system

Abstract

The system according to the embodiment aims to identify an accurate location in the event of an accident in a mountainous area. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, an identification unit, and a connection unit. The photographing unit photographs images of the surrounding area. The analysis unit analyzes the images photographed by the photographing unit. The identification unit identifies the location based on the results of the analysis by the analysis unit. The connection unit ensures an internet connection.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to pinpoint an accurate location in the event of a disaster in mountainous areas.

[0005] The system according to the embodiment aims to identify an accurate location in the event of an accident in a mountainous area. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, an identification unit, and a connection unit. The photographing unit photographs images of the surrounding area. The analysis unit analyzes the images photographed by the photographing unit. The identification unit identifies the location based on the results of the analysis by the analysis unit. The connection unit ensures an internet connection. [Effects of the Invention]

[0007] The system according to the embodiment can pinpoint an accurate location in the event of a disaster in a mountainous area. [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 mountain climber distress prevention system according to an embodiment of the present invention accurately identifies a climber's location by capturing multiple images of the surrounding area and analyzing them using a generation AI in addition to GPS location information. This system quickly and accurately identifies the climber's location when a climber becomes distressed, supporting rescue efforts. For example, when a climber uses an app to take a photo of the surrounding area, the generation AI analyzes the image and determines the climber's current location. This analysis uses technology that recognizes distinctive topography and landmarks in mountainous areas. For example, the shape of a specific mountain or the arrangement of rocks can be analyzed to determine the climber's current location. Furthermore, internet connectivity in mountainous areas is essential for utilizing this function. Therefore, satellite communication systems and high-altitude platform systems are utilized to provide stable internet connections, even in mountainous areas. This allows climbers to access the app at all times, helping to prevent distress. This app not only ensures the safety of climbers but also contributes to the promotion of mobile brand recognition. Furthermore, it contributes to the realization of the NTN concept and promotes the widespread use of internet connectivity in mountainous areas. This allows the mountain climber distress prevention system to accurately identify the climber's location and support rapid rescue efforts. In addition, by ensuring internet connectivity in mountainous areas, the app will always be available, helping to prevent people from getting lost.

[0029] A climber distress prevention system according to an embodiment includes a camera unit, an analysis unit, an identification unit, and a connection unit. The camera unit captures images of the surrounding area. For example, the camera unit allows a climber to capture images of their surroundings using an app. The camera unit captures images using, for example, a smartphone camera. The camera unit can also automatically capture images from multiple different angles. For example, images can be automatically captured from four directions: front, rear, left, and right. The analysis unit analyzes the images captured by the camera unit using a generation AI. For example, the analysis unit uses terrain recognition technology using deep learning. The generation AI analyzes, for example, the shape of a specific mountain or the arrangement of rocks to determine the current location. The identification unit determines the location based on the results of the analysis by the analysis unit. For example, the identification unit determines the location by combining GPS data and image analysis results. The connection unit secures an internet connection. For example, the connection unit secures an internet connection using a satellite communication system. The connection unit can also secure an internet connection using a high-altitude platform system. As a result, the mountain climber distress prevention system according to the embodiment accurately identifies the location of the climber and contributes to preventing distress. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit may ensure an internet connection using an AI model that monitors the status of the internet connection and selects the optimal connection method.

[0030] The analysis unit can use terrain recognition technology using deep learning. Deep learning includes, but is not limited to, for example, a convolutional neural network (CNN) or a recurrent neural network (RNN). The analysis unit, for example, uses a CNN to extract image features and perform terrain recognition. The analysis unit can also analyze continuous image data using an RNN to recognize changes in terrain. For example, the analysis unit can use a CNN to recognize the shape of a specific mountain or the arrangement of rocks and identify the current location. The analysis unit can also analyze continuous image data using an RNN to identify the route a climber has taken. This improves the accuracy of terrain recognition and makes location identification more accurate. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data to a generation AI and output terrain recognition results.

[0031] The connection unit can ensure an internet connection using a satellite communication system. Examples of satellite communication systems include, but are not limited to, low-earth orbit satellites, medium-earth orbit satellites, and geostationary satellites. The connection unit can ensure high-speed, low-latency communication using, for example, low-earth orbit satellites. The connection unit can also ensure wide-area communication using medium-earth orbit satellites. The connection unit can also ensure stable communication using geostationary satellites. For example, the connection unit can ensure internet connection in mountainous areas using low-earth orbit satellites, allowing climbers to always use apps. The connection unit can provide wide-area internet connection using medium-earth orbit satellites, allowing climbers to ensure internet connection no matter where they are. The connection unit can provide stable communication using geostationary satellites, allowing climbers to maintain internet connection for long periods of time. This enables stable internet connection even in mountainous areas. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without AI. For example, the connection unit can ensure internet connection using an AI model that monitors the status of the satellite communication system and selects the optimal communication method.

[0032] The connection unit may secure an internet connection using a high-altitude platform system. Examples of high-altitude platform systems include, but are not limited to, balloons, drones, and airships. For example, the connection unit may use a balloon to provide wide-area communications from high altitudes. The connection unit may also use a drone to provide communications in a specific area. The connection unit may also use an airship to provide stable communications over a long period of time. For example, the connection unit may use a balloon to ensure internet connectivity in mountainous areas, allowing climbers to use apps at all times. The connection unit may use a drone to provide internet connectivity in a specific area, allowing climbers to maintain their internet connection even while traveling. The connection unit may use an airship to provide stable communications over a long period of time, allowing climbers to maintain their internet connection over a long period of time. This enables stable internet connectivity even in mountainous areas. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without AI. For example, the connection unit may secure internet connectivity using an AI model that monitors the status of the high-altitude platform system and selects the optimal communication method.

[0033] The system may include a providing unit that provides the analysis results to the user. The providing unit provides the analysis results to the user. For example, the providing unit can display the analysis results in text format. The providing unit can also display the analysis results in graph format. For example, the providing unit displays the analysis results in text format, allowing the user to check their current location. The providing unit can also display the analysis results in graph format, allowing the user to visually understand the characteristics of the terrain. The providing unit can also provide the analysis results by voice. For example, the providing unit provides the analysis results by voice, allowing the user to check their current location without relying on vision. This allows the user to check the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and provide them to the user in a format optimal for the user.

[0034] The device may include a monitoring unit that monitors the status of the internet connection. The monitoring unit monitors the status of the internet connection. For example, the monitoring unit can monitor the connection speed. The monitoring unit can also monitor the stability of the connection. For example, the monitoring unit monitors the connection speed in real time to understand the status of the internet connection. The monitoring unit can also monitor the stability of the connection to ensure that the internet connection is not interrupted. The monitoring unit can also record the status of the internet connection and refer to past data. For example, the monitoring unit can record fluctuations in the connection speed and improve the stability of the connection based on the past data. This allows the status of the internet connection to be constantly monitored. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can monitor the status of the internet connection and ensure the internet connection using an AI model that selects the optimal connection method.

[0035] The camera unit can simultaneously record surrounding environmental sounds when taking a photo and use them for analysis. The camera unit, for example, records environmental sounds such as the sound of wind and birdsong when taking a photo. For example, the camera unit records the sound of wind and uses it for analysis. The camera unit can also record the sound of birdsong and use it for analysis. The camera unit can also record the voices of surrounding people and use it for analysis. For example, the camera unit can record the voices of surrounding people and use it for analysis. The camera unit can also record the sound of a flowing river or a waterfall and use it for analysis. For example, the camera unit can record the sound of a flowing river and use it for analysis. The camera unit can also record the sound of a waterfall and use it for analysis. By using environmental sounds in the analysis, the accuracy of the analysis is improved. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the recorded environmental sound data to a generation AI and have the generation AI analyze the environmental sounds.

