System

Abstract

An object of the system according to the embodiment is to automatically identify the type, sex, and rarity of wild birds and track the growth and behavior pattern of each individual.SOLUTION: A system according to an embodiment includes a fixed camera, an identification unit, and a feature analysis unit. The fixed camera photographs the wild birds. The identification unit identifies a type, sex, and rarity in the region of the wild birds from the video of the wild birds photographed by the fixed camera. The feature analysis unit analyzes a feature of each individual of the wild birds identified by the identification unit and tracks growth and a behavior pattern of the same individual.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of automatically identifying the species, sex, and rarity of wild birds, and tracking the growth and behavioral patterns of each individual bird.

[0005] The system according to the embodiment aims to automatically identify the species, sex, and rarity of wild birds and track the growth and behavioral patterns of each individual bird. [Means for solving the problem]

[0006] The system according to the embodiment includes a fixed camera, an identification unit, and a feature analysis unit. The fixed camera photographs wild birds. The identification unit identifies the species, sex, and local rarity of the wild bird from the video of the wild bird photographed by the fixed camera. The feature analysis unit analyzes the features of each individual wild bird identified by the identification unit and tracks the growth and behavioral patterns of the same individual bird. [Effects of the Invention]

[0007] The system according to the embodiment can automatically identify the species, sex, and rarity of wild birds and track the growth and behavioral patterns of each individual bird. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A wild bird observation system according to an embodiment of the present invention uses fixed cameras to automate the observation of wild birds and display the species, sex, and rarity of the wild birds in a given region. Furthermore, the system can analyze the characteristics of each individual bird and track the growth and behavioral patterns of the same individual. This allows the wild bird observation system to observe wild birds efficiently and in detail. For example, by automatically identifying the species, sex, and rarity of wild birds, observers can easily obtain detailed information. Furthermore, tracking the growth and behavioral patterns of the same individual bird allows for a deeper understanding of the ecology of wild birds.

[0029] A wild bird observation system according to an embodiment includes a fixed camera, an identification unit, and a feature analysis unit. The fixed camera photographs wild birds. For example, a fixed camera installed at a bird feeding area uses motion detection technology to detect the movement of wild birds and begin filming. The fixed camera can also detect the flight patterns and calls of wild birds and begin filming. The identification unit identifies the species, sex, and rarity of the wild bird in the area from the video of the wild bird captured by the fixed camera. For example, the identification unit extracts and identifies the characteristics of the wild bird from the video or image based on a pre-trained wild bird database. The feature analysis unit analyzes the characteristics of each individual wild bird identified by the identification unit and tracks the growth and behavior patterns of the same individual. For example, the feature analysis unit analyzes the feather patterns, body size, behavior patterns, etc. of the wild bird and compares them with past data to determine whether the bird is the same individual. This allows the wild bird observation system to automate wild bird observation, display the species, sex, and rarity, and track the growth and behavior patterns of the same individual. For example, observers can use the system to get a detailed understanding of the growth and behavioral patterns of wild birds.The system also stores observation results in a database and makes it possible to search for information such as period and frequency, making it possible to monitor the growth of the same individual.

[0030] Fixed cameras can detect not only the movements of wild birds, but also their flight patterns or calls and begin filming. Fixed cameras, for example, are equipped with highly sensitive microphones that analyze wild bird calls in real time. If a call pattern is detected, the camera automatically begins recording. For example, changes in specific frequencies or volume are used as triggers. Fixed cameras also analyze movements within the camera's field of view to detect flight patterns. For example, if a specific flight trajectory or speed is detected, the camera begins recording. This allows them to detect not only the movements of wild birds, but also their flight patterns and calls and begin filming.

[0031] With fixed cameras, the generation AI analyzes the surrounding environmental sounds before filming begins, allowing the camera to predict the approach of wild birds and prepare the camera in advance. For example, fixed cameras constantly monitor the surrounding environmental sounds using a microphone installed in the camera, and the generation AI analyzes the calls and flapping sounds of wild birds. For example, if a specific call pattern is detected, the camera begins preparing to record. The generation AI also analyzes changes in environmental sounds and predicts the approach of wild birds. For example, it predicts the approach of wild birds based on changes in the volume and frequency of the surrounding sounds. This makes it possible to predict the approach of wild birds and prepare the camera in advance.

