system

Abstract

The system according to this embodiment aims to identify celestial objects in the night sky and provide appropriate explanations to the user. [Solution] The system according to the embodiment comprises an imaging unit, an identification unit, a generation unit, and an explanation unit. The imaging unit allows the user to photograph the night sky. The identification unit analyzes the image captured by the imaging unit and identifies celestial objects. The generation unit generates a star chart based on the celestial objects identified by the identification unit. The explanation unit provides an explanation of the celestial objects based on the star chart generated by the generation unit.

Description

Technical Field

[0003] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately identify celestial bodies in the night sky and provide appropriate explanations to users.

[0005] The system according to the embodiment aims to identify celestial bodies in the night sky and provide appropriate explanations to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an imaging unit, an identification unit, a generation unit, and an explanation unit. The imaging unit allows the user to photograph the night sky. The identification unit analyzes the image captured by the imaging unit and identifies celestial objects. The generation unit generates a star chart based on the celestial objects identified by the identification unit. The explanation unit provides an explanation of the celestial objects based on the star chart generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify celestial objects in the night sky and provide appropriate explanations to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The stargazing system according to an embodiment of the present invention is a groundbreaking system that revolutionizes the stargazing experience by utilizing cutting-edge generative AI technology. When a user photographs the night sky with their smartphone or tablet, the AI ​​instantly identifies celestial objects and generates an accurate star chart based on the location and time. For example, even for faint stars visible in urban areas, the AI ​​considers surrounding light pollution and atmospheric conditions to provide a complete star map that includes even invisible celestial objects. Furthermore, the AI ​​provides explanations for each celestial object, tailored to the user's interests and knowledge level, covering everything from mythology and history to the latest astronomical discoveries. Users can deepen their knowledge of the night sky as if on a guided tour by an expert, engaging in natural conversations with the AI ​​through voice interaction. It is also possible to simulate the night sky during the daytime or recreate past and future starry skies using AR technology. The community function analyzes each user's observation history and interests, matching them with other users who share similar hobbies and notifying them of opportunities to observe rare astronomical phenomena. This will allow it to function as a new-era astronomy education and entertainment platform, going beyond a simple constellation app, making the mystery and wonder of the universe accessible to everyone. The stargazing system will enable users to photograph the night sky, identify celestial objects, generate star charts, and receive explanations of celestial bodies.

[0029] The stargazing system according to this embodiment comprises a shooting unit, an identification unit, a generation unit, and an explanation unit. The shooting unit allows the user to photograph the night sky. The shooting unit can, for example, use the camera of a smartphone or tablet to photograph the night sky. The shooting unit can also use a digital camera or a dedicated astronomical observation camera to photograph the night sky. Furthermore, the shooting unit can automatically select a shooting mode such as long exposure or high-sensitivity shooting. For example, the shooting unit can photograph the night sky using a smartphone camera, and the AI ​​will automatically select the optimal shooting mode. The shooting unit can also perform high-sensitivity shooting using a digital camera to capture faint starlight. The identification unit analyzes the images taken by the shooting unit and identifies celestial objects. For example, the identification unit can perform image analysis using generation AI to identify the position and type of celestial object. The identification unit can also analyze the characteristics of celestial objects such as brightness, color, and shape to identify them. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. For example, the identification unit can use generation AI to identify the position of celestial objects and generate a star chart. The identification unit can analyze the brightness and color of celestial objects to identify the type of star. The generation unit generates a star chart based on the celestial objects identified by the identification unit. For example, the generation unit can generate a star chart based on the positional information of celestial objects using generation AI. The generation unit can also generate a star chart considering surrounding light pollution and atmospheric conditions. Furthermore, the generation unit can recreate past and future starry skies. For example, the generation unit can generate an accurate star chart based on the positional information of celestial objects using generation AI. The generation unit can also generate a star chart that includes celestial objects that are not actually visible, taking into account light pollution and atmospheric conditions. The explanation unit provides explanations of celestial objects based on the star chart generated by the generation unit. For example, the explanation unit can use AI to explain myths, history, and the latest astronomical discoveries about each celestial object. The explanation unit can also provide explanations tailored to the user's interests and knowledge level. Furthermore, the explanation unit can provide explanations while having a natural conversation with the user through voice dialogue. For example, the explanation unit can use AI to explain the myths and history of a specific constellation. Furthermore, the commentary section can explain the latest astronomical discoveries, which can pique the user's interest.As a result, the stargazing system according to this embodiment allows the user to photograph the night sky, identify celestial objects, generate star charts, and provide explanations of celestial objects.

[0030] The shooting function allows users to photograph the night sky. For example, users can use the cameras on their smartphones or tablets to photograph the night sky. It can also use digital cameras or dedicated astronomical cameras. Furthermore, the shooting function can automatically select shooting modes such as long exposure and high-sensitivity shooting. For example, the shooting function can use a smartphone camera to photograph the night sky, and the AI ​​will automatically select the optimal shooting mode. It can also use a digital camera for high-sensitivity shooting to capture faint starlight. The shooting function has a feature that automatically adjusts camera settings when the user photographs the night sky. For example, the AI ​​analyzes the brightness of the night sky and weather conditions to set the optimal exposure time and ISO sensitivity. This allows users to easily take beautiful photos of the starry sky without specialized knowledge. The shooting function also has a function to generate high-resolution starry sky images by combining multiple images. For example, the AI ​​analyzes multiple images taken in succession, removing noise and generating a high-resolution image. This allows for the capture of faint starlight and the detailed structure of celestial objects. Furthermore, the camera's shooting function also includes a feature that allows users to save images they've taken to the cloud, making them accessible from other devices. This makes it easy for users to share their captured images of the starry sky and re-edit them later.

[0031] The identification unit analyzes images captured by the imaging unit to identify celestial objects. For example, the identification unit can use generative AI to analyze images and determine the position and type of celestial object. It can also analyze features such as brightness, color, and shape to identify celestial objects. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. For example, the identification unit uses generative AI to determine the position of a celestial object and generate a star chart. It can also analyze the brightness and color of a celestial object to identify its type. The identification unit uses AI to analyze captured images and determine the position and type of celestial object. Specifically, the AI ​​uses image recognition technology to identify celestial objects such as stars, planets, and nebulae, and to determine the position of each object. Furthermore, the AI ​​analyzes features such as brightness, color, and shape to identify the type of celestial object. For example, the AI ​​analyzes the brightness of a star to identify fixed stars and variable stars. The AI ​​can also analyze the color of a celestial object to estimate its temperature and age. The identification unit can track the movement and changes of celestial bodies in real time, improving identification accuracy. For example, the AI ​​analyzes a series of images to track the movement of celestial bodies. This allows for accurate determination of the position and movement of celestial bodies, improving identification accuracy. Furthermore, the identification unit can improve the accuracy of celestial body identification by utilizing past observation data and astronomical databases. For example, the AI ​​can predict the position and movement of a specific celestial body based on past observation data, improving identification accuracy.

[0032] The generation unit generates a star chart based on celestial objects identified by the identification unit. For example, the generation unit can generate a star chart based on the positional information of celestial objects using a generation AI. Furthermore, the generation unit can generate a star chart considering surrounding light pollution and atmospheric conditions. In addition, the generation unit can recreate past and future starry skies. For example, the generation unit uses a generation AI to generate an accurate star chart based on the positional information of celestial objects. Furthermore, the generation unit can create a star chart that includes celestial objects that are not actually visible, taking into account light pollution and atmospheric conditions. The generation unit generates an accurate star chart based on the positional information of celestial objects identified by the identification unit. Specifically, the AI ​​analyzes the positional information of celestial objects and generates a star chart. The generation unit can generate a star chart considering surrounding light pollution and atmospheric conditions. For example, the AI ​​analyzes the effects of light pollution and generates a star chart that includes celestial objects that are not actually visible. The AI ​​also analyzes atmospheric conditions and can accurately recreate how celestial objects appear. The generation unit can recreate past and future starry skies. For example, the AI ​​can recreate past starry skies based on past celestial object positional information. Furthermore, the AI ​​can predict the future positions of celestial bodies and recreate the future night sky. This allows users to observe past and future night skies. In addition, the generation unit can recreate the night sky visible from a specific location based on the user's location information. For example, the AI ​​can analyze the user's location information and accurately recreate the night sky visible from that location. This allows users to observe the night sky as seen from their own location.

