System

Abstract

An object of the system according to the embodiment is to provide a guide tour personalized for a visiting tourist.SOLUTION: A system according to an embodiment includes a learning unit, a receiving unit, a generating unit, and a providing unit. The learning unit learns a manner of a gesture, a way of speaking, an expression, and a way of developing a story of the guide. The reception unit receives a request from a visiting tourist. The generation unit generates a guide tour based on the request received by the reception unit. The providing unit provides the guide tour generated by the generating 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 makes it difficult to provide personalized guided tours to tourists visiting Japan, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized guided tours to tourists visiting Japan. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a reception unit, a generation unit, and a provision unit. The learning unit learns the gestures, speaking style, facial expressions, and story development of guides. The reception unit receives requests from tourists visiting Japan. The generation unit generates a guided tour based on the request received by the reception unit. The provision unit provides the guided tour generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized guided tours to tourists visiting Japan. [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 guided tour provision system according to an embodiment of the present invention utilizes generative AI to provide personalized guided tours to tourists visiting Japan. The guided tour provision system learns the guide's gestures, speaking style, facial expressions, and storytelling style, and generates and provides optimal guided tours based on the tourist's requests. For example, the guided tour provision system learns using video and audio data from the guide's past guided tours. The tourist then accesses the system and requests a guided tour tailored to their interests and preferences. The generative AI generates the optimal guided tour based on the input request. After the guided tour is completed, the guide can receive compensation. This allows the guided tour provision system to provide personalized guided tours to tourists visiting Japan and to receive compensation. Furthermore, the guided tour provision system can reach the mass market, which has not previously used guided tours, providing a new business model for the inbound tourism industry. This allows the guided tour provision system to provide personalized guided tours to tourists visiting Japan and to receive compensation. For example, tourists visiting Japan can receive guided tours tailored to their interests and preferences, and the guide can also receive compensation. It will also be possible to reach out to the masses who have not previously used guides, providing a new business model for the inbound tourism industry.

[0029] A guided tour providing system according to an embodiment includes a learning unit, a reception unit, a generation unit, and a provision unit. The learning unit learns a guide's gestures, speaking style, facial expressions, and story development. The learning unit, for example, learns using video and audio data from the guide's past guided tours. The learning unit can also use a generation AI to reproduce the guide's gestures and speaking style. For example, the learning unit collects video and audio of the guide guiding visitors around tourist spots, and the generation AI analyzes and learns from them. The reception unit receives requests from tourists visiting Japan. For example, the reception unit allows tourists visiting Japan to access the system and input requests tailored to their interests and wishes. For example, the tourists may input requests such as wanting to visit historical tourist spots or wanting to experience local food culture. The generation unit generates a guided tour based on the request. For example, the generation unit uses a generation AI to generate an optimal guided tour based on the input request. For example, in response to a request to visit a historical tourist spot, the generation unit collects information about the tourist spot and guides the tourist around the spot while reproducing the guide's gestures and speaking style. The providing unit provides the generated guided tour to tourists visiting Japan. The providing unit, for example, provides the generated guided tour to tourists, allowing the tourists to enjoy the tour. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. In this way, the guided tour providing system according to the embodiment provides personalized guided tours to tourists visiting Japan, and the guide himself / herself can also receive compensation.

[0030] The learning unit can learn using video or audio data of the guide's past guided tours. Examples of video or audio data include, but are not limited to, video clips and audio recordings. The learning unit can also learn using, for example, video of the guide's past guided tours. The learning unit can also learn using audio data of the guide's past guided tours. For example, the learning unit collects video and audio of the guide showing around tourist attractions, and the generation AI analyzes and learns from them. This allows the learning unit to accurately learn the guide's gestures, speaking style, facial expressions, and story development. Some or all of the above-described processing in the learning unit can be performed using, or without, the generation AI. For example, the learning unit can input video data of the guide's past guided tours into the generation AI and have the generation AI learn the guide's gestures and speaking style.

[0031] The reception unit can accept requests tailored to the interests and desires of tourists visiting Japan. Examples of interests and desires include, but are not limited to, an interest in historical places or a desire for a particular food. For example, the reception unit allows tourists visiting Japan to access the system and input requests tailored to their interests and desires. For example, they may input requests such as wanting to visit historical tourist sites or wanting to experience local food culture. This allows the reception unit to provide guided tours tailored to the tourists' individual interests and desires. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the requests of tourists visiting Japan into AI and have the AI ​​analyze the requests.

[0032] The generation unit can generate a guided tour based on the request. Optimal guided tours include, but are not limited to, tourist satisfaction and tour efficiency. The generation unit generates an optimal guided tour based on the input request, for example, using a generation AI. For example, in response to a request to visit a historical tourist spot, the generation unit collects information about the tourist spot and guides the tourist while reproducing the guide's gestures and speaking style. This allows the generation unit to generate an optimal guided tour in response to the tourist's request. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the tourist's request into the generation AI and have the generation AI generate an optimal guided tour.

