system

Abstract

The system according to this embodiment aims to make it easy for even beginners who enjoy taking airplane photos to find the optimal shooting spots and camera settings. [Solution] The system according to the embodiment comprises a collection unit, a recommendation unit, a discrimination unit, an advice unit, and an evaluation unit. The collection unit collects the user's location information or weather data. The recommendation unit introduces shooting spots based on the data collected by the collection unit. The discrimination unit identifies the aircraft type or airline from the photos taken by the user. The advice unit provides advice on adjusting camera settings or shooting conditions based on the information identified by the discrimination unit. The evaluation unit judges the composition or quality of the photos taken and suggests areas for improvement.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for beginners who enjoy airplane photography to find the optimal shooting spots and camera settings.

[0005] The system according to the embodiment aims to enable beginners who enjoy airplane photography to easily find the optimal shooting spots and camera settings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a recommendation unit, a discrimination unit, an advice unit, and an evaluation unit. The collection unit collects the user's location information or weather data. The recommendation unit introduces shooting spots based on the data collected by the collection unit. The discrimination unit identifies the aircraft type or airline from the photographs taken by the user. The advice unit provides advice on adjusting camera settings or shooting conditions based on the information identified by the discrimination unit. The evaluation unit judges the composition or quality of the photographs taken and suggests areas for improvement. [Effects of the Invention]

[0007] The system according to this embodiment makes it easy for even beginners who enjoy taking airplane photos to find the optimal shooting spots and camera settings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The airplane photography support system according to an embodiment of the present invention is a system that uses AI to further enhance the enjoyment of airplane photography for people who enjoy it. This airplane photography support system collects the user's location information and weather data and introduces the optimal shooting spot. Next, it uses a generating AI to automatically identify the aircraft type and airline from the airplane photographs taken by the user. Furthermore, the generating AI provides advice to optimize camera settings and shooting conditions. Finally, the AI ​​judges the composition and quality of the photographs taken and provides advice to make the photographs even better. For example, the airplane photography support system obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, it suggests airports near the user's current location or places where airplanes can easily be seen taking off and landing. It also guides users to shooting spots suitable for sunny or cloudy days based on weather information. Next, it uses a generating AI to automatically identify the aircraft type and airline from the airplane photographs taken by the user. This allows the user to know detailed information about the photographs they have taken. Furthermore, the generating AI provides advice to optimize camera settings and shooting conditions. For example, it suggests shutter speeds and aperture values ​​suitable for photographing specific aircraft types or airlines. Furthermore, the AI ​​analyzes the composition and quality of the photos taken and provides advice on how to improve them. For example, if the composition is unbalanced or the exposure is inappropriate, it will suggest specific areas for improvement. This allows users to improve their photography skills. This service can be expanded beyond airplane photography to other fields of photography. For instance, it can provide similar advice for various genres of photography, such as landscape and portrait photography. This will allow the airplane photography support system to enhance users' enjoyment of airplane photography.

[0029] The aircraft photography support system according to this embodiment comprises a collection unit, a recommendation unit, a discrimination unit, an advice unit, and an evaluation unit. The collection unit collects the user's location information or weather data. For example, the collection unit obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, the collection unit obtains GPS data from the user's smartphone to determine the current location. The collection unit can also obtain weather data from the internet to understand the current weather conditions. The recommendation unit introduces shooting spots based on the data collected by the collection unit. For example, the recommendation unit suggests airports near the user's current location or places where aircraft take off and land easily visible. The recommendation unit can also guide users to shooting spots suitable for sunny or cloudy days based on weather information. The discrimination unit uses a generation AI to determine the aircraft type and airline from the photos taken by the user. For example, the generation AI analyzes the airplane photos to identify the aircraft type and airline. The generation AI uses a deep learning model or image recognition algorithm to extract features from the photos and determine the aircraft type and airline. The advice unit provides advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. For example, the advice unit suggests suitable shutter speeds and aperture values ​​when photographing specific aircraft models or airlines. The advice unit can also use generative AI to provide optimal camera settings according to shooting conditions. The evaluation unit judges the composition and quality of the photographs taken and suggests specific areas for improvement. For example, the evaluation unit suggests specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. The evaluation unit uses AI to evaluate the composition and quality of the photographs and provides feedback to the user. As a result, the airplane photography support system according to this embodiment allows users to enjoy airplane photography more. Some or all of the above-described processes in the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit may be performed using AI, for example, or without AI. For example, the collection unit obtains location information from the user's smartphone and weather data from the internet. The introduction unit suggests the optimal shooting spot to the user based on the data collected by the collection unit.The discrimination unit uses generation AI to identify the aircraft type and airline from photos taken by the user. The advice unit provides advice to optimize camera settings and shooting conditions based on the information identified by the discrimination unit. The evaluation unit judges the composition and quality of the photos taken and suggests specific areas for improvement.

[0030] The data collection unit can acquire location information from the user's smartphone or GPS device and weather data from the internet. For example, the data collection unit can acquire GPS data from the user's smartphone to determine the current location. For example, the data collection unit can use the smartphone's GPS function to acquire the user's current location in real time. The data collection unit can also acquire weather data from the internet to understand the current weather conditions. For example, the data collection unit can acquire weather data from the Japan Meteorological Agency or private weather forecasting services. This allows the data collection unit to collect accurate location information and weather data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can acquire location information from the user's smartphone and weather data from the internet. This allows the data collection unit to collect accurate location information and weather data.

[0031] The recommendation unit can suggest optimal shooting locations to the user based on data collected by the data collection unit. For example, the recommendation unit can suggest airports or places where it is easy to see planes taking off and landing from the user's current location. For example, the recommendation unit can select the optimal shooting location based on the user's current location. The recommendation unit can also guide users to shooting locations suitable for sunny or cloudy days based on weather information. For example, the recommendation unit can suggest an airport observation deck on a sunny day and a place where it is easy to see planes taking off and landing on a cloudy day. This allows the recommendation unit to help users enjoy taking photos in the optimal location. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit suggests optimal shooting locations to the user based on data collected by the data collection unit. This allows the recommendation unit to help users enjoy taking photos in the optimal location.

[0032] The discrimination unit can identify the aircraft type and airline from a user-submitted photograph using a generative AI. For example, the discrimination unit uses the generative AI to analyze a photograph of an airplane and identify the aircraft type and airline. For example, the discrimination unit uses the generative AI to extract features of the airplane and identify the aircraft type and airline. The generative AI analyzes the features of the photograph using deep learning models and image recognition algorithms. For example, the generative AI identifies the airline based on the airplane's logo or paint pattern. The generative AI can also identify the aircraft type based on the airplane's shape and engine arrangement. This allows the discrimination unit to obtain detailed information about the user-submitted photograph. Some or all of the above processing in the discrimination unit may be performed using the generative AI, or it may be performed without using the generative AI. For example, the discrimination unit uses the generative AI to identify the aircraft type and airline from a user-submitted photograph. This allows the discrimination unit to obtain detailed information about the user-submitted photograph.

[0033] The advice unit can provide advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. For example, the advice unit may suggest suitable shutter speeds and aperture values ​​when photographing a specific aircraft model or airline. For example, the advice unit may use generative AI to provide optimal camera settings according to shooting conditions. The generative AI can suggest optimal camera settings based on past shooting data and expert knowledge. For example, the generative AI may recommend setting the shutter speed to 1 / 1000 second when photographing a specific aircraft model. The generative AI may also suggest setting the aperture value to f / 8 so that the airline logo is clearly visible. This allows the advice unit to take better photos. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or without using generative AI. For example, the advice unit provides advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. This allows the advice unit to take better photos.

[0034] The evaluation unit can judge the composition and quality of the photographs taken and suggest specific areas for improvement. For example, the evaluation unit can suggest specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. For example, the evaluation unit can use AI to evaluate the composition and quality of the photographs and provide feedback to the user. The AI ​​can evaluate the composition of a photograph based on evaluation criteria such as the rule of thirds or diagonal composition. For example, if the subject is placed in the center of the photograph, the AI ​​will suggest moving the subject based on the rule of thirds. The AI ​​can also suggest specific exposure correction methods if the exposure of the photograph is inappropriate. For example, if the photograph is too dark, the AI ​​will suggest setting the exposure compensation to +1. This allows the evaluation unit to help the user improve their photography skills. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit judges the composition and quality of the photographs taken and suggests specific areas for improvement. This allows the evaluation unit to help the user improve their photography skills.

