System

Abstract

An object of a system according to an embodiment is to make starry sky observation easier and more enjoyable and to promote information sharing and exchange between users.SOLUTION: A system includes an imaging unit, an identification unit, an explanation providing unit, an interaction unit, an information sharing unit, and a community unit. The photographing unit photographs a starry sky. The identification unit analyzes the image captured by the imaging unit to identify celestial objects and constellations. The explanation providing section provides an explanation about the celestial body or the constellation identified by the identifying section. The interaction unit performs interaction with a user. The information sharing unit performs information sharing between users. The community component facilitates interaction between users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, identifying and explaining celestial bodies and constellations during stargazing was time-consuming, and there was a problem of insufficient information sharing and interaction between users.

[0005] The system according to the embodiment aims to make stargazing easier and more enjoyable, and to promote information sharing and interaction between users. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an identification unit, an explanation providing unit, a dialogue unit, an information sharing unit, and a community unit. The photographing unit photographs the starry sky. The identification unit analyzes the image photographed by the photographing unit to identify celestial bodies and constellations. The explanation providing unit provides explanations about the celestial bodies and constellations identified by the identification unit. The dialogue unit dialogues with users. The information sharing unit shares information between users. The community unit promotes interaction between users. [Effects of the Invention]

[0007] The system according to the embodiment makes stargazing easier and more enjoyable, and can promote information sharing and interaction between users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The starry sky observation system according to an embodiment of the present invention allows users to take pictures of the starry sky using a smartphone or tablet, and the AI ​​generation system automatically identifies celestial bodies and constellations in that location and provides detailed explanations and related information. This allows users to enjoy stargazing more and deepen their knowledge of astronomy.

[0029] A stargazing system according to an embodiment includes a photographing unit, an identification unit, an explanation providing unit, a dialogue unit, an information sharing unit, and a community unit. The photographing unit allows a user to photograph a starry sky. For example, the photographing unit can photograph the starry sky using a smartphone or tablet camera. The photographing unit can also photograph a night sky image at high resolution. The photographing unit can also photograph a starry sky using a long exposure. The identification unit analyzes the image photographed by the photographing unit to identify celestial bodies and constellations. For example, the generation AI identifies celestial bodies and constellations using image analysis technology. The identification unit can also identify the positions of celestial bodies and constellations. The identification unit can also display the names of celestial bodies and constellations. The explanation providing unit provides explanations about the celestial bodies and constellations identified by the identification unit. For example, the generation AI provides detailed explanations about the identified celestial bodies and constellations in text format. The explanation providing unit can also provide explanations in audio format. The explanation providing unit can also provide related information in the form of links. The dialogue unit engages in dialogue with the user. For example, the generation AI generates appropriate answers to questions from users. The dialogue unit can also engage in real-time dialogue with users. Furthermore, the dialogue unit can also engage in dialogue to support users' learning. The information sharing unit shares information between users. For example, users can share images of starry skies they have taken or observation results with other users. The information sharing unit can also organize and display the shared information. Furthermore, the information sharing unit can also comment and rate the shared information. The community unit promotes interactions between users. For example, users can interact with other starry sky enthusiasts through the community function. The community unit can also promote discussions between users. Furthermore, the community unit can plan events and activities between users. This allows the starry sky observation system according to the embodiment to allow users to enjoy starry sky observation more and deepen their knowledge of astronomy. For example, a user can take a photo of the starry sky using their smartphone, and the generation AI can identify the celestial bodies and constellations and provide an explanation, further enhancing the enjoyment of starry sky observation.You can also interact with other stargazing enthusiasts and share your new discoveries through real-time interactions and community features.

[0030] The identification unit can display the positions of identified celestial bodies and constellations in real time, linked to the user's current location. For example, the identification unit links the position information of celestial bodies and constellations identified by the generation AI with GPS data and displays it in real time based on the user's current location. For example, when a user points their smartphone at the sky, the positions of identified celestial bodies and constellations are overlaid on the screen. This improves the accuracy of observation by displaying the positions of celestial bodies and constellations in real time based on the user's current location.

[0031] The identification unit can simulate the movements of the identified celestial bodies and constellations to recreate the appearance of the starry sky in the past or future. For example, the identification unit can simulate the movements of the celestial bodies and constellations identified by the generation AI to recreate the appearance of the starry sky in the past. For example, when a user inputs a specific date, the appearance of the night sky on that day is displayed on the screen. The identification unit can also recreate the appearance of the starry sky in the future. For example, when a user inputs a future date, the appearance of the night sky on that day is displayed on the screen. Furthermore, the identification unit can simulate the movements of celestial bodies and constellations in real time. This allows the user to more easily understand the movement of celestial bodies by recreating the appearance of the starry sky in the past or future.

