is in a specific area, the dialogue unit can provide information about events and activities related to that area. For example, if the traveler says, "I'm in X area," the dialogue unit provides information about events and activities related to that area. Furthermore, if the traveler is traveling, the dialogue unit can prioritize providing information about the traveler's destination. For example, if the traveler says, "I'm traveling," the dialogue unit prioritizes providing information about the traveler's destination. This allows for more appropriate information to be provided by taking into account the traveler's geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide relevant information.

[0038] The dialogue unit can analyze the traveler's social media activity during the dialogue and provide relevant information. For example, the dialogue unit can provide information about places the traveler checked in on social media. For example, if the traveler says, "I checked in to XX," the dialogue unit can provide information about the place. The dialogue unit can also analyze the traveler's social media posts and provide information about related tourist spots and stores. For example, if the traveler says, "I posted about XX," the dialogue unit can provide relevant information based on the post. The dialogue unit can also provide information about related places and events based on the traveler's friends' activities on social media. For example, if the traveler says, "My friend went to XX," the dialogue unit can provide information about the place or event. This allows for more relevant information to be provided by analyzing the traveler's social media activity. Some or all of the above-described processing in the dialogue unit can be performed using, or without, AI. For example, the dialogue unit can input the traveler's social media data into a generation AI and cause the generation AI to provide relevant information.

[0039] The dialogue unit can customize the dialogue method by reflecting the traveler's past feedback during the dialogue. For example, the dialogue unit selects a similar dialogue method based on the traveler's previously preferred dialogue style. For example, if the traveler has previously said, "I like casual dialogue," the dialogue unit selects a casual dialogue method based on that dialogue style. The dialogue unit can also improve the dialogue content based on feedback provided by the traveler in the past. For example, if the traveler has previously said, "I want more detailed information," the dialogue unit improves the dialogue content based on that feedback. The dialogue unit can also identify specific interests and concerns from the traveler's past feedback and select a dialogue method based on that interest. For example, if the traveler has previously said, "I'm interested in historical places," the dialogue unit selects a dialogue method based on that interest. This makes it possible to provide a more appropriate dialogue method by reflecting the traveler's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's past feedback data into a generation AI and have the generation AI customize the dialogue method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the dialogue content. For example, if a traveler asks an important question, the analysis unit performs a detailed analysis to provide highly accurate information. For example, if a traveler says, "I have an important question," the analysis unit analyzes the importance of the question and performs a detailed analysis. Furthermore, if a traveler asks a general question, the analysis unit can quickly analyze the question and provide the minimum necessary information. For example, if a traveler says, "I have a general question," the analysis unit analyzes the importance of the question and performs a quick analysis. Furthermore, if a traveler expresses specific interests or concerns, the analysis unit can perform a detailed analysis based on the content of those interests. For example, if a traveler says, "I have specific interests," the analysis unit performs a detailed analysis based on those interests. By adjusting the level of detail of the analysis based on the importance of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data of the dialogue content to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the dialogue content. For example, if a traveler asks a question about tourist spots, the analysis unit applies an analysis algorithm specialized for tourist spots. For example, if a traveler says, "Tell me about tourist spots," the analysis unit applies an analysis algorithm specialized for tourist spots based on the question. Furthermore, if a traveler asks a question about restaurants, the analysis unit can also apply an analysis algorithm specialized for restaurants. For example, if a traveler says, "Tell me about restaurants," the analysis unit applies an analysis algorithm specialized for restaurants based on the question. Furthermore, if a traveler asks a question about events, the analysis unit can also apply an analysis algorithm specialized for events. For example, if a traveler says, "Tell me about events," the analysis unit applies an analysis algorithm specialized for events based on the question. By applying different analysis algorithms depending on the category of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input category data of the dialogue content into a generation AI and cause the generation AI to apply an analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the traveler's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on feedback provided by the traveler in the past. For example, if the traveler has previously said, "This information was helpful," the analysis unit improves the accuracy of the analysis based on that feedback. The analysis unit can also improve the accuracy of analysis for similar questions by referring to the traveler's past analysis results. For example, if the traveler has previously said, "The answer to this question was good," the analysis unit improves the accuracy of the analysis by referring to the analysis results. The analysis unit can also identify specific interests and concerns from the traveler's past analysis results and perform analysis based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the analysis unit performs analysis based on those interests. By referring to the traveler's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the traveler's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the dialogue content. For example, the analysis unit prioritizes analysis of the dialogue content most recently submitted by the traveler. For example, if the traveler says, "I recently asked a question about XX," the analysis unit prioritizes analysis of that dialogue content. The analysis unit can also prioritize analysis of dialogue content submitted by the traveler during a specific time period. For example, if the traveler says, "I asked a question about XX at night," the analysis unit prioritizes analysis of the dialogue content based on that time period. The analysis unit can also perform analysis based on the time of submission, taking into account dialogue content previously submitted by the traveler. For example, if the traveler says, "I asked a question about XX in the past," the analysis unit analyzes the dialogue content based on the time of submission. This allows for more appropriate information to be provided by determining the priority of analysis based on the time of submission of the dialogue content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the time of submission of the dialogue content into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the dialogue content. For example, if a traveler asks related questions consecutively, the analysis unit adjusts the order of analysis based on that relevance. For example, if a traveler says, "I asked about XX. Then, I also asked about XX," the analysis unit adjusts the order of analysis based on that relevance. Furthermore, if a traveler asks a question related to a specific category, the analysis unit can adjust the order of analysis based on that category. For example, if a traveler says, "I asked about tourist spots. Then, I also asked about restaurants," the analysis unit adjusts the order of analysis based on that category. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of questions previously asked by the traveler. For example, if a traveler says, "I asked about XX in the past. Then, I also asked about XX," the analysis unit adjusts the order of analysis based on that relevance. By adjusting the order of analysis based on the relevance of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data of the dialogue content to the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the traveler's level of expertise. For example, if the traveler has specialized knowledge, the analysis unit performs a detailed analysis using technical terms. For example, if the traveler says, "I want specialized information," the analysis unit performs the analysis using technical terms based on the traveler's level of expertise. Furthermore, if the traveler has general knowledge, the analysis unit can avoid technical terms and perform an easy-to-understand analysis. For example, if the traveler says, "I want easy-to-understand information," the analysis unit performs the analysis by avoiding technical terms based on the traveler's level of expertise. Furthermore, the analysis unit can also perform an analysis according to the traveler's level of expertise by referring to the traveler's past dialogue history. For example, if the traveler has previously said, "I found specialized information useful," the analysis unit performs an analysis according to the traveler's level of expertise by referring to the dialogue history. This allows for the provision of more appropriate information by adjusting the use of technical terms in the analysis according to the traveler's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the traveler's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0046] The suggestion unit can adjust the level of detail of the suggestion based on the importance of tourist spots and restaurants when making a suggestion. For example, the suggestion unit provides detailed information when making a suggestion regarding important tourist spots and restaurants. For example, if a traveler says, "Please tell me about important tourist spots," the suggestion unit provides detailed information based on the importance of the tourist spots. The suggestion unit can also provide concise information when making a suggestion regarding general tourist spots and restaurants. For example, if a traveler says, "Please tell me about general tourist spots," the suggestion unit provides concise information based on the importance of the tourist spots. The suggestion unit can also prioritize suggesting information with high importance based on the traveler's interests. For example, if a traveler says, "I'm interested in historical places," the suggestion unit prioritizes suggesting information with high importance based on the traveler's interests. This allows for adjusting the level of detail of the suggestion based on the importance of tourist spots and restaurants, thereby providing more appropriate information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data of tourist spots and restaurants into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist spot or restaurant. For example, when making a suggestion regarding a tourist spot, the suggestion unit applies a suggestion algorithm specialized for tourist spots. For example, when a traveler says, "Tell me about tourist spots," the suggestion unit applies a suggestion algorithm specialized for tourist spots based on the category. Furthermore, when making a suggestion regarding a restaurant, the suggestion unit can also apply a suggestion algorithm specialized for restaurants. For example, when a traveler says, "Tell me about restaurants," the suggestion unit applies a suggestion algorithm specialized for restaurants based on the category. Furthermore, when making a suggestion regarding an event, the suggestion unit can also apply a suggestion algorithm specialized for events. For example, when a traveler says, "Tell me about events," the suggestion unit applies a suggestion algorithm specialized for events based on the category. This allows for more appropriate information to be provided by applying different suggestion algorithms depending on the category of the tourist spot or restaurant. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input category data of tourist spots or restaurants into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0048] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the traveler's past suggestion results. The suggestion unit, for example, makes similar suggestions based on tourist spots or restaurants that the traveler has previously liked. For example, if the traveler has previously said, "This tourist spot was good," the suggestion unit makes a similar suggestion based on that suggestion result. The suggestion unit can also analyze the traveler's past suggestion results to make more accurate suggestions. For example, if the traveler has previously said, "This restaurant was good," the suggestion unit can analyze that suggestion result to make more accurate suggestions. The suggestion unit can also identify specific interests and concerns from the traveler's past suggestion results and make suggestions based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the suggestion unit can make suggestions based on those interests. In this way, the accuracy of the suggestion can be improved by referring to the traveler's past suggestion results. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the traveler's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0049] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the tourist spots and restaurants. For example, the suggestion unit prioritizes the suggestions of tourist spots and restaurants most recently submitted by the traveler. For example, if the traveler says, "I recently got information about XX," the suggestion unit prioritizes the suggestions based on the time of submission. The suggestion unit can also prioritize the suggestions submitted by the traveler during a specific time period. For example, if the traveler says, "I got information about XX at night," the suggestion unit prioritizes the suggestions based on the time of submission. The suggestion unit can also make suggestions based on the time of submission, taking into account information previously submitted by the traveler. For example, if the traveler says, "I got information about XX in the past," the suggestion unit suggests information based on the time of submission. This allows for more appropriate information to be provided by prioritizing the suggestions based on the time of submission of the tourist spots and restaurants. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input submission date data for tourist spots and restaurants into the generation AI and have the generation AI determine the priority of the suggestions.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and restaurants when making suggestions. For example, the suggestion unit consecutively suggests tourist spots and restaurants related to the traveler. For example, if the traveler says, "Tell me about XX. Then, please tell me about XX," the suggestion unit adjusts the order of suggestions based on the relevance. Furthermore, when the traveler makes suggestions related to a specific category, the suggestion unit can also adjust the order of suggestions based on the category. For example, if the traveler says, "Tell me about tourist spots. Then, please tell me about restaurants," the suggestion unit adjusts the order of suggestions based on the category. Furthermore, the suggestion unit can also adjust the order of suggestions based on the relevance of suggestions made by the traveler in the past. For example, if the traveler says, "Tell me about XX in the past. Then, please tell me about XX," the suggestion unit adjusts the order of suggestions based on the relevance. By adjusting the order of suggestions based on the relevance of tourist spots and restaurants, more appropriate information can be provided. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input relevant data about tourist spots and restaurants into the generation AI and have the generation AI adjust the order of suggestions.

