system

Abstract

The system according to this embodiment aims to comprehensively manage the health status of pets and detect abnormalities at an early stage. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, and a recording unit. The collection unit collects data on the pet's food intake, exercise level, and excretion. The analysis unit analyzes the data collected by the collection unit and checks the pet's health status. The notification unit, based on the data analyzed by the analysis unit, alerts the pet to take necessary action if there are concerns about its health status. The recording unit records daily video data and detects changes in movement and injuries.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to comprehensively manage the health status of a pet and detect abnormalities at an early stage.

[0005] The system according to the embodiment aims to comprehensively manage the health status of a pet and detect abnormalities at an early stage.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, and a recording unit. The collection unit collects data on the pet's food intake, exercise level, and excretion. The analysis unit analyzes the data collected by the collection unit and checks the pet's health status. The notification unit, based on the data analyzed by the analysis unit, alerts the pet to take necessary action if there are concerns about its health status. The recording unit records daily video data and detects changes in movement and injuries. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively manage the health status of pets and detect abnormalities at an early stage. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 for managing pet health using AI. This pet health management system accumulates data on the pet's food intake, exercise level, and excretion, and checks its health status using AI. For example, it uses AI to collect data on the speed of the pet's walks, the amount of food (water intake), and changes in the color and amount of excrement. If there are concerns about the pet's health, it alerts the user to take necessary action. For example, this could involve adjusting the pet's diet, increasing or decreasing exercise, improving communication, or taking the pet to the vet. Next, it records video data of the pet's daily activities (indoor and outdoor walks) to detect changes in movement and injuries early. For example, even if a pet has a habit of hiding injuries when with family, videos of its movements and sleep (or lack thereof) when the family is away can be used to manage injuries, illnesses, and stress. Specifically, a chip is attached to the collar or other part of the pet to record indoor behavior on video and detect changes in behavior and movements. In addition, outdoor exercise levels are recorded using a harness (exercise level and body temperature), and a camera is attached to the leash to record movement. This allows for the accumulation of information that makes it easier to explain the pet's condition to a veterinarian. This system makes it possible to generate information from the "voices" of pets that cannot speak. This system can help extend the healthy lifespan of pets and enable early detection of illnesses and injuries. It can also help prevent obesity in pets that live indoors. The target audience is people who consider pets such as dogs and cats as family members, and this system is effective because there are limitations to manually recording information. As a result, the pet health management system can efficiently manage the health status of pets and take necessary actions quickly.

[0029] The pet health management system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, and a recording unit. The data collection unit collects data on the pet's food intake, exercise level, and excretion. For example, the data collection unit records the number of meals, amount, and type of food to measure the pet's food intake. The data collection unit can also record the number of steps, exercise time, and type of exercise to measure the pet's exercise level. Furthermore, the data collection unit can also record the number of excretions, amount, and time to measure excretion data. For example, the data collection unit records the number of meals to measure the pet's food intake. The data collection unit can also record the number of steps to measure the pet's exercise level. The data collection unit can also record the number of excretions to measure the pet's excretion data. The analysis unit analyzes the data collected by the data collection unit and checks the pet's health status. For example, the analysis unit analyzes the pet's weight, body temperature, and behavioral patterns based on the collected data. The analysis unit can also determine that there is a concern about the pet's health status if there is an abnormal data pattern or if certain thresholds are exceeded. Furthermore, the analysis unit can also evaluate the pet's health status based on the collected data. For example, the analysis unit can analyze the pet's weight based on the collected data. The analysis unit can also analyze the pet's body temperature based on the collected data. The analysis unit can also analyze the pet's behavioral patterns based on the collected data. The notification unit alerts the pet to take necessary action if there are concerns about its health status, based on the data analyzed by the analysis unit. For example, the notification unit can alert the pet to adjust its diet, increase or decrease exercise, improve communication, or take it to the vet. The notification unit can also alert the pet to contact a veterinarian if there are concerns about its health status. In addition, the notification unit can alert the pet to specific care methods if there are concerns about its health status. For example, the notification unit can alert the pet to adjust its diet if there are concerns about its health status. The notification unit can also alert the pet to increase or decrease exercise if there are concerns about its health status. The notification unit can also alert the pet to improve communication if there are concerns about its health status. The recording unit records everyday video data and detects changes in movement and injuries.The recording unit can, for example, record video using a camera attached to the leash, and the AI ​​analyzes the video to detect changes in movement or injuries. The recording unit can also collect data using sensors attached to the collar or harness, and the AI ​​analyzes this data to detect changes in movement or injuries. Furthermore, the recording unit can encrypt and store the data. For example, the recording unit can record video using a camera attached to the leash. The recording unit can also collect data using sensors attached to the collar. The recording unit can also collect data using sensors attached to the harness. This allows the pet health management system according to this embodiment to efficiently manage the pet's health status and take necessary actions quickly.

