System

Abstract

An object of the system according to the embodiment is to select an observation target, a method, equipment, and a place for enabling even a beginner to easily perform astronomical observation.SOLUTION: A system according to an embodiment includes an observation target selection unit, an observation method suggestion unit, a device selection unit, and an observed location suggestion unit. The observation target selection unit selects an observation target of the user using the generated AI. The observation method proposal unit proposes an observation method for the observation target selected by the observation target selection unit. The device selecting section selects a device necessary for observation based on the observation method proposed by the observation method proposing section. The observation place proposing section proposes a place suitable for observation based on the equipment selected by the equipment selecting section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that selecting the observation target, method, equipment, and location for astronomical observation is complicated, making it difficult for beginners.

[0005] The system according to the embodiment aims to enable even beginners to easily select an observation target, method, equipment, and location for astronomical observation. [Means for solving the problem]

[0006] The system according to the embodiment includes an observation target selection unit, an observation method proposal unit, an equipment selection unit, and an observation location proposal unit. The observation target selection unit selects an observation target for a user using a generation AI. The observation method proposal unit proposes an observation method for the observation target selected by the observation target selection unit. The equipment selection unit selects equipment required for observation based on the observation method proposed by the observation method proposal unit. The observation location proposal unit proposes a location suitable for observation based on the equipment selected by the equipment selection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even beginners to easily select the observation target, method, equipment, and location for astronomical observation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The astronomical observation support system according to the embodiment of the present invention utilizes a generative AI to enable anyone to easily observe the stars and broaden one's view of the universe. As a result, the astronomical observation support system can use the generative AI to solve various problems related to astronomical observation and present specific solutions to users.

[0029] An astronomical observation support system according to an embodiment includes an observation target selection unit, an observation method suggestion unit, an equipment selection unit, and an observation location suggestion unit. The observation target selection unit uses a generation AI to select a user's observation target. For example, if a user inputs, "I want to observe planets," the generation AI presents a list of currently observable planets and explains their characteristics and observation points. The observation method suggestion unit suggests an observation method for the observation target selected by the observation target selection unit. For example, if a user inputs, "I want to observe Mars," the generation AI provides detailed explanations of the best time and time of day for observing Mars, the type of equipment to use, observation points, and so on. The equipment selection unit selects the equipment necessary for observation based on the observation method suggested by the observation method suggestion unit. For example, if a user inputs, "I have a limited budget, but I want to observe Mars," the generation AI lists telescopes, cameras, and other equipment that can be purchased within the budget and explains the features and prices of each. The observation location suggestion unit suggests suitable locations for observation based on the equipment selected by the equipment selection unit. For example, if you input "Where is the best place to observe Mars?", the AI ​​generator will suggest the best observation location taking into account the weather, humidity, and a location with minimal light pollution. As a result, the astronomical observation support system according to the embodiment allows anyone to easily observe the stars and broaden their perspective on space.

[0030] The observation target selection unit can analyze the user's past observation history and suggest new celestial objects that may be of interest. For example, the observation target selection unit uses a generation AI to analyze the user's past observation history and list celestial objects that have not been observed. For example, it suggests new celestial objects based on the types and frequency of celestial objects observed in the past. The observation target selection unit also predicts and suggests celestial objects that may be of interest to the user based on the user's observation history. For example, it analyzes the characteristics of celestial objects observed in the past and suggests similar celestial objects. The observation target selection unit also analyzes the observation history and suggests celestial objects that the user has not yet observed but may be of interest. For example, it lists celestial objects that have not been observed based on past observation data. This increases the enjoyment of observation by suggesting new celestial objects based on the user's interests.

[0031] The observation target selection unit can obtain the latest scientific data on celestial bodies in real time and suggest the optimal timing for observation. For example, the generation AI of the observation target selection unit obtains the latest scientific data on celestial bodies in real time and suggests the optimal timing for observation. For example, it may suggest the optimal observation time based on the position, brightness, and observation conditions of the celestial body. The observation target selection unit also suggests the optimal timing for observation in real time based on the latest data on celestial bodies. For example, it may analyze the movement and position of the celestial body and suggest the optimal time period for observation. The observation target selection unit also suggests the optimal timing for observation based on the latest data on celestial bodies using the generation AI. For example, it may suggest the optimal observation time based on the position, brightness, and observation conditions of the celestial body. This makes it possible to optimize the timing of observation based on the latest scientific data.

