system

Abstract

An object of a system according to an embodiment is to enable a mountaineer to grasp a state of a mountain in real time.SOLUTION: A system includes an image collection part, an analysis part, a street view generation part, and a provision part. The image collection unit collects images from mountaineers and BLOG. The analysis part analyzes the image collected by the image collection part. The street view generation unit generates a street view based on the image analyzed by the analysis unit. The providing unit provides the mountain climber with the street view generated by the street view generating unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for climbers to grasp mountain conditions in real time, which may increase the risk of getting lost.

[0005] The system according to the embodiment aims to enable climbers to grasp mountain conditions in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an image collection unit, an analysis unit, a street view generation unit, and a provision unit. The image collection unit collects images from climbers and blogs. The analysis unit analyzes the images collected by the image collection unit. The street view generation unit generates a street view based on the images analyzed by the analysis unit. The provision unit provides the street view generated by the street view generation unit to climbers in real time. [Effects of the Invention]

[0007] The system according to the embodiment can enable climbers to understand the mountain conditions in real time. [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) The system for climbers according to an embodiment of the present invention uses generative AI to create a mountain street view on Google Maps and provide climbers with real-time mountain conditions. This allows climbers to understand the mountain conditions in advance and reduce the risk of getting lost. It also generates revenue through apps for climbers, insurance, and revenue sharing with travel agencies.

[0029] A system for climbers according to an embodiment includes an image collection unit, an analysis unit, a street view generation unit, and a provision unit. The image collection unit collects images from climbers and blogs. For example, images taken by climbers can be automatically collected using an API. Images can also be manually collected from blogs. The image collection unit also provides a function for climbers to upload images taken through an app. For example, climbers can upload images taken using the app to the cloud, and the image collection unit collects them. The analysis unit analyzes the collected images. For example, it can extract features within the images using an image recognition algorithm. It can also classify the content of the images using a machine learning model. The analysis unit also evaluates the quality of the images and selects images suitable for street view generation. For example, the analysis unit evaluates the image resolution and brightness and selects appropriate images. The street view generation unit generates a street view based on the analyzed images. For example, it can integrate multiple images into a single street view using panoramic image synthesis technology. It can also generate a three-dimensional street view using 3D modeling technology. The street view generation unit also provides a function for updating the generated street view in real time. For example, the Street View is dynamically updated based on the latest image data. The provider provides the generated Street View to climbers in real time. For example, streaming technology can be used to deliver Street View with low latency. It is also possible to allow climbers to view Street View through the app. Furthermore, the provider offers a function to provide the optimal Street View based on the climber's location information. For example, it can display the Street View closest to the climber's current location. This allows the climber-oriented system to understand mountain conditions in advance and reduce the risk of getting lost. For example, climbers can use the app to check mountain conditions and prepare appropriate equipment, ensuring a safe climb. Furthermore, by purchasing mountaineering insurance, they can enjoy mountaineering with peace of mind in the event of an accident. Furthermore, partnerships with travel agencies enable smooth booking of mountaineering tours.

[0030] The analysis unit analyzes a climber's past climbing history and can customize the optimal Street View for each individual climber. For example, the generation AI analyzes a climber's past climbing history and provides the optimal Street View for each individual climber. For example, it suggests mountains with similar scenery based on information about mountains visited in the past. The analysis unit also customizes routes to suit each individual climber based on the climber's past climbing data. For example, it suggests new routes that reflect the characteristics of routes that were preferred in the past. The analysis unit also customizes the optimal rest points and scenery for each individual climber based on the climber's past climbing history. For example, it suggests routes that include scenic spots that have been visited in the past. This makes it possible to provide the optimal Street View based on the climber's past history.

[0031] The analysis unit can acquire the climber's location information in real time and dynamically update the Street View based on that location. For example, the analysis unit allows the generation AI to acquire the climber's location information in real time and dynamically update the Street View based on that location. For example, the analysis unit displays the latest scenery every time the climber moves. The analysis unit also allows the generation AI to display the rest points and scenery closest to the climber's current location based on the climber's location information. For example, it suggests the best place for a climber to take a break. The analysis unit also allows the generation AI to analyze the climber's location information and provide a Street View that reflects weather and seasonal changes based on that location. For example, it displays scenery that matches the weather at the current location. This allows the Street View to be updated in real time based on the climber's location.

[0032] The analysis unit can analyze the mountain guide's audio commentary and add the audio guide to Street View. For example, the generation AI in the analysis unit analyzes the mountain guide's audio commentary and adds the audio guide to Street View. For example, the audio commentary is automatically played when a specific point is reached. The analysis unit also causes the generation AI to display information related to Street View based on the mountain guide's audio data. For example, the analysis unit provides audio commentary on historical background and topographical features. The analysis unit also causes the generation AI to analyze the mountain guide's audio commentary and provide audio commentary according to the climber's location. For example, the audio commentary is played along a specific route. This allows the mountain guide's audio commentary to be added to Street View.

[0033] The analysis unit can analyze aerial footage taken by a drone and integrate it into Street View. For example, the generation AI in the analysis unit analyzes aerial footage taken by a drone and integrates it into Street View. For example, it can provide a panoramic view from the top of a mountain. The analysis unit also uses the generation AI to add advanced visual effects to Street View based on the drone footage. For example, it can use aerial footage to display three-dimensional terrain. The analysis unit also uses the generation AI to analyze drone footage and provide an aerial view based on the climber's location. For example, it can display footage from above the climber's current location. This allows aerial footage taken by a drone to be integrated into Street View.