[0032] In addition to fixed cameras, drones can be used to track and film wild birds in flight. In addition to fixed cameras, for example, a system can be built to track and film wild birds in flight by equipping a drone with a high-resolution camera. For example, the drone can automatically track wild birds and film their flight patterns. The drone can also analyze the flight speed and trajectory of wild birds in real time to maintain the optimal filming position. For example, the drone can automatically adjust its position according to the bird's movements and film clear footage. This makes it possible to track and film wild birds in flight.

[0033] Fixed cameras can be equipped with an algorithm that automatically optimizes installation locations, allowing them to be moved to locations where wild birds are frequently seen. For example, an algorithm can be developed for fixed cameras that analyzes the frequency of wild bird appearances and automatically determines the optimal camera installation location. For example, the system can learn the appearance patterns of wild birds based on past data and identify the optimal location. The camera can also be equipped with an automatic movement mechanism that moves it to the optimal location. For example, the camera can move on a rail and be installed in a location where wild birds are frequently seen. This allows the camera to be moved to a location where wild birds are frequently seen.

[0034] Generative AI can analyze wild bird calls and identify the species or gender from the call patterns. For example, generative AI can analyze wild bird calls in real time and build a system that identifies the species and gender from the call patterns. For example, it can identify birds based on specific frequencies or pitches. Generative AI can also analyze the rhythm and volume changes of the calls to improve the accuracy of identification. For example, it can compare the call patterns with a database to identify the species and gender. This makes it possible to identify species and gender from the call patterns.

[0035] Generative AI can improve the accuracy of wild bird identification by taking into account environmental factors such as season or time of day. Generative AI builds a system that improves the accuracy of wild bird identification by taking into account environmental factors such as season and time of day. For example, it learns data on wild birds that appear in specific seasons or time of day. Generative AI also analyzes changes in environmental factors in real time and reflects them in the identification results. For example, it improves identification accuracy based on the behavioral patterns and appearance frequency of wild birds by season. This makes it possible to improve identification accuracy by taking into account environmental factors such as season and time of day.

[0036] The generative AI can upload wild bird identification results to the cloud in real time and share them with other observers. For example, the generative AI can build a system that uploads wild bird identification results to the cloud in real time and shares them with other observers. For example, the identification results can be displayed on a dedicated website or app. The generative AI can also automatically upload the identification results to the cloud, allowing observers to access them in real time. For example, when the identification results are uploaded, a notification is sent to the observer. This allows the identification results to be uploaded to the cloud in real time and shared with other observers.

[0037] The generation AI can automatically generate distribution maps for each region based on the wild bird identification results and provide them to observers. For example, the generation AI can build a system that automatically generates wild bird distribution maps for each region based on the wild bird identification results. For example, it can plot the identification results on a map to visually display the distribution of wild birds. The generation AI can also automatically update the distribution map to provide the latest information. For example, when data on newly identified wild birds is added, the distribution map is automatically updated. This makes it possible to automatically generate distribution maps for each region based on the identification results and provide them to observers.

[0038] Generative AI can analyze not only the feather patterns or body size of wild birds, but also the flight patterns or call characteristics, improving the accuracy of individual identification. For example, generative AI can build a system that not only analyzes the feather patterns or body size of wild birds, but also analyzes the flight patterns and call characteristics. For example, it can collect flight pattern data and use it for individual identification. Generative AI can also analyze the frequency and rhythm of calls to improve the accuracy of individual identification. For example, it can compare call patterns with a database to identify individuals. This allows it to analyze not only the feather patterns or body size, but also the flight patterns and call characteristics, improving the accuracy of individual identification.

[0039] When identifying individuals, generative AI can integrate and analyze not only past data but also data provided by other observers. For example, when identifying individuals, generative AI will build a system that integrates and analyzes not only past data but also data provided by other observers. For example, images and videos taken by observers will be added to a database and used for individual identification. Generative AI will also optimize data formats and integration algorithms to improve identification accuracy. For example, it will convert data in different formats into a unified format and use it for analysis. This will allow it to integrate and analyze not only past data but also data provided by other observers.