[0033] The commentary section provides explanations of celestial objects based on the star chart generated by the generation section. For example, the commentary section can use AI to explain myths, history, and the latest astronomical discoveries for each celestial object. Furthermore, the commentary section can tailor its explanations to the user's interests and knowledge level. In addition, the commentary section can engage in natural conversation with the user through voice dialogue. For example, the commentary section can use AI to explain the myths and history of a specific constellation. It can also explain the latest astronomical discoveries to pique the user's interest. The commentary section provides detailed explanations of each celestial object based on the star chart generated by the generation section. Specifically, the AI ​​analyzes the positional information and characteristics of celestial objects and explains their myths, history, and the latest astronomical discoveries. For example, the AI ​​can explain the myths and history of a specific constellation, describing how it was named and what stories surround it. The AI ​​can also explain the latest astronomical discoveries, providing the user with interesting information. The commentary section can tailor its explanations to the user's interests and knowledge level. For example, the AI ​​analyzes the user's past search history and interests, and customizes the explanations based on that information. This allows users to obtain information that matches their interests. Furthermore, the explanation unit can provide explanations while naturally conversing with the user through voice interaction. For instance, the AI ​​can answer user questions in real time and provide explanations in a conversational format. This allows users to gain a deeper understanding. The explanation unit can support users in enjoying stargazing and stimulate their interest in astronomy.

[0034] The explanatory section can explain the myths, history, and latest astronomical discoveries for each celestial body. For example, it can explain the myths and history of a particular constellation. For instance, it can explain the origins of constellations based on Greek and Roman mythology. Furthermore, the explanatory section can provide users with new knowledge by explaining the latest astronomical discoveries. For example, it can introduce the latest findings on a particular celestial body based on recent research papers and observational data. In addition, the explanatory section can analyze the user's observation history and interests regarding celestial bodies and provide relevant information. For example, it can explain the myths and history related to celestial bodies the user has observed in the past. This allows the explanatory section to explain the myths, history, and latest astronomical discoveries for each celestial body.

[0035] The explanation section can provide explanations tailored to the user's interests and knowledge level. For example, the explanation section can conduct surveys to identify the user's interests and knowledge level. For instance, it can ask users about celestial objects they are interested in or information they want to know, and adjust the explanation content based on their answers. The explanation section can also analyze the user's past usage history to identify their interests and knowledge level. For example, it can determine the user's interests and knowledge level based on celestial objects they have observed and explanations they have viewed in the past. Furthermore, the explanation section can create user profiles and provide individualized explanations. For example, it can select appropriate explanations based on information such as the user's age, education level, and occupation. This allows the explanation section to provide explanations tailored to the user's interests and knowledge level.

[0036] The generation unit can generate star charts while considering surrounding light pollution and atmospheric conditions. For example, the generation unit can generate star charts while considering the effects of light pollution. For instance, the generation unit can consider light pollution in urban areas and generate star charts that include celestial objects that are not actually visible. The generation unit can also generate star charts while considering atmospheric conditions. For example, the generation unit can consider atmospheric conditions such as cloud cover, humidity, and temperature and adjust how celestial objects appear. Furthermore, the generation unit can simulate past and future atmospheric conditions and generate star charts based on them. For example, the generation unit can recreate the atmospheric conditions of a specific date and time based on past observation data and recreate the night sky at that time. This allows the generation unit to generate star charts while considering surrounding light pollution and atmospheric conditions.

[0037] The generation unit can simulate the night sky even during the daytime using AR technology. For example, the generation unit can use AR technology to simulate the night sky during the day. For instance, the generation unit can use a smartphone or tablet camera to photograph the daytime sky and display the night sky using AR technology. The generation unit can also recreate the starry sky at a specific date and time. For example, the generation unit can simulate the starry sky at a specific date and time in the past or future and display it to the user. Furthermore, the generation unit can use AR technology to display the positions and movements of celestial bodies in real time. For example, the generation unit can use a smartphone camera to photograph the current sky and display the positions and movements of celestial bodies using AR technology. As a result, the generation unit can use AR technology to simulate the night sky even during the daytime.

[0038] The generation unit can recreate past and future starry skies. For example, it can recreate past starry skies. For instance, it can recreate the starry sky at a specific date and time based on past observational data. Furthermore, the generation unit can simulate future starry skies. For example, it can simulate the starry sky at a specific date and time in the future based on celestial position data. In addition, the generation unit can recreate specific astronomical phenomena from the past and future. For example, it can recreate and display past astronomical phenomena such as meteor showers, solar eclipses, and comet approaches to the user. In this way, the generation unit can recreate past and future starry skies.

[0039] The commentary unit can provide explanations while engaging in natural conversation with the user through voice interaction. For example, the commentary unit can respond to user questions using speech recognition technology. For instance, if a user asks about a specific celestial body, the commentary unit will provide an explanation via voice. The commentary unit can also engage in natural conversations with the user using a dialogue system. For example, the commentary unit can provide appropriate explanations according to the user's interests and level of knowledge. Furthermore, the commentary unit can provide explanations using speech synthesis technology. For example, the commentary unit can provide explanations in a natural, expert-like voice using speech synthesis technology. This allows the commentary unit to provide explanations while engaging in natural conversation with the user through voice interaction.

[0040] The commentary section can provide knowledge about the night sky, much like a guided tour by an expert. For example, it can offer guided tours based on expert commentary. For instance, it can provide detailed information about specific constellations or celestial bodies based on expert explanations. Furthermore, the commentary section can create tour scenarios and provide users with sequential explanations. For example, it can offer tours based on specific themes, deepening users' knowledge of the night sky. In addition, the commentary section can provide personalized guided tours tailored to the user's interests and knowledge level. For example, it can provide detailed explanations about specific celestial bodies or constellations based on the user's interests. In this way, the commentary section can provide knowledge about the night sky, much like a guided tour by an expert.

[0041] The commentary section provides community features, analyzing users' observation history and interests, and matching them with other users who share similar interests. For example, the commentary section analyzes a user's observation history to identify users with similar interests. For example, it finds common ground with other users based on celestial objects and interests that the user has observed in the past. The commentary section can also analyze users' interests and perform appropriate matching. For example, it matches users with other users based on their interests and knowledge level. Furthermore, the commentary section can provide community features that allow users to share information with each other. For example, it can provide forums and chat functions, creating a space for users to share observation information and knowledge. This allows the commentary section to analyze users' observation history and interests and match them with other users who share similar interests.

[0042] The information section can notify users of opportunities to observe rare astronomical phenomena. For example, it can notify users of the date, time, and location where a specific astronomical phenomenon can be observed. For instance, it can inform users of opportunities to observe astronomical phenomena such as meteor showers, solar eclipses, and comet approaches. The information section can also provide personalized notifications based on the user's observation history and interests. For example, it can prioritize notifications related to astronomical phenomena that the user has shown interest in in the past. Furthermore, the information section can also explain observation methods and precautions for astronomical phenomena. For example, it can provide users with the optimal location, time, and necessary equipment for observing a specific astronomical phenomenon. This allows the information section to notify users of opportunities to observe rare astronomical phenomena.

[0043] The imaging unit can analyze the user's past observation history and automatically adjust the optimal shooting settings during shooting. For example, the imaging unit can automatically set the optimal exposure time and ISO sensitivity based on data of celestial objects the user has previously photographed. For example, the imaging unit analyzes the user's past observation data and selects the optimal shooting settings. The imaging unit can also automatically adjust the camera orientation considering the positions of celestial objects the user has previously preferred to photograph. For example, the imaging unit optimizes the camera orientation based on the user's past observation history. Furthermore, the imaging unit can suggest the optimal shooting time based on the user's past observation history and perform shooting accordingly. For example, the imaging unit suggests the optimal shooting time based on the user's past observation data. This allows the imaging unit to analyze the user's past observation history and automatically adjust the optimal shooting settings.

[0044] The camera unit can suggest the optimal shooting angle and direction based on the user's current location information during shooting. For example, the camera unit can calculate the position of the most easily visible celestial object based on the user's current location and suggest the camera's orientation. For example, the camera unit can calculate the optimal shooting angle based on the user's current location's GPS data. The camera unit can also automatically set the optimal shooting angle considering the user's current altitude and azimuth. For example, the camera unit can optimize the camera's orientation based on the user's current altitude and azimuth. Furthermore, the camera unit can suggest the optimal shooting direction based on the user's current location's weather information. For example, the camera unit can suggest the optimal shooting direction based on the user's current location's weather data. In this way, the camera unit can suggest the optimal shooting angle and direction based on the user's current location information.

[0045] The camera unit can adjust shooting settings while considering the battery level of the user's device. For example, if the battery level is low, the AI ​​will shorten the exposure time to reduce the shooting time. For example, the camera unit detects the battery level and selects the optimal shooting settings. Also, if the battery level is sufficient, the AI ​​can shoot with high-quality settings. For example, the camera unit selects high-quality settings based on the battery level. Furthermore, if the battery level is moderate, the AI ​​can shoot with balanced settings. For example, the camera unit selects balanced shooting settings based on the battery level. In this way, the camera unit can adjust shooting settings while considering the battery level of the user's device.