[0051] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the traveler's level of expertise. For example, if the traveler has specialized knowledge, the suggestion unit makes a detailed suggestion using technical terminology. For example, if the traveler says, "I want specialized information," the suggestion unit makes a suggestion using technical terminology based on the traveler's level of expertise. Furthermore, if the traveler has general knowledge, the suggestion unit can also make an easy-to-understand suggestion avoiding technical terminology. For example, if the traveler says, "I want easy-to-understand information," the suggestion unit makes a suggestion avoiding technical terminology based on the traveler's level of expertise. Furthermore, the suggestion unit can also make a suggestion according to the traveler's level of expertise by referring to the traveler's past dialogue history. For example, if the traveler has previously said, "I found specialized information useful," the suggestion unit makes a suggestion according to the traveler's level of expertise by referring to the dialogue history. This allows for the provision of more appropriate information by adjusting the use of technical terminology in the suggestion according to the traveler's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the traveler's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] When providing information, the providing unit can analyze the traveler's past consumption behavior and select the optimal information provision method. The providing unit, for example, provides similar information based on the traveler's past favorite tourist spots and restaurants. For example, if the traveler has previously said, "This tourist spot was good," the providing unit provides similar information based on that consumption behavior. The providing unit can also analyze the traveler's past consumption behavior to provide highly accurate information. For example, if the traveler has previously said, "This restaurant was good," the providing unit analyzes that consumption behavior and provides highly accurate information. The providing unit can also identify specific interests and concerns from the traveler's past consumption behavior and provide information based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the providing unit provides information based on that interest. This allows for more appropriate information to be provided by analyzing the traveler's past consumption behavior. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's past consumption behavior data into a generation AI and have the generation AI select an information provision method.

[0053] When providing information, the providing unit can customize the means of providing information based on the traveler's current living situation. The providing unit, for example, selects the optimal means of providing information based on the traveler's current living situation. For example, when a traveler says, "I'm currently in XX," the providing unit selects the optimal means of providing information based on that living situation. The providing unit can also select the means of providing information according to a specific living situation when the traveler is in that situation. For example, when a traveler says, "I'm currently in XX," the providing unit selects the means of providing information according to that living situation. The providing unit can also analyze the traveler's current living situation and select the optimal means of providing information. For example, when a traveler says, "I'm currently in XX," the providing unit analyzes the living situation and selects the optimal means of providing information. This allows the provision of more appropriate information by customizing the means of providing information based on the traveler's current living situation. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data on the traveler's living situation into the generating AI and have the generating AI customize the information providing means.

[0054] The providing unit can improve the information provision method by reflecting traveler feedback when providing information. For example, the providing unit improves the information provision method based on feedback provided by the traveler in the past. For example, if a traveler has previously said, "This information was helpful," the providing unit improves the information provision method based on that feedback. The providing unit can also analyze the traveler's feedback and select the optimal information provision method. For example, if a traveler has previously said, "This information was helpful," the providing unit analyzes that feedback and selects the optimal information provision method. The providing unit can also identify specific interests and concerns from the traveler's past feedback and select an information provision method based on that interest. For example, if a traveler has previously said, "I'm interested in historical places," the providing unit selects an information provision method based on that interest. This allows more appropriate information to be provided by reflecting traveler feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input traveler feedback data into a generating AI and cause the generating AI to improve the information provision method.

[0055] When providing information, the providing unit can select the optimal information provision method by taking into account the traveler's geographical location information. For example, the providing unit provides information on nearby tourist spots and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in XX," the providing unit provides information on nearby tourist spots and restaurants based on the location. Furthermore, if the traveler is in a specific area, the providing unit can also provide information on events and activities related to that area. For example, if the traveler says, "I'm in XX," the providing unit provides information on events and activities related to that area. Furthermore, if the traveler is traveling, the providing unit can prioritize providing information on the traveler's destination. For example, if the traveler says, "I'm traveling," the providing unit prioritizes providing information on the traveler's destination. This allows for more appropriate information to be provided by selecting the optimal information provision method by taking into account the traveler's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the traveler's geographical location information into the generation AI and cause the generation AI to select the information provision method.

[0056] When providing information, the providing unit can analyze the traveler's social media activity and suggest ways to provide the information. For example, the providing unit provides information about places where the traveler checked in on social media. For example, if the traveler says, "I checked in to X," the providing unit provides information about that place. The providing unit can also analyze the traveler's social media posts and provide information about related tourist spots and stores. For example, if the traveler says, "I posted about X," the providing unit provides related information based on the content of the post. The providing unit can also provide information about related places and events based on the activities of the traveler's friends on social media. For example, if the traveler says, "My friend went to X," the providing unit provides information about the place or event. This allows for more appropriate information to be provided by analyzing the traveler's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's social media data into a generation AI and have the generation AI suggest ways to provide information.