[0036] The imaging unit can be added with a function to automatically capture images from multiple different angles during imaging. The imaging unit automatically captures images from four directions, for example, the front, rear, left, and right. For example, the imaging unit captures a front image and then a rear image. The imaging unit can also capture left and right images. The imaging unit can also automatically capture an overhead image from above. For example, the imaging unit captures an overhead image from above and uses it for analysis. The imaging unit can also automatically capture images from an oblique direction. For example, the imaging unit captures images from an oblique direction and uses it for analysis. This improves the accuracy of the analysis by obtaining images from multiple angles. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input image data captured from multiple angles to a generation AI and have the generation AI analyze the images.

[0037] The photographing unit can suggest the optimal photographing spot based on the user's location information when photographing. For example, when the user is on a mountaintop, the photographing unit suggests a spot to photograph the surrounding scenery. For example, when the user is on a mountaintop, the photographing unit suggests a spot to photograph the surrounding scenery. Furthermore, when the user is along a river, the photographing unit can also suggest a spot to photograph a beautiful scenery. For example, when the user is along a river, the photographing unit suggests a spot to photograph a beautiful scenery. Furthermore, when the user is in a forest, the photographing unit can also suggest a spot to photograph a place where light shines in. For example, when the user is in a forest, the photographing unit suggests a spot to photograph a place where light shines in. In this way, the optimal photographing spot can be suggested based on the user's location information. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's location information to the generation AI and cause the generation AI to suggest the optimal photographing spot.

[0038] The photographing unit can set optimal photographing conditions by taking weather information into consideration when photographing. For example, the photographing unit uses a waterproof camera when it is raining. For example, the photographing unit uses a waterproof camera when it is raining. The photographing unit can also set photographing conditions to avoid backlighting when it is sunny. For example, the photographing unit sets photographing conditions to avoid backlighting when it is sunny. The photographing unit can also set photographing conditions to prevent overexposure when it is snowy. For example, the photographing unit sets photographing conditions to prevent overexposure when it is snowy. In this way, optimal photographing conditions can be set by taking weather information into consideration. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input weather information to the generation AI and cause the generation AI to set optimal photographing conditions.

[0039] The analysis unit can improve the analysis accuracy by referring to past analysis data during analysis. The analysis unit, for example, improves the recognition accuracy of specific terrain or landmarks based on past analysis data. For example, the analysis unit improves the recognition accuracy of specific terrain or landmarks based on past analysis data. The analysis unit can also improve the analysis accuracy in different seasons or weather conditions based on past analysis data. For example, the analysis unit improves the analysis accuracy in different seasons or weather conditions based on past analysis data. The analysis unit can also improve the analysis accuracy in different time periods based on past analysis data. For example, the analysis unit improves the analysis accuracy in different time periods based on past analysis data. In this way, the analysis accuracy is improved by referring to past data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0040] The analysis unit can improve analysis accuracy by combining different analysis algorithms during analysis. The analysis unit can improve analysis accuracy by, for example, combining deep learning with conventional image analysis algorithms. For example, the analysis unit extracts image features using deep learning and performs detailed analysis using conventional image analysis algorithms. The analysis unit can also improve analysis accuracy by combining different feature extraction algorithms. For example, the analysis unit combines different feature extraction algorithms to extract more features and improve analysis accuracy. The analysis unit can also improve analysis accuracy by combining different machine learning models. For example, the analysis unit combines different machine learning models to utilize the strengths of each model and improve analysis accuracy. In this way, combining different algorithms improves analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input different analysis algorithms into the generation AI and have the generation AI execute the improvement in analysis accuracy.

[0041] The analysis unit can improve the accuracy of the analysis by also using surrounding environmental data during analysis. For example, the analysis unit can improve the analysis accuracy of a specific terrain by also using surrounding temperature data. For example, the analysis unit can improve the analysis accuracy of a specific terrain by also using surrounding temperature data. The analysis unit can also improve the analysis accuracy of a specific plant by also using surrounding humidity data. For example, the analysis unit can improve the analysis accuracy of a specific plant by also using surrounding humidity data. The analysis unit can also improve the analysis accuracy under specific weather conditions by also using surrounding atmospheric pressure data. For example, the analysis unit can improve the analysis accuracy under specific weather conditions by also using surrounding atmospheric pressure data. In this way, the analysis accuracy is improved by using environmental data in combination. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input surrounding environmental data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0042] The analysis unit can improve the analysis accuracy by comparing the captured data with other users' captured data during analysis. The analysis unit can improve the analysis accuracy by, for example, comparing the captured data with images of the same location taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of the same location taken by other users. The analysis unit can also improve the analysis accuracy by comparing the captured data with images of different seasons taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of different seasons taken by other users. The analysis unit can also improve the analysis accuracy by comparing the captured data with images of different time periods taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of different time periods taken by other users. In this way, the analysis accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the captured data of other users into the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] The identification unit can improve the identification accuracy by referring to past identification data during identification. The identification unit, for example, improves the recognition accuracy of specific terrain or landmarks based on past identification data. For example, the identification unit improves the recognition accuracy of specific terrain or landmarks based on past identification data. The identification unit can also improve the identification accuracy under different seasons or weather conditions based on past identification data. For example, the identification unit improves the identification accuracy under different seasons or weather conditions based on past identification data. The identification unit can also improve the identification accuracy under different time periods based on past identification data. For example, the identification unit improves the identification accuracy under different time periods based on past identification data. In this way, the identification accuracy is improved by referring to past data. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input past identification data into the generation AI and cause the generation AI to improve the identification accuracy.

[0044] The identification unit can improve identification accuracy by combining different identification algorithms during identification. The identification unit can improve identification accuracy by, for example, combining deep learning and conventional image analysis algorithms. For example, the identification unit extracts image features using deep learning and performs detailed analysis using conventional image analysis algorithms. The identification unit can also improve identification accuracy by combining different feature extraction algorithms. For example, the identification unit combines different feature extraction algorithms to extract more features and improve identification accuracy. The identification unit can also improve identification accuracy by combining different machine learning models. For example, the identification unit combines different machine learning models to utilize the strengths of each model and improve identification accuracy. In this way, the identification accuracy is improved by combining different algorithms. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input different identification algorithms into the generation AI and cause the generation AI to improve identification accuracy.

[0045] The identification unit can improve the identification accuracy by also using surrounding environmental data during identification. The identification unit can, for example, also use ambient temperature data to improve the identification accuracy of a specific terrain. For example, the identification unit can also use ambient temperature data to improve the identification accuracy of a specific terrain. The identification unit can also improve the identification accuracy of a specific plant by also using ambient humidity data. For example, the identification unit can also improve the identification accuracy of a specific plant by also using ambient humidity data. The identification unit can also improve the identification accuracy under specific weather conditions by also using ambient air pressure data. For example, the identification unit can also improve the identification accuracy under specific weather conditions by also using ambient air pressure data. In this way, the identification accuracy is improved by also using environmental data. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the identification unit can input surrounding environmental data into the generation AI and cause the generation AI to improve the identification accuracy.

[0046] The identification unit can improve the identification accuracy by comparing the data with the identification data of other users during identification. For example, the identification unit improves the identification accuracy by comparing the data with data of the same location identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of the same location identified by other users. The identification unit can also improve the identification accuracy by comparing the data with data of different seasons identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of different seasons identified by other users. The identification unit can also improve the identification accuracy by comparing the data with data of different time periods identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of different time periods identified by other users. In this way, the identification accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the identification data of other users into the generation AI and cause the generation AI to improve the identification accuracy.