[0035] The data collection unit can analyze the user's past shooting history and select the optimal data acquisition method. For example, the data collection unit can acquire data under similar conditions based on data of locations where the user has previously taken photos. For example, the data collection unit can analyze the user's past shooting history and acquire data at the same location based on shooting data from that specific location. The data collection unit can also acquire data at specific time periods based on the user's past shooting history. For example, the data collection unit can analyze the time periods when the user preferred to take photos in the past and acquire data at those times. Furthermore, the data collection unit can adjust the timing of data acquisition based on the weather conditions when the user preferred to take photos in the past. For example, if the user prefers to take photos on sunny days, the data collection unit will prioritize acquiring data from sunny days. This allows the data collection unit to analyze the user's past shooting history and select a more appropriate data acquisition method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit analyzes the user's past shooting history and selects the optimal data acquisition method. This allows the data collection unit to select a more appropriate data acquisition method.

[0036] The data collection unit can filter location and weather data based on the user's current photography purpose and areas of interest. For example, if a user wants to photograph a specific aircraft model, the data collection unit will prioritize acquiring data on locations where that aircraft model can be seen. For example, if a user wants to photograph a specific aircraft model, the data collection unit will prioritize acquiring data on airports and flight routes where that aircraft model can be seen. The data collection unit can also acquire data that matches specific weather conditions if the user wants to photograph under those conditions. For example, if a user wants to photograph on a sunny day, the data collection unit will prioritize acquiring weather data for sunny days. Furthermore, if a user wants to photograph at a specific time of day, the data collection unit can acquire data that matches that time of day. For example, if a user wants to photograph in the evening, the data collection unit will prioritize acquiring evening weather data and location information. This allows the data collection unit to filter data based on the user's photography purpose and areas of interest, providing more relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when acquiring location information and weather data, the data collection unit filters the data based on the user's current photography purpose and areas of interest. This allows the data collection unit to provide more relevant information.

[0037] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring location information and weather data. For example, if the user is near an airport, the data collection unit will prioritize the acquisition of weather data around the airport. For example, the data collection unit will prioritize the acquisition of weather data around the airport based on the user's geographical location. The data collection unit can also acquire detailed location information of a location where the user can easily see planes taking off and landing. For example, if the data collection unit determines that the user is in a location where planes taking off and landing can be easily seen, it will acquire detailed location information of that location. Furthermore, if the data collection unit is near a specific flight route, it can prioritize the acquisition of data related to that route. For example, if the data collection unit determines that the user is near a specific flight route, it will prioritize the acquisition of data related to that route. In this way, the data collection unit can provide highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit prioritizes the acquisition of highly relevant data by considering the user's geographical location when acquiring location information and weather data. This allows the data collection unit to provide more relevant data.

[0038] The data collection unit can analyze the user's social media activity and obtain relevant data when acquiring location information and weather data. For example, if the user mentions a specific aircraft model on social media, the data collection unit will prioritize acquiring data related to that aircraft model. For example, the data collection unit analyzes the user's social media activity and prioritizes acquiring data related to a specific aircraft model. The data collection unit can also prioritize acquiring data around a specific airport if the user mentions that airport on social media. For example, if the user mentions a specific airport, the data collection unit will prioritize acquiring weather data and location information around that airport. Furthermore, if the user mentions specific weather conditions on social media, the data collection unit will prioritize acquiring data related to those conditions. For example, if the user mentions specific weather conditions, the data collection unit will prioritize acquiring data related to those conditions. This allows the data collection unit to analyze the user's social media activity and provide more relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit analyzes the user's social media activity and obtains relevant data when acquiring location information and weather data. This allows the data collection unit to provide more relevant data.

[0039] The introduction section can adjust the level of detail in its recommendations based on the importance of the photo spot. For example, for important photo spots, the introduction section can provide detailed access information and shooting points. For example, for tourist destinations or popular photo spots, the introduction section can provide detailed access information and shooting points. The introduction section can also provide a brief description and access information for general photo spots. For example, for general photo spots, the introduction section can provide a brief description and access information for general photo spots. Furthermore, for minor photo spots, the introduction section can provide only an overview. For example, for minor photo spots, the introduction section can provide only an overview. In this way, the introduction section can adjust the level of detail in its recommendations based on the importance of the photo spot and provide information that is important to the user. Some or all of the above processing in the introduction section may be performed using AI, for example, or not using AI. For example, the introduction section adjusts the level of detail in its recommendations based on the importance of the photo spot. In this way, the introduction section can provide information that is important to the user.

[0040] The recommendation section can apply different recommendation algorithms depending on the category of the shooting spot when making recommendations. For example, in the case of shooting spots around airports, the recommendation section will highlight points where it is easy to see planes taking off and landing. For example, in the case of shooting spots around airports, the recommendation section will highlight points where it is easy to see planes taking off and landing. The recommendation section can also highlight points where it is easy to see buildings in the background when making recommendations in urban areas. For example, in the case of shooting spots in urban areas, the recommendation section will highlight points where it is easy to see buildings in the background. Furthermore, in the case of shooting spots in natural environments, the recommendation section will highlight points where it is easy to see the scenery and the plane in harmony. For example, in the case of shooting spots in natural environments, the recommendation section will highlight points where it is easy to see the scenery and the plane in harmony. In this way, the recommendation section can apply different recommendation algorithms depending on the category of the shooting spot and recommend the best shooting spot for the user. Some or all of the above processing in the recommendation section may be performed using AI, for example, or not. For example, the recommendation section applies different recommendation algorithms depending on the category of the shooting spot when making recommendations. In this way, the recommendation section can recommend the best shooting spot for the user.

[0041] The recommendation system can prioritize recommendations based on the popularity of the photo spots. For example, it might prioritize popular photo spots. For example, it might prioritize tourist spots or photo spots that are trending on social media. It could also recommend more common photo spots next. For example, it could recommend more common photo spots next. Furthermore, it could recommend less popular photo spots last. For example, it could recommend less popular photo spots last. This allows the recommendation system to prioritize recommendations based on the popularity of the photo spots and provide users with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system might prioritize recommendations based on the popularity of the photo spots when making recommendations. This allows the recommendation system to provide users with the most relevant information.

[0042] The recommendation system can adjust the order of recommendations based on the relevance of the shooting locations. For example, it may prioritize recommending shooting locations close to the user's current location. For example, it may prioritize recommending nearby shooting locations based on the user's current location. The recommendation system can also recommend shooting locations related to the user's areas of interest next. For example, it may recommend shooting locations related to the aircraft models or airlines the user is interested in next. Furthermore, the recommendation system may recommend highly relevant locations last based on the user's past shooting history. For example, it may analyze the user's past shooting history and recommend highly relevant locations last. In this way, the recommendation system can adjust the order of recommendations based on the relevance of the shooting locations and provide the user with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system adjusts the order of recommendations based on the relevance of the shooting locations when making recommendations. In this way, the recommendation system can provide the user with the most relevant information.

[0043] The discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs during discrimination. For example, the discrimination unit can determine the aircraft type based on photographs of the same aircraft taken from different angles. For example, the discrimination unit can determine the aircraft type based on multiple photographs of the same aircraft taken from different angles. The discrimination unit can also determine the airline based on multiple photographs taken at the same location. For example, the discrimination unit can determine the airline based on multiple photographs taken at the same location. Furthermore, the discrimination unit can determine the flight route based on photographs taken at the same time. For example, the discrimination unit can determine the flight route based on multiple photographs taken at the same time. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or without generative AI. For example, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs during discrimination. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs.

[0044] The discrimination unit can perform discrimination while considering the attribute information of the photographer of the photograph. For example, if the discrimination unit determines that the photographer is a professional, it will provide a detailed discrimination result. The discrimination unit can also provide a concise discrimination result if the photographer is an amateur. For example, if the discrimination unit determines that the photographer is an amateur, it will provide a concise discrimination result. Furthermore, if the discrimination unit determines that the photographer is a first-time photographer, it will provide a basic discrimination result. For example, if the discrimination unit determines that the photographer is a first-time photographer, it will provide a basic discrimination result. This allows the discrimination unit to perform discrimination while considering the attribute information of the photographer of the photograph and provide a more appropriate discrimination result. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or not using generative AI. For example, the discrimination unit performs discrimination while considering the attribute information of the photographer of the photograph. This allows the discrimination unit to provide a more appropriate discrimination result.

