system

Abstract

The system according to this embodiment aims to perform detailed health management by utilizing the internal information of pets. [Solution] The system according to the embodiment comprises a recording unit, a reading unit, an analysis unit, and an advice unit. The recording unit records information about the pet's internal organs. The reading unit reads the information recorded by the recording unit. The analysis unit analyzes the information read by the reading unit. The advice unit provides advice based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the health management of pets only stays at individual identification, and detailed health management using internal body information has not been sufficiently carried out.

[0005] The system according to the embodiment aims to perform detailed health management by utilizing the internal body information of pets.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, a reading unit, an analysis unit, and an advice unit. The recording unit records information about the pet's internal organs. The reading unit reads the information recorded by the recording unit. The analysis unit analyzes the information read by the reading unit. The advice unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform detailed health management by utilizing the internal information of pets. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet health management system according to an embodiment of the present invention is a system that efficiently records, reads, analyzes, and provides advice on a pet's internal bodily information. The pet health management system records internal bodily information (body temperature, pulse, blood data, exercise level, medical visit records, chronic illnesses, etc.) on a microchip implanted in the pet. Next, the owner scans the microchip using a smartphone and reads the recorded information. This information can be used by the owner for daily health management. Furthermore, if the system determines that the pet is not in good health, it provides advice on diet and medication use within the smartphone app. For example, a generating AI analyzes the health data and suggests appropriate food and medication. Also, if illness is suspected, showing the generating AI data to a veterinarian enables prompt treatment. Furthermore, it is possible to send data to a hospital for pre-consultation. This system streamlines pet health management and enables owners to detect abnormalities in their pets early. In the future, it is expected that this technology can be applied to humans, enabling rapid treatment in medical settings such as nursing care and accidents. As a result, the pet health management system can efficiently record, read, analyze, and provide advice on a pet's internal bodily information.

[0029] The pet health management system according to this embodiment comprises a recording unit, a reading unit, an analysis unit, and an advice unit. The recording unit records the pet's internal information. The pet's internal information includes, but is not limited to, body temperature, heart rate, and blood pressure. The recording unit records the internal information using, for example, digital recording. The recording unit can also record on paper. Furthermore, the recording unit can record the internal information using sensors. For example, the recording unit can record the pet's body temperature using a body temperature sensor. It can also record the heart rate using a heart rate sensor. It can also record blood pressure using a blood pressure sensor. The reading unit reads the information recorded by the recording unit. The reading unit reads the information using, for example, a smartphone. Furthermore, the reading unit can read the information using sensors. Furthermore, the reading unit can read the information manually. For example, the reading unit reads the information using a smartphone camera. It can also read the information using sensors. It can also read the information manually. The analysis unit analyzes the information read by the reading unit. The analysis unit analyzes the information using, for example, a data analysis algorithm. Furthermore, the analysis unit can analyze information using statistical methods. In addition, the analysis unit can analyze information using machine learning models. For example, the analysis unit can analyze body temperature data using a data analysis algorithm. It can also analyze heart rate data using statistical methods. It can also analyze blood pressure data using machine learning models. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit provides, for example, dietary advice. It can also provide exercise advice. Furthermore, the advice unit can provide medication advice. For example, the advice unit provides appropriate dietary advice based on the analysis results. It can also provide exercise advice. It can also provide medication advice. Thus, the pet health management system according to this embodiment can efficiently record, read, analyze, and provide advice on the pet's internal bodily information.Some or all of the above-described processes in the recording unit, reading unit, analysis unit, and advice unit may be performed using AI, for example, or without AI. For example, the recording unit can input body temperature data acquired by a body temperature sensor into a generating AI and have the generating AI record the body temperature data. The reading unit can input information acquired by a smartphone camera into a generating AI and have the generating AI read the information. The analysis unit can input the read information into a generating AI and have the generating AI analyze the information. The advice unit can input the analysis results into a generating AI and have the generating AI provide advice.

[0030] The recording unit records the pet's internal bodily information. This information includes, but is not limited to, body temperature, heart rate, and blood pressure. The recording unit can record internal bodily information using, for example, digital recording. It can also record on paper. Furthermore, the recording unit can record internal bodily information using sensors. For example, it can record the pet's body temperature using a body temperature sensor, its heart rate using a heart rate sensor, and its blood pressure using a blood pressure sensor. The recording unit can use multiple sensors in combination to accurately and efficiently record the pet's internal bodily information. For example, by using a body temperature sensor and a heart rate sensor simultaneously, the pet's body temperature and heart rate can be recorded at the same time. Furthermore, the recording unit can record data acquired from sensors in real time. This ensures that the pet's internal bodily information is always recorded in an up-to-date state. The recording unit can also save data acquired from sensors to the cloud. This ensures that the pet's internal bodily information is securely stored and can be accessed at any time as needed. Furthermore, the recording unit can transmit data acquired from sensors to an analysis unit. This allows for rapid analysis of the pet's internal bodily information and the provision of appropriate advice. The recording unit can utilize AI to efficiently record the pet's internal body information. For example, body temperature data acquired by a temperature sensor can be input into a generating AI, which can then record the body temperature data. This allows the recording unit to accurately and efficiently record the pet's internal body information.

[0031] The reading unit reads information recorded by the recording unit. The reading unit can read information using, for example, a smartphone. It can also read information using sensors. Furthermore, the reading unit can read information manually. For example, the reading unit can read information using a smartphone camera. It can also read information using sensors. It can also read information manually. The reading unit can use a combination of methods to accurately and quickly read information recorded by the recording unit. For example, by using a smartphone camera and sensors simultaneously, information can be read accurately and quickly. Furthermore, the reading unit can use AI when reading information. For example, information acquired by a smartphone camera can be input into a generating AI, and the generating AI can perform the information reading. This allows the reading unit to read information accurately and quickly. The reading unit can also transmit the read information to an analysis unit. This allows for rapid analysis of the read information and the provision of appropriate advice. The reading unit can use AI to accurately and quickly read information. For example, information acquired by a smartphone camera can be input into a generating AI, and the generating AI can perform the information reading. This allows the reading unit to read information accurately and quickly.

[0032] The analysis unit analyzes the information read by the reading unit. The analysis unit can analyze the information using, for example, data analysis algorithms. It can also analyze the information using statistical methods. Furthermore, it can analyze the information using machine learning models. For example, the analysis unit can analyze body temperature data using data analysis algorithms. It can also analyze heart rate data using statistical methods. It can also analyze blood pressure data using machine learning models. The analysis unit can combine multiple analysis methods to accurately and quickly analyze the information read by the reading unit. For example, by simultaneously using data analysis algorithms and statistical methods, the information can be analyzed accurately and quickly. Furthermore, the analysis unit can use AI when analyzing information. For example, the read information can be input into a generating AI, and the generating AI can perform the analysis. This allows the analysis unit to analyze the information accurately and quickly. The analysis unit can also send the analysis results to the advice unit. This allows the advice unit to provide appropriate advice based on the analysis results. The analysis unit can use AI to accurately and quickly analyze information. For example, the read information can be input into a generating AI, and the generating AI can perform the analysis. This allows the analysis unit to analyze the information accurately and quickly.

[0033] The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, the advice unit can provide dietary advice. It can also provide exercise advice. Furthermore, it can provide medication advice. For example, the advice unit can provide appropriate dietary advice based on the analysis results. It can also provide exercise advice. It can also provide medication advice. Based on the analysis results obtained by the analysis unit, the advice unit can combine multiple advice methods to provide appropriate advice tailored to the pet's health condition. For example, by providing dietary and exercise advice simultaneously, the pet's health condition can be comprehensively improved. Furthermore, the advice unit can use AI when providing advice. For example, the analysis results can be input into a generating AI, and the generating AI can be made to provide advice. This allows the advice unit to provide appropriate advice accurately and quickly based on the analysis results. The advice unit can also monitor the effectiveness of the advice provided and modify the advice content as needed. This allows the advice unit to continuously improve the pet's health condition. The advice unit can use AI to provide appropriate advice based on the analysis results. For example, the analysis results can be input into a generating AI, which can then be used to provide advice. This allows the advice unit to provide appropriate advice accurately and quickly based on the analysis results.

[0034] The reading unit can read information using a smartphone. For example, the reading unit can read information using the smartphone's camera. The reading unit can also read information using the smartphone's NFC function. Furthermore, the reading unit can read information using a dedicated smartphone app. For example, the reading unit can read microchip information using the smartphone's camera. It can also read microchip information using the smartphone's NFC function. It can also read microchip information using a dedicated smartphone app. This allows pet owners to easily read information using their smartphones. Smartphones include, but are not limited to, iOS devices and Android devices. Some or all of the above-described processes in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input information acquired by the smartphone's camera into a generating AI and have the generating AI perform the information reading.

[0035] The advice unit can analyze health data using generative AI and provide appropriate diet and medication advice. For example, the advice unit can analyze health data using generative AI. The advice unit can also provide appropriate dietary advice using generative AI. Furthermore, the advice unit can also provide appropriate medication advice using generative AI. For example, the advice unit can analyze body temperature data using generative AI and provide appropriate dietary advice. It can also analyze heart rate data using generative AI and provide appropriate medication advice. It can also analyze blood pressure data using generative AI and provide appropriate dietary advice. In this way, by using generative AI, appropriate advice based on health data can be provided. Generative AI includes, but is not limited to, machine learning models and deep learning algorithms. Health data includes, but is not limited to, body temperature, heart rate, and blood pressure. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input health data into generative AI and have the generative AI provide appropriate diet and medication advice.

[0036] The advisory unit can enable rapid treatment by showing the veterinarian the data generated by the AI. The advisory unit can, for example, show the veterinarian the data generated by the AI. The advisory unit can also enable rapid treatment by showing the veterinarian the data generated by the AI. Furthermore, the advisory unit can provide appropriate treatment by showing the veterinarian the data generated by the AI. For example, the advisory unit can show the veterinarian the analysis results of the AI ​​generated. It can also show the veterinarian the prediction data of the AI ​​generated. It can also show the veterinarian the diagnostic data of the AI ​​generated. This makes rapid treatment possible by showing the veterinarian the data generated by the AI. The data generated by the AI ​​includes, but is not limited to, analysis results, prediction data, and diagnostic data. Some or all of the processing described above in the advisory unit may be performed using, for example, AI, or not using AI. For example, the advisory unit can input data into the AI ​​and have the AI ​​generate data to show to the veterinarian in order to show the veterinarian the analysis results of the AI ​​generated.