[0034] The analysis unit can analyze past weather data and predict future weather changes to reflect them in Street View. For example, the analysis unit allows the generation AI to analyze past weather data and develop an algorithm to predict future weather changes. For example, the generation AI learns weather patterns based on past data and predicts future weather. The analysis unit also allows the generation AI to reflect weather changes in Street View based on future weather forecasts. For example, the landscape is dynamically updated according to the predicted weather. The analysis unit also allows the generation AI to predict future weather changes in real time based on past weather data and reflect them in Street View. For example, this allows hikers to check future weather conditions at their current location. This allows future weather changes to be predicted and reflected in Street View.

[0035] The analysis unit can analyze seasonal plant and animal ecological information and display it on Street View. For example, the generation AI in the analysis unit analyzes seasonal plant and animal ecological information and displays it on Street View. For example, it displays places where flowers bloom in spring and places where autumn leaves can be seen in autumn. The analysis unit also allows the generation AI to add information about plants and animals to Street View based on seasonal ecological information. For example, it displays the habitats of animals that can be seen in specific seasons. The analysis unit also allows the generation AI to analyze seasonal plant and animal ecological information and provide information according to the hiker's location. For example, it displays seasonal scenery around the current location. This allows seasonal plant and animal ecological information to be displayed on Street View.

[0036] The analysis unit can compare street views from different seasons and suggest the best time for climbers to climb. For example, the generation AI compares street views from different seasons and suggests the best time for climbers to climb. For example, suggestions are made based on seasonal attractions such as cherry blossom viewing in spring and autumn leaves. The analysis unit also uses weather data for each season to suggest the best time for climbers to climb. For example, it prioritizes suggestions for times with stable weather. The analysis unit also uses street views from different seasons to suggest a time for climbers to climb based on the climbers' preferences. For example, it suggests times that avoid summer for climbers who prefer cooler seasons. This allows the generation AI to compare street views from different seasons and suggest the best time for climbers to climb.

[0037] The analysis unit can analyze the comments of mountain weather forecasters and add weather commentary to Street View. For example, the generation AI of the analysis unit analyzes the comments of mountain weather forecasters and adds weather commentary to Street View. For example, a weather commentary is displayed when a specific point is reached. The analysis unit also causes the generation AI to display weather information related to Street View based on the mountain weather forecaster's comment data. For example, it explains points to be careful of regarding changes in the weather. The analysis unit also causes the generation AI to analyze the comments of mountain weather forecasters and provide weather commentary according to the climber's location. For example, a weather commentary is displayed along a specific route. This makes it possible to analyze the comments of mountain weather forecasters and add weather commentary.

[0038] The provision unit collects feedback from climbers, and the generation AI analyzes that feedback to improve the app's functions. For example, the provision unit allows the app to collect feedback from climbers, and the generation AI analyzes that feedback to improve the app's functions. For example, new functions are added based on user opinions. The provision unit also allows the generation AI to improve the app's user interface based on climber feedback data. For example, it improves usability and visual design. The provision unit also analyzes the feedback collected by the app in real time, and the generation AI immediately makes improvement suggestions. For example, it proposes specific improvement measures to resolve user dissatisfaction. This allows the app's functions to be improved based on climber feedback.

[0039] The provision unit collects the climber's health data, and the generation AI analyzes that data to provide advice based on the climber's health condition. For example, the provision unit uses an app to collect the climber's health data, such as heart rate and number of steps, and the generation AI analyzes that data to provide advice based on the climber's health condition. For example, if the heart rate is high, the provision unit suggests taking a break. The provision unit also uses the climber's health data to have the generation AI suggest the optimal amount of exercise and rest times for each climber. For example, it suggests an appropriate pace based on past data. The provision unit also analyzes the health data collected by the app in real time, and the generation AI instantly provides advice based on the climber's health condition. For example, if the climber feels very tired, the generation AI suggests drinking more water. This makes it possible to provide advice based on the climber's health data and according to the climber's health condition.

[0040] The provision unit collects climber's equipment information, and the generation AI analyzes that information to propose the optimal equipment list. For example, the provision unit's app collects climber's equipment information, and the generation AI analyzes that information to propose the optimal equipment list. For example, it proposes equipment according to the climber's experience and destination. The provision unit also customizes the optimal equipment list for each climber using the generation AI based on the climber's equipment data. For example, it suggests necessary items based on past equipment history. The provision unit also analyzes the equipment information collected by the app in real time, and the generation AI instantly provides the optimal equipment list. For example, it suggests equipment according to the weather and season. This makes it possible to propose the optimal equipment list based on the climber's equipment information.

[0041] The provision unit can analyze a climber's past insurance usage history and customize the optimal insurance plan for each individual climber. In the provision unit, for example, the generation AI analyzes a climber's past insurance usage history and provides the optimal insurance plan for each individual climber. For example, it proposes the necessary coverage based on the past usage history. In addition, the provision unit customizes an insurance plan to suit each individual climber using the generation AI based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. In addition, the provision unit analyzes a climber's past insurance usage history and provides the optimal insurance plan. For example, it proposes the optimal plan based on past usage frequency and compensation details. This makes it possible to customize the optimal insurance plan based on the climber's past insurance usage history.

