system

Abstract

We provide the system. [Solution] Means of acquiring astronomical data, A means for generating visual data using a generative model that simulates astronomical phenomena based on the aforementioned astronomical data, A means for distributing the generated visual data to a user terminal, A means by which the user interacts with the visual data on the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] To enjoy astronomical observations, weather and geographical conditions often become obstacles. Especially for people living in urban areas and in educational settings, it is difficult to conduct original astronomical observations. Therefore, there is a need for a method that can truly experience astronomical phenomena without relying on weather and location. In addition, there is a need for a means that can enable people to enjoy rare astronomical phenomena that can only be observed during limited periods or under specific conditions at any time.

Means for Solving the Problems

[0005] This invention provides a system that acquires astronomical data, simulates astronomical phenomena using a generative model, and generates visual data. This system delivers the generated visual data to user terminals, allowing users to interactively experience astronomical phenomena regardless of weather or geographical conditions. Furthermore, it customizes the generated visual data based on the user's profile, providing an optimized visual experience for each individual user. It also collects user interaction data and utilizes it for future content delivery, enabling a personalized experience tailored to the user's interests and preferences.

[0006] "Astronomical data" is a general term for observational information related to celestial bodies, such as the positions of stars, the date and time of celestial phenomena, and weather conditions.

[0007] A "generative model" is an algorithm or artificial intelligence framework that outputs a specific result based on input data, and in this invention, it is used to generate simulations of astronomical phenomena.

[0008] "Visual data" refers to visible information expressed in image or video format, intended to provide users with a visual experience.

[0009] A "user terminal" refers to an electronic device used directly by the user, such as a smartphone, tablet, or personal computer.

[0010] "Interaction" refers to the act of a user interacting with a system or application, including actions such as manipulating or inputting information on the screen.

[0011] A "profile" is a dataset containing information about a user, used to provide a customized experience. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] To implement this invention, it is necessary to collect astronomical data, generate simulations based on that data, distribute the generated visual data, and acquire and analyze user interactions. Specific embodiments are shown below.

[0034] The server retrieves star position information, astronomical phenomenon history, and weather data from an astronomical observation database. This data undergoes preprocessing such as standardization and timestamp conversion before being converted into a format for input into the generative model.

[0035] The generative model simulates astronomical phenomena based on this input data and generates high-resolution visual data. The simulation results can, for example, reproduce how a meteor shower would appear from a specific region of Earth.

[0036] Next, the server delivers the generated visual data to the user's terminal. During delivery, data compression and optimization are performed to ensure efficient transmission according to the network environment. This allows the user to receive high-quality visual content in real time.

[0037] On the user's terminal, the received visual data is displayed instantly, and the user can interact with it. By selecting specific celestial bodies or changing the viewpoint, the user can obtain an experience tailored to their individual interests.

[0038] Furthermore, the server collects user interaction data and uses it to improve the accuracy and relevance of the content it provides next. For example, if a user frequently displays a particular type of celestial object, the server will prioritize providing information about that object in subsequent content deliveries.

[0039] In this way, the present invention is a system that allows users to realistically experience diverse celestial phenomena in space, regardless of weather or geography. This system is considered to be extremely useful in deepening the interest of educational institutions and individuals in astronomy.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server retrieves necessary astronomical data from an astronomical observation database. This includes star position information, records of past astronomical phenomena, and related weather information. The retrieved data is standardized and timestamped, and converted into a format that is easy to process.

[0043] Step 2:

[0044] The server launches a generative model using the formatted astronomical data. The generative model performs simulations based on the input data and generates realistic visual data. In this process, for example, the movement of a meteor shower or the orbit of a specific comet can be reproduced.

[0045] Step 3:

[0046] The server compresses and optimizes the generated visual data into a format suitable for the user's terminal. This improves data transmission efficiency and ensures that data is delivered with quality appropriate to the communication environment.

[0047] Step 4:

[0048] The server delivers optimized visual data to the user's terminal. The user can receive and play the delivered content on their terminal.

[0049] Step 5:

[0050] Users can view visual data on their devices and initiate interaction. Specifically, they can tap on celestial bodies to display detailed information or change their viewpoint to observe them from different angles.

[0051] Step 6:

[0052] The server collects user interaction data. This data is used to analyze user interests and improve future content delivery. For example, users who spend a lot of time on specific celestial objects or phenomena will receive priority information related to those interests.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] The present invention aims to provide a system that effectively integrates the acquisition of astronomical data, the generation and distribution of real-time visual data, and the collection and analysis of user interactions, thereby providing users with a realistic and interactive astronomical experience. Furthermore, it solves the problem of improving the user experience by providing customized content based on the individual user's interests and operation history.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes data preprocessing means for standardizing and timestamp-transforming astronomical data, means for generating visual data using a generative model that simulates astronomical phenomena based on the astronomical data, and data compression and distribution means for encoding the generated visual data and performing adaptive streaming according to network conditions. This enables users to experience high-resolution and personalized astronomical simulations in real time.

[0058] "Astronomical data" refers to information about the positions of stars and planets, the history of celestial phenomena, and the weather conditions associated with them.

[0059] "Standardization and timestamp conversion" refers to the process of unifying data expressed in different formats and units into a consistent format, and converting temporal information to conform to a common standard.

[0060] "Data preprocessing means" refers to means that provide functions for processing acquired data into a format suitable for analysis and simulation.

[0061] A "generative model" refers to an algorithm or machine learning model used to generate new data or visuals based on input data.

[0062] "Visual data" refers to data that is visually recognizable, generated through computer graphics or simulations.

[0063] "Data compression and distribution means" refers to means that provide processing and functions for reducing the size of visual data and transmitting it efficiently over a network.

[0064] "Adaptive streaming" refers to a technology that dynamically adjusts the quality of video and audio in real time according to network conditions during delivery.

[0065] "Interaction" refers to the actions a user performs on visual data through their device, as well as the system's response to those actions.

[0066] "Interaction data" refers to information such as user actions, choices, and changes in perspective, and is used to analyze user interests and behavior.

[0067] This invention aims to realize a system that simulates astronomical phenomena in real time and provides them to the user visually. Specific embodiments are shown below.

[0068] The server first retrieves astronomical data from a database or API for astronomical observation. At this stage, it uses SQL queries to extract information about the positions of stars and astronomical phenomena from the database. For weather data, it uses an external API via the internet, such as a weather information API, to obtain weather data for a specific region.

[0069] Next, the server standardizes the acquired data and unifies the timestamps. Using Python and the Pandas library, it performs data imputation and format conversion to prepare the input data. This makes the data suitable for the generative AI model.

[0070] The server inputs pre-prepared astronomical data into generative AI models such as TENSORFLOW® and PyTorch, and performs simulations. This model generates high-resolution visual data, providing detailed representations of specific astronomical phenomena, such as meteor showers, as they appear from a particular region.

[0071] The generated visual data is encoded on the server using video compression technologies such as H.264. Depending on the network conditions, adaptive streaming technologies (e.g., MPEG-DASH) are used to efficiently deliver the data to the user's terminal.

[0072] The device instantly decodes the received visual data and displays it to the user in real time. This can utilize applications that leverage HTML5 or WebGL. The user can use the provided visual interface to select specific celestial objects or change their viewpoint.

[0073] Furthermore, the server collects user interaction data, including a history of user clicks, selections, and viewpoint changes. This data is analyzed after collection and used to optimize future content generation and delivery strategies.

[0074] As a concrete example, the following prompt statements can be used to instruct the system to operate.

[0075] "Generate a simulation of a meteor shower that beautifully illuminates the night sky, and specifically show how it would look from a particular location."

[0076] This system allows users to intuitively and interactively experience astronomical phenomena by utilizing astronomical data and generative AI models.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server initiates the input process, retrieving star position information and astronomical phenomenon history from an astronomical database or API. Specifically, it accesses the database using SQL queries and extracts the necessary data. It also retrieves weather data using an external API. The data obtained in this process is then used as output for the next processing step.

[0080] Step 2:

[0081] The server performs preprocessing on the data acquired in Step 1, standardizing and timestamping it. Using the Python Pandas library, it imputes missing values ​​and converts the time-series data into a unified format. As a result of this preprocessing, data in a unified format is output. The converted data is then input into the next generative model in its original format.

[0082] Step 3:

[0083] The server inputs pre-processed data into a generating AI model to simulate astronomical phenomena. Specifically, an AI model using TensorFlow or PyTorch generates high-resolution visual data from the input data. This process yields data that serves as a visual representation of a specific astronomical phenomenon. This output is later distributed to the user and is therefore subjected to data compression.

[0084] Step 4:

[0085] The server encodes the visual data generated in step 3, making it efficient for distribution over the network. Specifically, it compresses the data in H.264 format and prepares it for distribution using adaptive streaming technology. The output of this process is then provided as compressed data for transmission to each user terminal.

[0086] Step 5:

[0087] The terminal decodes the visual data received from the server and displays it to the user in real time. By utilizing HTML5 and WebGL, the visual data is provided to the user through an intuitive interface. The displayed data appears as output that enables user interaction.

[0088] Step 6:

[0089] Users interact with the on-device interface, selecting specific celestial bodies and changing their viewpoints to build experiences based on their interests. The data generated by this interaction is then sent back to the server and output as input data used for detailed analysis.

[0090] Step 7:

[0091] The server analyzes user interaction data and uses it to generate and deliver content in the future. Machine learning techniques are used to predict user behavior patterns and preferences, processing this data as input for generating optimized content. The results of this analysis are then used as output data to further enhance user engagement.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Experiencing astronomical phenomena in reality is usually limited by geographical and meteorological conditions. Furthermore, gaining a deep and accurate understanding of the night sky and celestial phenomena requires analyzing complex data and information, resulting in a considerable learning burden. Therefore, there is a need for a system that allows users to intuitively understand astronomy based on their own interests and gain unique experiences.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring astronomical information, means for generating visual information using a generative model that reproduces astronomical phenomena based on the astronomical information, and means for transmitting the generated visual information to a user device. This enables a more intuitive and personalized astronomical experience for the user by reproducing the starry sky based on location information and providing descriptive information about selected celestial bodies.

[0097] "Astronomical information" refers to data about stars, planets, and other cosmic structures obtained through observation.

[0098] A "generative model" is an algorithm or program that uses astronomical information to reproduce and visually represent specific astronomical phenomena.

[0099] "Visual information" refers to data generated to visually represent celestial bodies and astronomical phenomena to users.

[0100] A "user device" refers to an information terminal that can receive and display visual information, and includes smartphones and smart glasses.

[0101] "Operation" refers to actions such as selecting, zooming in, or zooming out of visual information performed by a user through their device.

[0102] "Location information" refers to data that indicates the geographical location of a user or device.

[0103] "Explanatory information" refers to data used to provide users with detailed information related to celestial bodies and astronomical phenomena.

[0104] The development of a system to implement this invention begins with a server acquiring astronomical information. This astronomical information consists of observational data about stars and planets, which a generative model uses to reproduce astronomical phenomena and generate visual information. For example, an AI model can be used for this generative model, enabling highly accurate simulations.

[0105] Next, the server transmits the generated visual information to the user's device. User devices include smartphones, smart glasses, and head-mounted displays. The user's device displays the received visual information, and the user can interact with that information. For example, when using smart glasses, the user can obtain information about constellations and meteor showers superimposed on the actual sky.

[0106] Information obtained through user actions is sent to the server and used for future content delivery. This makes it possible to provide a customized experience for each user. In addition, based on the user's location information, the system recreates the night sky visible from that location, and by selecting a specific celestial object, related explanatory information is provided.

[0107] For example, if a user looks up at the night sky and prompts "Show me the meteor showers visible tonight" through their smart glasses, the meteor showers visible from that location will be displayed. This allows users to intuitively explore specific astronomical phenomena, contributing to their learning and expanding their interest.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server retrieves astronomical information. In this step, the server accesses an astronomical observation database to collect positional data for stars and planets, a history of astronomical phenomena, and related meteorological data. The input is raw data from the observation database, and the output is standardized and timestamp-transformed astronomical information.