[0033] The providing unit can provide the generated guided tour to tourists visiting Japan. Examples of providing include, but are not limited to, direct guidance by a guide and provision of a digital guide. The providing unit, for example, provides the generated guided tour to tourists, allowing them to enjoy the tour. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. This allows the providing unit to provide the generated guided tour to tourists. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI provide the generated guided tour.

[0034] The providing unit can return a portion of the profits from the guided tour to the guide himself / herself. The portion of the profits can include, but is not limited to, for example, what percentage of the profits is returned to the guide. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. For example, a portion of the fee paid by the tourist is paid to the guide himself / herself. This allows the providing unit to allow the guide himself / herself to receive compensation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI execute the return of the profits from the guided tour.

[0035] During learning, the learning unit can analyze the success rate of the guide's past tours and select a learning pattern. Examples of success rates include, but are not limited to, tour participant satisfaction and repeat rates. For example, the learning unit may focus on learning the parts of the guide's past tours that received high ratings. The learning unit may also perform learning to improve the parts of the guide's past tours that received low ratings. The learning unit may also select the most effective learning pattern based on the success rate of the guide's past tours. This allows the learning unit to select the optimal learning pattern based on the success rate of the guide's past tours. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input the guide's past tour data into the generation AI and have the generation AI analyze the success rate.

[0036] The learning unit learns how the guide should respond in different situations during learning, thereby enabling flexible guiding. Different situations include, but are not limited to, changes in weather and the age range of participants. For example, the learning unit may learn how to respond during a sightseeing tour in rainy weather. The learning unit may also learn how to respond when guiding a large number of tourists. The learning unit may also learn how to respond to tourists with specific cultural backgrounds. This enables the learning unit to provide flexible guidance that can adapt to different situations. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit may input data of different situations into the generation AI and cause the generation AI to learn how to respond.

[0037] The learning unit can improve the accuracy of learning by incorporating the guide's cultural background and local knowledge during learning. Examples of cultural background and local knowledge include, but are not limited to, local history and traditional events. For example, the learning unit can learn knowledge based on the guide's hometown and cultural background. The learning unit can also learn the unique history and culture of the area where the guide will be guiding. The learning unit can also learn the latest tourist information for the area where the guide will be guiding. In this way, the learning unit improves the accuracy of learning by incorporating the guide's cultural background and local knowledge. Some or all of the above-mentioned processing in the learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the learning unit can input data on cultural background and local knowledge into the generation AI and cause the generation AI to improve the accuracy of learning.

[0038] The reception unit can analyze the tourist's past travel history at the time of reception and select the optimal request method. Past travel history includes, for example, places visited and activities participated in, but is not limited to these examples. For example, the reception unit can suggest candidate locations for the request based on places the tourist has visited in the past. The reception unit can also suggest places that the tourist may be interested in based on the tourist's past travel history. The reception unit can also analyze the tourist's past travel history and select the optimal request method. In this way, the reception unit can select the optimal request method based on the tourist's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's past travel history data into AI and have the AI ​​select the request method.

[0039] The reception unit can filter the request content based on the tourist's current interests when receiving the request. Current interests include, but are not limited to, recent search history, survey results, etc. The reception unit can filter the request content based on, for example, the tourist's current interests. The reception unit can also suggest related request content based on the tourist's current interests. The reception unit can also filter the most appropriate request content based on the tourist's current interests. This allows the reception unit to filter the request content based on the tourist's current interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's current interests data into AI and have the AI ​​filter the request content.

[0040] The reception unit can select a reception means according to the tourist's input method at the time of reception. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the tourist desires voice input, the reception unit can prioritize receiving the voice input. Furthermore, if the tourist desires text input, the reception unit can also prioritize receiving the text input. Furthermore, if the tourist desires image input, the reception unit can also prioritize receiving the image input. This allows the reception unit to select the optimal reception means according to the tourist's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's input data into AI and have the AI ​​select the reception means.

[0041] The generation unit can analyze the congestion status of tourist attractions in real time during generation and generate tour content. Examples of congestion status include, but are not limited to, real-time people flow data and past congestion data. For example, the generation unit can suggest a route that avoids crowded tourist attractions. The generation unit can also adjust the order in which tourist attractions are visited depending on the congestion status. The generation unit can also analyze the congestion status in real time and generate optimal tour content. This allows the generation unit to generate optimal tour content according to the congestion status of tourist attractions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input congestion status data of tourist attractions into the generation AI and have the generation AI generate the tour content.

[0042] The generation unit can customize the tour content by referring to the tourist's past travel history when generating the tour. The past travel history includes, for example, places visited and activities participated in, but is not limited to these examples. The generation unit customizes the tour content, for example, based on places visited by the tourist in the past. The generation unit can also incorporate places that the tourist is likely to be interested in from the tourist's past travel history into the tour. The generation unit can also customize the optimal tour content by referring to the tourist's past travel history. In this way, the generation unit can customize the tour content based on the tourist's past travel history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the tourist's past travel history data into the generation AI and have the generation AI customize the tour content.