[0040] Generative AI can analyze the social relationships or flock structure of wild birds based on the results of individual identification. For example, generative AI can build a system that analyzes the social relationships or flock structure of wild birds based on the results of individual identification. For example, it can analyze the relationship between specific individuals and other individuals. Generative AI can also analyze the structure of the flock and identify the roles of leader and follower. For example, it can determine social relationships based on the positional relationships and behavioral patterns within the flock. This makes it possible to analyze the social relationships or flock structure of wild birds based on the results of individual identification.

[0041] The generating AI can estimate the health or nutritional status of wild birds based on individual identification data and notify observers. For example, the generating AI can build a system that estimates the health and nutritional status of wild birds based on individual identification data. For example, it can analyze weight and feather condition to estimate health status. The generating AI can also analyze changes in health and nutritional status in real time and notify observers. For example, if it detects weight loss or feather discoloration, it will send an alert to the observer. This makes it possible to estimate the health and nutritional status of wild birds based on individual identification data and notify observers.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The wild bird observation system also includes a voice recognition unit that can analyze the calls of wild birds to identify their species and gender. For example, the voice recognition unit analyzes and identifies calls based on specific frequencies and pitches. Furthermore, analyzing the rhythm and volume changes of the calls can improve the accuracy of identification. This makes it possible to identify the species and gender of wild birds from their calls.

[0044] The wild bird observation system also includes an environmental analysis unit, which can improve the accuracy of wild bird identification by taking into account environmental factors such as season and time of day. For example, identification accuracy can be improved by learning data on wild birds that appear during specific seasons and times of day. The environmental analysis unit can also analyze changes in environmental factors in real time and reflect them in the identification results. This makes it possible to improve the accuracy of wild bird identification by taking into account environmental factors such as season and time of day.

[0045] Bird watching systems can also use drones to track and photograph wild birds in flight. For example, a drone equipped with a high-resolution camera can track and photograph wild birds while analyzing their flight patterns in real time. The drone can also analyze the flight speed and trajectory of the wild birds and maintain the optimal shooting position. This makes it possible to capture clear images of wild birds in flight.

[0046] The bird watching system can further use generative AI to analyze the calls of wild birds and identify species and gender from their call patterns. For example, identification can be performed based on specific frequencies and pitches, and the accuracy of identification can be improved by analyzing the rhythm and volume changes of the calls. This makes it possible to identify species and gender from call patterns.

[0047] The bird observation system can also use generative AI to upload bird identification results to the cloud in real time and share them with other observers. For example, the identification results can be displayed on a dedicated website or app, allowing observers to access them in real time. Also, when the identification results are uploaded, observers are notified. This allows the identification results to be uploaded to the cloud in real time and shared with other observers.

[0048] The bird watching system can also use generative AI to automatically generate a distribution map for each region based on the bird identification results and provide it to observers. For example, the identification results can be plotted on a map to visually display the distribution of wild birds. The distribution map can also be automatically updated to provide the latest information. This makes it possible to automatically generate a distribution map for each region based on the identification results and provide it to observers.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: A fixed camera photographs wild birds. For example, a fixed camera installed at a bird feeding area uses motion detection technology to detect the movement of wild birds and begin filming. The fixed camera can also detect the flight patterns and calls of wild birds and begin filming. Step 2: The identification unit identifies the species, sex, and local rarity of wild birds from the images of wild birds captured by the fixed camera. For example, the identification unit extracts and identifies the characteristics of wild birds from the images and videos based on a database of wild birds that has been trained in advance. Step 3: The feature analysis unit analyzes the features of each individual wild bird identified by the identification unit and tracks the growth and behavioral patterns of the same individual. For example, the feature analysis unit analyzes the feather patterns, body size, behavioral patterns, etc. of the wild bird and compares them with past data to determine whether they are the same individual.

[0051] (Example 2) A wild bird observation system according to an embodiment of the present invention uses fixed cameras to automate the observation of wild birds and display the species, sex, and rarity of the wild birds in a given region. Furthermore, the system can analyze the characteristics of each individual bird and track the growth and behavioral patterns of the same individual. This allows the wild bird observation system to observe wild birds efficiently and in detail. For example, by automatically identifying the species, sex, and rarity of wild birds, observers can easily obtain detailed information. Furthermore, tracking the growth and behavioral patterns of the same individual bird allows for a deeper understanding of the ecology of wild birds.