[0032] The observation method suggestion unit can evaluate the user's observation skill level and customize and suggest observation methods ranging from beginner to advanced. In the observation method suggestion unit, for example, the generation AI evaluates the user's observation skill level and customizes and suggests observation methods ranging from beginner to advanced. For example, it suggests an easy observation method for beginners. In addition, the observation method suggestion unit evaluates the observation skill level and customizes and suggests observation methods. For example, it suggests an easy observation method for beginners. In addition, the observation method suggestion unit evaluates the user's observation skill level and customizes and suggests observation methods. For example, it suggests an easy observation method for beginners. In this way, it is possible to suggest observation methods according to the user's skill level.

[0033] The observation method suggestion unit can acquire the environmental conditions required for observing celestial bodies in real time and propose the optimal observation method. For example, the generation AI of the observation method suggestion unit acquires the environmental conditions required for observing celestial bodies in real time and proposes the optimal observation method. For example, it proposes an observation method based on temperature and humidity. The observation method suggestion unit can also acquire environmental conditions in real time and propose the optimal observation method. For example, it proposes an observation method based on temperature and humidity. The observation method suggestion unit can also acquire the environmental conditions required for observing celestial bodies in real time and propose the optimal observation method. For example, it proposes an observation method based on temperature and humidity. In this way, environmental conditions can be acquired in real time and the optimal observation method can be proposed.

[0034] The observation method suggestion unit can suggest methods for scientific experiments other than astronomical observation. In the observation method suggestion unit, for example, the generation AI proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In addition, the observation method suggestion unit proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In addition, the observation method suggestion unit proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In this way, methods for scientific experiments other than astronomical observation can also be suggested.

[0035] The observation method suggestion unit can suggest a method for creating an observation report to be shared with other users based on the user's observation data. For example, the generation AI can suggest a method for creating an observation report to be shared with other users based on the user's observation data. For example, the observation data can be organized and compiled in a report format. The observation method suggestion unit can also suggest a method for creating an observation report based on the observation data. For example, the observation data can be organized and compiled in a report format. The observation method suggestion unit can also suggest a method for creating an observation report based on the user's observation data. For example, the observation data can be organized and compiled in a report format. This makes it possible to suggest a method for creating an observation report based on the observation data.

[0036] The equipment selection unit can propose the optimal combination of equipment depending on the user's observation purpose. In the equipment selection unit, for example, a generation AI proposes the optimal combination of equipment depending on the user's observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In addition, the equipment selection unit proposes the optimal combination of equipment depending on the observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In addition, the equipment selection unit proposes the optimal combination of equipment depending on the user's observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In this way, it is possible to propose the optimal combination of equipment depending on the observation purpose.

[0037] The equipment selection unit can analyze the latest equipment reviews and user feedback and suggest the equipment with the highest rating. In the equipment selection unit, for example, a generation AI analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. Also, the equipment selection unit analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. Also, the equipment selection unit analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. This makes it possible to suggest the equipment with the highest rating based on the latest reviews and feedback.

[0038] The equipment selection unit can suggest equipment suitable for uses other than astronomical observation. For example, the generation AI of the equipment selection unit suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. The equipment selection unit also suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. The equipment selection unit also suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. This makes it possible to suggest equipment suitable for uses other than astronomical observation.

[0039] The observation location suggestion unit can analyze the user's past observation data and suggest the observation location and conditions with the highest success rate. For example, the generation AI analyzes the user's past observation data and suggests the observation location and conditions with the highest success rate. For example, it suggests the optimal location based on the past observation success rate. The observation location suggestion unit also suggests the observation location and conditions with the highest success rate based on the past observation data. For example, it suggests the optimal location based on the past observation success rate. The observation location suggestion unit also suggests the user's past observation data and suggests the observation location and conditions with the highest success rate. For example, it suggests the optimal location based on the past observation success rate. This makes it possible to suggest the observation location and conditions with the highest success rate based on the past observation data.

[0040] The observation location suggestion unit can obtain the latest weather data for locations suitable for astronomical observation in real time and propose optimal observation conditions. For example, the generation AI of the observation location suggestion unit obtains the latest weather data for locations suitable for astronomical observation in real time and proposes optimal observation conditions. For example, the observation conditions are proposed based on weather and humidity. The observation location suggestion unit also proposes optimal observation conditions based on the latest weather data for locations suitable for astronomical observation. For example, the observation conditions are proposed based on weather and humidity. The observation location suggestion unit also proposes optimal observation conditions based on the latest weather data for locations suitable for astronomical observation in real time and proposes optimal observation conditions. For example, the observation conditions are proposed based on weather and humidity. This makes it possible to propose optimal observation conditions based on the latest weather data.