[0043] The generation unit can adjust the tour content by incorporating seasonal and event information of tourist destinations during generation. Seasonal and event information includes, but is not limited to, seasonal events and specific festivals. For example, the generation unit incorporates seasonal attractions into the tour. The generation unit can also incorporate information about currently held events into the tour. The generation unit can also generate optimal tour content based on seasonal and event information. This allows the generation unit to optimize the tour content based on seasonal and event information of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input seasonal and event information data of tourist destinations into the generation AI and have the generation AI adjust the tour content.

[0044] The providing unit can select a provision method by referring to the tourist's past tour history when providing the information. Past tour history includes, for example, the content of tours participated in and satisfaction level, but is not limited to these examples. For example, the providing unit can suggest an optimal guided tour based on the tourist's past tour history. The providing unit can also suggest guided tours that the tourist may be interested in based on the tourist's past tour history. The providing unit can also select an optimal provision method by referring to the tourist's past tour history. In this way, the providing unit can select an optimal provision method based on the tourist's past tour history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourist's past tour history data into AI and have the AI ​​select a provision method.

[0045] The providing unit can customize the content provided based on the tourist's current interests and concerns at the time of providing the content. Current interests and concerns include, but are not limited to, recent search history, survey results, etc. The providing unit customizes the guided tour, for example, based on the tourist's current interests. The providing unit can also suggest related guided tours based on the tourist's current interests. The providing unit can also provide the optimal guided tour based on the tourist's current interests and concerns. This allows the providing unit to customize the content provided based on the tourist's current interests and concerns. 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 can input the tourist's current interests and concerns data into AI and have the AI ​​customize the content provided.

[0046] The providing unit can improve the providing method by reflecting tourist feedback when providing the tour. Examples of feedback include, but are not limited to, survey results and review comments. The providing unit can improve the providing method for the guided tour, for example, based on the tourist feedback. The providing unit can also analyze the tourist feedback and select the optimal providing method. The providing unit can also customize the providing method by reflecting the tourist feedback. This allows the providing unit to improve the providing method based on the tourist feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input tourist feedback data into AI and have the AI ​​improve the providing method.

[0047] The providing unit can select a provision method based on the tourist's geographical location information at the time of provision. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, the providing unit can prioritize providing tourist spots closest to the tourist's current location. The providing unit can also provide tourist spots that are easily accessible based on the tourist's geographical location information. The providing unit can also select the optimal provision method taking into account the tourist's geographical location information. This allows the providing unit to select the optimal provision method based on the tourist's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourist's geographical location information data into AI and have the AI ​​select the provision method.

[0048] The providing unit can analyze the tourist's social media activity during provision and incorporate related information into the content provided. Examples of social media activity include, but are not limited to, the content of posts and the number of likes. For example, the providing unit can provide information about places where the tourist checked in on social media. The providing unit can also analyze the content of the tourist's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events based on the activity of the tourist's friends on social media. This allows the providing unit to incorporate related information based on the tourist's social media activity into the content provided. 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 can input the tourist's social media activity data into AI and have the AI ​​provide related information.

[0049] The providing unit can customize the provision method by reflecting the tourists' past feedback when providing the information. Past feedback includes, but is not limited to, for example, survey results and review comments. The providing unit can, for example, suggest an optimal provision method based on the tourists' past feedback. The providing unit can also improve the provision interface by reflecting the tourists' past feedback. The providing unit can also analyze the tourists' past feedback and customize the optimal provision method. This allows the providing unit to customize the provision method based on the tourists' past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourists' past feedback data into AI and have the AI ​​customize the provision method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The guided tour providing system can further include a real-time translation unit. When tourists speak a different language, the real-time translation unit can translate the guide's explanations in real time and provide them to the tourists. For example, for an English-speaking tourist, the guide's explanations, given in Japanese, can be translated into English and provided. In addition, when a tourist asks a question, the real-time translation unit can translate the question into a language the guide can understand, and can also translate the guide's answer back into the tourist's language and provide it to the tourist. This allows tourists who speak different languages ​​to smoothly enjoy the guided tour.

[0052] The learning department not only analyzes the success rate of guides' past tours, but also monitors the guide's health condition and adjusts the learning content accordingly. For example, if a guide is tired, the learning department can prioritize learning relaxation techniques and stress management methods. On the other hand, if a guide is healthy and energetic, the learning department can teach more active guiding methods. This allows the learning department to provide optimal learning content according to the guide's health condition.

[0053] The reception unit not only accepts requests based on tourists' interests and wishes, but can also suggest request contents by referring to the tourists' past reviews and ratings. For example, it can prioritize suggesting tourist spots and activities that have received high ratings in the past. It can also adjust the request contents to avoid places and activities that tourists have given low ratings in the past. This allows the reception unit to suggest optimal request contents based on the tourists' past experiences.

[0054] The generation unit not only generates guided tours based on requests, but can also adjust the tour content by incorporating environmental data from tourist destinations. For example, it can suggest optimal visit times and routes based on weather data and air quality data from tourist destinations. It can also analyze the congestion situation and traffic information of tourist destinations in real time and adjust the tour so that tourists can enjoy it comfortably. This allows the generation unit to provide optimal guided tours based on environmental data from tourist destinations.