[0046] The imaging team can analyze users' social media activity during imaging and prioritize capturing relevant celestial objects. For example, the team can prioritize capturing relevant celestial objects based on data of celestial objects shared by users on social media. For example, the team can analyze users' social media posts and select relevant celestial objects. The imaging team can also prioritize capturing relevant celestial objects based on astronomers and astronomical events that users follow on social media. For example, the team can select relevant celestial objects based on users' following information. Furthermore, the imaging team can analyze trends in astronomical communities that users participate in on social media and prioritize capturing relevant celestial objects. For example, the team can select relevant celestial objects based on trends in astronomical communities. This allows the imaging team to analyze users' social media activity and prioritize capturing relevant celestial objects.

[0047] The identification unit can improve identification accuracy by tracking the movement and changes of celestial bodies in real time during identification. For example, the identification unit can improve identification accuracy by updating the position information of celestial bodies in real time. For example, the identification unit can acquire position data of celestial bodies in real time and reflect it in the identification algorithm. The identification unit can also improve identification accuracy by tracking changes in the brightness and color of celestial bodies in real time. For example, the identification unit can analyze changes in the brightness and color of celestial bodies in real time and reflect it in the identification algorithm. Furthermore, the identification unit can improve identification accuracy by tracking the movement of celestial bodies in real time. For example, the identification unit can acquire data on the movement of celestial bodies in real time and reflect it in the identification algorithm. As a result, the identification unit can track the movement and changes of celestial bodies in real time and improve identification accuracy.

[0048] The identification unit can improve its identification accuracy by referring to past observational data of celestial objects during identification. For example, the identification unit can improve its identification accuracy by referring to past positional information of celestial objects. For example, the identification unit can acquire past positional data of celestial objects and reflect it in the identification algorithm. The identification unit can also improve its identification accuracy by referring to past brightness and color data of celestial objects. For example, the identification unit can acquire past brightness and color data of celestial objects and reflect it in the identification algorithm. Furthermore, the identification unit can improve its identification accuracy by referring to past movement data of celestial objects. For example, the identification unit can acquire past movement data of celestial objects and reflect it in the identification algorithm. In this way, the identification unit can improve its identification accuracy by referring to past observational data of celestial objects.

[0049] The identification unit can improve its identification accuracy by considering the color and brightness of celestial objects during identification. For example, the identification unit can apply an algorithm to improve identification accuracy based on the color of the celestial object. For example, the identification unit can acquire color data of the celestial object and reflect it in the identification algorithm. The identification unit can also apply an algorithm to improve identification accuracy based on the brightness of the celestial object. For example, the identification unit can acquire brightness data of the celestial object and reflect it in the identification algorithm. Furthermore, the identification unit can apply an algorithm to improve identification accuracy by simultaneously considering changes in the color and brightness of the celestial object. For example, the identification unit can acquire data on changes in the color and brightness of the celestial object and reflect it in the identification algorithm. In this way, the identification unit can improve its identification accuracy by considering the color and brightness of the celestial object.

[0050] The identification unit can improve its identification accuracy by referring to relevant literature on celestial objects during the identification process. For example, the identification unit can improve its identification accuracy by referring to academic papers on celestial objects. For example, the identification unit can acquire academic papers on celestial objects and incorporate them into the identification algorithm. The identification unit can also improve its identification accuracy by referring to observation reports on celestial objects. For example, the identification unit can acquire observation reports on celestial objects and incorporate them into the identification algorithm. Furthermore, the identification unit can improve its identification accuracy by adjusting the identification algorithm based on relevant literature on celestial objects. For example, the identification unit can acquire relevant literature on celestial objects and incorporate it into the identification algorithm. In this way, the identification unit can improve its identification accuracy by referring to relevant literature on celestial objects.

[0051] The generation unit can improve the accuracy of the star chart by considering the relationships between celestial bodies during generation. For example, the generation unit generates an accurate star chart based on the positional relationships of celestial bodies. For example, the generation unit acquires positional data of celestial bodies and generates a star chart considering their relationships. The generation unit can also generate a dynamic star chart by considering the movement of celestial bodies. For example, the generation unit acquires data on the movement of celestial bodies and generates a dynamic star chart. Furthermore, the generation unit can generate a detailed star chart based on the relationships between celestial bodies. For example, the generation unit acquires data on the relationships between celestial bodies and generates a detailed star chart. In this way, the generation unit can improve the accuracy of the star chart by considering the relationships between celestial bodies.

[0052] The generation unit can improve the accuracy of the star chart by referencing past observational data of celestial bodies during generation. For example, the generation unit can generate an accurate star chart by referencing past positional information of celestial bodies. For example, the generation unit can acquire past positional data of celestial bodies and generate a star chart. The generation unit can also generate a detailed star chart by referencing past brightness and color data of celestial bodies. For example, the generation unit can acquire past brightness and color data of celestial bodies and generate a star chart. Furthermore, the generation unit can generate a dynamic star chart by referencing data on the past movement of celestial bodies. For example, the generation unit can acquire data on the past movement of celestial bodies and generate a dynamic star chart. This allows the generation unit to improve the accuracy of the star chart by referencing past observational data of celestial bodies.

[0053] The generation unit can generate star charts while considering the geographical distribution of celestial bodies. For example, the generation unit can generate accurate star charts based on the geographical distribution of celestial bodies. For example, the generation unit can acquire geographical distribution data of celestial bodies and generate a star chart. The generation unit can also generate detailed star charts while considering the geographical distribution of celestial bodies. For example, the generation unit can generate detailed star charts based on geographical distribution data of celestial bodies. Furthermore, the generation unit can generate dynamic star charts based on the geographical distribution of celestial bodies. For example, the generation unit can acquire geographical distribution data of celestial bodies and generate dynamic star charts. In this way, the generation unit can generate star charts while considering the geographical distribution of celestial bodies.

[0054] The generation unit can improve the accuracy of the star chart by referring to relevant literature on celestial objects during generation. For example, the generation unit can refer to academic papers on celestial objects to generate accurate star charts. For example, the generation unit can obtain academic papers on celestial objects and generate star charts. The generation unit can also refer to observation reports on celestial objects to generate detailed star charts. For example, the generation unit can obtain observation reports on celestial objects and generate star charts. Furthermore, the generation unit can generate dynamic star charts based on relevant literature on celestial objects. For example, the generation unit can obtain relevant literature on celestial objects and generate dynamic star charts. This allows the generation unit to improve the accuracy of the star chart by referring to relevant literature on celestial objects.

[0055] The commentary section can adjust the level of detail in its explanations based on the importance of the celestial objects. For example, it can provide detailed explanations for important celestial objects. For instance, it can provide detailed explanations based on the scientific value or observation frequency of a particular celestial object. It can also provide concise explanations for common celestial objects. For example, it can provide a concise and to-the-point explanation for common celestial objects. Furthermore, the commentary section can prioritize explaining highly important celestial objects based on the user's interests. For example, it can select highly important celestial objects based on the user's interests and provide detailed explanations. This allows the commentary section to adjust the level of detail in its explanations based on the importance of the celestial objects.

[0056] The commentary section can apply different commentary algorithms depending on the category of the celestial object. For example, for constellations, the commentary section provides commentary based on mythology and history. For example, the commentary section provides detailed commentary on a specific constellation based on mythology and history. The commentary section can also provide commentary on planets based on the latest astronomical discoveries. For example, the commentary section provides commentary on a specific planet based on the latest research papers and observational data. Furthermore, the commentary section can provide commentary on galaxies based on scientific data. For example, the commentary section provides detailed commentary on a specific galaxy based on scientific data. This allows the commentary section to apply different commentary algorithms depending on the category of the celestial object.

[0057] The commentary section can determine the priority of its commentary based on the timing of celestial object observations. For example, it can prioritize commenting on currently observable objects. For instance, it can identify currently observable objects and provide commentary on them first. It can also prioritize commenting on objects that will be observable in the near future. For example, it can identify and provide commentary on objects that will be observable in the near future. Furthermore, it can provide commentary on previously observed objects last. For example, it can identify and provide commentary on previously observed objects. This allows the commentary section to determine the priority of its commentary based on the timing of celestial object observations.

[0058] The commentary section can adjust the order of explanations based on the relationships between celestial bodies. For example, it can group related celestial bodies and provide explanations in a specific order. For instance, it can group celestial bodies belonging to the same constellation and provide explanations in a specific order. Furthermore, the commentary section can consider the interrelationships of celestial bodies and provide explanations in order of relevance. For example, it can consider the physical interactions of celestial bodies and provide explanations in order of relevance. Additionally, the commentary section can prioritize explaining celestial bodies that are highly relevant to the user's interests. For instance, it can select and explain highly relevant celestial bodies based on the user's interests. This allows the commentary section to adjust the order of explanations based on the relationships between celestial bodies.

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

[0060] The stargazing system can further analyze the user's past observation history and propose the optimal observation plan. For example, it can suggest the next celestial object to observe based on data from celestial objects the user has observed in the past. It can also suggest the optimal observation time and location based on the user's observation history. Furthermore, it can analyze the user's observation history and provide the latest information on celestial objects of interest. In this way, the stargazing system can leverage the user's past observation history to provide a more effective observation experience.

