System

Abstract

An object of the system according to the embodiment is to analyze the bark of the dog and notify the owner of the feeling and request.SOLUTION: A system includes a sound collection unit, an analysis unit, and a notification unit. The bark collection unit collects the bark of the dog using the generated AI. The analysis unit analyzes the sound collected by the sound collection unit and specifies an emotion or a request indicated by the sound. The notification part notifies the owner of the feeling and the request specified by the analysis 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 had the problem of making it difficult to accurately understand a pet dog's emotions and needs from its barks.

[0005] The system according to the embodiment aims to analyze the barks of a pet dog and notify the owner of the dog's emotions and requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a bark collection unit, an analysis unit, and a notification unit. The bark collection unit collects barks from pet dogs using a generation AI. The analysis unit analyzes the barks collected by the bark collection unit and identifies the emotion or request indicated by the bark. The notification unit notifies the owner of the emotion or request identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the barking of a pet dog and notify the owner of the dog's emotions and needs. [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 system according to an embodiment of the present invention is a system that learns the barks of pet dogs to understand their feelings and enable owners to empathize with them. This system uses generative AI to analyze pet dogs' barks and identify the emotions and needs they express. This allows the system to gain a deeper understanding of the pet dog's feelings and take appropriate action.

[0029] The system according to the embodiment includes a bark collection unit, an analysis unit, and a notification unit. The bark collection unit collects barks from a pet dog using a generation AI. For example, the bark collection unit may record the bark of the pet dog using a microphone and collect the audio data. The bark collection unit may also collect barks using a smartphone or a dedicated device. The bark collection unit may also collect clearer bark data using filtering technology that removes environmental sounds. The analysis unit analyzes the barks collected by the bark collection unit and identifies the emotion or request indicated by the bark. For example, the analysis unit may use a generation AI to analyze the sound waveform, frequency, volume, etc. of the bark to identify the emotion or request. The analysis unit may also identify changes in emotion by comparing the bark data with past bark data. The analysis unit may also analyze emotions in real time using an emotion estimation function to instantly detect changes in emotion. The notification unit notifies the owner of the emotion or request identified by the analysis unit. For example, the notification unit notifies the owner via a smartphone app. The notification unit can also notify the owner using a voice message or visual feedback. Furthermore, the notification unit can suggest a response based on the owner's emotions and notify the owner in a format that is most acceptable to the owner. In this way, the system analyzes the dog's barks, identifies emotions and requests, and notifies the owner, allowing the owner to understand their dog's feelings and take appropriate action.

[0030] The bark collection unit can simultaneously collect environmental sounds when collecting barks and analyze the correlation between the environmental sounds and the barks. For example, when collecting barks, the bark collection unit simultaneously records surrounding environmental sounds and analyzes the correlation between the environmental sounds and the barks. For example, when a pet dog barks, the bark collection unit also collects surrounding noises and sounds of other animals and analyzes the influence of these sounds on the bark. The bark collection unit also simultaneously records environmental data such as weather and time of day and analyzes the correlation between this data and the bark. For example, it analyzes the difference in barks on rainy days and at night to improve the accuracy of emotion identification. The bark collection unit also simultaneously records the owner's voice and the voices of family members and analyzes the influence of these sounds on the pet dog's bark. For example, it analyzes changes in the bark when the owner speaks to the dog to improve the accuracy of emotion identification. In this way, by analyzing the correlation between environmental sounds and barks, the accuracy of emotion identification is improved.

[0031] When collecting barks, the bark collection unit collects not only sound waveforms but also vibration data, allowing analysis to be performed from both sound and vibration. For example, when collecting barks, the bark collection unit records vibration data simultaneously with sound waveform data and analyzes the correlation between sound and vibration. For example, it records vibrations on the floor or walls when a pet dog barks and analyzes the data. The bark collection unit also collects vibration data from sensors attached to the pet dog's collar or body and analyzes the correlation between sound and vibration. For example, it records the body movements and vibrations when the pet dog barks, improving the accuracy of emotion identification. The bark collection unit also collects data from vibration sensors attached to the pet dog's feet or surrounding objects and analyzes the correlation between sound and vibration. For example, it records the vibrations at the pet dog's feet and the vibrations of furniture when the pet dog barks, improving the accuracy of emotion identification. In this way, by performing analysis from both sound and vibration, the accuracy of emotion identification is improved.

[0032] The bark collection unit can simultaneously collect movement data when collecting barks and analyze the correlation between the barks and movements. For example, the bark collection unit simultaneously records movement data of the pet dog when collecting barks and analyzes it comprehensively. For example, data such as walking, tail wagging, and ear movement is collected and the correlation between the barks and movements is analyzed. The bark collection unit also records movement data of the pet dog in real time when collecting barks and builds a system that analyzes the correlation between the barks and movements. For example, the movement data when the pet dog barks is analyzed in real time to improve the accuracy of emotion identification. The bark collection unit also records movement data of the pet dog using a sensor or camera when collecting barks and analyzes the correlation between the barks and movements. For example, the movement data when the pet dog barks is recorded using a camera, and the movement data and audio data are integrated and analyzed. In this way, the correlation between the barks and movements is analyzed and the accuracy of emotion identification is improved.

[0033] The meow collection unit can also collect meows of different types of pets when collecting meows and perform a similar analysis. For example, when collecting meows, the meow collection unit simultaneously records the meows of different types of pets (e.g., cats and birds) and performs a similar analysis. For example, it collects cat meows and bird chirps and applies an emotion identification algorithm. The meow collection unit also collects meow data of different types of pets when collecting meows and builds a system that comprehensively analyzes the data. For example, it collects data on the meows of cats and birds to improve the accuracy of emotion identification. The meow collection unit also collects data on the meows of different types of pets when collecting meows and analyzes the correlation between the meows of different species. For example, it collects data on the meows of cats and birds and analyzes the correlation with the meows of a pet dog. In this way, by analyzing the meows of different types of pets, the accuracy of emotion identification is improved.

[0034] The analysis unit can compare past cry data with current cry data and analyze changes in emotion. For example, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and analyzes changes in emotion. For example, it analyzes changes in emotion in time series based on past data. Furthermore, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and builds a system that analyzes changes in emotion. For example, it displays changes in emotion in a graph based on past data. Furthermore, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and develops an algorithm that analyzes changes in emotion. For example, it predicts changes in emotion based on past data. In this way, by comparing past and current cry data, changes in emotion can be analyzed and the accuracy of emotion identification improved.

