system

Abstract

The system according to the embodiment aims to provide a new travel experience in the metaverse space based on the user's preferences. [Solution] A system according to an embodiment includes an analysis unit, a real-time analysis unit, and a reproduction unit. The analysis unit analyzes the results of a questionnaire to hear user preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions while traveling based on the results obtained by the analysis unit. The reproduction unit reproduces images and sounds in a metaverse space based on the results obtained by the real-time analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult to provide new travel experiences for people who love to travel, and there is room for improvement to increase satisfaction.

[0005] The system according to the embodiment aims to provide a new travel experience in the metaverse space based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a real-time analysis unit, and a reproduction unit. The analysis unit analyzes the results of a questionnaire to hear the user's preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions while traveling based on the results obtained by the analysis unit. The reproduction unit reproduces images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a new travel experience in the metaverse space based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel experience system according to an embodiment of the present invention listens to a user's preferences, analyzes the user's responses in real time, and provides a travel experience in a metaverse space. In this travel experience system, the user answers a questionnaire before traveling and is asked about their preferred travel destination. This information is input into an AI. The AI ​​then analyzes the input information and generates images and sounds of the travel destination based on the user's preferences. The generated images and sounds are reproduced in the metaverse space, allowing the user to experience the trip through a VR device. Furthermore, the AI ​​measures the user's brainwaves and facial expressions during the trip to understand their preferences in real time. This allows the AI ​​to continuously generate a more immersive space based on the user's responses. For example, if the user is relaxed, the AI ​​generates images of a tranquil beach and sounds of waves, while if the user is excited, the AI ​​generates images and sounds of an active city. This mechanism allows users to enjoy a highly satisfying travel experience that cannot be experienced on a real trip. For example, the system allows users to experience fictional places they have never visited or fantastical places that do not exist in reality. Furthermore, the travel destination is customized based on the user's preferences, providing a constantly new experience. This allows the travel experience system to provide travel experiences tailored to the user's preferences. For example, the user can experience fictional places they have never visited or fantastical places that do not exist in reality. Furthermore, because travel destinations are customized according to the user's preferences, new experiences can always be provided.

[0029] A travel experience system according to an embodiment includes an analysis unit, a real-time analysis unit, and a reproduction unit. The analysis unit analyzes survey results to gather user preferences. The survey results include, but are not limited to, destination preferences, activity preferences, and food preferences. The analysis unit, for example, aggregates the survey results and identifies the user's preferences. The analysis unit can also analyze the survey results using AI to understand the user's preferences in detail. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time while traveling. The brain waves can be analyzed using, for example, an EEG sensor. The facial expressions can be analyzed using, for example, a facial expression recognition algorithm. The real-time analysis unit, for example, analyzes the user's brain wave patterns to understand the user's emotional state. The real-time analysis unit can also analyze the user's facial expressions to understand the user's reactions in real time. The reproduction unit reproduces images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. For example, when the user is relaxing, the reproduction unit generates an image of a tranquil beach and the sound of waves. Furthermore, if the user is excited, the reproducing unit can generate images and sounds of an active city. The reproducing unit can generate images and sounds according to the user's reaction using AI. This allows the travel experience system according to the embodiment to provide a travel experience that suits the user's preferences.

[0030] The analysis unit can analyze the results of a survey to hear the user's preferences. The survey results include, but are not limited to, preferences for travel destinations, activities, and food. The analysis unit, for example, aggregates the survey results and identifies the user's preferences. The analysis unit can also use AI to analyze the survey results and understand the user's preferences in detail. This makes it possible to accurately understand the user's preferences.

[0031] The real-time analysis unit can analyze the brain waves and facial expressions of the user while traveling in real time. For example, the real-time analysis unit measures and analyzes the brain waves of the user while traveling using an EEG sensor. For example, the real-time analysis unit analyzes specific brain wave patterns to understand the emotional state of the user. The real-time analysis unit can also analyze the facial expressions of the user while traveling using a facial expression recognition algorithm. For example, the real-time analysis unit analyzes specific facial expression patterns to understand the user's reactions in real time. This makes it possible to understand the user's reactions in real time.

[0032] The reproduction unit can reproduce images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. The reproduction unit reproduces images and sounds in the metaverse space based on, for example, the user's emotional state obtained by the real-time analysis unit. For example, the reproduction unit can generate images of a quiet beach and the sound of waves if the user is relaxed. Alternatively, the reproduction unit can generate images and sounds of an active city if the user is excited. The reproduction unit can use AI to generate images and sounds according to the user's reactions. This makes it possible to provide images and sounds according to the user's real-time reactions.

[0033] The analysis unit can provide information for customizing travel destinations according to the user's preferences. For example, the analysis unit analyzes the results of a questionnaire to hear the user's preferences and provides information for customizing travel destinations according to the user's preferences. For example, the analysis unit provides customized travel destination information based on the user's preferred travel destinations and activities. The analysis unit can also analyze the survey results using AI and suggest travel destinations according to the user's preferences. This makes it possible to provide travel destinations according to the user's preferences.

[0034] The analysis unit can analyze the user's past travel history and reflect it in the analysis of the survey results. The analysis unit, for example, analyzes the user's past travel history and reflects it in the analysis of the survey results. For example, the analysis unit analyzes preference trends based on data on travel destinations the user has visited in the past. The analysis unit can also adjust the survey results based on activities the user has preferred in the past. The analysis unit can also suggest similar travel destinations that have not been visited based on the user's past travel history. This makes it possible to provide survey results that take the user's past travel history into consideration.