[0032] The photographing unit can also be applied to sky and weather observation, making it possible to provide an all-weather observation service. For example, the photographing unit applies the starry sky photographing and identification functions to daytime sky observation, and the generation AI identifies cloud shapes and weather changes. For example, when a user photographs the daytime sky, the generation AI displays the type of cloud and weather forecast. The photographing unit can also be applied to weather observation. For example, when a user photographs the sky to observe weather changes, the generation AI displays the weather forecast. This makes it possible to provide an all-weather observation service by applying it to daytime sky and weather observation.

[0033] The identification unit can use AR technology to overlay information about celestial bodies and constellations identified by the generation AI onto the user's device. For example, the identification unit can use AR technology to overlay information about celestial bodies and constellations identified by the generation AI onto the screen of the user's smartphone or tablet. For example, when the user points the device at the sky, the name and position of the identified celestial body or constellation are displayed on the screen. The identification unit can also use AR technology to display the movement of celestial bodies and constellations in real time. For example, when the user moves the device, the position of the celestial body or constellation on the screen also moves in tandem. This improves the observation experience by overlaying information about celestial bodies and constellations onto the device using AR technology.

[0034] The explanation providing unit can add personalized information based on the user's past observation history and interests. For example, the explanation providing unit adds personalized information based on the user's past observation history to the explanation provided by the generation AI. For example, the explanation providing unit can preferentially display information related to constellations and celestial bodies that the user has observed in the past. The explanation providing unit can also provide information based on the user's interests. For example, if the user is interested in a particular constellation, the explanation providing unit can provide detailed information about that constellation. This increases the user's motivation to learn by providing personalized information based on the user's past observation history and interests.

[0035] The commentary providing unit can reflect the latest astronomy research results and news in real time. For example, the commentary providing unit reflects the latest astronomy research results in real time in the commentary provided by the generation AI. For example, information about newly discovered celestial bodies and constellations is added to the commentary. The commentary providing unit can also provide the latest astronomy news. For example, news about the latest astronomical phenomena and observation results is displayed in real time. This allows users to obtain the latest information by reflecting the latest astronomy research results and news in real time.

[0036] The explanation providing unit provides explanations by voice through a voice assistant, making it possible to accommodate visually impaired people. The explanation providing unit, for example, builds a system that provides explanations provided by the generation AI by voice through a voice assistant. For example, when a user speaks to a smartphone, the generation AI provides explanations by voice. The explanation providing unit can also provide explanations using a voice assistant to accommodate visually impaired people. For example, when a visually impaired person speaks to a smartphone, the generation AI provides explanations by voice. In this way, explanations can be provided by voice through a voice assistant, making it possible to accommodate visually impaired people.

[0037] The explanation providing unit can convert the explanation provided by the generation AI into an interactive quiz format to increase the user's motivation to learn. The explanation providing unit, for example, converts the explanation provided by the generation AI into an interactive quiz format to build a system that increases the user's motivation to learn. For example, a quiz based on the content of the explanation is generated, and the user answers it. The explanation providing unit can also provide additional explanation based on the results of the quiz. For example, if the user answers the quiz correctly, more detailed information is provided. In this way, by converting it into an interactive quiz format, the user's motivation to learn is increased.

[0038] The dialogue unit can evaluate the user's learning progress and level of understanding through dialogue with the user and propose an appropriate study plan. For example, the generation AI can evaluate the user's learning progress through dialogue with the user and propose an appropriate study plan. For example, if the user says, "I want to know more about Orion," the generation AI will provide a study plan that meets that request. The dialogue unit can also evaluate the user's level of understanding. For example, if the user says, "I find this part difficult to understand," the generation AI will explain that part in detail. This improves learning effectiveness by evaluating the user's learning progress and level of understanding and proposing an appropriate study plan.

[0039] The dialogue unit can provide customized learning content based on the user's interests through dialogue with the user. For example, the generation AI can provide customized learning content based on the user's interests through dialogue with the user. For example, if the user says, "I want to know about planets," the generation AI can provide detailed learning content about planets. The dialogue unit can also update the learning content according to the user's interests. For example, if the user develops a new interest, the dialogue unit can provide content based on that interest. This provides customized learning content based on the user's interests, thereby increasing motivation to learn.

[0040] The dialogue unit can promote collaborative learning and discussion with other users through dialogue with the user. For example, the dialogue unit builds a system in which the generation AI promotes collaborative learning with other users through dialogue with the user. For example, it provides a function that connects users who are interested in the same constellations. The dialogue unit can also promote discussion between users. For example, it provides a forum for discussing specific astronomical phenomena. This promotes collaborative learning and discussion with other users, improving learning effectiveness.

[0041] The dialogue unit can provide knowledge of scientific fields other than astronomy through dialogue with the user, thereby supporting comprehensive science education. For example, the dialogue unit builds a system in which the generation AI provides knowledge of scientific fields other than astronomy through dialogue with the user. For example, if a user says, "I want to know about physics," the generation AI provides detailed information about physics. The dialogue unit can also provide knowledge of other scientific fields, such as chemistry and biology. For example, if a user says, "I want to know about chemical reactions," the generation AI provides detailed information about chemical reactions. This makes it possible to support comprehensive science education by providing knowledge of scientific fields other than astronomy.