[0041] The observation location suggestion unit can suggest locations suitable for outdoor activities other than astronomy observation. In the observation location suggestion unit, for example, the generation AI suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In addition, the observation location suggestion unit suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In addition, the observation location suggestion unit suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In this way, it is possible to suggest locations suitable for outdoor activities other than astronomy observation.

[0042] The observation location suggestion unit can provide information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, the generation AI provides information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, it suggests the nearest means of transportation and accommodation. The observation location suggestion unit also provides information on transportation access and accommodation for the observation location based on the geographical location information. For example, it suggests the nearest means of transportation and accommodation. The observation location suggestion unit also provides information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, it suggests the nearest means of transportation and accommodation. This makes it possible to provide information on transportation access and accommodation for the observation location.

[0043] The observation target selection unit can also suggest observation targets for natural phenomena other than astronomical observation. In the observation target selection unit, for example, the generation AI suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In addition, the observation target selection unit suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In addition, the observation target selection unit suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In this way, it is possible to suggest observation targets for natural phenomena other than astronomical observation.

[0044] The observation target selection unit can customize and suggest observable celestial objects for each region based on the user's geographical location information. In the observation target selection unit, for example, the generation AI customizes and suggests observable celestial objects for each region based on the user's geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In addition, the observation target selection unit customizes and suggests observable celestial objects for each region based on the geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In addition, the observation target selection unit customizes and suggests observable celestial objects for each region based on the user's geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In this way, it is possible to customize and suggest observable celestial objects for each region based on the geographical location information.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The observation target selection unit can also suggest scientific themes other than astronomical observation based on the user's interests. For example, if a user is interested in biology, the generation AI can suggest nighttime biological observation or insect behavior observation. For a user interested in geology, it can suggest nighttime geological surveys or mineral observations. Furthermore, for a user interested in meteorology, it can suggest nighttime weather observations or recording of meteorological phenomena. This allows a wide range of observation activities to be offered to users who are interested in scientific themes other than astronomical observation.

[0047] The observation method suggestion unit can evaluate the user's observation progress based on the user's observation history and suggest the next step to take. For example, after the user observes a specific celestial object, the unit can analyze the observation data and suggest the next celestial object and observation method to observe. The unit can also reevaluate the data of celestial objects previously observed by the user based on the observation history and suggest new observation points. Furthermore, the unit can suggest celestial objects and observation methods that the user has not yet observed based on the observation history. This allows the unit to continuously support the user's observation activities and make appropriate suggestions according to the user's observation progress.

[0048] The observation location suggestion unit can evaluate the safety of the observation location based on the user's geographical location information and suggest safe observation locations. For example, it can suggest safe observation locations taking into account security information and natural disaster risks at the observation location. It can also provide access information to the observation location and evacuation routes in case of an emergency. It can also provide information on the surrounding environment and facilities at the observation location and suggest a safe and comfortable observation environment. This can support the user so that they can carry out their observation activities with peace of mind.

[0049] The observation method suggestion unit can evaluate the user's observation skill level and suggest customized observation methods ranging from beginner to advanced. For example, it can suggest simple observation methods for beginners and advanced observation methods for advanced users. It can also provide detailed instructions on how to use and set up observation equipment depending on the observation skill level. It can also provide observation guides and tutorials according to the observation skill level to support the user's skill improvement. This makes it possible to suggest appropriate observation methods according to the user's skill level and increase the success rate of observation.

[0050] The observation method suggestion unit can obtain the environmental conditions necessary for observing celestial bodies in real time and suggest the optimal observation method. For example, it can suggest an observation method based on environmental conditions such as temperature, humidity, and wind speed. It can also obtain weather information and light pollution information for the observation location in real time and suggest the optimal observation method. It can also suggest settings and adjustment methods for observation equipment according to environmental conditions, improving the accuracy of observation. In this way, by obtaining environmental conditions in real time and suggesting the optimal observation method, it is possible to increase the success rate of observation.

[0051] The observation method suggestion unit can suggest methods for scientific experiments other than astronomical observation. For example, it can provide methods for meteorological observation and biological observation. It can also suggest methods for geological surveys and mineral observation. It can also suggest methods for observing biological behavior at night and insect behavior. This allows it to suggest methods for scientific experiments other than astronomical observation, broadening the user's scientific interest.