[0035] When analyzing the survey results, the analysis unit can adjust the analysis results based on the user's current mood and physical condition. For example, when analyzing the survey results, the analysis unit adjusts the analysis results by taking into account the user's current mood and physical condition. For example, if the user is tired, the analysis unit can preferentially suggest travel destinations where the user can relax. Furthermore, if the user is energetic, the analysis unit can also suggest travel destinations that are active. Furthermore, if the user is feeling stressed, the analysis unit can also suggest travel destinations that have a soothing effect. In this way, it is possible to provide analysis results that take into account the user's current mood and physical condition.

[0036] When analyzing the survey results, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, when analyzing the survey results, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit analyzes preferences based on travel destination information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts to identify travel destinations of interest. The analysis unit can also suggest related travel destinations by referring to the social media activity of the user's friends. This makes it possible to provide analysis results that take the user's social media activity into consideration.

[0037] When analyzing the survey results, the analysis unit can adjust the analysis results by taking into account the user's geographical location information. For example, when analyzing the survey results, the analysis unit adjusts the analysis results by taking into account the user's geographical location information. For example, the analysis unit can suggest travel destinations by taking into account the climate and culture of the user's current location. The analysis unit can also preferentially suggest nearby travel destinations based on the user's geographical location information. The analysis unit can also suggest easily accessible travel destinations based on the user's geographical location information. This makes it possible to provide analysis results that take into account the user's geographical location information.

[0038] When analyzing the survey results, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, when analyzing the survey results, the analysis unit customizes the analysis method by reflecting the user's past feedback. For example, the analysis unit adjusts the content of the survey questions based on the user's past feedback. The analysis unit can also optimize the analysis algorithm by reflecting the user's past feedback. The analysis unit can also improve the accuracy of the analysis results based on the user's past feedback. This makes it possible to provide an analysis method that takes the user's past feedback into consideration.

[0039] When analyzing the survey results, the analysis unit can prioritize the analysis results based on the user's interests and concerns. For example, when analyzing the survey results, the analysis unit prioritizes the analysis results based on the user's interests and concerns. For example, the analysis unit preferentially suggests theme parks that the user is interested in. The analysis unit can also preferentially suggest historical places that the user is interested in. The analysis unit can also preferentially suggest natural landscapes that the user is interested in. This makes it possible to provide analysis results that take the user's interests and concerns into consideration.

[0040] The real-time analysis unit can improve the accuracy of the analysis by referring to the user's past electroencephalogram and facial expression data during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by referring to the user's past electroencephalogram and facial expression data during real-time analysis, for example. For example, the real-time analysis unit analyzes current electroencephalograms based on the user's past electroencephalogram data. The real-time analysis unit can also analyze current facial expressions based on the user's past facial expression data. The real-time analysis unit can also integrate the user's past electroencephalogram and facial expression data to improve the accuracy of the analysis. This makes it possible to provide analysis results that take the user's past electroencephalogram and facial expression data into consideration.

[0041] The real-time analysis unit can adjust the analysis results based on the user's current activity status during real-time analysis. For example, the real-time analysis unit adjusts the analysis results taking into account the user's current activity status during real-time analysis. For example, when the user is sitting, the real-time analysis unit performs analysis assuming a relaxed state. Furthermore, when the user is walking, the real-time analysis unit can also perform analysis assuming an active state. Furthermore, when the user is exercising, the real-time analysis unit can also perform analysis assuming an excited state. This makes it possible to provide analysis results that take into account the user's current activity status.

[0042] The real-time analysis unit can improve the accuracy of the analysis by also using the user's physiological data (heart rate, respiratory rate, etc.) during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by also using the user's physiological data during real-time analysis. For example, the real-time analysis unit analyzes the stress level based on the user's heart rate. The real-time analysis unit can also analyze the user's relaxation level based on the user's respiratory rate. The real-time analysis unit can also integrate the user's physiological data to analyze the user's overall emotional state. This makes it possible to provide analysis results that take the user's physiological data into consideration.

[0043] The real-time analysis unit can adjust the analysis results during real-time analysis by taking into account the user's geographical location information. For example, the real-time analysis unit adjusts the analysis results by taking into account the user's geographical location information during real-time analysis. For example, if the user is in an urban area, the real-time analysis unit can perform analysis by taking into account the noise level. Furthermore, if the user is in a natural environment, the real-time analysis unit can also perform analysis by taking into account the silence. Furthermore, if the user is in a specific location, the real-time analysis unit can perform analysis by taking into account the characteristics of that location. This makes it possible to provide analysis results that take into account the user's geographical location information.

[0044] The real-time analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during real-time analysis, for example. For example, the real-time analysis unit performs real-time analysis based on the emotional state shared by the user on social media. The real-time analysis unit can also analyze the content of the user's social media posts to estimate the user's current emotional state. The real-time analysis unit can also estimate the related emotional state by referring to the social media activity of the user's friends. This makes it possible to provide analysis results that take the user's social media activity into consideration.

[0045] The real-time analysis unit can customize the analysis method by reflecting the user's past feedback during real-time analysis. The real-time analysis unit, for example, customizes the analysis method by reflecting the user's past feedback during real-time analysis. For example, the real-time analysis unit adjusts the real-time analysis algorithm based on feedback provided by the user in the past. The real-time analysis unit can also improve the accuracy of the analysis results by reflecting the user's past feedback. The real-time analysis unit can also optimize the analysis method based on the user's past feedback. This makes it possible to provide an analysis method that takes the user's past feedback into consideration.