[0061] The stargazing system can also suggest the optimal observation spot based on the user's current location. For example, it can calculate the position of the most easily visible celestial object based on the user's GPS data and suggest an observation spot. It can also automatically set the optimal observation spot considering the user's current altitude and azimuth. Furthermore, it can suggest the optimal observation spot based on the weather information of the user's current location. In short, the stargazing system can suggest the optimal observation spot based on the user's current location.

[0062] The stargazing system can further adjust observation settings considering the battery level of the user's device. For example, if the battery level is low, the system can shorten the exposure time to reduce the observation time. If the battery level is sufficient, the system can perform observations with high image quality settings. Furthermore, if the battery level is moderate, the system can perform observations with balanced settings. In this way, the stargazing system can adjust observation settings considering the battery level of the user's device.

[0063] The stargazing system can further analyze users' social media activity and prioritize the observation of relevant celestial objects. For example, it can prioritize observation of relevant celestial objects based on data shared by users on social media. It can also prioritize observation of relevant celestial objects based on astronomers and astronomical events followed by users on social media. Furthermore, it can analyze trends in astronomical communities that users participate in on social media and prioritize the observation of relevant celestial objects. In short, the stargazing system can analyze users' social media activity and prioritize the observation of relevant celestial objects.

[0064] The stargazing system can further improve identification accuracy by tracking the movement and changes of celestial bodies in real time. For example, it can update the position information of celestial bodies in real time to improve identification accuracy. It can also track changes in the brightness and color of celestial bodies in real time to improve identification accuracy. Furthermore, it can track the movement of celestial bodies in real time to improve identification accuracy. In this way, the stargazing system can improve identification accuracy by tracking the movement and changes of celestial bodies in real time.

[0065] The stargazing system can further improve its identification accuracy by referring to past observational data of celestial objects. For example, it can improve identification accuracy by referring to past positional information of celestial objects. It can also improve identification accuracy by referring to past brightness and color data of celestial objects. Furthermore, it can improve identification accuracy by referring to past movement data of celestial objects. In this way, the stargazing system can improve its identification accuracy by referring to past observational data of celestial objects.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The user takes a picture of the night sky. The shooting unit can take pictures of the night sky using a smartphone or tablet camera, a digital camera, or a dedicated astronomical observation camera. The shooting unit can also automatically select shooting modes such as long exposure and high-sensitivity shooting. For example, if you take a picture of the night sky using a smartphone camera, the AI ​​will automatically select the optimal shooting mode. Alternatively, you can use a digital camera to perform high-sensitivity shooting and capture faint starlight. Step 2: The identification unit analyzes the images captured by the imaging unit and identifies celestial objects. The identification unit uses generative AI to analyze the images and determine the position and type of celestial object. It can also analyze characteristics such as brightness, color, and shape of celestial objects to identify them. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. Step 3: The generation unit generates a star chart based on the celestial objects identified by the identification unit. The generation unit can generate a star chart based on the positional information of celestial objects using generation AI. It can also generate a star chart considering surrounding light pollution and atmospheric conditions. Furthermore, the generation unit can recreate past and future starry skies. Step 4: The commentary section provides explanations of celestial bodies based on the star chart generated by the generation section. The commentary section can use AI to explain myths, history, and the latest astronomical discoveries for each celestial body. It can also provide explanations tailored to the user's interests and knowledge level. Furthermore, the commentary section can provide explanations while engaging in natural conversation with the user through voice interaction.

[0068] (Example of form 2) The stargazing system according to an embodiment of the present invention is a groundbreaking system that revolutionizes the stargazing experience by utilizing cutting-edge generative AI technology. When a user photographs the night sky with their smartphone or tablet, the AI ​​instantly identifies celestial objects and generates an accurate star chart based on the location and time. For example, even for faint stars visible in urban areas, the AI ​​considers surrounding light pollution and atmospheric conditions to provide a complete star map that includes even invisible celestial objects. Furthermore, the AI ​​provides explanations for each celestial object, tailored to the user's interests and knowledge level, covering everything from mythology and history to the latest astronomical discoveries. Users can deepen their knowledge of the night sky as if on a guided tour by an expert, engaging in natural conversations with the AI ​​through voice interaction. It is also possible to simulate the night sky during the daytime or recreate past and future starry skies using AR technology. The community function analyzes each user's observation history and interests, matching them with other users who share similar hobbies and notifying them of opportunities to observe rare astronomical phenomena. This will allow it to function as a new-era astronomy education and entertainment platform, going beyond a simple constellation app, making the mystery and wonder of the universe accessible to everyone. The stargazing system will enable users to photograph the night sky, identify celestial objects, generate star charts, and receive explanations of celestial bodies.

[0069] The stargazing system according to this embodiment comprises a shooting unit, an identification unit, a generation unit, and an explanation unit. The shooting unit allows the user to photograph the night sky. The shooting unit can, for example, use the camera of a smartphone or tablet to photograph the night sky. The shooting unit can also use a digital camera or a dedicated astronomical observation camera to photograph the night sky. Furthermore, the shooting unit can automatically select a shooting mode such as long exposure or high-sensitivity shooting. For example, the shooting unit can photograph the night sky using a smartphone camera, and the AI ​​will automatically select the optimal shooting mode. The shooting unit can also perform high-sensitivity shooting using a digital camera to capture faint starlight. The identification unit analyzes the images taken by the shooting unit and identifies celestial objects. For example, the identification unit can perform image analysis using generation AI to identify the position and type of celestial object. The identification unit can also analyze the characteristics of celestial objects such as brightness, color, and shape to identify them. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. For example, the identification unit can use generation AI to identify the position of celestial objects and generate a star chart. The identification unit can analyze the brightness and color of celestial objects to identify the type of star. The generation unit generates a star chart based on the celestial objects identified by the identification unit. For example, the generation unit can generate a star chart based on the positional information of celestial objects using generation AI. The generation unit can also generate a star chart considering surrounding light pollution and atmospheric conditions. Furthermore, the generation unit can recreate past and future starry skies. For example, the generation unit can generate an accurate star chart based on the positional information of celestial objects using generation AI. The generation unit can also generate a star chart that includes celestial objects that are not actually visible, taking into account light pollution and atmospheric conditions. The explanation unit provides explanations of celestial objects based on the star chart generated by the generation unit. For example, the explanation unit can use AI to explain myths, history, and the latest astronomical discoveries about each celestial object. The explanation unit can also provide explanations tailored to the user's interests and knowledge level. Furthermore, the explanation unit can provide explanations while having a natural conversation with the user through voice dialogue. For example, the explanation unit can use AI to explain the myths and history of a specific constellation. Furthermore, the commentary section can explain the latest astronomical discoveries, which can pique the user's interest.As a result, the stargazing system according to this embodiment allows the user to photograph the night sky, identify celestial objects, generate star charts, and provide explanations of celestial objects.

[0070] The shooting function allows users to photograph the night sky. For example, users can use the cameras on their smartphones or tablets to photograph the night sky. It can also use digital cameras or dedicated astronomical cameras. Furthermore, the shooting function can automatically select shooting modes such as long exposure and high-sensitivity shooting. For example, the shooting function can use a smartphone camera to photograph the night sky, and the AI ​​will automatically select the optimal shooting mode. It can also use a digital camera for high-sensitivity shooting to capture faint starlight. The shooting function has a feature that automatically adjusts camera settings when the user photographs the night sky. For example, the AI ​​analyzes the brightness of the night sky and weather conditions to set the optimal exposure time and ISO sensitivity. This allows users to easily take beautiful photos of the starry sky without specialized knowledge. The shooting function also has a function to generate high-resolution starry sky images by combining multiple images. For example, the AI ​​analyzes multiple images taken in succession, removing noise and generating a high-resolution image. This allows for the capture of faint starlight and the detailed structure of celestial objects. Furthermore, the camera's shooting function also includes a feature that allows users to save images they've taken to the cloud, making them accessible from other devices. This makes it easy for users to share their captured images of the starry sky and re-edit them later.

[0071] The identification unit analyzes images captured by the imaging unit to identify celestial objects. For example, the identification unit can use generative AI to analyze images and determine the position and type of celestial object. It can also analyze features such as brightness, color, and shape to identify celestial objects. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. For example, the identification unit uses generative AI to determine the position of a celestial object and generate a star chart. It can also analyze the brightness and color of a celestial object to identify its type. The identification unit uses AI to analyze captured images and determine the position and type of celestial object. Specifically, the AI ​​uses image recognition technology to identify celestial objects such as stars, planets, and nebulae, and to determine the position of each object. Furthermore, the AI ​​analyzes features such as brightness, color, and shape to identify the type of celestial object. For example, the AI ​​analyzes the brightness of a star to identify fixed stars and variable stars. The AI ​​can also analyze the color of a celestial object to estimate its temperature and age. The identification unit can track the movement and changes of celestial bodies in real time, improving identification accuracy. For example, the AI ​​analyzes a series of images to track the movement of celestial bodies. This allows for accurate determination of the position and movement of celestial bodies, improving identification accuracy. Furthermore, the identification unit can improve the accuracy of celestial body identification by utilizing past observation data and astronomical databases. For example, the AI ​​can predict the position and movement of a specific celestial body based on past observation data, improving identification accuracy.