[0052] The observation method suggestion unit can suggest a method for creating an observation report to share with other users based on the user's observation data. For example, it can suggest a method for organizing the observation data and compiling it into a report format. It can also suggest a method for creating graphs and charts based on the observation data. Furthermore, it can suggest an online platform for sharing observation results with other users based on the observation data. This makes it possible to suggest a method for creating an observation report based on the observation data and promote information sharing with other users.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The observation target selection unit uses the generation AI to select the user's observation target. For example, if the user inputs "I want to observe planets," the generation AI will present a list of planets that are currently observable and explain the characteristics and observation points of each. Step 2: The observation method suggestion unit proposes an observation method for the observation target selected by the observation target selection unit. For example, if you input "I want to observe Mars," the generation AI will provide detailed explanations of the best time and date for observing Mars, the type of equipment to use, key observation points, etc. Step 3: The equipment selection section selects the equipment necessary for the observation based on the observation method proposed by the observation method proposal section. For example, if you input "I have a limited budget, but I want to observe Mars," the generation AI will list telescopes, cameras, and other equipment that can be purchased within your budget and explain the features and prices of each. Step 4: The observation location suggestion unit suggests suitable locations for observation based on the equipment selected by the equipment selection unit. For example, if you input "Where is the best place to observe Mars?", the AI ​​generator will suggest the best observation location taking into account factors such as weather, humidity, and locations with minimal light pollution.

[0055] (Example 2) The astronomical observation support system according to the embodiment of the present invention utilizes a generative AI to enable anyone to easily observe the stars and broaden one's view of the universe. As a result, the astronomical observation support system can use the generative AI to solve various problems related to astronomical observation and present specific solutions to users.

[0056] An astronomical observation support system according to an embodiment includes an observation target selection unit, an observation method suggestion unit, an equipment selection unit, and an observation location suggestion unit. The observation target selection unit uses a generation AI to select a user's observation target. For example, if a user inputs, "I want to observe planets," the generation AI presents a list of currently observable planets and explains their characteristics and observation points. The observation method suggestion unit suggests an observation method for the observation target selected by the observation target selection unit. For example, if a user inputs, "I want to observe Mars," the generation AI provides detailed explanations of the best time and time of day for observing Mars, the type of equipment to use, observation points, and so on. The equipment selection unit selects the equipment necessary for observation based on the observation method suggested by the observation method suggestion unit. For example, if a user inputs, "I have a limited budget, but I want to observe Mars," the generation AI lists telescopes, cameras, and other equipment that can be purchased within the budget and explains the features and prices of each. The observation location suggestion unit suggests suitable locations for observation based on the equipment selected by the equipment selection unit. For example, if you input "Where is the best place to observe Mars?", the AI ​​generator will suggest the best observation location taking into account the weather, humidity, and a location with minimal light pollution. As a result, the astronomical observation support system according to the embodiment allows anyone to easily observe the stars and broaden their perspective on space.

[0057] The observation target selection unit can analyze the user's past observation history and suggest new celestial objects that may be of interest. For example, the observation target selection unit uses a generation AI to analyze the user's past observation history and list celestial objects that have not been observed. For example, it suggests new celestial objects based on the types and frequency of celestial objects observed in the past. The observation target selection unit also predicts and suggests celestial objects that may be of interest to the user based on the user's observation history. For example, it analyzes the characteristics of celestial objects observed in the past and suggests similar celestial objects. The observation target selection unit also analyzes the observation history and suggests celestial objects that the user has not yet observed but may be of interest. For example, it lists celestial objects that have not been observed based on past observation data. This increases the enjoyment of observation by suggesting new celestial objects based on the user's interests.

[0058] The observation target selection unit can obtain the latest scientific data on celestial bodies in real time and suggest the optimal timing for observation. For example, the generation AI of the observation target selection unit obtains the latest scientific data on celestial bodies in real time and suggests the optimal timing for observation. For example, it may suggest the optimal observation time based on the position, brightness, and observation conditions of the celestial body. The observation target selection unit also suggests the optimal timing for observation in real time based on the latest data on celestial bodies. For example, it may analyze the movement and position of the celestial body and suggest the optimal time period for observation. The observation target selection unit also suggests the optimal timing for observation based on the latest data on celestial bodies using the generation AI. For example, it may suggest the optimal observation time based on the position, brightness, and observation conditions of the celestial body. This makes it possible to optimize the timing of observation based on the latest scientific data.

[0059] The observation target selection unit uses the emotion estimation function to analyze the user's emotions regarding the celestial object they wish to observe, and can suggest the most interesting celestial object. The observation target selection unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the celestial object they wish to observe, and can suggest the most interesting celestial object. For example, it selects a celestial object based on the user's emotion score. The observation target selection unit also analyzes the user's emotions in real time and suggests celestial objects they wish to observe. For example, it preferentially suggests celestial objects with high emotion scores. The observation target selection unit also uses the emotion estimation function to analyze the user's emotions and suggests the most interesting celestial object. For example, it selects a celestial object based on the emotion score. This makes it possible to suggest the most interesting celestial object based on the user's emotions.