[0037] The reading unit may include a transmitting unit that sends data to the hospital. The reading unit can send data using, for example, wireless communication. The reading unit can also send data via the internet. Furthermore, the reading unit can send data using a dedicated application. For example, the reading unit can send data to the hospital using wireless communication. It can also send data to the hospital via the internet. It can also send data to the hospital using a dedicated application. This makes it possible to have a preliminary consultation by sending data to the hospital. The transmitting unit includes, but is not limited to, wireless communication and transmission via the internet. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input data acquired via wireless communication into a generating AI and have the generating AI perform the data transmission.

[0038] The transmission unit may include a consultation unit for conducting preliminary consultations. The transmission unit may conduct preliminary consultations using, for example, online chat. It may also conduct preliminary consultations using telephone consultations. Furthermore, the transmission unit may conduct preliminary consultations using a dedicated application. For example, the transmission unit may conduct preliminary consultations with hospitals using online chat. It may also conduct preliminary consultations with hospitals using telephone consultations. It may also conduct preliminary consultations with hospitals using a dedicated application. This allows for a quicker response by conducting preliminary consultations. The consultation unit includes, but is not limited to, online chat, telephone consultations, and dedicated applications. Some or all of the above-described processes in the transmission unit may be performed using, for example, AI, or not using AI. For example, the transmission unit may input data acquired through online chat into a generating AI and have the generating AI perform the preliminary consultation.

[0039] The recording unit can analyze the pet's activity patterns during recording and select the optimal recording timing. For example, the recording unit can analyze the times when the pet is actively moving and record data during those times. It can also analyze the times when the pet is resting and record data during those times. Furthermore, the recording unit can analyze the times after the pet has eaten and record data during those times. For example, the recording unit can record data during the times when the pet is actively moving. It can also record data during the times when the pet is resting. It can also record data after the pet has eaten. By selecting the optimal recording timing based on the pet's activity patterns, more accurate data can be recorded. Activity patterns include, but are not limited to, exercise levels and sleep patterns. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input pet activity data into a generating AI and have the generating AI select the optimal recording timing.

[0040] The recording unit can supplement data by considering the pet's diet and exercise history during recording. For example, the recording unit can record data after the pet has consumed a particular meal and analyze the effect of the meal. It can also record data after the pet has exercised and analyze the effect of the exercise. Furthermore, the recording unit can record data after the pet has taken a particular medication and analyze the effect of the medication. For example, the recording unit can record data after the pet has consumed a particular meal. It can also record data after the pet has exercised. It can also record data after the pet has taken a particular medication. This makes it possible to supplement data by considering the pet's diet and exercise history. The diet and exercise history includes, but is not limited to, the type of meal and the frequency of exercise. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input pet diet data into a generating AI and have the generating AI perform data supplementation.

[0041] The recording unit can supplement data by considering the pet's living environment information during recording. For example, if the pet is indoors, the recording unit will record data considering indoor temperature and humidity. The recording unit can also record data considering outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the recording unit can record data considering the environmental information of that location. For example, if the pet is indoors, the recording unit will record data considering indoor temperature and humidity. If the pet is outdoors, it can also record data considering outside temperature and weather. If the pet is in a specific location, it can also record data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0042] The recording unit can analyze the pet owner's lifestyle patterns and record relevant data during recording. For example, the recording unit can analyze the time periods when the owner is with the pet and record data during those times. Furthermore, if the owner manages the pet's diet and exercise, the recording unit can also record data considering that management information. Additionally, if the owner manages the pet's health, the recording unit can also record data considering that management information. For example, the recording unit records data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, it can also record data considering that management information. If the owner manages the pet's health, it can also record data considering that management information. This allows the recording of relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the owner's lifestyle pattern data into the generating AI and have the generating AI record the related data.

[0043] The reading unit can detect changes in the pet's physical condition in real time during reading and immediately read the data. For example, if the pet's body temperature rises sharply, the reading unit can immediately read the data. The reading unit can also immediately read the data if the pet's pulse becomes abnormally fast. Furthermore, the reading unit can immediately read the data if the pet's respiratory rate increases sharply. For example, if the pet's body temperature rises sharply, the reading unit can immediately read the data. If the pet's pulse becomes abnormally fast, the reading unit can also immediately read the data. If the pet's respiratory rate increases sharply, the reading unit can also immediately read the data. This allows for immediate data reading by detecting changes in the pet's physical condition in real time. Real-time detection includes, but is not limited to, the type of sensor and the frequency of data updates. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input pet physical condition data into a generating AI and have the generating AI perform real-time detection and immediate data reading.

[0044] The reading unit can select the optimal reading method when reading data, taking into account the pet's activity level. For example, if the pet is exercising, the reading unit will select a reading method suitable for exercise. It can also select a reading method suitable for resting if the pet is resting, and a reading method suitable for eating if the pet is eating. This allows for more accurate data to be obtained by selecting the optimal reading method based on the pet's activity level. Optimal reading methods include, but are not limited to, non-contact sensors and contact sensors. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input pet activity data into a generating AI and have the generating AI select the optimal reading method.

[0045] The reading unit can supplement data by considering the pet's living environment information during reading. For example, if the pet is indoors, the reading unit will read the data considering the indoor temperature and humidity. The reading unit can also read the data considering the outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the reading unit can read the data considering the environmental information of that location. For example, if the pet is indoors, the reading unit will read the data considering the indoor temperature and humidity. If the pet is outdoors, it can also read the data considering the outside temperature and weather. If the pet is in a specific location, it can also read the data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0046] The reading unit can analyze the pet owner's lifestyle patterns during reading and read relevant data. For example, the reading unit can analyze the time periods when the owner is with the pet and read data during those times. Furthermore, if the owner manages the pet's diet and exercise, the reading unit can also consider that management information when reading data. Additionally, if the owner manages the pet's health, the reading unit can also consider that management information when reading data. For example, the reading unit reads data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, it can also consider that management information when reading data. If the owner manages the pet's health, it can also consider that management information when reading data. This allows the reading unit to extract relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not. For example, the reading unit can input the owner's lifestyle pattern data into a generating AI, which can then perform the reading of related data.

[0047] The analysis unit can estimate the pet's emotions and adjust the analysis method based on the estimated emotions. For example, if the pet is stressed, the analysis unit will focus on analyzing stress-related data. If the pet is relaxed, the analysis unit can also focus on analyzing normal health data. Furthermore, if the pet is excited, the analysis unit can also focus on analyzing data related to the excited state. This allows for more appropriate analysis by adjusting the analysis method based on the pet's emotions. The analysis method includes, but is not limited to, algorithm selection and analysis parameter adjustment. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the analysis method.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis. For example, the analysis unit can refer to the pet's past body temperature data to analyze the current body temperature data. It can also refer to the pet's past pulse rate data to analyze the current pulse rate data. Furthermore, it can refer to the pet's past exercise data to analyze the current exercise level data. For example, the analysis unit can refer to the pet's past body temperature data to analyze the current body temperature data. It can also refer to the pet's past pulse rate data to analyze the current pulse rate data. It can also refer to the pet's past exercise level data to analyze the current exercise level data. This improves the accuracy of the analysis by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnostic results and treatment history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the pet's past health data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0049] The analysis unit can perform analysis while considering the pet's living environment and dietary history. For example, the analysis unit can analyze data while considering the pet's living environment (indoors, outdoors, etc.). The analysis unit can also analyze data while considering the pet's dietary history. Furthermore, the analysis unit can also analyze data while considering the pet's exercise history. For example, the analysis unit can analyze data while considering the pet's living environment. It can also analyze data while considering the pet's dietary history. It can also analyze data while considering the pet's exercise history. This makes it possible to perform a more accurate analysis by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, living environment and type of food. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input pet living environment data into a generating AI and have the generating AI perform the analysis.

[0050] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. It can also highlight normal health data if the pet is relaxed. Furthermore, if the pet is excited, the analysis unit can highlight data related to the excited state. This allows for a more appropriate display by adjusting the display method of the analysis results based on the pet's emotions. Display methods for analysis results include, but are not limited to, graph displays and text displays. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0051] The analysis unit can perform analysis while considering the pet's living environment information. For example, if the pet is indoors, the analysis unit will analyze the data while considering indoor temperature and humidity. Furthermore, if the pet is outdoors, the analysis unit can also analyze the data while considering outside temperature and weather. In addition, if the pet is in a specific location, the analysis unit can also analyze the data while considering the environmental information of that location. For example, if the pet is indoors, the analysis unit will analyze the data while considering indoor temperature and humidity. If the pet is outdoors, it can also analyze the data while considering outside temperature and weather. If the pet is in a specific location, it can also analyze the data while considering the environmental information of that location. This allows for a more accurate analysis by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's living environment data into a generating AI and have the generating AI perform the analysis.

[0052] The analysis unit can analyze the lifestyle patterns of pet owners and analyze related data during the analysis process. For example, the analysis unit can analyze the time periods when owners are with their pets and analyze the data for those periods. Furthermore, if the owner manages the pet's diet and exercise, the analysis unit can also analyze the data while considering that management information. In addition, if the owner manages the pet's health, the analysis unit can also analyze the data while considering that management information. For example, the analysis unit analyzes the data for the time periods when owners are with their pets. If the owner manages the pet's diet and exercise, the analysis unit can also analyze the data while considering that management information. If the owner manages the pet's health, the analysis unit can also analyze the data while considering that management information. This allows for the analysis of related data by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input owner lifestyle pattern data into the generating AI and have the generating AI perform analysis of the related data.

