System

Abstract

An object of the system according to the embodiment is to detect an abnormal behavior of a dog in real time and promptly notify the owner of the abnormal behavior.SOLUTION: A system according to an embodiment includes a sensor, a camera, a AI processing unit, and a notifying unit. The sensor and camera monitor the behavior pattern of the dog. The data-processing unit processes AI acquired by the sensor and the camera. The notifying part notifies the owner of the abnormal behavior identified by the AI processing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to detect abnormal behavior in dogs in real time and respond quickly.

[0005] The system according to the embodiment aims to detect abnormal behavior in dogs in real time and promptly notify the owner. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor, a camera, an AI processing unit, and a notification unit. The sensor and the camera monitor the dog's behavioral patterns. The AI ​​processing unit processes data acquired by the sensor and the camera. The notification unit notifies the owner of abnormal behavior identified by the AI ​​processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect abnormal behavior in dogs in real time and quickly notify the owner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dog behavior abnormality detection system according to an embodiment of the present invention is a system that monitors dog behavior patterns in real time, uses a generation AI to learn normal behavior patterns, identifies abnormal behavior, and notifies the owner. This enables the dog behavior abnormality detection system to detect abnormal dog behavior early and promptly notify the owner.

[0029] A dog behavior abnormality detection system according to an embodiment includes a sensor, a camera, an AI processing unit, and a notification unit. The sensor monitors the dog's behavioral patterns. For example, the sensor detects the dog's movements and sounds and collects the data. The camera records the dog's behavior. For example, the camera records the dog's movements and barking sounds. The AI ​​processing unit processes the data acquired by the sensor and the camera. For example, the AI ​​processing unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to learn the dog's normal behavioral patterns and identify abnormal behavior. The notification unit notifies the owner of abnormal behavior identified by the AI ​​processing unit. For example, the notification unit sends a notification to the owner via a dedicated mobile app or web platform. This enables the dog behavior abnormality detection system to detect abnormal dog behavior in real time and notify the owner, enabling a prompt response.

[0030] The sensor may include a biosensor that monitors the dog's body temperature and heart rate. For example, a biosensor that can be attached to a dog's collar may be developed to monitor the dog's body temperature and heart rate in real time. This allows behavioral patterns and physiological data to be integrated and analyzed, improving the accuracy of detecting abnormal behavior. By monitoring the dog's body temperature and heart rate, the accuracy of detecting abnormal behavior can be improved.

[0031] The camera can record a dog's movements in detail using 3D motion capture technology. For example, a system can be developed that records a dog's movements in detail using 3D motion capture technology, making it possible to detect even the most minute changes in movement. For example, joint movement and walking patterns can be analyzed. This allows for detailed recording of a dog's movements, making it possible to detect even the most minute changes in movement.

[0032] The sensors and cameras can be expanded to monitor the behavioral patterns of other pets. For example, the sensors and cameras expand the device to monitor the behavioral patterns of pets other than dogs (e.g., cats and birds). For example, they record the movements of cats and the sounds of birds. This allows the behavioral patterns of other pets to be monitored, thereby expanding the range of abnormal behavior detection.

[0033] The sensors and cameras can also be used in outdoor environments, recording behavioral patterns during walks in real time. For example, sensors and cameras can be used in monitoring devices that can be used in outdoor environments, developing a system that records behavioral patterns during walks in real time. For example, a system equipped with a GPS function can track a dog's path. This allows behavioral patterns to be recorded in real time even in outdoor environments, expanding the range of abnormal behavior detection.

[0034] The AI ​​processing unit can take into account past health checkup data and medical history when learning a dog's behavioral patterns. For example, when learning a dog's behavioral patterns, the AI ​​processing unit takes into account past health checkup data and medical history to build an anomaly detection model. For example, it predicts specific abnormal behavior based on past medical history. By taking past health checkup data and medical history into account, the accuracy of detecting abnormal behavior is improved.

[0035] The AI ​​processing unit can share the dog's behavioral data on the cloud and compare it with data from other dogs. The AI ​​processing unit, for example, builds a system that shares the dog's behavioral data on the cloud and compares it with data from other dogs. This improves the accuracy of detecting abnormal behavior. By comparing it with data from other dogs, the accuracy of detecting abnormal behavior improves.