[0047] The connection unit can improve connection stability by referring to past connection data when connecting. The connection unit, for example, improves connection stability in a specific location based on the past connection data. For example, the connection unit improves connection stability in a specific location based on the past connection data. The connection unit can also improve connection stability under different weather conditions based on the past connection data. For example, the connection unit improves connection stability under different weather conditions based on the past connection data. The connection unit can also improve connection stability in different time periods based on the past connection data. For example, the connection unit improves connection stability in different time periods based on the past connection data. In this way, connection stability is improved by referring to past data. Some or all of the above-described processing in the connection unit may be performed using, or without, a generation AI. For example, the connection unit may input past connection data into the generation AI and cause the generation AI to improve connection stability.

[0048] The connection unit can improve connection stability by combining different connection means during connection. The connection unit, for example, combines satellite communication and terrestrial communication to improve connection stability. For example, the connection unit combines satellite communication and terrestrial communication to improve connection stability. The connection unit can also improve connection stability by combining a high-altitude platform system and terrestrial communication. For example, the connection unit combines a high-altitude platform system and terrestrial communication to improve connection stability. The connection unit can also improve connection stability by combining different communication protocols. For example, the connection unit combines different communication protocols to improve connection stability. As a result, connection stability is improved by combining different connection means. Some or all of the above-mentioned processing in the connection unit may be performed using, or without, the generation AI. For example, the connection unit can input different connection means into the generation AI and cause the generation AI to improve connection stability.

[0049] The connection unit can improve connection stability by also using surrounding environmental data when connecting. The connection unit, for example, also uses surrounding weather data to select the optimal connection means. For example, the connection unit also uses surrounding weather data to select the optimal connection means. The connection unit can also improve connection stability by also using surrounding terrain data. For example, the connection unit also uses surrounding terrain data to improve connection stability. The connection unit can also select the optimal connection means by also using surrounding radio wave conditions. For example, the connection unit selects the optimal connection means by also using surrounding radio wave conditions. In this way, connection stability is improved by also using environmental data. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can input surrounding environmental data to the generation AI and cause the generation AI to improve connection stability.

[0050] The connection unit can improve connection stability by comparing the connection data of other users at the time of connection. The connection unit, for example, selects the optimal connection means based on connection data used by other users in the same location. For example, the connection unit selects the optimal connection means based on connection data used by other users in the same location. The connection unit can also improve connection stability based on connection data used by other users under different weather conditions. For example, the connection unit improves connection stability based on connection data used by other users under different weather conditions. The connection unit can also improve connection stability based on connection data used by other users in different time periods. For example, the connection unit improves connection stability based on connection data used by other users in different time periods. In this way, connection stability is improved by comparing the data of other users. Some or all of the above-described processing in the connection unit may be performed using, or without, a generation AI. For example, the connection unit can input the connection data of other users into the generation AI and cause the generation AI to improve connection stability.

[0051] The providing unit can improve the accuracy of provision by referring to past provided data at the time of provision. The providing unit, for example, improves the accuracy of provision of specific information based on past provided data. For example, the providing unit improves the accuracy of provision of specific information based on past provided data. The providing unit can also improve the accuracy of provision in different seasons or weather conditions based on past provided data. For example, the providing unit improves the accuracy of provision in different seasons or weather conditions based on past provided data. The providing unit can also improve the accuracy of provision in different time periods based on past provided data. For example, the providing unit improves the accuracy of provision in different time periods based on past provided data. In this way, the accuracy of provision is improved by referring to past data. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input past provided data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0052] The providing unit can improve the accuracy of information provision by combining different provision algorithms during provision. The providing unit can improve the accuracy of information provision by, for example, combining deep learning with a conventional information provision algorithm. For example, the providing unit extracts information features using deep learning and performs detailed analysis using a conventional information provision algorithm. The providing unit can also improve the accuracy of information provision by combining different feature extraction algorithms. For example, the providing unit combines different feature extraction algorithms to extract more features and improve the accuracy of information provision. The providing unit can also improve the accuracy of information provision by combining different machine learning models. For example, the providing unit combines different machine learning models to utilize the strengths of each model and improve the accuracy of information provision. In this way, the accuracy of information provision is improved by combining different algorithms. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input different provision algorithms into the generation AI and cause the generation AI to improve the accuracy of information provision.

[0053] The providing unit can improve the accuracy of the information provided by also using surrounding environmental data when providing the information. The providing unit can, for example, also use surrounding weather data to provide optimal information. For example, the providing unit can also use surrounding weather data to provide optimal information. The providing unit can also improve the accuracy of the information provided by also using surrounding terrain data. For example, the providing unit can also use surrounding terrain data to improve the accuracy of the information provided. The providing unit can also provide optimal information by also using surrounding radio wave conditions. For example, the providing unit can also provide optimal information by also using surrounding radio wave conditions. In this way, the accuracy of the information provided is improved by also using environmental data. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input surrounding environmental data to the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0054] The providing unit can improve the accuracy of the information provided by comparing the data provided by other users with that provided by other users at the time of providing the information. The providing unit, for example, provides optimal information based on information provided by other users at the same location. For example, the providing unit provides optimal information based on information provided by other users at the same location. The providing unit can also improve the accuracy of the information provided by other users under different weather conditions. For example, the providing unit improves the accuracy of the information provided by other users under different weather conditions. The providing unit can also improve the accuracy of the information provided by other users at different time periods. For example, the providing unit improves the accuracy of the information provided by other users at different time periods. This improves the accuracy of the information provided by other users by comparing their data. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the data provided by other users into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0055] The monitoring unit can improve monitoring accuracy by referring to past monitoring data during monitoring. The monitoring unit, for example, improves monitoring accuracy at a specific location based on past monitoring data. For example, the monitoring unit improves monitoring accuracy at a specific location based on past monitoring data. The monitoring unit can also improve monitoring accuracy under different weather conditions based on past monitoring data. For example, the monitoring unit improves monitoring accuracy under different weather conditions based on past monitoring data. The monitoring unit can also improve monitoring accuracy at different time periods based on past monitoring data. For example, the monitoring unit improves monitoring accuracy at different time periods based on past monitoring data. In this way, monitoring accuracy is improved by referring to past data. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to improve monitoring accuracy.

[0056] The monitoring unit can improve monitoring accuracy by combining different monitoring algorithms during monitoring. The monitoring unit can improve monitoring accuracy by, for example, combining deep learning and conventional monitoring algorithms. For example, the monitoring unit can extract features of the monitored object using deep learning and perform detailed analysis using conventional monitoring algorithms. The monitoring unit can also improve monitoring accuracy by combining different feature extraction algorithms. For example, the monitoring unit can combine different feature extraction algorithms to extract more features and improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy by combining different machine learning models. For example, the monitoring unit can combine different machine learning models to utilize the strengths of each model to improve monitoring accuracy. In this way, combining different algorithms improves monitoring accuracy. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input different monitoring algorithms into the generation AI and have the generation AI improve monitoring accuracy.

[0057] The monitoring unit can improve monitoring accuracy by also using surrounding environmental data during monitoring. The monitoring unit, for example, also uses surrounding weather data to select the optimal monitoring means. For example, the monitoring unit also uses surrounding weather data to select the optimal monitoring means. The monitoring unit can also improve monitoring accuracy by also using surrounding terrain data. For example, the monitoring unit also uses surrounding terrain data to improve monitoring accuracy. The monitoring unit can also select the optimal monitoring means by also using surrounding radio wave conditions. For example, the monitoring unit selects the optimal monitoring means by also using surrounding radio wave conditions. In this way, the monitoring accuracy is improved by also using environmental data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input surrounding environmental data into the generation AI and cause the generation AI to improve monitoring accuracy.