[0053] The advice unit can provide optimal advice by referring to the pet's past health data when giving advice. For example, the advice unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. Furthermore, the advice unit can refer to the pet's past dietary data to provide advice on appropriate dietary content. For example, the advice unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. It can also refer to the pet's past dietary data to provide advice on appropriate dietary content. This allows the advice unit to provide optimal advice by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnostic results and treatment history. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input the pet's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0054] The advice unit can provide advice while considering the pet's living environment and dietary history. For example, the advice unit can provide appropriate advice on the pet's living environment (indoors, outdoors, etc.) by considering the pet's living environment. It can also provide advice on appropriate dietary content by considering the pet's dietary history. Furthermore, it can provide advice on appropriate exercise levels by considering the pet's exercise history. For example, the advice unit can provide appropriate advice on the pet's living environment by considering the pet's living environment. It can also provide advice on appropriate dietary content by considering the pet's dietary history. It can also provide advice on appropriate exercise levels by considering the pet's exercise history. This allows for the provision of more appropriate advice by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, living environment and type of food. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input pet living environment data into a generating AI and have the generating AI execute the advice.

[0055] The advice unit can provide advice while considering the pet's living environment information. For example, if the pet is indoors, the advice unit will provide advice appropriate to the indoor environment. It can also provide advice appropriate to the outdoor environment if the pet is outdoors. Furthermore, if the pet is in a specific location, the advice unit can provide advice appropriate to the environment of that location. This allows for more appropriate advice to be provided by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise levels. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not. For example, the advice unit can input the pet's living environment data into a generating AI and have the generating AI execute the advice.

[0056] The advice unit can analyze the pet owner's lifestyle patterns when providing advice and offer relevant advice. For example, the advice unit can analyze the time periods when the owner is with their pet and provide advice appropriate to those times. Furthermore, if the owner manages the pet's diet and exercise, the advice unit can also consider that management information when providing advice. Additionally, if the owner manages the pet's health, the advice unit can also consider that management information when providing advice. For example, the advice unit can provide advice appropriate to the time periods when the owner is with their pet. If the owner manages the pet's diet and exercise, the advice unit can also consider that management information when providing advice. If the owner manages the pet's health, the advice unit can also consider that management information when providing advice. This allows relevant advice to be provided by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not. For example, the advice unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI provide relevant advice.

[0057] The transmitting unit can detect changes in the pet's physical condition in real time during transmission and transmit data immediately. For example, if the pet's body temperature rises sharply, the transmitting unit will immediately transmit data. The transmitting unit can also immediately transmit data if the pet's pulse rate becomes abnormally fast. Furthermore, the transmitting unit can immediately transmit data if the pet's respiratory rate increases sharply. For example, if the pet's body temperature rises sharply, the transmitting unit will immediately transmit data. If the pet's pulse rate becomes abnormally fast, the transmitting unit can also immediately transmit data. If the pet's respiratory rate increases sharply, the transmitting unit can also immediately transmit data. This allows for real-time detection of changes in the pet's physical condition and immediate data transmission. Real-time detection includes, but is not limited to, the type of sensor and the frequency of data updates. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input pet physical condition data into a generating AI and have the generating AI perform real-time detection and immediate data transmission.

[0058] The transmitting unit can select the optimal transmission method when transmitting data, taking into account the pet's activity level. For example, if the pet is exercising, the transmitting unit will select a transmission method suitable for exercise. If the pet is resting, the transmitting unit can also select a transmission method suitable for resting. Furthermore, if the pet is eating, the transmitting unit can also select a transmission method suitable for eating. By selecting the optimal transmission method based on the pet's activity level, more accurate data can be transmitted. Optimal transmission methods include, but are not limited to, wireless communication and transmission via the internet. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input pet activity data into a generating AI and have the generating AI select the optimal transmission method.

[0059] The transmitting unit can supplement data by considering the pet's living environment information during transmission. For example, if the pet is indoors, the transmitting unit will transmit data considering the indoor temperature and humidity. The transmitting unit can also transmit data considering the outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the transmitting unit can transmit data considering the environmental information of that location. For example, if the pet is indoors, the transmitting unit will transmit data considering the indoor temperature and humidity. If the pet is outdoors, it can also transmit data considering the outside temperature and weather. If the pet is in a specific location, it can also transmit data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not. For example, the transmitting unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0060] The transmitting unit can analyze the pet owner's lifestyle patterns and transmit relevant data at the time of transmission. For example, the transmitting unit can analyze the time periods when the owner is with the pet and transmit data during those times. Furthermore, if the owner manages the pet's diet and exercise, the transmitting unit can also transmit data considering that management information. Additionally, if the owner manages the pet's health, the transmitting unit can also transmit data considering that management information. For example, the transmitting unit transmits data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, the transmitting unit can also transmit data considering that management information. If the owner manages the pet's health, the transmitting unit can also transmit data considering that management information. This allows the transmitting unit to transmit relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not. For example, the transmitting unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI execute the transmission of relevant data.

[0061] The consultation unit can provide optimal advice by referring to the pet's past health data during a consultation. For example, the consultation unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. Furthermore, the consultation unit can refer to the pet's past dietary data to provide advice on appropriate dietary content. For example, the consultation unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. It can also refer to the pet's past dietary data to provide advice on appropriate dietary content. In this way, optimal advice can be provided by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnosis results and treatment history. Some or all of the above processing in the consultation unit may be performed using, for example, AI, or not using AI. For example, the consultation unit can input the pet's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0062] The consultation department can provide advice while considering the pet's living environment and dietary history. For example, the consultation department can provide appropriate advice on the pet's living environment (indoors, outdoors, etc.) by considering the pet's living environment. The consultation department can also provide appropriate advice on the pet's diet by considering the pet's dietary history. Furthermore, the consultation department can provide advice on the appropriate amount of exercise by considering the pet's exercise history. For example, the consultation department can provide appropriate advice on the pet's living environment by considering the pet's living environment. It can also provide appropriate advice on the pet's diet by considering the pet's dietary history. It can also provide advice on the appropriate amount of exercise by considering the pet's exercise history. This allows for more appropriate advice to be provided by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, the living environment and the type of food eaten. Some or all of the above processing in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input pet living environment data into a generating AI and have the generating AI execute the consultation.

[0063] The consultation unit can provide advice while considering the pet's living environment information. For example, if the pet is indoors, the consultation unit can provide advice appropriate to the indoor environment. If the pet is outdoors, the consultation unit can also provide advice appropriate to the outdoor environment. Furthermore, if the pet is in a specific location, the consultation unit can also provide advice appropriate to the environment of that location. For example, if the pet is indoors, the consultation unit can provide advice appropriate to the indoor environment. If the pet is outdoors, it can also provide advice appropriate to the outdoor environment. If the pet is in a specific location, it can also provide advice appropriate to the environment of that location. This allows for the provision of more appropriate advice by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the consultation unit may be performed using, for example, AI, or not using AI. For example, the consultation unit can input the pet's living environment data into a generating AI and have the generating AI execute the consultation.

[0064] The consultation department can analyze the pet owner's lifestyle patterns during consultations and provide relevant consultation content. For example, the consultation department can analyze the time periods when the owner is with their pet and provide consultation content appropriate to those times. Furthermore, if the owner manages the pet's diet and exercise, the consultation department can also provide consultation content considering that management information. In addition, if the owner manages the pet's health, the consultation department can also provide consultation content considering that management information. For example, the consultation department can provide consultation content appropriate to the time periods when the owner is with their pet. If the owner manages the pet's diet and exercise, the consultation department can also provide consultation content considering that management information. If the owner manages the pet's health, the consultation department can also provide consultation content considering that management information. In this way, relevant consultation content can be provided by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input data on the pet owner's lifestyle patterns into a generating AI and have the AI ​​provide relevant consultation content.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The pet health management system may further include a behavioral analysis unit that analyzes the pet's behavioral patterns. The behavioral analysis unit can monitor the pet's movements and behaviors and detect abnormal behavioral patterns. For example, if the behavioral analysis unit drinks water more frequently than usual, it may suggest a potential health problem. The behavioral analysis unit can also detect signs of lack of exercise or stress if the pet is not maintaining a normal activity level. Furthermore, if the behavioral analysis unit repeatedly performs a specific action during a specific time period, it can record that behavioral pattern and notify the owner. This allows for early detection of health problems and appropriate countermeasures by analyzing the pet's behavioral patterns. Some or all of the above processing in the behavioral analysis unit may be performed using, for example, machine learning algorithms, or without machine learning algorithms. For example, the behavioral analysis unit can input pet behavioral data into a generating AI and have the generating AI perform abnormal behavior detection.

[0067] The pet health management system may also include a cloud storage unit that stores pet health data in the cloud, making it accessible from multiple devices. The cloud storage unit can store data such as the pet's body temperature, pulse rate, and activity level in the cloud. It can also store records of pet medical visits and data related to pre-existing conditions. Furthermore, the cloud storage unit can allow pet owners to access the data from multiple devices such as smartphones, tablets, and computers. For example, the cloud storage unit can store the pet's body temperature data in the cloud. It can also store records of pet medical visits in the cloud. It can also allow pet owners to access the data from multiple devices such as smartphones, tablets, and computers. This makes data management easier and allows access from multiple devices by storing pet health data in the cloud. Some or all of the above-described processes in the cloud storage unit may be performed using, for example, cloud computing technology, or not. For example, the cloud storage unit can upload pet health data to the cloud and have cloud computing technology perform the data storage.

[0068] The pet health management system may further include a data display unit that visually displays the pet's health data. For example, the data display unit can display changes in the pet's body temperature and pulse rate in a graph. It can also display the pet's exercise level and feeding history in a timeline format. Furthermore, the data display unit can provide a dashboard that allows owners to grasp the pet's health status at a glance. This allows pet owners to easily understand the data and take appropriate action by visually displaying the pet's health data. Some or all of the above-described processing in the data display unit may be performed using, for example, data visualization technology, or without data visualization technology. For example, the data display unit can input pet health data into a generating AI and have the generating AI perform the visual display.

[0069] The pet health management system may further include a sharing section for sharing pet health data. This sharing section can, for example, share pet health data with family members or veterinarians. It can also share pet health data on social media or dedicated community sites. Furthermore, the sharing section can allow for privacy settings when sharing pet health data. This allows family members and veterinarians to understand the pet's health status and take appropriate action. Some or all of the above-described processes in the sharing section may be performed using data sharing technology, or without it. For example, the sharing section can input pet health data into a generating AI and have the generating AI perform the data sharing.