[0035] The analysis unit can combine and analyze biometric data such as the dog's body temperature and heart rate when identifying the emotion of the bark. For example, the analysis unit combines and analyzes biometric data such as the dog's body temperature and heart rate when identifying the emotion of the bark. For example, the emotion is identified based on changes in body temperature and heart rate. Furthermore, the analysis unit records the dog's biometric data in real time when identifying the emotion of the bark, and builds a system that analyzes the correlation between the bark and the biometric data. For example, it analyzes changes in body temperature and heart rate in real time. Furthermore, the analysis unit records the dog's biometric data using a sensor and analyzes the correlation between the bark and the biometric data. For example, it records changes in body temperature and heart rate using a sensor to improve the accuracy of emotion identification. As a result, the accuracy of emotion identification is improved by combining and analyzing biometric data such as body temperature and heart rate.

[0036] The analysis unit can construct an emotion identification model for each breed of dog, taking into account the characteristics of each breed. For example, when identifying the emotion of a dog's bark, the analysis unit constructs an emotion identification model for each breed of dog, taking into account the characteristics of each breed of dog. For example, a different emotion identification algorithm is developed for each breed of dog, such as a Shiba Inu or a Golden Retriever. The analysis unit also registers the characteristics of each breed of dog in a database when identifying the emotion of a dog's bark and constructs a system to refer to when identifying the emotion. For example, the analysis unit registers the characteristics of each breed of dog in a database. The analysis unit also develops an emotion identification model for each breed of dog, taking into account the characteristics of each breed of dog, when identifying the emotion of a dog's bark, thereby improving the accuracy of emotion identification. For example, the analysis unit analyzes the bark patterns of each breed of dog and reflects this in the emotion identification algorithm. In this way, by constructing an emotion identification model for each breed of dog, the accuracy of emotion identification is improved.

[0037] The analysis unit can combine attribute data such as the dog's age and gender to perform analysis when identifying the emotion of a bark. For example, the analysis unit combines attribute data such as the dog's age and gender to perform analysis when identifying the emotion of a bark. For example, the analysis unit analyzes the differences between the barks of young and old dogs to improve the accuracy of emotion identification. The analysis unit also registers the dog's attribute data in a database to build a system that is referenced when identifying the emotion of a bark. For example, the analysis unit registers the characteristics of barks by gender and age in the database. The analysis unit also develops an emotion identification model that takes the dog's attribute data into consideration to improve the accuracy of emotion identification when identifying the emotion of a bark. For example, the analysis unit analyzes bark patterns by age and gender and reflects this in the emotion identification algorithm. In this way, the accuracy of emotion identification is improved by combining and analyzing attribute data such as age and gender.

[0038] The notification unit can send notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, the notification unit will build a system that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by voice through a smart speaker. The notification unit will also develop an algorithm that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by vibration to a smart watch. The notification unit will also develop a system that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by voice in cooperation with a smart speaker. This will allow owners to understand their dog's emotions no matter where they are.

[0039] The analysis unit can take into account changes in bird calls by season and time of day and build models for each season and time of day. For example, when learning bird call data, the analysis unit takes into account changes in bird calls by season and time of day and builds models for each season and time of day. For example, it analyzes the differences between calls in summer and winter, and between day and night. The analysis unit also develops a system that takes into account changes in bird calls by season and time of day and builds models for each season and time of day when learning bird call data. For example, it collects and analyzes bird call data by season and time of day. The analysis unit also develops an algorithm that takes into account changes in bird calls by season and time of day and builds models for each season and time of day when learning bird call data. For example, it analyzes patterns of bird calls by season and time of day and reflects them in the model. This improves the accuracy of emotion identification by taking into account changes in bird calls by season and time of day.

[0040] The analysis unit can combine and analyze lifestyle data such as the dog's diet and amount of exercise when identifying emotions from barks. For example, the analysis unit combines and analyzes lifestyle data such as the dog's diet and amount of exercise when learning bark data. For example, it analyzes the impact of changes in diet and amount of exercise on barks. Furthermore, when learning bark data, the analysis unit records the dog's lifestyle data in real time and builds a system that analyzes the correlation between the barks and the lifestyle data. For example, it analyzes data on diet and amount of exercise in real time. Furthermore, when learning bark data, the analysis unit records the dog's lifestyle data using a sensor or an app and analyzes the correlation between the barks and the lifestyle data. For example, it records data on diet and amount of exercise and analyzes bark patterns. In this way, by combining and analyzing lifestyle data such as diet and amount of exercise, the accuracy of emotion identification is improved.

[0041] The analysis unit can combine and analyze data from different pets to build a general pet emotion identification model. For example, when learning meow data, the analysis unit combines and analyzes data from different pets to build a general pet emotion identification model. For example, it collects meow data from cats and birds to develop an emotion identification algorithm. In addition, when learning meow data, the analysis unit registers data from different pets in a database and builds a system to refer to when identifying emotions. For example, it registers data from cats and birds in the database. In addition, when learning meow data, the analysis unit combines and analyzes data from different pets to develop an algorithm to build a general pet emotion identification model. For example, it analyzes the meow patterns of cats and birds and reflects this in the model. In this way, the accuracy of emotion identification is improved by combining and analyzing data from different pets.

[0042] The analysis unit can combine and analyze the owner's lifestyle data when identifying the emotion of a dog's bark. For example, the analysis unit combines and analyzes the owner's lifestyle data when learning the bark data. For example, it analyzes the influence of the time the dog leaves work and the time the dog returns home on the bark. In addition, when learning the bark data, the analysis unit records the owner's lifestyle data in real time and builds a system that analyzes the correlation between the bark and the lifestyle data. For example, it analyzes the data on the time the dog leaves work and the time the dog returns home in real time. In addition, when learning the bark data, the analysis unit records the owner's lifestyle data using a sensor or an app and analyzes the correlation between the bark and the lifestyle data. For example, it records the data on the time the dog leaves work and the time the dog returns home and analyzes the bark pattern. In this way, by combining and analyzing the owner's lifestyle data, the accuracy of emotion identification is improved.

[0043] The analysis unit can combine and analyze health data such as the dog's weight and appetite when monitoring the health condition through barks. For example, the analysis unit combines and analyzes health data such as the dog's weight and appetite when monitoring the health condition through barks. For example, it analyzes the impact of weight changes and loss of appetite on the barks. Furthermore, the analysis unit records the dog's health data in real time when monitoring the health condition through barks, and builds a system that analyzes the correlation between the barks and the health data. For example, it analyzes weight and appetite data in real time. Furthermore, the analysis unit records the dog's health data using a sensor or an app and analyzes the correlation between the barks and the health data. For example, it records weight and appetite data and analyzes the bark pattern. In this way, by combining and analyzing health data such as weight and appetite, the accuracy of health condition monitoring is improved.