[0111] Step 2:

[0112] The server generates visual information using a generative AI model. Acquired astronomical information is input into this generative model, and simulations of astronomical phenomena are performed. The output is high-resolution visual information, ready to be delivered to the user.

[0113] Step 3:

[0114] The server transmits the generated visual information to the user device. In this step, data compression and optimization are performed according to the communication conditions, and the visual information is sent to the user device. The input is the generated visual information, and the output is the compressed visual information that arrives at the user terminal.

[0115] Step 4:

[0116] The terminal displays visual information, and the user interacts with it. The user device decompresses the received visual information and displays it on the screen. Through interaction, the user can select specific celestial bodies or change the viewpoint. The input is compressed visual information, and the output is interaction data resulting from the user's actions.

[0117] Step 5:

[0118] The server analyzes user interaction data and uses it to improve future content delivery. It identifies user interests and preferences from their operation history and individually optimizes the content delivered next. The input is user interaction data, and the output is an optimized content delivery plan.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention provides a more personalized experience by combining a system that allows users to visually experience astronomical phenomena using astronomical data with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0121] The server first retrieves data on star positions and past astronomical phenomena from an astronomical observation database. This data is formatted into a format suitable for the generative model and input into the model. The generative model then uses this data to perform realistic simulations and generate visual data for the user to view.

[0122] The server then sends the generated visual data to the user's terminal. The user can then receive and view high-quality, real-time visuals of celestial phenomena through the terminal. The user can interact with these visuals, for example, by selecting a celestial object to obtain detailed information about it.

[0123] Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. While the user is viewing visual data, the terminal determines the user's emotional state through facial recognition and voice analysis. For example, if the user shows expressions of surprise or excitement, the emotion engine detects this and sends that information to the server.

[0124] The server can adjust the display of visual data and the provision of additional information in real time based on the emotional data received from the emotion engine. For example, if a user indicates excitement, it can recommend and display interesting astronomical phenomena related to that excitement.

[0125] Ultimately, the server collects user interaction and sentiment data, which is then used to improve future content delivery. This allows for a more personalized experience by referencing the user's past behavior and emotional history.

[0126] This embodiment makes the astronomical observation experience more interactive and personalized, enabling the delivery of content optimized according to the user's emotions. Thus, the present invention makes a significant contribution to the field of astronomy education and to stimulating individual interest.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server retrieves the latest astronomical data from an astronomical observation database. This includes star positions and information on the occurrence of specific celestial phenomena. The obtained data is then formatted into a format suitable for simulation.

[0130] Step 2:

[0131] The server inputs the formatted astronomical data into a generative model to generate visual data of astronomical phenomena. The generative model performs realistic simulations based on the input data and converts the astronomical phenomena into a format that users can observe.

[0132] Step 3:

[0133] The server sends the generated visual data to the user's terminal. The visual data is compressed and optimized for smooth playback on the terminal.

[0134] Step 4:

[0135] The user receives and displays visual data on their device. The user can observe and interact with celestial phenomena. For example, they can tap on a specific celestial body to view detailed information.

[0136] Step 5:

[0137] The device recognizes the user's emotions in real time through an emotion engine. It uses a facial recognition camera and voice analysis to determine the user's emotional state from their facial expressions and voice.

[0138] Step 6:

[0139] The server analyzes emotional data obtained from the emotion engine and adjusts the displayed visual data in real time. For example, if the user expresses surprise, it automatically introduces new and interesting astronomical phenomena.

[0140] Step 7:

[0141] The server records user interaction and sentiment data, which will be used to improve future content delivery. The recorded data is used to provide experiences tailored to the user's preferences and interests.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Current astronomical observation systems typically provide users with uniform visual information, lacking personalization of the experience based on individual user emotions and interactions. This limits the opportunities for users to deeply enjoy the observation experience.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generative model that simulates astronomical events based on the astronomical information, and means for distributing the generated visual data to an information processing device. This makes it possible to personalize the visual data based on the user's emotional state and interactions, providing a richer and more personalized astronomical observation experience.

[0147] "Astronomical information" refers to data related to the position, movement, and past events of celestial bodies existing in outer space.

[0148] "Astronomical events" refer to phenomena that describe the movement or changes of stars and celestial bodies in the universe.

[0149] A "generative model" refers to an algorithm or model that generates new output based on input data, and is particularly used in computer simulations.

[0150] "Visual data" refers to digital images and video data that are generated so that users can experience them through their vision.

[0151] "Information processing equipment" refers to hardware devices such as computers, tablets, and smartphones that receive, process, and display digital data.

[0152] "Interaction" refers to the actions or responses that a user performs with a system, either directly or indirectly.

[0153] "Emotional state" refers to the emotional state inferred from the user's psychological reactions, facial expressions, and tone of voice.

[0154] This invention is a system that individualizes astronomical events and provides users with a personalized observation experience. This system is realized by utilizing multiple computer systems and software.

[0155] The server first accesses an astronomical database to obtain celestial information. The data obtained from the database includes the positions of stars and past celestial events, and the server formats this data into a format suitable for the generative model. The advanced data processing programming language Python is used for data formatting. The formatted data is used as input to a generative AI model (e.g., TensorFlow) to perform real-time simulations of celestial phenomena. The generative AI model outputs visual data that the user can experience visually.

[0156] Visual data from the server is transmitted to information processing devices (computers, tablets, and smartphones). These devices utilize 3D rendering software, such as Unity or Unreal Engine, to display the visual data in high quality. The devices also use cameras and microphones to detect the user's real-time emotional state. This emotional data is analyzed using facial recognition and voice analysis technologies and then transmitted back to the server.

[0157] The generated visual data is customized based on the user's emotions and interactions. The server can dynamically adjust the content of the visual data using the user's emotional state information. For example, if the user indicates excitement, it can provide additional information about relevant new celestial events.

[0158] To give a specific example, when a user experiences a simulation of a meteor shower in the night sky, if the server detects emotions such as surprise or excitement, it will display additional visuals explaining the causes and characteristics of past large-scale meteor showers.

[0159] A concrete example of a prompt sentence to input into the generating AI model is, "Simulate the following celestial event: Real-time simulation of a meteor shower in the night sky and provide detailed information."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server retrieves celestial information from an astronomical database. First, the server uses a specific query to access the astronomical database and extract data on star positions and past celestial events. The input is a database query, and the output is raw celestial data. This step involves executing SQL queries on the database server.

[0163] Step 2:

[0164] The server formats the acquired celestial object information and converts it into a format suitable for the generating AI model. The server uses a Python script to remove unnecessary fields from the data and reformat the necessary data. The input is raw celestial object data, and the output is formatted celestial object data. Specifically, data cleaning and filtering are performed.

[0165] Step 3:

[0166] The server inputs formatted astronomical data into a generative AI model to generate visual data. The generative AI model (e.g., TensorFlow) analyzes the data and performs real-time simulations of astronomical events. The input is formatted astronomical data, and the output is simulated visual data. Specifically, the generative model constructs images and 3D models based on the data.

[0167] Step 4:

[0168] The server sends the generated visual data to the terminal. The server distributes the visual data to the information processing device via the network, and the terminal displays the data using Unity or Unreal Engine. The input is simulated visual data, and the output is a visually displayed astronomical event. Specifically, data transmission and rendering take place.

[0169] Step 5:

[0170] The device recognizes the user's emotional state in real time using facial recognition and voice analysis technologies. Input consists of facial expression and voice data acquired by the camera and microphone, while output is analyzed emotional data. Specifically, the device uses video analysis software to classify emotions.

[0171] Step 6:

[0172] The server adjusts the content of the visual data based on the user's emotional data. The server analyzes the emotional data and selects new celestial information that should be focused on. The input is the user's emotional data, and the output is the adjusted visual data. Specific operations include searching for relevant information and selecting applicable visual content.

[0173] Step 7:

[0174] The server collects user interaction and sentiment information and uses it to provide information in the future. The server records the data as logs and stores it to analyze the user's past behavior history. The input is user interaction data and sentiment data, and the output is accumulated user history data. Specifically, data recording and preparation for analysis are performed.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] Modern astronomical observation experiences are limited to providing users with uniform visual information, making it difficult to offer experiences that take into account the individual reactions and interests of each user. Furthermore, dynamic adjustments to content based on users' emotional states and their use in future content provision are not being adequately implemented.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generation algorithm that simulates astronomical phenomena based on the astronomical information, means for distributing the generated visual data to a user device, means for recognizing the user's emotional state, and means for dynamically adjusting the visual data based on the emotional state. This makes it possible to provide a personalized astronomical observation experience that responds to the user's emotional state.

[0180] "Astronomical information" refers to data about the positions and movements of stars, planets, and other celestial bodies in the universe.

[0181] A "generative algorithm" is a computational method that generates visual content by performing simulations based on input data.

[0182] "Visual data" refers to digital information displayed in a format that users can visually recognize.

[0183] A "user device" is an electronic device that can be operated by a user and has an interface for displaying and manipulating visual data.

[0184] A "user" is a person who operates the system and experiences the content provided.

[0185] "Emotional state" refers to the psychological response exhibited by the user, and this includes emotions such as joy, surprise, and excitement.

[0186] "Dynamic adjustment" means changing the displayed content in real time according to the user's emotional state and actions.

[0187] To implement this invention, the system is configured as follows: The server first retrieves celestial information from a celestial database. This celestial information includes detailed data on the positions and movements of stars, planets, and other celestial bodies in the universe. Based on this information, a generation algorithm performs celestial simulations and generates visual data. The visual data is delivered to a user device, which provides the user with intuitive and high-quality visuals of celestial phenomena.

[0188] The user device is an electronic device such as a smartphone or computer, equipped with an interface for the user to view and interact with visual data. The server also uses an emotion analysis engine to recognize the user's emotional state when they view the displayed visual data. This allows the server to determine the user's psychological response, such as joy or surprise, in real time. Based on this emotional state, the server dynamically adjusts the visual content to provide a personalized astronomical observation experience.

[0189] For example, suppose a user is observing a meteor shower using smart glasses. If the emotion analysis engine recognizes the user's surprised expression, the server can display additional detailed information about the meteor shower and other interesting celestial phenomena. This allows the user to have a personalized and emotionally optimal experience.

[0190] An example of a prompt might be: "A user was observing the night sky with smart glasses when they saw an unexpected meteor shower and showed a surprised expression. How is this emotion data analyzed and reflected in the next content displayed?"

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server retrieves celestial object information from a celestial object database. The input is the query results from the celestial object database, which are used to extract information about the object's position and movement. The output is the celestial object data necessary for the simulation. Based on this data, the next simulation process is performed.

[0194] Step 2:

[0195] The server inputs acquired celestial information into a generation algorithm and simulates celestial phenomena. This simulation generates visual data. The input is celestial information, and the output is high-quality visual data to be provided to the user. During this process, calculations are performed to model realistic celestial phenomena.

[0196] Step 3:

[0197] The server delivers the generated visual data to the user's device. The input is the visual data, and the output is the completion of data delivery to the user's device. This allows the user to receive the visual data in real time through their device.

[0198] Step 4:

[0199] The user device displays received visual data and provides an interface to the user. The input is visual data received from the server, and the output is a graphical display that allows user interaction. This allows the user to observe simulated celestial phenomena and interact with details about celestial objects of interest.

[0200] Step 5:

[0201] The device uses its built-in camera and microphone to recognize the user's emotional state and runs an emotion analysis engine. Input is the user's facial expressions and voice data, and output is data related to the user's emotions. Image processing and voice analysis are performed to analyze the emotional state.