[0070] The pet health management system may further include a preventative measures suggestion unit that proposes preventative measures based on the pet's health data. For example, the preventative measures suggestion unit may suggest the appropriate timing for vaccinations based on the pet's body temperature and pulse rate data. It can also provide advice on appropriate exercise and diet based on the pet's exercise and diet data. Furthermore, it can suggest a schedule for regular health checkups based on the pet's medical visit records. This allows for the maintenance of pet health and disease prevention by suggesting preventative measures based on the pet's health data. Some or all of the above-described processes in the preventative measures suggestion unit may be performed using, for example, a data analysis algorithm, or without one. For example, the preventative measures suggestion unit can input pet health data into a generating AI and have the generating AI execute preventative measures suggestions.

[0071] The pet health management system may further include a risk prediction unit that predicts health risks based on the pet's health data. For example, the risk prediction unit can predict future health risks based on data such as the pet's body temperature and pulse rate. It can also predict the risk of lifestyle-related diseases based on data such as the pet's exercise level and diet. Furthermore, it can predict the risk of specific diseases based on the pet's medical visit records. This allows for early intervention by predicting health risks based on the pet's health data. Some or all of the above-described processes in the risk prediction unit may be performed using, for example, data analysis algorithms, or without them. For example, the risk prediction unit can input pet health data into a generating AI and have the generating AI perform health risk predictions.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The recording unit records the pet's internal information. This information includes, for example, body temperature, heart rate, and blood pressure. The recording unit records this information using digital recording, paper media, or sensors. For example, it records body temperature using a body temperature sensor, heart rate using a heart rate sensor, and blood pressure using a blood pressure sensor. Step 2: The reading unit reads the information recorded by the recording unit. The reading unit can read the information using a smartphone, a sensor, or manually. For example, it can read the information using a smartphone camera. Step 3: The analysis unit analyzes the information read by the reading unit. The analysis unit analyzes the information using data analysis algorithms, statistical methods, or machine learning models. For example, it might analyze body temperature data using a data analysis algorithm, heart rate data using statistical methods, and blood pressure data using a machine learning model. Step 4: The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit can provide advice on diet, exercise, or medication. For example, it can provide appropriate dietary advice, exercise advice, or medication advice based on the analysis results.

[0074] (Example of form 2) The pet health management system according to an embodiment of the present invention is a system that efficiently records, reads, analyzes, and provides advice on a pet's internal bodily information. The pet health management system records internal bodily information (body temperature, pulse, blood data, exercise level, medical visit records, chronic illnesses, etc.) on a microchip implanted in the pet. Next, the owner scans the microchip using a smartphone and reads the recorded information. This information can be used by the owner for daily health management. Furthermore, if the system determines that the pet is not in good health, it provides advice on diet and medication use within the smartphone app. For example, a generating AI analyzes the health data and suggests appropriate food and medication. Also, if illness is suspected, showing the generating AI data to a veterinarian enables prompt treatment. Furthermore, it is possible to send data to a hospital for pre-consultation. This system streamlines pet health management and enables owners to detect abnormalities in their pets early. In the future, it is expected that this technology can be applied to humans, enabling rapid treatment in medical settings such as nursing care and accidents. As a result, the pet health management system can efficiently record, read, analyze, and provide advice on a pet's internal bodily information.

[0075] The pet health management system according to this embodiment comprises a recording unit, a reading unit, an analysis unit, and an advice unit. The recording unit records the pet's internal information. The pet's internal information includes, but is not limited to, body temperature, heart rate, and blood pressure. The recording unit records the internal information using, for example, digital recording. The recording unit can also record on paper. Furthermore, the recording unit can record the internal information using sensors. For example, the recording unit can record the pet's body temperature using a body temperature sensor. It can also record the heart rate using a heart rate sensor. It can also record blood pressure using a blood pressure sensor. The reading unit reads the information recorded by the recording unit. The reading unit reads the information using, for example, a smartphone. Furthermore, the reading unit can read the information using sensors. Furthermore, the reading unit can read the information manually. For example, the reading unit reads the information using a smartphone camera. It can also read the information using sensors. It can also read the information manually. The analysis unit analyzes the information read by the reading unit. The analysis unit analyzes the information using, for example, a data analysis algorithm. Furthermore, the analysis unit can analyze information using statistical methods. In addition, the analysis unit can analyze information using machine learning models. For example, the analysis unit can analyze body temperature data using a data analysis algorithm. It can also analyze heart rate data using statistical methods. It can also analyze blood pressure data using machine learning models. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit provides, for example, dietary advice. It can also provide exercise advice. Furthermore, the advice unit can provide medication advice. For example, the advice unit provides appropriate dietary advice based on the analysis results. It can also provide exercise advice. It can also provide medication advice. Thus, the pet health management system according to this embodiment can efficiently record, read, analyze, and provide advice on the pet's internal bodily information.Some or all of the above-described processes in the recording unit, reading unit, analysis unit, and advice unit may be performed using AI, for example, or without AI. For example, the recording unit can input body temperature data acquired by a body temperature sensor into a generating AI and have the generating AI record the body temperature data. The reading unit can input information acquired by a smartphone camera into a generating AI and have the generating AI read the information. The analysis unit can input the read information into a generating AI and have the generating AI analyze the information. The advice unit can input the analysis results into a generating AI and have the generating AI provide advice.

[0076] The recording unit records the pet's internal bodily information. This information includes, but is not limited to, body temperature, heart rate, and blood pressure. The recording unit can record internal bodily information using, for example, digital recording. It can also record on paper. Furthermore, the recording unit can record internal bodily information using sensors. For example, it can record the pet's body temperature using a body temperature sensor, its heart rate using a heart rate sensor, and its blood pressure using a blood pressure sensor. The recording unit can use multiple sensors in combination to accurately and efficiently record the pet's internal bodily information. For example, by using a body temperature sensor and a heart rate sensor simultaneously, the pet's body temperature and heart rate can be recorded at the same time. Furthermore, the recording unit can record data acquired from sensors in real time. This ensures that the pet's internal bodily information is always recorded in an up-to-date state. The recording unit can also save data acquired from sensors to the cloud. This ensures that the pet's internal bodily information is securely stored and can be accessed at any time as needed. Furthermore, the recording unit can transmit data acquired from sensors to an analysis unit. This allows for rapid analysis of the pet's internal bodily information and the provision of appropriate advice. The recording unit can utilize AI to efficiently record the pet's internal body information. For example, body temperature data acquired by a temperature sensor can be input into a generating AI, which can then record the body temperature data. This allows the recording unit to accurately and efficiently record the pet's internal body information.

[0077] The reading unit reads information recorded by the recording unit. The reading unit can read information using, for example, a smartphone. It can also read information using sensors. Furthermore, the reading unit can read information manually. For example, the reading unit can read information using a smartphone camera. It can also read information using sensors. It can also read information manually. The reading unit can use a combination of methods to accurately and quickly read information recorded by the recording unit. For example, by using a smartphone camera and sensors simultaneously, information can be read accurately and quickly. Furthermore, the reading unit can use AI when reading information. For example, information acquired by a smartphone camera can be input into a generating AI, and the generating AI can perform the information reading. This allows the reading unit to read information accurately and quickly. The reading unit can also transmit the read information to an analysis unit. This allows for rapid analysis of the read information and the provision of appropriate advice. The reading unit can use AI to accurately and quickly read information. For example, information acquired by a smartphone camera can be input into a generating AI, and the generating AI can perform the information reading. This allows the reading unit to read information accurately and quickly.

[0078] The analysis unit analyzes the information read by the reading unit. The analysis unit can analyze the information using, for example, data analysis algorithms. It can also analyze the information using statistical methods. Furthermore, it can analyze the information using machine learning models. For example, the analysis unit can analyze body temperature data using data analysis algorithms. It can also analyze heart rate data using statistical methods. It can also analyze blood pressure data using machine learning models. The analysis unit can combine multiple analysis methods to accurately and quickly analyze the information read by the reading unit. For example, by simultaneously using data analysis algorithms and statistical methods, the information can be analyzed accurately and quickly. Furthermore, the analysis unit can use AI when analyzing information. For example, the read information can be input into a generating AI, and the generating AI can perform the analysis. This allows the analysis unit to analyze the information accurately and quickly. The analysis unit can also send the analysis results to the advice unit. This allows the advice unit to provide appropriate advice based on the analysis results. The analysis unit can use AI to accurately and quickly analyze information. For example, the read information can be input into a generating AI, and the generating AI can perform the analysis. This allows the analysis unit to analyze the information accurately and quickly.

[0079] The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, the advice unit can provide dietary advice. It can also provide exercise advice. Furthermore, it can provide medication advice. For example, the advice unit can provide appropriate dietary advice based on the analysis results. It can also provide exercise advice. It can also provide medication advice. Based on the analysis results obtained by the analysis unit, the advice unit can combine multiple advice methods to provide appropriate advice tailored to the pet's health condition. For example, by providing dietary and exercise advice simultaneously, the pet's health condition can be comprehensively improved. Furthermore, the advice unit can use AI when providing advice. For example, the analysis results can be input into a generating AI, and the generating AI can be made to provide advice. This allows the advice unit to provide appropriate advice accurately and quickly based on the analysis results. The advice unit can also monitor the effectiveness of the advice provided and modify the advice content as needed. This allows the advice unit to continuously improve the pet's health condition. The advice unit can use AI to provide appropriate advice based on the analysis results. For example, the analysis results can be input into a generating AI, which can then be used to provide advice. This allows the advice unit to provide appropriate advice accurately and quickly based on the analysis results.

[0080] The reading unit can read information using a smartphone. For example, the reading unit can read information using the smartphone's camera. The reading unit can also read information using the smartphone's NFC function. Furthermore, the reading unit can read information using a dedicated smartphone app. For example, the reading unit can read microchip information using the smartphone's camera. It can also read microchip information using the smartphone's NFC function. It can also read microchip information using a dedicated smartphone app. This allows pet owners to easily read information using their smartphones. Smartphones include, but are not limited to, iOS devices and Android devices. Some or all of the above-described processes in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input information acquired by the smartphone's camera into a generating AI and have the generating AI perform the information reading.