[0058] The monitoring unit can improve monitoring accuracy by comparing the monitoring data of other users during monitoring. The monitoring unit, for example, selects the optimal monitoring means based on monitoring data used by other users at the same location. For example, the monitoring unit selects the optimal monitoring means based on monitoring data used by other users at the same location. The monitoring unit can also improve monitoring accuracy based on monitoring data used by other users under different weather conditions. For example, the monitoring unit improves monitoring accuracy based on monitoring data used by other users under different weather conditions. The monitoring unit can also improve monitoring accuracy based on monitoring data used by other users at different time periods. For example, the monitoring unit improves monitoring accuracy based on monitoring data used by other users at different time periods. In this way, monitoring accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the monitoring data of other users into the generation AI and cause the generation AI to improve monitoring accuracy.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] A climber distress prevention system can also be equipped with an automatic emergency notification function. For example, the system can automatically make an emergency call if the climber remains motionless for a certain period of time or if a sudden change in altitude is detected. The system can also monitor the climber's biometric data, such as heart rate and body temperature, and make an emergency call if it detects an abnormality. The system can also be equipped with a button that allows the climber to manually make an emergency call. This allows rescue to be requested quickly if the climber is distressed.

[0061] The connection unit can monitor the status of the internet connection and ensure internet connection using an AI model that selects the optimal connection method. For example, the connection unit can monitor the status of a satellite communication system and ensure internet connection using an AI model that selects the optimal communication method. The connection unit can also monitor the status of the internet connection in real time to prevent connection interruptions. Furthermore, the connection unit can improve connection stability in specific locations based on past connection data. This allows for stable internet connection even in mountainous areas.

[0062] The monitoring unit monitors the status of the internet connection and uses an AI model to select the optimal connection method to ensure internet connection. For example, the monitoring unit monitors the connection speed in real time to understand the status of the internet connection. The monitoring unit can also monitor the stability of the connection to prevent internet connection interruptions. Furthermore, the monitoring unit can record the status of the internet connection and improve the stability of the connection based on past data. This allows the status of the internet connection to be constantly monitored.

[0063] The analysis unit can improve the accuracy of analysis by referring to past analysis data during analysis. For example, the accuracy of recognizing specific terrain or landmarks can be improved based on past analysis data. It can also improve the accuracy of analysis under different seasons or weather conditions. It can also improve the accuracy of analysis under different time periods. In this way, by referring to past data, the accuracy of analysis can be improved.

[0064] The connection unit can improve connection stability by combining different connection means during connection. For example, connection stability can be improved by combining satellite communication and terrestrial communication. Connection stability can also be improved by combining a high altitude platform system and terrestrial communication. Connection stability can also be improved by combining different communication protocols. As a result, connection stability can be improved by combining different connection means.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The camera captures images of the surrounding area. For example, a climber can use the app to capture images of their surroundings. The camera can also capture images using a smartphone camera and automatically capture images from multiple different angles. For example, it can automatically capture images from four directions: front, back, left and right. Step 2: The analysis unit uses the generation AI to analyze the images captured by the photography unit. For example, it uses terrain recognition technology based on deep learning to analyze the shape of a specific mountain or the arrangement of rocks. Step 3: The identification unit identifies the location based on the results of the analysis by the analysis unit, for example, by combining GPS data and image analysis results. Step 4: The connection unit secures the internet connection. For example, the connection can be secured using a satellite communication system or a high altitude platform system. The connection unit can also secure the internet connection using an AI model that monitors the status of the internet connection and selects the optimal connection method.

[0067] (Example 2) The mountain climber distress prevention system according to an embodiment of the present invention accurately identifies a climber's location by capturing multiple images of the surrounding area and analyzing them using a generation AI in addition to GPS location information. This system quickly and accurately identifies the climber's location when a climber becomes distressed, supporting rescue efforts. For example, when a climber uses an app to take a photo of the surrounding area, the generation AI analyzes the image and determines the climber's current location. This analysis uses technology that recognizes distinctive topography and landmarks in mountainous areas. For example, the shape of a specific mountain or the arrangement of rocks can be analyzed to determine the climber's current location. Furthermore, internet connectivity in mountainous areas is essential for utilizing this function. Therefore, satellite communication systems and high-altitude platform systems are utilized to provide stable internet connections, even in mountainous areas. This allows climbers to access the app at all times, helping to prevent distress. This app not only ensures the safety of climbers but also contributes to the promotion of mobile brand recognition. Furthermore, it contributes to the realization of the NTN concept and promotes the widespread use of internet connectivity in mountainous areas. This allows the mountain climber distress prevention system to accurately identify the climber's location and support rapid rescue efforts. In addition, by ensuring internet connectivity in mountainous areas, the app will always be available, helping to prevent people from getting lost.

[0068] A climber distress prevention system according to an embodiment includes a camera unit, an analysis unit, an identification unit, and a connection unit. The camera unit captures images of the surrounding area. For example, the camera unit allows a climber to capture images of their surroundings using an app. The camera unit captures images using, for example, a smartphone camera. The camera unit can also automatically capture images from multiple different angles. For example, images can be automatically captured from four directions: front, rear, left, and right. The analysis unit analyzes the images captured by the camera unit using a generation AI. For example, the analysis unit uses terrain recognition technology using deep learning. The generation AI analyzes, for example, the shape of a specific mountain or the arrangement of rocks to determine the current location. The identification unit determines the location based on the results of the analysis by the analysis unit. For example, the identification unit determines the location by combining GPS data and image analysis results. The connection unit secures an internet connection. For example, the connection unit secures an internet connection using a satellite communication system. The connection unit can also secure an internet connection using a high-altitude platform system. As a result, the mountain climber distress prevention system according to the embodiment accurately identifies the location of the climber and contributes to preventing distress. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit may ensure an internet connection using an AI model that monitors the status of the internet connection and selects the optimal connection method.

[0069] The analysis unit can use terrain recognition technology using deep learning. Deep learning includes, but is not limited to, for example, a convolutional neural network (CNN) or a recurrent neural network (RNN). The analysis unit, for example, uses a CNN to extract image features and perform terrain recognition. The analysis unit can also analyze continuous image data using an RNN to recognize changes in terrain. For example, the analysis unit can use a CNN to recognize the shape of a specific mountain or the arrangement of rocks and identify the current location. The analysis unit can also analyze continuous image data using an RNN to identify the route a climber has taken. This improves the accuracy of terrain recognition and makes location identification more accurate. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data to a generation AI and output terrain recognition results.

[0070] The connection unit can ensure an internet connection using a satellite communication system. Examples of satellite communication systems include, but are not limited to, low-earth orbit satellites, medium-earth orbit satellites, and geostationary satellites. The connection unit can ensure high-speed, low-latency communication using, for example, low-earth orbit satellites. The connection unit can also ensure wide-area communication using medium-earth orbit satellites. The connection unit can also ensure stable communication using geostationary satellites. For example, the connection unit can ensure internet connection in mountainous areas using low-earth orbit satellites, allowing climbers to always use apps. The connection unit can provide wide-area internet connection using medium-earth orbit satellites, allowing climbers to ensure internet connection no matter where they are. The connection unit can provide stable communication using geostationary satellites, allowing climbers to maintain internet connection for long periods of time. This enables stable internet connection even in mountainous areas. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without AI. For example, the connection unit can ensure internet connection using an AI model that monitors the status of the satellite communication system and selects the optimal communication method.