[0057] When providing information, the providing unit can customize the information provision method by reflecting the traveler's past feedback. For example, the providing unit improves the information provision method based on the traveler's past feedback. For example, if the traveler has previously said, "This information was helpful," the providing unit improves the information provision method based on that feedback. The providing unit can also analyze the traveler's feedback and select the optimal information provision method. For example, if the traveler has previously said, "This information was helpful," the providing unit analyzes that feedback and selects the optimal information provision method. The providing unit can also identify specific interests and concerns from the traveler's past feedback and select an information provision method based on that interest. For example, if the traveler has previously said, "I'm interested in historical places," the providing unit selects an information provision method based on that interest. This allows more appropriate information to be provided by reflecting the traveler's feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's feedback data into a generating AI and have the generating AI customize the information provision method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When a traveler interacts with locals, the dialogue unit can customize the dialogue content by referring to the traveler's past travel history. For example, the dialogue unit can suggest new related tourist spots and activities based on the places the traveler has visited and the activities the traveler has experienced in the past. The dialogue unit can also suggest similar styles based on the travel style the traveler has preferred in the past (e.g., adventurous travel, relaxed travel, etc.). Furthermore, the dialogue unit can suggest related events based on information about events and festivals the traveler has participated in in the past. This makes it possible to utilize the traveler's past travel history to enable more personalized dialogue.

[0060] When analyzing the content of a traveler's dialogue, the suggestion unit can customize the suggestions by taking into account the traveler's cultural background. For example, if a traveler is interested in a particular culture or religion, the suggestion unit can suggest tourist spots and events related to that culture or religion. Also, if a traveler is interested in a particular food culture, the suggestion unit can suggest restaurants and dishes related to that food culture. Furthermore, if a traveler is interested in a particular historical background, the suggestion unit can suggest tourist spots and museums related to that history. This makes it possible to make suggestions based on the traveler's cultural background, providing a more personalized travel experience.

[0061] When analyzing the content of a traveler's dialogue, the providing unit can adjust the content of the information provided by taking into account the traveler's current weather information. For example, if the traveler says, "It's raining," the providing unit can analyze the weather information and suggest indoor tourist spots and activities. Also, if the traveler says, "It's sunny," the providing unit can analyze the weather information and suggest outdoor tourist spots and activities. Furthermore, if the traveler says, "It's cold," the providing unit can analyze the weather information and suggest warm places and activities. This makes it possible to provide information according to the traveler's current weather information, resulting in more appropriate suggestions.

[0062] When analyzing the content of a traveler's conversation, the analysis unit can improve the accuracy of the analysis by taking into account the traveler's past purchasing history. For example, the analysis unit can suggest related tourist spots and activities based on products and services the traveler has purchased in the past. The analysis unit can also suggest related restaurants and cuisines based on food and drink the traveler has purchased in the past. Furthermore, the analysis unit can suggest related events and tours based on events and tours the traveler has participated in in the past. This makes it possible to make more personalized suggestions by utilizing the traveler's past purchasing history.

[0063] When analyzing the content of the traveler's dialogue, the suggestion unit can adjust the suggestions taking into account the traveler's current travel budget. For example, if the traveler says, "I have a limited budget," the suggestion unit can analyze the budget information and suggest cost-effective tourist spots and restaurants. Also, if the traveler says, "I want to take a luxurious trip," the suggestion unit can analyze the budget information and suggest high-end tourist spots and restaurants. Furthermore, if the traveler says, "I want to save money," the suggestion unit can analyze the budget information and suggest free or low-cost tourist spots and activities. This makes it possible to make suggestions based on the traveler's budget, thereby providing a more appropriate travel plan.

[0064] When analyzing the content of a traveler's dialogue, the providing unit can adjust the content of the information provided by taking into account the traveler's current mode of transportation. For example, if the traveler says, "I'm traveling by foot," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants within walking distance. Also, if the traveler says, "I'm traveling by car," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants with parking. Furthermore, if the traveler says, "I'm using public transportation," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants accessible by public transportation. This makes it possible to provide information according to the traveler's mode of transportation, resulting in more appropriate suggestions.

[0065] When analyzing the content of a traveler's dialogue, the analysis unit can improve the accuracy of the analysis by taking into account the traveler's current travel purpose. For example, if a traveler says, "I'm here for sightseeing," the analysis unit can analyze that travel purpose and suggest tourist spots and activities. Also, if a traveler says, "I'm here for business," the analysis unit can analyze that travel purpose and suggest business-related facilities and services. Furthermore, if a traveler says, "I'm here for relaxation," the analysis unit can analyze that travel purpose and suggest places and activities where you can relax. This makes it possible to perform an analysis based on the travel purpose of the traveler, resulting in more appropriate suggestions.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The dialogue unit supports dialogue between travelers and local people. The dialogue unit provides an interface that allows travelers to chat and video call with local people in real time using their smartphones. The dialogue unit is also designed to enable travelers to easily communicate with local people. For example, the dialogue unit provides a user interface for travelers to interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, etc. Step 2: The analysis unit uses the generation AI to analyze the dialogue content collected by the dialogue unit. The analysis unit analyzes the text and voice data entered when travelers interact with local people to understand the travelers' interests and concerns. For example, the generation AI uses natural language processing technology to analyze travelers' questions and requests and identify their interests and concerns. The analysis unit can also use voice recognition technology to convert travelers' voice data into text data for analysis. Step 3: The suggestion unit suggests tourist spots and restaurants based on the information analyzed by the analysis unit. The generation AI collects the latest information on the internet and information provided by local people, and makes suggestions to travelers. For example, the generation AI collects information on tourist spots that have recently become popular and restaurants that are popular among locals, and provides this to travelers. The suggestion unit can also suggest tourist spots and restaurants that can help avoid overtourism based on travelers' interests and preferences. Step 4: The providing unit provides the traveler with the information suggested by the suggesting unit. Based on the information acquired by the generating AI, the providing unit suggests tourist spots and restaurants that are best suited to the traveler. For example, the providing unit may send a notification to the traveler's smartphone and display information about the suggested tourist spots and restaurants. The providing unit may also provide the best information based on the traveler's location information and time of day.

[0068] (Example 2) A travel support system according to an embodiment of the present invention allows travelers to interact with locals in real time, and a generation AI analyzes the content of the conversation to suggest nearby popular spots and hidden gems. The travel support system provides an interface for travelers to interact with locals, and the generation AI analyzes the content of the conversation to suggest tourist spots and restaurants that are best suited to the traveler. For example, the travel support system provides an interface that allows travelers to chat and video call with locals in real time using their smartphones. This interface is designed to allow travelers to easily communicate with locals. Next, the travel support system uses a generation AI to analyze the content of the conversation between the traveler and the locals. The generation AI analyzes the text and voice data entered when the traveler interacts with locals to understand the traveler's interests. For example, if a traveler asks, "What local restaurants do you recommend?", the generation AI analyzes the question and suggests the best restaurants for the traveler based on the information provided by locals. Furthermore, the travel support system uses a generation AI to collect the latest information from the internet and information provided by locals and suggest them to the traveler. For example, the generation AI collects information on popular tourist spots and restaurants popular among locals and provides it to the traveler. Finally, the travel support system recommends tourist spots and restaurants that are best suited to travelers based on the information obtained by the generation AI. Based on the travelers' interests, the generation AI recommends tourist spots and restaurants that avoid overtourism. This allows travelers to avoid crowds and visit places beloved by locals, improving their trip satisfaction. The travel support system allows travelers to interact with locals in real time, and the generation AI analyzes the content of the conversation to recommend popular spots and hidden gems, thereby avoiding overtourism and improving trip satisfaction. For example, the system provides an interface for travelers to interact with locals, and the generation AI analyzes the content of the conversation to recommend tourist spots and restaurants that are best suited to travelers. This allows travelers to avoid crowds and visit places beloved by locals, improving their trip satisfaction.