[0081] The advice unit can analyze health data using generative AI and provide appropriate diet and medication advice. For example, the advice unit can analyze health data using generative AI. The advice unit can also provide appropriate dietary advice using generative AI. Furthermore, the advice unit can also provide appropriate medication advice using generative AI. For example, the advice unit can analyze body temperature data using generative AI and provide appropriate dietary advice. It can also analyze heart rate data using generative AI and provide appropriate medication advice. It can also analyze blood pressure data using generative AI and provide appropriate dietary advice. In this way, by using generative AI, appropriate advice based on health data can be provided. Generative AI includes, but is not limited to, machine learning models and deep learning algorithms. Health data includes, but is not limited to, body temperature, heart rate, and blood pressure. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input health data into generative AI and have the generative AI provide appropriate diet and medication advice.

[0082] The advisory unit can enable rapid treatment by showing the veterinarian the data generated by the AI. The advisory unit can, for example, show the veterinarian the data generated by the AI. The advisory unit can also enable rapid treatment by showing the veterinarian the data generated by the AI. Furthermore, the advisory unit can provide appropriate treatment by showing the veterinarian the data generated by the AI. For example, the advisory unit can show the veterinarian the analysis results of the AI ​​generated. It can also show the veterinarian the prediction data of the AI ​​generated. It can also show the veterinarian the diagnostic data of the AI ​​generated. This makes rapid treatment possible by showing the veterinarian the data generated by the AI. The data generated by the AI ​​includes, but is not limited to, analysis results, prediction data, and diagnostic data. Some or all of the processing described above in the advisory unit may be performed using, for example, AI, or not using AI. For example, the advisory unit can input data into the AI ​​and have the AI ​​generate data to show to the veterinarian in order to show the veterinarian the analysis results of the AI ​​generated.

[0083] The reading unit may include a transmitting unit that sends data to the hospital. The reading unit can send data using, for example, wireless communication. The reading unit can also send data via the internet. Furthermore, the reading unit can send data using a dedicated application. For example, the reading unit can send data to the hospital using wireless communication. It can also send data to the hospital via the internet. It can also send data to the hospital using a dedicated application. This makes it possible to have a preliminary consultation by sending data to the hospital. The transmitting unit includes, but is not limited to, wireless communication and transmission via the internet. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input data acquired via wireless communication into a generating AI and have the generating AI perform the data transmission.

[0084] The transmission unit may include a consultation unit for conducting preliminary consultations. The transmission unit may conduct preliminary consultations using, for example, online chat. It may also conduct preliminary consultations using telephone consultations. Furthermore, the transmission unit may conduct preliminary consultations using a dedicated application. For example, the transmission unit may conduct preliminary consultations with hospitals using online chat. It may also conduct preliminary consultations with hospitals using telephone consultations. It may also conduct preliminary consultations with hospitals using a dedicated application. This allows for a quicker response by conducting preliminary consultations. The consultation unit includes, but is not limited to, online chat, telephone consultations, and dedicated applications. Some or all of the above-described processes in the transmission unit may be performed using, for example, AI, or not using AI. For example, the transmission unit may input data acquired through online chat into a generating AI and have the generating AI perform the preliminary consultation.

[0085] The recording unit can estimate the pet's emotions and adjust the types of data recorded based on the estimated emotions. For example, if the pet is stressed, the recording unit will prioritize recording data related to the stress level (heart rate, body temperature, etc.). If the pet is relaxed, the recording unit can also record normal health data (body temperature, activity level, etc.). Furthermore, if the pet is excited, the recording unit can also record data related to the state of excitement (pulse rate, respiratory rate, etc.). For example, if the pet is stressed, the recording unit will prioritize recording heart rate and body temperature. If the pet is relaxed, it can also record normal health data. If the pet is excited, it can also record pulse rate and respiratory rate. This allows for the recording of more appropriate data by adjusting the types of data recorded based on the pet's emotions. The pet's emotions are estimated using, for example, behavioral analysis, facial expression analysis, etc., but are not limited to these examples. Some or all of the above processing in the recording unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. For example, the recording unit can input pet behavior data into the generative AI and have the generative AI perform emotion estimation.

[0086] The recording unit can analyze the pet's activity patterns during recording and select the optimal recording timing. For example, the recording unit can analyze the times when the pet is actively moving and record data during those times. It can also analyze the times when the pet is resting and record data during those times. Furthermore, the recording unit can analyze the times after the pet has eaten and record data during those times. For example, the recording unit can record data during the times when the pet is actively moving. It can also record data during the times when the pet is resting. It can also record data after the pet has eaten. By selecting the optimal recording timing based on the pet's activity patterns, more accurate data can be recorded. Activity patterns include, but are not limited to, exercise levels and sleep patterns. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input pet activity data into a generating AI and have the generating AI select the optimal recording timing.

[0087] The recording unit can supplement data by considering the pet's diet and exercise history during recording. For example, the recording unit can record data after the pet has consumed a particular meal and analyze the effect of the meal. It can also record data after the pet has exercised and analyze the effect of the exercise. Furthermore, the recording unit can record data after the pet has taken a particular medication and analyze the effect of the medication. For example, the recording unit can record data after the pet has consumed a particular meal. It can also record data after the pet has exercised. It can also record data after the pet has taken a particular medication. This makes it possible to supplement data by considering the pet's diet and exercise history. The diet and exercise history includes, but is not limited to, the type of meal and the frequency of exercise. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input pet diet data into a generating AI and have the generating AI perform data supplementation.

[0088] The recording unit can estimate the pet's emotions and determine the priority of data to record based on the estimated emotions. For example, if the pet is stressed, the recording unit will prioritize recording stress-related data. It can also prioritize recording normal health data if the pet is relaxed. Furthermore, if the pet is excited, the recording unit can prioritize recording data related to the excited state. This allows for the priority recording of important data by prioritizing data based on the pet's emotions. Data prioritization includes, but is not limited to, importance and urgency. Some or all of the above processing in the recording unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the recording unit can input pet emotional data into a generating AI and have the generating AI determine the priority of the data.

[0089] The recording unit can supplement data by considering the pet's living environment information during recording. For example, if the pet is indoors, the recording unit will record data considering indoor temperature and humidity. The recording unit can also record data considering outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the recording unit can record data considering the environmental information of that location. For example, if the pet is indoors, the recording unit will record data considering indoor temperature and humidity. If the pet is outdoors, it can also record data considering outside temperature and weather. If the pet is in a specific location, it can also record data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0090] The recording unit can analyze the pet owner's lifestyle patterns and record relevant data during recording. For example, the recording unit can analyze the time periods when the owner is with the pet and record data during those times. Furthermore, if the owner manages the pet's diet and exercise, the recording unit can also record data considering that management information. Additionally, if the owner manages the pet's health, the recording unit can also record data considering that management information. For example, the recording unit records data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, it can also record data considering that management information. If the owner manages the pet's health, it can also record data considering that management information. This allows the recording of relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the owner's lifestyle pattern data into the generating AI and have the generating AI record the related data.

[0091] The reading unit can estimate the pet's emotions and adjust the timing of readings based on the estimated emotions. For example, the reading unit can select a time to read if the pet is relaxed. It can also select a time to read if the pet is excited. Furthermore, it can select a time to read if the pet is stressed. By adjusting the timing of readings based on the pet's emotions, data can be read at a more appropriate time. Timing of readings includes, but is not limited to, periodic readings and event-based readings. Some or all of the above processing in the reading unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the reading unit can input pet emotional data into a generating AI, which can then adjust the timing of the reading.

[0092] The reading unit can detect changes in the pet's physical condition in real time during reading and immediately read the data. For example, if the pet's body temperature rises sharply, the reading unit can immediately read the data. The reading unit can also immediately read the data if the pet's pulse becomes abnormally fast. Furthermore, the reading unit can immediately read the data if the pet's respiratory rate increases sharply. For example, if the pet's body temperature rises sharply, the reading unit can immediately read the data. If the pet's pulse becomes abnormally fast, the reading unit can also immediately read the data. If the pet's respiratory rate increases sharply, the reading unit can also immediately read the data. This allows for immediate data reading by detecting changes in the pet's physical condition in real time. Real-time detection includes, but is not limited to, the type of sensor and the frequency of data updates. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input pet physical condition data into a generating AI and have the generating AI perform real-time detection and immediate data reading.

[0093] The reading unit can select the optimal reading method when reading data, taking into account the pet's activity level. For example, if the pet is exercising, the reading unit will select a reading method suitable for exercise. It can also select a reading method suitable for resting if the pet is resting, and a reading method suitable for eating if the pet is eating. This allows for more accurate data to be obtained by selecting the optimal reading method based on the pet's activity level. Optimal reading methods include, but are not limited to, non-contact sensors and contact sensors. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input pet activity data into a generating AI and have the generating AI select the optimal reading method.

[0094] The reading unit can estimate the pet's emotions and determine the priority of data to read based on the estimated emotions. For example, if the pet is stressed, the reading unit will prioritize reading stress-related data. It can also prioritize reading normal health data if the pet is relaxed. Furthermore, if the pet is excited, the reading unit can prioritize reading data related to the excited state. This allows for the priority of important data to be read by determining data priorities based on the pet's emotions. Data priorities include, but are not limited to, importance and urgency. Some or all of the above processing in the reading unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the reading unit can input pet emotional data into a generating AI, which can then perform the task of determining the priority of the data.

[0095] The reading unit can supplement data by considering the pet's living environment information during reading. For example, if the pet is indoors, the reading unit will read the data considering the indoor temperature and humidity. The reading unit can also read the data considering the outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the reading unit can read the data considering the environmental information of that location. For example, if the pet is indoors, the reading unit will read the data considering the indoor temperature and humidity. If the pet is outdoors, it can also read the data considering the outside temperature and weather. If the pet is in a specific location, it can also read the data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0096] The reading unit can analyze the pet owner's lifestyle patterns during reading and read relevant data. For example, the reading unit can analyze the time periods when the owner is with the pet and read data during those times. Furthermore, if the owner manages the pet's diet and exercise, the reading unit can also consider that management information when reading data. Additionally, if the owner manages the pet's health, the reading unit can also consider that management information when reading data. For example, the reading unit reads data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, it can also consider that management information when reading data. If the owner manages the pet's health, it can also consider that management information when reading data. This allows the reading unit to extract relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not. For example, the reading unit can input the owner's lifestyle pattern data into a generating AI, which can then perform the reading of related data.