[0071] The connection unit may secure an internet connection using a high-altitude platform system. Examples of high-altitude platform systems include, but are not limited to, balloons, drones, and airships. For example, the connection unit may use a balloon to provide wide-area communications from high altitudes. The connection unit may also use a drone to provide communications in a specific area. The connection unit may also use an airship to provide stable communications over a long period of time. For example, the connection unit may use a balloon to ensure internet connectivity in mountainous areas, allowing climbers to use apps at all times. The connection unit may use a drone to provide internet connectivity in a specific area, allowing climbers to maintain their internet connection even while traveling. The connection unit may use an airship to provide stable communications over a long period of time, allowing climbers to maintain their internet connection over a long period of time. This enables stable internet connectivity even in mountainous areas. Some or all of the above-described processing in the connection unit may be performed using, for example, AI, or may be performed without AI. For example, the connection unit may secure internet connectivity using an AI model that monitors the status of the high-altitude platform system and selects the optimal communication method.

[0072] The system may include a providing unit that provides the analysis results to the user. The providing unit provides the analysis results to the user. For example, the providing unit can display the analysis results in text format. The providing unit can also display the analysis results in graph format. For example, the providing unit displays the analysis results in text format, allowing the user to check their current location. The providing unit can also display the analysis results in graph format, allowing the user to visually understand the characteristics of the terrain. The providing unit can also provide the analysis results by voice. For example, the providing unit provides the analysis results by voice, allowing the user to check their current location without relying on vision. This allows the user to check the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and provide them to the user in a format optimal for the user.

[0073] The device may include a monitoring unit that monitors the status of the internet connection. The monitoring unit monitors the status of the internet connection. For example, the monitoring unit can monitor the connection speed. The monitoring unit can also monitor the stability of the connection. For example, the monitoring unit monitors the connection speed in real time to understand the status of the internet connection. The monitoring unit can also monitor the stability of the connection to ensure that the internet connection is not interrupted. The monitoring unit can also record the status of the internet connection and refer to past data. For example, the monitoring unit can record fluctuations in the connection speed and improve the stability of the connection based on the past data. This allows the status of the internet connection to be constantly monitored. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can monitor the status of the internet connection and ensure the internet connection using an AI model that selects the optimal connection method.

[0074] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, the image capture unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the image capture unit calculates an emotion score based on changes in facial expressions and adjusts the timing of capturing images. The image capture unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the image capture unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of capturing images. The image capture unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the image capture unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of capturing images. This allows capturing images at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input image data of a user captured by a camera to the generation AI, and have the generation AI estimate emotions.

[0075] The camera unit can simultaneously record surrounding environmental sounds when taking a photo and use them for analysis. The camera unit, for example, records environmental sounds such as the sound of wind and birdsong when taking a photo. For example, the camera unit records the sound of wind and uses it for analysis. The camera unit can also record the sound of birdsong and use it for analysis. The camera unit can also record the voices of surrounding people and use it for analysis. For example, the camera unit can record the voices of surrounding people and use it for analysis. The camera unit can also record the sound of a flowing river or a waterfall and use it for analysis. For example, the camera unit can record the sound of a flowing river and use it for analysis. The camera unit can also record the sound of a waterfall and use it for analysis. By using environmental sounds in the analysis, the accuracy of the analysis is improved. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without using AI. For example, the camera unit can input the recorded environmental sound data to a generation AI and have the generation AI analyze the environmental sounds.

[0076] The imaging unit can be added with a function to automatically capture images from multiple different angles during imaging. The imaging unit automatically captures images from four directions, for example, the front, rear, left, and right. For example, the imaging unit captures a front image and then a rear image. The imaging unit can also capture left and right images. The imaging unit can also automatically capture an overhead image from above. For example, the imaging unit captures an overhead image from above and uses it for analysis. The imaging unit can also automatically capture images from an oblique direction. For example, the imaging unit captures images from an oblique direction and uses it for analysis. This improves the accuracy of the analysis by obtaining images from multiple angles. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input image data captured from multiple angles to a generation AI and have the generation AI analyze the images.

[0077] The image capture unit can estimate the user's emotions and determine the priority of images to be captured based on the estimated user's emotions. For example, if the user is excited, the image capture unit prioritizes capturing images of scenic locations. For example, if the user is excited, the image capture unit prioritizes capturing images of scenic locations. Furthermore, if the user is tired, the image capture unit can prioritize capturing images of resting points. For example, if the user is tired, the image capture unit prioritizes capturing images of resting points. Furthermore, if the user is relaxed, the image capture unit can prioritize capturing images of natural scenery. For example, if the user is relaxed, the image capture unit prioritizes capturing images of natural scenery. This allows for prioritized capture of optimal images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit can be performed, for example, using AI or without AI. For example, the photographing unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0078] The photographing unit can suggest the optimal photographing spot based on the user's location information when photographing. For example, when the user is on a mountaintop, the photographing unit suggests a spot to photograph the surrounding scenery. For example, when the user is on a mountaintop, the photographing unit suggests a spot to photograph the surrounding scenery. Furthermore, when the user is along a river, the photographing unit can also suggest a spot to photograph a beautiful scenery. For example, when the user is along a river, the photographing unit suggests a spot to photograph a beautiful scenery. Furthermore, when the user is in a forest, the photographing unit can also suggest a spot to photograph a place where light shines in. For example, when the user is in a forest, the photographing unit suggests a spot to photograph a place where light shines in. In this way, the optimal photographing spot can be suggested based on the user's location information. Some or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's location information to the generation AI and cause the generation AI to suggest the optimal photographing spot.

[0079] The photographing unit can set optimal photographing conditions by taking weather information into consideration when photographing. For example, the photographing unit uses a waterproof camera when it is raining. For example, the photographing unit uses a waterproof camera when it is raining. The photographing unit can also set photographing conditions to avoid backlighting when it is sunny. For example, the photographing unit sets photographing conditions to avoid backlighting when it is sunny. The photographing unit can also set photographing conditions to prevent overexposure when it is snowy. For example, the photographing unit sets photographing conditions to prevent overexposure when it is snowy. In this way, optimal photographing conditions can be set by taking weather information into consideration. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input weather information to the generation AI and cause the generation AI to set optimal photographing conditions.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This makes it possible to provide an optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis results are displayed.

[0081] The analysis unit can improve the analysis accuracy by referring to past analysis data during analysis. The analysis unit, for example, improves the recognition accuracy of specific terrain or landmarks based on past analysis data. For example, the analysis unit improves the recognition accuracy of specific terrain or landmarks based on past analysis data. The analysis unit can also improve the analysis accuracy in different seasons or weather conditions based on past analysis data. For example, the analysis unit improves the analysis accuracy in different seasons or weather conditions based on past analysis data. The analysis unit can also improve the analysis accuracy in different time periods based on past analysis data. For example, the analysis unit improves the analysis accuracy in different time periods based on past analysis data. In this way, the analysis accuracy is improved by referring to past data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0082] The analysis unit can improve analysis accuracy by combining different analysis algorithms during analysis. The analysis unit can improve analysis accuracy by, for example, combining deep learning with conventional image analysis algorithms. For example, the analysis unit extracts image features using deep learning and performs detailed analysis using conventional image analysis algorithms. The analysis unit can also improve analysis accuracy by combining different feature extraction algorithms. For example, the analysis unit combines different feature extraction algorithms to extract more features and improve analysis accuracy. The analysis unit can also improve analysis accuracy by combining different machine learning models. For example, the analysis unit combines different machine learning models to utilize the strengths of each model and improve analysis accuracy. In this way, combining different algorithms improves analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input different analysis algorithms into the generation AI and have the generation AI execute the improvement in analysis accuracy.