[0052] A wild bird observation system according to an embodiment includes a fixed camera, an identification unit, and a feature analysis unit. The fixed camera photographs wild birds. For example, a fixed camera installed at a bird feeding area uses motion detection technology to detect the movement of wild birds and begin filming. The fixed camera can also detect the flight patterns and calls of wild birds and begin filming. The identification unit identifies the species, sex, and rarity of the wild bird in the area from the video of the wild bird captured by the fixed camera. For example, the identification unit extracts and identifies the characteristics of the wild bird from the video or image based on a pre-trained wild bird database. The feature analysis unit analyzes the characteristics of each individual wild bird identified by the identification unit and tracks the growth and behavior patterns of the same individual. For example, the feature analysis unit analyzes the feather patterns, body size, behavior patterns, etc. of the wild bird and compares them with past data to determine whether the bird is the same individual. This allows the wild bird observation system to automate wild bird observation, display the species, sex, and rarity, and track the growth and behavior patterns of the same individual. For example, observers can use the system to get a detailed understanding of the growth and behavioral patterns of wild birds.The system also stores observation results in a database and makes it possible to search for information such as period and frequency, making it possible to monitor the growth of the same individual.

[0053] Fixed cameras can detect not only the movements of wild birds, but also their flight patterns or calls and begin filming. Fixed cameras, for example, are equipped with highly sensitive microphones that analyze wild bird calls in real time. If a call pattern is detected, the camera automatically begins recording. For example, changes in specific frequencies or volume are used as triggers. Fixed cameras also analyze movements within the camera's field of view to detect flight patterns. For example, if a specific flight trajectory or speed is detected, the camera begins recording. This allows them to detect not only the movements of wild birds, but also their flight patterns and calls and begin filming.

[0054] With fixed cameras, the generation AI analyzes the surrounding environmental sounds before filming begins, allowing the camera to predict the approach of wild birds and prepare the camera in advance. For example, fixed cameras constantly monitor the surrounding environmental sounds using a microphone installed in the camera, and the generation AI analyzes the calls and flapping sounds of wild birds. For example, if a specific call pattern is detected, the camera begins preparing to record. The generation AI also analyzes changes in environmental sounds and predicts the approach of wild birds. For example, it predicts the approach of wild birds based on changes in the volume and frequency of the surrounding sounds. This makes it possible to predict the approach of wild birds and prepare the camera in advance.

[0055] The fixed camera uses an emotion estimation function to estimate stress or vigilance from the behavior of wild birds, making it possible to photograph wild birds at times when they are relaxed. For example, the fixed camera analyzes the behavioral patterns of wild birds and develops an algorithm to detect movements that indicate stress or vigilance. For example, sudden movements and frequent flapping of wings are used as indicators of stress. The emotion estimation function also analyzes changes in the facial expressions and calls of wild birds to estimate their state of relaxation. For example, it determines a state of relaxation based on changes in the tone and frequency of their calls. This makes it possible to photograph wild birds at times when they are relaxed.

[0056] In addition to fixed cameras, drones can be used to track and film wild birds in flight. In addition to fixed cameras, for example, a system can be built to track and film wild birds in flight by equipping a drone with a high-resolution camera. For example, the drone can automatically track wild birds and film their flight patterns. The drone can also analyze the flight speed and trajectory of wild birds in real time to maintain the optimal filming position. For example, the drone can automatically adjust its position according to the bird's movements and film clear footage. This makes it possible to track and film wild birds in flight.

[0057] Fixed cameras can be equipped with an algorithm that automatically optimizes installation locations, allowing them to be moved to locations where wild birds are frequently seen. For example, an algorithm can be developed for fixed cameras that analyzes the frequency of wild bird appearances and automatically determines the optimal camera installation location. For example, the system can learn the appearance patterns of wild birds based on past data and identify the optimal location. The camera can also be equipped with an automatic movement mechanism that moves it to the optimal location. For example, the camera can move on a rail and be installed in a location where wild birds are frequently seen. This allows the camera to be moved to a location where wild birds are frequently seen.