[0097] The analysis unit can estimate the pet's emotions and adjust the analysis method based on the estimated emotions. For example, if the pet is stressed, the analysis unit will focus on analyzing stress-related data. If the pet is relaxed, the analysis unit can also focus on analyzing normal health data. Furthermore, if the pet is excited, the analysis unit can also focus on analyzing data related to the excited state. This allows for more appropriate analysis by adjusting the analysis method based on the pet's emotions. The analysis method includes, but is not limited to, algorithm selection and analysis parameter adjustment. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the analysis method.

[0098] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis. For example, the analysis unit can refer to the pet's past body temperature data to analyze the current body temperature data. It can also refer to the pet's past pulse rate data to analyze the current pulse rate data. Furthermore, it can refer to the pet's past exercise data to analyze the current exercise level data. For example, the analysis unit can refer to the pet's past body temperature data to analyze the current body temperature data. It can also refer to the pet's past pulse rate data to analyze the current pulse rate data. It can also refer to the pet's past exercise level data to analyze the current exercise level data. This improves the accuracy of the analysis by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnostic results and treatment history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the pet's past health data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0099] The analysis unit can perform analysis while considering the pet's living environment and dietary history. For example, the analysis unit can analyze data while considering the pet's living environment (indoors, outdoors, etc.). The analysis unit can also analyze data while considering the pet's dietary history. Furthermore, the analysis unit can also analyze data while considering the pet's exercise history. For example, the analysis unit can analyze data while considering the pet's living environment. It can also analyze data while considering the pet's dietary history. It can also analyze data while considering the pet's exercise history. This makes it possible to perform a more accurate analysis by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, living environment and type of food. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input pet living environment data into a generating AI and have the generating AI perform the analysis.

[0100] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. It can also highlight normal health data if the pet is relaxed. Furthermore, if the pet is excited, the analysis unit can highlight data related to the excited state. This allows for a more appropriate display by adjusting the display method of the analysis results based on the pet's emotions. Display methods for analysis results include, but are not limited to, graph displays and text displays. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0101] The analysis unit can perform analysis while considering the pet's living environment information. For example, if the pet is indoors, the analysis unit will analyze the data while considering indoor temperature and humidity. Furthermore, if the pet is outdoors, the analysis unit can also analyze the data while considering outside temperature and weather. In addition, if the pet is in a specific location, the analysis unit can also analyze the data while considering the environmental information of that location. For example, if the pet is indoors, the analysis unit will analyze the data while considering indoor temperature and humidity. If the pet is outdoors, it can also analyze the data while considering outside temperature and weather. If the pet is in a specific location, it can also analyze the data while considering the environmental information of that location. This allows for a more accurate analysis by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's living environment data into a generating AI and have the generating AI perform the analysis.

[0102] The analysis unit can analyze the lifestyle patterns of pet owners and analyze related data during the analysis process. For example, the analysis unit can analyze the time periods when owners are with their pets and analyze the data for those periods. Furthermore, if the owner manages the pet's diet and exercise, the analysis unit can also analyze the data while considering that management information. In addition, if the owner manages the pet's health, the analysis unit can also analyze the data while considering that management information. For example, the analysis unit analyzes the data for the time periods when owners are with their pets. If the owner manages the pet's diet and exercise, the analysis unit can also analyze the data while considering that management information. If the owner manages the pet's health, the analysis unit can also analyze the data while considering that management information. This allows for the analysis of related data by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input owner lifestyle pattern data into the generating AI and have the generating AI perform analysis of the related data.

[0103] The advice unit can estimate the pet's emotions and adjust the advice based on the estimated emotions. For example, if the pet is stressed, the advice unit will provide advice to reduce stress. If the pet is relaxed, the advice unit can also provide advice on normal health management. Furthermore, if the pet is excited, the advice unit can also provide advice to calm the excited state. For example, if the pet is stressed, the advice unit will provide advice to reduce stress. If the pet is relaxed, it can also provide advice on normal health management. If the pet is excited, it can also provide advice to calm the excited state. By adjusting the advice based on the pet's emotions, more appropriate advice can be provided. The advice may include, but is not limited to, dietary advice and exercise advice. Some or all of the above processing in the advice unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the advice unit can input the pet's emotional data into a generating AI, which then adjusts the content of the advice.

[0104] The advice unit can provide optimal advice by referring to the pet's past health data when giving advice. For example, the advice unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. Furthermore, the advice unit can refer to the pet's past dietary data to provide advice on appropriate dietary content. For example, the advice unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. It can also refer to the pet's past dietary data to provide advice on appropriate dietary content. This allows the advice unit to provide optimal advice by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnostic results and treatment history. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input the pet's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0105] The advice unit can provide advice while considering the pet's living environment and dietary history. For example, the advice unit can provide appropriate advice on the pet's living environment (indoors, outdoors, etc.) by considering the pet's living environment. It can also provide advice on appropriate dietary content by considering the pet's dietary history. Furthermore, it can provide advice on appropriate exercise levels by considering the pet's exercise history. For example, the advice unit can provide appropriate advice on the pet's living environment by considering the pet's living environment. It can also provide advice on appropriate dietary content by considering the pet's dietary history. It can also provide advice on appropriate exercise levels by considering the pet's exercise history. This allows for the provision of more appropriate advice by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, living environment and type of food. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input pet living environment data into a generating AI and have the generating AI execute the advice.

[0106] The advice unit can estimate the pet's emotions and prioritize advice based on those emotions. For example, if the pet is stressed, the advice unit will prioritize stress reduction advice. If the pet is relaxed, the advice unit can also prioritize general health care advice. Furthermore, if the pet is excited, the advice unit can prioritize advice to calm the excited state. This allows important advice to be prioritized by determining the priority of advice based on the pet's emotions. The priority of advice includes, but is not limited to, importance and urgency. Some or all of the above processing in the advice unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI can be, but is not limited to, text-generating AI (e.g., LLM) or multimodal-generating AI. For example, the advice unit can input pet emotion data into the generative AI and have the generative AI determine the priority of advice.

[0107] The advice unit can provide advice while considering the pet's living environment information. For example, if the pet is indoors, the advice unit will provide advice appropriate to the indoor environment. It can also provide advice appropriate to the outdoor environment if the pet is outdoors. Furthermore, if the pet is in a specific location, the advice unit can provide advice appropriate to the environment of that location. This allows for more appropriate advice to be provided by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise levels. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not. For example, the advice unit can input the pet's living environment data into a generating AI and have the generating AI execute the advice.

[0108] The advice unit can analyze the pet owner's lifestyle patterns when providing advice and offer relevant advice. For example, the advice unit can analyze the time periods when the owner is with their pet and provide advice appropriate to those times. Furthermore, if the owner manages the pet's diet and exercise, the advice unit can also consider that management information when providing advice. Additionally, if the owner manages the pet's health, the advice unit can also consider that management information when providing advice. For example, the advice unit can provide advice appropriate to the time periods when the owner is with their pet. If the owner manages the pet's diet and exercise, the advice unit can also consider that management information when providing advice. If the owner manages the pet's health, the advice unit can also consider that management information when providing advice. This allows relevant advice to be provided by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not. For example, the advice unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI provide relevant advice.

[0109] The transmitting unit can estimate the pet's emotions and adjust the type of data it transmits based on the estimated emotions. For example, if the pet is stressed, the transmitting unit will prioritize transmitting stress-related data. If the pet is relaxed, the transmitting unit can also prioritize transmitting normal health data. Furthermore, if the pet is excited, the transmitting unit can also prioritize transmitting data related to the excited state. This allows for the transmission of more appropriate data by adjusting the type of data transmitted based on the pet's emotions. The types of data transmitted include, but are not limited to, importance and urgency. Some or all of the above processing in the transmitting unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the transmission unit can input pet emotional data into a generating AI and have the generating AI adjust the type of data to be transmitted.

[0110] The transmitting unit can detect changes in the pet's physical condition in real time during transmission and transmit data immediately. For example, if the pet's body temperature rises sharply, the transmitting unit will immediately transmit data. The transmitting unit can also immediately transmit data if the pet's pulse rate becomes abnormally fast. Furthermore, the transmitting unit can immediately transmit data if the pet's respiratory rate increases sharply. For example, if the pet's body temperature rises sharply, the transmitting unit will immediately transmit data. If the pet's pulse rate becomes abnormally fast, the transmitting unit can also immediately transmit data. If the pet's respiratory rate increases sharply, the transmitting unit can also immediately transmit data. This allows for real-time detection of changes in the pet's physical condition and immediate data transmission. Real-time detection includes, but is not limited to, the type of sensor and the frequency of data updates. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input pet physical condition data into a generating AI and have the generating AI perform real-time detection and immediate data transmission.

[0111] The transmitting unit can select the optimal transmission method when transmitting data, taking into account the pet's activity level. For example, if the pet is exercising, the transmitting unit will select a transmission method suitable for exercise. If the pet is resting, the transmitting unit can also select a transmission method suitable for resting. Furthermore, if the pet is eating, the transmitting unit can also select a transmission method suitable for eating. By selecting the optimal transmission method based on the pet's activity level, more accurate data can be transmitted. Optimal transmission methods include, but are not limited to, wireless communication and transmission via the internet. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input pet activity data into a generating AI and have the generating AI select the optimal transmission method.

[0112] The transmitting unit can estimate the pet's emotions and determine the priority of data to transmit based on the estimated emotions. For example, if the pet is stressed, the transmitting unit will prioritize transmitting stress-related data. It can also prioritize transmitting normal health data if the pet is relaxed. Furthermore, if the pet is excited, the transmitting unit can prioritize transmitting data related to the excited state. This allows for the priority transmission of important data by prioritizing data based on the pet's emotions. Prioritization of data includes, but is not limited to, importance and urgency. Some or all of the above processing in the transmitting unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the transmission unit can input pet emotional data into a generating AI and have the generating AI determine the priority of the data to be transmitted.