[0060] The observation method suggestion unit can evaluate the user's observation skill level and customize and suggest observation methods ranging from beginner to advanced. In the observation method suggestion unit, for example, the generation AI evaluates the user's observation skill level and customizes and suggests observation methods ranging from beginner to advanced. For example, it suggests an easy observation method for beginners. In addition, the observation method suggestion unit evaluates the observation skill level and customizes and suggests observation methods. For example, it suggests an easy observation method for beginners. In addition, the observation method suggestion unit evaluates the user's observation skill level and customizes and suggests observation methods. For example, it suggests an easy observation method for beginners. In this way, it is possible to suggest observation methods according to the user's skill level.

[0061] The observation method suggestion unit can acquire the environmental conditions required for observing celestial bodies in real time and propose the optimal observation method. For example, the generation AI of the observation method suggestion unit acquires the environmental conditions required for observing celestial bodies in real time and proposes the optimal observation method. For example, it proposes an observation method based on temperature and humidity. The observation method suggestion unit can also acquire environmental conditions in real time and propose the optimal observation method. For example, it proposes an observation method based on temperature and humidity. The observation method suggestion unit can also acquire the environmental conditions required for observing celestial bodies in real time and propose the optimal observation method. For example, it proposes an observation method based on temperature and humidity. In this way, environmental conditions can be acquired in real time and the optimal observation method can be proposed.

[0062] The observation method suggestion unit can use the emotion estimation function to analyze the user's emotions during observation and suggest an observation method for reducing stress. The observation method suggestion unit, for example, uses the emotion estimation function to analyze the user's emotions during observation and suggest an observation method for reducing stress. For example, it suggests an observation method that will help relax. The observation method suggestion unit also analyzes the user's emotions in real time and suggests an observation method for reducing stress. For example, it suggests an observation method that will help relax. The observation method suggestion unit also uses the emotion estimation function to analyze the user's emotions and suggest an observation method for reducing stress. For example, it suggests an observation method that will help relax. In this way, it is possible to suggest an observation method that will reduce stress based on the user's emotions.

[0063] The observation method suggestion unit can suggest methods for scientific experiments other than astronomical observation. In the observation method suggestion unit, for example, the generation AI proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In addition, the observation method suggestion unit proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In addition, the observation method suggestion unit proposes methods for scientific experiments other than astronomical observation. For example, it provides methods for meteorological observation or biological observation. In this way, methods for scientific experiments other than astronomical observation can also be suggested.

[0064] The observation method suggestion unit can suggest a method for creating an observation report to be shared with other users based on the user's observation data. For example, the generation AI can suggest a method for creating an observation report to be shared with other users based on the user's observation data. For example, the observation data can be organized and compiled in a report format. The observation method suggestion unit can also suggest a method for creating an observation report based on the observation data. For example, the observation data can be organized and compiled in a report format. The observation method suggestion unit can also suggest a method for creating an observation report based on the user's observation data. For example, the observation data can be organized and compiled in a report format. This makes it possible to suggest a method for creating an observation report based on the observation data.

[0065] The observation method proposal unit can use the emotion estimation function to monitor the user's emotions during observation in real time and dynamically adjust the observation method. The observation method proposal unit, for example, uses the emotion estimation function to monitor the user's emotions during observation in real time and dynamically adjust the observation method. For example, if the emotion score changes, it re-proposes the observation method. The observation method proposal unit also monitors the user's emotions in real time and dynamically adjusts the observation method. For example, if the emotion score changes, it re-proposes the observation method. The observation method proposal unit also uses the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the observation method. For example, if the emotion score changes, it re-proposes the observation method. This makes it possible to dynamically adjust the observation method based on the user's emotions.

[0066] The equipment selection unit can propose the optimal combination of equipment depending on the user's observation purpose. In the equipment selection unit, for example, a generation AI proposes the optimal combination of equipment depending on the user's observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In addition, the equipment selection unit proposes the optimal combination of equipment depending on the observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In addition, the equipment selection unit proposes the optimal combination of equipment depending on the user's observation purpose. For example, it proposes a combination of a telescope and a camera for planetary observation. In this way, it is possible to propose the optimal combination of equipment depending on the observation purpose.