[0083] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user's emotions. For example, if the user is excited, the analysis unit prioritizes analysis results of scenic locations. For example, if the user is excited, the analysis unit prioritizes analysis results of scenic locations. The analysis unit can also prioritize analysis results of rest points if the user is tired. For example, if the user is tired, the analysis unit prioritizes analysis results of rest points. The analysis unit can also prioritize analysis results of natural landscapes if the user is relaxed. For example, if the user is relaxed, the analysis unit prioritizes analysis results of natural landscapes. This allows for preferential provision of optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the analysis results.

[0084] The analysis unit can improve the accuracy of the analysis by also using surrounding environmental data during analysis. For example, the analysis unit can improve the analysis accuracy of a specific terrain by also using surrounding temperature data. For example, the analysis unit can improve the analysis accuracy of a specific terrain by also using surrounding temperature data. The analysis unit can also improve the analysis accuracy of a specific plant by also using surrounding humidity data. For example, the analysis unit can improve the analysis accuracy of a specific plant by also using surrounding humidity data. The analysis unit can also improve the analysis accuracy under specific weather conditions by also using surrounding atmospheric pressure data. For example, the analysis unit can improve the analysis accuracy under specific weather conditions by also using surrounding atmospheric pressure data. In this way, the analysis accuracy is improved by using environmental data in combination. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input surrounding environmental data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0085] The analysis unit can improve the analysis accuracy by comparing the captured data with other users' captured data during analysis. The analysis unit can improve the analysis accuracy by, for example, comparing the captured data with images of the same location taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of the same location taken by other users. The analysis unit can also improve the analysis accuracy by comparing the captured data with images of different seasons taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of different seasons taken by other users. The analysis unit can also improve the analysis accuracy by comparing the captured data with images of different time periods taken by other users. For example, the analysis unit can improve the analysis accuracy by comparing the captured data with images of different time periods taken by other users. In this way, the analysis accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the captured data of other users into the generation AI and cause the generation AI to improve the analysis accuracy.

[0086] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. For example, if the user is nervous, the identification unit provides a simple, highly visible display method. For example, if the user is nervous, the identification unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the identification unit can provide a display method including detailed information. For example, if the user is relaxed, the identification unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the identification unit provides a display method that focuses on the main points. This makes it possible to provide an optimal display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the identification results.

[0087] The identification unit can improve the identification accuracy by referring to past identification data during identification. The identification unit, for example, improves the recognition accuracy of specific terrain or landmarks based on past identification data. For example, the identification unit improves the recognition accuracy of specific terrain or landmarks based on past identification data. The identification unit can also improve the identification accuracy under different seasons or weather conditions based on past identification data. For example, the identification unit improves the identification accuracy under different seasons or weather conditions based on past identification data. The identification unit can also improve the identification accuracy under different time periods based on past identification data. For example, the identification unit improves the identification accuracy under different time periods based on past identification data. In this way, the identification accuracy is improved by referring to past data. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input past identification data into the generation AI and cause the generation AI to improve the identification accuracy.

[0088] The identification unit can improve identification accuracy by combining different identification algorithms during identification. The identification unit can improve identification accuracy by, for example, combining deep learning and conventional image analysis algorithms. For example, the identification unit extracts image features using deep learning and performs detailed analysis using conventional image analysis algorithms. The identification unit can also improve identification accuracy by combining different feature extraction algorithms. For example, the identification unit combines different feature extraction algorithms to extract more features and improve identification accuracy. The identification unit can also improve identification accuracy by combining different machine learning models. For example, the identification unit combines different machine learning models to utilize the strengths of each model and improve identification accuracy. In this way, the identification accuracy is improved by combining different algorithms. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input different identification algorithms into the generation AI and cause the generation AI to improve identification accuracy.

[0089] The identification unit can estimate the user's emotions and prioritize the identification results based on the estimated user's emotions. For example, if the user is excited, the identification unit prioritizes the identification results of scenic places. For example, if the user is excited, the identification unit prioritizes the identification results of scenic places. Furthermore, if the user is tired, the identification unit can prioritize the identification results of rest points. For example, if the user is tired, the identification unit prioritizes the identification results of rest points. Furthermore, if the user is relaxed, the identification unit can prioritize the identification results of natural landscapes. For example, if the user is relaxed, the identification unit prioritizes the identification results of natural landscapes. This makes it possible to provide the optimal identification results according to the user's emotions preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed, for example, using AI or without AI. For example, the identification unit can input user emotion data to the generation AI and have the generation AI determine the priority of the identification results.

[0090] The identification unit can improve the identification accuracy by also using surrounding environmental data during identification. The identification unit can, for example, also use ambient temperature data to improve the identification accuracy of a specific terrain. For example, the identification unit can also use ambient temperature data to improve the identification accuracy of a specific terrain. The identification unit can also improve the identification accuracy of a specific plant by also using ambient humidity data. For example, the identification unit can also improve the identification accuracy of a specific plant by also using ambient humidity data. The identification unit can also improve the identification accuracy under specific weather conditions by also using ambient air pressure data. For example, the identification unit can also improve the identification accuracy under specific weather conditions by also using ambient air pressure data. In this way, the identification accuracy is improved by also using environmental data. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the identification unit can input surrounding environmental data into the generation AI and cause the generation AI to improve the identification accuracy.

[0091] The identification unit can improve the identification accuracy by comparing the data with the identification data of other users during identification. For example, the identification unit improves the identification accuracy by comparing the data with data of the same location identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of the same location identified by other users. The identification unit can also improve the identification accuracy by comparing the data with data of different seasons identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of different seasons identified by other users. The identification unit can also improve the identification accuracy by comparing the data with data of different time periods identified by other users. For example, the identification unit improves the identification accuracy by comparing the data with data of different time periods identified by other users. In this way, the identification accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the identification data of other users into the generation AI and cause the generation AI to improve the identification accuracy.

[0092] The connection unit can estimate the user's emotions and adjust the connection method based on the estimated user's emotions. For example, if the user is nervous, the connection unit prioritizes a stable connection method. For example, if the user is nervous, the connection unit prioritizes a stable connection method. The connection unit can also suggest an optimal connection method if the user is relaxed. For example, if the user is relaxed, the connection unit can suggest an optimal connection method. The connection unit can also suggest a method that allows for quick connection if the user is in a hurry. For example, if the user is in a hurry, the connection unit suggests a method that allows for quick connection. This makes it possible to provide an optimal connection method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the connection unit may be performed using an AI, for example, or without an AI. For example, the connection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the connection method.

[0093] The connection unit can improve connection stability by referring to past connection data when connecting. The connection unit, for example, improves connection stability in a specific location based on the past connection data. For example, the connection unit improves connection stability in a specific location based on the past connection data. The connection unit can also improve connection stability under different weather conditions based on the past connection data. For example, the connection unit improves connection stability under different weather conditions based on the past connection data. The connection unit can also improve connection stability in different time periods based on the past connection data. For example, the connection unit improves connection stability in different time periods based on the past connection data. In this way, connection stability is improved by referring to past data. Some or all of the above-described processing in the connection unit may be performed using, or without, a generation AI. For example, the connection unit may input past connection data into the generation AI and cause the generation AI to improve connection stability.