[0113] The transmitting unit can supplement data by considering the pet's living environment information during transmission. For example, if the pet is indoors, the transmitting unit will transmit data considering the indoor temperature and humidity. The transmitting unit can also transmit data considering the outside temperature and weather if the pet is outdoors. Furthermore, if the pet is in a specific location, the transmitting unit can transmit data considering the environmental information of that location. For example, if the pet is indoors, the transmitting unit will transmit data considering the indoor temperature and humidity. If the pet is outdoors, it can also transmit data considering the outside temperature and weather. If the pet is in a specific location, it can also transmit data considering the environmental information of that location. This allows for data supplementation by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not. For example, the transmitting unit can input the pet's living environment data into a generating AI and have the generating AI perform data supplementation.

[0114] The transmitting unit can analyze the pet owner's lifestyle patterns and transmit relevant data at the time of transmission. For example, the transmitting unit can analyze the time periods when the owner is with the pet and transmit data during those times. Furthermore, if the owner manages the pet's diet and exercise, the transmitting unit can also transmit data considering that management information. Additionally, if the owner manages the pet's health, the transmitting unit can also transmit data considering that management information. For example, the transmitting unit transmits data during the time the owner is with the pet. If the owner manages the pet's diet and exercise, the transmitting unit can also transmit data considering that management information. If the owner manages the pet's health, the transmitting unit can also transmit data considering that management information. This allows the transmitting unit to transmit relevant data by considering the owner's lifestyle patterns. Examples of the owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not. For example, the transmitting unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI execute the transmission of relevant data.

[0115] The consultation unit can estimate the pet's emotions and adjust the consultation content based on the estimated emotions. For example, if the pet is stressed, the consultation unit can provide consultation content to reduce stress. If the pet is relaxed, the consultation unit can also provide consultation content for normal health management. Furthermore, if the pet is excited, the consultation unit can also provide consultation content to calm the excited state. For example, if the pet is stressed, the consultation unit can provide consultation content to reduce stress. If the pet is relaxed, it can also provide consultation content for normal health management. If the pet is excited, it can also provide consultation content to calm the excited state. By adjusting the consultation content based on the pet's emotions, more appropriate consultations can be provided. Consultation content includes, but is not limited to, health consultations and behavioral consultations. Some or all of the above processing in the consultation unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. For example, the consultation department can input pet emotional data into a generating AI and have the AI ​​adjust the consultation content.

[0116] The consultation unit can provide optimal advice by referring to the pet's past health data during a consultation. For example, the consultation unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. Furthermore, the consultation unit can refer to the pet's past dietary data to provide advice on appropriate dietary content. For example, the consultation unit can refer to the pet's past body temperature data to provide advice on appropriate body temperature management. It can also refer to the pet's past pulse rate data to provide advice on appropriate exercise levels. It can also refer to the pet's past dietary data to provide advice on appropriate dietary content. In this way, optimal advice can be provided by referring to the pet's past health data. Past health data includes, but is not limited to, past diagnosis results and treatment history. Some or all of the above processing in the consultation unit may be performed using, for example, AI, or not using AI. For example, the consultation unit can input the pet's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0117] The consultation department can provide advice while considering the pet's living environment and dietary history. For example, the consultation department can provide appropriate advice on the pet's living environment (indoors, outdoors, etc.) by considering the pet's living environment. The consultation department can also provide appropriate advice on the pet's diet by considering the pet's dietary history. Furthermore, the consultation department can provide advice on the appropriate amount of exercise by considering the pet's exercise history. For example, the consultation department can provide appropriate advice on the pet's living environment by considering the pet's living environment. It can also provide appropriate advice on the pet's diet by considering the pet's dietary history. It can also provide advice on the appropriate amount of exercise by considering the pet's exercise history. This allows for more appropriate advice to be provided by considering the pet's living environment and dietary history. Living environment and dietary history include, but are not limited to, the living environment and the type of food eaten. Some or all of the above processing in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input pet living environment data into a generating AI and have the generating AI execute the consultation.

[0118] The consultation department can estimate the pet's emotions and determine the priority of consultations based on the estimated emotions. For example, if the pet is stressed, the consultation department will prioritize providing stress reduction consultations. It can also prioritize providing routine health management consultations if the pet is relaxed. Furthermore, if the pet is agitated, the consultation department can prioritize providing consultations to calm the agitated state. This allows for prioritizing important consultations based on the pet's emotions. Consultation priorities include, but are not limited to, importance and urgency. Some or all of the above processing in the consultation department is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the consultation department can input pet emotional data into the generative AI and have the generative AI determine the priority of consultations.

[0119] The consultation unit can provide advice while considering the pet's living environment information. For example, if the pet is indoors, the consultation unit can provide advice appropriate to the indoor environment. If the pet is outdoors, the consultation unit can also provide advice appropriate to the outdoor environment. Furthermore, if the pet is in a specific location, the consultation unit can also provide advice appropriate to the environment of that location. For example, if the pet is indoors, the consultation unit can provide advice appropriate to the indoor environment. If the pet is outdoors, it can also provide advice appropriate to the outdoor environment. If the pet is in a specific location, it can also provide advice appropriate to the environment of that location. This allows for the provision of more appropriate advice by considering the pet's living environment information. Living environment information includes, but is not limited to, room temperature, humidity, and noise level. Some or all of the above processing in the consultation unit may be performed using, for example, AI, or not using AI. For example, the consultation unit can input the pet's living environment data into a generating AI and have the generating AI execute the consultation.

[0120] The consultation department can analyze the pet owner's lifestyle patterns during consultations and provide relevant consultation content. For example, the consultation department can analyze the time periods when the owner is with their pet and provide consultation content appropriate to those times. Furthermore, if the owner manages the pet's diet and exercise, the consultation department can also provide consultation content considering that management information. In addition, if the owner manages the pet's health, the consultation department can also provide consultation content considering that management information. For example, the consultation department can provide consultation content appropriate to the time periods when the owner is with their pet. If the owner manages the pet's diet and exercise, the consultation department can also provide consultation content considering that management information. If the owner manages the pet's health, the consultation department can also provide consultation content considering that management information. In this way, relevant consultation content can be provided by considering the owner's lifestyle patterns. The owner's lifestyle patterns include, but are not limited to, working hours and frequency of going out. Some or all of the above processing in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input data on the pet owner's lifestyle patterns into a generating AI and have the AI ​​provide relevant consultation content.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The pet health management system may further include a behavioral analysis unit that analyzes the pet's behavioral patterns. The behavioral analysis unit can monitor the pet's movements and behaviors and detect abnormal behavioral patterns. For example, if the behavioral analysis unit drinks water more frequently than usual, it may suggest a potential health problem. The behavioral analysis unit can also detect signs of lack of exercise or stress if the pet is not maintaining a normal activity level. Furthermore, if the behavioral analysis unit repeatedly performs a specific action during a specific time period, it can record that behavioral pattern and notify the owner. This allows for early detection of health problems and appropriate countermeasures by analyzing the pet's behavioral patterns. Some or all of the above processing in the behavioral analysis unit may be performed using, for example, machine learning algorithms, or without machine learning algorithms. For example, the behavioral analysis unit can input pet behavioral data into a generating AI and have the generating AI perform abnormal behavior detection.

[0123] The pet health management system may further include a notification unit that estimates the pet's emotions and notifies the owner based on the estimated emotions. For example, if the pet is stressed, the notification unit may notify the owner with advice on stress reduction. It can also notify the owner if the pet is relaxed. Furthermore, if the pet is agitated, the notification unit may notify the owner of this state and provide advice on appropriate measures. This allows owners to understand their pet's condition and take appropriate action by notifying them based on their emotions. Some or all of the above processing in the notification unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the notification unit can input pet emotional data into a generating AI and have the generating AI determine the content of the notification.

[0124] The pet health management system may also include a cloud storage unit that stores pet health data in the cloud, making it accessible from multiple devices. The cloud storage unit can store data such as the pet's body temperature, pulse rate, and activity level in the cloud. It can also store records of pet medical visits and data related to pre-existing conditions. Furthermore, the cloud storage unit can allow pet owners to access the data from multiple devices such as smartphones, tablets, and computers. For example, the cloud storage unit can store the pet's body temperature data in the cloud. It can also store records of pet medical visits in the cloud. It can also allow pet owners to access the data from multiple devices such as smartphones, tablets, and computers. This makes data management easier and allows access from multiple devices by storing pet health data in the cloud. Some or all of the above-described processes in the cloud storage unit may be performed using, for example, cloud computing technology, or not. For example, the cloud storage unit can upload pet health data to the cloud and have cloud computing technology perform the data storage.

[0125] The pet health management system may further include a data display unit that visually displays the pet's health data. For example, the data display unit can display changes in the pet's body temperature and pulse rate in a graph. It can also display the pet's exercise level and feeding history in a timeline format. Furthermore, the data display unit can provide a dashboard that allows owners to grasp the pet's health status at a glance. This allows pet owners to easily understand the data and take appropriate action by visually displaying the pet's health data. Some or all of the above-described processing in the data display unit may be performed using, for example, data visualization technology, or without data visualization technology. For example, the data display unit can input pet health data into a generating AI and have the generating AI perform the visual display.

[0126] The pet health management system may further include a sharing section for sharing pet health data. This sharing section can, for example, share pet health data with family members or veterinarians. It can also share pet health data on social media or dedicated community sites. Furthermore, the sharing section can allow for privacy settings when sharing pet health data. This allows family members and veterinarians to understand the pet's health status and take appropriate action. Some or all of the above-described processes in the sharing section may be performed using data sharing technology, or without it. For example, the sharing section can input pet health data into a generating AI and have the generating AI perform the data sharing.