[0046] The reproducing unit can customize the content of the video and sound by referring to the user's past travel experiences during the reproducing process. For example, the reproducing unit customizes the content of the video and sound by referring to the user's past travel experiences during the reproducing process. For example, the reproducing unit reproduces the video and sound of a travel destination that the user has previously visited. The reproducing unit can also generate video and sound of a similar travel destination based on the user's past travel experiences. The reproducing unit can also generate customized video and sound by referring to the user's past travel experiences. This makes it possible to provide video and sound that take the user's past travel experiences into consideration.

[0047] The reproduction unit can adjust the content of the video and sound during reproduction, taking into account the user's current mood and physical condition. For example, the reproduction unit adjusts the content of the video and sound during reproduction, taking into account the user's current mood and physical condition. For example, if the user is tired, the reproduction unit can generate relaxing video and sound. Furthermore, if the user is energetic, the reproduction unit can generate active video and sound. Furthermore, if the user is feeling stressed, the reproduction unit can generate video and sound that has a soothing effect. In this way, it is possible to provide video and sound that take into account the user's current mood and physical condition.

[0048] The reproduction unit can customize the content of the video and sound by referring to the user's social media activity during reproduction. For example, the reproduction unit customizes the content of the video and sound by referring to the user's social media activity during reproduction. For example, the reproduction unit reproduces video and sound of travel destinations shared by the user on social media. The reproduction unit can also generate video and sound of interest based on the content posted by the user on social media. The reproduction unit can also generate related video and sound by referring to the social media activity of the user's friends. This makes it possible to provide video and sound that takes the user's social media activity into consideration.

[0049] The reproduction unit can adjust the content of the video and sound during reproduction, taking into account the user's geographical location information. For example, the reproduction unit adjusts the content of the video and sound during reproduction, taking into account the user's geographical location information. For example, if the user is in an urban area, the reproduction unit reproduces urban video and sound. Furthermore, if the user is in a natural environment, the reproduction unit can also reproduce natural video and sound. Furthermore, if the user is in a specific location, the reproduction unit can reproduce video and sound taking into account the characteristics of that location. This makes it possible to provide video and sound that take into account the user's geographical location information.

[0050] The reproduction unit can customize the content of the video and sound by reflecting the user's past feedback during reproduction. For example, the reproduction unit customizes the content of the video and sound by reflecting the user's past feedback during reproduction. For example, the reproduction unit adjusts the content of the video and sound based on feedback previously provided by the user. The reproduction unit can also optimize the content of the video and sound by reflecting the user's past feedback. The reproduction unit can also customize the content of the video and sound based on the user's past feedback. This makes it possible to provide video and sound that take the user's past feedback into consideration.

[0051] The reproduction unit can prioritize the video and audio content based on the user's interests and concerns during reproduction. For example, the reproduction unit prioritizes the video and audio content based on the user's interests and concerns during reproduction. For example, the reproduction unit prioritizes the reproduction of video and audio content based on the user's interests and concerns. The reproduction unit can also prioritize the reproduction of video and audio content of a theme park in which the user is interested. The reproduction unit can also prioritize the reproduction of video and audio content of a historical place in which the user is interested. The reproduction unit can also prioritize the reproduction of video and audio content of natural landscapes in which the user is interested. This makes it possible to provide video and audio content that takes the user's interests and concerns into consideration.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When analyzing the results of a survey to hear a user's preferences, the analysis unit can improve the accuracy of the analysis by referring to the user's past purchasing history. For example, the analysis unit analyzes preference trends based on travel-related products and services purchased by the user in the past. The analysis unit can also adjust the survey results by referring to the contents of guidebooks and travel magazines purchased by the user in the past. Furthermore, the analysis unit can suggest similar travel destinations that the user has not yet visited based on the user's past purchasing history. This makes it possible to provide analysis results that take the user's purchasing history into consideration.

[0054] When analyzing the user's brain waves and facial expressions in real time while traveling, the real-time analysis unit can adjust the analysis results by taking into account the user's current weather information. For example, if the user is traveling on a rainy day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming a relaxed state. Also, if the user is traveling on a sunny day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming an active state. Furthermore, if the user is traveling on a snowy day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming a specific emotional state. This makes it possible to provide analysis results that take into account the user's current weather information.

[0055] When reproducing video and sound in the metaverse space based on the results obtained by the real-time analysis unit, the reproduction unit can customize the content of the video and sound by referring to the user's past music playlists. For example, the reproduction unit selects music for a travel destination based on the genre of music the user has listened to in the past. The reproduction unit can also adjust the atmosphere of the travel destination by referring to the user's past music playlists. Furthermore, the reproduction unit can play specific music according to the user's music preferences. This makes it possible to provide video and sound that take the user's music preferences into consideration.

[0056] When analyzing the results of a questionnaire to hear the user's preferences, the analysis unit can adjust the analysis results by taking into account the user's current health condition. For example, the analysis unit may preferentially suggest travel destinations where the user can relax based on the results of a health check. The analysis unit may also suggest active travel destinations if the user is in good health. Furthermore, if the user has a specific health condition, the analysis unit may suggest travel destinations that are suitable for that condition. This makes it possible to provide analysis results that take the user's health condition into account.