[0036] The AI ​​processing unit can be expanded to learn the behavioral patterns of other animal species. For example, the AI ​​processing unit expands the AI ​​model so that it can learn the behavioral patterns of other animal species. For example, it learns the behavioral patterns of cats and birds. This makes it possible to learn the behavioral patterns of other animal species, thereby improving the accuracy of detecting abnormal behavior.

[0037] The AI ​​processing unit can also incorporate data on the owner's lifestyle and environment when learning behavioral patterns. For example, the AI ​​processing unit can incorporate data on the owner's lifestyle and environment when learning behavioral patterns, developing a system that can perform comprehensive anomaly detection. For example, it can associate the owner's lifestyle rhythm with the dog's behavioral patterns. By incorporating data on the owner's lifestyle and environment, this will enable more comprehensive anomaly detection.

[0038] The AI ​​processing unit can identify abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations. The AI ​​processing unit will develop a system that identifies abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations. For example, it will analyze changes in behavioral patterns by season. This will improve the accuracy of identifying abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations.

[0039] The notification unit can notify the owner's smart device of the abnormal behavior identification result in real time. For example, the notification unit will develop a system that notifies the owner's smart device of the abnormal behavior identification result in real time. For example, the notification will be sent via a smartphone app. This allows the owner to respond immediately by notifying the owner of the abnormal behavior identification result in real time.

[0040] The notification unit can share the abnormal behavior identification results with veterinarians and pet trainers. The notification unit develops a system for sharing the abnormal behavior identification results with veterinarians and pet trainers, for example. For example, the data can be shared through an online platform. By sharing the abnormal behavior identification results, professional advice can be received.

[0041] The notification unit can link the abnormal behavior identification results with a smart home system and automatically adjust the environment. For example, the notification unit can link the abnormal behavior identification results with a smart home system and develop a system that automatically adjusts the environment. For example, if a dog feels anxious, relaxing music can be played. This automatically adjusts the environment and reduces the dog's stress.

[0042] The AI ​​processing unit can refer to past data and similar cases to identify the cause of abnormal behavior. For example, the AI ​​processing unit will develop a system that refers to past data and similar cases to identify the cause of abnormal behavior and provides specific guidance. For example, it will refer to past abnormal behavior and countermeasures. This makes it easier to identify the cause of abnormal behavior by referring to past data and similar cases.

[0043] The notification unit can provide guidance and advice as video or interactive content. The notification unit will develop a system that provides guidance and advice as video or interactive content, for example. For example, measures to deal with abnormal behaviors are explained in video. By providing the information as video or interactive content, it becomes easier for pet owners to understand.

[0044] The notification unit can be expanded to apply guidance and advice to other pets (e.g., cats and birds). The notification unit will develop a system that expands the guidance and advice so that it can be applied to other pets (e.g., cats and birds). For example, advice on abnormal behavior in cats will be provided. This will allow the measures to address abnormal behavior to be applied to a wider range of pets.

[0045] The notification unit can provide guidance and advice in cooperation with local pet care services, enabling the user to receive professional support. The notification unit, for example, develops a system for providing guidance and advice in cooperation with local pet care services, enabling the user to receive professional support. For example, the notification unit may provide guidance and advice in cooperation with local veterinarians. This allows the user to receive professional support by linking with local pet care services.

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

[0047] The dog behavior abnormality detection system can further include a voice recognition unit. The voice recognition unit analyzes the dog's barks and the owner's voice to identify specific voice patterns. For example, abnormal behavior can be detected when the dog makes a specific bark. It can also analyze the tone and content of the owner's voice to identify factors that affect the dog's behavior. This makes it possible to improve the accuracy of detecting abnormal behavior by utilizing voice data.

[0048] The dog behavior abnormality detection system can further include an environmental sensor. The environmental sensor collects environmental data such as temperature, humidity, and illuminance, and analyzes it in association with the dog's behavioral patterns. For example, it can detect abnormal behavior in a dog when the room temperature is too high. It can also analyze behavioral patterns in low-illuminance environments to identify the cause of abnormal behavior. This makes it possible to detect abnormal behavior while taking environmental data into consideration.