[0072] The generation unit generates a star chart based on celestial objects identified by the identification unit. For example, the generation unit can generate a star chart based on the positional information of celestial objects using a generation AI. Furthermore, the generation unit can generate a star chart considering surrounding light pollution and atmospheric conditions. In addition, the generation unit can recreate past and future starry skies. For example, the generation unit uses a generation AI to generate an accurate star chart based on the positional information of celestial objects. Furthermore, the generation unit can create a star chart that includes celestial objects that are not actually visible, taking into account light pollution and atmospheric conditions. The generation unit generates an accurate star chart based on the positional information of celestial objects identified by the identification unit. Specifically, the AI ​​analyzes the positional information of celestial objects and generates a star chart. The generation unit can generate a star chart considering surrounding light pollution and atmospheric conditions. For example, the AI ​​analyzes the effects of light pollution and generates a star chart that includes celestial objects that are not actually visible. The AI ​​also analyzes atmospheric conditions and can accurately recreate how celestial objects appear. The generation unit can recreate past and future starry skies. For example, the AI ​​can recreate past starry skies based on past celestial object positional information. Furthermore, the AI ​​can predict the future positions of celestial bodies and recreate the future night sky. This allows users to observe past and future night skies. In addition, the generation unit can recreate the night sky visible from a specific location based on the user's location information. For example, the AI ​​can analyze the user's location information and accurately recreate the night sky visible from that location. This allows users to observe the night sky as seen from their own location.

[0073] The commentary section provides explanations of celestial objects based on the star chart generated by the generation section. For example, the commentary section can use AI to explain myths, history, and the latest astronomical discoveries for each celestial object. Furthermore, the commentary section can tailor its explanations to the user's interests and knowledge level. In addition, the commentary section can engage in natural conversation with the user through voice dialogue. For example, the commentary section can use AI to explain the myths and history of a specific constellation. It can also explain the latest astronomical discoveries to pique the user's interest. The commentary section provides detailed explanations of each celestial object based on the star chart generated by the generation section. Specifically, the AI ​​analyzes the positional information and characteristics of celestial objects and explains their myths, history, and the latest astronomical discoveries. For example, the AI ​​can explain the myths and history of a specific constellation, describing how it was named and what stories surround it. The AI ​​can also explain the latest astronomical discoveries, providing the user with interesting information. The commentary section can tailor its explanations to the user's interests and knowledge level. For example, the AI ​​analyzes the user's past search history and interests, and customizes the explanations based on that information. This allows users to obtain information that matches their interests. Furthermore, the explanation unit can provide explanations while naturally conversing with the user through voice interaction. For instance, the AI ​​can answer user questions in real time and provide explanations in a conversational format. This allows users to gain a deeper understanding. The explanation unit can support users in enjoying stargazing and stimulate their interest in astronomy.

[0074] The explanatory section can explain the myths, history, and latest astronomical discoveries for each celestial body. For example, it can explain the myths and history of a particular constellation. For instance, it can explain the origins of constellations based on Greek and Roman mythology. Furthermore, the explanatory section can provide users with new knowledge by explaining the latest astronomical discoveries. For example, it can introduce the latest findings on a particular celestial body based on recent research papers and observational data. In addition, the explanatory section can analyze the user's observation history and interests regarding celestial bodies and provide relevant information. For example, it can explain the myths and history related to celestial bodies the user has observed in the past. This allows the explanatory section to explain the myths, history, and latest astronomical discoveries for each celestial body.

[0075] The explanation section can provide explanations tailored to the user's interests and knowledge level. For example, the explanation section can conduct surveys to identify the user's interests and knowledge level. For instance, it can ask users about celestial objects they are interested in or information they want to know, and adjust the explanation content based on their answers. The explanation section can also analyze the user's past usage history to identify their interests and knowledge level. For example, it can determine the user's interests and knowledge level based on celestial objects they have observed and explanations they have viewed in the past. Furthermore, the explanation section can create user profiles and provide individualized explanations. For example, it can select appropriate explanations based on information such as the user's age, education level, and occupation. This allows the explanation section to provide explanations tailored to the user's interests and knowledge level.

[0076] The generation unit can generate star charts while considering surrounding light pollution and atmospheric conditions. For example, the generation unit can generate star charts while considering the effects of light pollution. For instance, the generation unit can consider light pollution in urban areas and generate star charts that include celestial objects that are not actually visible. The generation unit can also generate star charts while considering atmospheric conditions. For example, the generation unit can consider atmospheric conditions such as cloud cover, humidity, and temperature and adjust how celestial objects appear. Furthermore, the generation unit can simulate past and future atmospheric conditions and generate star charts based on them. For example, the generation unit can recreate the atmospheric conditions of a specific date and time based on past observation data and recreate the night sky at that time. This allows the generation unit to generate star charts while considering surrounding light pollution and atmospheric conditions.

[0077] The generation unit can simulate the night sky even during the daytime using AR technology. For example, the generation unit can use AR technology to simulate the night sky during the day. For instance, the generation unit can use a smartphone or tablet camera to photograph the daytime sky and display the night sky using AR technology. The generation unit can also recreate the starry sky at a specific date and time. For example, the generation unit can simulate the starry sky at a specific date and time in the past or future and display it to the user. Furthermore, the generation unit can use AR technology to display the positions and movements of celestial bodies in real time. For example, the generation unit can use a smartphone camera to photograph the current sky and display the positions and movements of celestial bodies using AR technology. As a result, the generation unit can use AR technology to simulate the night sky even during the daytime.

[0078] The generation unit can recreate past and future starry skies. For example, it can recreate past starry skies. For instance, it can recreate the starry sky at a specific date and time based on past observational data. Furthermore, the generation unit can simulate future starry skies. For example, it can simulate the starry sky at a specific date and time in the future based on celestial position data. In addition, the generation unit can recreate specific astronomical phenomena from the past and future. For example, it can recreate and display past astronomical phenomena such as meteor showers, solar eclipses, and comet approaches to the user. In this way, the generation unit can recreate past and future starry skies.

[0079] The commentary unit can provide explanations while engaging in natural conversation with the user through voice interaction. For example, the commentary unit can respond to user questions using speech recognition technology. For instance, if a user asks about a specific celestial body, the commentary unit will provide an explanation via voice. The commentary unit can also engage in natural conversations with the user using a dialogue system. For example, the commentary unit can provide appropriate explanations according to the user's interests and level of knowledge. Furthermore, the commentary unit can provide explanations using speech synthesis technology. For example, the commentary unit can provide explanations in a natural, expert-like voice using speech synthesis technology. This allows the commentary unit to provide explanations while engaging in natural conversation with the user through voice interaction.

[0080] The commentary section can provide knowledge about the night sky, much like a guided tour by an expert. For example, it can offer guided tours based on expert commentary. For instance, it can provide detailed information about specific constellations or celestial bodies based on expert explanations. Furthermore, the commentary section can create tour scenarios and provide users with sequential explanations. For example, it can offer tours based on specific themes, deepening users' knowledge of the night sky. In addition, the commentary section can provide personalized guided tours tailored to the user's interests and knowledge level. For example, it can provide detailed explanations about specific celestial bodies or constellations based on the user's interests. In this way, the commentary section can provide knowledge about the night sky, much like a guided tour by an expert.

[0081] The commentary section provides community features, analyzing users' observation history and interests, and matching them with other users who share similar interests. For example, the commentary section analyzes a user's observation history to identify users with similar interests. For example, it finds common ground with other users based on celestial objects and interests that the user has observed in the past. The commentary section can also analyze users' interests and perform appropriate matching. For example, it matches users with other users based on their interests and knowledge level. Furthermore, the commentary section can provide community features that allow users to share information with each other. For example, it can provide forums and chat functions, creating a space for users to share observation information and knowledge. This allows the commentary section to analyze users' observation history and interests and match them with other users who share similar interests.

[0082] The information section can notify users of opportunities to observe rare astronomical phenomena. For example, it can notify users of the date, time, and location where a specific astronomical phenomenon can be observed. For instance, it can inform users of opportunities to observe astronomical phenomena such as meteor showers, solar eclipses, and comet approaches. The information section can also provide personalized notifications based on the user's observation history and interests. For example, it can prioritize notifications related to astronomical phenomena that the user has shown interest in in the past. Furthermore, the information section can also explain observation methods and precautions for astronomical phenomena. For example, it can provide users with the optimal location, time, and necessary equipment for observing a specific astronomical phenomenon. This allows the information section to notify users of opportunities to observe rare astronomical phenomena.