[0044] The analysis unit can combine and analyze health data of different pets to build a health monitoring model for pets in general. For example, in health monitoring of pets' cries, the analysis unit combines and analyzes health data of different pets to build a health monitoring model for pets in general. For example, health data of cats and birds is collected and a health monitoring algorithm is developed. In addition, in health monitoring of pets' cries, the analysis unit registers the health data of different pets in a database and builds a system to be referenced during health monitoring. For example, the health data of cats and birds is registered in a database. In addition, in health monitoring of pets' cries, the analysis unit combines and analyzes the health data of different pets to develop an algorithm to build a health monitoring model for pets in general. For example, the health data of cats and birds is analyzed and reflected in the model. In this way, the accuracy of health monitoring is improved by combining and analyzing the health data of different pets.

[0045] The analysis unit can analyze the correlation between the health conditions of the owner and the dog by combining and analyzing the health data of the owner when monitoring the health condition of the dog through barks. For example, the analysis unit analyzes the correlation between the health conditions of the owner and the dog when monitoring the health condition of the dog through barks by combining and analyzing the health data of the owner when monitoring the health condition of the dog through barks. For example, the analysis unit analyzes the influence of the health condition of the owner on the barks of the dog. Furthermore, the analysis unit records the health data of the owner in real time when monitoring the health condition of the dog through barks, and builds a system that analyzes the correlation between the health conditions of the owner and the dog. For example, the analysis unit analyzes the health data of the owner in real time. Furthermore, the analysis unit records the health data of the owner using a sensor or an app when monitoring the health condition of the dog through barks, and analyzes the correlation between the health conditions of the owner and the dog. For example, the analysis unit records the health data of the owner and analyzes the pattern of the dog's barks. In this way, the accuracy of health condition monitoring is improved by combining and analyzing the health data of the owner.

[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 system may further include a location information acquisition unit. The location information acquisition unit collects location information of the dog and provides it to the analysis unit. For example, if the dog frequently barks in a particular location, the environmental factors of that location can be analyzed. The location information acquisition unit can also record the dog's movement patterns and analyze the correlation with the dog's barks. For example, it can analyze the difference between barks made during walks and barks made at home, improving the accuracy of emotion identification. Furthermore, the location information acquisition unit can notify the owner when the dog enters a specific area. For example, notifying the owner when the dog goes out into the yard makes it easier to understand the dog's behavior.

[0048] The system may further include a temperature sensor. The temperature sensor measures the temperature around the dog and provides the data to the analysis unit. For example, if the dog barks in a hot environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The temperature sensor can also measure the dog's body temperature and analyze the correlation with the bark. For example, analyzing the difference between bark when the dog has a high body temperature and bark when normal can be useful for monitoring the dog's health. Furthermore, the temperature sensor can determine whether the dog is within a comfortable temperature range and notify the owner. For example, if the dog is in a cold environment, the system can suggest moving the dog to a warmer place.

[0049] The system may further include a light sensor. The light sensor measures the brightness of the dog's surroundings and provides the data to the analysis unit. For example, if the dog barks in a dark environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The light sensor can also record the dog's activity times and analyze the correlation with the bark. For example, the difference between nighttime barks and daytime barks can be analyzed to improve the accuracy of emotion identification. The light sensor can also determine whether the dog is in a comfortable lighting environment and notify the owner. For example, if the dog is in a dark place, the system can suggest moving the dog to a brighter place.

[0050] The system can further include a humidity sensor. The humidity sensor measures the humidity around the dog and provides the data to the analysis unit. For example, if the dog barks in a humid environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The humidity sensor can also monitor the condition of the dog's skin and coat and analyze the correlation with the bark. For example, analyzing the difference between barks in high humidity and normal conditions can be useful for monitoring the dog's health. Furthermore, the humidity sensor can determine whether the dog is within a comfortable humidity range and notify the owner. For example, if the dog is in a dry environment, the system can suggest adding humidity.

[0051] The system may further include an air quality sensor. The air quality sensor measures the air quality around the dog and provides the data to the analysis unit. For example, if the dog barks in a polluted air environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The air quality sensor can also monitor the dog's breathing and analyze correlations with the bark. For example, analyzing the difference between barks during poor air quality and normal barks can be useful for monitoring the dog's health. Furthermore, the air quality sensor can determine whether the dog is in a comfortable air environment and notify the owner. For example, if the dog is in a polluted air environment, the system can suggest ventilating the house.

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

[0053] Step 1: The bark collection unit uses the generation AI to collect the barks of the pet dog. For example, the bark collection unit may use a microphone to record the barks of the pet dog and collect the audio data. The bark collection unit may also collect barks using a smartphone or dedicated device. Furthermore, the bark collection unit may use filtering technology to remove environmental sounds to collect clearer bark data. Step 2: The analysis unit analyzes the cries collected by the cry collection unit and identifies the emotions and requests indicated by the cries. For example, the analysis unit uses a generation AI to analyze the sound waveform, frequency, volume, etc. of the cries to identify emotions and requests. The analysis unit can also identify changes in emotions by comparing with past cry data. Furthermore, the analysis unit can analyze emotions in real time using an emotion estimation function and instantly detect changes in emotions. Step 3: The notification unit notifies the owner of the emotions and requests identified by the analysis unit. For example, the notification unit notifies the owner via a smartphone app. The notification unit can also notify the owner using a voice message or visual feedback. Furthermore, the notification unit can suggest a response based on the owner's emotions and notify the owner in a form that is most acceptable to the owner.

[0054] (Example 2) A system according to an embodiment of the present invention is a system that learns the barks of pet dogs to understand their feelings and enable owners to empathize with them. This system uses generative AI to analyze pet dogs' barks and identify the emotions and needs they express. This allows the system to gain a deeper understanding of the pet dog's feelings and take appropriate action.