[0057] When analyzing the user's brain waves and facial expressions in real time while traveling, the real-time analysis unit can adjust the analysis results by taking into account the user's current activity level. For example, if the user is exercising, the real-time analysis unit can analyze the user's state based on the assumption that the user is in an excited state. Also, if the user is resting, the real-time analysis unit can analyze the user's state based on the assumption that the user is in a relaxed state. Furthermore, if the user is performing a specific activity, the real-time analysis unit can analyze the user's state based on the emotional state corresponding to that activity. This makes it possible to provide analysis results that take into account the user's current activity level.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The analysis unit analyzes the survey results to hear the user's preferences. The survey results include travel destination preferences, activity preferences, and food preferences. The analysis unit compiles the survey results and identifies the user's preferences. It can also use AI to analyze the survey results and gain a detailed understanding of the user's preferences. Step 2: The real-time analysis unit analyzes the user's brain waves and facial expressions in real time while traveling. An EEG sensor is used to analyze brain waves, and a facial expression recognition algorithm is used to analyze facial expressions. The real-time analysis unit analyzes the user's brain wave patterns to understand their emotional state. It can also analyze the user's facial expressions to understand their reactions in real time. Step 3: The reproduction unit recreates images and sounds in the Metaverse space based on the results obtained by the real-time analysis unit. For example, if the user is relaxed, it generates images of a quiet beach and the sound of waves, while if the user is excited, it generates images and sounds of an active city. The reproduction unit can use AI to generate images and sounds based on the user's reactions.

[0060] (Example 2) A travel experience system according to an embodiment of the present invention listens to a user's preferences, analyzes the user's responses in real time, and provides a travel experience in a metaverse space. In this travel experience system, the user answers a questionnaire before traveling and is asked about their preferred travel destination. This information is input into an AI. The AI ​​then analyzes the input information and generates images and sounds of the travel destination based on the user's preferences. The generated images and sounds are reproduced in the metaverse space, allowing the user to experience the trip through a VR device. Furthermore, the AI ​​measures the user's brainwaves and facial expressions during the trip to understand their preferences in real time. This allows the AI ​​to continuously generate a more immersive space based on the user's responses. For example, if the user is relaxed, the AI ​​generates images of a tranquil beach and sounds of waves, while if the user is excited, the AI ​​generates images and sounds of an active city. This mechanism allows users to enjoy a highly satisfying travel experience that cannot be experienced on a real trip. For example, the system allows users to experience fictional places they have never visited or fantastical places that do not exist in reality. Furthermore, the travel destination is customized based on the user's preferences, providing a constantly new experience. This allows the travel experience system to provide travel experiences tailored to the user's preferences. For example, the user can experience fictional places they have never visited or fantastical places that do not exist in reality. Furthermore, because travel destinations are customized according to the user's preferences, new experiences can always be provided.

[0061] A travel experience system according to an embodiment includes an analysis unit, a real-time analysis unit, and a reproduction unit. The analysis unit analyzes survey results to gather user preferences. The survey results include, but are not limited to, destination preferences, activity preferences, and food preferences. The analysis unit, for example, aggregates the survey results and identifies the user's preferences. The analysis unit can also analyze the survey results using AI to understand the user's preferences in detail. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time while traveling. The brain waves can be analyzed using, for example, an EEG sensor. The facial expressions can be analyzed using, for example, a facial expression recognition algorithm. The real-time analysis unit, for example, analyzes the user's brain wave patterns to understand the user's emotional state. The real-time analysis unit can also analyze the user's facial expressions to understand the user's reactions in real time. The reproduction unit reproduces images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. For example, when the user is relaxing, the reproduction unit generates an image of a tranquil beach and the sound of waves. Furthermore, if the user is excited, the reproducing unit can generate images and sounds of an active city. The reproducing unit can generate images and sounds according to the user's reaction using AI. This allows the travel experience system according to the embodiment to provide a travel experience that suits the user's preferences.

[0062] The analysis unit can analyze the results of a survey to hear the user's preferences. The survey results include, but are not limited to, preferences for travel destinations, activities, and food. The analysis unit, for example, aggregates the survey results and identifies the user's preferences. The analysis unit can also use AI to analyze the survey results and understand the user's preferences in detail. This makes it possible to accurately understand the user's preferences.

[0063] The real-time analysis unit can analyze the brain waves and facial expressions of the user while traveling in real time. For example, the real-time analysis unit measures and analyzes the brain waves of the user while traveling using an EEG sensor. For example, the real-time analysis unit analyzes specific brain wave patterns to understand the emotional state of the user. The real-time analysis unit can also analyze the facial expressions of the user while traveling using a facial expression recognition algorithm. For example, the real-time analysis unit analyzes specific facial expression patterns to understand the user's reactions in real time. This makes it possible to understand the user's reactions in real time.

[0064] The reproduction unit can reproduce images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. The reproduction unit reproduces images and sounds in the metaverse space based on, for example, the user's emotional state obtained by the real-time analysis unit. For example, the reproduction unit can generate images of a quiet beach and the sound of waves if the user is relaxed. Alternatively, the reproduction unit can generate images and sounds of an active city if the user is excited. The reproduction unit can use AI to generate images and sounds according to the user's reactions. This makes it possible to provide images and sounds according to the user's real-time reactions.

[0065] The reproduction unit can generate an image of a quiet beach and the sound of waves when the user is relaxed, and can generate an image of an active city and the sound when the user is excited. For example, the reproduction unit generates an image of a quiet beach and the sound of waves when the user is relaxed. For example, the reproduction unit generates an image of a quiet beach and reproduces the sound of waves. The reproduction unit can also generate an image of an active city and the sound when the user is excited. For example, the reproduction unit generates an image of an active city and reproduces the sound of the city. This makes it possible to provide images and sounds that correspond to the user's emotions.