[0083] The camera unit can estimate the user's emotions and adjust the shooting timing based on those emotions. For example, if the user is excited, the AI ​​can detect that emotion and take a picture at the moment when the most interesting celestial object is visible. For example, the camera unit can detect the user's excitement and select the moment when a particular celestial object is brightest to take a picture. Also, if the user is relaxed, the AI ​​can detect that emotion and take a picture at a stable, shake-free moment. For example, the camera unit can detect the user's relaxation and take a picture at a time with minimal camera shake. Furthermore, if the user is tired, the AI ​​can detect that emotion and adjust the settings to complete the shooting in a short amount of time. For example, the camera unit can detect the user's fatigue and select the optimal settings to complete the shooting in a short amount of time. In this way, the camera unit can adjust the shooting timing based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The imaging unit can analyze the user's past observation history and automatically adjust the optimal shooting settings during shooting. For example, the imaging unit can automatically set the optimal exposure time and ISO sensitivity based on data of celestial objects the user has previously photographed. For example, the imaging unit analyzes the user's past observation data and selects the optimal shooting settings. The imaging unit can also automatically adjust the camera orientation considering the positions of celestial objects the user has previously preferred to photograph. For example, the imaging unit optimizes the camera orientation based on the user's past observation history. Furthermore, the imaging unit can suggest the optimal shooting time based on the user's past observation history and perform shooting accordingly. For example, the imaging unit suggests the optimal shooting time based on the user's past observation data. This allows the imaging unit to analyze the user's past observation history and automatically adjust the optimal shooting settings.

[0085] The camera unit can suggest the optimal shooting angle and direction based on the user's current location information during shooting. For example, the camera unit can calculate the position of the most easily visible celestial object based on the user's current location and suggest the camera's orientation. For example, the camera unit can calculate the optimal shooting angle based on the user's current location's GPS data. The camera unit can also automatically set the optimal shooting angle considering the user's current altitude and azimuth. For example, the camera unit can optimize the camera's orientation based on the user's current altitude and azimuth. Furthermore, the camera unit can suggest the optimal shooting direction based on the user's current location's weather information. For example, the camera unit can suggest the optimal shooting direction based on the user's current location's weather data. In this way, the camera unit can suggest the optimal shooting angle and direction based on the user's current location information.

[0086] The imaging unit can estimate the user's emotions and determine the priority of celestial objects to photograph based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and prioritize photographing the most interesting celestial objects. For example, the imaging unit can detect the user's excitement and prioritize photographing specific celestial objects. Also, if the user is relaxed, the AI ​​can detect that emotion and prioritize photographing stable, shake-free celestial objects. For example, the imaging unit can detect the user's relaxation and prioritize photographing celestial objects with minimal shake. Furthermore, if the user is tired, the AI ​​can detect that emotion and prioritize photographing celestial objects that can be photographed in a short amount of time. For example, the imaging unit can detect the user's fatigue and select celestial objects that can be photographed in a short amount of time. In this way, the imaging unit can determine the priority of celestial objects to photograph based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0087] The camera unit can adjust shooting settings while considering the battery level of the user's device. For example, if the battery level is low, the AI ​​will shorten the exposure time to reduce the shooting time. For example, the camera unit detects the battery level and selects the optimal shooting settings. Also, if the battery level is sufficient, the AI ​​can shoot with high-quality settings. For example, the camera unit selects high-quality settings based on the battery level. Furthermore, if the battery level is moderate, the AI ​​can shoot with balanced settings. For example, the camera unit selects balanced shooting settings based on the battery level. In this way, the camera unit can adjust shooting settings while considering the battery level of the user's device.

[0088] The imaging team can analyze users' social media activity during imaging and prioritize capturing relevant celestial objects. For example, the team can prioritize capturing relevant celestial objects based on data of celestial objects shared by users on social media. For example, the team can analyze users' social media posts and select relevant celestial objects. The imaging team can also prioritize capturing relevant celestial objects based on astronomers and astronomical events that users follow on social media. For example, the team can select relevant celestial objects based on users' following information. Furthermore, the imaging team can analyze trends in astronomical communities that users participate in on social media and prioritize capturing relevant celestial objects. For example, the team can select relevant celestial objects based on trends in astronomical communities. This allows the imaging team to analyze users' social media activity and prioritize capturing relevant celestial objects.

[0089] The identification unit can estimate the user's emotions and adjust the identification algorithm based on the estimated emotions. For example, if the user is excited, the identification unit can detect that emotion and apply an algorithm that performs rapid identification. For example, the identification unit detects the user's excitement and selects a rapid identification algorithm. The identification unit can also detect the user's relaxation and apply an algorithm that performs detailed identification. For example, the identification unit detects the user's relaxation and selects a detailed identification algorithm. Furthermore, if the user is tired, the identification unit can detect that emotion and apply a simplified identification algorithm. For example, the identification unit detects the user's fatigue and selects a simplified identification algorithm. In this way, the identification unit can adjust the identification algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The identification unit can improve identification accuracy by tracking the movement and changes of celestial bodies in real time during identification. For example, the identification unit can improve identification accuracy by updating the position information of celestial bodies in real time. For example, the identification unit can acquire position data of celestial bodies in real time and reflect it in the identification algorithm. The identification unit can also improve identification accuracy by tracking changes in the brightness and color of celestial bodies in real time. For example, the identification unit can analyze changes in the brightness and color of celestial bodies in real time and reflect it in the identification algorithm. Furthermore, the identification unit can improve identification accuracy by tracking the movement of celestial bodies in real time. For example, the identification unit can acquire data on the movement of celestial bodies in real time and reflect it in the identification algorithm. As a result, the identification unit can track the movement and changes of celestial bodies in real time and improve identification accuracy.

[0091] The identification unit can improve its identification accuracy by referring to past observational data of celestial objects during identification. For example, the identification unit can improve its identification accuracy by referring to past positional information of celestial objects. For example, the identification unit can acquire past positional data of celestial objects and reflect it in the identification algorithm. The identification unit can also improve its identification accuracy by referring to past brightness and color data of celestial objects. For example, the identification unit can acquire past brightness and color data of celestial objects and reflect it in the identification algorithm. Furthermore, the identification unit can improve its identification accuracy by referring to past movement data of celestial objects. For example, the identification unit can acquire past movement data of celestial objects and reflect it in the identification algorithm. In this way, the identification unit can improve its identification accuracy by referring to past observational data of celestial objects.

[0092] 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 excited, the AI ​​can detect that emotion and provide a visually stimulating display method. For example, the identification unit can detect the user's excitement and select a colorful and dynamic display method. Also, if the user is relaxed, the AI ​​can detect that emotion and provide a calm display method. For example, the identification unit can detect the user's relaxation and select a simple and calm display method. Furthermore, if the user is tired, the AI ​​can detect that emotion and provide a simple and highly visible display method. For example, the identification unit can detect the user's fatigue and select a highly visible display method. In this way, the identification unit can adjust the display method of the identification results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The identification unit can improve its identification accuracy by considering the color and brightness of celestial objects during identification. For example, the identification unit can apply an algorithm to improve identification accuracy based on the color of the celestial object. For example, the identification unit can acquire color data of the celestial object and reflect it in the identification algorithm. The identification unit can also apply an algorithm to improve identification accuracy based on the brightness of the celestial object. For example, the identification unit can acquire brightness data of the celestial object and reflect it in the identification algorithm. Furthermore, the identification unit can apply an algorithm to improve identification accuracy by simultaneously considering changes in the color and brightness of the celestial object. For example, the identification unit can acquire data on changes in the color and brightness of the celestial object and reflect it in the identification algorithm. In this way, the identification unit can improve its identification accuracy by considering the color and brightness of the celestial object.

[0094] The identification unit can improve its identification accuracy by referring to relevant literature on celestial objects during the identification process. For example, the identification unit can improve its identification accuracy by referring to academic papers on celestial objects. For example, the identification unit can acquire academic papers on celestial objects and incorporate them into the identification algorithm. The identification unit can also improve its identification accuracy by referring to observation reports on celestial objects. For example, the identification unit can acquire observation reports on celestial objects and incorporate them into the identification algorithm. Furthermore, the identification unit can improve its identification accuracy by adjusting the identification algorithm based on relevant literature on celestial objects. For example, the identification unit can acquire relevant literature on celestial objects and incorporate it into the identification algorithm. In this way, the identification unit can improve its identification accuracy by referring to relevant literature on celestial objects.