[0042] The provision unit can analyze the climber's travel plan and optimize the partnership plan with the travel agency. In the provision unit, for example, the generation AI analyzes the climber's travel plan and optimizes the partnership plan with the travel agency. For example, it proposes the optimal tour plan based on the travel destination and dates. In addition, the provision unit customizes the partnership plan with the travel agency based on the climber's travel plan data by the generation AI. For example, it proposes a plan based on the travel purpose and budget. In addition, the provision unit analyzes the climber's travel plan and optimizes the partnership plan with the travel agency. For example, it proposes a plan with benefits based on the travel destination and season. In this way, the partnership plan with the travel agency can be optimized based on the climber's travel plan.

[0043] The provision unit can analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. In the provision unit, for example, the generation AI analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the necessary coverage based on the past usage history. In addition, the provision unit has the generation AI customize the partnership plan with the insurance company based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accident and trouble history. In addition, the provision unit has the generation AI analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. For example, it proposes the optimal plan based on past usage frequency and compensation content. In this way, the partnership plan with the insurance company can be optimized based on the climber's insurance usage history.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The provider collects the climber's health data, and the generation AI analyzes that data to provide advice based on their health condition. For example, if their heart rate is high, it will suggest taking a break. The provider also uses the climber's health data to allow the generation AI to suggest the optimal amount of exercise and rest times for each individual climber. For example, it will suggest an appropriate pace based on past data. The provider also analyzes the health data collected by the app in real time, and the generation AI instantly provides advice based on their health condition. For example, if they feel very tired, it will suggest they drink more water. This makes it possible to provide advice based on the climber's health data and their health condition.

[0046] The provision unit collects climber equipment information, and the generation AI analyzes that information to suggest the optimal equipment list. For example, it suggests equipment that suits the climber's experience and destination. The provision unit also uses the climber's equipment data to allow the generation AI to customize the optimal equipment list for each climber. For example, it suggests necessary items based on past equipment history. The provision unit also analyzes the equipment information collected by the app in real time, and the generation AI instantly provides the optimal equipment list. For example, it suggests equipment according to the weather and season. This makes it possible to suggest the optimal equipment list based on the climber's equipment information.

[0047] The provision unit can analyze a climber's past insurance usage history and customize the optimal insurance plan for each individual climber. For example, the generation AI analyzes a climber's past insurance usage history and provides the optimal insurance plan for each individual climber. For example, it proposes the necessary coverage based on the past usage history. The provision unit also customizes an insurance plan to suit each individual climber based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. The provision unit also analyzes a climber's past insurance usage history and provides the optimal insurance plan. For example, it proposes the optimal plan based on past usage frequency and coverage. This makes it possible to customize the optimal insurance plan based on the climber's past insurance usage history.

[0048] The provision unit can analyze the climber's travel plans and optimize partnership plans with travel agencies. For example, the generation AI analyzes the climber's travel plans and optimizes partnership plans with travel agencies. For example, it proposes the optimal tour plan based on the travel destination and dates. The provision unit also customizes partnership plans with travel agencies based on the climber's travel plan data by the generation AI. For example, it proposes plans based on the travel purpose and budget. The provision unit also analyzes the climber's travel plans and optimizes partnership plans with travel agencies. For example, it proposes plans with benefits based on the travel destination and season. This makes it possible to optimize partnership plans with travel agencies based on the climber's travel plans.

[0049] The provision unit can analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. For example, the generation AI analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the necessary coverage based on past usage history. The provision unit also customizes the partnership plan with the insurance company based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. The provision unit also analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the optimal plan based on past usage frequency and compensation content. This makes it possible to optimize the partnership plan with the insurance company based on the climber's insurance usage history.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The image collection unit collects images from climbers and blogs. For example, it can automatically collect images taken by climbers using an API. It can also collect images manually from blogs. Furthermore, the image collection unit provides a function for climbers to upload images taken through the app. For example, climbers can upload images taken using the app to the cloud, and the image collection unit collects them. Step 2: The analysis unit analyzes the collected images. For example, it uses an image recognition algorithm to extract features within the images. It can also use a machine learning model to classify the content of the images. The analysis unit then evaluates the quality of the images and selects images suitable for Street View generation. For example, the analysis unit evaluates the resolution and brightness of the images to select suitable images. Step 3: The street view generation unit generates a street view based on the analyzed images. For example, it can use panoramic image synthesis technology to integrate multiple images into a single street view. It can also use 3D modeling technology to generate a three-dimensional street view. The street view generation unit also provides a function to update the generated street view in real time. For example, it can dynamically update the street view based on the latest image data. Step 4: The provider provides the generated Street View to the climber in real time. For example, streaming technology can be used to deliver the Street View with low latency. It is also possible to allow the climber to view the Street View through an app. Furthermore, the provider provides a function to provide the optimal Street View based on the climber's location information. For example, it displays the Street View closest to the climber's current location.

[0052] (Example 2) The system for climbers according to an embodiment of the present invention uses generative AI to create a mountain street view on Google Maps and provide climbers with real-time mountain conditions. This allows climbers to understand the mountain conditions in advance and reduce the risk of getting lost. It also generates revenue through apps for climbers, insurance, and revenue sharing with travel agencies.