[0058] The fixed camera can use the emotion estimation function to customize the content of footage based on the observer's interests and concerns, and prioritize filming of specific wild birds or behaviors. For example, the fixed camera can analyze the observer's interests and concerns to create a system that prioritizes filming of specific wild birds and behaviors. For example, if the observer is interested in a particular wild bird species, the camera can automatically start recording when that wild bird is captured on camera. The emotion estimation function can also analyze the observer's emotional responses and identify their interests and concerns. For example, it can analyze changes in the observer's facial expressions and voice to determine what they are interested in. This allows the content of footage to be customized based on the observer's interests and concerns.

[0059] Generative AI can analyze wild bird calls and identify the species or gender from the call patterns. For example, generative AI can analyze wild bird calls in real time and build a system that identifies the species and gender from the call patterns. For example, it can identify birds based on specific frequencies or pitches. Generative AI can also analyze the rhythm and volume changes of the calls to improve the accuracy of identification. For example, it can compare the call patterns with a database to identify the species and gender. This makes it possible to identify species and gender from the call patterns.

[0060] Generative AI can improve the accuracy of wild bird identification by taking into account environmental factors such as season or time of day. Generative AI builds a system that improves the accuracy of wild bird identification by taking into account environmental factors such as season and time of day. For example, it learns data on wild birds that appear in specific seasons or time of day. Generative AI also analyzes changes in environmental factors in real time and reflects them in the identification results. For example, it improves identification accuracy based on the behavioral patterns and appearance frequency of wild birds by season. This makes it possible to improve identification accuracy by taking into account environmental factors such as season and time of day.

[0061] The generative AI can use the emotion estimation function to infer the emotional state of a wild bird from its facial expression or behavior and add this information to the identification results. For example, the generative AI analyzes the facial expressions and behavior of wild birds to develop algorithms that infer their emotional state. For example, the state of their wings being spread or the tone of their calls are used as indicators of emotion. The emotion estimation function also analyzes the behavioral patterns of wild birds and tracks changes in their emotions. For example, it determines their emotional state based on changes in their behavior or calls. This makes it possible to infer the emotional state of wild birds from their facial expressions and behavior and add this information to the identification results.

[0062] The generative AI can upload wild bird identification results to the cloud in real time and share them with other observers. For example, the generative AI can build a system that uploads wild bird identification results to the cloud in real time and shares them with other observers. For example, the identification results can be displayed on a dedicated website or app. The generative AI can also automatically upload the identification results to the cloud, allowing observers to access them in real time. For example, when the identification results are uploaded, a notification is sent to the observer. This allows the identification results to be uploaded to the cloud in real time and shared with other observers.

[0063] The generation AI can automatically generate distribution maps for each region based on the wild bird identification results and provide them to observers. For example, the generation AI can build a system that automatically generates wild bird distribution maps for each region based on the wild bird identification results. For example, it can plot the identification results on a map to visually display the distribution of wild birds. The generation AI can also automatically update the distribution map to provide the latest information. For example, when data on newly identified wild birds is added, the distribution map is automatically updated. This makes it possible to automatically generate distribution maps for each region based on the identification results and provide them to observers.

[0064] Using its emotion estimation function, the generative AI can prioritize identifying the species or behavior of wild birds that interest the observer most and notify the observer. For example, the generative AI can analyze the observer's emotional responses and build a system that prioritizes identifying the species and behavior of wild birds that interest the observer most. For example, if the observer is excited about a particular wild bird species, it will prioritize identifying that wild bird. The emotion estimation function also analyzes the observer's facial expressions and voice changes to determine what the observer is interested in. For example, it can identify the wild bird or behavior that the observer is interested in based on changes in the observer's facial expression and tone of voice. This allows the generative AI to prioritize identifying and notify the species and behavior of wild birds that interest the observer most.

[0065] Generative AI can analyze not only the feather patterns or body size of wild birds, but also the flight patterns or call characteristics, improving the accuracy of individual identification. For example, generative AI can build a system that not only analyzes the feather patterns or body size of wild birds, but also analyzes the flight patterns and call characteristics. For example, it can collect flight pattern data and use it for individual identification. Generative AI can also analyze the frequency and rhythm of calls to improve the accuracy of individual identification. For example, it can compare call patterns with a database to identify individuals. This allows it to analyze not only the feather patterns or body size, but also the flight patterns and call characteristics, improving the accuracy of individual identification.