[0095] The generation unit can estimate the user's emotions and adjust the star chart generation method based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect that emotion and generate a visually stimulating star chart. For example, the generation unit can detect the user's excitement and generate a colorful and dynamic star chart. Also, if the user is relaxed, the AI ​​can detect that emotion and generate a star chart with a calm design. For example, the generation unit can detect the user's relaxation and generate a simple and calm design. Furthermore, if the user is tired, the AI ​​can detect that emotion and generate a simple and highly visible star chart. For example, the generation unit can detect the user's fatigue and generate a highly visible star chart. In this way, the generation unit can adjust the star chart generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The generation unit can improve the accuracy of the star chart by considering the relationships between celestial bodies during generation. For example, the generation unit generates an accurate star chart based on the positional relationships of celestial bodies. For example, the generation unit acquires positional data of celestial bodies and generates a star chart considering their relationships. The generation unit can also generate a dynamic star chart by considering the movement of celestial bodies. For example, the generation unit acquires data on the movement of celestial bodies and generates a dynamic star chart. Furthermore, the generation unit can generate a detailed star chart based on the relationships between celestial bodies. For example, the generation unit acquires data on the relationships between celestial bodies and generates a detailed star chart. In this way, the generation unit can improve the accuracy of the star chart by considering the relationships between celestial bodies.

[0097] The generation unit can improve the accuracy of the star chart by referencing past observational data of celestial bodies during generation. For example, the generation unit can generate an accurate star chart by referencing past positional information of celestial bodies. For example, the generation unit can acquire past positional data of celestial bodies and generate a star chart. The generation unit can also generate a detailed star chart by referencing past brightness and color data of celestial bodies. For example, the generation unit can acquire past brightness and color data of celestial bodies and generate a star chart. Furthermore, the generation unit can generate a dynamic star chart by referencing data on the past movement of celestial bodies. For example, the generation unit can acquire data on the past movement of celestial bodies and generate a dynamic star chart. This allows the generation unit to improve the accuracy of the star chart by referencing past observational data of celestial bodies.

[0098] The generation unit can estimate the user's emotions and adjust the display method of the star chart based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and provide a visually stimulating display method. For example, the generation unit can detect the user's excitement and select a colorful and dynamic display method. Also, if the user is relaxed, the AI ​​can detect that emotion and provide a calm display method. For example, the generation unit can detect the user's relaxation and select a simple and calm display method. Furthermore, if the user is tired, the AI ​​can detect that emotion and provide a simple and highly visible display method. For example, the generation unit can detect the user's fatigue and select a highly visible display method. In this way, the generation unit can adjust the display method of the star chart based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The generation unit can generate star charts while considering the geographical distribution of celestial bodies. For example, the generation unit can generate accurate star charts based on the geographical distribution of celestial bodies. For example, the generation unit can acquire geographical distribution data of celestial bodies and generate a star chart. The generation unit can also generate detailed star charts while considering the geographical distribution of celestial bodies. For example, the generation unit can generate detailed star charts based on geographical distribution data of celestial bodies. Furthermore, the generation unit can generate dynamic star charts based on the geographical distribution of celestial bodies. For example, the generation unit can acquire geographical distribution data of celestial bodies and generate dynamic star charts. In this way, the generation unit can generate star charts while considering the geographical distribution of celestial bodies.

[0100] The generation unit can improve the accuracy of the star chart by referring to relevant literature on celestial objects during generation. For example, the generation unit can refer to academic papers on celestial objects to generate accurate star charts. For example, the generation unit can obtain academic papers on celestial objects and generate star charts. The generation unit can also refer to observation reports on celestial objects to generate detailed star charts. For example, the generation unit can obtain observation reports on celestial objects and generate star charts. Furthermore, the generation unit can generate dynamic star charts based on relevant literature on celestial objects. For example, the generation unit can obtain relevant literature on celestial objects and generate dynamic star charts. This allows the generation unit to improve the accuracy of the star chart by referring to relevant literature on celestial objects.

[0101] The commentary unit can estimate the user's emotions and adjust the way it presents the commentary based on those emotions. For example, if the user is excited, the AI ​​can detect that emotion and provide a visually stimulating commentary. For example, the commentary unit can detect the user's excitement and select a colorful and dynamic commentary. Also, if the user is relaxed, the AI ​​can detect that emotion and provide a calm commentary. For example, the commentary unit can detect the user's relaxation and select a simple and calm commentary. Furthermore, if the user is tired, the AI ​​can detect that emotion and provide a simple and highly visible commentary. For example, the commentary unit can detect the user's fatigue and select a highly visible commentary. In this way, the commentary unit can adjust the way it presents the commentary based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The commentary section can adjust the level of detail in its explanations based on the importance of the celestial objects. For example, it can provide detailed explanations for important celestial objects. For instance, it can provide detailed explanations based on the scientific value or observation frequency of a particular celestial object. It can also provide concise explanations for common celestial objects. For example, it can provide a concise and to-the-point explanation for common celestial objects. Furthermore, the commentary section can prioritize explaining highly important celestial objects based on the user's interests. For example, it can select highly important celestial objects based on the user's interests and provide detailed explanations. This allows the commentary section to adjust the level of detail in its explanations based on the importance of the celestial objects.

[0103] The commentary section can apply different commentary algorithms depending on the category of the celestial object. For example, for constellations, the commentary section provides commentary based on mythology and history. For example, the commentary section provides detailed commentary on a specific constellation based on mythology and history. The commentary section can also provide commentary on planets based on the latest astronomical discoveries. For example, the commentary section provides commentary on a specific planet based on the latest research papers and observational data. Furthermore, the commentary section can provide commentary on galaxies based on scientific data. For example, the commentary section provides detailed commentary on a specific galaxy based on scientific data. This allows the commentary section to apply different commentary algorithms depending on the category of the celestial object.

[0104] The commentary section can estimate the user's emotions and adjust the length of the commentary based on the estimated emotions. For example, if the user is excited, the AI ​​can detect that emotion and provide a detailed commentary. For example, the commentary section can detect the user's excitement and provide a long, detailed commentary. Also, if the user is relaxed, the AI ​​can detect that emotion and provide a commentary of appropriate length. For example, the commentary section can detect the user's relaxation and provide a commentary of appropriate length. Furthermore, if the user is tired, the AI ​​can detect that emotion and provide a short, concise commentary. For example, the commentary section can detect the user's fatigue and provide a short, concise commentary. In this way, the commentary section can adjust the length of the commentary based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The commentary section can determine the priority of its commentary based on the timing of celestial object observations. For example, it can prioritize commenting on currently observable objects. For instance, it can identify currently observable objects and provide commentary on them first. It can also prioritize commenting on objects that will be observable in the near future. For example, it can identify and provide commentary on objects that will be observable in the near future. Furthermore, it can provide commentary on previously observed objects last. For example, it can identify and provide commentary on previously observed objects. This allows the commentary section to determine the priority of its commentary based on the timing of celestial object observations.

[0106] The commentary section can adjust the order of explanations based on the relationships between celestial bodies. For example, it can group related celestial bodies and provide explanations in a specific order. For instance, it can group celestial bodies belonging to the same constellation and provide explanations in a specific order. Furthermore, the commentary section can consider the interrelationships of celestial bodies and provide explanations in order of relevance. For example, it can consider the physical interactions of celestial bodies and provide explanations in order of relevance. Additionally, the commentary section can prioritize explaining celestial bodies that are highly relevant to the user's interests. For instance, it can select and explain highly relevant celestial bodies based on the user's interests. This allows the commentary section to adjust the order of explanations based on the relationships between celestial bodies.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The stargazing system can further estimate the user's emotions and customize the observation experience based on those emotions. For example, if the user is excited, the system can detect that emotion and generate a visually stimulating star chart. If the user is relaxed, the system can detect that emotion and generate a calming star chart. Furthermore, if the user is tired, the system can detect that emotion and generate a simple, easy-to-read star chart. In this way, the stargazing system can customize the observation experience based on the user's emotions.

[0109] The stargazing system can further analyze the user's past observation history and propose the optimal observation plan. For example, it can suggest the next celestial object to observe based on data from celestial objects the user has observed in the past. It can also suggest the optimal observation time and location based on the user's observation history. Furthermore, it can analyze the user's observation history and provide the latest information on celestial objects of interest. In this way, the stargazing system can leverage the user's past observation history to provide a more effective observation experience.

[0110] The stargazing system can also suggest the optimal observation spot based on the user's current location. For example, it can calculate the position of the most easily visible celestial object based on the user's GPS data and suggest an observation spot. It can also automatically set the optimal observation spot considering the user's current altitude and azimuth. Furthermore, it can suggest the optimal observation spot based on the weather information of the user's current location. In short, the stargazing system can suggest the optimal observation spot based on the user's current location.

[0111] The stargazing system can also estimate the user's emotions and prioritize celestial objects to observe based on those emotions. For example, if the user is excited, the system can detect this emotion and prioritize observing the most interesting celestial objects. If the user is relaxed, the system can detect this emotion and prioritize observing stable, shake-free celestial objects. Furthermore, if the user is tired, the system can detect this emotion and prioritize observing celestial objects that can be observed in a short amount of time. In this way, the stargazing system can prioritize celestial objects to observe based on the user's emotions.