[0053] A system for climbers according to an embodiment includes an image collection unit, an analysis unit, a street view generation unit, and a provision unit. The image collection unit collects images from climbers and blogs. For example, images taken by climbers can be automatically collected using an API. Images can also be manually collected from blogs. The image collection unit also provides a function for climbers to upload images taken through an app. For example, climbers can upload images taken using the app to the cloud, and the image collection unit collects them. The analysis unit analyzes the collected images. For example, it can extract features within the images using an image recognition algorithm. It can also classify the content of the images using a machine learning model. The analysis unit also evaluates the quality of the images and selects images suitable for street view generation. For example, the analysis unit evaluates the image resolution and brightness and selects appropriate images. The street view generation unit generates a street view based on the analyzed images. For example, it can integrate multiple images into a single street view using panoramic image synthesis technology. It can also generate a three-dimensional street view using 3D modeling technology. The street view generation unit also provides a function for updating the generated street view in real time. For example, the Street View is dynamically updated based on the latest image data. The provider provides the generated Street View to climbers in real time. For example, streaming technology can be used to deliver Street View with low latency. It is also possible to allow climbers to view Street View through the app. Furthermore, the provider offers a function to provide the optimal Street View based on the climber's location information. For example, it can display the Street View closest to the climber's current location. This allows the climber-oriented system to understand mountain conditions in advance and reduce the risk of getting lost. For example, climbers can use the app to check mountain conditions and prepare appropriate equipment, ensuring a safe climb. Furthermore, by purchasing mountaineering insurance, they can enjoy mountaineering with peace of mind in the event of an accident. Furthermore, partnerships with travel agencies enable smooth booking of mountaineering tours.

[0054] The analysis unit can estimate the climber's emotions and suggest the optimal route and scenery based on those emotions. For example, the analysis unit uses a generation AI to analyze the climber's emotions in real time and suggest the optimal route based on those emotions. For example, if the climber is feeling very tired, the analysis unit will suggest a route that includes many rest points. The analysis unit also uses the generation AI to prioritize displaying scenic spots based on the climber's emotional data. For example, if the climber is feeling very moved or happy, the analysis unit will suggest a route that includes scenic spots. The analysis unit also uses the generation AI to analyze the climber's emotions and suggest a safe route based on those emotions. For example, if the climber is feeling very anxious or scared, the analysis unit will suggest a route that avoids dangerous areas. This makes it possible to provide the optimal route and scenery according to the climber's emotions.

[0055] The analysis unit analyzes a climber's past climbing history and can customize the optimal Street View for each individual climber. For example, the generation AI analyzes a climber's past climbing history and provides the optimal Street View for each individual climber. For example, it suggests mountains with similar scenery based on information about mountains visited in the past. The analysis unit also customizes routes to suit each individual climber based on the climber's past climbing data. For example, it suggests new routes that reflect the characteristics of routes that were preferred in the past. The analysis unit also customizes the optimal rest points and scenery for each individual climber based on the climber's past climbing history. For example, it suggests routes that include scenic spots that have been visited in the past. This makes it possible to provide the optimal Street View based on the climber's past history.

[0056] The analysis unit can acquire the climber's location information in real time and dynamically update the Street View based on that location. For example, the analysis unit allows the generation AI to acquire the climber's location information in real time and dynamically update the Street View based on that location. For example, the analysis unit displays the latest scenery every time the climber moves. The analysis unit also allows the generation AI to display the rest points and scenery closest to the climber's current location based on the climber's location information. For example, it suggests the best place for a climber to take a break. The analysis unit also allows the generation AI to analyze the climber's location information and provide a Street View that reflects weather and seasonal changes based on that location. For example, it displays scenery that matches the weather at the current location. This allows the Street View to be updated in real time based on the climber's location.

[0057] The analysis unit can analyze the mountain guide's audio commentary and add the audio guide to Street View. For example, the generation AI in the analysis unit analyzes the mountain guide's audio commentary and adds the audio guide to Street View. For example, the audio commentary is automatically played when a specific point is reached. The analysis unit also causes the generation AI to display information related to Street View based on the mountain guide's audio data. For example, the analysis unit provides audio commentary on historical background and topographical features. The analysis unit also causes the generation AI to analyze the mountain guide's audio commentary and provide audio commentary according to the climber's location. For example, the audio commentary is played along a specific route. This allows the mountain guide's audio commentary to be added to Street View.

[0058] The analysis unit can analyze aerial footage taken by a drone and integrate it into Street View. For example, the generation AI in the analysis unit analyzes aerial footage taken by a drone and integrates it into Street View. For example, it can provide a panoramic view from the top of a mountain. The analysis unit also uses the generation AI to add advanced visual effects to Street View based on the drone footage. For example, it can use aerial footage to display three-dimensional terrain. The analysis unit also uses the generation AI to analyze drone footage and provide an aerial view based on the climber's location. For example, it can display footage from above the climber's current location. This allows aerial footage taken by a drone to be integrated into Street View.