[0202] Step 6:

[0203] The server dynamically adjusts the visual data provided to the user based on the acquired emotional data. The input is emotional state data and the currently displayed visual data, while the output is updated visual data optimized for the user's emotions. This step makes the user experience more personalized.

[0204] Step 7:

[0205] The server collects user interaction data and emotional state data and stores it in a database. Inputs include user operation logs and emotional data, while output is the completion of saving to the database for future content delivery. This data contributes to improving the quality of personalization in the future.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] To implement this invention, it is necessary to collect astronomical data, generate simulations based on that data, distribute the generated visual data, and acquire and analyze user interactions. Specific embodiments are shown below.

[0223] The server retrieves star position information, astronomical phenomenon history, and weather data from an astronomical observation database. This data undergoes preprocessing such as standardization and timestamp conversion before being converted into a format for input into the generative model.

[0224] The generative model simulates astronomical phenomena based on this input data and generates high-resolution visual data. The simulation results can, for example, reproduce how a meteor shower would appear from a specific region of Earth.

[0225] Next, the server delivers the generated visual data to the user's terminal. During delivery, data compression and optimization are performed to ensure efficient transmission according to the network environment. This allows the user to receive high-quality visual content in real time.

[0226] On the user's terminal, the received visual data is displayed instantly, and the user can interact with it. By selecting specific celestial bodies or changing the viewpoint, the user can obtain an experience tailored to their individual interests.

[0227] Furthermore, the server collects user interaction data and uses it to improve the accuracy and relevance of the content it provides next. For example, if a user frequently displays a particular type of celestial object, the server will prioritize providing information about that object in subsequent content deliveries.

[0228] In this way, the present invention is a system that allows users to realistically experience diverse celestial phenomena in space, regardless of weather or geography. This system is considered to be extremely useful in deepening the interest of educational institutions and individuals in astronomy.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server retrieves necessary astronomical data from an astronomical observation database. This includes star position information, records of past astronomical phenomena, and related weather information. The retrieved data is standardized and timestamped, and converted into a format that is easy to process.

[0232] Step 2:

[0233] The server launches a generative model using the formatted astronomical data. The generative model performs simulations based on the input data and generates realistic visual data. In this process, for example, the movement of a meteor shower or the orbit of a specific comet can be reproduced.

[0234] Step 3:

[0235] The server compresses and optimizes the generated visual data into a format suitable for the user's terminal. This improves data transmission efficiency and ensures that data is delivered with quality appropriate to the communication environment.

[0236] Step 4:

[0237] The server delivers optimized visual data to the user's terminal. The user can receive and play the delivered content on their terminal.

[0238] Step 5:

[0239] Users can view visual data on their devices and initiate interaction. Specifically, they can tap on celestial bodies to display detailed information or change their viewpoint to observe them from different angles.

[0240] Step 6:

[0241] The server collects user interaction data. This data is used to analyze user interests and improve future content delivery. For example, users who spend a lot of time on specific celestial objects or phenomena will receive priority information related to those interests.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] The present invention aims to provide a system that effectively integrates the acquisition of astronomical data, the generation and distribution of real-time visual data, and the collection and analysis of user interactions, thereby providing users with a realistic and interactive astronomical experience. Furthermore, it solves the problem of improving the user experience by providing customized content based on the individual user's interests and operation history.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes data preprocessing means for standardizing and timestamp-transforming astronomical data, means for generating visual data using a generative model that simulates astronomical phenomena based on the astronomical data, and data compression and distribution means for encoding the generated visual data and performing adaptive streaming according to network conditions. This enables users to experience high-resolution and personalized astronomical simulations in real time.

[0247] "Astronomical data" refers to information about the positions of stars and planets, the history of celestial phenomena, and the weather conditions associated with them.

[0248] "Standardization and timestamp conversion" refers to the process of unifying data expressed in different formats and units into a consistent format, and converting temporal information to conform to a common standard.

[0249] "Data preprocessing means" refers to means that provide functions for processing acquired data into a format suitable for analysis and simulation.

[0250] A "generative model" refers to an algorithm or machine learning model used to generate new data or visuals based on input data.

[0251] "Visual data" refers to data that is visually recognizable, generated through computer graphics or simulations.

[0252] "Data compression and distribution means" refers to means that provide processing and functions for reducing the size of visual data and transmitting it efficiently over a network.

[0253] "Adaptive streaming" refers to a technology that dynamically adjusts the quality of video and audio in real time according to network conditions during delivery.

[0254] "Interaction" refers to the actions a user performs on visual data through their device, as well as the system's response to those actions.

[0255] "Interaction data" refers to information such as user actions, choices, and changes in perspective, and is used to analyze user interests and behavior.

[0256] This invention aims to realize a system that simulates astronomical phenomena in real time and provides them to the user visually. Specific embodiments are shown below.

[0257] The server first retrieves astronomical data from a database or API for astronomical observation. At this stage, it uses SQL queries to extract information about the positions of stars and astronomical phenomena from the database. For weather data, it uses an external API via the internet, such as a weather information API, to obtain weather data for a specific region.

[0258] Next, the server standardizes the acquired data and unifies the timestamps. Using Python and the Pandas library, it performs data imputation and format conversion to prepare the input data. This makes the data suitable for the generative AI model.

[0259] The server inputs pre-prepared astronomical data into generative AI models such as TensorFlow and PyTorch, and performs simulations. This model generates high-resolution visual data, providing detailed representations of specific astronomical phenomena, such as meteor showers, as they appear from a particular region.

[0260] The generated visual data is encoded on the server using video compression technologies such as H.264. Depending on the network conditions, adaptive streaming technologies (e.g., MPEG-DASH) are used to efficiently deliver the data to the user's terminal.

[0261] The device instantly decodes the received visual data and displays it to the user in real time. This can utilize applications that leverage HTML5 or WebGL. The user can use the provided visual interface to select specific celestial objects or change their viewpoint.

[0262] Furthermore, the server collects user interaction data, including a history of user clicks, selections, and viewpoint changes. This data is analyzed after collection and used to optimize future content generation and delivery strategies.

[0263] As a concrete example, the following prompt statements can be used to instruct the system to operate.

[0264] "Generate a simulation of a meteor shower that beautifully illuminates the night sky, and specifically show how it would look from a particular location."

[0265] This system allows users to intuitively and interactively experience astronomical phenomena by utilizing astronomical data and generative AI models.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server initiates the input process, retrieving star position information and astronomical phenomenon history from an astronomical database or API. Specifically, it accesses the database using SQL queries and extracts the necessary data. It also retrieves weather data using an external API. The data obtained in this process is then used as output for the next processing step.

[0269] Step 2:

[0270] The server performs preprocessing on the data acquired in Step 1, standardizing and timestamping it. Using the Python Pandas library, it imputes missing values ​​and converts the time-series data into a unified format. As a result of this preprocessing, data in a unified format is output. The converted data is then input into the next generative model in its original format.

[0271] Step 3:

[0272] The server inputs pre-processed data into a generating AI model to simulate astronomical phenomena. Specifically, an AI model using TensorFlow or PyTorch generates high-resolution visual data from the input data. This process yields data that serves as a visual representation of a specific astronomical phenomenon. This output is later distributed to the user and is therefore subjected to data compression.

[0273] Step 4:

[0274] The server encodes the visual data generated in step 3, making it efficient for distribution over the network. Specifically, it compresses the data in H.264 format and prepares it for distribution using adaptive streaming technology. The output of this process is then provided as compressed data for transmission to each user terminal.

[0275] Step 5:

[0276] The terminal decodes the visual data received from the server and displays it to the user in real time. By utilizing HTML5 and WebGL, the visual data is provided to the user through an intuitive interface. The displayed data appears as output that enables user interaction.

[0277] Step 6:

[0278] Users interact with the on-device interface, selecting specific celestial bodies and changing their viewpoints to build experiences based on their interests. The data generated by this interaction is then sent back to the server and output as input data used for detailed analysis.

[0279] Step 7:

[0280] The server analyzes user interaction data and uses it to generate and deliver content in the future. Machine learning techniques are used to predict user behavior patterns and preferences, processing this data as input for generating optimized content. The results of this analysis are then used as output data to further enhance user engagement.

[0281] (Application Example 1)

[0282] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0283] Actually experiencing astronomical phenomena usually has restrictions depending on geographical and meteorological conditions. Also, in order to accurately deepen the understanding of the starry sky and celestial phenomena, it is necessary to analyze complex data and information, and the learning burden associated with this is not small. Therefore, there is a desire to provide a system that allows users to intuitively understand astronomy based on their own interests and obtain special experiences.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for acquiring celestial body information, means for generating visual information using a generation model that reproduces astronomical phenomena based on the celestial body information, and means for transmitting the generated visual information to a user device. As a result, the user can reproduce the starry sky based on the position information and is provided with explanatory information about the selected celestial body, enabling a more intuitive and individualized astronomical experience.

[0286] "Celestial body information" is data related to stars, planets, and other cosmic structures obtained through observations.

[0287] "Generation model" is an algorithm or program for reproducing a specific astronomical phenomenon using celestial body information and visually expressing it.

[0288] "Visual information" is data generated to visually show celestial bodies and astronomical phenomena to the user.

[0289] "User device" is an information terminal that can receive and display visual information, including smartphones, smart glasses, etc.

[0290] "Operation" is an action such as selection, zooming in or out, etc. that the user performs on the visual information through the user device.

[0291] "Position information" is data indicating the geographical position of the user or device.

[0292] "Explanatory information" refers to data used to provide users with detailed information related to celestial bodies and astronomical phenomena.

[0293] The development of a system to implement this invention begins with a server acquiring astronomical information. This astronomical information consists of observational data about stars and planets, which a generative model uses to reproduce astronomical phenomena and generate visual information. For example, an AI model can be used for this generative model, enabling highly accurate simulations.

[0294] Next, the server transmits the generated visual information to the user's device. User devices include smartphones, smart glasses, and head-mounted displays. The user's device displays the received visual information, and the user can interact with that information. For example, when using smart glasses, the user can obtain information about constellations and meteor showers superimposed on the actual sky.

[0295] Information obtained through user actions is sent to the server and used for future content delivery. This makes it possible to provide a customized experience for each user. In addition, based on the user's location information, the system recreates the night sky visible from that location, and by selecting a specific celestial object, related explanatory information is provided.

[0296] For example, if a user looks up at the night sky and prompts "Show me the meteor showers visible tonight" through their smart glasses, the meteor showers visible from that location will be displayed. This allows users to intuitively explore specific astronomical phenomena, contributing to their learning and expanding their interest.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server obtains celestial body information. In this step, the server accesses the celestial body observation database and collects the position data of stars and planets, the history of astronomical phenomena, and related meteorological data. The input is raw data from the observation database, and the output is celestial body information that has been standardized and whose timestamp has been converted.

[0300] Step 2:

[0301] The server uses a generated AI model to generate visual information. The obtained celestial body information is input into this generation model, and a simulation of astronomical phenomena is performed. The output is high-resolution visual information, which is data ready to be distributed to users.

[0302] Step 3:

[0303] The server transmits the generated visual information to the user device. In this step, data compression and optimization according to the communication situation are performed, and the visual information is sent to the user device. The input is the generated visual information, and the output is the compressed visual information that reaches the user terminal.

[0304] Step 4:

[0305] The terminal displays the visual information and the user performs operations. The user device decompresses the received visual information and displays it on the display. The user selects a specific celestial body or changes the viewing point through operations. The input is the compressed visual information, and the output is interaction data based on the user's operations.

[0306] Step 5:

[0307] The server analyzes the user's interaction data and utilizes it for subsequent distributions. It identifies the interests and concerns from the user's operation history and individually optimizes the content to be provided next. The input is the user's interaction data, and the output is an optimized content distribution plan.