[0055] The system according to the embodiment includes a bark collection unit, an analysis unit, and a notification unit. The bark collection unit collects barks from a pet dog using a generation AI. For example, the bark collection unit may record the bark of the pet dog using a microphone and collect the audio data. The bark collection unit may also collect barks using a smartphone or a dedicated device. The bark collection unit may also collect clearer bark data using filtering technology that removes environmental sounds. The analysis unit analyzes the barks collected by the bark collection unit and identifies the emotion or request indicated by the bark. For example, the analysis unit may use a generation AI to analyze the sound waveform, frequency, volume, etc. of the bark to identify the emotion or request. The analysis unit may also identify changes in emotion by comparing the bark data with past bark data. The analysis unit may also analyze emotions in real time using an emotion estimation function to instantly detect changes in emotion. The notification unit notifies the owner of the emotion or request identified by the analysis unit. For example, the notification unit notifies the owner via a smartphone app. The notification unit can also notify the owner using a voice message or visual feedback. Furthermore, the notification unit can suggest a response based on the owner's emotions and notify the owner in a format that is most acceptable to the owner. In this way, the system analyzes the dog's barks, identifies emotions and requests, and notifies the owner, allowing the owner to understand their dog's feelings and take appropriate action.

[0056] The bark collection unit can simultaneously collect environmental sounds when collecting barks and analyze the correlation between the environmental sounds and the barks. For example, when collecting barks, the bark collection unit simultaneously records surrounding environmental sounds and analyzes the correlation between the environmental sounds and the barks. For example, when a pet dog barks, the bark collection unit also collects surrounding noises and sounds of other animals and analyzes the influence of these sounds on the bark. The bark collection unit also simultaneously records environmental data such as weather and time of day and analyzes the correlation between this data and the bark. For example, it analyzes the difference in barks on rainy days and at night to improve the accuracy of emotion identification. The bark collection unit also simultaneously records the owner's voice and the voices of family members and analyzes the influence of these sounds on the pet dog's bark. For example, it analyzes changes in the bark when the owner speaks to the dog to improve the accuracy of emotion identification. In this way, by analyzing the correlation between environmental sounds and barks, the accuracy of emotion identification is improved.

[0057] When collecting barks, the bark collection unit collects not only sound waveforms but also vibration data, allowing analysis to be performed from both sound and vibration. For example, when collecting barks, the bark collection unit records vibration data simultaneously with sound waveform data and analyzes the correlation between sound and vibration. For example, it records vibrations on the floor or walls when a pet dog barks and analyzes the data. The bark collection unit also collects vibration data from sensors attached to the pet dog's collar or body and analyzes the correlation between sound and vibration. For example, it records the body movements and vibrations when the pet dog barks, improving the accuracy of emotion identification. The bark collection unit also collects data from vibration sensors attached to the pet dog's feet or surrounding objects and analyzes the correlation between sound and vibration. For example, it records the vibrations at the pet dog's feet and the vibrations of furniture when the pet dog barks, improving the accuracy of emotion identification. In this way, by performing analysis from both sound and vibration, the accuracy of emotion identification is improved.

[0058] The analysis unit uses the emotion estimation function to analyze the emotion of the bark in real time and can instantly detect changes in emotion. For example, when collecting barks, the analysis unit uses the emotion estimation function to analyze the emotion in real time and instantly detect changes in emotion. For example, the analysis unit analyzes audio data of a pet dog barking in real time and instantly notifies the owner of changes in emotion. The analysis unit also uses the emotion estimation function to analyze emotions in real time and builds a system that instantly provides feedback on changes in emotion. For example, it allows owners to check changes in their dog's emotions in real time. The analysis unit also develops a system that uses the emotion estimation function to analyze emotions in real time and instantly records changes in emotion. For example, it records changes in emotion as time-series data so that they can be analyzed later. This improves the accuracy of identifying emotions by detecting changes in emotion in real time.

[0059] The bark collection unit can simultaneously collect movement data when collecting barks and analyze the correlation between the barks and movements. For example, the bark collection unit simultaneously records movement data of the pet dog when collecting barks and analyzes it comprehensively. For example, data such as walking, tail wagging, and ear movement is collected and the correlation between the barks and movements is analyzed. The bark collection unit also records movement data of the pet dog in real time when collecting barks and builds a system that analyzes the correlation between the barks and movements. For example, the movement data when the pet dog barks is analyzed in real time to improve the accuracy of emotion identification. The bark collection unit also records movement data of the pet dog using a sensor or camera when collecting barks and analyzes the correlation between the barks and movements. For example, the movement data when the pet dog barks is recorded using a camera, and the movement data and audio data are integrated and analyzed. In this way, the correlation between the barks and movements is analyzed and the accuracy of emotion identification is improved.

[0060] The meow collection unit can also collect meows of different types of pets when collecting meows and perform a similar analysis. For example, when collecting meows, the meow collection unit simultaneously records the meows of different types of pets (e.g., cats and birds) and performs a similar analysis. For example, it collects cat meows and bird chirps and applies an emotion identification algorithm. The meow collection unit also collects meow data of different types of pets when collecting meows and builds a system that comprehensively analyzes the data. For example, it collects data on the meows of cats and birds to improve the accuracy of emotion identification. The meow collection unit also collects data on the meows of different types of pets when collecting meows and analyzes the correlation between the meows of different species. For example, it collects data on the meows of cats and birds and analyzes the correlation with the meows of a pet dog. In this way, by analyzing the meows of different types of pets, the accuracy of emotion identification is improved.

[0061] The bark collection unit can simultaneously collect the owner's emotions when collecting the bark and analyze the correlation between the owner's and the dog's emotions. For example, the bark collection unit simultaneously records the owner's emotional data when collecting the bark and analyzes the correlation between the owner's and the dog's emotions. For example, it analyzes the owner's facial expressions and voice and analyzes the correlation with the dog's bark. The bark collection unit also records the owner's emotional data in real time when collecting the bark, building a system that analyzes the correlation between the owner's and the dog's emotions. For example, it analyzes the owner's emotional changes in real time and analyzes the correlation with the dog's bark. The bark collection unit also records the owner's emotional data using a sensor or camera when collecting the bark and analyzes the correlation between the owner's and the dog's emotions. For example, it records the owner's facial expressions and voice with a camera and analyzes the correlation with the dog's bark. This will improve the accuracy of identifying emotions by analyzing the correlation between the emotions of owners and their dogs.

[0062] The analysis unit can compare past cry data with current cry data and analyze changes in emotion. For example, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and analyzes changes in emotion. For example, it analyzes changes in emotion in time series based on past data. Furthermore, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and builds a system that analyzes changes in emotion. For example, it displays changes in emotion in a graph based on past data. Furthermore, in identifying the emotion of a cry, the analysis unit compares past cry data with current cry data and develops an algorithm that analyzes changes in emotion. For example, it predicts changes in emotion based on past data. In this way, by comparing past and current cry data, changes in emotion can be analyzed and the accuracy of emotion identification improved.