[0049] The dog behavior abnormality detection system can further include a feedback unit. When abnormal behavior is detected, the feedback unit suggests specific measures to the owner. For example, if the dog is feeling stressed, it can suggest relaxation methods. It can also identify the cause of the abnormal behavior and provide training methods based on that. This makes it possible to support the owner in taking appropriate measures.

[0050] The dog behavior abnormality detection system can further include a health management unit. The health management unit monitors the dog's health condition and notifies the owner if an abnormality is detected. For example, it can monitor fluctuations in weight and appetite and issue an alert if an abnormality is detected. It can also provide reminders for regular health checkups. This supports the dog's health management and enables early detection of abnormalities.

[0051] The dog behavior abnormality detection system can further include an exercise amount measurement unit. The exercise amount measurement unit measures the dog's exercise amount in real time and provides advice on maintaining an appropriate amount of exercise. For example, if abnormal behavior is caused by a lack of exercise, an appropriate exercise plan can be suggested. Also, if abnormal behavior is caused by excessive exercise, advice can be provided on adjusting the amount of exercise. This can support a healthy lifestyle for dogs.

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

[0053] Step 1: The sensor monitors the dog's behavioral patterns. For example, the sensor detects the dog's movements and sounds and collects that data. The camera records the dog's behavior. For example, the camera records the dog's movements and barking sounds. Step 2: The AI ​​processing unit processes the data acquired by the sensors and cameras. For example, the AI ​​processing unit uses generative AI (e.g., text generation AI or multimodal generation AI) to learn the dog's normal behavior patterns and identify abnormal behavior. Step 3: The notification unit notifies the owner of the abnormal behavior identified by the AI ​​processing unit. For example, the notification unit sends a notification to the owner via a dedicated mobile app or web platform.

[0054] (Example 2) The dog behavior abnormality detection system according to an embodiment of the present invention is a system that monitors dog behavior patterns in real time, uses a generation AI to learn normal behavior patterns, identifies abnormal behavior, and notifies the owner. This enables the dog behavior abnormality detection system to detect abnormal dog behavior early and promptly notify the owner.

[0055] A dog behavior abnormality detection system according to an embodiment includes a sensor, a camera, an AI processing unit, and a notification unit. The sensor monitors the dog's behavioral patterns. For example, the sensor detects the dog's movements and sounds and collects the data. The camera records the dog's behavior. For example, the camera records the dog's movements and barking sounds. The AI ​​processing unit processes the data acquired by the sensor and the camera. For example, the AI ​​processing unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to learn the dog's normal behavioral patterns and identify abnormal behavior. The notification unit notifies the owner of abnormal behavior identified by the AI ​​processing unit. For example, the notification unit sends a notification to the owner via a dedicated mobile app or web platform. This enables the dog behavior abnormality detection system to detect abnormal dog behavior in real time and notify the owner, enabling a prompt response.

[0056] The sensor may include a biosensor that monitors the dog's body temperature and heart rate. For example, a biosensor that can be attached to a dog's collar may be developed to monitor the dog's body temperature and heart rate in real time. This allows behavioral patterns and physiological data to be integrated and analyzed, improving the accuracy of detecting abnormal behavior. By monitoring the dog's body temperature and heart rate, the accuracy of detecting abnormal behavior can be improved.

[0057] The camera can record a dog's movements in detail using 3D motion capture technology. For example, a system can be developed that records a dog's movements in detail using 3D motion capture technology, making it possible to detect even the most minute changes in movement. For example, joint movement and walking patterns can be analyzed. This allows for detailed recording of a dog's movements, making it possible to detect even the most minute changes in movement.

[0058] The AI ​​processing unit may include an emotion estimation function that estimates a dog's emotional state from its facial expressions and tone of voice. For example, the AI ​​processing unit may develop an emotion estimation function that analyzes a dog's facial expressions and tone of voice, and analyze the relationship between behavioral patterns and emotions. For example, it may identify behavioral patterns when a dog is feeling anxious. This makes it possible to detect abnormal behavior based on emotions by estimating the dog's emotional state.

[0059] The sensors and cameras can be expanded to monitor the behavioral patterns of other pets. For example, the sensors and cameras expand the device to monitor the behavioral patterns of pets other than dogs (e.g., cats and birds). For example, they record the movements of cats and the sounds of birds. This allows the behavioral patterns of other pets to be monitored, thereby expanding the range of abnormal behavior detection.