[0308] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0309] This invention provides a more personalized experience by combining a system that allows users to visually experience astronomical phenomena using astronomical data with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0310] The server first retrieves data on star positions and past astronomical phenomena from an astronomical observation database. This data is formatted into a format suitable for the generative model and input into the model. The generative model then uses this data to perform realistic simulations and generate visual data for the user to view.

[0311] The server then sends the generated visual data to the user's terminal. The user can then receive and view high-quality, real-time visuals of celestial phenomena through the terminal. The user can interact with these visuals, for example, by selecting a celestial object to obtain detailed information about it.

[0312] Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. While the user is viewing visual data, the terminal determines the user's emotional state through facial recognition and voice analysis. For example, if the user shows expressions of surprise or excitement, the emotion engine detects this and sends that information to the server.

[0313] The server can adjust the display of visual data and the provision of additional information in real time based on the emotional data received from the emotion engine. For example, if a user indicates excitement, it can recommend and display interesting astronomical phenomena related to that excitement.

[0314] Ultimately, the server collects user interaction and sentiment data, which is then used to improve future content delivery. This allows for a more personalized experience by referencing the user's past behavior and emotional history.

[0315] This embodiment makes the astronomical observation experience more interactive and personalized, enabling the delivery of content optimized according to the user's emotions. Thus, the present invention makes a significant contribution to the field of astronomy education and to stimulating individual interest.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server retrieves the latest astronomical data from an astronomical observation database. This includes star positions and information on the occurrence of specific celestial phenomena. The obtained data is then formatted into a format suitable for simulation.

[0319] Step 2:

[0320] The server inputs the formatted astronomical data into a generative model to generate visual data of astronomical phenomena. The generative model performs realistic simulations based on the input data and converts the astronomical phenomena into a format that users can observe.

[0321] Step 3:

[0322] The server sends the generated visual data to the user's terminal. The visual data is compressed and optimized for smooth playback on the terminal.

[0323] Step 4:

[0324] The user receives and displays visual data on their device. The user can observe and interact with celestial phenomena. For example, they can tap on a specific celestial body to view detailed information.

[0325] Step 5:

[0326] The device recognizes the user's emotions in real time through an emotion engine. It uses a facial recognition camera and voice analysis to determine the user's emotional state from their facial expressions and voice.

[0327] Step 6:

[0328] The server analyzes emotional data obtained from the emotion engine and adjusts the displayed visual data in real time. For example, if the user expresses surprise, it automatically introduces new and interesting astronomical phenomena.

[0329] Step 7:

[0330] The server records user interaction and sentiment data, which will be used to improve future content delivery. The recorded data is used to provide experiences tailored to the user's preferences and interests.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Current astronomical observation systems typically provide users with uniform visual information, lacking personalization of the experience based on individual user emotions and interactions. This limits the opportunities for users to deeply enjoy the observation experience.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generative model that simulates astronomical events based on the astronomical information, and means for distributing the generated visual data to an information processing device. This makes it possible to personalize the visual data based on the user's emotional state and interactions, providing a richer and more personalized astronomical observation experience.

[0336] "Astronomical information" refers to data related to the position, movement, and past events of celestial bodies existing in outer space.

[0337] "Astronomical events" refer to phenomena that describe the movement or changes of stars and celestial bodies in the universe.

[0338] A "generative model" refers to an algorithm or model that generates new output based on input data, and is particularly used in computer simulations.

[0339] "Visual data" refers to digital images and video data that are generated so that users can experience them through their vision.

[0340] "Information processing equipment" refers to hardware devices such as computers, tablets, and smartphones that receive, process, and display digital data.

[0341] "Interaction" refers to the actions or responses that a user performs with a system, either directly or indirectly.

[0342] "Emotional state" refers to the emotional state inferred from the user's psychological reactions, facial expressions, and tone of voice.

[0343] This invention is a system that individualizes astronomical events and provides users with a personalized observation experience. This system is realized by utilizing multiple computer systems and software.

[0344] The server first accesses an astronomical database to obtain celestial information. The data obtained from the database includes the positions of stars and past celestial events, and the server formats this data into a format suitable for the generative model. The advanced data processing programming language Python is used for data formatting. The formatted data is used as input to a generative AI model (e.g., TensorFlow) to perform real-time simulations of celestial phenomena. The generative AI model outputs visual data that the user can experience visually.

[0345] Visual data from the server is transmitted to information processing devices (computers, tablets, and smartphones). These devices utilize 3D rendering software, such as Unity or Unreal Engine, to display the visual data in high quality. The devices also use cameras and microphones to detect the user's real-time emotional state. This emotional data is analyzed using facial recognition and voice analysis technologies and then transmitted back to the server.

[0346] The generated visual data is customized based on the user's emotions and interactions. The server can dynamically adjust the content of the visual data using the user's emotional state information. For example, if the user indicates excitement, it can provide additional information about relevant new celestial events.

[0347] To give a specific example, when a user experiences a simulation of a meteor shower in the night sky, if the server detects emotions such as surprise or excitement, it will display additional visuals explaining the causes and characteristics of past large-scale meteor showers.

[0348] A concrete example of a prompt sentence to input into the generating AI model is, "Simulate the following celestial event: Real-time simulation of a meteor shower in the night sky and provide detailed information."

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The server retrieves celestial information from an astronomical database. First, the server uses a specific query to access the astronomical database and extract data on star positions and past celestial events. The input is a database query, and the output is raw celestial data. This step involves executing SQL queries on the database server.

[0352] Step 2:

[0353] The server formats the acquired celestial object information and converts it into a format suitable for the generating AI model. The server uses a Python script to remove unnecessary fields from the data and reformat the necessary data. The input is raw celestial object data, and the output is formatted celestial object data. Specifically, data cleaning and filtering are performed.

[0354] Step 3:

[0355] The server inputs formatted astronomical data into a generative AI model to generate visual data. The generative AI model (e.g., TensorFlow) analyzes the data and performs real-time simulations of astronomical events. The input is formatted astronomical data, and the output is simulated visual data. Specifically, the generative model constructs images and 3D models based on the data.

[0356] Step 4:

[0357] The server sends the generated visual data to the terminal. The server distributes the visual data to the information processing device via the network, and the terminal displays the data using Unity or Unreal Engine. The input is simulated visual data, and the output is a visually displayed astronomical event. Specifically, data transmission and rendering take place.

[0358] Step 5:

[0359] The device recognizes the user's emotional state in real time using facial recognition and voice analysis technologies. Input consists of facial expression and voice data acquired by the camera and microphone, while output is analyzed emotional data. Specifically, the device uses video analysis software to classify emotions.

[0360] Step 6:

[0361] The server adjusts the content of the visual data based on the user's emotional data. The server analyzes the emotional data and selects new celestial information that should be focused on. The input is the user's emotional data, and the output is the adjusted visual data. Specific operations include searching for relevant information and selecting applicable visual content.

[0362] Step 7:

[0363] The server collects user interaction and sentiment information and uses it to provide information in the future. The server records the data as logs and stores it to analyze the user's past behavior history. The input is user interaction data and sentiment data, and the output is accumulated user history data. Specifically, data recording and preparation for analysis are performed.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] Modern astronomical observation experiences are limited to providing users with uniform visual information, making it difficult to offer experiences that take into account the individual reactions and interests of each user. Furthermore, dynamic adjustments to content based on users' emotional states and their use in future content provision are not being adequately implemented.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generation algorithm that simulates astronomical phenomena based on the astronomical information, means for distributing the generated visual data to a user device, means for recognizing the user's emotional state, and means for dynamically adjusting the visual data based on the emotional state. This makes it possible to provide a personalized astronomical observation experience that responds to the user's emotional state.

[0369] "Astronomical information" refers to data about the positions and movements of stars, planets, and other celestial bodies in the universe.

[0370] A "generative algorithm" is a computational method that generates visual content by performing simulations based on input data.

[0371] "Visual data" refers to digital information displayed in a format that users can visually recognize.

[0372] A "user device" is an electronic device that can be operated by a user and has an interface for displaying and manipulating visual data.

[0373] A "user" is a person who operates the system and experiences the content provided.

[0374] "Emotional state" refers to the psychological response exhibited by the user, and this includes emotions such as joy, surprise, and excitement.

[0375] "Dynamic adjustment" means changing the displayed content in real time according to the user's emotional state and actions.

[0376] To implement this invention, the system is configured as follows: The server first retrieves celestial information from a celestial database. This celestial information includes detailed data on the positions and movements of stars, planets, and other celestial bodies in the universe. Based on this information, a generation algorithm performs celestial simulations and generates visual data. The visual data is delivered to a user device, which provides the user with intuitive and high-quality visuals of celestial phenomena.

[0377] The user device is an electronic device such as a smartphone or computer, equipped with an interface for the user to view and interact with visual data. The server also uses an emotion analysis engine to recognize the user's emotional state when they view the displayed visual data. This allows the server to determine the user's psychological response, such as joy or surprise, in real time. Based on this emotional state, the server dynamically adjusts the visual content to provide a personalized astronomical observation experience.

[0378] For example, suppose a user is observing a meteor shower using smart glasses. If the emotion analysis engine recognizes the user's surprised expression, the server can display additional detailed information about the meteor shower and other interesting celestial phenomena. This allows the user to have a personalized and emotionally optimal experience.

[0379] An example of a prompt might be: "A user was observing the night sky with smart glasses when they saw an unexpected meteor shower and showed a surprised expression. How is this emotion data analyzed and reflected in the next content displayed?"

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server retrieves celestial object information from a celestial object database. The input is the query results from the celestial object database, which are used to extract information about the object's position and movement. The output is the celestial object data necessary for the simulation. Based on this data, the next simulation process is performed.

[0383] Step 2:

[0384] The server inputs acquired celestial information into a generation algorithm and simulates celestial phenomena. This simulation generates visual data. The input is celestial information, and the output is high-quality visual data to be provided to the user. During this process, calculations are performed to model realistic celestial phenomena.

[0385] Step 3:

[0386] The server delivers the generated visual data to the user's device. The input is the visual data, and the output is the completion of data delivery to the user's device. This allows the user to receive the visual data in real time through their device.

[0387] Step 4:

[0388] The user device displays received visual data and provides an interface to the user. The input is visual data received from the server, and the output is a graphical display that allows user interaction. This allows the user to observe simulated celestial phenomena and interact with details about celestial objects of interest.

[0389] Step 5:

[0390] The device uses its built-in camera and microphone to recognize the user's emotional state and runs an emotion analysis engine. Input is the user's facial expressions and voice data, and output is data related to the user's emotions. Image processing and voice analysis are performed to analyze the emotional state.

[0391] Step 6:

[0392] The server dynamically adjusts the visual data provided to the user based on the acquired emotional data. The input is emotional state data and the currently displayed visual data, while the output is updated visual data optimized for the user's emotions. This step makes the user experience more personalized.

[0393] Step 7:

[0394] The server collects user interaction data and emotional state data and stores it in a database. Inputs include user operation logs and emotional data, while output is the completion of saving to the database for future content delivery. This data contributes to improving the quality of personalization in the future.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] To implement this invention, it is necessary to collect astronomical data, generate simulations based on that data, distribute the generated visual data, and acquire and analyze user interactions. Specific embodiments are shown below.

[0412] The server retrieves star position information, astronomical phenomenon history, and weather data from an astronomical observation database. This data undergoes preprocessing such as standardization and timestamp conversion before being converted into a format for input into the generative model.

[0413] The generative model simulates astronomical phenomena based on this input data and generates high-resolution visual data. The simulation results can, for example, reproduce how a meteor shower would appear from a specific region of Earth.

[0414] Next, the server delivers the generated visual data to the user's terminal. During delivery, data compression and optimization are performed to ensure efficient transmission according to the network environment. This allows the user to receive high-quality visual content in real time.

[0415] On the user's terminal, the received visual data is displayed instantly, and the user can interact with it. By selecting specific celestial bodies or changing the viewpoint, the user can obtain an experience tailored to their individual interests.