[0127] The pet health management system may further include an analysis adjustment unit that estimates the pet's emotions and adjusts the method of analyzing health data based on the estimated emotions. For example, if the pet is stressed, the analysis adjustment unit will focus on analyzing stress-related data. If the pet is relaxed, the analysis adjustment unit can also focus on analyzing normal health data. Furthermore, if the pet is excited, the analysis adjustment unit can also focus on analyzing data related to the state of excitement. For example, if the pet is stressed, the analysis adjustment unit will focus on analyzing stress-related data. If the pet is relaxed, it can also focus on analyzing normal health data. If the pet is excited, it can also focus on analyzing data related to the state of excitement. This allows for more appropriate analysis by adjusting the method of analyzing health data based on the pet's emotions. Some or all of the above processing in the analysis adjustment unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the analysis and adjustment unit can input pet emotional data into the generating AI and have the generating AI perform adjustments to the analysis method.

[0128] The pet health management system may further include a preventative measures suggestion unit that proposes preventative measures based on the pet's health data. For example, the preventative measures suggestion unit may suggest the appropriate timing for vaccinations based on the pet's body temperature and pulse rate data. It can also provide advice on appropriate exercise and diet based on the pet's exercise and diet data. Furthermore, it can suggest a schedule for regular health checkups based on the pet's medical visit records. This allows for the maintenance of pet health and disease prevention by suggesting preventative measures based on the pet's health data. Some or all of the above-described processes in the preventative measures suggestion unit may be performed using, for example, a data analysis algorithm, or without one. For example, the preventative measures suggestion unit can input pet health data into a generating AI and have the generating AI execute preventative measures suggestions.

[0129] The pet health management system may further include a display adjustment unit that estimates the pet's emotions and adjusts how health data is displayed based on the estimated emotions. For example, if the pet is stressed, the display adjustment unit may highlight stress-related data. It may also highlight normal health data if the pet is relaxed. Furthermore, if the pet is excited, the display adjustment unit may highlight data related to the state of excitement. For example, if the pet is stressed, the display adjustment unit may highlight stress-related data. If the pet is relaxed, it may also highlight normal health data. If the pet is excited, it may also highlight data related to the state of excitement. This allows pet owners to easily grasp important data by adjusting how health data is displayed based on the pet's emotions. Some or all of the above processing in the display adjustment unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the display adjustment unit can input pet emotion data into a generating AI and have the generating AI adjust the display method.

[0130] The pet health management system may further include a risk prediction unit that predicts health risks based on the pet's health data. For example, the risk prediction unit can predict future health risks based on data such as the pet's body temperature and pulse rate. It can also predict the risk of lifestyle-related diseases based on data such as the pet's exercise level and diet. Furthermore, it can predict the risk of specific diseases based on the pet's medical visit records. This allows for early intervention by predicting health risks based on the pet's health data. Some or all of the above-described processes in the risk prediction unit may be performed using, for example, data analysis algorithms, or without them. For example, the risk prediction unit can input pet health data into a generating AI and have the generating AI perform health risk predictions.

[0131] The pet health management system may further include a priority determination unit that estimates the pet's emotions and determines the priority of health data based on the estimated emotions. For example, if the pet is stressed, the priority determination unit will prioritize the analysis of stress-related data. The priority determination unit can also prioritize the analysis of normal health data if the pet is relaxed. Furthermore, if the pet is excited, the priority determination unit can prioritize the analysis of data related to the excited state. For example, if the pet is stressed, the priority determination unit will prioritize the analysis of stress-related data. If the pet is relaxed, it can also prioritize the analysis of normal health data. If the pet is excited, it can also prioritize the analysis of data related to the excited state. This allows for the priority of important data analysis by determining the priority of health data based on the pet's emotions. Some or all of the above processing in the priority determination unit is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the priority determination unit can input the pet's emotion data into the generative AI and have the generative AI perform the data priority determination.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The recording unit records the pet's internal information. This information includes, for example, body temperature, heart rate, and blood pressure. The recording unit records this information using digital recording, paper media, or sensors. For example, it records body temperature using a body temperature sensor, heart rate using a heart rate sensor, and blood pressure using a blood pressure sensor. Step 2: The reading unit reads the information recorded by the recording unit. The reading unit can read the information using a smartphone, a sensor, or manually. For example, it can read the information using a smartphone camera. Step 3: The analysis unit analyzes the information read by the reading unit. The analysis unit analyzes the information using data analysis algorithms, statistical methods, or machine learning models. For example, it might analyze body temperature data using a data analysis algorithm, heart rate data using statistical methods, and blood pressure data using a machine learning model. Step 4: The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit can provide advice on diet, exercise, or medication. For example, it can provide appropriate dietary advice, exercise advice, or medication advice based on the analysis results.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the recording unit, reading unit, analysis unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the pet's internal information using the sensors of the smart device 14 and stores the data using the specific processing unit 290 of the data processing unit 12. The reading unit reads the information using the camera and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the pet's internal information. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate advice based on the analysis results. The recording unit can also be implemented by the control unit 46A of the smart device 14, for example, which can estimate the pet's emotions and adjust the type of data to be recorded. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the recording unit, reading unit, analysis unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the pet's internal information using the sensors of the smart glasses 214 and stores the data using the identification processing unit 290 of the data processing unit 12. The reading unit reads the information using the camera and communication I / F 44 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the pet's internal information. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides appropriate advice based on the analysis results. The recording unit can also be implemented by the control unit 46A of the smart glasses 214, which can estimate the pet's emotions and adjust the type of data to be recorded. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the recording unit, reading unit, analysis unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the pet's internal information using the sensors of the headset terminal 314 and stores the data using the identification processing unit 290 of the data processing unit 12. The reading unit reads the information using the camera and communication I / F 44 of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the pet's internal information. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides appropriate advice based on the analysis results. The recording unit can also be implemented by the control unit 46A of the headset terminal 314, which can estimate the pet's emotions and adjust the type of data to be recorded. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the recording unit, reading unit, analysis unit, and advice unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit records internal information of the pet using the sensors of the robot 414 and stores the data using the specific processing unit 290 of the data processing unit 12. The reading unit reads the information using the camera and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the internal information of the pet. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate advice based on the analysis results. The recording unit can also be implemented by the control unit 46A of the robot 414, which can estimate the pet's emotions and adjust the type of data to be recorded. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) A recording unit that records the internal information of the pet, A reading unit that reads the information recorded by the recording unit, An analysis unit analyzes the information read by the reading unit, The system includes an advice unit that provides advice based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The reading unit is Read information using a smartphone The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, The generated AI analyzes health data and provides appropriate diet and medication advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, Showing veterinarians the data generated by the AI ​​will enable faster treatment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The reading unit is Equipped with a transmission unit that sends data to the hospital. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned transmitting unit We have a consultation department that provides preliminary consultations. The system described in Appendix 5, characterized by the features described herein. (Note 7) The recording unit is, It estimates the pet's emotions and adjusts the type of data recorded based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recording unit is, During recording, the system analyzes the pet's activity patterns and selects the optimal recording timing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recording unit is, When recording data, supplement it by considering the pet's diet and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recording unit is, It estimates the pet's emotions and determines the priority of data to record based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recording unit is, When recording, supplement the data by taking into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recording unit is, During recording, analyze the pet owner's lifestyle patterns and record relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The reading unit is It estimates the pet's emotions and adjusts the timing of readings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reading unit is During scanning, changes in the pet's physical condition are detected in real time, and the data is read immediately. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reading unit is When reading, the optimal reading method is selected considering the pet's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reading unit is The system estimates the pet's emotions and prioritizes the data to read based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reading unit is When reading the data, the system supplements it by taking into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reading unit is During the reading process, the system analyzes the pet owner's lifestyle patterns and extracts relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate the pet's emotions and adjust the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the pet's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During the analysis, the pet's living environment and dietary history will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the pet's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, information about the pet's living environment will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the lifestyle patterns of pet owners are analyzed, and related data is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, It estimates the pet's emotions and adjusts the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, we refer to your pet's past health data to offer the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, we take into consideration the pet's living environment and dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the pet's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we take into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, we analyze the pet owner's lifestyle patterns and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned transmitting unit It estimates the pet's emotions and adjusts the type of data sent based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 32) The aforementioned transmitting unit During transmission, changes in the pet's health are detected in real time, and the data is sent immediately. The system described in Appendix 5, characterized by the features described herein. (Note 33) The aforementioned transmitting unit When sending, the system selects the most suitable sending method considering the pet's activity level. The system described in Appendix 5, characterized by the features described herein. (Note 34) The aforementioned transmitting unit It estimates the pet's emotions and prioritizes the data to send based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 35) The aforementioned transmitting unit When sending data, the system will supplement it by taking into account information about the pet's living environment. The system described in Appendix 5, characterized by the features described herein. (Note 36) The aforementioned transmitting unit When sending data, the system analyzes the pet owner's lifestyle patterns and sends relevant data. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned consultation department, We estimate the pet's emotions and adjust the consultation based on those estimates. The system described in Appendix 6, characterized by the features described herein. (Note 38) The aforementioned consultation department, During consultations, we refer to your pet's past health data to provide the most appropriate advice. The system described in Appendix 6, characterized by the features described herein. (Note 39) The aforementioned consultation department, When providing advice, we will take into consideration the pet's living environment and dietary history. The system described in Appendix 6, characterized by the features described herein. (Note 40) The aforementioned consultation department, The system estimates the pet's emotions and prioritizes consultations based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 41) The aforementioned consultation department, When providing advice, we will take into consideration information about your pet's living environment. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned consultation department, During the consultation, we analyze the pet owner's lifestyle patterns and provide relevant advice. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit that records the internal information of the pet, A reading unit that reads the information recorded by the recording unit, An analysis unit analyzes the information read by the reading unit, The system includes an advice unit that provides advice based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The reading unit is Read information using a smartphone The system according to feature 1.

3. The aforementioned advice section, The system uses AI to analyze health data and provide appropriate advice on diet and medication. The system according to feature 1.

4. The aforementioned advice section, Showing veterinarians the data generated by AI will enable faster treatment. The system according to feature 1.

5. The reading unit is Equipped with a transmission unit that sends data to the hospital. The system according to feature 1.

6. The aforementioned transmitting unit We have a consultation department that provides preliminary consultations. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned recording unit is It estimates the pet's emotions and adjusts the type of data recorded based on the estimated emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the system analyzes the pet's activity patterns and selects the optimal recording timing. The system according to feature 1.

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