[0059] The analysis unit can estimate the climber's emotions and suggest optimal rest points based on their emotions. For example, the generation AI in the analysis unit analyzes the climber's emotions in real time and suggests optimal rest points based on their emotions. For example, if the climber feels very tired, nearby rest points will be displayed. The analysis unit also allows the generation AI to prioritize suggesting rest points with beautiful scenery based on the climber's emotional data. For example, if the climber feels strong emotions of emotion or joy, the generation AI will suggest rest spots that include scenic spots. The analysis unit also allows the generation AI to analyze the climber's emotions and suggest safe rest points based on their emotions. For example, if the climber feels strong emotions of anxiety or fear, the generation AI will suggest places where they can rest in peace. This makes it possible to provide optimal rest points according to the climber's emotions.

[0060] The analysis unit can analyze past weather data and predict future weather changes to reflect them in Street View. For example, the analysis unit allows the generation AI to analyze past weather data and develop an algorithm to predict future weather changes. For example, the generation AI learns weather patterns based on past data and predicts future weather. The analysis unit also allows the generation AI to reflect weather changes in Street View based on future weather forecasts. For example, the landscape is dynamically updated according to the predicted weather. The analysis unit also allows the generation AI to predict future weather changes in real time based on past weather data and reflect them in Street View. For example, this allows hikers to check future weather conditions at their current location. This allows future weather changes to be predicted and reflected in Street View.

[0061] The analysis unit can analyze seasonal plant and animal ecological information and display it on Street View. For example, the generation AI in the analysis unit analyzes seasonal plant and animal ecological information and displays it on Street View. For example, it displays places where flowers bloom in spring and places where autumn leaves can be seen in autumn. The analysis unit also allows the generation AI to add information about plants and animals to Street View based on seasonal ecological information. For example, it displays the habitats of animals that can be seen in specific seasons. The analysis unit also allows the generation AI to analyze seasonal plant and animal ecological information and provide information according to the hiker's location. For example, it displays seasonal scenery around the current location. This allows seasonal plant and animal ecological information to be displayed on Street View.

[0062] The analysis unit can estimate the climber's emotions and provide advice about weather changes based on those emotions. In the analysis unit, for example, the generation AI analyzes the climber's emotions in real time and provides advice about weather changes based on those emotions. For example, if there is a strong sense of anxiety, the generation AI will warn the climber about weather changes. The analysis unit also provides specific advice about weather changes based on the climber's emotional data. For example, if the climber is calm, the generation AI will suggest measures to take in response to weather changes. The analysis unit also analyzes the climber's emotions and provides safety measures against weather changes based on those emotions. For example, if there is a strong sense of fear, the generation AI will suggest a safe evacuation site. In this way, advice about weather changes can be provided based on the climber's emotions.

[0063] The analysis unit can compare street views from different seasons and suggest the best time for climbers to climb. For example, the generation AI compares street views from different seasons and suggests the best time for climbers to climb. For example, suggestions are made based on seasonal attractions such as cherry blossom viewing in spring and autumn leaves. The analysis unit also uses weather data for each season to suggest the best time for climbers to climb. For example, it prioritizes suggestions for times with stable weather. The analysis unit also uses street views from different seasons to suggest a time for climbers to climb based on the climbers' preferences. For example, it suggests times that avoid summer for climbers who prefer cooler seasons. This allows the generation AI to compare street views from different seasons and suggest the best time for climbers to climb.

[0064] The analysis unit can analyze the comments of mountain weather forecasters and add weather commentary to Street View. For example, the generation AI of the analysis unit analyzes the comments of mountain weather forecasters and adds weather commentary to Street View. For example, a weather commentary is displayed when a specific point is reached. The analysis unit also causes the generation AI to display weather information related to Street View based on the mountain weather forecaster's comment data. For example, it explains points to be careful of regarding changes in the weather. The analysis unit also causes the generation AI to analyze the comments of mountain weather forecasters and provide weather commentary according to the climber's location. For example, a weather commentary is displayed along a specific route. This makes it possible to analyze the comments of mountain weather forecasters and add weather commentary.

[0065] The analysis unit can estimate the climber's emotions and suggest countermeasures for weather changes based on the emotions. In the analysis unit, for example, the generation AI analyzes the climber's emotions in real time and suggests countermeasures for weather changes based on the emotions. For example, if there is a strong sense of anxiety, the generation AI suggests specific countermeasures for weather changes. In addition, the analysis unit uses the generation AI to suggest safety countermeasures for weather changes based on the climber's emotional data. For example, if there is a strong sense of fear, the generation AI suggests a safe evacuation site. In addition, the analysis unit uses the generation AI to analyze the climber's emotions and suggest equipment for weather changes based on the emotions. For example, if the climber is calm, the necessary equipment is suggested. In this way, countermeasures for weather changes can be suggested based on the climber's emotions.

[0066] The providing unit can estimate the climber's emotions and customize the climbing plan based on the emotions. For example, the providing unit has an app that analyzes the climber's emotions in real time and customizes the climbing plan based on the emotions. For example, if the climber feels very tired, the app will suggest a plan that includes many rest points. The providing unit also has the app suggest the optimal route for each climber based on the climber's emotional data. For example, if the climber feels very moved or happy, the app will suggest a route that includes scenic spots. The providing unit also has the app analyze the climber's emotions and suggest a safe climbing plan based on the emotions. For example, if the climber feels very anxious or scared, the app will suggest a plan that avoids dangerous areas. In this way, the climbing plan can be customized based on the climber's emotions.