[0063] The analysis unit can combine and analyze biometric data such as the dog's body temperature and heart rate when identifying the emotion of the bark. For example, the analysis unit combines and analyzes biometric data such as the dog's body temperature and heart rate when identifying the emotion of the bark. For example, the emotion is identified based on changes in body temperature and heart rate. Furthermore, the analysis unit records the dog's biometric data in real time when identifying the emotion of the bark, and builds a system that analyzes the correlation between the bark and the biometric data. For example, it analyzes changes in body temperature and heart rate in real time. Furthermore, the analysis unit records the dog's biometric data using a sensor and analyzes the correlation between the bark and the biometric data. For example, it records changes in body temperature and heart rate using a sensor to improve the accuracy of emotion identification. As a result, the accuracy of emotion identification is improved by combining and analyzing biometric data such as body temperature and heart rate.

[0064] The analysis unit can use the emotion estimation function to identify the emotion of the dog's bark and suggest an appropriate response based on that emotion. The analysis unit, for example, uses the emotion estimation function to build a system that identifies the emotion of a dog's bark and suggests an appropriate response based on that emotion. For example, if the dog is feeling anxious, the analysis unit suggests that the owner pet the dog. The analysis unit also uses the emotion estimation function to develop an algorithm that identifies the emotion of a dog's bark and suggests an appropriate response based on that emotion. For example, if the dog wants to play, the analysis unit suggests playing with a toy. The analysis unit also uses the emotion estimation function to develop a system that identifies the emotion of a dog's bark and suggests an appropriate response based on that emotion. For example, if the dog is wary, the analysis unit suggests a way to reassure the dog. In this way, the accuracy of emotion identification is improved by using the emotion estimation function to identify the emotion and suggest an appropriate response.

[0065] The analysis unit can construct an emotion identification model for each breed of dog, taking into account the characteristics of each breed. For example, when identifying the emotion of a dog's bark, the analysis unit constructs an emotion identification model for each breed of dog, taking into account the characteristics of each breed of dog. For example, a different emotion identification algorithm is developed for each breed of dog, such as a Shiba Inu or a Golden Retriever. The analysis unit also registers the characteristics of each breed of dog in a database when identifying the emotion of a dog's bark and constructs a system to refer to when identifying the emotion. For example, the analysis unit registers the characteristics of each breed of dog in a database. The analysis unit also develops an emotion identification model for each breed of dog, taking into account the characteristics of each breed of dog, when identifying the emotion of a dog's bark, thereby improving the accuracy of emotion identification. For example, the analysis unit analyzes the bark patterns of each breed of dog and reflects this in the emotion identification algorithm. In this way, by constructing an emotion identification model for each breed of dog, the accuracy of emotion identification is improved.

[0066] The analysis unit can combine attribute data such as the dog's age and gender to perform analysis when identifying the emotion of a bark. For example, the analysis unit combines attribute data such as the dog's age and gender to perform analysis when identifying the emotion of a bark. For example, the analysis unit analyzes the differences between the barks of young and old dogs to improve the accuracy of emotion identification. The analysis unit also registers the dog's attribute data in a database to build a system that is referenced when identifying the emotion of a bark. For example, the analysis unit registers the characteristics of barks by gender and age in the database. The analysis unit also develops an emotion identification model that takes the dog's attribute data into consideration to improve the accuracy of emotion identification when identifying the emotion of a bark. For example, the analysis unit analyzes bark patterns by age and gender and reflects this in the emotion identification algorithm. In this way, the accuracy of emotion identification is improved by combining and analyzing attribute data such as age and gender.

[0067] The analysis unit can simultaneously analyze the owner's emotion when identifying the emotion in the bark and evaluate the degree of agreement between the owner's and the dog's emotions. For example, the analysis unit simultaneously analyzes the owner's emotion data when identifying the emotion in the bark and evaluates the degree of agreement between the owner's and the dog's emotions. For example, the analysis unit analyzes the owner's facial expression and voice and evaluates the degree of agreement with the dog's bark. Furthermore, the analysis unit records the owner's emotional data in real time when identifying the emotion in the bark and builds a system that evaluates the degree of agreement between the owner's and the dog's emotions. For example, the analysis unit analyzes the owner's emotional changes in real time and evaluates the degree of agreement with the dog's bark. Furthermore, the analysis unit records the owner's emotional data using a sensor or camera and evaluates the degree of agreement between the owner's and the dog's emotions when identifying the emotion in the bark. For example, the analysis unit records the owner's facial expression and voice using a camera and evaluates the degree of agreement with the dog's bark. This improves the accuracy of emotion identification by evaluating the degree of agreement between the owner's and the dog's emotions.

[0068] The notification unit can generate a voice message according to the emotion and provide it to the owner. For example, the notification unit builds a system that generates a voice message according to the emotion of the dog when notifying the owner and provides it to the owner. For example, if the dog is feeling anxious, it provides a voice message that reassures the owner. Furthermore, the notification unit develops an algorithm that generates a voice message according to the emotion of the dog when notifying the owner and provides it to the owner. For example, if the dog wants to play, it provides a voice message encouraging the dog to play with a toy. Furthermore, the notification unit develops a system that generates a voice message according to the emotion of the dog when notifying the owner and provides it to the owner. For example, if the dog is wary, it provides a voice message to reassure the dog. In this way, by providing a voice message according to the emotion of the dog, it becomes easier for the owner to understand the emotion of their dog.

[0069] The notification unit can provide visual feedback according to the emotion. For example, the notification unit builds a system that provides visual feedback according to the emotion of the dog when notifying the owner. For example, if the dog is feeling anxious, it displays a reassuring icon or animation. The notification unit also develops an algorithm that provides visual feedback according to the emotion of the dog when notifying the owner. For example, if the dog wants to play, it displays an icon or animation that encourages the dog to play with a toy. The notification unit also develops a system that provides visual feedback according to the emotion of the dog when notifying the owner. For example, if the dog is wary, it displays a reassuring icon or animation. In this way, by providing visual feedback according to the emotion of the dog, it becomes easier for the owner to understand the emotion of their dog.