[0060] The sensors and cameras can also be used in outdoor environments, recording behavioral patterns during walks in real time. For example, sensors and cameras can be used in monitoring devices that can be used in outdoor environments, developing a system that records behavioral patterns during walks in real time. For example, a system equipped with a GPS function can track a dog's path. This allows behavioral patterns to be recorded in real time even in outdoor environments, expanding the range of abnormal behavior detection.

[0061] The AI ​​processing unit may include an emotion estimation function that simultaneously monitors the owner's emotional state and analyzes the interaction between the pet and the owner. For example, the AI ​​processing unit may use the emotion estimation function to simultaneously monitor the owner's emotional state and develop a system that analyzes the interaction between the pet and the owner. For example, the system may analyze the relationship between the owner's stress level and the dog's behavioral patterns. This makes it possible to analyze the interaction between the pet and the owner by monitoring the owner's emotional state.

[0062] The AI ​​processing unit can take into account past health checkup data and medical history when learning a dog's behavioral patterns. For example, when learning a dog's behavioral patterns, the AI ​​processing unit takes into account past health checkup data and medical history to build an anomaly detection model. For example, it predicts specific abnormal behavior based on past medical history. By taking past health checkup data and medical history into account, the accuracy of detecting abnormal behavior is improved.

[0063] The AI ​​processing unit can share the dog's behavioral data on the cloud and compare it with data from other dogs. The AI ​​processing unit, for example, builds a system that shares the dog's behavioral data on the cloud and compares it with data from other dogs. This improves the accuracy of detecting abnormal behavior. By comparing it with data from other dogs, the accuracy of detecting abnormal behavior improves.

[0064] The AI ​​processing unit can use the emotion estimation function to integrate the dog's behavioral data and emotional data, and detect abnormal behavior based on the dog's emotional state. The AI ​​processing unit, for example, will use the emotion estimation function to develop a system that integrates the dog's behavioral data and emotional data, and detects abnormal behavior based on the dog's emotional state. For example, it will identify abnormal behavior when the dog is feeling anxious. This will enable the detection of abnormal behavior based on the dog's emotional state.

[0065] The AI ​​processing unit can be expanded to learn the behavioral patterns of other animal species. For example, the AI ​​processing unit expands the AI ​​model so that it can learn the behavioral patterns of other animal species. For example, it learns the behavioral patterns of cats and birds. This makes it possible to learn the behavioral patterns of other animal species, thereby improving the accuracy of detecting abnormal behavior.

[0066] The AI ​​processing unit can also incorporate data on the owner's lifestyle and environment when learning behavioral patterns. For example, the AI ​​processing unit can incorporate data on the owner's lifestyle and environment when learning behavioral patterns, developing a system that can perform comprehensive anomaly detection. For example, it can associate the owner's lifestyle rhythm with the dog's behavioral patterns. By incorporating data on the owner's lifestyle and environment, this will enable more comprehensive anomaly detection.

[0067] The AI ​​processing unit uses the emotion estimation function to learn the owner's emotional data and detect abnormal behavior based on the relationship between the pet and its owner. For example, the AI ​​processing unit will use the emotion estimation function to learn the owner's emotional data and develop a system that detects abnormal behavior based on the relationship between the pet and its owner. For example, it will analyze the impact of the owner's stress on the dog's behavior. By learning the owner's emotional data, it will be possible to detect abnormal behavior based on the relationship between the pet and its owner.

[0068] The AI ​​processing unit can identify abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations. The AI ​​processing unit will develop a system that identifies abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations. For example, it will analyze changes in behavioral patterns by season. This will improve the accuracy of identifying abnormal behavior by taking into account the dog's past behavioral history and seasonal fluctuations.

[0069] The notification unit can notify the owner's smart device of the abnormal behavior identification result in real time. For example, the notification unit will develop a system that notifies the owner's smart device of the abnormal behavior identification result in real time. For example, the notification will be sent via a smartphone app. This allows the owner to respond immediately by notifying the owner of the abnormal behavior identification result in real time.

[0070] The notification unit can use the emotion estimation function to simultaneously notify the dog's emotional state when identifying abnormal behavior. For example, the notification unit will develop a system that uses the emotion estimation function to simultaneously notify the dog's emotional state when identifying abnormal behavior. For example, it will notify the dog if it is feeling anxious. This allows the owner to provide emotional care by simultaneously notifying the dog's emotional state.