[0045] The discrimination unit can perform discrimination while considering the geographical distribution of the photographs. For example, the discrimination unit can determine the aircraft type based on photographs taken around a specific airport. For example, the discrimination unit can determine the aircraft type based on photographs taken around a specific airport. The discrimination unit can also determine the airline based on photographs taken along a specific flight route. For example, the discrimination unit can determine the airline based on photographs taken along a specific flight route. Furthermore, the discrimination unit can determine the model of the aircraft based on photographs taken in a specific region. For example, the discrimination unit can determine the model of the aircraft based on photographs taken in a specific region. This allows the discrimination unit to perform discrimination while considering the geographical distribution of the photographs and provide more accurate discrimination results. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or without generative AI. For example, the discrimination unit performs discrimination while considering the geographical distribution of the photographs. This allows the discrimination unit to provide more accurate discrimination results.

[0046] The discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph during discrimination. For example, the discrimination unit can identify the aircraft type by referring to relevant literature based on the characteristics of the aircraft in the photograph. For example, the discrimination unit can identify the aircraft type by referring to relevant literature based on the characteristics of the aircraft in the photograph. The discrimination unit can also identify the airline by referring to relevant literature based on the airline logo in the photograph. For example, the discrimination unit can identify the airline by referring to relevant literature based on the airline logo in the photograph. Furthermore, the discrimination unit can identify the model by referring to relevant literature based on the paintwork of the aircraft in the photograph. For example, the discrimination unit can identify the model by referring to relevant literature based on the paintwork of the aircraft in the photograph. In this way, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or not using generative AI. For example, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph during discrimination. In this way, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph.

[0047] The advice unit can adjust the level of detail in its advice based on the importance of camera settings and shooting conditions. For example, for important camera settings, the advice unit provides a detailed explanation and specific setting values. For example, for important camera settings, the advice unit provides a detailed explanation and specific setting values. The advice unit can also provide a brief explanation and recommended setting values ​​for general shooting conditions. For example, for general shooting conditions, the advice unit provides a brief explanation and recommended setting values. Furthermore, for minor shooting conditions, the advice unit can provide only an overview. For example, for minor shooting conditions, the advice unit provides only an overview. This allows the advice unit to adjust the level of detail in its advice based on the importance of camera settings and shooting conditions, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not using generative AI. For example, the advice unit adjusts the level of detail in its advice based on the importance of camera settings and shooting conditions when providing advice. This allows the advice unit to provide the best possible advice for the user.

[0048] The advice unit can apply different advice algorithms depending on the camera settings and shooting conditions category when providing advice. For example, in the case of landscape photography, the advice unit may recommend the use of a wide-angle lens. The advice unit can also provide advice on how to blur the background in the case of portrait photography. Furthermore, the advice unit may provide advice on long exposure settings in the case of night photography. This allows the advice unit to apply different advice algorithms depending on the camera settings and shooting conditions category, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not. For example, the advice unit applies different advice algorithms depending on the camera settings and shooting conditions category when providing advice. This allows the advice unit to provide the best possible advice for the user.

[0049] The advice unit can prioritize advice based on the timing of submission of camera settings and shooting conditions. For example, the advice unit may prioritize advice regarding recent shooting conditions. The advice unit may also provide advice regarding general shooting conditions next. Furthermore, the advice unit may provide advice regarding past shooting conditions last. This allows the advice unit to prioritize advice based on the timing of submission of camera settings and shooting conditions, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not. For example, the advice unit may prioritize advice based on the timing of submission of camera settings and shooting conditions. This allows the advice unit to provide the best possible advice for the user.

[0050] The advice unit can adjust the order of advice based on the relevance of camera settings and shooting conditions when providing advice. For example, the advice unit may first provide advice that is most relevant to the user's current shooting conditions. The advice unit may also next provide advice related to the user's past shooting conditions. Furthermore, the advice unit may last provide advice related to the user's future shooting conditions. In this way, the advice unit can adjust the order of advice based on the relevance of camera settings and shooting conditions to provide the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not using generative AI. For example, the advice unit adjusts the order of advice based on the relevance of camera settings and shooting conditions when providing advice. In this way, the advice unit can provide the best possible advice for the user.

[0051] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data of the photograph during the evaluation process. For example, the evaluation unit can evaluate the current photograph based on the characteristics of photographs that have received high ratings in the past. The evaluation unit can also suggest areas for improvement based on the characteristics of photographs that have received low ratings in the past. Furthermore, the evaluation unit can analyze past evaluation data and adjust the evaluation criteria. For example, the evaluation unit can analyze past evaluation data and adjust the evaluation criteria. This allows the evaluation unit to optimize its evaluation algorithm by referring to past evaluation data of the photograph and provide a more accurate evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit optimizes its evaluation algorithm by referring to past evaluation data of the photograph during the evaluation process. This allows the evaluation unit to provide a more accurate evaluation.

[0052] The evaluation unit can apply different evaluation methods to each category of photograph during evaluation. For example, in the case of landscape photographs, the evaluation unit may prioritize the evaluation of composition and color. For example, in the case of portrait photographs, the evaluation unit may prioritize the evaluation of the subject's expression and background. For example, in the case of portrait photographs, the evaluation unit may prioritize the evaluation of the subject's expression and background. Furthermore, in the case of nightscape photographs, the evaluation unit may prioritize the evaluation of exposure and sharpness. For example, in the case of nightscape photographs, the evaluation unit may prioritize the evaluation of exposure and sharpness. This allows the evaluation unit to apply different evaluation methods to each category of photograph and provide a more appropriate evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit applies different evaluation methods to each category of photograph during evaluation. This allows the evaluation unit to provide a more appropriate evaluation.

[0053] The evaluation unit can weight the evaluation based on when the photos were submitted. For example, the evaluation unit may prioritize the evaluation of the most recently submitted photos. The evaluation unit may also evaluate photos submitted around the typical time period next. Furthermore, the evaluation unit may evaluate photos submitted in the past last. This allows the evaluation unit to weight the evaluation based on when the photos were submitted and provide a more appropriate evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit weights the evaluation based on when the photos were submitted during the evaluation process. This allows the evaluation unit to provide a more appropriate evaluation.

[0054] The evaluation unit can perform evaluations by referring to relevant market data for the photographs during the evaluation process. For example, the evaluation unit can evaluate the current photographs based on the characteristics of photographs that have received high ratings in the market. The evaluation unit can also suggest areas for improvement based on the characteristics of photographs that have received low ratings in the market. Furthermore, the evaluation unit can analyze market data and adjust the evaluation criteria. For example, the evaluation unit can analyze market data and adjust the evaluation criteria. This allows the evaluation unit to perform evaluations by referring to relevant market data for the photographs and provide more appropriate evaluations. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations by referring to relevant market data for the photographs during the evaluation process. This allows the evaluation unit to provide more appropriate evaluations.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can also analyze the user's past shooting history and select the optimal data acquisition method. For example, it can acquire data under similar conditions based on data from locations the user has previously photographed. Furthermore, it can acquire data at specific time periods based on the user's past shooting history. In addition, it can adjust the timing of data acquisition based on weather conditions the user has previously preferred for photography. This allows the data collection unit to analyze the user's past shooting history and select a more appropriate data acquisition method.

[0057] The data collection unit can also filter location and weather data based on the user's current photography purpose and areas of interest. For example, if a user wants to photograph a specific aircraft model, the unit will prioritize acquiring data for locations where that model can be seen. Furthermore, if a user wishes to photograph under specific weather conditions, the unit can acquire data that matches those conditions. Additionally, if a user wishes to photograph at a specific time of day, the unit can acquire data that matches that time. This allows the data collection unit to filter data based on the user's photography purpose and areas of interest, providing more relevant information.

[0058] The data collection unit can also analyze users' social media activity when acquiring location information and weather data, and obtain relevant data. For example, if a user mentions a specific aircraft model on social media, it can prioritize acquiring data related to that model. Similarly, if a user mentions a specific airport on social media, it can prioritize acquiring data related to the area around that airport. Furthermore, if a user mentions specific weather conditions on social media, it can prioritize acquiring data related to those conditions. This allows the data collection unit to analyze users' social media activity and provide more relevant data.