[0070] The notification unit uses the emotion estimation function to suggest a response based on the owner's emotions, and can notify the owner in a format that is most acceptable to the owner. The notification unit, for example, uses the emotion estimation function to suggest a response based on the owner's emotions, and builds a system that notifies the owner in a format that is most acceptable to the owner. For example, if the owner is feeling stressed, a simple response method is suggested. The notification unit also uses the emotion estimation function to develop an algorithm that suggests a response based on the owner's emotions and notifies the owner in a format that is most acceptable to the owner. For example, if the owner is relaxed, a detailed response method is suggested. The notification unit also uses the emotion estimation function to develop a system that suggests a response based on the owner's emotions and notifies the owner in a format that is most acceptable to the owner. For example, if the owner is busy, a response method that can be done in a short amount of time is suggested. As a result, by suggesting a response based on the owner's emotions, the owner can respond appropriately to their dog's emotions.

[0071] The notification unit can send notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, the notification unit will build a system that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by voice through a smart speaker. The notification unit will also develop an algorithm that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by vibration to a smart watch. The notification unit will also develop a system that sends notifications to owners not only through smartphone apps but also through devices such as smart speakers and smart watches. For example, notifications can be sent by voice in cooperation with a smart speaker. This will allow owners to understand their dog's emotions no matter where they are.

[0072] The notification unit can simultaneously analyze the owner's emotions and make a response suggestion based on the owner's emotions. The notification unit, for example, builds a system that analyzes the owner's emotions and makes a response suggestion based on those emotions. For example, if the owner is feeling stressed, a simple response method is suggested. The notification unit also develops an algorithm that analyzes the owner's emotions and makes a response suggestion based on those emotions. For example, if the owner is relaxed, a detailed response method is suggested. The notification unit also develops a system that analyzes the owner's emotions and makes a response suggestion based on those emotions. For example, if the owner is busy, a response method that can be done in a short amount of time is suggested. In this way, by making a response suggestion based on the owner's emotions, the owner can respond appropriately to the emotions of their dog.

[0073] The analysis unit can take into account changes in bird calls by season and time of day and build models for each season and time of day. For example, when learning bird call data, the analysis unit takes into account changes in bird calls by season and time of day and builds models for each season and time of day. For example, it analyzes the differences between calls in summer and winter, and between day and night. The analysis unit also develops a system that takes into account changes in bird calls by season and time of day and builds models for each season and time of day when learning bird call data. For example, it collects and analyzes bird call data by season and time of day. The analysis unit also develops an algorithm that takes into account changes in bird calls by season and time of day and builds models for each season and time of day when learning bird call data. For example, it analyzes patterns of bird calls by season and time of day and reflects them in the model. This improves the accuracy of emotion identification by taking into account changes in bird calls by season and time of day.

[0074] The analysis unit can combine and analyze lifestyle data such as the dog's diet and amount of exercise when identifying emotions from barks. For example, the analysis unit combines and analyzes lifestyle data such as the dog's diet and amount of exercise when learning bark data. For example, it analyzes the impact of changes in diet and amount of exercise on barks. Furthermore, when learning bark data, the analysis unit records the dog's lifestyle data in real time and builds a system that analyzes the correlation between the barks and the lifestyle data. For example, it analyzes data on diet and amount of exercise in real time. Furthermore, when learning bark data, the analysis unit records the dog's lifestyle data using a sensor or an app and analyzes the correlation between the barks and the lifestyle data. For example, it records data on diet and amount of exercise and analyzes bark patterns. In this way, by combining and analyzing lifestyle data such as diet and amount of exercise, the accuracy of emotion identification is improved.

[0075] The analysis unit can use the emotion estimation function to continuously learn the emotional changes of the dog and identify emotional patterns. The analysis unit, for example, uses the emotion estimation function to build a system that continuously learns the emotional changes of the dog and identifies emotional patterns. For example, the emotional changes of the dog are recorded chronologically and the patterns are analyzed. The analysis unit also uses the emotion estimation function to continuously learn the emotional changes of the dog and develop an algorithm that identifies emotional patterns. For example, the emotional changes of the dog are analyzed in real time and the patterns are identified. The analysis unit also uses the emotion estimation function to continuously learn the emotional changes of the dog and develop a system that identifies emotional patterns. For example, the emotional changes of the dog are recorded with a sensor and the patterns are analyzed. In this way, the accuracy of emotion identification is improved by continuously learning the emotional changes and identifying emotional patterns.

[0076] The analysis unit can combine and analyze data from different pets to build a general pet emotion identification model. For example, when learning meow data, the analysis unit combines and analyzes data from different pets to build a general pet emotion identification model. For example, it collects meow data from cats and birds to develop an emotion identification algorithm. In addition, when learning meow data, the analysis unit registers data from different pets in a database and builds a system to refer to when identifying emotions. For example, it registers data from cats and birds in the database. In addition, when learning meow data, the analysis unit combines and analyzes data from different pets to develop an algorithm to build a general pet emotion identification model. For example, it analyzes the meow patterns of cats and birds and reflects this in the model. In this way, the accuracy of emotion identification is improved by combining and analyzing data from different pets.

[0077] The analysis unit can combine and analyze the owner's lifestyle data when identifying the emotion of a dog's bark. For example, the analysis unit combines and analyzes the owner's lifestyle data when learning the bark data. For example, it analyzes the influence of the time the dog leaves work and the time the dog returns home on the bark. In addition, when learning the bark data, the analysis unit records the owner's lifestyle data in real time and builds a system that analyzes the correlation between the bark and the lifestyle data. For example, it analyzes the data on the time the dog leaves work and the time the dog returns home in real time. In addition, when learning the bark data, the analysis unit records the owner's lifestyle data using a sensor or an app and analyzes the correlation between the bark and the lifestyle data. For example, it records the data on the time the dog leaves work and the time the dog returns home and analyzes the bark pattern. In this way, by combining and analyzing the owner's lifestyle data, the accuracy of emotion identification is improved.

[0078] When identifying the emotion in a dog's bark, the analysis unit simultaneously learns the owner's emotional data and can analyze the correlation between the owner's and the dog's emotions. For example, when learning bark data, the analysis unit simultaneously learns the owner's emotional data and builds a system that analyzes the correlation between the owner's and the dog's emotions. For example, the analysis unit records the owner's emotional changes over time and analyzes the correlation with the dog's barks. Furthermore, when learning the bark data, the analysis unit records the owner's emotional data in real time and develops an algorithm that analyzes the correlation between the owner's and the dog's emotions. For example, the analysis unit analyzes the owner's emotional changes in real time and identifies the correlation with the dog's barks. Furthermore, when learning the bark data, the analysis unit records the owner's emotional data using a sensor or camera and develops a system that analyzes the correlation between the owner's and the dog's emotions. For example, the analysis unit records the owner's facial expressions and voice with a camera and analyzes the correlation with the dog's barks. This will improve the accuracy of identifying emotions by analyzing the correlation between the emotions of owners and their dogs.