[0069] A travel support system according to an embodiment includes a dialogue unit, an analysis unit, a suggestion unit, and a provision unit. The dialogue unit supports dialogue between travelers and local people. For example, the dialogue unit provides an interface that allows travelers to chat or video call with local people in real time using a smartphone. The dialogue unit is also designed to enable travelers to easily communicate with local people. For example, the dialogue unit provides a user interface for travelers to interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, or the like. The analysis unit uses a generation AI to analyze the dialogue content collected by the dialogue unit. For example, the analysis unit analyzes text and voice data entered when the travelers interact with local people to understand the travelers' interests. For example, the generation AI uses natural language processing technology to analyze the travelers' questions and requests and identify the travelers' interests. The analysis unit can also use voice recognition technology to convert the travelers' voice data into text data and perform analysis. The suggestion unit suggests tourist spots and restaurants based on the information analyzed by the analysis unit. The suggestion unit, for example, uses a generation AI to collect the latest information on the Internet and information provided by local people and suggest it to travelers. For example, the generation AI collects information on recently popular tourist spots and restaurants popular among locals and provides it to travelers. The suggestion unit can also suggest tourist spots and restaurants that can avoid overtourism based on the traveler's interests. The provision unit provides the traveler with the information suggested by the suggestion unit. The provision unit, for example, suggests tourist spots and restaurants that are best suited to the traveler based on the information acquired by the generation AI. For example, the provision unit sends a notification to the traveler's smartphone and displays information on the suggested tourist spots and restaurants. The provision unit can also provide optimal information based on the traveler's location information and time zone. As a result, the travel support system according to the embodiment allows travelers to interact with local people in real time and suggests and provides tourist spots and restaurants based on the analyzed information, thereby increasing travel satisfaction.

[0070] The dialogue unit may provide an interface through which travelers can chat or video call with local people in real time using their smartphones. Examples of interfaces include, but are not limited to, the design and operation of a user interface. For example, the dialogue unit may provide an interface through which travelers can chat with local people in real time using their smartphones. The dialogue unit may also provide an interface through which travelers can video call with local people using their smartphones. For example, the dialogue unit may provide a user interface through which travelers can interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, or the like. This allows travelers to interact with local people in real time using their smartphones. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit may input traveler input data to a generation AI and have the generation AI analyze the content of the dialogue.

[0071] The analysis unit can analyze text or voice data input when a traveler converses with local people to understand the traveler's interests. The analysis unit can, for example, use natural language processing technology to analyze a traveler's questions or requests and identify the traveler's interests. For example, if a traveler asks, "Please tell me some recommended local restaurants," the analysis unit analyzes the question and understands the traveler's interests. The analysis unit can also use voice recognition technology to convert the traveler's voice data into text data and analyze it. For example, the analysis unit can analyze voice data input when a traveler converses with local people to understand the traveler's interests. The analysis unit can also use a generation AI to analyze the content of the traveler's conversation and understand the traveler's interests. For example, the generation AI can analyze the content of the traveler's conversation and identify the traveler's interests. This allows for understanding the traveler's interests and makes more appropriate suggestions. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the traveler's dialogue data into the generation AI and have the generation AI understand the traveler's interests and concerns.

[0072] The suggestion unit allows the generation AI to collect the latest information on the internet or information provided by local people and suggest it to travelers. The generation AI, for example, collects the latest information on the internet and suggests it to travelers. For example, the generation AI collects information on popular tourist spots and restaurants popular among locals and provides it to travelers. The generation AI can also collect information provided by local people and suggest it to travelers. For example, the generation AI collects information on tourist spots and restaurants provided by local people and provides it to travelers. Furthermore, the generation AI can suggest the best tourist spots and restaurants for travelers based on the latest information on the internet and information provided by local people. For example, the generation AI suggests tourist spots and restaurants that can avoid overtourism based on the traveler's interests. This makes it possible to make the best suggestions for travelers based on the latest information and information from local people. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit suggests the best tourist spots and restaurants for travelers based on the information collected by the generation AI.

[0073] The providing unit can suggest tourist spots or restaurants to the traveler based on the information acquired by the generation AI. The providing unit, for example, suggests tourist spots or restaurants that are optimal for the traveler based on the information acquired by the generation AI. For example, the providing unit sends a notification to the traveler's smartphone and displays information about the suggested tourist spots or restaurants. The providing unit can also provide optimal information based on the traveler's location information and time of day. For example, the providing unit provides information about nearby tourist spots and restaurants based on the traveler's current location. Furthermore, if the traveler is having a conversation at night, the providing unit can also provide information about stores and spots that are open at night. Furthermore, if the traveler is in a location where a specific event is being held, the providing unit can provide information related to the event. This can increase travel satisfaction by suggesting tourist spots and restaurants that are optimal for the traveler. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit suggests tourist spots and restaurants that are optimal for the traveler based on the information acquired by the generation AI.

[0074] The dialogue unit can estimate the traveler's emotions and adjust the tone and content of the dialogue based on the estimated traveler's emotions. For example, if the traveler is nervous, the dialogue unit can calm the tone of the dialogue and provide relaxing content. For example, if the traveler says, "I'm nervous," the dialogue unit can analyze the traveler's emotions and speak in a calm tone. Also, if the traveler is excited, the dialogue unit can also calm the tone of the dialogue and provide interesting content. For example, if the traveler says, "I'm excited," the dialogue unit can analyze the traveler's emotions and speak in a lively tone. Also, if the traveler is tired, the dialogue unit can calm the tone of the dialogue and provide concise, easy-to-understand content. For example, if the traveler says, "I'm tired," the dialogue unit can analyze the traveler's emotions and speak in a calm tone. This allows for more appropriate dialogue by adjusting the tone and content of the dialogue according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue unit may be performed using AI, or may be performed without using AI. For example, the dialogue unit may input traveler emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The dialogue unit can analyze the traveler's past dialogue history and select the optimal dialogue method. For example, the dialogue unit selects a similar dialogue method based on the traveler's preferred dialogue style in the past. For example, if the traveler has previously said, "I like casual dialogue," the dialogue unit selects a casual dialogue method based on that dialogue style. The dialogue unit can also prioritize providing related information based on questions the traveler has frequently asked in the past. For example, if the traveler has frequently asked, "What are some recommended tourist spots?" in the past, the dialogue unit prioritizes providing information related to that question. The dialogue unit can also identify specific interests and concerns from the traveler's past dialogue history and select a dialogue method based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the dialogue unit selects a dialogue method based on that interest. This enables more effective dialogue by selecting the optimal dialogue method based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's past dialogue data into a generation AI and have the generation AI select the optimal dialogue method.

[0076] During the dialogue, the dialogue unit can customize the dialogue content based on the traveler's current location information and time of day. For example, the dialogue unit provides information on nearby tourist spots and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in XX," the dialogue unit provides information on nearby tourist spots and restaurants based on the traveler's location. Furthermore, if the traveler is having a dialogue at night, the dialogue unit can provide information on stores and spots that are open at night. For example, if the traveler says, "I want to go to XX at night," the dialogue unit can provide information on stores and spots that are open at night. Furthermore, if the traveler is in a location where a specific event is being held, the dialogue unit can provide information related to that event. For example, if the traveler says, "I'm participating in the XX event," the dialogue unit provides information related to the event. This allows for customizing the dialogue content based on the traveler's current location information and time of day, thereby providing more appropriate information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the traveler's location information and time of day data into a generation AI and have the generation AI customize the dialogue content.

[0077] The dialogue unit can select the optimal dialogue means depending on the traveler's input method during dialogue. For example, if the traveler uses voice input, the dialogue unit prioritizes voice dialogue. For example, if the traveler says, "I want to talk by voice," the dialogue unit prioritizes voice dialogue based on the input method. The dialogue unit can also prioritize text dialogue if the traveler uses text input. For example, if the traveler says, "I want to talk by text," the dialogue unit prioritizes text dialogue based on the input method. The dialogue unit can also provide related information based on the image if the traveler sends an image. For example, if the traveler says, "Tell me about this image," the dialogue unit provides related information based on the image. This enables more effective dialogue by selecting the optimal dialogue means depending on the traveler's input method. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's input data into a generation AI and have the generation AI select the optimal dialogue means.

[0078] The dialogue unit can estimate the traveler's emotions and prioritize dialogue based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the dialogue unit can quickly initiate dialogue and provide reassuring information. For example, if the traveler says, "I'm anxious," the dialogue unit can analyze the traveler's emotions and quickly initiate dialogue. Furthermore, if the traveler is excited, the dialogue unit can prioritize providing interesting information. For example, if the traveler says, "I'm excited," the dialogue unit can analyze the traveler's emotions and prioritize providing interesting information. Furthermore, if the traveler is tired, the dialogue unit can prioritize providing concise and easy-to-understand information. For example, if the traveler says, "I'm tired," the dialogue unit can analyze the traveler's emotions and prioritize providing concise and easy-to-understand information. This enables more appropriate dialogue by prioritizing dialogue based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input traveler emotion data to the generation AI and have the generation AI estimate the emotion.