[0055] The provider not only provides the generated guided tour, but also collects tourists' feedback in real time and can instantly adjust the tour content. For example, if a tourist shows interest in a particular place, the provider can provide additional information about that place. Also, if a tourist is dissatisfied, the provider can identify the cause and improve the tour content. This allows the provider to flexibly adjust the tour content based on tourists' real-time feedback.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The learning unit learns the guide's gestures, speaking style, facial expressions, and story development. For example, it can learn using video and audio data from the guide's past guided tours, and then use the generation AI to reproduce the guide's gestures and speaking style. Specifically, it collects video and audio of the guide showing around tourist spots, and the generation AI analyzes and learns from them. Step 2: The reception department accepts requests from tourists visiting Japan. For example, tourists visiting Japan can access the system and input requests tailored to their interests and wishes. Specifically, they can input requests such as wanting to visit historical tourist sites or experience local food culture. Step 3: The generator generates a guided tour based on the request. For example, using a generation AI, it generates the optimal guided tour based on the input request. Specifically, in response to a request to visit a historical tourist spot, it collects information about the tourist spot and guides the tourist around while reproducing the guide's gestures and speaking style. Step 4: The provider provides the generated guided tour to tourists visiting Japan. For example, the provider provides the generated guided tour to tourists so that they can enjoy the tour. Specifically, a portion of the profits from the guided tour is returned to the guide himself.

[0058] (Example 2) A guided tour provision system according to an embodiment of the present invention utilizes generative AI to provide personalized guided tours to tourists visiting Japan. The guided tour provision system learns the guide's gestures, speaking style, facial expressions, and storytelling style, and generates and provides optimal guided tours based on the tourist's requests. For example, the guided tour provision system learns using video and audio data from the guide's past guided tours. The tourist then accesses the system and requests a guided tour tailored to their interests and preferences. The generative AI generates the optimal guided tour based on the input request. After the guided tour is completed, the guide can receive compensation. This allows the guided tour provision system to provide personalized guided tours to tourists visiting Japan and to receive compensation. Furthermore, the guided tour provision system can reach the mass market, which has not previously used guided tours, providing a new business model for the inbound tourism industry. This allows the guided tour provision system to provide personalized guided tours to tourists visiting Japan and to receive compensation. For example, tourists visiting Japan can receive guided tours tailored to their interests and preferences, and the guide can also receive compensation. It will also be possible to reach out to the masses who have not previously used guides, providing a new business model for the inbound tourism industry.

[0059] A guided tour providing system according to an embodiment includes a learning unit, a reception unit, a generation unit, and a provision unit. The learning unit learns a guide's gestures, speaking style, facial expressions, and story development. The learning unit, for example, learns using video and audio data from the guide's past guided tours. The learning unit can also use a generation AI to reproduce the guide's gestures and speaking style. For example, the learning unit collects video and audio of the guide guiding visitors around tourist spots, and the generation AI analyzes and learns from them. The reception unit receives requests from tourists visiting Japan. For example, the reception unit allows tourists visiting Japan to access the system and input requests tailored to their interests and wishes. For example, the tourists may input requests such as wanting to visit historical tourist spots or wanting to experience local food culture. The generation unit generates a guided tour based on the request. For example, the generation unit uses a generation AI to generate an optimal guided tour based on the input request. For example, in response to a request to visit a historical tourist spot, the generation unit collects information about the tourist spot and guides the tourist around the spot while reproducing the guide's gestures and speaking style. The providing unit provides the generated guided tour to tourists visiting Japan. The providing unit, for example, provides the generated guided tour to tourists, allowing the tourists to enjoy the tour. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. In this way, the guided tour providing system according to the embodiment provides personalized guided tours to tourists visiting Japan, and the guide himself / herself can also receive compensation.

[0060] The learning unit can learn using video or audio data of the guide's past guided tours. Examples of video or audio data include, but are not limited to, video clips and audio recordings. The learning unit can also learn using, for example, video of the guide's past guided tours. The learning unit can also learn using audio data of the guide's past guided tours. For example, the learning unit collects video and audio of the guide showing around tourist attractions, and the generation AI analyzes and learns from them. This allows the learning unit to accurately learn the guide's gestures, speaking style, facial expressions, and story development. Some or all of the above-described processing in the learning unit can be performed using, or without, the generation AI. For example, the learning unit can input video data of the guide's past guided tours into the generation AI and have the generation AI learn the guide's gestures and speaking style.

[0061] The reception unit can accept requests tailored to the interests and desires of tourists visiting Japan. Examples of interests and desires include, but are not limited to, an interest in historical places or a desire for a particular food. For example, the reception unit allows tourists visiting Japan to access the system and input requests tailored to their interests and desires. For example, they may input requests such as wanting to visit historical tourist sites or wanting to experience local food culture. This allows the reception unit to provide guided tours tailored to the tourists' individual interests and desires. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the requests of tourists visiting Japan into AI and have the AI ​​analyze the requests.