[0066] When identifying individuals, generative AI can integrate and analyze not only past data but also data provided by other observers. For example, when identifying individuals, generative AI will build a system that integrates and analyzes not only past data but also data provided by other observers. For example, images and videos taken by observers will be added to a database and used for individual identification. Generative AI will also optimize data formats and integration algorithms to improve identification accuracy. For example, it will convert data in different formats into a unified format and use it for analysis. This will allow it to integrate and analyze not only past data but also data provided by other observers.

[0067] Using the emotion estimation function, the generative AI can infer the emotional state of wild birds from their behavioral patterns and track changes in their emotions. For example, the generative AI analyzes the behavioral patterns of wild birds and develops algorithms to infer their emotional state. For example, the frequency of wing flapping and the tone of their calls are used as indicators of emotion. The emotion estimation function also analyzes changes in wild birds' behavior in real time and tracks changes in their emotions. For example, it determines the emotional state based on changes in behavior and calls. This makes it possible to infer the emotional state of wild birds from their behavioral patterns and track changes in their emotions.

[0068] Generative AI can analyze the social relationships or flock structure of wild birds based on the results of individual identification. For example, generative AI can build a system that analyzes the social relationships or flock structure of wild birds based on the results of individual identification. For example, it can analyze the relationship between specific individuals and other individuals. Generative AI can also analyze the structure of the flock and identify the roles of leader and follower. For example, it can determine social relationships based on the positional relationships and behavioral patterns within the flock. This makes it possible to analyze the social relationships or flock structure of wild birds based on the results of individual identification.

[0069] The generating AI can estimate the health or nutritional status of wild birds based on individual identification data and notify observers. For example, the generating AI can build a system that estimates the health and nutritional status of wild birds based on individual identification data. For example, it can analyze weight and feather condition to estimate health status. The generating AI can also analyze changes in health and nutritional status in real time and notify observers. For example, if it detects weight loss or feather discoloration, it will send an alert to the observer. This makes it possible to estimate the health and nutritional status of wild birds based on individual identification data and notify observers.

[0070] The generation AI can use the emotion estimation function to prioritize tracking the growth or behavior patterns of individuals that the observer is particularly interested in and generate reports. For example, the generation AI can analyze the observer's emotional reactions and build a system that prioritizes tracking the growth and behavior patterns of individuals that the observer is particularly interested in. For example, if the observer is excited about a particular individual, it will prioritize tracking that individual. The emotion estimation function can also analyze changes in the observer's facial expressions and voice to identify individuals that the observer is interested in. For example, it can determine which individuals the observer is interested in based on changes in the observer's facial expressions and tone of voice. This allows the generation AI to prioritize tracking the growth and behavior patterns of individuals that the observer is particularly interested in and generate reports.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The wild bird observation system also includes a voice recognition unit that can analyze the calls of wild birds to identify their species and gender. For example, the voice recognition unit analyzes and identifies calls based on specific frequencies and pitches. Furthermore, analyzing the rhythm and volume changes of the calls can improve the accuracy of identification. This makes it possible to identify the species and gender of wild birds from their calls.

[0073] The wild bird observation system also includes an environmental analysis unit, which can improve the accuracy of wild bird identification by taking into account environmental factors such as season and time of day. For example, identification accuracy can be improved by learning data on wild birds that appear during specific seasons and times of day. The environmental analysis unit can also analyze changes in environmental factors in real time and reflect them in the identification results. This makes it possible to improve the accuracy of wild bird identification by taking into account environmental factors such as season and time of day.

[0074] Bird watching systems can also use drones to track and photograph wild birds in flight. For example, a drone equipped with a high-resolution camera can track and photograph wild birds while analyzing their flight patterns in real time. The drone can also analyze the flight speed and trajectory of the wild birds and maintain the optimal shooting position. This makes it possible to capture clear images of wild birds in flight.

[0075] The bird watching system also uses an emotion estimation function to estimate stress and alertness from the behavior of wild birds, allowing it to capture images when the birds are relaxed. For example, sudden movements and frequent flapping of wings can be used as indicators of stress to estimate a state of relaxation. It can also analyze changes in the tone and frequency of birds' calls to determine a state of relaxation. This makes it possible to capture images when the birds are relaxed.