[0042] The information sharing unit can automatically organize information shared within a community and prioritize displaying highly relevant information. For example, the information sharing unit constructs a system in which a generation AI automatically organizes information shared within a community and prioritizes displaying highly relevant information. For example, information about the same constellation is displayed together. The information sharing unit can also display information based on the user's interests. For example, if a user is interested in a particular astronomical phenomenon, information about that phenomenon is displayed preferentially. This automatically organizes information shared within a community and prioritizes displaying highly relevant information, improving information searchability.

[0043] The information sharing unit can automatically translate information shared within a community and promote interaction between users who speak different languages. For example, the information sharing unit constructs a system in which a generation AI automatically translates information shared within a community and promotes interaction between users who speak different languages. For example, information posted in Japanese is translated into English and displayed. The information sharing unit can also display information based on the user's language settings. For example, if the user selects English, Japanese information is translated into English and displayed. This promotes interaction between users who speak different languages, thereby improving the diversity of the community.

[0044] The community section can be expanded to include sub-communities based on other hobbies and interests, promoting diverse interactions. For example, the community section can expand the community function to include sub-communities based on other hobbies and interests, building a system that promotes diverse interactions. For example, sub-communities based on hobbies and interests other than astronomy can be created. The community section can also promote information sharing between sub-communities. For example, it can provide a forum for discussions between different sub-communities. This allows the system to be expanded to include sub-communities based on other hobbies and interests, promoting diverse interactions.

[0045] The information sharing unit can automatically tag information shared within a community, improving information searchability. For example, the information sharing unit constructs a system in which a generation AI automatically tags information shared within a community, improving information searchability. For example, information about a specific astronomical phenomenon is tagged and displayed. The information sharing unit can also tag based on a user's interests. For example, if a user is interested in a specific constellation, information about that constellation is tagged and displayed. This automatically tags information shared within a community, improving information searchability, allowing users to quickly find the information they need.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The identification unit can estimate the physical properties of celestial objects based on images of the starry sky taken by the user and provide detailed scientific data. For example, the identification unit can analyze the brightness and spectral data of a star to estimate the temperature and age of the star. The identification unit can also estimate the atmospheric composition of a planet. For example, the identification unit can analyze light of specific wavelengths to identify the type of gas contained in the planet's atmosphere. Furthermore, the identification unit can analyze the shape and structure of a galaxy and estimate the evolutionary process of that galaxy. This allows users to obtain detailed scientific data on the physical properties of celestial objects through stargazing.

[0048] The commentary provider may provide historical anecdotes and myths related to the celestial body or constellation observed by the user. For example, the commentary provider may introduce ancient Greek myths related to a particular constellation. The commentary provider may also provide information about the history of astronomical observation. For example, the commentary provider may introduce when a particular celestial body was first observed and stories about astronomers involved in the observation. Furthermore, the commentary provider may provide cultural background related to the celestial body or constellation. For example, the commentary provider may introduce how a particular constellation is perceived in different cultures. This allows the user to enjoy a deeper observation experience through historical anecdotes and myths related to the celestial body or constellation.

[0049] The dialogue unit can generate quizzes about celestial bodies and constellations observed by the user to increase motivation to learn. For example, the dialogue unit generates quizzes about constellations observed by the user, and the user answers them. The dialogue unit can also provide additional commentary based on the results of the quiz. For example, if the user answers the quiz correctly, more detailed information is provided. Furthermore, the dialogue unit can adjust the difficulty of the quiz according to the user's learning progress. This allows the user to enjoy learning through quizzes and deepen their knowledge of astronomy.

[0050] The information sharing unit can automatically tag images of starry skies taken by users, allowing other users to easily search for them. For example, the information sharing unit can automatically tag images related to specific constellations or celestial bodies, allowing users to search for images using those tags. The information sharing unit can also customize tags based on the user's interests. For example, if a user is interested in a particular astronomical phenomenon, the information sharing unit can prioritize tags related to that phenomenon. Furthermore, the information sharing unit can organize and display tagged images by category. This allows users to quickly find the information they need, improving information searchability.