[0416] Furthermore, the server collects user interaction data and uses it to improve the accuracy and relevance of the content it provides next. For example, if a user frequently displays a particular type of celestial object, the server will prioritize providing information about that object in subsequent content deliveries.

[0417] In this way, the present invention is a system that allows users to realistically experience diverse celestial phenomena in space, regardless of weather or geography. This system is considered to be extremely useful in deepening the interest of educational institutions and individuals in astronomy.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server retrieves necessary astronomical data from an astronomical observation database. This includes star position information, records of past astronomical phenomena, and related weather information. The retrieved data is standardized and timestamped, and converted into a format that is easy to process.

[0421] Step 2:

[0422] The server launches a generative model using the formatted astronomical data. The generative model performs simulations based on the input data and generates realistic visual data. In this process, for example, the movement of a meteor shower or the orbit of a specific comet can be reproduced.

[0423] Step 3:

[0424] The server compresses and optimizes the generated visual data into a format suitable for the user's terminal. This improves data transmission efficiency and ensures that data is delivered with quality appropriate to the communication environment.

[0425] Step 4:

[0426] The server delivers optimized visual data to the user's terminal. The user can receive and play the delivered content on their terminal.

[0427] Step 5:

[0428] Users can view visual data on their devices and initiate interaction. Specifically, they can tap on celestial bodies to display detailed information or change their viewpoint to observe them from different angles.

[0429] Step 6:

[0430] The server collects user interaction data. This data is used to analyze user interests and improve future content delivery. For example, users who spend a lot of time on specific celestial objects or phenomena will receive priority information related to those interests.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] The present invention aims to provide a system that effectively integrates the acquisition of astronomical data, the generation and distribution of real-time visual data, and the collection and analysis of user interactions, thereby providing users with a realistic and interactive astronomical experience. Furthermore, it solves the problem of improving the user experience by providing customized content based on the individual user's interests and operation history.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes data preprocessing means for standardizing and timestamp-transforming astronomical data, means for generating visual data using a generative model that simulates astronomical phenomena based on the astronomical data, and data compression and distribution means for encoding the generated visual data and performing adaptive streaming according to network conditions. This enables users to experience high-resolution and personalized astronomical simulations in real time.

[0436] "Astronomical data" refers to information about the positions of stars and planets, the history of celestial phenomena, and the weather conditions associated with them.

[0437] "Standardization and timestamp conversion" refers to the process of unifying data expressed in different formats and units into a consistent format, and converting temporal information to conform to a common standard.

[0438] "Data preprocessing means" refers to means that provide functions for processing acquired data into a format suitable for analysis and simulation.

[0439] A "generative model" refers to an algorithm or machine learning model used to generate new data or visuals based on input data.

[0440] "Visual data" refers to data that is visually recognizable, generated through computer graphics or simulations.

[0441] "Data compression and distribution means" refers to means that provide processing and functions for reducing the size of visual data and transmitting it efficiently over a network.

[0442] "Adaptive streaming" refers to a technology that dynamically adjusts the quality of video and audio in real time according to network conditions during delivery.

[0443] "Interaction" refers to the actions a user performs on visual data through their device, as well as the system's response to those actions.

[0444] "Interaction data" refers to information such as user actions, choices, and changes in perspective, and is used to analyze user interests and behavior.

[0445] This invention aims to realize a system that simulates astronomical phenomena in real time and provides them to the user visually. Specific embodiments are shown below.

[0446] The server first retrieves astronomical data from a database or API for astronomical observation. At this stage, it uses SQL queries to extract information about the positions of stars and astronomical phenomena from the database. For weather data, it uses an external API via the internet, such as a weather information API, to obtain weather data for a specific region.

[0447] Next, the server standardizes the acquired data and unifies the timestamps. Using Python and the Pandas library, it performs data imputation and format conversion to prepare the input data. This makes the data suitable for the generative AI model.

[0448] The server inputs pre-prepared astronomical data into generative AI models such as TensorFlow and PyTorch, and performs simulations. This model generates high-resolution visual data, providing detailed representations of specific astronomical phenomena, such as meteor showers, as they appear from a particular region.

[0449] The generated visual data is encoded on the server using video compression technologies such as H.264. Depending on the network conditions, adaptive streaming technologies (e.g., MPEG-DASH) are used to efficiently deliver the data to the user's terminal.

[0450] The device instantly decodes the received visual data and displays it to the user in real time. This can utilize applications that leverage HTML5 or WebGL. The user can use the provided visual interface to select specific celestial objects or change their viewpoint.

[0451] Furthermore, the server collects user interaction data, including a history of user clicks, selections, and viewpoint changes. This data is analyzed after collection and used to optimize future content generation and delivery strategies.

[0452] As a concrete example, the following prompt statements can be used to instruct the system to operate.

[0453] "Generate a simulation of a meteor shower that beautifully illuminates the night sky, and specifically show how it would look from a particular location."

[0454] This system allows users to intuitively and interactively experience astronomical phenomena by utilizing astronomical data and generative AI models.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The server initiates the input process, retrieving star position information and astronomical phenomenon history from an astronomical database or API. Specifically, it accesses the database using SQL queries and extracts the necessary data. It also retrieves weather data using an external API. The data obtained in this process is then used as output for the next processing step.

[0458] Step 2:

[0459] The server performs preprocessing on the data acquired in Step 1, standardizing and timestamping it. Using the Python Pandas library, it imputes missing values ​​and converts the time-series data into a unified format. As a result of this preprocessing, data in a unified format is output. The converted data is then input into the next generative model in its original format.

[0460] Step 3:

[0461] The server inputs pre-processed data into a generating AI model to simulate astronomical phenomena. Specifically, an AI model using TensorFlow or PyTorch generates high-resolution visual data from the input data. This process yields data that serves as a visual representation of a specific astronomical phenomenon. This output is later distributed to the user and is therefore subjected to data compression.

[0462] Step 4:

[0463] The server encodes the visual data generated in step 3, making it efficient for distribution over the network. Specifically, it compresses the data in H.264 format and prepares it for distribution using adaptive streaming technology. The output of this process is then provided as compressed data for transmission to each user terminal.

[0464] Step 5:

[0465] The terminal decodes the visual data received from the server and displays it to the user in real time. By utilizing HTML5 and WebGL, the visual data is provided to the user through an intuitive interface. The displayed data appears as output that enables user interaction.

[0466] Step 6:

[0467] Users interact with the on-device interface, selecting specific celestial bodies and changing their viewpoints to build experiences based on their interests. The data generated by this interaction is then sent back to the server and output as input data used for detailed analysis.

[0468] Step 7:

[0469] The server analyzes user interaction data and uses it to generate and deliver content in the future. Machine learning techniques are used to predict user behavior patterns and preferences, processing this data as input for generating optimized content. The results of this analysis are then used as output data to further enhance user engagement.

[0470] (Application Example 1)

[0471] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0472] Experiencing astronomical phenomena in reality is usually limited by geographical and meteorological conditions. Furthermore, gaining a deep and accurate understanding of the night sky and celestial phenomena requires analyzing complex data and information, resulting in a considerable learning burden. Therefore, there is a need for a system that allows users to intuitively understand astronomy based on their own interests and gain unique experiences.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0474] In this invention, the server includes means for acquiring astronomical information, means for generating visual information using a generative model that reproduces astronomical phenomena based on the astronomical information, and means for transmitting the generated visual information to a user device. This enables a more intuitive and personalized astronomical experience for the user by reproducing the starry sky based on location information and providing descriptive information about selected celestial bodies.

[0475] "Astronomical information" refers to data about stars, planets, and other cosmic structures obtained through observation.

[0476] A "generative model" is an algorithm or program that uses astronomical information to reproduce and visually represent specific astronomical phenomena.

[0477] "Visual information" refers to data generated to visually represent celestial bodies and astronomical phenomena to users.

[0478] A "user device" refers to an information terminal that can receive and display visual information, and includes smartphones and smart glasses.

[0479] "Operation" refers to actions such as selecting, zooming in, or zooming out of visual information performed by a user through their device.

[0480] "Location information" refers to data that indicates the geographical location of a user or device.

[0481] "Explanatory information" refers to data used to provide users with detailed information related to celestial bodies and astronomical phenomena.

[0482] The development of a system to implement this invention begins with a server acquiring astronomical information. This astronomical information consists of observational data about stars and planets, which a generative model uses to reproduce astronomical phenomena and generate visual information. For example, an AI model can be used for this generative model, enabling highly accurate simulations.

[0483] Next, the server transmits the generated visual information to the user's device. User devices include smartphones, smart glasses, and head-mounted displays. The user's device displays the received visual information, and the user can interact with that information. For example, when using smart glasses, the user can obtain information about constellations and meteor showers superimposed on the actual sky.

[0484] Information obtained through user actions is sent to the server and used for future content delivery. This makes it possible to provide a customized experience for each user. In addition, based on the user's location information, the system recreates the night sky visible from that location, and by selecting a specific celestial object, related explanatory information is provided.

[0485] For example, if a user looks up at the night sky and prompts "Show me the meteor showers visible tonight" through their smart glasses, the meteor showers visible from that location will be displayed. This allows users to intuitively explore specific astronomical phenomena, contributing to their learning and expanding their interest.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server retrieves astronomical information. In this step, the server accesses an astronomical observation database to collect positional data for stars and planets, a history of astronomical phenomena, and related meteorological data. The input is raw data from the observation database, and the output is standardized and timestamp-transformed astronomical information.

[0489] Step 2:

[0490] The server generates visual information using a generative AI model. Acquired astronomical information is input into this generative model, and simulations of astronomical phenomena are performed. The output is high-resolution visual information, ready to be delivered to the user.

[0491] Step 3:

[0492] The server transmits the generated visual information to the user device. In this step, data compression and optimization are performed according to the communication conditions, and the visual information is sent to the user device. The input is the generated visual information, and the output is the compressed visual information that arrives at the user terminal.

[0493] Step 4:

[0494] The terminal displays visual information, and the user interacts with it. The user device decompresses the received visual information and displays it on the screen. Through interaction, the user can select specific celestial bodies or change the viewpoint. The input is compressed visual information, and the output is interaction data resulting from the user's actions.

[0495] Step 5:

[0496] The server analyzes user interaction data and uses it to improve future content delivery. It identifies user interests and preferences from their operation history and individually optimizes the content delivered next. The input is user interaction data, and the output is an optimized content delivery plan.

[0497] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0498] This invention provides a more personalized experience by combining a system that allows users to visually experience astronomical phenomena using astronomical data with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0499] The server first retrieves data on star positions and past astronomical phenomena from an astronomical observation database. This data is formatted into a format suitable for the generative model and input into the model. The generative model then uses this data to perform realistic simulations and generate visual data for the user to view.

[0500] The server then sends the generated visual data to the user's terminal. The user can then receive and view high-quality, real-time visuals of celestial phenomena through the terminal. The user can interact with these visuals, for example, by selecting a celestial object to obtain detailed information about it.

[0501] Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. While the user is viewing visual data, the terminal determines the user's emotional state through facial recognition and voice analysis. For example, if the user shows expressions of surprise or excitement, the emotion engine detects this and sends that information to the server.

[0502] The server can adjust the display of visual data and the provision of additional information in real time based on the emotional data received from the emotion engine. For example, if a user indicates excitement, it can recommend and display interesting astronomical phenomena related to that excitement.

[0503] Ultimately, the server collects user interaction and sentiment data, which is then used to improve future content delivery. This allows for a more personalized experience by referencing the user's past behavior and emotional history.

[0504] This embodiment makes the astronomical observation experience more interactive and personalized, enabling the delivery of content optimized according to the user's emotions. Thus, the present invention makes a significant contribution to the field of astronomy education and to stimulating individual interest.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The server retrieves the latest astronomical data from an astronomical observation database. This includes star positions and information on the occurrence of specific celestial phenomena. The obtained data is then formatted into a format suitable for simulation.