[0066] The analysis unit can provide information for customizing travel destinations according to the user's preferences. For example, the analysis unit analyzes the results of a questionnaire to hear the user's preferences and provides information for customizing travel destinations according to the user's preferences. For example, the analysis unit provides customized travel destination information based on the user's preferred travel destinations and activities. The analysis unit can also analyze the survey results using AI and suggest travel destinations according to the user's preferences. This makes it possible to provide travel destinations according to the user's preferences.

[0067] The analysis unit can estimate the user's emotions and dynamically change the content of the questionnaire questions based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and dynamically changes the content of the questionnaire questions based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can add detailed questions to inquire about more specific preferences. Furthermore, if the user is feeling stressed, the analysis unit can reduce the number of questions and change them to simpler questions. Furthermore, if the user is excited, the analysis unit can add questions that pique the user's interest to explore their favorite travel destinations. In this way, a questionnaire can be provided that corresponds to the user's emotions.

[0068] The analysis unit can analyze the user's past travel history and reflect it in the analysis of the survey results. The analysis unit, for example, analyzes the user's past travel history and reflects it in the analysis of the survey results. For example, the analysis unit analyzes preference trends based on data on travel destinations the user has visited in the past. The analysis unit can also adjust the survey results based on activities the user has preferred in the past. The analysis unit can also suggest similar travel destinations that have not been visited based on the user's past travel history. This makes it possible to provide survey results that take the user's past travel history into consideration.

[0069] When analyzing the survey results, the analysis unit can adjust the analysis results based on the user's current mood and physical condition. For example, when analyzing the survey results, the analysis unit adjusts the analysis results by taking into account the user's current mood and physical condition. For example, if the user is tired, the analysis unit can preferentially suggest travel destinations where the user can relax. Furthermore, if the user is energetic, the analysis unit can also suggest travel destinations that are active. Furthermore, if the user is feeling stressed, the analysis unit can also suggest travel destinations that have a soothing effect. In this way, it is possible to provide analysis results that take into account the user's current mood and physical condition.

[0070] When analyzing the survey results, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, when analyzing the survey results, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit analyzes preferences based on travel destination information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts to identify travel destinations of interest. The analysis unit can also suggest related travel destinations by referring to the social media activity of the user's friends. This makes it possible to provide analysis results that take the user's social media activity into consideration.

[0071] The analysis unit can estimate the user's emotions and select a method for answering the questionnaire based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and selects a method for answering the questionnaire based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can prompt the user to enter detailed text. If the user is feeling stressed, the analysis unit can also suggest simple voice input. If the user is excited, the analysis unit can also provide a method for answering the questionnaire using images. This makes it possible to provide a method for answering the questionnaire that corresponds to the user's emotions.

[0072] When analyzing the survey results, the analysis unit can adjust the analysis results by taking into account the user's geographical location information. For example, when analyzing the survey results, the analysis unit adjusts the analysis results by taking into account the user's geographical location information. For example, the analysis unit can suggest travel destinations by taking into account the climate and culture of the user's current location. The analysis unit can also preferentially suggest nearby travel destinations based on the user's geographical location information. The analysis unit can also suggest easily accessible travel destinations based on the user's geographical location information. This makes it possible to provide analysis results that take into account the user's geographical location information.

[0073] When analyzing the survey results, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, when analyzing the survey results, the analysis unit customizes the analysis method by reflecting the user's past feedback. For example, the analysis unit adjusts the content of the survey questions based on the user's past feedback. The analysis unit can also optimize the analysis algorithm by reflecting the user's past feedback. The analysis unit can also improve the accuracy of the analysis results based on the user's past feedback. This makes it possible to provide an analysis method that takes the user's past feedback into consideration.

[0074] When analyzing the survey results, the analysis unit can prioritize the analysis results based on the user's interests and concerns. For example, when analyzing the survey results, the analysis unit prioritizes the analysis results based on the user's interests and concerns. For example, the analysis unit preferentially suggests theme parks that the user is interested in. The analysis unit can also preferentially suggest historical places that the user is interested in. The analysis unit can also preferentially suggest natural landscapes that the user is interested in. This makes it possible to provide analysis results that take the user's interests and concerns into consideration.

[0075] The real-time analysis unit can estimate the user's emotions and adjust the analysis method of brain waves and facial expressions based on the estimated user emotions. The real-time analysis unit, for example, estimates the user's emotions and adjusts the analysis method of brain waves and facial expressions based on the estimated user emotions. For example, when the user is relaxed, the real-time analysis unit can analyze the brain waves by focusing on alpha waves. Furthermore, when the user is excited, the real-time analysis unit can analyze the brain waves by focusing on beta waves. Furthermore, when the user is stressed, the real-time analysis unit can analyze the brain waves by focusing on changes in facial expressions. This makes it possible to provide a method for analyzing brain waves and facial expressions according to the user's emotions.

[0076] The real-time analysis unit can improve the accuracy of the analysis by referring to the user's past electroencephalogram and facial expression data during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by referring to the user's past electroencephalogram and facial expression data during real-time analysis, for example. For example, the real-time analysis unit analyzes current electroencephalograms based on the user's past electroencephalogram data. The real-time analysis unit can also analyze current facial expressions based on the user's past facial expression data. The real-time analysis unit can also integrate the user's past electroencephalogram and facial expression data to improve the accuracy of the analysis. This makes it possible to provide analysis results that take the user's past electroencephalogram and facial expression data into consideration.