[0079] During the dialogue, the dialogue unit can prioritize providing relevant information by taking into account the traveler's geographical location information. For example, the dialogue unit prioritizes providing information about nearby tourist attractions and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in X," the dialogue unit prioritizes providing information about nearby tourist attractions and restaurants based on the traveler's location. Furthermore, if the traveler is in a specific area, the dialogue unit can provide information about events and activities related to that area. For example, if the traveler says, "I'm in X area," the dialogue unit provides information about events and activities related to that area. Furthermore, if the traveler is traveling, the dialogue unit can prioritize providing information about the traveler's destination. For example, if the traveler says, "I'm traveling," the dialogue unit prioritizes providing information about the traveler's destination. This allows for more appropriate information to be provided by taking into account the traveler's geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide relevant information.

[0080] The dialogue unit can analyze the traveler's social media activity during the dialogue and provide relevant information. For example, the dialogue unit can provide information about places the traveler checked in on social media. For example, if the traveler says, "I checked in to XX," the dialogue unit can provide information about the place. The dialogue unit can also analyze the traveler's social media posts and provide information about related tourist spots and stores. For example, if the traveler says, "I posted about XX," the dialogue unit can provide relevant information based on the post. The dialogue unit can also provide information about related places and events based on the traveler's friends' activities on social media. For example, if the traveler says, "My friend went to XX," the dialogue unit can provide information about the place or event. This allows for more relevant information to be provided by analyzing the traveler's social media activity. Some or all of the above-described processing in the dialogue unit can be performed using, or without, AI. For example, the dialogue unit can input the traveler's social media data into a generation AI and cause the generation AI to provide relevant information.

[0081] The dialogue unit can customize the dialogue method by reflecting the traveler's past feedback during the dialogue. For example, the dialogue unit selects a similar dialogue method based on the traveler's previously preferred dialogue style. For example, if the traveler has previously said, "I like casual dialogue," the dialogue unit selects a casual dialogue method based on that dialogue style. The dialogue unit can also improve the dialogue content based on feedback provided by the traveler in the past. For example, if the traveler has previously said, "I want more detailed information," the dialogue unit improves the dialogue content based on that feedback. The dialogue unit can also identify specific interests and concerns from the traveler's past feedback and select a dialogue method based on that interest. For example, if the traveler has previously said, "I'm interested in historical places," the dialogue unit selects a dialogue method based on that interest. This makes it possible to provide a more appropriate dialogue method by reflecting the traveler's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the traveler's past feedback data into a generation AI and have the generation AI customize the dialogue method.

[0082] The analysis unit can estimate the traveler's emotions and adjust the accuracy of the analysis based on the estimated traveler's emotions. For example, if the traveler is relaxed, the analysis unit performs a detailed analysis and provides highly accurate information. For example, if the traveler says, "I'm relaxed," the analysis unit analyzes the traveler's emotions and performs a detailed analysis. Furthermore, if the traveler is in a hurry, the analysis unit can quickly analyze the traveler's emotions and provide the minimum necessary information. For example, if the traveler says, "I'm in a hurry," the analysis unit analyzes the traveler's emotions and performs a quick analysis. Furthermore, if the traveler is excited, the analysis unit can prioritize analyzing information that is interesting. For example, if the traveler says, "I'm excited," the analysis unit analyzes the traveler's emotions and prioritizes analyzing information that is interesting. This allows the accuracy of the analysis to be adjusted based on the traveler's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input traveler emotion data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the dialogue content. For example, if a traveler asks an important question, the analysis unit performs a detailed analysis to provide highly accurate information. For example, if a traveler says, "I have an important question," the analysis unit analyzes the importance of the question and performs a detailed analysis. Furthermore, if a traveler asks a general question, the analysis unit can quickly analyze the question and provide the minimum necessary information. For example, if a traveler says, "I have a general question," the analysis unit analyzes the importance of the question and performs a quick analysis. Furthermore, if a traveler expresses specific interests or concerns, the analysis unit can perform a detailed analysis based on the content of those interests. For example, if a traveler says, "I have specific interests," the analysis unit performs a detailed analysis based on those interests. By adjusting the level of detail of the analysis based on the importance of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data of the dialogue content to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the dialogue content. For example, if a traveler asks a question about tourist spots, the analysis unit applies an analysis algorithm specialized for tourist spots. For example, if a traveler says, "Tell me about tourist spots," the analysis unit applies an analysis algorithm specialized for tourist spots based on the question. Furthermore, if a traveler asks a question about restaurants, the analysis unit can also apply an analysis algorithm specialized for restaurants. For example, if a traveler says, "Tell me about restaurants," the analysis unit applies an analysis algorithm specialized for restaurants based on the question. Furthermore, if a traveler asks a question about events, the analysis unit can also apply an analysis algorithm specialized for events. For example, if a traveler says, "Tell me about events," the analysis unit applies an analysis algorithm specialized for events based on the question. By applying different analysis algorithms depending on the category of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input category data of the dialogue content into a generation AI and cause the generation AI to apply an analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the traveler's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on feedback provided by the traveler in the past. For example, if the traveler has previously said, "This information was helpful," the analysis unit improves the accuracy of the analysis based on that feedback. The analysis unit can also improve the accuracy of analysis for similar questions by referring to the traveler's past analysis results. For example, if the traveler has previously said, "The answer to this question was good," the analysis unit improves the accuracy of the analysis by referring to the analysis results. The analysis unit can also identify specific interests and concerns from the traveler's past analysis results and perform analysis based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the analysis unit performs analysis based on those interests. By referring to the traveler's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the traveler's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the traveler's emotions and determine analysis priorities based on the estimated traveler's emotions. For example, if a traveler is feeling anxious, the analysis unit can quickly analyze the emotion and provide reassuring information. For example, if a traveler says, "I'm anxious," the analysis unit can analyze the emotion and quickly analyze it. Furthermore, if a traveler is excited, the analysis unit can prioritize analyzing information that will interest them. For example, if a traveler says, "I'm excited," the analysis unit can analyze the emotion and prioritize analyzing information that will interest them. Furthermore, if a traveler is tired, the analysis unit can prioritize analyzing concise and easy-to-understand information. For example, if a traveler says, "I'm tired," the analysis unit can analyze the emotion and prioritize analyzing concise and easy-to-understand information. By determining analysis priorities based on the traveler's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input traveler emotion data into the generation AI and have the generation AI determine the priorities of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the dialogue content. For example, the analysis unit prioritizes analysis of the dialogue content most recently submitted by the traveler. For example, if the traveler says, "I recently asked a question about XX," the analysis unit prioritizes analysis of that dialogue content. The analysis unit can also prioritize analysis of dialogue content submitted by the traveler during a specific time period. For example, if the traveler says, "I asked a question about XX at night," the analysis unit prioritizes analysis of the dialogue content based on that time period. The analysis unit can also perform analysis based on the time of submission, taking into account dialogue content previously submitted by the traveler. For example, if the traveler says, "I asked a question about XX in the past," the analysis unit analyzes the dialogue content based on the time of submission. This allows for more appropriate information to be provided by determining the priority of analysis based on the time of submission of the dialogue content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the time of submission of the dialogue content into the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the dialogue content. For example, if a traveler asks related questions consecutively, the analysis unit adjusts the order of analysis based on that relevance. For example, if a traveler says, "I asked about XX. Then, I also asked about XX," the analysis unit adjusts the order of analysis based on that relevance. Furthermore, if a traveler asks a question related to a specific category, the analysis unit can adjust the order of analysis based on that category. For example, if a traveler says, "I asked about tourist spots. Then, I also asked about restaurants," the analysis unit adjusts the order of analysis based on that category. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of questions previously asked by the traveler. For example, if a traveler says, "I asked about XX in the past. Then, I also asked about XX," the analysis unit adjusts the order of analysis based on that relevance. By adjusting the order of analysis based on the relevance of the dialogue content, more appropriate information can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data of the dialogue content to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the traveler's level of expertise. For example, if the traveler has specialized knowledge, the analysis unit performs a detailed analysis using technical terms. For example, if the traveler says, "I want specialized information," the analysis unit performs the analysis using technical terms based on the traveler's level of expertise. Furthermore, if the traveler has general knowledge, the analysis unit can avoid technical terms and perform an easy-to-understand analysis. For example, if the traveler says, "I want easy-to-understand information," the analysis unit performs the analysis by avoiding technical terms based on the traveler's level of expertise. Furthermore, the analysis unit can also perform an analysis according to the traveler's level of expertise by referring to the traveler's past dialogue history. For example, if the traveler has previously said, "I found specialized information useful," the analysis unit performs an analysis according to the traveler's level of expertise by referring to the dialogue history. This allows for the provision of more appropriate information by adjusting the use of technical terms in the analysis according to the traveler's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the traveler's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0090] The suggestion unit can estimate the traveler's emotions and adjust the way suggestions are expressed based on the estimated traveler's emotions. For example, if the traveler is relaxed, the suggestion unit can provide detailed suggestions and interesting information. For example, if the traveler says, "I'm relaxed," the suggestion unit can analyze the traveler's emotions and provide detailed suggestions. Furthermore, if the traveler is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. For example, if the traveler says, "I'm in a hurry," the suggestion unit can analyze the traveler's emotions and provide concise and to-the-point suggestions. Furthermore, if the traveler is excited, the suggestion unit can provide visually stimulating suggestions. For example, if the traveler says, "I'm excited," the suggestion unit can analyze the traveler's emotions and provide visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the way suggestions are expressed based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input traveler emotion data to the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0091] The suggestion unit can adjust the level of detail of the suggestion based on the importance of tourist spots and restaurants when making a suggestion. For example, the suggestion unit provides detailed information when making a suggestion regarding important tourist spots and restaurants. For example, if a traveler says, "Please tell me about important tourist spots," the suggestion unit provides detailed information based on the importance of the tourist spots. The suggestion unit can also provide concise information when making a suggestion regarding general tourist spots and restaurants. For example, if a traveler says, "Please tell me about general tourist spots," the suggestion unit provides concise information based on the importance of the tourist spots. The suggestion unit can also prioritize suggesting information with high importance based on the traveler's interests. For example, if a traveler says, "I'm interested in historical places," the suggestion unit prioritizes suggesting information with high importance based on the traveler's interests. This allows for adjusting the level of detail of the suggestion based on the importance of tourist spots and restaurants, thereby providing more appropriate information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data of tourist spots and restaurants into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0092] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist spot or restaurant. For example, when making a suggestion regarding a tourist spot, the suggestion unit applies a suggestion algorithm specialized for tourist spots. For example, when a traveler says, "Tell me about tourist spots," the suggestion unit applies a suggestion algorithm specialized for tourist spots based on the category. Furthermore, when making a suggestion regarding a restaurant, the suggestion unit can also apply a suggestion algorithm specialized for restaurants. For example, when a traveler says, "Tell me about restaurants," the suggestion unit applies a suggestion algorithm specialized for restaurants based on the category. Furthermore, when making a suggestion regarding an event, the suggestion unit can also apply a suggestion algorithm specialized for events. For example, when a traveler says, "Tell me about events," the suggestion unit applies a suggestion algorithm specialized for events based on the category. This allows for more appropriate information to be provided by applying different suggestion algorithms depending on the category of the tourist spot or restaurant. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input category data of tourist spots or restaurants into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0093] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the traveler's past suggestion results. The suggestion unit, for example, makes similar suggestions based on tourist spots or restaurants that the traveler has previously liked. For example, if the traveler has previously said, "This tourist spot was good," the suggestion unit makes a similar suggestion based on that suggestion result. The suggestion unit can also analyze the traveler's past suggestion results to make more accurate suggestions. For example, if the traveler has previously said, "This restaurant was good," the suggestion unit can analyze that suggestion result to make more accurate suggestions. The suggestion unit can also identify specific interests and concerns from the traveler's past suggestion results and make suggestions based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the suggestion unit can make suggestions based on those interests. In this way, the accuracy of the suggestion can be improved by referring to the traveler's past suggestion results. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the traveler's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0094] The suggestion unit can estimate the traveler's emotions and adjust the length of the suggestions based on the estimated traveler's emotions. For example, if the traveler is relaxed, the suggestion unit can provide detailed suggestions and interesting information. For example, if the traveler says, "I'm relaxed," the suggestion unit can analyze the traveler's emotions and provide detailed suggestions. Furthermore, if the traveler is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. For example, if the traveler says, "I'm in a hurry," the suggestion unit can analyze the traveler's emotions and provide concise and to-the-point suggestions. Furthermore, if the traveler is excited, the suggestion unit can provide visually stimulating suggestions. For example, if the traveler says, "I'm excited," the suggestion unit can analyze the traveler's emotions and provide visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestions based on the traveler's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input traveler emotion data to the generation AI and have the generation AI adjust the length of the suggestion.