[0076] The bird watching system can also use emotion estimation to customize the content of footage based on the observer's interests and focus, prioritizing the capture of specific birds and their behaviors. For example, if the observer is interested in a particular bird species, the system will automatically start recording when that bird appears on screen. It can also analyze changes in the observer's facial expressions and voice to determine what the observer is interested in. This makes it possible to customize the content of footage based on the observer's interests and focus.

[0077] The bird watching system can further use generative AI to analyze the calls of wild birds and identify species and gender from their call patterns. For example, identification can be performed based on specific frequencies and pitches, and the accuracy of identification can be improved by analyzing the rhythm and volume changes of the calls. This makes it possible to identify species and gender from call patterns.

[0078] The bird observation system can also use generative AI to upload bird identification results to the cloud in real time and share them with other observers. For example, the identification results can be displayed on a dedicated website or app, allowing observers to access them in real time. Also, when the identification results are uploaded, observers are notified. This allows the identification results to be uploaded to the cloud in real time and shared with other observers.

[0079] The bird watching system can also use generative AI to automatically generate a distribution map for each region based on the bird identification results and provide it to observers. For example, the identification results can be plotted on a map to visually display the distribution of wild birds. The distribution map can also be automatically updated to provide the latest information. This makes it possible to automatically generate a distribution map for each region based on the identification results and provide it to observers.

[0080] The bird watching system can also use emotion estimation to prioritize and notify the observer of the type and behavior of wild birds that interest them most. For example, if the observer is excited about a particular wild bird species, it will prioritize identifying that bird. It can also analyze changes in the observer's facial expressions and voice to determine what the observer is interested in. This makes it possible to prioritize and notify the observer of the type and behavior of wild birds that interest them most.

[0081] The bird observation system can also use emotion estimation to prioritize tracking and generate reports on the growth and behavioral patterns of individuals that the observer is particularly interested in. For example, if the observer is excited about a particular bird, that individual will be prioritized for tracking. The system can also analyze changes in the observer's facial expressions and voice to determine which individual the observer is interested in. This makes it possible to prioritize tracking and generate reports on the growth and behavioral patterns of individuals that the observer is particularly interested in.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: A fixed camera photographs wild birds. For example, a fixed camera installed at a bird feeding area uses motion detection technology to detect the movement of wild birds and begin filming. The fixed camera can also detect the flight patterns and calls of wild birds and begin filming. Step 2: The identification unit identifies the species, sex, and local rarity of wild birds from the images of wild birds captured by the fixed camera. For example, the identification unit extracts and identifies the characteristics of wild birds from the images and videos based on a database of wild birds that has been trained in advance. Step 3: The feature analysis unit analyzes the features of each individual wild bird identified by the identification unit and tracks the growth and behavioral patterns of the same individual. For example, the feature analysis unit analyzes the feather patterns, body size, behavioral patterns, etc. of the wild bird and compares them with past data to determine whether they are the same individual.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with fixed cameras, The fixed camera is Photographing wild birds, An identification unit is provided, The identification unit Identifying the type, sex, and rarity of the wild bird in the area from the image of the wild bird captured by the fixed camera; A feature analysis unit is provided, The feature analysis unit The characteristics of each individual wild bird identified by the identification unit are analyzed, and the growth and behavioral patterns of the same individual are tracked. A system characterized by:

2. The fixed camera is Not only the movement of the wild bird, but also the flight pattern or call of the bird is detected and recording begins.

2. The system of claim 1.

3. In addition to the fixed cameras, Using a drone to track and photograph the wild birds in flight 2. The system of claim 1.

4. The generating AI is Analyzing the calls of the wild birds and identifying the species or sex from the call patterns.

2. The system of claim 1.

5. The generating AI is Analyzing not only the feather patterns or the body size of the wild bird but also the flight patterns or the characteristics of the calls of the wild bird improves the accuracy of individual identification.

2. The system of claim 1.

6. The fixed camera is Stress or vigilance of the wild bird is estimated from the behavior of the wild bird, and the bird is photographed when it is relaxed.

2. The system of claim 1.

7. The generating AI is Estimating the emotional state of the wild bird from its facial expression or behavior and adding that information to the classification result 2. The system of claim 1.

8. The generating AI is Prioritizing tracking and generating reports on the growth or behavioral patterns of the individuals that are of particular interest to the observer.

2. The system of claim 1.

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