[0062] The generation unit can generate a guided tour based on the request. Optimal guided tours include, but are not limited to, tourist satisfaction and tour efficiency. The generation unit generates an optimal guided tour based on the input request, for example, using a generation AI. For example, in response to a request to visit a historical tourist spot, the generation unit collects information about the tourist spot and guides the tourist while reproducing the guide's gestures and speaking style. This allows the generation unit to generate an optimal guided tour in response to the tourist's request. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the tourist's request into the generation AI and have the generation AI generate an optimal guided tour.

[0063] The providing unit can provide the generated guided tour to tourists visiting Japan. Examples of providing include, but are not limited to, direct guidance by a guide and provision of a digital guide. The providing unit, for example, provides the generated guided tour to tourists, allowing them to enjoy the tour. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. This allows the providing unit to provide the generated guided tour to tourists. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can have AI provide the generated guided tour.

[0064] The providing unit can return a portion of the profits from the guided tour to the guide himself / herself. The portion of the profits can include, but is not limited to, for example, what percentage of the profits is returned to the guide. For example, the providing unit returns a portion of the profits from the guided tour to the guide himself / herself. For example, a portion of the fee paid by the tourist is paid to the guide himself / herself. This allows the providing unit to allow the guide himself / herself to receive compensation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI execute the return of the profits from the guided tour.

[0065] The learning unit can estimate the guide's emotions and adjust the learning content based on the estimated emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the guide is nervous, the learning unit can prioritize relaxation training. Furthermore, if the guide is enjoying themselves, the learning unit can incorporate positive feedback into the learning to reinforce that emotion. Furthermore, if the guide is tired, the learning unit can teach relaxation techniques to encourage rest. This enables the learning unit to adjust the learning content according to the guide's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 learning unit can be performed using, for example, the generation AI. For example, the learning unit can input the guide's emotion data into the generation AI and have the generation AI adjust the learning content.

[0066] During learning, the learning unit can analyze the success rate of the guide's past tours and select a learning pattern. Examples of success rates include, but are not limited to, tour participant satisfaction and repeat rates. For example, the learning unit may focus on learning the parts of the guide's past tours that received high ratings. The learning unit may also perform learning to improve the parts of the guide's past tours that received low ratings. The learning unit may also select the most effective learning pattern based on the success rate of the guide's past tours. This allows the learning unit to select the optimal learning pattern based on the success rate of the guide's past tours. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input the guide's past tour data into the generation AI and have the generation AI analyze the success rate.

[0067] The learning unit learns how the guide should respond in different situations during learning, thereby enabling flexible guiding. Different situations include, but are not limited to, changes in weather and the age range of participants. For example, the learning unit may learn how to respond during a sightseeing tour in rainy weather. The learning unit may also learn how to respond when guiding a large number of tourists. The learning unit may also learn how to respond to tourists with specific cultural backgrounds. This enables the learning unit to provide flexible guidance that can adapt to different situations. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit may input data of different situations into the generation AI and cause the generation AI to learn how to respond.

[0068] The learning unit can improve the accuracy of learning by incorporating the guide's cultural background and local knowledge during learning. Examples of cultural background and local knowledge include, but are not limited to, local history and traditional events. For example, the learning unit can learn knowledge based on the guide's hometown and cultural background. The learning unit can also learn the unique history and culture of the area where the guide will be guiding. The learning unit can also learn the latest tourist information for the area where the guide will be guiding. In this way, the learning unit improves the accuracy of learning by incorporating the guide's cultural background and local knowledge. Some or all of the above-mentioned processing in the learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the learning unit can input data on cultural background and local knowledge into the generation AI and cause the generation AI to improve the accuracy of learning.

[0069] The reception unit can estimate the tourist's emotions and adjust the request acceptance method based on the estimated tourist's emotions. Examples of tourist emotions include, but are not limited to, satisfaction, dissatisfaction, and excitement. For example, if the tourist is excited, the reception unit can provide detailed request options. Furthermore, if the tourist is relaxed, the reception unit can provide a simple request interface. Furthermore, if the tourist is stressed, the reception unit can prioritize voice input when accepting requests. This enables the reception unit to adjust the request acceptance method according to the tourist's emotions. The emotion estimation is realized 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 reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input tourist's emotion data into the generation AI and have the generation AI adjust the request acceptance method.

[0070] The reception unit can analyze the tourist's past travel history at the time of reception and select the optimal request method. Past travel history includes, for example, places visited and activities participated in, but is not limited to these examples. For example, the reception unit can suggest candidate locations for the request based on places the tourist has visited in the past. The reception unit can also suggest places that the tourist may be interested in based on the tourist's past travel history. The reception unit can also analyze the tourist's past travel history and select the optimal request method. In this way, the reception unit can select the optimal request method based on the tourist's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's past travel history data into AI and have the AI ​​select the request method.