[0094] The connection unit can improve connection stability by combining different connection means during connection. The connection unit, for example, combines satellite communication and terrestrial communication to improve connection stability. For example, the connection unit combines satellite communication and terrestrial communication to improve connection stability. The connection unit can also improve connection stability by combining a high-altitude platform system and terrestrial communication. For example, the connection unit combines a high-altitude platform system and terrestrial communication to improve connection stability. The connection unit can also improve connection stability by combining different communication protocols. For example, the connection unit combines different communication protocols to improve connection stability. As a result, connection stability is improved by combining different connection means. Some or all of the above-mentioned processing in the connection unit may be performed using, or without, the generation AI. For example, the connection unit can input different connection means into the generation AI and cause the generation AI to improve connection stability.

[0095] The connection unit can estimate the user's emotions and determine connection priorities based on the estimated user's emotions. For example, if the user is nervous, the connection unit prioritizes the most stable connection means. For example, if the user is nervous, the connection unit prioritizes the most stable connection means. The connection unit can also suggest the optimal connection means if the user is relaxed. For example, if the user is relaxed, the connection unit suggests the optimal connection means. The connection unit can also prioritize a means that allows for quick connection if the user is in a hurry. For example, if the user is in a hurry, the connection unit prioritizes a means that allows for quick connection. This allows the optimal connection means to be preferentially provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the connection unit may be performed using an AI, or may be performed without using an AI. For example, the connection unit can input the user's emotion data into the generation AI and have the generation AI determine the connection priorities.

[0096] The connection unit can improve connection stability by also using surrounding environmental data when connecting. The connection unit, for example, also uses surrounding weather data to select the optimal connection means. For example, the connection unit also uses surrounding weather data to select the optimal connection means. The connection unit can also improve connection stability by also using surrounding terrain data. For example, the connection unit also uses surrounding terrain data to improve connection stability. The connection unit can also select the optimal connection means by also using surrounding radio wave conditions. For example, the connection unit selects the optimal connection means by also using surrounding radio wave conditions. In this way, connection stability is improved by also using environmental data. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can input surrounding environmental data to the generation AI and cause the generation AI to improve connection stability.

[0097] The connection unit can improve connection stability by comparing the connection data of other users at the time of connection. The connection unit, for example, selects the optimal connection means based on connection data used by other users in the same location. For example, the connection unit selects the optimal connection means based on connection data used by other users in the same location. The connection unit can also improve connection stability based on connection data used by other users under different weather conditions. For example, the connection unit improves connection stability based on connection data used by other users under different weather conditions. The connection unit can also improve connection stability based on connection data used by other users in different time periods. For example, the connection unit improves connection stability based on connection data used by other users in different time periods. In this way, connection stability is improved by comparing the data of other users. Some or all of the above-described processing in the connection unit may be performed using, or without, a generation AI. For example, the connection unit can input the connection data of other users into the generation AI and cause the generation AI to improve connection stability.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the provided information based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This makes it possible to provide an optimal display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the display method of the provided information.

[0099] The providing unit can improve the accuracy of provision by referring to past provided data at the time of provision. The providing unit, for example, improves the accuracy of provision of specific information based on past provided data. For example, the providing unit improves the accuracy of provision of specific information based on past provided data. The providing unit can also improve the accuracy of provision in different seasons or weather conditions based on past provided data. For example, the providing unit improves the accuracy of provision in different seasons or weather conditions based on past provided data. The providing unit can also improve the accuracy of provision in different time periods based on past provided data. For example, the providing unit improves the accuracy of provision in different time periods based on past provided data. In this way, the accuracy of provision is improved by referring to past data. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input past provided data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0100] The providing unit can improve the accuracy of information provision by combining different provision algorithms during provision. The providing unit can improve the accuracy of information provision by, for example, combining deep learning with a conventional information provision algorithm. For example, the providing unit extracts information features using deep learning and performs detailed analysis using a conventional information provision algorithm. The providing unit can also improve the accuracy of information provision by combining different feature extraction algorithms. For example, the providing unit combines different feature extraction algorithms to extract more features and improve the accuracy of information provision. The providing unit can also improve the accuracy of information provision by combining different machine learning models. For example, the providing unit combines different machine learning models to utilize the strengths of each model and improve the accuracy of information provision. In this way, the accuracy of information provision is improved by combining different algorithms. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input different provision algorithms into the generation AI and cause the generation AI to improve the accuracy of information provision.

[0101] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit prioritizes information about scenic places. For example, if the user is excited, the providing unit prioritizes information about scenic places. Furthermore, if the user is tired, the providing unit can prioritize information about rest points. For example, if the user is tired, the providing unit prioritizes information about rest points. Furthermore, if the user is relaxed, the providing unit can prioritize information about natural scenery. For example, if the user is relaxed, the providing unit prioritizes information about natural scenery. This allows optimal information to be provided preferentially according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input user emotion data to the generating AI and have the generating AI determine the priority of the information to be provided.

[0102] The providing unit can improve the accuracy of the information provided by also using surrounding environmental data when providing the information. The providing unit can, for example, also use surrounding weather data to provide optimal information. For example, the providing unit can also use surrounding weather data to provide optimal information. The providing unit can also improve the accuracy of the information provided by also using surrounding terrain data. For example, the providing unit can also use surrounding terrain data to improve the accuracy of the information provided. The providing unit can also provide optimal information by also using surrounding radio wave conditions. For example, the providing unit can also provide optimal information by also using surrounding radio wave conditions. In this way, the accuracy of the information provided is improved by also using environmental data. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input surrounding environmental data to the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0103] The providing unit can improve the accuracy of the information provided by comparing the data provided by other users with that provided by other users at the time of providing the information. The providing unit, for example, provides optimal information based on information provided by other users at the same location. For example, the providing unit provides optimal information based on information provided by other users at the same location. The providing unit can also improve the accuracy of the information provided by other users under different weather conditions. For example, the providing unit improves the accuracy of the information provided by other users under different weather conditions. The providing unit can also improve the accuracy of the information provided by other users at different time periods. For example, the providing unit improves the accuracy of the information provided by other users at different time periods. This improves the accuracy of the information provided by other users by comparing their data. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the data provided by other users into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0104] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is nervous, the monitoring unit prioritizes a stable monitoring method. For example, if the user is nervous, the monitoring unit prioritizes a stable monitoring method. The monitoring unit can also suggest an optimal monitoring method if the user is relaxed. For example, if the user is relaxed, the monitoring unit can suggest an optimal monitoring method. The monitoring unit can also suggest a method that allows for quick monitoring if the user is in a hurry. For example, if the user is in a hurry, the monitoring unit suggests a method that allows for quick monitoring. This makes it possible to provide an optimal monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the monitoring method.

[0105] The monitoring unit can improve monitoring accuracy by referring to past monitoring data during monitoring. The monitoring unit, for example, improves monitoring accuracy at a specific location based on past monitoring data. For example, the monitoring unit improves monitoring accuracy at a specific location based on past monitoring data. The monitoring unit can also improve monitoring accuracy under different weather conditions based on past monitoring data. For example, the monitoring unit improves monitoring accuracy under different weather conditions based on past monitoring data. The monitoring unit can also improve monitoring accuracy at different time periods based on past monitoring data. For example, the monitoring unit improves monitoring accuracy at different time periods based on past monitoring data. In this way, monitoring accuracy is improved by referring to past data. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to improve monitoring accuracy.