[0079] The analysis unit can combine and analyze health data such as the dog's weight and appetite when monitoring the health condition through barks. For example, the analysis unit combines and analyzes health data such as the dog's weight and appetite when monitoring the health condition through barks. For example, it analyzes the impact of weight changes and loss of appetite on the barks. Furthermore, the analysis unit records the dog's health data in real time when monitoring the health condition through barks, and builds a system that analyzes the correlation between the barks and the health data. For example, it analyzes weight and appetite data in real time. Furthermore, the analysis unit records the dog's health data using a sensor or an app and analyzes the correlation between the barks and the health data. For example, it records weight and appetite data and analyzes the bark pattern. In this way, by combining and analyzing health data such as weight and appetite, the accuracy of health condition monitoring is improved.

[0080] The analysis unit uses the emotion estimation function to analyze changes in the dog's emotions as an indicator of its health state, allowing for early detection of abnormalities. For example, the analysis unit uses the emotion estimation function to analyze changes in the dog's emotions as an indicator of its health state, building a system for early detection of abnormalities. For example, the analysis unit records changes in the dog's emotions over time and detects abnormalities. The analysis unit also uses the emotion estimation function to analyze changes in the dog's emotions as an indicator of its health state, developing an algorithm for early detection of abnormalities. For example, the analysis unit analyzes changes in the dog's emotions in real time and identifies abnormalities. The analysis unit also uses the emotion estimation function to analyze changes in the dog's emotions as an indicator of its health state, allowing for early detection of abnormalities. For example, the analysis unit records changes in the dog's emotions with a sensor and analyzes abnormalities. This allows for analyzing changes in emotions as an indicator of its health state, allowing for early detection of abnormalities, improving the accuracy of health monitoring.

[0081] The analysis unit can combine and analyze health data of different pets to build a health monitoring model for pets in general. For example, in health monitoring of pets' cries, the analysis unit combines and analyzes health data of different pets to build a health monitoring model for pets in general. For example, health data of cats and birds is collected and a health monitoring algorithm is developed. In addition, in health monitoring of pets' cries, the analysis unit registers the health data of different pets in a database and builds a system to be referenced during health monitoring. For example, the health data of cats and birds is registered in a database. In addition, in health monitoring of pets' cries, the analysis unit combines and analyzes the health data of different pets to develop an algorithm to build a health monitoring model for pets in general. For example, the health data of cats and birds is analyzed and reflected in the model. In this way, the accuracy of health monitoring is improved by combining and analyzing the health data of different pets.

[0082] The analysis unit can analyze the correlation between the health conditions of the owner and the dog by combining and analyzing the health data of the owner when monitoring the health condition of the dog through barks. For example, the analysis unit analyzes the correlation between the health conditions of the owner and the dog when monitoring the health condition of the dog through barks by combining and analyzing the health data of the owner when monitoring the health condition of the dog through barks. For example, the analysis unit analyzes the influence of the health condition of the owner on the barks of the dog. Furthermore, the analysis unit records the health data of the owner in real time when monitoring the health condition of the dog through barks, and builds a system that analyzes the correlation between the health conditions of the owner and the dog. For example, the analysis unit analyzes the health data of the owner in real time. Furthermore, the analysis unit records the health data of the owner using a sensor or an app when monitoring the health condition of the dog through barks, and analyzes the correlation between the health conditions of the owner and the dog. For example, the analysis unit records the health data of the owner and analyzes the pattern of the dog's barks. In this way, the accuracy of health condition monitoring is improved by combining and analyzing the health data of the owner.

[0083] The analysis unit can simultaneously analyze the owner's emotional data when monitoring the dog's health condition through barks, and evaluate the impact of the owner's emotions on the dog's health. For example, the analysis unit simultaneously analyzes the owner's emotional data when monitoring the dog's health condition through barks, and builds a system that evaluates the impact of the owner's emotions on the dog's health. For example, the analysis unit records the owner's emotional changes over time and analyzes the correlation with the dog's health condition. The analysis unit also records the owner's emotional data in real time when monitoring the dog's health condition through barks, and develops an algorithm that evaluates the impact of the owner's emotions on the dog's health. For example, the analysis unit analyzes the owner's emotional changes in real time and identifies the correlation with the dog's health condition. The analysis unit also records the owner's emotional data using a sensor or camera when monitoring the dog's health condition through barks, and develops a system that evaluates the impact of the owner's emotions on the dog's health. For example, the analysis unit records the owner's facial expressions and voice with a camera, and analyzes the correlation with the dog's health condition. This will improve the accuracy of health monitoring by assessing the impact of owners' emotions on their dogs' health.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The system may further include a location information acquisition unit. The location information acquisition unit collects location information of the dog and provides it to the analysis unit. For example, if the dog frequently barks in a particular location, the environmental factors of that location can be analyzed. The location information acquisition unit can also record the dog's movement patterns and analyze the correlation with the dog's barks. For example, it can analyze the difference between barks made during walks and barks made at home, improving the accuracy of emotion identification. Furthermore, the location information acquisition unit can notify the owner when the dog enters a specific area. For example, notifying the owner when the dog goes out into the yard makes it easier to understand the dog's behavior.

[0086] The system may further include a temperature sensor. The temperature sensor measures the temperature around the dog and provides the data to the analysis unit. For example, if the dog barks in a hot environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The temperature sensor can also measure the dog's body temperature and analyze the correlation with the bark. For example, analyzing the difference between bark when the dog has a high body temperature and bark when normal can be useful for monitoring the dog's health. Furthermore, the temperature sensor can determine whether the dog is within a comfortable temperature range and notify the owner. For example, if the dog is in a cold environment, the system can suggest moving the dog to a warmer place.

[0087] The system may further include a light sensor. The light sensor measures the brightness of the dog's surroundings and provides the data to the analysis unit. For example, if the dog barks in a dark environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The light sensor can also record the dog's activity times and analyze the correlation with the bark. For example, the difference between nighttime barks and daytime barks can be analyzed to improve the accuracy of emotion identification. The light sensor can also determine whether the dog is in a comfortable lighting environment and notify the owner. For example, if the dog is in a dark place, the system can suggest moving the dog to a brighter place.