[0071] The reception unit can filter the request content based on the tourist's current interests when receiving the request. Current interests include, but are not limited to, recent search history, survey results, etc. The reception unit can filter the request content based on, for example, the tourist's current interests. The reception unit can also suggest related request content based on the tourist's current interests. The reception unit can also filter the most appropriate request content based on the tourist's current interests. This allows the reception unit to filter the request content based on the tourist's current interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's current interests data into AI and have the AI ​​filter the request content.

[0072] The reception unit can select a reception means according to the tourist's input method at the time of reception. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the tourist desires voice input, the reception unit can prioritize receiving the voice input. Furthermore, if the tourist desires text input, the reception unit can also prioritize receiving the text input. Furthermore, if the tourist desires image input, the reception unit can also prioritize receiving the image input. This allows the reception unit to select the optimal reception means according to the tourist's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the tourist's input data into AI and have the AI ​​select the reception means.

[0073] The generation unit can estimate the tourist's emotions and adjust the content of the guided tour based on the estimated tourist's emotions. Examples of tourist emotions include, but are not limited to, satisfaction, dissatisfaction, and excitement. For example, if the tourist is excited, the generation unit can provide active tour content. Furthermore, if the tourist is relaxed, the generation unit can provide leisurely tour content. Furthermore, if the tourist is stressed, the generation unit can provide relaxing tour content. This enables the generation unit to adjust the content of the guided tour according to the tourist's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit can input tourist emotion data into the generation AI and have the generation AI adjust the content of the guided tour.

[0074] The generation unit can analyze the congestion status of tourist attractions in real time during generation and generate tour content. Examples of congestion status include, but are not limited to, real-time people flow data and past congestion data. For example, the generation unit can suggest a route that avoids crowded tourist attractions. The generation unit can also adjust the order in which tourist attractions are visited depending on the congestion status. The generation unit can also analyze the congestion status in real time and generate optimal tour content. This allows the generation unit to generate optimal tour content according to the congestion status of tourist attractions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input congestion status data of tourist attractions into the generation AI and have the generation AI generate the tour content.

[0075] The generation unit can customize the tour content by referring to the tourist's past travel history when generating the tour. The past travel history includes, for example, places visited and activities participated in, but is not limited to these examples. The generation unit customizes the tour content, for example, based on places visited by the tourist in the past. The generation unit can also incorporate places that the tourist is likely to be interested in from the tourist's past travel history into the tour. The generation unit can also customize the optimal tour content by referring to the tourist's past travel history. In this way, the generation unit can customize the tour content based on the tourist's past travel history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the tourist's past travel history data into the generation AI and have the generation AI customize the tour content.

[0076] The generation unit can adjust the tour content by incorporating seasonal and event information of tourist destinations during generation. Seasonal and event information includes, but is not limited to, seasonal events and specific festivals. For example, the generation unit incorporates seasonal attractions into the tour. The generation unit can also incorporate information about currently held events into the tour. The generation unit can also generate optimal tour content based on seasonal and event information. This allows the generation unit to optimize the tour content based on seasonal and event information of tourist destinations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input seasonal and event information data of tourist destinations into the generation AI and have the generation AI adjust the tour content.

[0077] The providing unit can estimate the tourist's emotions and adjust the method of providing the guided tour based on the estimated tourist's emotions. Examples of tourist emotions include, but are not limited to, satisfaction, dissatisfaction, and excitement. For example, if the tourist is excited, the providing unit can provide an active guided tour. Furthermore, if the tourist is relaxed, the providing unit can provide a leisurely guided tour. Furthermore, if the tourist is stressed, the providing unit can provide a relaxing guided tour. This enables the providing unit to adjust the method of providing the guided tour according to the tourist's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input tourist's emotion data into the generation AI and have the generation AI adjust the method of providing the guided tour.

[0078] The providing unit can select a provision method by referring to the tourist's past tour history when providing the information. Past tour history includes, for example, the content of tours participated in and satisfaction level, but is not limited to these examples. For example, the providing unit can suggest an optimal guided tour based on the tourist's past tour history. The providing unit can also suggest guided tours that the tourist may be interested in based on the tourist's past tour history. The providing unit can also select an optimal provision method by referring to the tourist's past tour history. In this way, the providing unit can select an optimal provision method based on the tourist's past tour history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourist's past tour history data into AI and have the AI ​​select a provision method.

[0079] The providing unit can customize the content provided based on the tourist's current interests and concerns at the time of providing the content. Current interests and concerns include, but are not limited to, recent search history, survey results, etc. The providing unit customizes the guided tour, for example, based on the tourist's current interests. The providing unit can also suggest related guided tours based on the tourist's current interests. The providing unit can also provide the optimal guided tour based on the tourist's current interests and concerns. This allows the providing unit to customize the content provided based on the tourist's current interests and concerns. 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 can input the tourist's current interests and concerns data into AI and have the AI ​​customize the content provided.