[0106] The monitoring unit can improve monitoring accuracy by combining different monitoring algorithms during monitoring. The monitoring unit can improve monitoring accuracy by, for example, combining deep learning and conventional monitoring algorithms. For example, the monitoring unit can extract features of the monitored object using deep learning and perform detailed analysis using conventional monitoring algorithms. The monitoring unit can also improve monitoring accuracy by combining different feature extraction algorithms. For example, the monitoring unit can combine different feature extraction algorithms to extract more features and improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy by combining different machine learning models. For example, the monitoring unit can combine different machine learning models to utilize the strengths of each model to improve monitoring accuracy. In this way, combining different algorithms improves monitoring accuracy. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input different monitoring algorithms into the generation AI and have the generation AI improve monitoring accuracy.

[0107] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is nervous, the monitoring unit prioritizes the most stable monitoring means. For example, if the user is nervous, the monitoring unit prioritizes the most stable monitoring means. The monitoring unit can also suggest the optimal monitoring means if the user is relaxed. For example, if the user is relaxed, the monitoring unit suggests the optimal monitoring means. The monitoring unit can also prioritize a means that can monitor quickly if the user is in a hurry. For example, if the user is in a hurry, the monitoring unit prioritizes a means that can monitor quickly. This makes it possible to prioritize the optimal monitoring means according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotional data into the generation AI and have the generation AI determine the monitoring priorities.

[0108] The monitoring unit can improve monitoring accuracy by also using surrounding environmental data during monitoring. The monitoring unit, for example, also uses surrounding weather data to select the optimal monitoring means. For example, the monitoring unit also uses surrounding weather data to select the optimal monitoring means. The monitoring unit can also improve monitoring accuracy by also using surrounding terrain data. For example, the monitoring unit also uses surrounding terrain data to improve monitoring accuracy. The monitoring unit can also select the optimal monitoring means by also using surrounding radio wave conditions. For example, the monitoring unit selects the optimal monitoring means by also using surrounding radio wave conditions. In this way, the monitoring accuracy is improved by also using environmental data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input surrounding environmental data into the generation AI and cause the generation AI to improve monitoring accuracy.

[0109] The monitoring unit can improve monitoring accuracy by comparing the monitoring data of other users during monitoring. The monitoring unit, for example, selects the optimal monitoring means based on monitoring data used by other users at the same location. For example, the monitoring unit selects the optimal monitoring means based on monitoring data used by other users at the same location. The monitoring unit can also improve monitoring accuracy based on monitoring data used by other users under different weather conditions. For example, the monitoring unit improves monitoring accuracy based on monitoring data used by other users under different weather conditions. The monitoring unit can also improve monitoring accuracy based on monitoring data used by other users at different time periods. For example, the monitoring unit improves monitoring accuracy based on monitoring data used by other users at different time periods. In this way, monitoring accuracy is improved by comparing the data of other users. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the monitoring data of other users into the generation AI and cause the generation AI to improve monitoring accuracy. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, and connection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit captures an image using the camera 42 of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the location by combining GPS data and the image analysis results. The connection unit secures an internet connection, for example, by using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, and connection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit captures an image using the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the location by combining GPS data and the image analysis results. The connection unit secures an internet connection, for example, by the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, and connection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit photographs an image using the camera 42 of the headset-type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the location by combining GPS data and the image analysis results. The connection unit secures an internet connection, for example, by using the communication I / F 44 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, and connection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit photographs an image using the camera 42 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the image using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the location by combining GPS data and the image analysis results. The connection unit secures an internet connection, for example, by using the communication I / F 44 of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] A climber distress prevention system can also be equipped with an automatic emergency notification function. For example, the system can automatically make an emergency call if the climber remains motionless for a certain period of time or if a sudden change in altitude is detected. The system can also monitor the climber's biometric data, such as heart rate and body temperature, and make an emergency call if it detects an abnormality. The system can also be equipped with a button that allows the climber to manually make an emergency call. This allows rescue to be requested quickly if the climber is distressed.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide the optimal display method according to the user's emotions.

[0113] The connection unit can monitor the status of the internet connection and ensure internet connection using an AI model that selects the optimal connection method. For example, the connection unit can monitor the status of a satellite communication system and ensure internet connection using an AI model that selects the optimal communication method. The connection unit can also monitor the status of the internet connection in real time to prevent connection interruptions. Furthermore, the connection unit can improve connection stability in specific locations based on past connection data. This allows for stable internet connection even in mountainous areas.

[0114] The connection unit can estimate the user's emotions and adjust the connection method based on the estimated user's emotions. For example, if the user is nervous, a stable connection method is prioritized. Also, if the user is relaxed, the connection unit can suggest the optimal connection method. Furthermore, if the user is in a hurry, the connection unit can suggest a method that allows for a quick connection. In this way, the optimal connection method can be provided according to the user's emotions.

[0115] When providing the analysis results to the user, the providing unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide the optimal display method according to the user's emotions.

[0116] The monitoring unit monitors the status of the internet connection and uses an AI model to select the optimal connection method to ensure internet connection. For example, the monitoring unit monitors the connection speed in real time to understand the status of the internet connection. The monitoring unit can also monitor the stability of the connection to prevent internet connection interruptions. Furthermore, the monitoring unit can record the status of the internet connection and improve the stability of the connection based on past data. This allows the status of the internet connection to be constantly monitored.

[0117] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, the camera can capture the user's facial expression and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, it can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. This allows the image capture to be performed at the optimal timing according to the user's emotions.

[0118] The analysis unit can improve the accuracy of analysis by referring to past analysis data during analysis. For example, the accuracy of recognizing specific terrain or landmarks can be improved based on past analysis data. It can also improve the accuracy of analysis under different seasons or weather conditions. It can also improve the accuracy of analysis under different time periods. In this way, by referring to past data, the accuracy of analysis can be improved.

[0119] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide the optimal display method according to the user's emotions.

[0120] The connection unit can improve connection stability by combining different connection means during connection. For example, connection stability can be improved by combining satellite communication and terrestrial communication. Connection stability can also be improved by combining a high altitude platform system and terrestrial communication. Connection stability can also be improved by combining different communication protocols. As a result, connection stability can be improved by combining different connection means.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The camera captures images of the surrounding area. For example, a climber can use the app to capture images of their surroundings. The camera can also capture images using a smartphone camera and automatically capture images from multiple different angles. For example, it can automatically capture images from four directions: front, back, left and right. Step 2: The analysis unit uses the generation AI to analyze the images captured by the photography unit. For example, it uses terrain recognition technology based on deep learning to analyze the shape of a specific mountain or the arrangement of rocks. Step 3: The identification unit identifies the location based on the results of the analysis by the analysis unit, for example, by combining GPS data and image analysis results. Step 4: The connection unit secures the internet connection. For example, the connection can be secured using a satellite communication system or a high altitude platform system. The connection unit can also secure the internet connection using an AI model that monitors the status of the internet connection and selects the optimal connection method.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photographing unit that photographs images of the surrounding area; an analysis unit that analyzes the image captured by the imaging unit; an identification unit that identifies a position based on the result of the analysis by the analysis unit; A connection part that ensures a network connection is provided. A system characterized by:

2. The analysis unit Uses deep learning-based terrain recognition technology 2. The system of claim 1.

3. The connection portion is Secure internet connectivity using satellite communication systems 2. The system of claim 1.

4. The connection portion is Ensuring internet connectivity using high altitude platform systems 2. The system of claim 1.

5. A providing unit is provided to provide the analysis results to the user.

2. The system of claim 1.

6. Equipped with a monitoring unit that monitors the status of the internet connection 2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is Records surrounding environmental sounds while shooting and uses them for analysis 2. The system of claim 1.

9. The imaging unit is Add a feature to automatically capture images from multiple angles when shooting 2. The system of claim 1.

Images