[0059] The introduction section can also adjust the level of detail in its descriptions based on the importance of the photo spot. For example, for important photo spots, it can provide detailed access information and shooting points. For general photo spots, it can provide a brief description and access information. Furthermore, for minor photo spots, it can provide only an overview. In this way, the introduction section can adjust the level of detail in its descriptions based on the importance of the photo spot, providing users with important information.

[0060] The discrimination unit can also improve its discrimination accuracy by considering the interrelationships between photographs during the discrimination process. For example, it can identify the aircraft type based on photographs of the same airplane taken from different angles. It can also identify the airline based on multiple photographs taken at the same location. Furthermore, it can identify the flight route based on photographs taken at the same time. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs.

[0061] The advice section can apply different advice algorithms depending on the camera settings and shooting conditions category when providing advice. For example, in the case of landscape photography, it can recommend the use of a wide-angle lens. In the case of portrait photography, it can also provide advice on how to blur the background. Furthermore, in the case of night photography, it can provide advice on long exposure settings. In this way, the advice section can apply different advice algorithms depending on the camera settings and shooting conditions category, providing the best possible advice for the user.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects the user's location information or weather data. The data collection unit obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, the data collection unit obtains GPS data from the user's smartphone to determine their current location. The data collection unit also obtains weather data from the internet to understand the current weather conditions. Step 2: The introduction section introduces photo spots based on the data collected by the data collection section. The introduction section suggests airports near the user's current location or places where it is easy to see planes taking off and landing. The introduction section can also guide users to photo spots suitable for sunny or cloudy days based on weather information. Step 3: The discrimination unit uses generative AI to identify the aircraft type and airline from the user's photograph. The discrimination unit uses generative AI to analyze the airplane photograph and identify the aircraft type and airline. The generative AI uses deep learning models and image recognition algorithms to extract features from the photograph and identify the aircraft type and airline. Step 4: The advice unit provides advice to optimize camera settings and shooting conditions based on the information identified by the discrimination unit. The advice unit suggests suitable shutter speeds and aperture values ​​when photographing specific aircraft models or airlines. The advice unit can also use generative AI to provide optimal camera settings according to the shooting conditions. Step 5: The evaluation unit assesses the composition and quality of the photographs taken and suggests specific areas for improvement. The evaluation unit will suggest specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. The evaluation unit uses AI to evaluate the composition and quality of the photographs and provides feedback to the user.

[0064] (Example of form 2) The airplane photography support system according to an embodiment of the present invention is a system that uses AI to further enhance the enjoyment of airplane photography for people who enjoy it. This airplane photography support system collects the user's location information and weather data and introduces the optimal shooting spot. Next, it uses a generating AI to automatically identify the aircraft type and airline from the airplane photographs taken by the user. Furthermore, the generating AI provides advice to optimize camera settings and shooting conditions. Finally, the AI ​​judges the composition and quality of the photographs taken and provides advice to make the photographs even better. For example, the airplane photography support system obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, it suggests airports near the user's current location or places where airplanes can easily be seen taking off and landing. It also guides users to shooting spots suitable for sunny or cloudy days based on weather information. Next, it uses a generating AI to automatically identify the aircraft type and airline from the airplane photographs taken by the user. This allows the user to know detailed information about the photographs they have taken. Furthermore, the generating AI provides advice to optimize camera settings and shooting conditions. For example, it suggests shutter speeds and aperture values ​​suitable for photographing specific aircraft types or airlines. Furthermore, the AI ​​analyzes the composition and quality of the photos taken and provides advice on how to improve them. For example, if the composition is unbalanced or the exposure is inappropriate, it will suggest specific areas for improvement. This allows users to improve their photography skills. This service can be expanded beyond airplane photography to other fields of photography. For instance, it can provide similar advice for various genres of photography, such as landscape and portrait photography. This will allow the airplane photography support system to enhance users' enjoyment of airplane photography.

[0065] The aircraft photography support system according to this embodiment comprises a collection unit, a recommendation unit, a discrimination unit, an advice unit, and an evaluation unit. The collection unit collects the user's location information or weather data. For example, the collection unit obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, the collection unit obtains GPS data from the user's smartphone to determine the current location. The collection unit can also obtain weather data from the internet to understand the current weather conditions. The recommendation unit introduces shooting spots based on the data collected by the collection unit. For example, the recommendation unit suggests airports near the user's current location or places where aircraft take off and land easily visible. The recommendation unit can also guide users to shooting spots suitable for sunny or cloudy days based on weather information. The discrimination unit uses a generation AI to determine the aircraft type and airline from the photos taken by the user. For example, the generation AI analyzes the airplane photos to identify the aircraft type and airline. The generation AI uses a deep learning model or image recognition algorithm to extract features from the photos and determine the aircraft type and airline. The advice unit provides advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. For example, the advice unit suggests suitable shutter speeds and aperture values ​​when photographing specific aircraft models or airlines. The advice unit can also use generative AI to provide optimal camera settings according to shooting conditions. The evaluation unit judges the composition and quality of the photographs taken and suggests specific areas for improvement. For example, the evaluation unit suggests specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. The evaluation unit uses AI to evaluate the composition and quality of the photographs and provides feedback to the user. As a result, the airplane photography support system according to this embodiment allows users to enjoy airplane photography more. Some or all of the above-described processes in the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit may be performed using AI, for example, or without AI. For example, the collection unit obtains location information from the user's smartphone and weather data from the internet. The introduction unit suggests the optimal shooting spot to the user based on the data collected by the collection unit.The discrimination unit uses generation AI to identify the aircraft type and airline from photos taken by the user. The advice unit provides advice to optimize camera settings and shooting conditions based on the information identified by the discrimination unit. The evaluation unit judges the composition and quality of the photos taken and suggests specific areas for improvement.

[0066] The data collection unit can acquire location information from the user's smartphone or GPS device and weather data from the internet. For example, the data collection unit can acquire GPS data from the user's smartphone to determine the current location. For example, the data collection unit can use the smartphone's GPS function to acquire the user's current location in real time. The data collection unit can also acquire weather data from the internet to understand the current weather conditions. For example, the data collection unit can acquire weather data from the Japan Meteorological Agency or private weather forecasting services. This allows the data collection unit to collect accurate location information and weather data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can acquire location information from the user's smartphone and weather data from the internet. This allows the data collection unit to collect accurate location information and weather data.

[0067] The recommendation unit can suggest optimal shooting locations to the user based on data collected by the data collection unit. For example, the recommendation unit can suggest airports or places where it is easy to see planes taking off and landing from the user's current location. For example, the recommendation unit can select the optimal shooting location based on the user's current location. The recommendation unit can also guide users to shooting locations suitable for sunny or cloudy days based on weather information. For example, the recommendation unit can suggest an airport observation deck on a sunny day and a place where it is easy to see planes taking off and landing on a cloudy day. This allows the recommendation unit to help users enjoy taking photos in the optimal location. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit suggests optimal shooting locations to the user based on data collected by the data collection unit. This allows the recommendation unit to help users enjoy taking photos in the optimal location.

[0068] The discrimination unit can identify the aircraft type and airline from a user-submitted photograph using a generative AI. For example, the discrimination unit uses the generative AI to analyze a photograph of an airplane and identify the aircraft type and airline. For example, the discrimination unit uses the generative AI to extract features of the airplane and identify the aircraft type and airline. The generative AI analyzes the features of the photograph using deep learning models and image recognition algorithms. For example, the generative AI identifies the airline based on the airplane's logo or paint pattern. The generative AI can also identify the aircraft type based on the airplane's shape and engine arrangement. This allows the discrimination unit to obtain detailed information about the user-submitted photograph. Some or all of the above processing in the discrimination unit may be performed using the generative AI, or it may be performed without using the generative AI. For example, the discrimination unit uses the generative AI to identify the aircraft type and airline from a user-submitted photograph. This allows the discrimination unit to obtain detailed information about the user-submitted photograph.

[0069] The advice unit can provide advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. For example, the advice unit may suggest suitable shutter speeds and aperture values ​​when photographing a specific aircraft model or airline. For example, the advice unit may use generative AI to provide optimal camera settings according to shooting conditions. The generative AI can suggest optimal camera settings based on past shooting data and expert knowledge. For example, the generative AI may recommend setting the shutter speed to 1 / 1000 second when photographing a specific aircraft model. The generative AI may also suggest setting the aperture value to f / 8 so that the airline logo is clearly visible. This allows the advice unit to take better photos. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or without using generative AI. For example, the advice unit provides advice to optimize camera settings and shooting conditions based on the information determined by the discrimination unit. This allows the advice unit to take better photos.