[0067] The equipment selection unit can analyze the latest equipment reviews and user feedback and suggest the equipment with the highest rating. In the equipment selection unit, for example, a generation AI analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. Also, the equipment selection unit analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. Also, the equipment selection unit analyzes the latest equipment reviews and user feedback and suggests the equipment with the highest rating. For example, it suggests a telescope with a high rating in user reviews. This makes it possible to suggest the equipment with the highest rating based on the latest reviews and feedback.

[0068] The equipment selection unit can use the emotion estimation function to analyze the emotions the user has regarding equipment selection and suggest the equipment that will provide the highest level of satisfaction. The equipment selection unit, for example, uses the emotion estimation function to analyze the emotions the user has regarding equipment selection and suggest the equipment that will provide the highest level of satisfaction. For example, it prioritizes suggesting equipment with a high emotion score. The equipment selection unit also analyzes the user's emotions in real time and suggests the equipment that will provide the highest level of satisfaction. For example, it prioritizes suggesting equipment with a high emotion score. The equipment selection unit also uses the emotion estimation function to analyze the user's emotions and suggest the equipment that will provide the highest level of satisfaction. For example, it prioritizes suggesting equipment with a high emotion score. This makes it possible to suggest the equipment that will provide the highest level of satisfaction based on the user's emotions.

[0069] The equipment selection unit can suggest equipment suitable for uses other than astronomical observation. For example, the generation AI of the equipment selection unit suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. The equipment selection unit also suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. The equipment selection unit also suggests equipment suitable for uses other than astronomical observation. For example, it suggests telescopes and cameras suitable for bird watching and landscape photography. This makes it possible to suggest equipment suitable for uses other than astronomical observation.

[0070] The equipment selection unit can use the emotion estimation function to provide support in real time to alleviate any anxieties or questions that the user may have while selecting equipment. The equipment selection unit, for example, uses the emotion estimation function to provide support in real time to alleviate any anxieties or questions that the user may have while selecting equipment. For example, a support message is displayed when the emotion score is low. The equipment selection unit also monitors the user's emotions in real time to provide support to alleviate any anxieties or questions. For example, a support message is displayed when the emotion score is low. The equipment selection unit also uses the emotion estimation function to monitor the user's emotions in real time to provide support to alleviate any anxieties or questions. For example, a support message is displayed when the emotion score is low. This makes it possible to provide support to alleviate the user's anxieties or questions in real time.

[0071] The observation location suggestion unit can analyze the user's past observation data and suggest the observation location and conditions with the highest success rate. For example, the generation AI analyzes the user's past observation data and suggests the observation location and conditions with the highest success rate. For example, it suggests the optimal location based on the past observation success rate. The observation location suggestion unit also suggests the observation location and conditions with the highest success rate based on the past observation data. For example, it suggests the optimal location based on the past observation success rate. The observation location suggestion unit also suggests the user's past observation data and suggests the observation location and conditions with the highest success rate. For example, it suggests the optimal location based on the past observation success rate. This makes it possible to suggest the observation location and conditions with the highest success rate based on the past observation data.

[0072] The observation location suggestion unit can obtain the latest weather data for locations suitable for astronomical observation in real time and propose optimal observation conditions. For example, the generation AI of the observation location suggestion unit obtains the latest weather data for locations suitable for astronomical observation in real time and proposes optimal observation conditions. For example, the observation conditions are proposed based on weather and humidity. The observation location suggestion unit also proposes optimal observation conditions based on the latest weather data for locations suitable for astronomical observation. For example, the observation conditions are proposed based on weather and humidity. The observation location suggestion unit also proposes optimal observation conditions based on the latest weather data for locations suitable for astronomical observation in real time and proposes optimal observation conditions. For example, the observation conditions are proposed based on weather and humidity. This makes it possible to propose optimal observation conditions based on the latest weather data.

[0073] The observation location suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the observation location and suggest the most relaxing observation location. The observation location suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user has toward the observation location and suggest the most relaxing observation location. For example, it prioritizes suggesting locations with a high emotion score. The observation location suggestion unit also analyzes the user's emotion in real time and suggests the most relaxing observation location. For example, it prioritizes suggesting locations with a high emotion score. The observation location suggestion unit also uses the emotion estimation function to analyze the user's emotion and suggest the most relaxing observation location. For example, it prioritizes suggesting locations with a high emotion score. In this way, it is possible to suggest the most relaxing observation location based on the user's emotion.

[0074] The observation location suggestion unit can suggest locations suitable for outdoor activities other than astronomy observation. In the observation location suggestion unit, for example, the generation AI suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In addition, the observation location suggestion unit suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In addition, the observation location suggestion unit suggests locations suitable for outdoor activities other than astronomy observation. For example, it provides locations suitable for camping and hiking. In this way, it is possible to suggest locations suitable for outdoor activities other than astronomy observation.