[0077] The real-time analysis unit can adjust the analysis results based on the user's current activity status during real-time analysis. For example, the real-time analysis unit adjusts the analysis results taking into account the user's current activity status during real-time analysis. For example, when the user is sitting, the real-time analysis unit performs analysis assuming a relaxed state. Furthermore, when the user is walking, the real-time analysis unit can also perform analysis assuming an active state. Furthermore, when the user is exercising, the real-time analysis unit can also perform analysis assuming an excited state. This makes it possible to provide analysis results that take into account the user's current activity status.

[0078] The real-time analysis unit can improve the accuracy of the analysis by also using the user's physiological data (heart rate, respiratory rate, etc.) during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by also using the user's physiological data during real-time analysis. For example, the real-time analysis unit analyzes the stress level based on the user's heart rate. The real-time analysis unit can also analyze the user's relaxation level based on the user's respiratory rate. The real-time analysis unit can also integrate the user's physiological data to analyze the user's overall emotional state. This makes it possible to provide analysis results that take the user's physiological data into consideration.

[0079] The real-time analysis unit can estimate the user's emotions and adjust the frequency of real-time analysis based on the estimated user's emotions. The real-time analysis unit, for example, estimates the user's emotions and adjusts the frequency of real-time analysis based on the estimated user's emotions. For example, the real-time analysis unit sets the analysis frequency low when the user is relaxed. The real-time analysis unit can also set the analysis frequency high when the user is excited. The real-time analysis unit can also set the analysis frequency to medium when the user is feeling stressed. This makes it possible to provide a real-time analysis frequency that corresponds to the user's emotions.

[0080] The real-time analysis unit can adjust the analysis results during real-time analysis by taking into account the user's geographical location information. For example, the real-time analysis unit adjusts the analysis results by taking into account the user's geographical location information during real-time analysis. For example, if the user is in an urban area, the real-time analysis unit can perform analysis by taking into account the noise level. Furthermore, if the user is in a natural environment, the real-time analysis unit can also perform analysis by taking into account the silence. Furthermore, if the user is in a specific location, the real-time analysis unit can perform analysis by taking into account the characteristics of that location. This makes it possible to provide analysis results that take into account the user's geographical location information.

[0081] The real-time analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during real-time analysis. The real-time analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during real-time analysis, for example. For example, the real-time analysis unit performs real-time analysis based on the emotional state shared by the user on social media. The real-time analysis unit can also analyze the content of the user's social media posts to estimate the user's current emotional state. The real-time analysis unit can also estimate the related emotional state by referring to the social media activity of the user's friends. This makes it possible to provide analysis results that take the user's social media activity into consideration.

[0082] The real-time analysis unit can customize the analysis method by reflecting the user's past feedback during real-time analysis. The real-time analysis unit, for example, customizes the analysis method by reflecting the user's past feedback during real-time analysis. For example, the real-time analysis unit adjusts the real-time analysis algorithm based on feedback provided by the user in the past. The real-time analysis unit can also improve the accuracy of the analysis results by reflecting the user's past feedback. The real-time analysis unit can also optimize the analysis method based on the user's past feedback. This makes it possible to provide an analysis method that takes the user's past feedback into consideration.

[0083] The reproduction unit can estimate the user's emotion and adjust the content of the video and sound to be reproduced based on the estimated user's emotion. For example, the reproduction unit can estimate the user's emotion and adjust the content of the video and sound to be reproduced based on the estimated user's emotion. For example, if the user is relaxed, the reproduction unit can generate video of a quiet beach and the sound of waves. If the user is excited, the reproduction unit can also generate video and sound of an active city. If the user is stressed, the reproduction unit can also generate video and sound of nature that has a soothing effect. In this way, video and sound can be provided that correspond to the user's emotion.

[0084] The reproducing unit can customize the content of the video and sound by referring to the user's past travel experiences during the reproducing process. For example, the reproducing unit customizes the content of the video and sound by referring to the user's past travel experiences during the reproducing process. For example, the reproducing unit reproduces the video and sound of a travel destination that the user has previously visited. The reproducing unit can also generate video and sound of a similar travel destination based on the user's past travel experiences. The reproducing unit can also generate customized video and sound by referring to the user's past travel experiences. This makes it possible to provide video and sound that take the user's past travel experiences into consideration.

[0085] The reproduction unit can adjust the content of the video and sound during reproduction, taking into account the user's current mood and physical condition. For example, the reproduction unit adjusts the content of the video and sound during reproduction, taking into account the user's current mood and physical condition. For example, if the user is tired, the reproduction unit can generate relaxing video and sound. Furthermore, if the user is energetic, the reproduction unit can generate active video and sound. Furthermore, if the user is feeling stressed, the reproduction unit can generate video and sound that has a soothing effect. In this way, it is possible to provide video and sound that take into account the user's current mood and physical condition.

[0086] The reproduction unit can customize the content of the video and sound by referring to the user's social media activity during reproduction. For example, the reproduction unit customizes the content of the video and sound by referring to the user's social media activity during reproduction. For example, the reproduction unit reproduces video and sound of travel destinations shared by the user on social media. The reproduction unit can also generate video and sound of interest based on the content posted by the user on social media. The reproduction unit can also generate related video and sound by referring to the social media activity of the user's friends. This makes it possible to provide video and sound that takes the user's social media activity into consideration.