[0051] The community section can provide discussion forums for celestial objects and constellations observed by users to promote knowledge sharing. For example, the community section can create a discussion forum for a particular constellation, allowing users to share information and observations about that constellation. The community section can also provide a forum for sharing the latest research results and news about astronomy. For example, a forum can be created to share information about newly discovered celestial objects. Furthermore, the community section can organize collaborative observation projects among users. This allows users to share knowledge through the discussion forums and deepen their understanding of astronomy.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The user photographs the starry sky using the camera. For example, the starry sky can be photographed using a smartphone or tablet camera. The camera can also capture high-resolution images of the night sky. The camera can also capture the starry sky using a long exposure. Step 2: The identification unit analyzes the image captured by the photographing unit to identify celestial bodies and constellations. For example, the generation AI uses image analysis technology to identify celestial bodies and constellations. The identification unit can also identify the positions of celestial bodies and constellations. Furthermore, the identification unit can display the names of celestial bodies and constellations. Step 3: The explanation provider provides an explanation about the celestial body or constellation identified by the identifier. For example, the generation AI provides a detailed explanation about the identified celestial body or constellation in text format. The explanation provider can also provide the explanation in audio format. Furthermore, the explanation provider can also provide related information in the form of a link. Step 4: The dialogue unit engages in dialogue with the user. For example, the generation AI generates appropriate answers to questions from the user. The dialogue unit can also engage in real-time dialogue with the user. Furthermore, the dialogue unit can also engage in dialogue to support the user's learning. Step 5: The information sharing unit shares information between users. For example, a user can share images of the starry sky that he or she has taken or observation results with other users. The information sharing unit can also organize and display the shared information. Furthermore, the information sharing unit can also comment on and rate the shared information. Step 6: The community section promotes interactions between users. For example, users can interact with other stargazing enthusiasts through the community function. The community section can also promote discussions between users. Furthermore, the community section can also plan events and activities between users.

[0054] (Example 2) The starry sky observation system according to an embodiment of the present invention allows users to take pictures of the starry sky using a smartphone or tablet, and the AI ​​generation system automatically identifies celestial bodies and constellations in that location and provides detailed explanations and related information. This allows users to enjoy stargazing more and deepen their knowledge of astronomy.

[0055] A stargazing system according to an embodiment includes a photographing unit, an identification unit, an explanation providing unit, a dialogue unit, an information sharing unit, and a community unit. The photographing unit allows a user to photograph a starry sky. For example, the photographing unit can photograph the starry sky using a smartphone or tablet camera. The photographing unit can also photograph a night sky image at high resolution. The photographing unit can also photograph a starry sky using a long exposure. The identification unit analyzes the image photographed by the photographing unit to identify celestial bodies and constellations. For example, the generation AI identifies celestial bodies and constellations using image analysis technology. The identification unit can also identify the positions of celestial bodies and constellations. The identification unit can also display the names of celestial bodies and constellations. The explanation providing unit provides explanations about the celestial bodies and constellations identified by the identification unit. For example, the generation AI provides detailed explanations about the identified celestial bodies and constellations in text format. The explanation providing unit can also provide explanations in audio format. The explanation providing unit can also provide related information in the form of links. The dialogue unit engages in dialogue with the user. For example, the generation AI generates appropriate answers to questions from users. The dialogue unit can also engage in real-time dialogue with users. Furthermore, the dialogue unit can also engage in dialogue to support users' learning. The information sharing unit shares information between users. For example, users can share images of starry skies they have taken or observation results with other users. The information sharing unit can also organize and display the shared information. Furthermore, the information sharing unit can also comment and rate the shared information. The community unit promotes interactions between users. For example, users can interact with other starry sky enthusiasts through the community function. The community unit can also promote discussions between users. Furthermore, the community unit can plan events and activities between users. This allows the starry sky observation system according to the embodiment to allow users to enjoy starry sky observation more and deepen their knowledge of astronomy. For example, a user can take a photo of the starry sky using their smartphone, and the generation AI can identify the celestial bodies and constellations and provide an explanation, further enhancing the enjoyment of starry sky observation.You can also interact with other stargazing enthusiasts and share your new discoveries through real-time interactions and community features.

[0056] The identification unit can display the positions of identified celestial bodies and constellations in real time, linked to the user's current location. For example, the identification unit links the position information of celestial bodies and constellations identified by the generation AI with GPS data and displays it in real time based on the user's current location. For example, when a user points their smartphone at the sky, the positions of identified celestial bodies and constellations are overlaid on the screen. This improves the accuracy of observation by displaying the positions of celestial bodies and constellations in real time based on the user's current location.

[0057] The identification unit can simulate the movements of the identified celestial bodies and constellations to recreate the appearance of the starry sky in the past or future. For example, the identification unit can simulate the movements of the celestial bodies and constellations identified by the generation AI to recreate the appearance of the starry sky in the past. For example, when a user inputs a specific date, the appearance of the night sky on that day is displayed on the screen. The identification unit can also recreate the appearance of the starry sky in the future. For example, when a user inputs a future date, the appearance of the night sky on that day is displayed on the screen. Furthermore, the identification unit can simulate the movements of celestial bodies and constellations in real time. This allows the user to more easily understand the movement of celestial bodies by recreating the appearance of the starry sky in the past or future.

[0058] The identification unit can use the emotion estimation function to analyze the user's emotion toward the starry sky image taken by the user and provide observation advice based on the emotion. For example, the identification unit uses the emotion estimation function to analyze the user's emotion toward the starry sky image taken by the user, and if the user's emotion is strong positive, the identification unit provides advice recommending continued observation. For example, the identification unit displays a message such as "What a wonderful starry sky! Let's continue observing." If the user's emotion is strong negative, the identification unit provides advice suggesting improvements to the observation. For example, the identification unit displays a message such as "Try extending the exposure time a little longer." This improves the observation experience by providing observation advice based on the user's emotion.