[0070] The evaluation unit can judge the composition and quality of the photographs taken and suggest specific areas for improvement. For example, the evaluation unit can suggest specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. For example, the evaluation unit can use AI to evaluate the composition and quality of the photographs and provide feedback to the user. The AI ​​can evaluate the composition of a photograph based on evaluation criteria such as the rule of thirds or diagonal composition. For example, if the subject is placed in the center of the photograph, the AI ​​will suggest moving the subject based on the rule of thirds. The AI ​​can also suggest specific exposure correction methods if the exposure of the photograph is inappropriate. For example, if the photograph is too dark, the AI ​​will suggest setting the exposure compensation to +1. This allows the evaluation unit to help the user improve their photography skills. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit judges the composition and quality of the photographs taken and suggests specific areas for improvement. This allows the evaluation unit to help the user improve their photography skills.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of location and weather data acquisition based on the estimated emotions. For example, if the user is excited, the data collection unit will acquire location and weather data frequently in real time to provide the latest information. For example, if the data collection unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it will acquire location and weather data every minute. Also, if the user is relaxed, the data collection unit will acquire location and weather data at regular intervals to provide stable information. For example, if the data collection unit determines that the user is relaxed, it will acquire location and weather data every 10 minutes. Furthermore, if the user is stressed, the data collection unit will reduce the frequency of data acquisition to alleviate the user's burden. For example, if the data collection unit determines that the user is stressed, it will acquire location and weather data every 30 minutes. In this way, the data collection unit can adjust the timing of data acquisition based on the user's emotions, reduce the user's burden, and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit estimates the user's emotions and adjusts the timing of acquiring location information and weather data based on the estimated user emotions. This allows the collection unit to reduce the burden on the user and provide more appropriate information.

[0072] The data collection unit can analyze the user's past shooting history and select the optimal data acquisition method. For example, the data collection unit can acquire data under similar conditions based on data of locations where the user has previously taken photos. For example, the data collection unit can analyze the user's past shooting history and acquire data at the same location based on shooting data from that specific location. The data collection unit can also acquire data at specific time periods based on the user's past shooting history. For example, the data collection unit can analyze the time periods when the user preferred to take photos in the past and acquire data at those times. Furthermore, the data collection unit can adjust the timing of data acquisition based on the weather conditions when the user preferred to take photos in the past. For example, if the user prefers to take photos on sunny days, the data collection unit will prioritize acquiring data from sunny days. This allows the data collection unit to analyze the user's past shooting history and select a more appropriate data acquisition method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit analyzes the user's past shooting history and selects the optimal data acquisition method. This allows the data collection unit to select a more appropriate data acquisition method.

[0073] The data collection unit can filter location and weather data based on the user's current photography purpose and areas of interest. For example, if a user wants to photograph a specific aircraft model, the data collection unit will prioritize acquiring data on locations where that aircraft model can be seen. For example, if a user wants to photograph a specific aircraft model, the data collection unit will prioritize acquiring data on airports and flight routes where that aircraft model can be seen. The data collection unit can also acquire data that matches specific weather conditions if the user wants to photograph under those conditions. For example, if a user wants to photograph on a sunny day, the data collection unit will prioritize acquiring weather data for sunny days. Furthermore, if a user wants to photograph at a specific time of day, the data collection unit can acquire data that matches that time of day. For example, if a user wants to photograph in the evening, the data collection unit will prioritize acquiring evening weather data and location information. This allows the data collection unit to filter data based on the user's photography purpose and areas of interest, providing more relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when acquiring location information and weather data, the data collection unit filters the data based on the user's current photography purpose and areas of interest. This allows the data collection unit to provide more relevant information.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize acquiring information on airplane takeoffs and landings. For example, if the data collection unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it will prioritize acquiring information on airplane takeoffs and landings. The data collection unit can also prioritize acquiring weather data if the user is relaxed. For example, if the data collection unit determines that the user is relaxed, it will prioritize acquiring weather data. Furthermore, if the data collection unit is stressed, it can also prioritize acquiring location information. For example, if the data collection unit determines that the user is stressed, it will prioritize acquiring location information. In this way, the data collection unit can prioritize data based on the user's emotions and provide information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. This allows the data collection unit to provide information that meets the user's needs.

[0075] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring location information and weather data. For example, if the user is near an airport, the data collection unit will prioritize the acquisition of weather data around the airport. For example, the data collection unit will prioritize the acquisition of weather data around the airport based on the user's geographical location. The data collection unit can also acquire detailed location information of a location where the user can easily see planes taking off and landing. For example, if the data collection unit determines that the user is in a location where planes taking off and landing can be easily seen, it will acquire detailed location information of that location. Furthermore, if the data collection unit is near a specific flight route, it can prioritize the acquisition of data related to that route. For example, if the data collection unit determines that the user is near a specific flight route, it will prioritize the acquisition of data related to that route. In this way, the data collection unit can provide highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit prioritizes the acquisition of highly relevant data by considering the user's geographical location when acquiring location information and weather data. This allows the data collection unit to provide more relevant data.

[0076] The data collection unit can analyze the user's social media activity and obtain relevant data when acquiring location information and weather data. For example, if the user mentions a specific aircraft model on social media, the data collection unit will prioritize acquiring data related to that aircraft model. For example, the data collection unit analyzes the user's social media activity and prioritizes acquiring data related to a specific aircraft model. The data collection unit can also prioritize acquiring data around a specific airport if the user mentions that airport on social media. For example, if the user mentions a specific airport, the data collection unit will prioritize acquiring weather data and location information around that airport. Furthermore, if the user mentions specific weather conditions on social media, the data collection unit will prioritize acquiring data related to those conditions. For example, if the user mentions specific weather conditions, the data collection unit will prioritize acquiring data related to those conditions. This allows the data collection unit to analyze the user's social media activity and provide more relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit analyzes the user's social media activity and obtains relevant data when acquiring location information and weather data. This allows the data collection unit to provide more relevant data.

[0077] The recommendation system can estimate the user's emotions and adjust how it recommends photo spots based on those emotions. For example, if the user is excited, the recommendation system will highlight visually appealing spots. For instance, it can estimate the user's emotions using facial recognition or voice analysis, and if it determines the user is excited, it will highlight visually appealing spots. It can also recommend quiet and calming spots if the user is relaxed. For example, if it determines the user is relaxed, it will recommend quiet and calming spots. Furthermore, if the user is stressed, the recommendation system can recommend easily accessible spots. For example, if it determines the user is stressed, it will recommend easily accessible spots. In this way, the recommendation system can adjust its recommendation method based on the user's emotions and recommend the most suitable photo spots for the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the introduction section may be performed using AI, for example, or without AI. For example, the introduction section may estimate the user's emotions and adjust the method of introducing photo spots based on the estimated user emotions. This allows the introduction section to introduce the most suitable photo spots for the user.

[0078] The introduction section can adjust the level of detail in its recommendations based on the importance of the photo spot. For example, for important photo spots, the introduction section can provide detailed access information and shooting points. For example, for tourist destinations or popular photo spots, the introduction section can provide detailed access information and shooting points. The introduction section can also provide a brief description and access information for general photo spots. For example, for general photo spots, the introduction section can provide a brief description and access information for general photo spots. Furthermore, for minor photo spots, the introduction section can provide only an overview. For example, for minor photo spots, the introduction section can provide only an overview. In this way, the introduction section can adjust the level of detail in its recommendations based on the importance of the photo spot and provide information that is important to the user. Some or all of the above processing in the introduction section may be performed using AI, for example, or not using AI. For example, the introduction section adjusts the level of detail in its recommendations based on the importance of the photo spot. In this way, the introduction section can provide information that is important to the user.