[0071] The notification unit can share the abnormal behavior identification results with veterinarians and pet trainers. The notification unit develops a system for sharing the abnormal behavior identification results with veterinarians and pet trainers, for example. For example, the data can be shared through an online platform. By sharing the abnormal behavior identification results, professional advice can be received.

[0072] The notification unit can link the abnormal behavior identification results with a smart home system and automatically adjust the environment. For example, the notification unit can link the abnormal behavior identification results with a smart home system and develop a system that automatically adjusts the environment. For example, if a dog feels anxious, relaxing music can be played. This automatically adjusts the environment and reduces the dog's stress.

[0073] The notification unit can also notify the owner of the emotional state using the emotion estimation function. For example, the notification unit can develop a system that also notifies the owner of the emotional state using the emotion estimation function, thereby promoting care for both the pet and the owner. For example, a notification is made when the owner is feeling stressed. This allows for notification of the owner's emotional state, thereby promoting care for both the pet and the owner.

[0074] The AI ​​processing unit can refer to past data and similar cases to identify the cause of abnormal behavior. For example, the AI ​​processing unit will develop a system that refers to past data and similar cases to identify the cause of abnormal behavior and provides specific guidance. For example, it will refer to past abnormal behavior and countermeasures. This makes it easier to identify the cause of abnormal behavior by referring to past data and similar cases.

[0075] The notification unit can provide guidance and advice as video or interactive content. The notification unit will develop a system that provides guidance and advice as video or interactive content, for example. For example, measures to deal with abnormal behaviors are explained in video. By providing the information as video or interactive content, it becomes easier for pet owners to understand.

[0076] The notification unit can be expanded to apply guidance and advice to other pets (e.g., cats and birds). The notification unit will develop a system that expands the guidance and advice so that it can be applied to other pets (e.g., cats and birds). For example, advice on abnormal behavior in cats will be provided. This will allow the measures to address abnormal behavior to be applied to a wider range of pets.

[0077] The notification unit can provide guidance and advice in cooperation with local pet care services, enabling the user to receive professional support. The notification unit, for example, develops a system for providing guidance and advice in cooperation with local pet care services, enabling the user to receive professional support. For example, the notification unit may provide guidance and advice in cooperation with local veterinarians. This allows the user to receive professional support by linking with local pet care services.

[0078] The notification unit can use the emotion estimation function to suggest relaxation methods and stress relief methods based on the emotional state of the owner. The notification unit, for example, uses the emotion estimation function to develop a system that suggests relaxation methods and stress relief methods based on the emotional state of the owner. For example, the notification unit suggests relaxation methods when the owner is feeling anxious. This makes it possible to reduce the stress of the owner by suggesting relaxation methods and stress relief methods based on the emotional state of the owner.

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

[0080] The dog behavior abnormality detection system can further include a voice recognition unit. The voice recognition unit analyzes the dog's barks and the owner's voice to identify specific voice patterns. For example, abnormal behavior can be detected when the dog makes a specific bark. It can also analyze the tone and content of the owner's voice to identify factors that affect the dog's behavior. This makes it possible to improve the accuracy of detecting abnormal behavior by utilizing voice data.

[0081] The dog behavior abnormality detection system can further include an environmental sensor. The environmental sensor collects environmental data such as temperature, humidity, and illuminance, and analyzes it in association with the dog's behavioral patterns. For example, it can detect abnormal behavior in a dog when the room temperature is too high. It can also analyze behavioral patterns in low-illuminance environments to identify the cause of abnormal behavior. This makes it possible to detect abnormal behavior while taking environmental data into consideration.

[0082] The dog behavior abnormality detection system can further include a feedback unit. When abnormal behavior is detected, the feedback unit suggests specific measures to the owner. For example, if the dog is feeling stressed, it can suggest relaxation methods. It can also identify the cause of the abnormal behavior and provide training methods based on that. This makes it possible to support the owner in taking appropriate measures.

[0083] The dog behavior abnormality detection system can further include a health management unit. The health management unit monitors the dog's health condition and notifies the owner if an abnormality is detected. For example, it can monitor fluctuations in weight and appetite and issue an alert if an abnormality is detected. It can also provide reminders for regular health checkups. This supports the dog's health management and enables early detection of abnormalities.