[0059] The photographing unit can also be applied to sky and weather observation, making it possible to provide an all-weather observation service. For example, the photographing unit applies the starry sky photographing and identification functions to daytime sky observation, and the generation AI identifies cloud shapes and weather changes. For example, when a user photographs the daytime sky, the generation AI displays the type of cloud and weather forecast. The photographing unit can also be applied to weather observation. For example, when a user photographs the sky to observe weather changes, the generation AI displays the weather forecast. This makes it possible to provide an all-weather observation service by applying it to daytime sky and weather observation.

[0060] The identification unit can use AR technology to overlay information about celestial bodies and constellations identified by the generation AI onto the user's device. For example, the identification unit can use AR technology to overlay information about celestial bodies and constellations identified by the generation AI onto the screen of the user's smartphone or tablet. For example, when the user points the device at the sky, the name and position of the identified celestial body or constellation are displayed on the screen. The identification unit can also use AR technology to display the movement of celestial bodies and constellations in real time. For example, when the user moves the device, the position of the celestial body or constellation on the screen also moves in tandem. This improves the observation experience by overlaying information about celestial bodies and constellations onto the device using AR technology.

[0061] The identification unit can use the emotion estimation function to collect other users' emotional reactions to starry sky images taken by the user, thereby promoting the formation of an emotion-based community. For example, the identification unit uses the emotion estimation function to collect other users' emotional reactions to starry sky images taken by the user, and shares images with many positive reactions within the community. For example, it displays a message such as, "This starry sky photo has received high ratings from many users." In addition, the identification unit provides feedback suggesting improvements to images with many negative reactions. For example, it displays a message such as, "Try extending the exposure time a little more." In this way, by collecting other users' emotional reactions and promoting the formation of an emotion-based community, interactions between users are deepened.

[0062] The explanation providing unit can add personalized information based on the user's past observation history and interests. For example, the explanation providing unit adds personalized information based on the user's past observation history to the explanation provided by the generation AI. For example, the explanation providing unit can preferentially display information related to constellations and celestial bodies that the user has observed in the past. The explanation providing unit can also provide information based on the user's interests. For example, if the user is interested in a particular constellation, the explanation providing unit can provide detailed information about that constellation. This increases the user's motivation to learn by providing personalized information based on the user's past observation history and interests.

[0063] The commentary providing unit can reflect the latest astronomy research results and news in real time. For example, the commentary providing unit reflects the latest astronomy research results in real time in the commentary provided by the generation AI. For example, information about newly discovered celestial bodies and constellations is added to the commentary. The commentary providing unit can also provide the latest astronomy news. For example, news about the latest astronomical phenomena and observation results is displayed in real time. This allows users to obtain the latest information by reflecting the latest astronomy research results and news in real time.

[0064] The explanation providing unit can use the emotion estimation function to analyze the emotion a user feels when reading an explanation, and provide additional information and related content based on the emotion. For example, the explanation providing unit uses the emotion estimation function to analyze the emotion a user feels when reading an explanation, and if the emotion is strong positive, provides related additional information. For example, it displays a message such as, "If you're interested in this constellation, try observing this constellation next." Furthermore, if the emotion is strong negative, the explanation providing unit provides feedback suggesting improvements to the explanation. For example, it displays a message such as, "If you find this part difficult to understand, please refer to this explanation." In this way, providing additional information and related content based on the user's emotion increases the user's motivation to learn.

[0065] The explanation providing unit provides explanations by voice through a voice assistant, making it possible to accommodate visually impaired people. The explanation providing unit, for example, builds a system that provides explanations provided by the generation AI by voice through a voice assistant. For example, when a user speaks to a smartphone, the generation AI provides explanations by voice. The explanation providing unit can also provide explanations using a voice assistant to accommodate visually impaired people. For example, when a visually impaired person speaks to a smartphone, the generation AI provides explanations by voice. In this way, explanations can be provided by voice through a voice assistant, making it possible to accommodate visually impaired people.

[0066] The explanation providing unit can convert the explanation provided by the generation AI into an interactive quiz format to increase the user's motivation to learn. The explanation providing unit, for example, converts the explanation provided by the generation AI into an interactive quiz format to build a system that increases the user's motivation to learn. For example, a quiz based on the content of the explanation is generated, and the user answers it. The explanation providing unit can also provide additional explanation based on the results of the quiz. For example, if the user answers the quiz correctly, more detailed information is provided. In this way, by converting it into an interactive quiz format, the user's motivation to learn is increased.