[0088] The system can further include a humidity sensor. The humidity sensor measures the humidity around the dog and provides the data to the analysis unit. For example, if the dog barks in a humid environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The humidity sensor can also monitor the condition of the dog's skin and coat and analyze the correlation with the bark. For example, analyzing the difference between barks in high humidity and normal conditions can be useful for monitoring the dog's health. Furthermore, the humidity sensor can determine whether the dog is within a comfortable humidity range and notify the owner. For example, if the dog is in a dry environment, the system can suggest adding humidity.

[0089] The system may further include an air quality sensor. The air quality sensor measures the air quality around the dog and provides the data to the analysis unit. For example, if the dog barks in a polluted air environment, the characteristics of the bark can be analyzed to improve the accuracy of emotion identification. The air quality sensor can also monitor the dog's breathing and analyze correlations with the bark. For example, analyzing the difference between barks during poor air quality and normal barks can be useful for monitoring the dog's health. Furthermore, the air quality sensor can determine whether the dog is in a comfortable air environment and notify the owner. For example, if the dog is in a polluted air environment, the system can suggest ventilating the house.

[0090] The analysis unit can take into account the owner's stress level when identifying the emotion in a dog's bark. For example, if the owner is in a high-stress state, the accuracy of identifying the emotion in the dog's bark may decrease, so the stress level can be incorporated into the analysis. The analysis unit can also build a system that records the owner's stress level in real time and analyzes the correlation with the dog's bark. For example, the analysis unit can analyze the difference between the owner's bark when the owner's stress level is high and the owner's normal bark, thereby improving the accuracy of identifying the emotion. Furthermore, the analysis unit can record the owner's stress level using a sensor or an app and analyze the correlation with the dog's bark. In this way, the accuracy of identifying the emotion can be improved by taking the owner's stress level into account.

[0091] The analysis unit can take into account the owner's sleep data when identifying the emotion in a dog's bark. For example, if the owner does not get enough sleep, the accuracy of identifying the emotion in the dog's bark may decrease, so sleep data can be incorporated into the analysis. The analysis unit can also build a system that records the owner's sleep data in real time and analyzes the correlation with the dog's bark. For example, it can analyze the difference between the owner's bark when sleep-deprived and when normal, thereby improving the accuracy of identifying the emotion. Furthermore, the analysis unit can record the owner's sleep data using a sensor or app and analyze the correlation with the dog's bark. In this way, by taking the owner's sleep data into consideration, the accuracy of identifying the emotion can be improved.

[0092] The analysis unit can take into account the owner's dietary data when identifying emotions from the dog's barks. For example, if the owner has irregular eating habits, the accuracy of identifying emotions from the dog's barks may decrease, so dietary data can be incorporated into the analysis. The analysis unit can also build a system that records the owner's dietary data in real time and analyzes the correlation with the dog's barks. For example, the analysis unit can analyze the difference between the owner's barks when the owner's mealtimes are irregular and when the owner's barks are normal, thereby improving the accuracy of identifying emotions. Furthermore, the analysis unit can record the owner's dietary data using a sensor or an app and analyze the correlation with the dog's barks. In this way, by taking the owner's dietary data into consideration, the accuracy of identifying emotions can be improved.

[0093] The analysis unit can take into account the owner's exercise data when identifying the emotion in a dog's bark. For example, if the owner does not exercise enough, the accuracy of identifying the emotion in a dog's bark may decrease, so the exercise data can be incorporated into the analysis. The analysis unit can also build a system that records the owner's exercise data in real time and analyzes the correlation with the dog's bark. For example, the analysis unit can analyze the difference between the owner's bark when he or she is not exercising and the owner's normal bark, thereby improving the accuracy of identifying the emotion. Furthermore, the analysis unit can record the owner's exercise data using a sensor or an app and analyze the correlation with the dog's bark. In this way, by taking the owner's exercise data into consideration, the accuracy of identifying the emotion is improved.

[0094] The analysis unit can take into account the owner's social data when identifying the emotion in the dog's bark. For example, if the owner is isolated, the accuracy of identifying the emotion in the dog's bark may decrease, so the social data can be incorporated into the analysis. The analysis unit can also build a system that records the owner's social data in real time and analyzes the correlation with the dog's bark. For example, the analysis unit can analyze the difference between the owner's bark when isolated and the owner's normal bark, thereby improving the accuracy of identifying the emotion. Furthermore, the analysis unit can record the owner's social data using a sensor or an app and analyze the correlation with the dog's bark. In this way, by taking the owner's social data into account, the accuracy of identifying the emotion can be improved.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The bark collection unit uses the generation AI to collect the barks of the pet dog. For example, the bark collection unit may use a microphone to record the barks of the pet dog and collect the audio data. The bark collection unit may also collect barks using a smartphone or dedicated device. Furthermore, the bark collection unit may use filtering technology to remove environmental sounds to collect clearer bark data. Step 2: The analysis unit analyzes the cries collected by the cry collection unit and identifies the emotions and requests indicated by the cries. For example, the analysis unit uses a generation AI to analyze the sound waveform, frequency, volume, etc. of the cries to identify emotions and requests. The analysis unit can also identify changes in emotions by comparing with past cry data. Furthermore, the analysis unit can analyze emotions in real time using an emotion estimation function and instantly detect changes in emotions. Step 3: The notification unit notifies the owner of the emotions and requests identified by the analysis unit. For example, the notification unit notifies the owner via a smartphone app. The notification unit can also notify the owner using a voice message or visual feedback. Furthermore, the notification unit can suggest a response based on the owner's emotions and notify the owner in a form that is most acceptable to the owner.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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. A bark collection unit that uses generative AI to collect barks from pet dogs, an analysis unit that analyzes the cries collected by the cries collection unit and identifies emotions or requests indicated by the cries; a notification unit that notifies the owner of the emotion or request identified by the analysis unit. A system characterized by:

2. The bird cry collection unit includes: When collecting the sounds, not only sound waveforms but also vibration data are collected, and analysis is performed based on both the sound and vibration.

2. The system of claim 1.

3. The analysis unit Comparing past and current call data to analyze emotional transitions 2. The system of claim 1.

4. The notification unit A voice message corresponding to the emotion is generated and provided to the owner.

2. The system of claim 1.

5. The analysis unit In the health condition monitoring of the barking sound, the emotional data of the owner is also analyzed at the same time to evaluate the influence of the owner's emotion on the health of the pet dog.

2. The system of claim 1.

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