[0030] The data collection unit collects data on the pet's food intake, exercise level, and excretion. Specifically, to measure the pet's food intake, it records the frequency, amount, and type of meals. For example, a weight sensor attached to the pet's food bowl can accurately measure the amount of food, and the type of food can be automatically recognized using a barcode scanner or RFID tag. The data collection unit can also record steps, exercise time, and type of exercise to measure exercise level. Accelerometers and gyroscopes attached to the pet's collar or harness can be used to track the pet's movements in detail and identify the type of exercise (e.g., walking, playing, running). Furthermore, the data collection unit can record the frequency, amount, and time of excretion to measure excretion data. For example, a sensor installed in the pet's litter box measures the amount of excrement and records the time of excretion. This allows the data collection unit to collect diverse data for detailed monitoring of the pet's health. The collected data is transmitted to a central database using wireless communication technology (e.g., Wi-Fi, Bluetooth®) and updated in real time. This allows the collection unit to continuously monitor the pet's health and respond quickly if any abnormalities occur.

[0031] The analysis unit analyzes the data collected by the data collection unit to check the pet's health status. Specifically, it analyzes the pet's weight, body temperature, and behavioral patterns based on the collected data. For example, a pet's weight can be estimated by combining data on food intake and exercise. Body temperature is measured using temperature sensors attached to collars or harnesses. Behavioral patterns can be understood by analyzing data from acceleration sensors and gyroscope sensors to grasp the pet's activity level and rest time. The analysis unit uses AI to analyze this data in real time and can determine if there are health concerns if abnormal data patterns or certain thresholds are exceeded. For example, if a pet's weight increases or decreases rapidly, its body temperature exceeds the normal range, or abnormal behavioral patterns are observed, the analysis unit can detect this and suggest appropriate actions. Furthermore, the analysis unit can also use historical data and statistical information to evaluate long-term health trends and predict future health risks. This allows the analysis unit to comprehensively evaluate the pet's health status, detect problems early, and take countermeasures.

[0032] The notification unit, based on data analyzed by the analysis unit, alerts owners to necessary actions if there are concerns about their pet's health. Specifically, it alerts owners to actions such as adjusting their pet's diet, increasing or decreasing exercise, improving communication, and taking their pet to the vet. For example, if a pet's weight is increasing, the notification unit can alert owners to reduce their pet's food intake. If a pet is not getting enough exercise, the notification unit can alert owners to increase exercise. Furthermore, if an abnormality is detected in the pet's behavioral patterns, the notification unit can alert owners to contact a veterinarian. The notification unit can quickly transmit information to pet owners via smartphone apps, email, SMS, etc. In addition, the notification unit can customize the content of alerts, allowing owners to adjust the notification method and frequency according to their preferences. This enables the notification unit to quickly provide appropriate actions based on the pet's health condition, allowing pet owners to efficiently manage their pet's health.

[0033] The recording unit records daily video data and detects changes in movement and injuries. Specifically, it records video using a camera attached to the leash, and AI analyzes the video to detect changes in movement and injuries. For example, if a pet is walking differently than usual or frequently licking a particular body part, the AI ​​can detect this and flag it as an abnormality. The recording unit can also collect data using sensors attached to the collar or harness, and the AI ​​can analyze this data to detect changes in movement and injuries. For example, by analyzing data from acceleration sensors and gyroscope sensors, it can understand the pet's movement patterns in detail and issue an alert if abnormal movement is detected. Furthermore, the recording unit can encrypt and store the data. This ensures data security while protecting the pet's privacy. The recording unit can also store the collected data on a cloud server, allowing the owner to access it at any time. This allows the recording unit to record the pet's health in detail and respond quickly if an abnormality occurs.

[0034] The data collection unit can collect data using sensors attached to the collar and sensors attached to the harness. For example, the data collection unit can collect the pet's location information using a GPS sensor attached to the collar. The data collection unit can also collect the pet's activity level using an accelerometer attached to the collar. Furthermore, the data collection unit can collect the pet's heart rate using a heart rate sensor attached to the harness. For example, the data collection unit can collect the pet's location information using a GPS sensor attached to the collar. The data collection unit can also collect the pet's activity level using an accelerometer attached to the collar. The data collection unit can also collect the pet's heart rate using a heart rate sensor attached to the harness. This allows for accurate collection of pet data using sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the sensors attached to the collar into a generating AI and have the generating AI perform data analysis.

[0035] The recording unit records video using a camera attached to the leash, and AI can analyze the video to detect changes in movement and injuries. For example, the recording unit records video of the pet using a high-resolution camera attached to the leash. The recording unit can also use AI to analyze the recorded video and detect changes in the pet's movement. Furthermore, the recording unit can use AI to analyze the recorded video and detect injuries to the pet. For example, the recording unit records video of the pet using a high-resolution camera attached to the leash. The recording unit can also use AI to analyze the recorded video and detect changes in the pet's movement. The recording unit can also use AI to analyze the recorded video and detect injuries to the pet. This allows for early detection of changes in the pet's movement and injuries by analyzing the video. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input video data acquired by a camera attached to the leash into a generating AI and have the generating AI perform the analysis of the video data.