[0067] The explanation providing unit can use the emotion estimation function to collect emotional reactions when users read an explanation and improve the explanation based on the emotions. For example, the explanation providing unit uses the emotion estimation function to collect emotional reactions when users read an explanation, and if the emotional reaction is strong, recommends the explanation to other users. For example, it displays a message such as "This explanation has been highly rated by many users." Furthermore, if the emotional reaction is strong, the explanation providing unit provides feedback suggesting improvements to the explanation. For example, it displays a message such as "If this part is difficult to understand, please refer to this explanation." In this way, the quality of the explanation is improved by collecting the emotional reactions of users and improving the explanation based on the emotions.

[0068] The dialogue unit can evaluate the user's learning progress and level of understanding through dialogue with the user and propose an appropriate study plan. For example, the generation AI can evaluate the user's learning progress through dialogue with the user and propose an appropriate study plan. For example, if the user says, "I want to know more about Orion," the generation AI will provide a study plan that meets that request. The dialogue unit can also evaluate the user's level of understanding. For example, if the user says, "I find this part difficult to understand," the generation AI will explain that part in detail. This improves learning effectiveness by evaluating the user's learning progress and level of understanding and proposing an appropriate study plan.

[0069] The dialogue unit can provide customized learning content based on the user's interests through dialogue with the user. For example, the generation AI can provide customized learning content based on the user's interests through dialogue with the user. For example, if the user says, "I want to know about planets," the generation AI can provide detailed learning content about planets. The dialogue unit can also update the learning content according to the user's interests. For example, if the user develops a new interest, the dialogue unit can provide content based on that interest. This provides customized learning content based on the user's interests, thereby increasing motivation to learn.

[0070] The dialogue unit can use the emotion estimation function to analyze emotions during a dialogue with the user and adjust the dialogue content based on those emotions. For example, the dialogue unit can use the emotion estimation function to analyze emotions during a dialogue with the user, and if positive emotions are strong, it can dig deeper into the dialogue content. For example, if a user says, "I want to know more about this constellation," the generation AI will provide more detailed information. The dialogue unit can also adjust the dialogue content if negative emotions are strong. For example, if a user says, "I don't understand this part," the generation AI will explain that part in more detail. In this way, the quality of the dialogue can be improved by adjusting the dialogue content based on the user's emotions.

[0071] The dialogue unit can promote collaborative learning and discussion with other users through dialogue with the user. For example, the dialogue unit builds a system in which the generation AI promotes collaborative learning with other users through dialogue with the user. For example, it provides a function that connects users who are interested in the same constellations. The dialogue unit can also promote discussion between users. For example, it provides a forum for discussing specific astronomical phenomena. This promotes collaborative learning and discussion with other users, improving learning effectiveness.

[0072] The dialogue unit can provide knowledge of scientific fields other than astronomy through dialogue with the user, thereby supporting comprehensive science education. For example, the dialogue unit builds a system in which the generation AI provides knowledge of scientific fields other than astronomy through dialogue with the user. For example, if a user says, "I want to know about physics," the generation AI provides detailed information about physics. The dialogue unit can also provide knowledge of other scientific fields, such as chemistry and biology. For example, if a user says, "I want to know about chemical reactions," the generation AI provides detailed information about chemical reactions. This makes it possible to support comprehensive science education by providing knowledge of scientific fields other than astronomy.

[0073] The dialogue unit can use the emotion estimation function to collect emotional responses during a dialogue with the user and improve the quality of the dialogue based on emotions. For example, the dialogue unit can use the emotion estimation function to collect emotional responses during a dialogue with the user and improve the quality of the dialogue if the positive emotion is strong. For example, if the user says, "This information is very interesting," the generation AI will provide more detailed information. The dialogue unit can also adjust the content of the dialogue if the negative emotion is strong. For example, if the user says, "I find this part difficult to understand," the generation AI will explain that part in more detail. In this way, by collecting the user's emotional responses and improving the quality of the dialogue based on emotions, the dialogue experience is improved.

[0074] The information sharing unit can automatically organize information shared within a community and prioritize displaying highly relevant information. For example, the information sharing unit constructs a system in which a generation AI automatically organizes information shared within a community and prioritizes displaying highly relevant information. For example, information about the same constellation is displayed together. The information sharing unit can also display information based on the user's interests. For example, if a user is interested in a particular astronomical phenomenon, information about that phenomenon is displayed preferentially. This automatically organizes information shared within a community and prioritizes displaying highly relevant information, improving information searchability.

[0075] The information sharing unit can automatically translate information shared within a community and promote interaction between users who speak different languages. For example, the information sharing unit constructs a system in which a generation AI automatically translates information shared within a community and promotes interaction between users who speak different languages. For example, information posted in Japanese is translated into English and displayed. The information sharing unit can also display information based on the user's language settings. For example, if the user selects English, Japanese information is translated into English and displayed. This promotes interaction between users who speak different languages, thereby improving the diversity of the community.