[0508] Step 2:

[0509] The server inputs the formatted astronomical data into a generative model to generate visual data of astronomical phenomena. The generative model performs realistic simulations based on the input data and converts the astronomical phenomena into a format that users can observe.

[0510] Step 3:

[0511] The server sends the generated visual data to the user's terminal. The visual data is compressed and optimized for smooth playback on the terminal.

[0512] Step 4:

[0513] The user receives and displays visual data on their device. The user can observe and interact with celestial phenomena. For example, they can tap on a specific celestial body to view detailed information.

[0514] Step 5:

[0515] The device recognizes the user's emotions in real time through an emotion engine. It uses a facial recognition camera and voice analysis to determine the user's emotional state from their facial expressions and voice.

[0516] Step 6:

[0517] The server analyzes emotional data obtained from the emotion engine and adjusts the displayed visual data in real time. For example, if the user expresses surprise, it automatically introduces new and interesting astronomical phenomena.

[0518] Step 7:

[0519] The server records user interaction and sentiment data, which will be used to improve future content delivery. The recorded data is used to provide experiences tailored to the user's preferences and interests.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] Current astronomical observation systems typically provide users with uniform visual information, lacking personalization of the experience based on individual user emotions and interactions. This limits the opportunities for users to deeply enjoy the observation experience.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generative model that simulates astronomical events based on the astronomical information, and means for distributing the generated visual data to an information processing device. This makes it possible to personalize the visual data based on the user's emotional state and interactions, providing a richer and more personalized astronomical observation experience.

[0525] "Astronomical information" refers to data related to the position, movement, and past events of celestial bodies existing in outer space.

[0526] "Astronomical events" refer to phenomena that describe the movement or changes of stars and celestial bodies in the universe.

[0527] A "generative model" refers to an algorithm or model that generates new output based on input data, and is particularly used in computer simulations.

[0528] "Visual data" refers to digital images and video data that are generated so that users can experience them through their vision.

[0529] "Information processing equipment" refers to hardware devices such as computers, tablets, and smartphones that receive, process, and display digital data.

[0530] "Interaction" refers to the actions or responses that a user performs with a system, either directly or indirectly.

[0531] "Emotional state" refers to the emotional state inferred from the user's psychological reactions, facial expressions, and tone of voice.

[0532] This invention is a system that individualizes astronomical events and provides users with a personalized observation experience. This system is realized by utilizing multiple computer systems and software.

[0533] The server first accesses an astronomical database to obtain celestial information. The data obtained from the database includes the positions of stars and past celestial events, and the server formats this data into a format suitable for the generative model. The advanced data processing programming language Python is used for data formatting. The formatted data is used as input to a generative AI model (e.g., TensorFlow) to perform real-time simulations of celestial phenomena. The generative AI model outputs visual data that the user can experience visually.

[0534] Visual data from the server is transmitted to information processing devices (computers, tablets, and smartphones). These devices utilize 3D rendering software, such as Unity or Unreal Engine, to display the visual data in high quality. The devices also use cameras and microphones to detect the user's real-time emotional state. This emotional data is analyzed using facial recognition and voice analysis technologies and then transmitted back to the server.

[0535] The generated visual data is customized based on the user's emotions and interactions. The server can dynamically adjust the content of the visual data using the user's emotional state information. For example, if the user indicates excitement, it can provide additional information about relevant new celestial events.

[0536] To give a specific example, when a user experiences a simulation of a meteor shower in the night sky, if the server detects emotions such as surprise or excitement, it will display additional visuals explaining the causes and characteristics of past large-scale meteor showers.

[0537] A concrete example of a prompt sentence to input into the generating AI model is, "Simulate the following celestial event: Real-time simulation of a meteor shower in the night sky and provide detailed information."

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The server retrieves celestial information from an astronomical database. First, the server uses a specific query to access the astronomical database and extract data on star positions and past celestial events. The input is a database query, and the output is raw celestial data. This step involves executing SQL queries on the database server.

[0541] Step 2:

[0542] The server formats the acquired celestial object information and converts it into a format suitable for the generating AI model. The server uses a Python script to remove unnecessary fields from the data and reformat the necessary data. The input is raw celestial object data, and the output is formatted celestial object data. Specifically, data cleaning and filtering are performed.

[0543] Step 3:

[0544] The server inputs formatted astronomical data into a generative AI model to generate visual data. The generative AI model (e.g., TensorFlow) analyzes the data and performs real-time simulations of astronomical events. The input is formatted astronomical data, and the output is simulated visual data. Specifically, the generative model constructs images and 3D models based on the data.

[0545] Step 4:

[0546] The server sends the generated visual data to the terminal. The server distributes the visual data to the information processing device via the network, and the terminal displays the data using Unity or Unreal Engine. The input is simulated visual data, and the output is a visually displayed astronomical event. Specifically, data transmission and rendering take place.

[0547] Step 5:

[0548] The device recognizes the user's emotional state in real time using facial recognition and voice analysis technologies. Input consists of facial expression and voice data acquired by the camera and microphone, while output is analyzed emotional data. Specifically, the device uses video analysis software to classify emotions.

[0549] Step 6:

[0550] The server adjusts the content of the visual data based on the user's emotional data. The server analyzes the emotional data and selects new celestial information that should be focused on. The input is the user's emotional data, and the output is the adjusted visual data. Specific operations include searching for relevant information and selecting applicable visual content.

[0551] Step 7:

[0552] The server collects user interaction and sentiment information and uses it to provide information in the future. The server records the data as logs and stores it to analyze the user's past behavior history. The input is user interaction data and sentiment data, and the output is accumulated user history data. Specifically, data recording and preparation for analysis are performed.

[0553] (Application Example 2)

[0554] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] Modern astronomical observation experiences are limited to providing users with uniform visual information, making it difficult to offer experiences that take into account the individual reactions and interests of each user. Furthermore, dynamic adjustments to content based on users' emotional states and their use in future content provision are not being adequately implemented.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0557] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generation algorithm that simulates astronomical phenomena based on the astronomical information, means for distributing the generated visual data to a user device, means for recognizing the user's emotional state, and means for dynamically adjusting the visual data based on the emotional state. This makes it possible to provide a personalized astronomical observation experience that responds to the user's emotional state.

[0558] "Astronomical information" refers to data about the positions and movements of stars, planets, and other celestial bodies in the universe.

[0559] A "generative algorithm" is a computational method that generates visual content by performing simulations based on input data.

[0560] "Visual data" refers to digital information displayed in a format that users can visually recognize.

[0561] A "user device" is an electronic device that can be operated by a user and has an interface for displaying and manipulating visual data.

[0562] A "user" is a person who operates the system and experiences the content provided.

[0563] "Emotional state" refers to the psychological response exhibited by the user, and this includes emotions such as joy, surprise, and excitement.

[0564] "Dynamic adjustment" means changing the displayed content in real time according to the user's emotional state and actions.

[0565] To implement this invention, the system is configured as follows: The server first retrieves celestial information from a celestial database. This celestial information includes detailed data on the positions and movements of stars, planets, and other celestial bodies in the universe. Based on this information, a generation algorithm performs celestial simulations and generates visual data. The visual data is delivered to a user device, which provides the user with intuitive and high-quality visuals of celestial phenomena.

[0566] The user device is an electronic device such as a smartphone or computer, equipped with an interface for the user to view and interact with visual data. The server also uses an emotion analysis engine to recognize the user's emotional state when they view the displayed visual data. This allows the server to determine the user's psychological response, such as joy or surprise, in real time. Based on this emotional state, the server dynamically adjusts the visual content to provide a personalized astronomical observation experience.

[0567] For example, suppose a user is observing a meteor shower using smart glasses. If the emotion analysis engine recognizes the user's surprised expression, the server can display additional detailed information about the meteor shower and other interesting celestial phenomena. This allows the user to have a personalized and emotionally optimal experience.

[0568] An example of a prompt might be: "A user was observing the night sky with smart glasses when they saw an unexpected meteor shower and showed a surprised expression. How is this emotion data analyzed and reflected in the next content displayed?"

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server retrieves celestial object information from a celestial object database. The input is the query results from the celestial object database, which are used to extract information about the object's position and movement. The output is the celestial object data necessary for the simulation. Based on this data, the next simulation process is performed.

[0572] Step 2:

[0573] The server inputs acquired celestial information into a generation algorithm and simulates celestial phenomena. This simulation generates visual data. The input is celestial information, and the output is high-quality visual data to be provided to the user. During this process, calculations are performed to model realistic celestial phenomena.

[0574] Step 3:

[0575] The server delivers the generated visual data to the user's device. The input is the visual data, and the output is the completion of data delivery to the user's device. This allows the user to receive the visual data in real time through their device.

[0576] Step 4:

[0577] The user device displays received visual data and provides an interface to the user. The input is visual data received from the server, and the output is a graphical display that allows user interaction. This allows the user to observe simulated celestial phenomena and interact with details about celestial objects of interest.

[0578] Step 5:

[0579] The device uses its built-in camera and microphone to recognize the user's emotional state and runs an emotion analysis engine. Input is the user's facial expressions and voice data, and output is data related to the user's emotions. Image processing and voice analysis are performed to analyze the emotional state.

[0580] Step 6:

[0581] The server dynamically adjusts the visual data provided to the user based on the acquired emotional data. The input is emotional state data and the currently displayed visual data, while the output is updated visual data optimized for the user's emotions. This step makes the user experience more personalized.

[0582] Step 7:

[0583] The server collects user interaction data and emotional state data and stores it in a database. Inputs include user operation logs and emotional data, while output is the completion of saving to the database for future content delivery. This data contributes to improving the quality of personalization in the future.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] To implement this invention, it is necessary to collect astronomical data, generate simulations based on that data, distribute the generated visual data, and acquire and analyze user interactions. Specific embodiments are shown below.

[0602] The server retrieves star position information, astronomical phenomenon history, and weather data from an astronomical observation database. This data undergoes preprocessing such as standardization and timestamp conversion before being converted into a format for input into the generative model.

[0603] The generative model simulates astronomical phenomena based on this input data and generates high-resolution visual data. The simulation results can, for example, reproduce how a meteor shower would appear from a specific region of Earth.

[0604] Next, the server delivers the generated visual data to the user's terminal. During delivery, data compression and optimization are performed to ensure efficient transmission according to the network environment. This allows the user to receive high-quality visual content in real time.

[0605] On the user's terminal, the received visual data is displayed instantly, and the user can interact with it. By selecting specific celestial bodies or changing the viewpoint, the user can obtain an experience tailored to their individual interests.

[0606] Furthermore, the server collects user interaction data and uses it to improve the accuracy and relevance of the content it provides next. For example, if a user frequently displays a particular type of celestial object, the server will prioritize providing information about that object in subsequent content deliveries.

[0607] In this way, the present invention is a system that allows users to realistically experience diverse celestial phenomena in space, regardless of weather or geography. This system is considered to be extremely useful in deepening the interest of educational institutions and individuals in astronomy.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The server retrieves necessary astronomical data from an astronomical observation database. This includes star position information, records of past astronomical phenomena, and related weather information. The retrieved data is standardized and timestamped, and converted into a format that is easy to process.

[0611] Step 2:

[0612] The server launches a generative model using the formatted astronomical data. The generative model performs simulations based on the input data and generates realistic visual data. In this process, for example, the movement of a meteor shower or the orbit of a specific comet can be reproduced.

[0613] Step 3:

[0614] The server compresses and optimizes the generated visual data into a format suitable for the user's terminal. This improves data transmission efficiency and ensures that data is delivered with quality appropriate to the communication environment.

[0615] Step 4:

[0616] The server delivers optimized visual data to the user's terminal. The user can receive and play the delivered content on their terminal.