[0075] The observation location suggestion unit can provide information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, the generation AI provides information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, it suggests the nearest means of transportation and accommodation. The observation location suggestion unit also provides information on transportation access and accommodation for the observation location based on the geographical location information. For example, it suggests the nearest means of transportation and accommodation. The observation location suggestion unit also provides information on transportation access and accommodation for the observation location based on the user's geographical location information. For example, it suggests the nearest means of transportation and accommodation. This makes it possible to provide information on transportation access and accommodation for the observation location.

[0076] The observation target selection unit can also suggest observation targets for natural phenomena other than astronomical observation. In the observation target selection unit, for example, the generation AI suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In addition, the observation target selection unit suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In addition, the observation target selection unit suggests natural phenomena other than astronomical observation. For example, it provides observation information for meteor showers and auroras. In this way, it is possible to suggest observation targets for natural phenomena other than astronomical observation.

[0077] The observation target selection unit can customize and suggest observable celestial objects for each region based on the user's geographical location information. In the observation target selection unit, for example, the generation AI customizes and suggests observable celestial objects for each region based on the user's geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In addition, the observation target selection unit customizes and suggests observable celestial objects for each region based on the geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In addition, the observation target selection unit customizes and suggests observable celestial objects for each region based on the user's geographical location information. For example, it selects celestial objects taking into consideration the local weather and light pollution. In this way, it is possible to customize and suggest observable celestial objects for each region based on the geographical location information.

[0078] The observation target selection unit uses the emotion estimation function to monitor in real time the user's emotions regarding the celestial body they wish to observe, and can dynamically change the observation target. The observation target selection unit, for example, uses the emotion estimation function to monitor in real time the user's emotions regarding the celestial body they wish to observe, and dynamically change the observation target. For example, it reselects a celestial body when the emotion score changes. The observation target selection unit also monitors the user's emotions in real time, and dynamically changes the observation target. For example, it reselects a celestial body when the emotion score changes. The observation target selection unit also uses the emotion estimation function to monitor the user's emotions in real time, and dynamically change the observation target. For example, it reselects a celestial body when the emotion score changes. This makes it possible to dynamically change the observation target based on the user's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The observation target selection unit can also suggest scientific themes other than astronomical observation based on the user's interests. For example, if a user is interested in biology, the generation AI can suggest nighttime biological observation or insect behavior observation. For a user interested in geology, it can suggest nighttime geological surveys or mineral observations. Furthermore, for a user interested in meteorology, it can suggest nighttime weather observations or recording of meteorological phenomena. This allows a wide range of observation activities to be offered to users who are interested in scientific themes other than astronomical observation.

[0081] The observation method suggestion unit can evaluate the user's observation progress based on the user's observation history and suggest the next step to take. For example, after the user observes a specific celestial object, the unit can analyze the observation data and suggest the next celestial object and observation method to observe. The unit can also reevaluate the data of celestial objects previously observed by the user based on the observation history and suggest new observation points. Furthermore, the unit can suggest celestial objects and observation methods that the user has not yet observed based on the observation history. This allows the unit to continuously support the user's observation activities and make appropriate suggestions according to the user's observation progress.

[0082] The observation location suggestion unit can evaluate the safety of the observation location based on the user's geographical location information and suggest safe observation locations. For example, it can suggest safe observation locations taking into account security information and natural disaster risks at the observation location. It can also provide access information to the observation location and evacuation routes in case of an emergency. It can also provide information on the surrounding environment and facilities at the observation location and suggest a safe and comfortable observation environment. This can support the user so that they can carry out their observation activities with peace of mind.

[0083] The observation target selection unit uses the emotion estimation function to analyze the user's emotions regarding the celestial object they wish to observe and can suggest the most interesting celestial object. For example, if the user shows a strong interest in a particular celestial object, it can suggest other celestial objects or observation targets related to that celestial object. It can also prioritize observation targets based on the user's emotion score and suggest the most interesting celestial object. Furthermore, it can analyze the user's emotions in real time and dynamically change the observation target. This can suggest the most interesting celestial object based on the user's emotions, increasing the enjoyment of observation.

[0084] The observation method suggestion unit can evaluate the user's observation skill level and suggest customized observation methods ranging from beginner to advanced. For example, it can suggest simple observation methods for beginners and advanced observation methods for advanced users. It can also provide detailed instructions on how to use and set up observation equipment depending on the observation skill level. It can also provide observation guides and tutorials according to the observation skill level to support the user's skill improvement. This makes it possible to suggest appropriate observation methods according to the user's skill level and increase the success rate of observation.