[0076] The information sharing unit can use the emotion estimation function to analyze emotional reactions to posts and comments within the community and provide feedback based on the emotions. For example, the information sharing unit uses the emotion estimation function to analyze emotional reactions to posts and comments within the community, and if there is a strong positive emotion, it recommends the post or comment to other users. For example, it displays a message such as "This post has been highly rated by many users." Furthermore, if there is a strong negative emotion, the information sharing unit provides feedback suggesting areas for improvement. For example, it displays a message such as "If you find this part difficult to understand, please refer to this explanation." In this way, providing feedback based on emotions stimulates interaction within the community.

[0077] The community section can be expanded to include sub-communities based on other hobbies and interests, promoting diverse interactions. For example, the community section can expand the community function to include sub-communities based on other hobbies and interests, building a system that promotes diverse interactions. For example, sub-communities based on hobbies and interests other than astronomy can be created. The community section can also promote information sharing between sub-communities. For example, it can provide a forum for discussions between different sub-communities. This allows the system to be expanded to include sub-communities based on other hobbies and interests, promoting diverse interactions.

[0078] The information sharing unit can automatically tag information shared within a community, improving information searchability. For example, the information sharing unit constructs a system in which a generation AI automatically tags information shared within a community, improving information searchability. For example, information about a specific astronomical phenomenon is tagged and displayed. The information sharing unit can also tag based on a user's interests. For example, if a user is interested in a specific constellation, information about that constellation is tagged and displayed. This automatically tags information shared within a community, improving information searchability, allowing users to quickly find the information they need.

[0079] The community unit can use the emotion estimation function to analyze emotional trends within the community and suggest events and activities based on emotions. For example, the community unit uses the emotion estimation function to analyze emotional trends within the community, and if positive emotions are strong, suggests events and activities based on emotions. For example, it makes a suggestion such as, "Let's hold an event related to this zodiac sign." Furthermore, if negative emotions are strong, the community unit provides feedback suggesting areas for improvement. For example, it displays a message such as, "If you don't understand this part, please refer to this explanation." In this way, suggesting events and activities based on emotions stimulates interaction within the community.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The identification unit can estimate the physical properties of celestial objects based on images of the starry sky taken by the user and provide detailed scientific data. For example, the identification unit can analyze the brightness and spectral data of a star to estimate the temperature and age of the star. The identification unit can also estimate the atmospheric composition of a planet. For example, the identification unit can analyze light of specific wavelengths to identify the type of gas contained in the planet's atmosphere. Furthermore, the identification unit can analyze the shape and structure of a galaxy and estimate the evolutionary process of that galaxy. This allows users to obtain detailed scientific data on the physical properties of celestial objects through stargazing.

[0082] The commentary provider may provide historical anecdotes and myths related to the celestial body or constellation observed by the user. For example, the commentary provider may introduce ancient Greek myths related to a particular constellation. The commentary provider may also provide information about the history of astronomical observation. For example, the commentary provider may introduce when a particular celestial body was first observed and stories about astronomers involved in the observation. Furthermore, the commentary provider may provide cultural background related to the celestial body or constellation. For example, the commentary provider may introduce how a particular constellation is perceived in different cultures. This allows the user to enjoy a deeper observation experience through historical anecdotes and myths related to the celestial body or constellation.

[0083] The dialogue unit can generate quizzes about celestial bodies and constellations observed by the user to increase motivation to learn. For example, the dialogue unit generates quizzes about constellations observed by the user, and the user answers them. The dialogue unit can also provide additional commentary based on the results of the quiz. For example, if the user answers the quiz correctly, more detailed information is provided. Furthermore, the dialogue unit can adjust the difficulty of the quiz according to the user's learning progress. This allows the user to enjoy learning through quizzes and deepen their knowledge of astronomy.

[0084] The information sharing unit can automatically tag images of starry skies taken by users, allowing other users to easily search for them. For example, the information sharing unit can automatically tag images related to specific constellations or celestial bodies, allowing users to search for images using those tags. The information sharing unit can also customize tags based on the user's interests. For example, if a user is interested in a particular astronomical phenomenon, the information sharing unit can prioritize tags related to that phenomenon. Furthermore, the information sharing unit can organize and display tagged images by category. This allows users to quickly find the information they need, improving information searchability.

[0085] The community section can provide discussion forums for celestial objects and constellations observed by users to promote knowledge sharing. For example, the community section can create a discussion forum for a particular constellation, allowing users to share information and observations about that constellation. The community section can also provide a forum for sharing the latest research results and news about astronomy. For example, a forum can be created to share information about newly discovered celestial objects. Furthermore, the community section can organize collaborative observation projects among users. This allows users to share knowledge through the discussion forums and deepen their understanding of astronomy.

[0086] The identification unit can use the emotion estimation function to analyze the user's emotion toward the starry sky image taken by the user and provide observation advice based on the emotion. For example, the identification unit can use the emotion estimation function to analyze the user's emotion toward the starry sky image taken by the user, and if the user's emotion is strong positive, provide advice recommending continued observation. For example, the identification unit can display a message such as "What a wonderful starry sky! Let's continue observing." In addition, if the user's emotion is strong negative, the identification unit can provide advice suggesting improvements to the observation. For example, the identification unit can display a message such as "Try extending the exposure time a little longer." This improves the observation experience by providing observation advice based on the user's emotion.