[0079] The recommendation section can apply different recommendation algorithms depending on the category of the shooting spot when making recommendations. For example, in the case of shooting spots around airports, the recommendation section will highlight points where it is easy to see planes taking off and landing. For example, in the case of shooting spots around airports, the recommendation section will highlight points where it is easy to see planes taking off and landing. The recommendation section can also highlight points where it is easy to see buildings in the background when making recommendations in urban areas. For example, in the case of shooting spots in urban areas, the recommendation section will highlight points where it is easy to see buildings in the background. Furthermore, in the case of shooting spots in natural environments, the recommendation section will highlight points where it is easy to see the scenery and the plane in harmony. For example, in the case of shooting spots in natural environments, the recommendation section will highlight points where it is easy to see the scenery and the plane in harmony. In this way, the recommendation section can apply different recommendation algorithms depending on the category of the shooting spot and recommend the best shooting spot for the user. Some or all of the above processing in the recommendation section may be performed using AI, for example, or not. For example, the recommendation section applies different recommendation algorithms depending on the category of the shooting spot when making recommendations. In this way, the recommendation section can recommend the best shooting spot for the user.

[0080] The introduction section can estimate the user's emotions and adjust the length of the introduction based on those emotions. For example, if the user is excited, the introduction section will provide a short, concise introduction. For example, the introduction section can estimate the user's emotions using facial recognition or voice analysis, and if it determines that the user is excited, it will provide a short, concise introduction. The introduction section can also provide a longer, more detailed introduction if the user is relaxed. For example, if it determines that the user is relaxed, it will provide a longer introduction with detailed explanations. Furthermore, if the introduction section is stressed, it can provide a concise and easy-to-understand introduction. For example, if it determines that the user is stressed, it will provide a concise and easy-to-understand introduction. In this way, the introduction section can adjust the length of the introduction based on the user's emotions and provide the user with the most appropriate information. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the referral section may be performed using AI, for example, or without AI. For example, the referral section may estimate the user's emotions and adjust the length of the referral based on the estimated user emotions. This allows the referral section to provide the user with the most relevant information.

[0081] The recommendation system can prioritize recommendations based on the popularity of the photo spots. For example, it might prioritize popular photo spots. For example, it might prioritize tourist spots or photo spots that are trending on social media. It could also recommend more common photo spots next. For example, it could recommend more common photo spots next. Furthermore, it could recommend less popular photo spots last. For example, it could recommend less popular photo spots last. This allows the recommendation system to prioritize recommendations based on the popularity of the photo spots and provide users with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system might prioritize recommendations based on the popularity of the photo spots when making recommendations. This allows the recommendation system to provide users with the most relevant information.

[0082] The recommendation system can adjust the order of recommendations based on the relevance of the shooting locations. For example, it may prioritize recommending shooting locations close to the user's current location. For example, it may prioritize recommending nearby shooting locations based on the user's current location. The recommendation system can also recommend shooting locations related to the user's areas of interest next. For example, it may recommend shooting locations related to the aircraft models or airlines the user is interested in next. Furthermore, the recommendation system may recommend highly relevant locations last based on the user's past shooting history. For example, it may analyze the user's past shooting history and recommend highly relevant locations last. In this way, the recommendation system can adjust the order of recommendations based on the relevance of the shooting locations and provide the user with the most relevant information. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system adjusts the order of recommendations based on the relevance of the shooting locations when making recommendations. In this way, the recommendation system can provide the user with the most relevant information.

[0083] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated user emotions. For example, if the user is excited, the discrimination unit can quickly provide a discrimination result. For example, if the discrimination unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it can quickly provide a discrimination result. The discrimination unit can also provide a detailed discrimination result if the user is relaxed. For example, if the discrimination unit determines that the user is relaxed, it can provide a detailed discrimination result. Furthermore, if the discrimination unit is stressed, it can provide a concise and easy-to-understand discrimination result. For example, if the discrimination unit determines that the user is stressed, it can provide a concise and easy-to-understand discrimination result. In this way, the discrimination unit can adjust the discrimination criteria based on the user's emotions and provide the optimal discrimination result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discrimination unit estimates the user's emotions and adjusts the discrimination criteria based on the estimated emotions. This allows the discrimination unit to provide the optimal discrimination result for the user.

[0084] The discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs during discrimination. For example, the discrimination unit can determine the aircraft type based on photographs of the same aircraft taken from different angles. For example, the discrimination unit can determine the aircraft type based on multiple photographs of the same aircraft taken from different angles. The discrimination unit can also determine the airline based on multiple photographs taken at the same location. For example, the discrimination unit can determine the airline based on multiple photographs taken at the same location. Furthermore, the discrimination unit can determine the flight route based on photographs taken at the same time. For example, the discrimination unit can determine the flight route based on multiple photographs taken at the same time. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or without generative AI. For example, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs during discrimination. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs.

[0085] The discrimination unit can perform discrimination while considering the attribute information of the photographer of the photograph. For example, if the discrimination unit determines that the photographer is a professional, it will provide a detailed discrimination result. The discrimination unit can also provide a concise discrimination result if the photographer is an amateur. For example, if the discrimination unit determines that the photographer is an amateur, it will provide a concise discrimination result. Furthermore, if the discrimination unit determines that the photographer is a first-time photographer, it will provide a basic discrimination result. For example, if the discrimination unit determines that the photographer is a first-time photographer, it will provide a basic discrimination result. This allows the discrimination unit to perform discrimination while considering the attribute information of the photographer of the photograph and provide a more appropriate discrimination result. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or not using generative AI. For example, the discrimination unit performs discrimination while considering the attribute information of the photographer of the photograph. This allows the discrimination unit to provide a more appropriate discrimination result.

[0086] The discrimination unit can estimate the user's emotions and adjust the order in which the discrimination results are displayed based on the estimated emotions. For example, if the user is excited, the discrimination unit can display important information first. For example, if the discrimination unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it can display important information first. The discrimination unit can also sequentially display detailed information if the user is relaxed. For example, if the discrimination unit determines that the user is relaxed, it can sequentially display detailed information. Furthermore, if the discrimination unit is stressed, it can also display concise information first. For example, if the discrimination unit determines that the user is stressed, it can display concise information first. In this way, the discrimination unit can adjust the order in which the discrimination results are displayed based on the user's emotions and provide the user with the most appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discrimination unit estimates the user's emotions and adjusts the order in which the discrimination results are displayed based on the estimated emotions. This allows the discrimination unit to provide the user with the most relevant information.

[0087] The discrimination unit can perform discrimination while considering the geographical distribution of the photographs. For example, the discrimination unit can determine the aircraft type based on photographs taken around a specific airport. For example, the discrimination unit can determine the aircraft type based on photographs taken around a specific airport. The discrimination unit can also determine the airline based on photographs taken along a specific flight route. For example, the discrimination unit can determine the airline based on photographs taken along a specific flight route. Furthermore, the discrimination unit can determine the model of the aircraft based on photographs taken in a specific region. For example, the discrimination unit can determine the model of the aircraft based on photographs taken in a specific region. This allows the discrimination unit to perform discrimination while considering the geographical distribution of the photographs and provide more accurate discrimination results. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or without generative AI. For example, the discrimination unit performs discrimination while considering the geographical distribution of the photographs. This allows the discrimination unit to provide more accurate discrimination results.

[0088] The discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph during discrimination. For example, the discrimination unit can identify the aircraft type by referring to relevant literature based on the characteristics of the aircraft in the photograph. For example, the discrimination unit can identify the aircraft type by referring to relevant literature based on the characteristics of the aircraft in the photograph. The discrimination unit can also identify the airline by referring to relevant literature based on the airline logo in the photograph. For example, the discrimination unit can identify the airline by referring to relevant literature based on the airline logo in the photograph. Furthermore, the discrimination unit can identify the model by referring to relevant literature based on the paintwork of the aircraft in the photograph. For example, the discrimination unit can identify the model by referring to relevant literature based on the paintwork of the aircraft in the photograph. In this way, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph. Some or all of the above processing in the discrimination unit may be performed using, for example, generative AI, or not using generative AI. For example, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph during discrimination. In this way, the discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the photograph.

[0089] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is excited, the advice unit will provide advice in a positive and energetic way. For instance, if the advice unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it will provide advice in a positive and energetic way. The advice unit can also provide advice in a calm and soothing way if the user is relaxed. For example, if the advice unit determines that the user is relaxed, it will provide advice in a calm and soothing way. Furthermore, if the advice unit is stressed, it can provide advice in a concise and easy-to-understand way. For example, if the advice unit determines that the user is stressed, it will provide advice in a concise and easy-to-understand way. In this way, the advice unit can adjust the way it expresses advice based on the user's emotions and provide the best possible advice for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the advice unit may be performed using, for example, generative AI, or without generative AI. For example, the advice unit estimates the user's emotions and adjusts the way the advice is expressed based on the estimated user emotions. This allows the advice unit to provide the best possible advice for the user.