[0085] The observation method suggestion unit can obtain the environmental conditions necessary for observing celestial bodies in real time and suggest the optimal observation method. For example, it can suggest an observation method based on environmental conditions such as temperature, humidity, and wind speed. It can also obtain weather information and light pollution information for the observation location in real time and suggest the optimal observation method. It can also suggest settings and adjustment methods for observation equipment according to environmental conditions, improving the accuracy of observation. In this way, by obtaining environmental conditions in real time and suggesting the optimal observation method, it is possible to increase the success rate of observation.

[0086] The observation method suggestion unit can use the emotion estimation function to analyze the user's emotions during observation and suggest an observation method to reduce stress. For example, if the user feels stressed during observation, it can suggest an observation method that will help them relax. It can also adjust the observation method based on the user's emotion score and provide advice to reduce stress. Furthermore, it can analyze the user's emotions in real time and dynamically change the observation method. This makes it possible to suggest an observation method that reduces stress based on the user's emotions, making observation more enjoyable.

[0087] The observation method suggestion unit can suggest methods for scientific experiments other than astronomical observation. For example, it can provide methods for meteorological observation and biological observation. It can also suggest methods for geological surveys and mineral observation. It can also suggest methods for observing biological behavior at night and insect behavior. This allows it to suggest methods for scientific experiments other than astronomical observation, broadening the user's scientific interest.

[0088] The observation method suggestion unit can suggest a method for creating an observation report to share with other users based on the user's observation data. For example, it can suggest a method for organizing the observation data and compiling it into a report format. It can also suggest a method for creating graphs and charts based on the observation data. Furthermore, it can suggest an online platform for sharing observation results with other users based on the observation data. This makes it possible to suggest a method for creating an observation report based on the observation data and promote information sharing with other users.

[0089] The observation method suggestion unit can use the emotion estimation function to monitor the user's emotions during observation in real time and dynamically adjust the observation method. For example, it can re-suggest an observation method if the user's emotion score changes. In addition, by analyzing the user's emotions in real time and adjusting the observation method, it is possible to increase the success rate of observation. Furthermore, it can also suggest settings and adjustment methods for observation equipment based on the user's emotions. This makes it possible to dynamically adjust the observation method based on the user's emotions and increase the success rate of observation.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The observation target selection unit uses the generation AI to select the user's observation target. For example, if the user inputs "I want to observe planets," the generation AI will present a list of planets that are currently observable and explain the characteristics and observation points of each. Step 2: The observation method suggestion unit proposes an observation method for the observation target selected by the observation target selection unit. For example, if you input "I want to observe Mars," the generation AI will provide detailed explanations of the best time and date for observing Mars, the type of equipment to use, key observation points, etc. Step 3: The equipment selection section selects the equipment necessary for the observation based on the observation method proposed by the observation method proposal section. For example, if you input "I have a limited budget, but I want to observe Mars," the generation AI will list telescopes, cameras, and other equipment that can be purchased within your budget and explain the features and prices of each. Step 4: The observation location suggestion unit suggests suitable locations for observation based on the equipment selected by the equipment selection unit. For example, if you input "Where is the best place to observe Mars?", the AI ​​generator will suggest the best observation location taking into account factors such as weather, humidity, and locations with minimal light pollution.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an observation target selection unit that selects a user's observation target using a generation AI; an observation method suggestion unit that proposes an observation method for the observation target selected by the observation target selection unit; an equipment selection unit that selects equipment necessary for observation based on the observation method proposed by the observation method proposal unit; an observation location suggestion unit that suggests a location suitable for observation based on the equipment selected by the equipment selection unit; A system characterized by:

2. The observation target selection unit Analyze the user's past observation history and suggest new celestial objects that may be of interest 2. The system of claim 1.

3. The observation target selection unit Obtaining the latest scientific data on celestial bodies in real time and suggesting the best time to observe 2. The system of claim 1.

4. The observation target selection unit Analyze the user's feelings about the celestial objects they want to observe and suggest the most interesting celestial objects 2. The system of claim 1.

5. The observation method suggestion unit Evaluate the user's observation skill level and customize and suggest observation methods for beginners to advanced users.

2. The system of claim 1.

6. The observation method suggestion unit Obtaining the environmental conditions necessary for celestial observation in real time and proposing the optimal observation method 2. The system of claim 1.

7. The observation method suggestion unit Analyzing the user's emotions during observation and proposing observation methods to reduce stress 2. The system of claim 1.

8. The observation method suggestion unit Proposing methods for scientific experiments other than astronomical observations 2. The system of claim 1.

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