[0080] The providing unit can improve the providing method by reflecting tourist feedback when providing the tour. Examples of feedback include, but are not limited to, survey results and review comments. The providing unit can improve the providing method for the guided tour, for example, based on the tourist feedback. The providing unit can also analyze the tourist feedback and select the optimal providing method. The providing unit can also customize the providing method by reflecting the tourist feedback. This allows the providing unit to improve the providing method based on the tourist feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input tourist feedback data into AI and have the AI ​​improve the providing method.

[0081] The providing unit can estimate the tourist's emotions and prioritize the tours to be provided based on the estimated tourist's emotions. Examples of tourist emotions include, but are not limited to, satisfaction, dissatisfaction, and excitement. For example, if the tourist is excited, the providing unit can prioritize active tours. Furthermore, if the tourist is relaxed, the providing unit can prioritize leisurely tours. Furthermore, if the tourist is stressed, the providing unit can prioritize relaxing tours. This allows the providing unit to prioritize tours according to the tourist's emotions. The emotion estimation is realized 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 without AI. For example, the providing unit can input tourist's emotion data into the generation AI and have the generation AI determine the tour priorities.

[0082] The providing unit can select a provision method based on the tourist's geographical location information at the time of provision. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, the providing unit can prioritize providing tourist spots closest to the tourist's current location. The providing unit can also provide tourist spots that are easily accessible based on the tourist's geographical location information. The providing unit can also select the optimal provision method taking into account the tourist's geographical location information. This allows the providing unit to select the optimal provision method based on the tourist's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourist's geographical location information data into AI and have the AI ​​select the provision method.

[0083] The providing unit can analyze the tourist's social media activity during provision and incorporate related information into the content provided. Examples of social media activity include, but are not limited to, the content of posts and the number of likes. For example, the providing unit can provide information about places where the tourist checked in on social media. The providing unit can also analyze the content of the tourist's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events based on the activity of the tourist's friends on social media. This allows the providing unit to incorporate related information based on the tourist's social media activity into the content provided. 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 can input the tourist's social media activity data into AI and have the AI ​​provide related information.

[0084] The providing unit can customize the provision method by reflecting the tourists' past feedback when providing the information. Past feedback includes, but is not limited to, for example, survey results and review comments. The providing unit can, for example, suggest an optimal provision method based on the tourists' past feedback. The providing unit can also improve the provision interface by reflecting the tourists' past feedback. The providing unit can also analyze the tourists' past feedback and customize the optimal provision method. This allows the providing unit to customize the provision method based on the tourists' past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the tourists' past feedback data into AI and have the AI ​​customize the provision method. === Hard Collateral 1-1 === Each of the multiple elements including the learning unit, reception unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the learning unit learns the guide's gestures and speaking style using the camera 42 and microphone 38B of the smart device 14, and processes the learning results using the control unit 46A. The reception unit receives requests from tourists using the touch panel 38A and microphone 38B of the smart device 14, and processes the requests using the control unit 46A. The generation unit generates a guided tour using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated guided tour to tourists using the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the learning unit, reception unit, generation 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 learning unit learns the guide's gestures and speaking style using the camera 42 and microphone 238 of the smart glasses 214, and processes the learning results using the control unit 46A. The reception unit receives requests from tourists using the microphone 238 of the smart glasses 214, and processes the requests using the control unit 46A. The generation unit generates a guided tour using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated guided tour to tourists using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the learning unit, reception unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the learning unit learns the gestures and speaking style of the guide using the camera 42 and microphone 238 of the headset terminal 314, and processes the learning results using the control unit 46A. The reception unit receives requests from tourists using the microphone 238 of the headset terminal 314, and processes the requests using the control unit 46A. The generation unit generates a guided tour using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated guided tour to tourists using the display 343 and speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, reception unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the learning unit learns the gestures and speaking style of the guide using the camera 42 and microphone 238 of the robot 414, and the control unit 46A processes the learning results. The reception unit receives requests from tourists using the microphone 238 of the robot 414, and processes the requests using the control unit 46A. The generation unit generates a guided tour using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides tourists with a guided tour generated using the speaker 240 and control target 443 of the robot 414.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The guided tour providing system can further include a real-time translation unit. When tourists speak a different language, the real-time translation unit can translate the guide's explanations in real time and provide them to the tourists. For example, for an English-speaking tourist, the guide's explanations, given in Japanese, can be translated into English and provided. In addition, when a tourist asks a question, the real-time translation unit can translate the question into a language the guide can understand, and can also translate the guide's answer back into the tourist's language and provide it to the tourist. This allows tourists who speak different languages ​​to smoothly enjoy the guided tour.

[0087] The learning department not only analyzes the success rate of guides' past tours, but also monitors the guide's health condition and adjusts the learning content accordingly. For example, if a guide is tired, the learning department can prioritize learning relaxation techniques and stress management methods. On the other hand, if a guide is healthy and energetic, the learning department can teach more active guiding methods. This allows the learning department to provide optimal learning content according to the guide's health condition.