[0090] The advice unit can adjust the level of detail in its advice based on the importance of camera settings and shooting conditions. For example, for important camera settings, the advice unit provides a detailed explanation and specific setting values. For example, for important camera settings, the advice unit provides a detailed explanation and specific setting values. The advice unit can also provide a brief explanation and recommended setting values ​​for general shooting conditions. For example, for general shooting conditions, the advice unit provides a brief explanation and recommended setting values. Furthermore, for minor shooting conditions, the advice unit can provide only an overview. For example, for minor shooting conditions, the advice unit provides only an overview. This allows the advice unit to adjust the level of detail in its advice based on the importance of camera settings and shooting conditions, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not using generative AI. For example, the advice unit adjusts the level of detail in its advice based on the importance of camera settings and shooting conditions when providing advice. This allows the advice unit to provide the best possible advice for the user.

[0091] The advice unit can apply different advice algorithms depending on the camera settings and shooting conditions category when providing advice. For example, in the case of landscape photography, the advice unit may recommend the use of a wide-angle lens. The advice unit can also provide advice on how to blur the background in the case of portrait photography. Furthermore, the advice unit may provide advice on long exposure settings in the case of night photography. This allows the advice unit to apply different advice algorithms depending on the camera settings and shooting conditions category, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not. For example, the advice unit applies different advice algorithms depending on the camera settings and shooting conditions category when providing advice. This allows the advice unit to provide the best possible advice for the user.

[0092] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is excited, the advice unit will provide short, concise advice. For example, if the advice unit estimates the user's emotions using facial recognition or voice analysis and determines that the user is excited, it will provide short, concise advice. The advice unit can also provide longer advice with more detailed explanations if the user is relaxed. For example, if the advice unit determines that the user is relaxed, it will provide longer advice with more detailed explanations. Furthermore, if the advice unit is stressed, it can provide concise and easy-to-understand advice. For example, if the advice unit determines that the user is stressed, it will provide concise and easy-to-understand advice. In this way, the advice unit can adjust the length of the advice based on the user's emotions and provide the most appropriate advice for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using, for example, generative AI, or without generative AI. For example, the advice unit estimates the user's emotions and adjusts the length of the advice based on the estimated user emotions. This allows the advice unit to provide the best possible advice for the user.

[0093] The advice unit can prioritize advice based on the timing of submission of camera settings and shooting conditions. For example, the advice unit may prioritize advice regarding recent shooting conditions. The advice unit may also provide advice regarding general shooting conditions next. Furthermore, the advice unit may provide advice regarding past shooting conditions last. This allows the advice unit to prioritize advice based on the timing of submission of camera settings and shooting conditions, providing the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not. For example, the advice unit may prioritize advice based on the timing of submission of camera settings and shooting conditions. This allows the advice unit to provide the best possible advice for the user.

[0094] The advice unit can adjust the order of advice based on the relevance of camera settings and shooting conditions when providing advice. For example, the advice unit may first provide advice that is most relevant to the user's current shooting conditions. The advice unit may also next provide advice related to the user's past shooting conditions. Furthermore, the advice unit may last provide advice related to the user's future shooting conditions. In this way, the advice unit can adjust the order of advice based on the relevance of camera settings and shooting conditions to provide the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, generative AI, or not using generative AI. For example, the advice unit adjusts the order of advice based on the relevance of camera settings and shooting conditions when providing advice. In this way, the advice unit can provide the best possible advice for the user.

[0095] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, if the user is excited, the evaluation unit can emphasize positive feedback. For example, the evaluation unit can estimate the user's emotions using facial recognition or voice analysis, and if it determines that the user is excited, it can emphasize positive feedback. The evaluation unit can also provide a detailed evaluation if the user is relaxed. For example, if the evaluation unit determines that the user is relaxed, it can provide a detailed evaluation. Furthermore, if the evaluation unit is stressed, it can provide a concise and easy-to-understand evaluation. For example, if the evaluation unit determines that the user is stressed, it can provide a concise and easy-to-understand evaluation. In this way, the evaluation unit can adjust the evaluation method based on the user's emotions and provide the optimal evaluation for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit estimates the user's emotions and adjusts the evaluation method based on those estimated emotions. This allows the evaluation unit to provide the optimal evaluation for the user.

[0096] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data of the photograph during the evaluation process. For example, the evaluation unit can evaluate the current photograph based on the characteristics of photographs that have received high ratings in the past. The evaluation unit can also suggest areas for improvement based on the characteristics of photographs that have received low ratings in the past. Furthermore, the evaluation unit can analyze past evaluation data and adjust the evaluation criteria. For example, the evaluation unit can analyze past evaluation data and adjust the evaluation criteria. This allows the evaluation unit to optimize its evaluation algorithm by referring to past evaluation data of the photograph and provide a more accurate evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit optimizes its evaluation algorithm by referring to past evaluation data of the photograph during the evaluation process. This allows the evaluation unit to provide a more accurate evaluation.

[0097] The evaluation unit can apply different evaluation methods to each category of photograph during evaluation. For example, in the case of landscape photographs, the evaluation unit may prioritize the evaluation of composition and color. For example, in the case of portrait photographs, the evaluation unit may prioritize the evaluation of the subject's expression and background. For example, in the case of portrait photographs, the evaluation unit may prioritize the evaluation of the subject's expression and background. Furthermore, in the case of nightscape photographs, the evaluation unit may prioritize the evaluation of exposure and sharpness. For example, in the case of nightscape photographs, the evaluation unit may prioritize the evaluation of exposure and sharpness. This allows the evaluation unit to apply different evaluation methods to each category of photograph and provide a more appropriate evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit applies different evaluation methods to each category of photograph during evaluation. This allows the evaluation unit to provide a more appropriate evaluation.

[0098] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is excited, the evaluation unit will prioritize providing positive evaluations. For example, the evaluation unit can estimate the user's emotions using facial recognition or voice analysis, and if it determines that the user is excited, it will prioritize providing positive evaluations. The evaluation unit can also prioritize providing detailed evaluations if the user is relaxed. For example, if the evaluation unit determines that the user is relaxed, it will prioritize providing detailed evaluations. Furthermore, if the evaluation unit is stressed, it will prioritize providing concise evaluations. For example, if the evaluation unit determines that the user is stressed, it will prioritize providing concise evaluations. In this way, the evaluation unit can determine the priority of evaluations based on the user's emotions and provide the optimal evaluation for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit estimates the user's emotions and determines the evaluation priority based on the estimated user emotions. This allows the evaluation unit to provide the best possible evaluation for the user.

[0099] The evaluation unit can weight the evaluation based on when the photos were submitted. For example, the evaluation unit may prioritize the evaluation of the most recently submitted photos. The evaluation unit may also evaluate photos submitted around the typical time period next. Furthermore, the evaluation unit may evaluate photos submitted in the past last. This allows the evaluation unit to weight the evaluation based on when the photos were submitted and provide a more appropriate evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit weights the evaluation based on when the photos were submitted during the evaluation process. This allows the evaluation unit to provide a more appropriate evaluation.