[0617] Step 5:

[0618] Users can view visual data on their devices and initiate interaction. Specifically, they can tap on celestial bodies to display detailed information or change their viewpoint to observe them from different angles.

[0619] Step 6:

[0620] The server collects user interaction data. This data is used to analyze user interests and improve future content delivery. For example, users who spend a lot of time on specific celestial objects or phenomena will receive priority information related to those interests.

[0621] (Example 1)

[0622] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] The present invention aims to provide a system that effectively integrates the acquisition of astronomical data, the generation and distribution of real-time visual data, and the collection and analysis of user interactions, thereby providing users with a realistic and interactive astronomical experience. Furthermore, it solves the problem of improving the user experience by providing customized content based on the individual user's interests and operation history.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0625] In this invention, the server includes data preprocessing means for standardizing and timestamp-transforming astronomical data, means for generating visual data using a generative model that simulates astronomical phenomena based on the astronomical data, and data compression and distribution means for encoding the generated visual data and performing adaptive streaming according to network conditions. This enables users to experience high-resolution and personalized astronomical simulations in real time.

[0626] "Astronomical data" refers to information about the positions of stars and planets, the history of celestial phenomena, and the weather conditions associated with them.

[0627] "Standardization and timestamp conversion" refers to the process of unifying data expressed in different formats and units into a consistent format, and converting temporal information to conform to a common standard.

[0628] "Data preprocessing means" refers to means that provide functions for processing acquired data into a format suitable for analysis and simulation.

[0629] A "generative model" refers to an algorithm or machine learning model used to generate new data or visuals based on input data.

[0630] "Visual data" refers to data that is visually recognizable, generated through computer graphics or simulations.

[0631] "Data compression and distribution means" refers to means that provide processing and functions for reducing the size of visual data and transmitting it efficiently over a network.

[0632] "Adaptive streaming" refers to a technology that dynamically adjusts the quality of video and audio in real time according to network conditions during delivery.

[0633] "Interaction" refers to the actions a user performs on visual data through their device, as well as the system's response to those actions.

[0634] "Interaction data" refers to information such as user actions, choices, and changes in perspective, and is used to analyze user interests and behavior.

[0635] This invention aims to realize a system that simulates astronomical phenomena in real time and provides them to the user visually. Specific embodiments are shown below.

[0636] The server first retrieves astronomical data from a database or API for astronomical observation. At this stage, it uses SQL queries to extract information about the positions of stars and astronomical phenomena from the database. For weather data, it uses an external API via the internet, such as a weather information API, to obtain weather data for a specific region.

[0637] Next, the server standardizes the acquired data and unifies the timestamps. Using Python and the Pandas library, it performs data imputation and format conversion to prepare the input data. This makes the data suitable for the generative AI model.

[0638] The server inputs pre-prepared astronomical data into generative AI models such as TensorFlow and PyTorch, and performs simulations. This model generates high-resolution visual data, providing detailed representations of specific astronomical phenomena, such as meteor showers, as they appear from a particular region.

[0639] The generated visual data is encoded on the server using video compression technologies such as H.264. Depending on the network conditions, adaptive streaming technologies (e.g., MPEG-DASH) are used to efficiently deliver the data to the user's terminal.

[0640] The device instantly decodes the received visual data and displays it to the user in real time. This can utilize applications that leverage HTML5 or WebGL. The user can use the provided visual interface to select specific celestial objects or change their viewpoint.

[0641] Furthermore, the server collects user interaction data, including a history of user clicks, selections, and viewpoint changes. This data is analyzed after collection and used to optimize future content generation and delivery strategies.

[0642] As a concrete example, the following prompt statements can be used to instruct the system to operate.

[0643] "Generate a simulation of a meteor shower that beautifully illuminates the night sky, and specifically show how it would look from a particular location."

[0644] This system allows users to intuitively and interactively experience astronomical phenomena by utilizing astronomical data and generative AI models.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The server initiates the input process, retrieving star position information and astronomical phenomenon history from an astronomical database or API. Specifically, it accesses the database using SQL queries and extracts the necessary data. It also retrieves weather data using an external API. The data obtained in this process is then used as output for the next processing step.

[0648] Step 2:

[0649] The server performs preprocessing on the data acquired in Step 1, standardizing and timestamping it. Using the Python Pandas library, it imputes missing values ​​and converts the time-series data into a unified format. As a result of this preprocessing, data in a unified format is output. The converted data is then input into the next generative model in its original format.

[0650] Step 3:

[0651] The server inputs pre-processed data into a generating AI model to simulate astronomical phenomena. Specifically, an AI model using TensorFlow or PyTorch generates high-resolution visual data from the input data. This process yields data that serves as a visual representation of a specific astronomical phenomenon. This output is later distributed to the user and is therefore subjected to data compression.

[0652] Step 4:

[0653] The server encodes the visual data generated in step 3, making it efficient for distribution over the network. Specifically, it compresses the data in H.264 format and prepares it for distribution using adaptive streaming technology. The output of this process is then provided as compressed data for transmission to each user terminal.

[0654] Step 5:

[0655] The terminal decodes the visual data received from the server and displays it to the user in real time. By utilizing HTML5 and WebGL, the visual data is provided to the user through an intuitive interface. The displayed data appears as output that enables user interaction.

[0656] Step 6:

[0657] Users interact with the on-device interface, selecting specific celestial bodies and changing their viewpoints to build experiences based on their interests. The data generated by this interaction is then sent back to the server and output as input data used for detailed analysis.

[0658] Step 7:

[0659] The server analyzes user interaction data and uses it to generate and deliver content in the future. Machine learning techniques are used to predict user behavior patterns and preferences, processing this data as input for generating optimized content. The results of this analysis are then used as output data to further enhance user engagement.

[0660] (Application Example 1)

[0661] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] Experiencing astronomical phenomena in reality is usually limited by geographical and meteorological conditions. Furthermore, gaining a deep and accurate understanding of the night sky and celestial phenomena requires analyzing complex data and information, resulting in a considerable learning burden. Therefore, there is a need for a system that allows users to intuitively understand astronomy based on their own interests and gain unique experiences.

[0663] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0664] In this invention, the server includes means for acquiring astronomical information, means for generating visual information using a generative model that reproduces astronomical phenomena based on the astronomical information, and means for transmitting the generated visual information to a user device. This enables a more intuitive and personalized astronomical experience for the user by reproducing the starry sky based on location information and providing descriptive information about selected celestial bodies.

[0665] "Astronomical information" refers to data about stars, planets, and other cosmic structures obtained through observation.

[0666] A "generative model" is an algorithm or program that uses astronomical information to reproduce and visually represent specific astronomical phenomena.

[0667] "Visual information" refers to data generated to visually represent celestial bodies and astronomical phenomena to users.

[0668] A "user device" refers to an information terminal that can receive and display visual information, and includes smartphones and smart glasses.

[0669] "Operation" refers to actions such as selecting, zooming in, or zooming out of visual information performed by a user through their device.

[0670] "Location information" refers to data that indicates the geographical location of a user or device.

[0671] "Explanatory information" refers to data used to provide users with detailed information related to celestial bodies and astronomical phenomena.

[0672] The development of a system to implement this invention begins with a server acquiring astronomical information. This astronomical information consists of observational data about stars and planets, which a generative model uses to reproduce astronomical phenomena and generate visual information. For example, an AI model can be used for this generative model, enabling highly accurate simulations.

[0673] Next, the server transmits the generated visual information to the user's device. User devices include smartphones, smart glasses, and head-mounted displays. The user's device displays the received visual information, and the user can interact with that information. For example, when using smart glasses, the user can obtain information about constellations and meteor showers superimposed on the actual sky.

[0674] Information obtained through user actions is sent to the server and used for future content delivery. This makes it possible to provide a customized experience for each user. In addition, based on the user's location information, the system recreates the night sky visible from that location, and by selecting a specific celestial object, related explanatory information is provided.

[0675] For example, if a user looks up at the night sky and prompts "Show me the meteor showers visible tonight" through their smart glasses, the meteor showers visible from that location will be displayed. This allows users to intuitively explore specific astronomical phenomena, contributing to their learning and expanding their interest.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server retrieves astronomical information. In this step, the server accesses an astronomical observation database to collect positional data for stars and planets, a history of astronomical phenomena, and related meteorological data. The input is raw data from the observation database, and the output is standardized and timestamp-transformed astronomical information.

[0679] Step 2:

[0680] The server generates visual information using a generative AI model. Acquired astronomical information is input into this generative model, and simulations of astronomical phenomena are performed. The output is high-resolution visual information, ready to be delivered to the user.

[0681] Step 3:

[0682] The server transmits the generated visual information to the user device. In this step, data compression and optimization are performed according to the communication conditions, and the visual information is sent to the user device. The input is the generated visual information, and the output is the compressed visual information that arrives at the user terminal.

[0683] Step 4:

[0684] The terminal displays visual information, and the user interacts with it. The user device decompresses the received visual information and displays it on the screen. Through interaction, the user can select specific celestial bodies or change the viewpoint. The input is compressed visual information, and the output is interaction data resulting from the user's actions.

[0685] Step 5:

[0686] The server analyzes user interaction data and uses it to improve future content delivery. It identifies user interests and preferences from their operation history and individually optimizes the content delivered next. The input is user interaction data, and the output is an optimized content delivery plan.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention provides a more personalized experience by combining a system that allows users to visually experience astronomical phenomena using astronomical data with an emotion engine that recognizes user emotions. Specific embodiments are shown below.

[0689] The server first retrieves data on star positions and past astronomical phenomena from an astronomical observation database. This data is formatted into a format suitable for the generative model and input into the model. The generative model then uses this data to perform realistic simulations and generate visual data for the user to view.

[0690] The server then sends the generated visual data to the user's terminal. The user can then receive and view high-quality, real-time visuals of celestial phenomena through the terminal. The user can interact with these visuals, for example, by selecting a celestial object to obtain detailed information about it.

[0691] Furthermore, this system includes an emotion engine that recognizes the user's emotions in real time. While the user is viewing visual data, the terminal determines the user's emotional state through facial recognition and voice analysis. For example, if the user shows expressions of surprise or excitement, the emotion engine detects this and sends that information to the server.

[0692] The server can adjust the display of visual data and the provision of additional information in real time based on the emotional data received from the emotion engine. For example, if a user indicates excitement, it can recommend and display interesting astronomical phenomena related to that excitement.

[0693] Ultimately, the server collects user interaction and sentiment data, which is then used to improve future content delivery. This allows for a more personalized experience by referencing the user's past behavior and emotional history.

[0694] This embodiment makes the astronomical observation experience more interactive and personalized, enabling the delivery of content optimized according to the user's emotions. Thus, the present invention makes a significant contribution to the field of astronomy education and to stimulating individual interest.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The server retrieves the latest astronomical data from an astronomical observation database. This includes star positions and information on the occurrence of specific celestial phenomena. The obtained data is then formatted into a format suitable for simulation.

[0698] Step 2:

[0699] The server inputs the formatted astronomical data into a generative model to generate visual data of astronomical phenomena. The generative model performs realistic simulations based on the input data and converts the astronomical phenomena into a format that users can observe.

[0700] Step 3:

[0701] The server sends the generated visual data to the user's terminal. The visual data is compressed and optimized for smooth playback on the terminal.

[0702] Step 4:

[0703] The user receives and displays visual data on their device. The user can observe and interact with celestial phenomena. For example, they can tap on a specific celestial body to view detailed information.

[0704] Step 5:

[0705] The device recognizes the user's emotions in real time through an emotion engine. It uses a facial recognition camera and voice analysis to determine the user's emotional state from their facial expressions and voice.

[0706] Step 6:

[0707] The server analyzes emotional data obtained from the emotion engine and adjusts the displayed visual data in real time. For example, if the user expresses surprise, it automatically introduces new and interesting astronomical phenomena.