[0067] The provision unit collects feedback from climbers, and the generation AI analyzes that feedback to improve the app's functions. For example, the provision unit allows the app to collect feedback from climbers, and the generation AI analyzes that feedback to improve the app's functions. For example, new functions are added based on user opinions. The provision unit also allows the generation AI to improve the app's user interface based on climber feedback data. For example, it improves usability and visual design. The provision unit also analyzes the feedback collected by the app in real time, and the generation AI immediately makes improvement suggestions. For example, it proposes specific improvement measures to resolve user dissatisfaction. This allows the app's functions to be improved based on climber feedback.

[0068] The provision unit collects the climber's health data, and the generation AI analyzes that data to provide advice based on the climber's health condition. For example, the provision unit uses an app to collect the climber's health data, such as heart rate and number of steps, and the generation AI analyzes that data to provide advice based on the climber's health condition. For example, if the heart rate is high, the provision unit suggests taking a break. The provision unit also uses the climber's health data to have the generation AI suggest the optimal amount of exercise and rest times for each climber. For example, it suggests an appropriate pace based on past data. The provision unit also analyzes the health data collected by the app in real time, and the generation AI instantly provides advice based on the climber's health condition. For example, if the climber feels very tired, the generation AI suggests drinking more water. This makes it possible to provide advice based on the climber's health data and according to the climber's health condition.

[0069] The providing unit can estimate the climber's emotions and provide entertainment content during the climb based on the emotions. For example, the providing unit allows an app to analyze the climber's emotions in real time and provide entertainment content based on the emotions. For example, if the climber feels very tired, the providing unit plays relaxing music. The providing unit also allows the app to suggest entertainment content that is optimal for each climber based on the climber's emotional data. For example, if the climber feels very moved or happy, the providing unit plays a video of a scenic spot. The providing unit also allows the app to analyze the climber's emotions and customize the entertainment content based on the emotions. For example, if the climber feels very anxious or scared, the providing unit provides content that gives a sense of security. In this way, entertainment content can be provided based on the climber's emotions.

[0070] The provision unit collects climber's equipment information, and the generation AI analyzes that information to propose the optimal equipment list. For example, the provision unit's app collects climber's equipment information, and the generation AI analyzes that information to propose the optimal equipment list. For example, it proposes equipment according to the climber's experience and destination. The provision unit also customizes the optimal equipment list for each climber using the generation AI based on the climber's equipment data. For example, it suggests necessary items based on past equipment history. The provision unit also analyzes the equipment information collected by the app in real time, and the generation AI instantly provides the optimal equipment list. For example, it suggests equipment according to the weather and season. This makes it possible to propose the optimal equipment list based on the climber's equipment information.

[0071] The information providing unit can estimate the climber's emotions and suggest post-climbing recovery methods based on the emotions. For example, the information providing unit has an app that analyzes the climber's emotions in real time and suggests post-climbing recovery methods based on the emotions. For example, if the climber feels very tired, it suggests stretching or massage. The information providing unit also has an app that suggests the optimal recovery method for each individual climber based on the climber's emotional data. For example, if the climber feels very moved or happy, it suggests a relaxing activity. The information providing unit also has an app that analyzes the climber's emotions and customizes the recovery method based on the emotions. For example, if the climber feels very anxious or scared, it suggests a mental care method. In this way, it is possible to suggest post-climbing recovery methods based on the climber's emotions.

[0072] The provision unit can estimate the climber's emotions and propose the optimal insurance plan based on those emotions. In the provision unit, for example, the generation AI analyzes the climber's emotions in real time and proposes the optimal insurance plan based on those emotions. For example, if there is a strong sense of anxiety, an insurance plan with a wide range of coverage is proposed. In addition, the provision unit customizes the optimal insurance plan for each climber based on the climber's emotional data using the generation AI. For example, if there are strong feelings of emotion or joy, an insurance plan with special benefits is proposed. In addition, the provision unit analyzes the climber's emotions using the generation AI and provides insurance plan options based on those emotions. For example, if there is a strong sense of fear, a plan with comprehensive emergency support is proposed. In this way, the optimal insurance plan can be proposed based on the climber's emotions.

[0073] The provision unit can analyze a climber's past insurance usage history and customize the optimal insurance plan for each individual climber. In the provision unit, for example, the generation AI analyzes a climber's past insurance usage history and provides the optimal insurance plan for each individual climber. For example, it proposes the necessary coverage based on the past usage history. In addition, the provision unit customizes an insurance plan to suit each individual climber using the generation AI based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. In addition, the provision unit analyzes a climber's past insurance usage history and provides the optimal insurance plan. For example, it proposes the optimal plan based on past usage frequency and compensation details. This makes it possible to customize the optimal insurance plan based on the climber's past insurance usage history.

[0074] The provision unit can analyze the climber's travel plan and optimize the partnership plan with the travel agency. In the provision unit, for example, the generation AI analyzes the climber's travel plan and optimizes the partnership plan with the travel agency. For example, it proposes the optimal tour plan based on the travel destination and dates. In addition, the provision unit customizes the partnership plan with the travel agency based on the climber's travel plan data by the generation AI. For example, it proposes a plan based on the travel purpose and budget. In addition, the provision unit analyzes the climber's travel plan and optimizes the partnership plan with the travel agency. For example, it proposes a plan with benefits based on the travel destination and season. In this way, the partnership plan with the travel agency can be optimized based on the climber's travel plan.