[0100] The evaluation unit can perform evaluations by referring to relevant market data for the photographs during the evaluation process. For example, the evaluation unit can evaluate the current photographs based on the characteristics of photographs that have received high ratings in the market. The evaluation unit can also suggest areas for improvement based on the characteristics of photographs that have received low ratings in the market. Furthermore, the evaluation unit can analyze market data and adjust the evaluation criteria. For example, the evaluation unit can analyze market data and adjust the evaluation criteria. This allows the evaluation unit to perform evaluations by referring to relevant market data for the photographs and provide more appropriate evaluations. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations by referring to relevant market data for the photographs during the evaluation process. This allows the evaluation unit to provide more appropriate evaluations. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires location information and weather data using the GPS function and internet connection of the smart device 14. The introduction unit suggests shooting spots through the display 40A of the smart device 14 based on the data obtained from the collection unit. The discrimination unit uses the identification processing unit 290 of the data processing unit 12 to identify the aircraft model and airline in the photograph using generated AI. The advice unit uses the identification processing unit 290 of the data processing unit 12 to provide advice on optimizing camera settings based on the results of the discrimination unit. The evaluation unit uses the control unit 46A of the smart device 14 to evaluate the composition and quality of the photograph and suggests areas for improvement. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires location information and weather data using the GPS function and internet connection of the smart glasses 214. The introduction unit suggests shooting locations through the display of the smart glasses 214 based on the data obtained from the collection unit. The discrimination unit uses the identification processing unit 290 of the data processing unit 12 to identify the aircraft model and airline in the photograph using generated AI. The advice unit uses the identification processing unit 290 of the data processing unit 12 to provide advice on optimizing camera settings based on the results of the discrimination unit. The evaluation unit uses the control unit 46A of the smart glasses 214 to evaluate the composition and quality of the photograph and suggests areas for improvement. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires location information and weather data using the GPS function and internet connection of the headset terminal 314. The introduction unit suggests shooting spots through the display 343 of the headset terminal 314 based on the data obtained from the collection unit. The discrimination unit uses the identification processing unit 290 of the data processing unit 12 to identify the aircraft model and airline in the photograph using generated AI. The advice unit uses the identification processing unit 290 of the data processing unit 12 to provide advice on optimizing camera settings based on the results of the discrimination unit. The evaluation unit uses the control unit 46A of the headset terminal 314 to evaluate the composition and quality of the photograph and suggests areas for improvement. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, introduction unit, discrimination unit, advice unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires location information and weather data using the GPS function and internet connection of the robot 414. The introduction unit suggests shooting spots through the display of the robot 414 based on the data obtained from the collection unit. The discrimination unit uses the identification processing unit 290 of the data processing unit 12 to identify the aircraft type and airline in the photograph using generated AI. The advice unit uses the identification processing unit 290 of the data processing unit 12 to provide advice on optimizing camera settings based on the results of the discrimination unit. The evaluation unit uses the control unit 46A of the robot 414 to evaluate the composition and quality of the photograph and suggests areas for improvement.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can also analyze the user's past shooting history and select the optimal data acquisition method. For example, it can acquire data under similar conditions based on data from locations the user has previously photographed. Furthermore, it can acquire data at specific time periods based on the user's past shooting history. In addition, it can adjust the timing of data acquisition based on weather conditions the user has previously preferred for photography. This allows the data collection unit to analyze the user's past shooting history and select a more appropriate data acquisition method.

[0103] The data collection unit can also filter location and weather data based on the user's current photography purpose and areas of interest. For example, if a user wants to photograph a specific aircraft model, the unit will prioritize acquiring data for locations where that model can be seen. Furthermore, if a user wishes to photograph under specific weather conditions, the unit can acquire data that matches those conditions. Additionally, if a user wishes to photograph at a specific time of day, the unit can acquire data that matches that time. This allows the data collection unit to filter data based on the user's photography purpose and areas of interest, providing more relevant information.

[0104] The recommendation system can also estimate the user's emotions and adjust how it presents photo spots based on those emotions. For example, if the user is excited, it can highlight visually appealing spots. If the user is relaxed, it can recommend quiet and calming spots. Furthermore, if the user is stressed, it can recommend easily accessible spots. In this way, the recommendation system can adjust its presentation based on the user's emotions and recommend the most suitable photo spots for the user.

[0105] The discrimination unit can also estimate the user's emotions and adjust the discrimination criteria based on the estimated emotions. For example, if the user is excited, it can provide a quick discrimination result. If the user is relaxed, it can provide a detailed discrimination result. Furthermore, if the user is stressed, it can provide a concise and easy-to-understand discrimination result. In this way, the discrimination unit can adjust the discrimination criteria based on the user's emotions and provide the optimal discrimination result for the user.

[0106] The advice unit can also estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is excited, it can provide advice in a positive and energetic way. If the user is relaxed, it can provide advice in a calm and soothing way. Furthermore, if the user is stressed, it can provide advice in a concise and easy-to-understand way. In this way, the advice unit can adjust the way it expresses advice based on the user's emotions and provide the most appropriate advice for the user.

[0107] The evaluation unit can also estimate the user's emotions and adjust the evaluation method based on those emotions. For example, if the user is excited, it can emphasize positive feedback. If the user is relaxed, it can provide a more detailed evaluation. Furthermore, if the user is stressed, it can provide a concise and easy-to-understand evaluation. In this way, the evaluation unit can adjust the evaluation method based on the user's emotions and provide the most appropriate evaluation for the user.

[0108] The data collection unit can also analyze users' social media activity when acquiring location information and weather data, and obtain relevant data. For example, if a user mentions a specific aircraft model on social media, it can prioritize acquiring data related to that model. Similarly, if a user mentions a specific airport on social media, it can prioritize acquiring data related to the area around that airport. Furthermore, if a user mentions specific weather conditions on social media, it can prioritize acquiring data related to those conditions. This allows the data collection unit to analyze users' social media activity and provide more relevant data.

[0109] The introduction section can also adjust the level of detail in its descriptions based on the importance of the photo spot. For example, for important photo spots, it can provide detailed access information and shooting points. For general photo spots, it can provide a brief description and access information. Furthermore, for minor photo spots, it can provide only an overview. In this way, the introduction section can adjust the level of detail in its descriptions based on the importance of the photo spot, providing users with important information.

[0110] The discrimination unit can also improve its discrimination accuracy by considering the interrelationships between photographs during the discrimination process. For example, it can identify the aircraft type based on photographs of the same airplane taken from different angles. It can also identify the airline based on multiple photographs taken at the same location. Furthermore, it can identify the flight route based on photographs taken at the same time. In this way, the discrimination unit can improve its discrimination accuracy by considering the interrelationships between photographs.

[0111] The advice section can apply different advice algorithms depending on the camera settings and shooting conditions category when providing advice. For example, in the case of landscape photography, it can recommend the use of a wide-angle lens. In the case of portrait photography, it can also provide advice on how to blur the background. Furthermore, in the case of night photography, it can provide advice on long exposure settings. In this way, the advice section can apply different advice algorithms depending on the camera settings and shooting conditions category, providing the best possible advice for the user.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects the user's location information or weather data. The data collection unit obtains location information from the user's smartphone or GPS device and weather data from the internet. For example, the data collection unit obtains GPS data from the user's smartphone to determine their current location. The data collection unit also obtains weather data from the internet to understand the current weather conditions. Step 2: The introduction section introduces photo spots based on the data collected by the data collection section. The introduction section suggests airports near the user's current location or places where it is easy to see planes taking off and landing. The introduction section can also guide users to photo spots suitable for sunny or cloudy days based on weather information. Step 3: The discrimination unit uses generative AI to identify the aircraft type and airline from the user's photograph. The discrimination unit uses generative AI to analyze the airplane photograph and identify the aircraft type and airline. The generative AI uses deep learning models and image recognition algorithms to extract features from the photograph and identify the aircraft type and airline. Step 4: The advice unit provides advice to optimize camera settings and shooting conditions based on the information identified by the discrimination unit. The advice unit suggests suitable shutter speeds and aperture values ​​when photographing specific aircraft models or airlines. The advice unit can also use generative AI to provide optimal camera settings according to the shooting conditions. Step 5: The evaluation unit assesses the composition and quality of the photographs taken and suggests specific areas for improvement. The evaluation unit will suggest specific areas for improvement if the composition of the photograph is unbalanced or the exposure is inappropriate. The evaluation unit uses AI to evaluate the composition and quality of the photographs and provides feedback to the user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user location information or weather data, Based on the data collected by the aforementioned collection unit, there is an introduction unit that introduces shooting locations, A discrimination unit that identifies the aircraft type or airline from a photo taken by the user, An advice unit provides advice on adjusting camera settings or shooting conditions based on the information determined by the aforementioned determination unit, It includes an evaluation unit that judges the composition or quality of the captured photograph and suggests areas for improvement. A system characterized by the following features.

2. The aforementioned collection unit is The system obtains location information from the user's smartphone or GPS device and weather data from the internet. The system according to feature 1.

3. The aforementioned introduction section is, Based on the data collected by the aforementioned data collection unit, the system proposes the optimal shooting location to the user. The system according to feature 1.

4. The aforementioned discrimination unit is The AI ​​generates data to identify the aircraft type and airline from photos taken by the user. The system according to feature 1.

5. The aforementioned advice section, Based on the information determined by the aforementioned discrimination unit, the system provides advice to optimize camera settings and shooting conditions. The system according to feature 1.

6. The evaluation unit, Evaluate the composition and quality of the photographs taken and suggest specific areas for improvement. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of location and weather data acquisition based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past shooting history and select the optimal data acquisition method. The system according to feature 1.

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