[0112] The stargazing system can further adjust observation settings considering the battery level of the user's device. For example, if the battery level is low, the system can shorten the exposure time to reduce the observation time. If the battery level is sufficient, the system can perform observations with high image quality settings. Furthermore, if the battery level is moderate, the system can perform observations with balanced settings. In this way, the stargazing system can adjust observation settings considering the battery level of the user's device.

[0113] The stargazing system can further analyze users' social media activity and prioritize the observation of relevant celestial objects. For example, it can prioritize observation of relevant celestial objects based on data shared by users on social media. It can also prioritize observation of relevant celestial objects based on astronomers and astronomical events followed by users on social media. Furthermore, it can analyze trends in astronomical communities that users participate in on social media and prioritize the observation of relevant celestial objects. In short, the stargazing system can analyze users' social media activity and prioritize the observation of relevant celestial objects.

[0114] The stargazing system can further estimate the user's emotions and adjust its identification algorithm based on those emotions. For example, if the user is excited, the system can detect that emotion and apply a rapid identification algorithm. If the user is relaxed, the system can detect that emotion and apply a more detailed identification algorithm. Furthermore, if the user is tired, the system can detect that emotion and apply a simplified identification algorithm. In this way, the stargazing system can adjust its identification algorithm based on the user's emotions.

[0115] The stargazing system can further improve identification accuracy by tracking the movement and changes of celestial bodies in real time. For example, it can update the position information of celestial bodies in real time to improve identification accuracy. It can also track changes in the brightness and color of celestial bodies in real time to improve identification accuracy. Furthermore, it can track the movement of celestial bodies in real time to improve identification accuracy. In this way, the stargazing system can improve identification accuracy by tracking the movement and changes of celestial bodies in real time.

[0116] The stargazing system can further improve its identification accuracy by referring to past observational data of celestial objects. For example, it can improve identification accuracy by referring to past positional information of celestial objects. It can also improve identification accuracy by referring to past brightness and color data of celestial objects. Furthermore, it can improve identification accuracy by referring to past movement data of celestial objects. In this way, the stargazing system can improve its identification accuracy by referring to past observational data of celestial objects.

[0117] The stargazing system can further estimate the user's emotions and adjust the display method of the identification results based on those emotions. For example, if the user is excited, the system can detect that emotion and provide a visually stimulating display method. If the user is relaxed, the system can detect that emotion and provide a calming display method. Furthermore, if the user is tired, the system can detect that emotion and provide a simple and easy-to-read display method. In this way, the stargazing system can adjust the display method of the identification results based on the user's emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The user takes a picture of the night sky. The shooting unit can take pictures of the night sky using a smartphone or tablet camera, a digital camera, or a dedicated astronomical observation camera. The shooting unit can also automatically select shooting modes such as long exposure and high-sensitivity shooting. For example, if you take a picture of the night sky using a smartphone camera, the AI ​​will automatically select the optimal shooting mode. Alternatively, you can use a digital camera to perform high-sensitivity shooting and capture faint starlight. Step 2: The identification unit analyzes the images captured by the imaging unit and identifies celestial objects. The identification unit uses generative AI to analyze the images and determine the position and type of celestial object. It can also analyze characteristics such as brightness, color, and shape of celestial objects to identify them. Furthermore, the identification unit can track the movement and changes of celestial objects in real time to improve identification accuracy. Step 3: The generation unit generates a star chart based on the celestial objects identified by the identification unit. The generation unit can generate a star chart based on the positional information of celestial objects using generation AI. It can also generate a star chart considering surrounding light pollution and atmospheric conditions. Furthermore, the generation unit can recreate past and future starry skies. Step 4: The commentary section provides explanations of celestial bodies based on the star chart generated by the generation section. The commentary section can use AI to explain myths, history, and the latest astronomical discoveries for each celestial body. It can also provide explanations tailored to the user's interests and knowledge level. Furthermore, the commentary section can provide explanations while engaging in natural conversation with the user through voice interaction.

[0120] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0123] Each of the multiple elements described above, including the imaging unit, identification unit, generation unit, and explanation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit can photograph the night sky using the camera 42 of the smart device 14. The identification unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which analyzes the captured image to identify celestial objects. The generation unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which generates a star chart based on the identified celestial objects. The explanation unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which provides an explanation of the celestial objects based on the generated star chart. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the imaging unit, identification unit, generation unit, and explanation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit can photograph the night sky using the camera 42 of the smart glasses 214. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the captured image to identify celestial objects. The generation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which generates a star chart based on the identified celestial objects. The explanation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which provides an explanation of the celestial objects based on the generated star chart. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the imaging unit, identification unit, generation unit, and explanation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit can photograph the night sky using the camera 42 of the headset terminal 314. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and identifies celestial objects by analyzing the captured image. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates a star chart based on the identified celestial objects. The explanation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides an explanation of the celestial objects based on the generated star chart. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the imaging unit, identification unit, generation unit, and explanation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the imaging unit can photograph the night sky using the camera 42 of the robot 414. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the captured image to identify celestial objects. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a star chart based on the identified celestial objects. The explanation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides an explanation of the celestial objects based on the generated star chart. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0173] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) The photography team, where users take pictures of the night sky, An identification unit analyzes the image captured by the aforementioned imaging unit and identifies celestial objects, A generation unit that generates a star chart based on the celestial bodies identified by the identification unit, The system includes an explanatory unit that provides explanations of celestial bodies based on the star chart generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned explanatory section is, This book explains the myths, history, and latest astronomical discoveries surrounding each celestial body. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned explanatory section is, Provide explanations tailored to the user's interests and knowledge level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a star chart that takes into account surrounding light pollution and atmospheric conditions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Using AR technology to simulate the night sky even during the daytime. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Recreating past and future starry skies The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned explanatory section is, The system provides explanations while engaging in natural conversations with the user through voice interaction. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned explanatory section is, Providing knowledge about the night sky, like a guided tour by an expert. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned explanatory section is, It provides community features, analyzes users' browsing history and interests, and matches them with other users who have similar hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned explanatory section is, Notify you of opportunities to observe rare astronomical phenomena. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is It estimates the user's emotions and adjusts the shooting timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is During shooting, the system analyzes the user's past observation history and automatically adjusts the optimal shooting settings. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned imaging unit is During shooting, the system suggests the optimal shooting angle and direction based on the user's current location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of celestial objects to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned imaging unit is When shooting, adjust the shooting settings considering the battery level of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned imaging unit is During shooting, the system analyzes the user's social media activity and prioritizes photographing relevant celestial objects. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned identification unit is The system estimates the user's emotions and adjusts the identification algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned identification unit is During identification, the movement and changes of celestial bodies are tracked in real time to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned identification unit is During identification, past observational data of celestial objects is referenced to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned identification unit is It estimates the user's emotions and adjusts how the identification results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned identification unit is When identifying celestial objects, the color and brightness of the objects are taken into consideration to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned identification unit is When identifying celestial objects, refer to relevant literature to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is The system estimates the user's emotions and adjusts the star chart generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the accuracy of the star chart is improved by considering the relationships between celestial bodies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the accuracy of the star chart is improved by referencing past observational data of celestial bodies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is The system estimates the user's emotions and adjusts how the star chart is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, the star chart is generated taking into account the geographical distribution of celestial bodies. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the accuracy of the star chart is improved by referring to relevant literature on celestial objects. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned explanatory section is, The system estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned explanatory section is, During the explanation, adjust the level of detail based on the importance of the celestial object. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned explanatory section is, When providing commentary, different commentary algorithms are applied depending on the category of the celestial object. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned explanatory section is, When providing explanations, the priority of the explanations is determined based on the timing of celestial object observations. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned explanatory section is, During the explanation, the order of explanation will be adjusted based on the relationships between celestial bodies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography section where users take pictures of the night sky, An identification unit analyzes the image captured by the aforementioned imaging unit and identifies celestial objects, A generation unit that generates a star chart based on the celestial bodies identified by the identification unit, The system includes an explanatory unit that provides explanations of celestial bodies based on the star chart generated by the generation unit. A system characterized by the following features.

2. The aforementioned explanatory section is, This book explains the myths, history, and latest astronomical discoveries surrounding each celestial body. The system according to feature 1.

3. The aforementioned explanatory section is, Provide explanations tailored to the user's interests and knowledge level. The system according to feature 1.

4. The generating unit is Generate a star chart that takes into account surrounding light pollution and atmospheric conditions. The system according to feature 1.

5. The generating unit is Using AR technology to simulate the night sky even during the daytime. The system according to feature 1.

6. The generating unit is Recreating past and future starry skies The system according to feature 1.

7. The aforementioned explanatory section is, The system provides explanations while engaging in natural conversations with the user through voice interaction. The system according to feature 1.

8. The aforementioned explanatory section is, Providing knowledge about the night sky, like a guided tour by an expert. The system according to feature 1.

Images