[0708] Step 7:

[0709] The server records user interaction and sentiment data, which will be used to improve future content delivery. The recorded data is used to provide experiences tailored to the user's preferences and interests.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Current astronomical observation systems typically provide users with uniform visual information, lacking personalization of the experience based on individual user emotions and interactions. This limits the opportunities for users to deeply enjoy the observation experience.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generative model that simulates astronomical events based on the astronomical information, and means for distributing the generated visual data to an information processing device. This makes it possible to personalize the visual data based on the user's emotional state and interactions, providing a richer and more personalized astronomical observation experience.

[0715] "Astronomical information" refers to data related to the position, movement, and past events of celestial bodies existing in outer space.

[0716] "Astronomical events" refer to phenomena that describe the movement or changes of stars and celestial bodies in the universe.

[0717] A "generative model" refers to an algorithm or model that generates new output based on input data, and is particularly used in computer simulations.

[0718] "Visual data" refers to digital images and video data that are generated so that users can experience them through their vision.

[0719] "Information processing equipment" refers to hardware devices such as computers, tablets, and smartphones that receive, process, and display digital data.

[0720] "Interaction" refers to the actions or responses that a user performs with a system, either directly or indirectly.

[0721] "Emotional state" refers to the emotional state inferred from the user's psychological reactions, facial expressions, and tone of voice.

[0722] This invention is a system that individualizes astronomical events and provides users with a personalized observation experience. This system is realized by utilizing multiple computer systems and software.

[0723] The server first accesses an astronomical database to obtain celestial information. The data obtained from the database includes the positions of stars and past celestial events, and the server formats this data into a format suitable for the generative model. The advanced data processing programming language Python is used for data formatting. The formatted data is used as input to a generative AI model (e.g., TensorFlow) to perform real-time simulations of celestial phenomena. The generative AI model outputs visual data that the user can experience visually.

[0724] Visual data from the server is transmitted to information processing devices (computers, tablets, and smartphones). These devices utilize 3D rendering software, such as Unity or Unreal Engine, to display the visual data in high quality. The devices also use cameras and microphones to detect the user's real-time emotional state. This emotional data is analyzed using facial recognition and voice analysis technologies and then transmitted back to the server.

[0725] The generated visual data is customized based on the user's emotions and interactions. The server can dynamically adjust the content of the visual data using the user's emotional state information. For example, if the user indicates excitement, it can provide additional information about relevant new celestial events.

[0726] To give a specific example, when a user experiences a simulation of a meteor shower in the night sky, if the server detects emotions such as surprise or excitement, it will display additional visuals explaining the causes and characteristics of past large-scale meteor showers.

[0727] A concrete example of a prompt sentence to input into the generating AI model is, "Simulate the following celestial event: Real-time simulation of a meteor shower in the night sky and provide detailed information."

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The server retrieves celestial information from an astronomical database. First, the server uses a specific query to access the astronomical database and extract data on star positions and past celestial events. The input is a database query, and the output is raw celestial data. This step involves executing SQL queries on the database server.

[0731] Step 2:

[0732] The server formats the acquired celestial object information and converts it into a format suitable for the generating AI model. The server uses a Python script to remove unnecessary fields from the data and reformat the necessary data. The input is raw celestial object data, and the output is formatted celestial object data. Specifically, data cleaning and filtering are performed.

[0733] Step 3:

[0734] The server inputs formatted astronomical data into a generative AI model to generate visual data. The generative AI model (e.g., TensorFlow) analyzes the data and performs real-time simulations of astronomical events. The input is formatted astronomical data, and the output is simulated visual data. Specifically, the generative model constructs images and 3D models based on the data.

[0735] Step 4:

[0736] The server sends the generated visual data to the terminal. The server distributes the visual data to the information processing device via the network, and the terminal displays the data using Unity or Unreal Engine. The input is simulated visual data, and the output is a visually displayed astronomical event. Specifically, data transmission and rendering take place.

[0737] Step 5:

[0738] The device recognizes the user's emotional state in real time using facial recognition and voice analysis technologies. Input consists of facial expression and voice data acquired by the camera and microphone, while output is analyzed emotional data. Specifically, the device uses video analysis software to classify emotions.

[0739] Step 6:

[0740] The server adjusts the content of the visual data based on the user's emotional data. The server analyzes the emotional data and selects new celestial information that should be focused on. The input is the user's emotional data, and the output is the adjusted visual data. Specific operations include searching for relevant information and selecting applicable visual content.

[0741] Step 7:

[0742] The server collects user interaction and sentiment information and uses it to provide information in the future. The server records the data as logs and stores it to analyze the user's past behavior history. The input is user interaction data and sentiment data, and the output is accumulated user history data. Specifically, data recording and preparation for analysis are performed.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] Modern astronomical observation experiences are limited to providing users with uniform visual information, making it difficult to offer experiences that take into account the individual reactions and interests of each user. Furthermore, dynamic adjustments to content based on users' emotional states and their use in future content provision are not being adequately implemented.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes means for acquiring astronomical information, means for generating visual data using a generation algorithm that simulates astronomical phenomena based on the astronomical information, means for distributing the generated visual data to a user device, means for recognizing the user's emotional state, and means for dynamically adjusting the visual data based on the emotional state. This makes it possible to provide a personalized astronomical observation experience that responds to the user's emotional state.

[0748] "Astronomical information" refers to data about the positions and movements of stars, planets, and other celestial bodies in the universe.

[0749] A "generative algorithm" is a computational method that generates visual content by performing simulations based on input data.

[0750] "Visual data" refers to digital information displayed in a format that users can visually recognize.

[0751] A "user device" is an electronic device that can be operated by a user and has an interface for displaying and manipulating visual data.

[0752] A "user" is a person who operates the system and experiences the content provided.

[0753] "Emotional state" refers to the psychological response exhibited by the user, and this includes emotions such as joy, surprise, and excitement.

[0754] "Dynamic adjustment" means changing the displayed content in real time according to the user's emotional state and actions.

[0755] To implement this invention, the system is configured as follows: The server first retrieves celestial information from a celestial database. This celestial information includes detailed data on the positions and movements of stars, planets, and other celestial bodies in the universe. Based on this information, a generation algorithm performs celestial simulations and generates visual data. The visual data is delivered to a user device, which provides the user with intuitive and high-quality visuals of celestial phenomena.

[0756] The user device is an electronic device such as a smartphone or computer, equipped with an interface for the user to view and interact with visual data. The server also uses an emotion analysis engine to recognize the user's emotional state when they view the displayed visual data. This allows the server to determine the user's psychological response, such as joy or surprise, in real time. Based on this emotional state, the server dynamically adjusts the visual content to provide a personalized astronomical observation experience.

[0757] For example, suppose a user is observing a meteor shower using smart glasses. If the emotion analysis engine recognizes the user's surprised expression, the server can display additional detailed information about the meteor shower and other interesting celestial phenomena. This allows the user to have a personalized and emotionally optimal experience.

[0758] An example of a prompt might be: "A user was observing the night sky with smart glasses when they saw an unexpected meteor shower and showed a surprised expression. How is this emotion data analyzed and reflected in the next content displayed?"

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The server retrieves celestial object information from a celestial object database. The input is the query results from the celestial object database, which are used to extract information about the object's position and movement. The output is the celestial object data necessary for the simulation. Based on this data, the next simulation process is performed.

[0762] Step 2:

[0763] The server inputs acquired celestial information into a generation algorithm and simulates celestial phenomena. This simulation generates visual data. The input is celestial information, and the output is high-quality visual data to be provided to the user. During this process, calculations are performed to model realistic celestial phenomena.

[0764] Step 3:

[0765] The server delivers the generated visual data to the user's device. The input is the visual data, and the output is the completion of data delivery to the user's device. This allows the user to receive the visual data in real time through their device.

[0766] Step 4:

[0767] The user device displays received visual data and provides an interface to the user. The input is visual data received from the server, and the output is a graphical display that allows user interaction. This allows the user to observe simulated celestial phenomena and interact with details about celestial objects of interest.

[0768] Step 5:

[0769] The device uses its built-in camera and microphone to recognize the user's emotional state and runs an emotion analysis engine. Input is the user's facial expressions and voice data, and output is data related to the user's emotions. Image processing and voice analysis are performed to analyze the emotional state.

[0770] Step 6:

[0771] The server dynamically adjusts the visual data provided to the user based on the acquired emotional data. The input is emotional state data and the currently displayed visual data, while the output is updated visual data optimized for the user's emotions. This step makes the user experience more personalized.

[0772] Step 7:

[0773] The server collects user interaction data and emotional state data and stores it in a database. Inputs include user operation logs and emotional data, while output is the completion of saving to the database for future content delivery. This data contributes to improving the quality of personalization in the future.

[0774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] 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.

[0788] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] Means of acquiring astronomical data,

[0798] A means for generating visual data using a generative model that simulates astronomical phenomena based on the aforementioned astronomical data,

[0799] A means for distributing the generated visual data to a user terminal,

[0800] A means by which the user interacts with the visual data on the user terminal,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for customizing the generated visual data based on a user profile.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for collecting the user's interaction data and utilizing it for subsequent content delivery.

[0806] "Example 1"

[0807] (Claim 1)

[0808] Means of acquiring astronomical data,

[0809] Data preprocessing means for standardizing and timestamp-transforming the aforementioned celestial data,

[0810] A means for generating visual data using a generative model that simulates astronomical phenomena based on the aforementioned astronomical data,

[0811] A data compression and distribution means that encodes the generated visual data and performs adaptive streaming according to network conditions,

[0812] A means by which the user interacts with the visual data on the user terminal,

[0813] Means for collecting the aforementioned user interaction data,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, further comprising means for customizing the generated visual data based on user interests.

[0817] (Claim 3)

[0818] The system according to claim 1, further comprising means for analyzing the collected interaction data and utilizing it for generating and distributing visual data in the future.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] Means of obtaining astronomical information,

[0822] A means for generating visual information using a generative model that reproduces astronomical phenomena based on the aforementioned celestial information,

[0823] Means for transmitting the generated visual information to a user device,

[0824] The user device includes means for the user to operate on the visual information,

[0825] A means of providing a realistic astronomical experience through visual information,

[0826] A means for recreating the starry sky based on the user's location information and providing descriptive information about selected celestial objects,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for individually responding to the generated visual information based on user characteristic information.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising means for collecting user operation information and utilizing it for future information distribution.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] Means of obtaining astronomical information,

[0835] A means for generating visual data using a generative model that simulates celestial events based on the aforementioned celestial information,

[0836] Means for distributing the generated visual data to an information processing device,

[0837] The information processing device includes means for a user to interact with the visual data,

[0838] A means of recognizing the user's emotional state in real time,

[0839] Means for adjusting the content of visual data based on the user's emotional state,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for customizing the generated visual data based on the user's personal information.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for collecting user interaction information and emotional information and utilizing them for providing information in the future.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] Means of obtaining astronomical information,

[0848] A means for generating visual data using a generation algorithm that simulates astronomical phenomena based on the aforementioned astronomical information,

[0849] Means for distributing the generated visual data to a user device,

[0850] The user device provides means for the user to interact with the visual data,

[0851] Means for recognizing the emotional state of the user,

[0852] Means for dynamically adjusting visual data based on the aforementioned emotional state,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for personalizing the generated visual data based on user attribute information.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for collecting the user's interaction data and emotional state and utilizing them for future information distribution. [Explanation of symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring astronomical data, A means for generating visual data using a generative model that simulates astronomical phenomena based on the aforementioned astronomical data, A means for distributing the generated visual data to a user terminal, A means by which the user interacts with the visual data on the user terminal, A system that includes this.

2. The system according to claim 1, further comprising means for customizing the generated visual data based on a user profile.

3. The system according to claim 1, further comprising means for collecting the user's interaction data and utilizing it for subsequent content delivery.

Images