[0095] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the tourist spots and restaurants. For example, the suggestion unit prioritizes the suggestions of tourist spots and restaurants most recently submitted by the traveler. For example, if the traveler says, "I recently got information about XX," the suggestion unit prioritizes the suggestions based on the time of submission. The suggestion unit can also prioritize the suggestions submitted by the traveler during a specific time period. For example, if the traveler says, "I got information about XX at night," the suggestion unit prioritizes the suggestions based on the time of submission. The suggestion unit can also make suggestions based on the time of submission, taking into account information previously submitted by the traveler. For example, if the traveler says, "I got information about XX in the past," the suggestion unit suggests information based on the time of submission. This allows for more appropriate information to be provided by prioritizing the suggestions based on the time of submission of the tourist spots and restaurants. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input submission date data for tourist spots and restaurants into the generation AI and have the generation AI determine the priority of the suggestions.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and restaurants when making suggestions. For example, the suggestion unit consecutively suggests tourist spots and restaurants related to the traveler. For example, if the traveler says, "Tell me about XX. Then, please tell me about XX," the suggestion unit adjusts the order of suggestions based on the relevance. Furthermore, when the traveler makes suggestions related to a specific category, the suggestion unit can also adjust the order of suggestions based on the category. For example, if the traveler says, "Tell me about tourist spots. Then, please tell me about restaurants," the suggestion unit adjusts the order of suggestions based on the category. Furthermore, the suggestion unit can also adjust the order of suggestions based on the relevance of suggestions made by the traveler in the past. For example, if the traveler says, "Tell me about XX in the past. Then, please tell me about XX," the suggestion unit adjusts the order of suggestions based on the relevance. By adjusting the order of suggestions based on the relevance of tourist spots and restaurants, more appropriate information can be provided. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input relevant data about tourist spots and restaurants into the generation AI and have the generation AI adjust the order of suggestions.

[0097] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the traveler's level of expertise. For example, if the traveler has specialized knowledge, the suggestion unit makes a detailed suggestion using technical terminology. For example, if the traveler says, "I want specialized information," the suggestion unit makes a suggestion using technical terminology based on the traveler's level of expertise. Furthermore, if the traveler has general knowledge, the suggestion unit can also make an easy-to-understand suggestion avoiding technical terminology. For example, if the traveler says, "I want easy-to-understand information," the suggestion unit makes a suggestion avoiding technical terminology based on the traveler's level of expertise. Furthermore, the suggestion unit can also make a suggestion according to the traveler's level of expertise by referring to the traveler's past dialogue history. For example, if the traveler has previously said, "I found specialized information useful," the suggestion unit makes a suggestion according to the traveler's level of expertise by referring to the dialogue history. This allows for the provision of more appropriate information by adjusting the use of technical terminology in the suggestion according to the traveler's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the traveler's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terminology.