[0087] The reproduction unit can estimate the user's emotion and determine the priority of the images and sounds to be reproduced based on the estimated user's emotion. The reproduction unit can, for example, estimate the user's emotion and determine the priority of the images and sounds to be reproduced based on the estimated user's emotion. For example, if the user is relaxed, the reproduction unit can preferentially reproduce images of a quiet beach and the sound of waves. Also, if the user is excited, the reproduction unit can preferentially reproduce images and sounds of an active city. Also, if the user is stressed, the reproduction unit can preferentially reproduce images and sounds of nature that have a soothing effect. This makes it possible to provide a priority of images and sounds according to the user's emotion.

[0088] The reproduction unit can adjust the content of the video and sound during reproduction, taking into account the user's geographical location information. For example, the reproduction unit adjusts the content of the video and sound during reproduction, taking into account the user's geographical location information. For example, if the user is in an urban area, the reproduction unit reproduces urban video and sound. Furthermore, if the user is in a natural environment, the reproduction unit can also reproduce natural video and sound. Furthermore, if the user is in a specific location, the reproduction unit can reproduce video and sound taking into account the characteristics of that location. This makes it possible to provide video and sound that take into account the user's geographical location information.

[0089] The reproduction unit can customize the content of the video and sound by reflecting the user's past feedback during reproduction. For example, the reproduction unit customizes the content of the video and sound by reflecting the user's past feedback during reproduction. For example, the reproduction unit adjusts the content of the video and sound based on feedback previously provided by the user. The reproduction unit can also optimize the content of the video and sound by reflecting the user's past feedback. The reproduction unit can also customize the content of the video and sound based on the user's past feedback. This makes it possible to provide video and sound that take the user's past feedback into consideration.

[0090] The reproduction unit can prioritize the video and audio content based on the user's interests and concerns during reproduction. For example, the reproduction unit prioritizes the video and audio content based on the user's interests and concerns during reproduction. For example, the reproduction unit prioritizes the reproduction of video and audio content based on the user's interests and concerns. The reproduction unit can also prioritize the reproduction of video and audio content of a theme park in which the user is interested. The reproduction unit can also prioritize the reproduction of video and audio content of a historical place in which the user is interested. The reproduction unit can also prioritize the reproduction of video and audio content of natural landscapes in which the user is interested. This makes it possible to provide video and audio content that takes the user's interests and concerns into consideration. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, real-time analysis unit, and reproduction 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 analysis unit analyzes survey results using the control unit 46A of the smart device 14 to identify the user's preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time using the camera 42 and microphone 38B of the smart device 14. The reproduction unit generates images and sounds in the Metaverse space using the specific processing unit 290 of the data processing device 12, providing a travel experience based on the user's reactions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, real-time analysis unit, and reproduction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes survey results using the control unit 46A of the smart glasses 214 to identify the user's preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time using the camera 42 and microphone 238 of the smart glasses 214. The reproduction unit generates images and sounds in the metaverse space using the specific processing unit 290 of the data processing device 12, providing a travel experience based on the user's reactions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, real-time analysis unit, and reproduction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit analyzes questionnaire results using the control unit 46A of the headset-type terminal 314 to identify the user's preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time using the camera 42 and microphone 238 of the headset-type terminal 314. The reproduction unit generates images and sounds in the Metaverse space using the specific processing unit 290 of the data processing device 12, providing a travel experience based on the user's reactions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, real-time analysis unit, and reproduction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes questionnaire results using the control unit 46A of the robot 414 to identify the user's preferences. The real-time analysis unit analyzes the user's brain waves and facial expressions in real time using the camera 42 and microphone 238 of the robot 414. The reproduction unit generates images and sounds in the Metaverse space using the specific processing unit 290 of the data processing device 12, providing a travel experience according to the user's reactions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing the results of a survey to hear a user's preferences, the analysis unit can improve the accuracy of the analysis by referring to the user's past purchasing history. For example, the analysis unit analyzes preference trends based on travel-related products and services purchased by the user in the past. The analysis unit can also adjust the survey results by referring to the contents of guidebooks and travel magazines purchased by the user in the past. Furthermore, the analysis unit can suggest similar travel destinations that the user has not yet visited based on the user's past purchasing history. This makes it possible to provide analysis results that take the user's purchasing history into consideration.

[0093] When analyzing the user's brain waves and facial expressions in real time while traveling, the real-time analysis unit can adjust the analysis results by taking into account the user's current weather information. For example, if the user is traveling on a rainy day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming a relaxed state. Also, if the user is traveling on a sunny day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming an active state. Furthermore, if the user is traveling on a snowy day, the real-time analysis unit can analyze the user's brain waves and facial expressions assuming a specific emotional state. This makes it possible to provide analysis results that take into account the user's current weather information.

[0094] When reproducing video and sound in the metaverse space based on the results obtained by the real-time analysis unit, the reproduction unit can customize the content of the video and sound by referring to the user's past music playlists. For example, the reproduction unit selects music for a travel destination based on the genre of music the user has listened to in the past. The reproduction unit can also adjust the atmosphere of the travel destination by referring to the user's past music playlists. Furthermore, the reproduction unit can play specific music according to the user's music preferences. This makes it possible to provide video and sound that take the user's music preferences into consideration.