[0075] The provision unit can estimate the climber's emotions and customize the travel agency's tour plan based on the emotions. In the provision unit, for example, the generation AI analyzes the climber's emotions in real time and customizes the travel agency's tour plan based on the emotions. For example, if the climber feels strong emotions of excitement or joy, the generation AI will suggest a tour that includes scenic spots. In addition, in the provision unit, the generation AI will suggest the optimal tour plan for each climber based on the climber's emotional data. For example, if the climber feels strong anxiety, the generation AI will suggest a tour that emphasizes safety. In addition, in the provision unit, the generation AI will analyze the climber's emotions and customize the travel agency's tour plan based on the emotions. For example, if the climber feels strong fatigue, the generation AI will suggest a tour that includes many rest points. In this way, the travel agency's tour plan can be customized based on the climber's emotions.

[0076] The provision unit can analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. In the provision unit, for example, the generation AI analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the necessary coverage based on the past usage history. In addition, the provision unit has the generation AI customize the partnership plan with the insurance company based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accident and trouble history. In addition, the provision unit has the generation AI analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. For example, it proposes the optimal plan based on past usage frequency and compensation content. In this way, the partnership plan with the insurance company can be optimized based on the climber's insurance usage history.

[0077] The provision unit can estimate the climber's emotions and propose travel agency reward plans based on the emotions. In the provision unit, for example, the generation AI analyzes the climber's emotions in real time and proposes travel agency reward plans based on the emotions. For example, if the climber feels strong emotions of excitement or joy, a tour with rewards is proposed. In addition, the provision unit uses the generation AI to propose reward plans that are optimal for each climber based on the climber's emotional data. For example, if the climber feels strong anxiety, a reward plan that emphasizes safety is proposed. In addition, the provision unit uses the generation AI to analyze the climber's emotions and propose travel agency reward plans based on the emotions. For example, if the climber feels strong fatigue, a plan that includes rewards for relaxation is proposed. In this way, travel agency reward plans can be proposed based on the climber's emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The provider collects the climber's health data, and the generation AI analyzes that data to provide advice based on their health condition. For example, if their heart rate is high, it will suggest taking a break. The provider also uses the climber's health data to allow the generation AI to suggest the optimal amount of exercise and rest times for each individual climber. For example, it will suggest an appropriate pace based on past data. The provider also analyzes the health data collected by the app in real time, and the generation AI instantly provides advice based on their health condition. For example, if they feel very tired, it will suggest they drink more water. This makes it possible to provide advice based on the climber's health data and their health condition.

[0080] The provision unit collects climber equipment information, and the generation AI analyzes that information to suggest the optimal equipment list. For example, it suggests equipment that suits the climber's experience and destination. The provision unit also uses the climber's equipment data to allow the generation AI to customize the optimal equipment list for each climber. For example, it suggests necessary items based on past equipment history. The provision unit also analyzes the equipment information collected by the app in real time, and the generation AI instantly provides the optimal equipment list. For example, it suggests equipment according to the weather and season. This makes it possible to suggest the optimal equipment list based on the climber's equipment information.

[0081] The providing unit can estimate the climber's emotions and provide entertainment content during the climb based on the emotions. For example, the app analyzes the climber's emotions in real time and provides entertainment content based on the emotions. For example, if the climber feels very tired, it can play relaxing music. The providing unit also suggests entertainment content that is optimal for each climber based on the climber's emotional data. For example, if the climber feels very moved or happy, it can play a video of a scenic spot. The providing unit also customizes the entertainment content based on the climber's emotions. For example, if the climber feels very anxious or scared, it can provide content that gives a sense of security. In this way, entertainment content can be provided based on the climber's emotions.

[0082] The information providing unit can estimate the climber's emotions and suggest post-climbing recovery methods based on those emotions. For example, the app analyzes the climber's emotions in real time and suggests post-climbing recovery methods based on those emotions. For example, if the climber feels very tired, it would suggest stretching or massage. The information providing unit also uses the climber's emotional data to have the app suggest the optimal recovery method for each individual climber. For example, if the climber feels very moved or happy, it would suggest a relaxing activity. The information providing unit also analyzes the climber's emotions and customizes the recovery method based on those emotions. For example, if the climber feels very anxious or scared, it would suggest a mental care method. In this way, it is possible to suggest post-climbing recovery methods based on the climber's emotions.

[0083] The provision unit can estimate the climber's emotions and propose the optimal insurance plan based on those emotions. For example, the generation AI analyzes the climber's emotions in real time and proposes the optimal insurance plan based on those emotions. For example, if there is a strong sense of anxiety, an insurance plan with a wide range of coverage is proposed. The provision unit also customizes the optimal insurance plan for each climber based on the climber's emotional data. For example, if there are strong feelings of emotion or joy, an insurance plan with special benefits is proposed. The provision unit also analyzes the climber's emotions and provides insurance plan options based on those emotions. For example, if there is a strong sense of fear, a plan with comprehensive emergency support is proposed. This makes it possible to propose the optimal insurance plan based on the climber's emotions.