[0098] The providing unit can estimate the traveler's emotions and adjust the way information is provided based on the estimated traveler's emotions. For example, if the traveler is relaxed, the providing unit can provide detailed information and interesting content. For example, if the traveler says, "I'm relaxed," the providing unit can analyze the traveler's emotions and provide detailed information. Furthermore, if the traveler is in a hurry, the providing unit can provide concise, to-the-point information. For example, if the traveler says, "I'm in a hurry," the providing unit can analyze the traveler's emotions and provide concise, to-the-point information. Furthermore, if the traveler is excited, the providing unit can provide visually stimulating information. For example, if the traveler says, "I'm excited," the providing unit can analyze the traveler's emotions and provide visually stimulating information. This allows the information providing method to be adjusted based on the traveler's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input traveler emotion data to the generating AI and have the generating AI adjust the method of providing information.

[0099] When providing information, the providing unit can analyze the traveler's past consumption behavior and select the optimal information provision method. The providing unit, for example, provides similar information based on the traveler's past favorite tourist spots and restaurants. For example, if the traveler has previously said, "This tourist spot was good," the providing unit provides similar information based on that consumption behavior. The providing unit can also analyze the traveler's past consumption behavior to provide highly accurate information. For example, if the traveler has previously said, "This restaurant was good," the providing unit analyzes that consumption behavior and provides highly accurate information. The providing unit can also identify specific interests and concerns from the traveler's past consumption behavior and provide information based on those interests. For example, if the traveler has previously said, "I'm interested in historical places," the providing unit provides information based on that interest. This allows for more appropriate information to be provided by analyzing the traveler's past consumption behavior. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's past consumption behavior data into a generation AI and have the generation AI select an information provision method.

[0100] When providing information, the providing unit can customize the means of providing information based on the traveler's current living situation. The providing unit, for example, selects the optimal means of providing information based on the traveler's current living situation. For example, when a traveler says, "I'm currently in XX," the providing unit selects the optimal means of providing information based on that living situation. The providing unit can also select the means of providing information according to a specific living situation when the traveler is in that situation. For example, when a traveler says, "I'm currently in XX," the providing unit selects the means of providing information according to that living situation. The providing unit can also analyze the traveler's current living situation and select the optimal means of providing information. For example, when a traveler says, "I'm currently in XX," the providing unit analyzes the living situation and selects the optimal means of providing information. This allows the provision of more appropriate information by customizing the means of providing information based on the traveler's current living situation. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data on the traveler's living situation into the generating AI and have the generating AI customize the information providing means.

[0101] The providing unit can improve the information provision method by reflecting traveler feedback when providing information. For example, the providing unit improves the information provision method based on feedback provided by the traveler in the past. For example, if a traveler has previously said, "This information was helpful," the providing unit improves the information provision method based on that feedback. The providing unit can also analyze the traveler's feedback and select the optimal information provision method. For example, if a traveler has previously said, "This information was helpful," the providing unit analyzes that feedback and selects the optimal information provision method. The providing unit can also identify specific interests and concerns from the traveler's past feedback and select an information provision method based on that interest. For example, if a traveler has previously said, "I'm interested in historical places," the providing unit selects an information provision method based on that interest. This allows more appropriate information to be provided by reflecting traveler feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input traveler feedback data into a generating AI and cause the generating AI to improve the information provision method.

[0102] The providing unit can estimate the traveler's emotions and determine the priority of information provision based on the estimated traveler's emotions. For example, if a traveler is feeling anxious, the providing unit can quickly provide information and reassure the traveler. For example, if a traveler says, "I'm anxious," the providing unit can analyze the traveler's emotions and quickly provide information. Furthermore, if a traveler is excited, the providing unit can prioritize providing interesting information. For example, if a traveler says, "I'm excited," the providing unit can analyze the traveler's emotions and prioritize providing interesting information. Furthermore, if a traveler is tired, the providing unit can prioritize providing concise and easy-to-understand information. For example, if a traveler says, "I'm tired," the providing unit can analyze the traveler's emotions and prioritize providing concise and easy-to-understand information. This allows the information provided to be prioritized based on the traveler's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input traveler emotion data into the generating AI and have the generating AI determine the priority of information provision.

[0103] When providing information, the providing unit can select the optimal information provision method by taking into account the traveler's geographical location information. For example, the providing unit provides information on nearby tourist spots and restaurants based on the traveler's current location. For example, if the traveler says, "I'm currently in XX," the providing unit provides information on nearby tourist spots and restaurants based on the location. Furthermore, if the traveler is in a specific area, the providing unit can also provide information on events and activities related to that area. For example, if the traveler says, "I'm in XX," the providing unit provides information on events and activities related to that area. Furthermore, if the traveler is traveling, the providing unit can prioritize providing information on the traveler's destination. For example, if the traveler says, "I'm traveling," the providing unit prioritizes providing information on the traveler's destination. This allows for more appropriate information to be provided by selecting the optimal information provision method by taking into account the traveler's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the traveler's geographical location information into the generation AI and cause the generation AI to select the information provision method.

[0104] When providing information, the providing unit can analyze the traveler's social media activity and suggest ways to provide the information. For example, the providing unit provides information about places where the traveler checked in on social media. For example, if the traveler says, "I checked in to X," the providing unit provides information about that place. The providing unit can also analyze the traveler's social media posts and provide information about related tourist spots and stores. For example, if the traveler says, "I posted about X," the providing unit provides related information based on the content of the post. The providing unit can also provide information about related places and events based on the activities of the traveler's friends on social media. For example, if the traveler says, "My friend went to X," the providing unit provides information about the place or event. This allows for more appropriate information to be provided by analyzing the traveler's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's social media data into a generation AI and have the generation AI suggest ways to provide information.

[0105] When providing information, the providing unit can customize the information provision method by reflecting the traveler's past feedback. For example, the providing unit improves the information provision method based on the traveler's past feedback. For example, if the traveler has previously said, "This information was helpful," the providing unit improves the information provision method based on that feedback. The providing unit can also analyze the traveler's feedback and select the optimal information provision method. For example, if the traveler has previously said, "This information was helpful," the providing unit analyzes that feedback and selects the optimal information provision method. The providing unit can also identify specific interests and concerns from the traveler's past feedback and select an information provision method based on that interest. For example, if the traveler has previously said, "I'm interested in historical places," the providing unit selects an information provision method based on that interest. This allows more appropriate information to be provided by reflecting the traveler's feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the traveler's feedback data into a generating AI and have the generating AI customize the information provision method. === Hard Collateral 1-1 === Each of the multiple elements, including the dialogue unit, analysis unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the dialogue unit is implemented by the control unit 46A of the smart device 14 and provides an interface that allows travelers to chat or video call with local people in real time using their smartphones. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dialogue using a generation AI to understand the travelers' interests. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest information on the Internet and information provided by local people, and suggests tourist spots and restaurants that are best suited to the travelers. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies and displays the suggested information on the travelers' smartphones. === Hard Collateral 1-2 === Each of the multiple elements, including the dialogue unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart glasses 214 and provides an interface that allows travelers to chat or video call with local people in real time using the smart glasses. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dialogue using a generation AI to understand the travelers' interests. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest information on the Internet and information provided by local people, and suggests tourist spots and restaurants that are best suited to the travelers. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies and displays the suggested information on the travelers' smart glasses. === Hard Collateral 1-3 === Each of the multiple elements, including the dialogue unit, analysis unit, suggestion unit, and provision unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the dialogue unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface that allows travelers to chat or video call with local people in real time using the headset terminal. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the dialogue using a generation AI to understand the travelers' interests and concerns. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest information on the Internet and information provided by local people, and suggests tourist spots and restaurants that are best suited to the travelers. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and notifies and displays the suggested information on the travelers' headset terminal. === Hard Collateral 1-4 === Each of the multiple elements, including the dialogue unit, analysis unit, suggestion unit, and provision unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue unit is implemented by the control unit 46A of the robot 414 and provides an interface that allows travelers to use the robot to chat or video call local people in real time. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the dialogue content using a generation AI to understand the travelers' interests. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest information on the Internet and information provided by local people, and suggests tourist spots and restaurants that are best suited to the travelers. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and notifies and displays the suggested information to the traveler's robot.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When a traveler interacts with locals, the dialogue unit can customize the dialogue content by referring to the traveler's past travel history. For example, the dialogue unit can suggest new related tourist spots and activities based on the places the traveler has visited and the activities the traveler has experienced in the past. The dialogue unit can also suggest similar styles based on the travel style the traveler has preferred in the past (e.g., adventurous travel, relaxed travel, etc.). Furthermore, the dialogue unit can suggest related events based on information about events and festivals the traveler has participated in in the past. This makes it possible to utilize the traveler's past travel history to enable more personalized dialogue.