[0095] When analyzing the results of a questionnaire to hear the user's preferences, the analysis unit can adjust the analysis results by taking into account the user's current health condition. For example, the analysis unit may preferentially suggest travel destinations where the user can relax based on the results of a health check. The analysis unit may also suggest active travel destinations if the user is in good health. Furthermore, if the user has a specific health condition, the analysis unit may suggest travel destinations that are suitable for that condition. This makes it possible to provide analysis results that take the user's health condition into account.

[0096] When analyzing the user's brain waves and facial expressions in real time while traveling, the real-time analysis unit can adjust the analysis results by taking into account the user's current activity level. For example, if the user is exercising, the real-time analysis unit can analyze the user's state based on the assumption that the user is in an excited state. Also, if the user is resting, the real-time analysis unit can analyze the user's state based on the assumption that the user is in a relaxed state. Furthermore, if the user is performing a specific activity, the real-time analysis unit can analyze the user's state based on the emotional state corresponding to that activity. This makes it possible to provide analysis results that take into account the user's current activity level.

[0097] The analysis unit can estimate the user's emotions and dynamically change the content of the questionnaire questions based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can add detailed questions to hear more specific preferences. If the user is feeling stressed, the analysis unit can reduce the number of questions and change them to simpler questions. If the user is excited, the analysis unit can add questions that pique the user's interest to explore their favorite travel destinations. This makes it possible to provide a questionnaire that corresponds to the user's emotions.

[0098] The real-time analysis unit can estimate the user's emotions and adjust the analysis method of brain waves and facial expressions based on the estimated user's emotions. For example, if the user is relaxed, the real-time analysis unit can analyze the brain waves by focusing on alpha waves. If the user is excited, the real-time analysis unit can also analyze the brain waves by focusing on beta waves. If the user is stressed, the real-time analysis unit can analyze the brain waves by focusing on changes in facial expressions. This makes it possible to provide a method for analyzing brain waves and facial expressions according to the user's emotions.

[0099] The reproduction unit can estimate the user's emotions and adjust the content of the images and sounds to be reproduced based on the estimated user's emotions. For example, if the user is relaxed, the reproduction unit can generate images of a quiet beach and the sound of waves. If the user is excited, the reproduction unit can generate images and sounds of an active city. If the user is stressed, the reproduction unit can generate images and sounds of nature that have a soothing effect. In this way, it is possible to provide images and sounds that correspond to the user's emotions.

[0100] The analysis unit can estimate the user's emotions and select a method for answering the questionnaire based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prompt the user to enter detailed text. If the user is feeling stressed, the analysis unit can also suggest simple voice input. If the user is excited, the analysis unit can also provide a method for answering the questionnaire using images. This makes it possible to provide a method for answering the questionnaire that corresponds to the user's emotions.

[0101] The real-time analysis unit can estimate the user's emotions and adjust the frequency of real-time analysis based on the estimated user's emotions. For example, the real-time analysis unit can set the analysis frequency low when the user is relaxed. The real-time analysis unit can also set the analysis frequency high when the user is excited. The real-time analysis unit can also set the analysis frequency to medium when the user is feeling stressed. This makes it possible to provide a real-time analysis frequency that corresponds to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The analysis unit analyzes the survey results to hear the user's preferences. The survey results include travel destination preferences, activity preferences, and food preferences. The analysis unit compiles the survey results and identifies the user's preferences. It can also use AI to analyze the survey results and gain a detailed understanding of the user's preferences. Step 2: The real-time analysis unit analyzes the user's brain waves and facial expressions in real time while traveling. An EEG sensor is used to analyze brain waves, and a facial expression recognition algorithm is used to analyze facial expressions. The real-time analysis unit analyzes the user's brain wave patterns to understand their emotional state. It can also analyze the user's facial expressions to understand their reactions in real time. Step 3: The reproduction unit recreates images and sounds in the Metaverse space based on the results obtained by the real-time analysis unit. For example, if the user is relaxed, it generates images of a quiet beach and the sound of waves, while if the user is excited, it generates images and sounds of an active city. The reproduction unit can use AI to generate images and sounds based on the user's reactions.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. an analysis unit that analyzes the results of a questionnaire to hear user preferences; a real-time analysis unit that analyzes the user's brain waves and facial expressions during the trip based on the results obtained by the analysis unit; A reproduction unit that reproduces images and sounds in the metaverse space based on the results obtained by the real-time analysis unit. A system characterized by:

2. The analysis unit Analyze survey results to hear user preferences 2. The system of claim 1.

3. The real-time analysis unit Analyzing users' brain waves and facial expressions in real time while traveling 2. The system of claim 1.

4. The reproducing section Based on the results obtained by the real-time analysis unit, images and sounds are reproduced in the Metaverse space.

2. The system of claim 1.

5. The reproducing section If the user is relaxed, it generates images of a tranquil beach and sounds of waves, and if the user is excited, it generates images and sounds of an active city.

2. The system of claim 1.

6. The analysis unit Providing information to customize travel destinations based on user preferences 2. The system of claim 1.

7. The analysis unit Estimate user emotions and dynamically change survey questions based on the estimated user emotions 2. The system of claim 1.

8. The analysis unit Analyze users' past travel history and reflect it in the analysis of survey results 2. The system of claim 1.

9. The analysis unit When analyzing survey results, adjust the analysis results based on the user's current mood and physical condition.

2. The system of claim 1.

10. The analysis unit When analyzing survey results, we refer to users' social media activity to improve the accuracy of the analysis.

2. The system of claim 1.

Images