[0084] The dog behavior abnormality detection system can further include an exercise amount measurement unit. The exercise amount measurement unit measures the dog's exercise amount in real time and provides advice on maintaining an appropriate amount of exercise. For example, if abnormal behavior is caused by a lack of exercise, an appropriate exercise plan can be suggested. Also, if abnormal behavior is caused by excessive exercise, advice can be provided on adjusting the amount of exercise. This can support a healthy lifestyle for dogs.

[0085] The dog behavior anomaly detection system can further use emotion estimation functionality to analyze and correlate a dog's behavioral patterns with its emotional state. For example, it can identify behavioral patterns when a dog is feeling anxious and suggest measures to reduce anxiety to the owner. It can also analyze behavioral patterns when a dog is happy and provide advice on reinforcing positive behavior. This makes it possible to detect and address abnormal behavior based on a dog's emotional state.

[0086] The dog behavior anomaly detection system can also use emotion estimation to monitor the owner's emotional state and analyze the impact it has on the dog's behavior. For example, it can identify changes in the dog's behavior patterns when the owner is stressed and provide the owner with advice on how to reduce stress. It can also analyze the dog's behavior patterns when the owner is relaxed and make suggestions to promote positive interactions. This can help improve the relationship between owner and dog.

[0087] The dog behavior abnormality detection system can also use its emotion estimation function to provide training programs based on the dog's emotional state. For example, if a dog is feeling anxious, it can suggest a training method to help the dog relax. It can also provide a training method to help the dog calm down if the dog is excited. This allows the system to support behavior improvement by providing a training program that corresponds to the dog's emotional state.

[0088] The dog behavior abnormality detection system can also use its emotion estimation function to suggest a meal plan based on the dog's emotional state. For example, if a dog is feeling stressed, it can suggest a meal plan that includes ingredients that have a relaxing effect. Also, if a dog is excited, it can suggest a meal plan that includes ingredients that have a calming effect. In this way, by providing a meal plan that corresponds to the dog's emotional state, it is possible to support a healthy lifestyle.

[0089] The dog behavior anomaly detection system can also use emotion estimation to suggest play activities based on the dog's emotional state. For example, if a dog is bored, it can suggest an interesting activity. It can also provide a relaxing activity if the dog is feeling anxious. This allows us to suggest activities that suit the dog's emotional state, reducing stress and promoting positive behavior.

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

[0091] Step 1: The sensor monitors the dog's behavioral patterns. For example, the sensor detects the dog's movements and sounds and collects that data. The camera records the dog's behavior. For example, the camera records the dog's movements and barking sounds. Step 2: The AI ​​processing unit processes the data acquired by the sensors and cameras. For example, the AI ​​processing unit uses generative AI (e.g., text generation AI or multimodal generation AI) to learn the dog's normal behavior patterns and identify abnormal behavior. Step 3: The notification unit notifies the owner of the abnormal behavior identified by the AI ​​processing unit. For example, the notification unit sends a notification to the owner via a dedicated mobile app or web platform.

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

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

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

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

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

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

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

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

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

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

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

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

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

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

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

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

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

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

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

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

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

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

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

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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.

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

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

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

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

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

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0150] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. Sensors and cameras that monitor the dog's behavioral patterns, an AI processing unit that processes data acquired by the sensor and the camera; A notification unit that notifies the owner of abnormal behavior identified by the AI ​​processing unit. A system characterized by:

2. The sensor Includes biometric sensors to monitor your dog's temperature and heart rate 2. The system of claim 1.

3. The camera is Dog movements are recorded in detail using 3D motion capture technology 2. The system of claim 1.

4. The AI ​​processing unit Includes an emotion estimation function that estimates a dog's emotional state from its facial expressions and tone of voice.

2. The system of claim 1.

5. The sensor and the camera It will be expanded to monitor the behavioral patterns of other pets as well.

2. The system of claim 1.

6. The AI ​​processing unit When learning the dog's behavioral patterns, past health check data and medical history are taken into consideration.

2. The system of claim 1.

7. The notification unit The abnormal behavior identification result is notified to the owner's smart device in real time.

2. The system of claim 1.

8. The notification unit Suggest relaxation and stress relief methods based on the owner's emotional state 2. The system of claim 1.

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