[0108] When analyzing the content of a traveler's conversation, the analysis unit can adjust the accuracy of the analysis by taking into account the traveler's current health condition. For example, if a traveler says, "I'm not feeling well," the analysis unit can analyze their health condition and suggest tourist spots and restaurants that take their health into consideration. Also, if a traveler says, "I'm tired," the analysis unit can analyze their health condition and suggest relaxing places and activities. Furthermore, if a traveler says, "I'm fine," the analysis unit can analyze their health condition and suggest active activities and spots. This makes it possible to perform an analysis based on the traveler's health condition, resulting in more appropriate suggestions.

[0109] When analyzing the content of a traveler's dialogue, the suggestion unit can customize the suggestions by taking into account the traveler's cultural background. For example, if a traveler is interested in a particular culture or religion, the suggestion unit can suggest tourist spots and events related to that culture or religion. Also, if a traveler is interested in a particular food culture, the suggestion unit can suggest restaurants and dishes related to that food culture. Furthermore, if a traveler is interested in a particular historical background, the suggestion unit can suggest tourist spots and museums related to that history. This makes it possible to make suggestions based on the traveler's cultural background, providing a more personalized travel experience.

[0110] When analyzing the content of a traveler's dialogue, the providing unit can adjust the content of the information provided by taking into account the traveler's current weather information. For example, if the traveler says, "It's raining," the providing unit can analyze the weather information and suggest indoor tourist spots and activities. Also, if the traveler says, "It's sunny," the providing unit can analyze the weather information and suggest outdoor tourist spots and activities. Furthermore, if the traveler says, "It's cold," the providing unit can analyze the weather information and suggest warm places and activities. This makes it possible to provide information according to the traveler's current weather information, resulting in more appropriate suggestions.

[0111] The dialogue unit can estimate the traveler's emotions and adjust the tempo of the dialogue based on the estimated traveler's emotions. For example, if the traveler says, "I'm in a hurry," the dialogue unit can analyze the traveler's emotions and speed up the tempo of the dialogue. Also, if the traveler says, "I'm relaxed," the dialogue unit can analyze the traveler's emotions and slow down the tempo of the dialogue. Furthermore, if the traveler says, "I'm excited," the dialogue unit can analyze the traveler's emotions and speed up the tempo of the dialogue. This makes it possible to adjust the tempo of the dialogue according to the traveler's emotions, resulting in more appropriate dialogue.

[0112] When analyzing the content of a traveler's conversation, the analysis unit can improve the accuracy of the analysis by taking into account the traveler's past purchasing history. For example, the analysis unit can suggest related tourist spots and activities based on products and services the traveler has purchased in the past. The analysis unit can also suggest related restaurants and cuisines based on food and drink the traveler has purchased in the past. Furthermore, the analysis unit can suggest related events and tours based on events and tours the traveler has participated in in the past. This makes it possible to make more personalized suggestions by utilizing the traveler's past purchasing history.

[0113] When analyzing the content of the traveler's dialogue, the suggestion unit can adjust the suggestions taking into account the traveler's current travel budget. For example, if the traveler says, "I have a limited budget," the suggestion unit can analyze the budget information and suggest cost-effective tourist spots and restaurants. Also, if the traveler says, "I want to take a luxurious trip," the suggestion unit can analyze the budget information and suggest high-end tourist spots and restaurants. Furthermore, if the traveler says, "I want to save money," the suggestion unit can analyze the budget information and suggest free or low-cost tourist spots and activities. This makes it possible to make suggestions based on the traveler's budget, thereby providing a more appropriate travel plan.

[0114] When analyzing the content of a traveler's dialogue, the providing unit can adjust the content of the information provided by taking into account the traveler's current mode of transportation. For example, if the traveler says, "I'm traveling by foot," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants within walking distance. Also, if the traveler says, "I'm traveling by car," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants with parking. Furthermore, if the traveler says, "I'm using public transportation," the providing unit can analyze the mode of transportation and suggest tourist spots and restaurants accessible by public transportation. This makes it possible to provide information according to the traveler's mode of transportation, resulting in more appropriate suggestions.

[0115] The dialogue unit can estimate the traveler's emotions and adjust the content of the dialogue based on the estimated traveler's emotions. For example, if the traveler says, "I'm anxious," the dialogue unit can analyze the traveler's emotions and provide reassuring content. Also, if the traveler says, "I'm excited," the dialogue unit can analyze the traveler's emotions and provide interesting content. Furthermore, if the traveler says, "I'm tired," the dialogue unit can analyze the traveler's emotions and provide relaxing content. This makes it possible to adjust the content of the dialogue according to the traveler's emotions, resulting in more appropriate dialogue.

[0116] When analyzing the content of a traveler's dialogue, the analysis unit can improve the accuracy of the analysis by taking into account the traveler's current travel purpose. For example, if a traveler says, "I'm here for sightseeing," the analysis unit can analyze that travel purpose and suggest tourist spots and activities. Also, if a traveler says, "I'm here for business," the analysis unit can analyze that travel purpose and suggest business-related facilities and services. Furthermore, if a traveler says, "I'm here for relaxation," the analysis unit can analyze that travel purpose and suggest places and activities where you can relax. This makes it possible to perform an analysis based on the travel purpose of the traveler, resulting in more appropriate suggestions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The dialogue unit supports dialogue between travelers and local people. The dialogue unit provides an interface that allows travelers to chat and video call with local people in real time using their smartphones. The dialogue unit is also designed to enable travelers to easily communicate with local people. For example, the dialogue unit provides a user interface for travelers to interact with local people, allowing travelers to engage in dialogue in the form of text chat, voice call, video call, etc. Step 2: The analysis unit uses the generation AI to analyze the dialogue content collected by the dialogue unit. The analysis unit analyzes the text and voice data entered when travelers interact with local people to understand the travelers' interests and concerns. For example, the generation AI uses natural language processing technology to analyze travelers' questions and requests and identify their interests and concerns. The analysis unit can also use voice recognition technology to convert travelers' voice data into text data for analysis. Step 3: The suggestion unit suggests tourist spots and restaurants based on the information analyzed by the analysis unit. The generation AI collects the latest information on the internet and information provided by local people, and makes suggestions to travelers. For example, the generation AI collects information on tourist spots that have recently become popular and restaurants that are popular among locals, and provides this to travelers. The suggestion unit can also suggest tourist spots and restaurants that can help avoid overtourism based on travelers' interests and preferences. Step 4: The providing unit provides the traveler with the information suggested by the suggesting unit. Based on the information acquired by the generating AI, the providing unit suggests tourist spots and restaurants that are best suited to the traveler. For example, the providing unit may send a notification to the traveler's smartphone and display information about the suggested tourist spots and restaurants. The providing unit may also provide the best information based on the traveler's location information and time of day.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 dialogue section that supports dialogue between travelers and local people, an analysis unit that analyzes the dialogue content collected by the dialogue unit; a suggestion unit that suggests tourist spots or restaurants based on the information analyzed by the analysis unit; a providing unit that provides the information proposed by the proposal unit to the traveler. A system characterized by:

2. The dialogue unit Providing an interface that allows travelers to chat or video call with local people in real time using their smartphones 2. The system of claim 1.

3. The analysis unit Analyzing text or voice data entered when a traveler interacts with locals to understand their interests 2. The system of claim 1.

4. The proposal unit Generative AI collects the latest information from the internet or information provided by local people and suggests it to travelers.

2. The system of claim 1.

5. The providing unit Based on the information acquired by the AI, the system suggests tourist spots or restaurants to travelers.

2. The system of claim 1.

6. The dialogue unit Estimate traveler sentiment and adjust the tone and content of conversations based on the estimated traveler sentiment 2. The system of claim 1.

7. The dialogue unit Analyze the traveler's past conversation history and select the most appropriate conversation method 2. The system of claim 1.

8. The dialogue unit At the time of interaction, customize the conversation based on the traveler's current location and time of day 2. The system of claim 1.

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