[0088] The reception unit not only accepts requests based on tourists' interests and wishes, but can also suggest request contents by referring to the tourists' past reviews and ratings. For example, it can prioritize suggesting tourist spots and activities that have received high ratings in the past. It can also adjust the request contents to avoid places and activities that tourists have given low ratings in the past. This allows the reception unit to suggest optimal request contents based on the tourists' past experiences.

[0089] The generation unit not only generates guided tours based on requests, but can also adjust the tour content by incorporating environmental data from tourist destinations. For example, it can suggest optimal visit times and routes based on weather data and air quality data from tourist destinations. It can also analyze the congestion situation and traffic information of tourist destinations in real time and adjust the tour so that tourists can enjoy it comfortably. This allows the generation unit to provide optimal guided tours based on environmental data from tourist destinations.

[0090] The provider not only provides the generated guided tour, but also collects tourists' feedback in real time and can instantly adjust the tour content. For example, if a tourist shows interest in a particular place, the provider can provide additional information about that place. Also, if a tourist is dissatisfied, the provider can identify the cause and improve the tour content. This allows the provider to flexibly adjust the tour content based on tourists' real-time feedback.

[0091] The learning unit can not only estimate the guide's emotions and adjust the learning content based on the estimated emotions, but also learn methods for communicating the guide's emotions to tourists. For example, if the guide is having fun, the learning unit can learn ways to express those emotions to tourists. Also, if the guide is nervous, the learning unit can learn communication techniques to relieve that tension. In this way, the learning unit can provide a method for effectively communicating the guide's emotions to tourists.

[0092] The reception unit can estimate the tourist's emotions and adjust the method of accepting requests based on the estimated tourist's emotions, as well as provide special offers or discounts according to the tourist's emotions. For example, if the tourist is excited, it can provide discounts on special activities or events. Also, if the tourist is relaxed, it can suggest offers related to relaxation. This allows the reception unit to provide special offers according to the tourist's emotions.

[0093] The generation unit not only estimates the tourists' emotions and adjusts the content of the guided tour based on the estimated tourists' emotions, but also incorporates interactive elements into the tour according to the tourists' emotions. For example, if the tourists are excited, active games or quizzes can be added to the tour. Also, if the tourists are relaxed, activities related to relaxation can be provided. In this way, the generation unit can incorporate interactive elements into the tour according to the tourists' emotions.

[0094] The providing unit not only estimates the tourist's emotions and adjusts the way the guided tour is provided based on the estimated tourist's emotions, but also transmits personalized messages according to the tourist's emotions. For example, if the tourist is excited, the providing unit can transmit an encouraging message. Also, if the tourist is relaxed, the providing unit can transmit a message encouraging the tourist to relax. In this way, the providing unit can provide personalized messages according to the tourist's emotions.

[0095] The provision unit not only estimates the tourist's emotions and determines the priority of tours to be provided based on the estimated tourist's emotions, but also provides a special surprise according to the tourist's emotions. For example, if the tourist is excited, a special event or activity can be provided as a surprise. Also, if the tourist is relaxed, a special service related to relaxation can be provided. In this way, the provision unit can provide a special surprise according to the tourist's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The learning unit learns the guide's gestures, speaking style, facial expressions, and story development. For example, it can learn using video and audio data from the guide's past guided tours, and then use the generation AI to reproduce the guide's gestures and speaking style. Specifically, it collects video and audio of the guide showing around tourist spots, and the generation AI analyzes and learns from them. Step 2: The reception department accepts requests from tourists visiting Japan. For example, tourists visiting Japan can access the system and input requests tailored to their interests and wishes. Specifically, they can input requests such as wanting to visit historical tourist sites or experience local food culture. Step 3: The generator generates a guided tour based on the request. For example, using a generation AI, it generates the optimal guided tour based on the input request. Specifically, in response to a request to visit a historical tourist spot, it collects information about the tourist spot and guides the tourist around while reproducing the guide's gestures and speaking style. Step 4: The provider provides the generated guided tour to tourists visiting Japan. For example, the provider provides the generated guided tour to tourists so that they can enjoy the tour. Specifically, a portion of the profits from the guided tour is returned to the guide himself.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

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

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 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. There is a learning section where you learn the gestures, speaking style, facial expressions, and how to develop a story as a guide. A reception desk that accepts requests from tourists visiting Japan, a generation unit that generates a guided tour based on the request received by the reception unit; a providing unit that provides the guided tour generated by the generating unit. A system characterized by:

2. The learning unit Learning using video or audio data from the guide's past guided tours 2. The system of claim 1.

3. The reception unit Accepting requests tailored to the interests and desires of tourists visiting Japan 2. The system of claim 1.

4. The generation unit Generate guided tours based on requests 2. The system of claim 1.

5. The providing unit Providing the generated guided tours to tourists visiting Japan 2. The system of claim 1.

6. The providing unit A portion of the profits from the guided tour will be returned to the guide himself.

2. The system of claim 1.

7. The learning unit Estimate the guide's emotions and adjust the learning content based on the estimated emotions of the guide 2. The system of claim 1.

8. The learning unit During learning, the success rate of the guide's past tours is analyzed and a learning pattern is selected.

2. The system of claim 1.

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