[0084] The provision unit can analyze a climber's past insurance usage history and customize the optimal insurance plan for each individual climber. For example, the generation AI analyzes a climber's past insurance usage history and provides the optimal insurance plan for each individual climber. For example, it proposes the necessary coverage based on the past usage history. The provision unit also customizes an insurance plan to suit each individual climber based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. The provision unit also analyzes a climber's past insurance usage history and provides the optimal insurance plan. For example, it proposes the optimal plan based on past usage frequency and coverage. This makes it possible to customize the optimal insurance plan based on the climber's past insurance usage history.

[0085] The provision unit can analyze the climber's travel plans and optimize partnership plans with travel agencies. For example, the generation AI analyzes the climber's travel plans and optimizes partnership plans with travel agencies. For example, it proposes the optimal tour plan based on the travel destination and dates. The provision unit also customizes partnership plans with travel agencies based on the climber's travel plan data by the generation AI. For example, it proposes plans based on the travel purpose and budget. The provision unit also analyzes the climber's travel plans and optimizes partnership plans with travel agencies. For example, it proposes plans with benefits based on the travel destination and season. This makes it possible to optimize partnership plans with travel agencies based on the climber's travel plans.

[0086] The provision unit can estimate the climber's emotions and customize the travel agency's tour plan based on the emotions. For example, the generation AI analyzes the climber's emotions in real time and customizes the travel agency's tour plan based on the emotions. For example, if the climber feels strong emotions of excitement or joy, the generation AI will suggest a tour that includes scenic spots. The provision unit also uses the climber's emotional data to suggest the optimal tour plan for each climber. For example, if the climber feels strong anxiety, the generation AI will suggest a tour that emphasizes safety. The provision unit also uses the generation AI to analyze the climber's emotions and customize the travel agency's tour plan based on the emotions. For example, if the climber feels strong fatigue, the generation AI will suggest a tour that includes many rest points. This allows the travel agency's tour plan to be customized based on the climber's emotions.

[0087] The provision unit can analyze the climber's insurance usage history and optimize the partnership plan with the insurance company. For example, the generation AI analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the necessary coverage based on past usage history. The provision unit also customizes the partnership plan with the insurance company based on the climber's insurance usage data. For example, it proposes a plan that takes into account past accidents and trouble history. The provision unit also analyzes the climber's insurance usage history and optimizes the partnership plan with the insurance company. For example, it proposes the optimal plan based on past usage frequency and compensation content. This makes it possible to optimize the partnership plan with the insurance company based on the climber's insurance usage history.

[0088] The provision unit can estimate the climber's emotions and suggest travel agency reward plans based on the emotions. For example, the generation AI analyzes the climber's emotions in real time and suggests travel agency reward plans based on the emotions. For example, if the climber feels strong emotions of excitement or joy, a tour with rewards is suggested. The provision unit also uses the generation AI to suggest reward plans that are optimal for each individual climber based on the climber's emotional data. For example, if the climber feels strong anxiety, a reward plan that emphasizes safety is suggested. The provision unit also uses the generation AI to analyze the climber's emotions and suggest travel agency reward plans based on the emotions. For example, if the climber feels strong fatigue, a plan that includes rewards for relaxation is suggested. In this way, travel agency reward plans can be suggested based on the climber's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The image collection unit collects images from climbers and blogs. For example, it can automatically collect images taken by climbers using an API. It can also collect images manually from blogs. Furthermore, the image collection unit provides a function for climbers to upload images taken through the app. For example, climbers can upload images taken using the app to the cloud, and the image collection unit collects them. Step 2: The analysis unit analyzes the collected images. For example, it uses an image recognition algorithm to extract features within the images. It can also use a machine learning model to classify the content of the images. The analysis unit then evaluates the quality of the images and selects images suitable for Street View generation. For example, the analysis unit evaluates the resolution and brightness of the images to select suitable images. Step 3: The street view generation unit generates a street view based on the analyzed images. For example, it can use panoramic image synthesis technology to integrate multiple images into a single street view. It can also use 3D modeling technology to generate a three-dimensional street view. The street view generation unit also provides a function to update the generated street view in real time. For example, it can dynamically update the street view based on the latest image data. Step 4: The provider provides the generated Street View to the climber in real time. For example, streaming technology can be used to deliver the Street View with low latency. It is also possible to allow the climber to view the Street View through an app. Furthermore, the provider provides a function to provide the optimal Street View based on the climber's location information. For example, it displays the Street View closest to the climber's current location.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[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 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.

[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 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).

[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] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, 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 robot 414 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 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 image collection department that collects images from climbers and blogs, an analysis unit that analyzes the images collected by the image collection unit; a street view generation unit that generates a street view based on the image analyzed by the analysis unit; a providing unit that provides the street view generated by the street view generating unit to climbers in real time. A system characterized by:

2. The analysis unit Estimate the climber's emotions and suggest the best route and scenery based on those emotions.

2. The system of claim 1.

3. The analysis unit Analyzing the climber's past climbing history and customizing the Street View that is best suited to each individual climber 2. The system of claim 1.

4. The analysis unit The location information of the climber is acquired in real time, and the street view is dynamically updated based on the location.

2. The system of claim 1.

5. The analysis unit Analyze the audio commentary of mountain guides and add the audio guide to the Street View.

2. The system of claim 1.

Images