[0087] The explanation providing unit can use the emotion estimation function to analyze the emotion a user feels when reading an explanation, and provide additional information or related content based on the emotion. For example, the explanation providing unit uses the emotion estimation function to analyze the emotion a user feels when reading an explanation, and if the emotion is strong positive, provides related additional information. For example, it displays a message such as, "If you're interested in this constellation, try observing this constellation next." Furthermore, if the emotion is strong negative, the explanation providing unit provides feedback suggesting improvements to the explanation. For example, it displays a message such as, "If you find this part difficult to understand, please refer to this explanation." In this way, providing additional information or related content based on the user's emotion increases the user's motivation to learn.

[0088] The dialogue unit can use the emotion estimation function to analyze emotions during a dialogue with the user and adjust the dialogue content based on those emotions. For example, the dialogue unit can use the emotion estimation function to analyze emotions during a dialogue with the user, and if positive emotions are strong, the dialogue content will be dug deeper. For example, if a user says, "I want to know more about this constellation," the generation AI will provide more detailed information. The dialogue unit can also adjust the dialogue content if negative emotions are strong. For example, if a user says, "I don't understand this part," the generation AI will explain that part in more detail. This improves the quality of the dialogue by adjusting the dialogue content based on the user's emotions.

[0089] The information sharing unit can use the emotion estimation function to analyze emotional reactions to posts and comments within the community and provide feedback based on the emotions. For example, the information sharing unit uses the emotion estimation function to analyze emotional reactions to posts and comments within the community, and if there is a strong positive emotion, it recommends the post or comment to other users. For example, it displays a message such as "This post has been highly rated by many users." Furthermore, if there is a strong negative emotion, the information sharing unit provides feedback suggesting areas for improvement. For example, it displays a message such as "If you find this part difficult to understand, please refer to this explanation." In this way, providing feedback based on emotions stimulates interaction within the community.

[0090] The community unit can use the emotion estimation function to analyze emotional trends within the community and suggest events and activities based on emotions. For example, the community unit uses the emotion estimation function to analyze emotional trends within the community, and if positive emotions are strong, it suggests events and activities based on emotions. For example, it makes a suggestion such as, "Let's hold an event related to this zodiac sign." Furthermore, if negative emotions are strong, the community unit provides feedback suggesting areas for improvement. For example, it displays a message such as, "If you don't understand this part, please refer to this explanation." In this way, suggesting events and activities based on emotions stimulates interaction within the community.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The user photographs the starry sky using the camera. For example, the starry sky can be photographed using a smartphone or tablet camera. The camera can also capture high-resolution images of the night sky. The camera can also capture the starry sky using a long exposure. Step 2: The identification unit analyzes the image captured by the photographing unit to identify celestial bodies and constellations. For example, the generation AI uses image analysis technology to identify celestial bodies and constellations. The identification unit can also identify the positions of celestial bodies and constellations. Furthermore, the identification unit can display the names of celestial bodies and constellations. Step 3: The explanation provider provides an explanation about the celestial body or constellation identified by the identifier. For example, the generation AI provides a detailed explanation about the identified celestial body or constellation in text format. The explanation provider can also provide the explanation in audio format. Furthermore, the explanation provider can also provide related information in the form of a link. Step 4: The dialogue unit engages in dialogue with the user. For example, the generation AI generates appropriate answers to questions from the user. The dialogue unit can also engage in real-time dialogue with the user. Furthermore, the dialogue unit can also engage in dialogue to support the user's learning. Step 5: The information sharing unit shares information between users. For example, a user can share images of the starry sky that he or she has taken or observation results with other users. The information sharing unit can also organize and display the shared information. Furthermore, the information sharing unit can also comment on and rate the shared information. Step 6: The community section promotes interactions between users. For example, users can interact with other stargazing enthusiasts through the community function. The community section can also promote discussions between users. Furthermore, the community section can also plan events and activities between users.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photography department that photographs the starry sky, an identification unit that analyzes the image captured by the imaging unit and identifies celestial bodies and constellations; an explanation providing unit that provides explanations about the celestial bodies and constellations identified by the identifying unit; a dialogue unit that dialogues with a user; an information sharing unit for sharing information between users; A community section that promotes interaction between users. A system characterized by:

2. The imaging unit is It will also be applied to sky and weather observations, providing all-weather observation services.

2. The system of claim 1.

3. The explanation providing unit Add personalized information based on the user's past observations and interests 2. The system of claim 1.

4. The dialogue unit Through dialogue with the user, the learning progress and level of understanding of the user are evaluated, and an appropriate learning plan is proposed.

2. The system of claim 1.

5. The information sharing unit Automatically organize the information shared within the community and prioritize the most relevant information.

2. The system of claim 1.

Images