[0036] The recording unit can encrypt and securely store data. For example, the recording unit can encrypt data using AES encryption. Alternatively, the recording unit can encrypt data using RSA encryption. Furthermore, the recording unit can use dedicated hardware for encrypting and storing data. For example, the recording unit can encrypt data using AES encryption. The recording unit can also encrypt data using RSA encryption. The recording unit can also use dedicated hardware for encrypting and storing data. This ensures the security of pet data through data encryption. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data into a generating AI and have the generating AI perform data encryption.

[0037] The data collection unit can estimate the pet's emotions and appropriately adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data on food intake and excretion when the pet is relaxed. It can also prioritize collecting data on exercise levels when the pet is excited. Furthermore, the data collection unit can temporarily suspend data collection when the pet is stressed and resume it later. This allows for the collection of more accurate data by adjusting the timing of data collection according to the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's emotional data into a generating AI and have the generating AI perform the adjustment of data collection timing based on emotions.

[0038] The data collection unit can analyze the pet's past health data and select the optimal sensor placement. For example, based on past data, the data collection unit can place sensors in areas where the pet's body temperature fluctuates significantly. The data collection unit can also improve the accuracy of data collection by adjusting the harness position based on past activity data. Furthermore, the data collection unit can place sensors in positions that allow for accurate measurement of the color and amount of excrement, referencing past excretion data. For example, based on past data, the data collection unit can place sensors in areas where the pet's body temperature fluctuates significantly. The data collection unit can also improve the accuracy of data collection by adjusting the harness position based on past activity data. The data collection unit can also place sensors in positions that allow for accurate measurement of the color and amount of excrement, referencing past excretion data. This allows for improved data collection accuracy by selecting the optimal sensor placement based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal sensor placement.

[0039] The data collection unit can appropriately adjust the data collection frequency based on the pet's activity level during data collection. For example, the data collection unit can increase the data collection frequency when the pet is active. It can also decrease the data collection frequency when the pet is resting. Furthermore, if the pet is active at night, the data collection unit can adjust the data collection frequency at night. For example, the data collection unit can increase the data collection frequency when the pet is active. It can also decrease the data collection frequency when the pet is resting. If the pet is active at night, the data collection unit can also adjust the data collection frequency at night. This allows for efficient data collection by adjusting the collection frequency according to the pet's activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet activity level data into a generating AI and have the generating AI adjust the collection frequency.

[0040] The data collection unit can estimate the pet's emotions and appropriately determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting data on excrement. If the pet is relaxed, the data collection unit can also prioritize collecting data on food intake. Furthermore, if the pet is excited, the data collection unit can also prioritize collecting data on exercise level. In this way, by prioritizing data according to the pet's emotions, important data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI perform the data prioritization.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information during data collection. For example, if a pet spends a lot of time indoors, the data collection unit will prioritize the collection of indoor activity data. Similarly, if a pet spends a lot of time outdoors, the data collection unit can prioritize the collection of outdoor exercise data. Furthermore, the data collection unit can prioritize the collection of body temperature data by considering the temperature and humidity of the living environment. This allows for the efficient collection of highly relevant data by considering the living environment information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's living environment information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0042] The data collection unit can appropriately adjust the timing of data collection, taking into account the pet owner's schedule. For example, the data collection unit can collect pet behavior data during times when the owner is away. It can also collect exercise data during times when the owner takes the pet for a walk. Furthermore, it can collect food intake data during times when the owner feeds the pet. For example, the data collection unit can collect pet behavior data during times when the owner is away. It can also collect exercise data during times when the owner takes the pet for a walk. It can also collect food intake data during times when the owner feeds the pet. This enables efficient data collection by taking the owner's schedule into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's schedule data into a generating AI and have the generating AI adjust the timing of data collection.

[0043] The analysis unit can estimate the pet's emotions and appropriately adjust the analysis algorithm based on the estimated emotions. For example, if the pet is relaxed, the analysis unit uses the normal analysis algorithm. If the pet is stressed, the analysis unit can also use an algorithm that emphasizes stress-related data. Furthermore, if the pet is excited, the analysis unit can also use an algorithm that emphasizes activity level data. This allows for improved analysis accuracy by adjusting the analysis algorithm according to the pet's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's emotional data into a generating AI and have the generating AI adjust the analysis algorithm.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis process. For example, the analysis unit can more accurately analyze the current health status based on past health data. The analysis unit can also detect outliers from past data and compare them with current data. Furthermore, the analysis unit can analyze long-term health trends by referring to past health data. For example, the analysis unit can more accurately analyze the current health status based on past health data. The analysis unit can also detect outliers from past data and compare them with current data. The analysis unit can also analyze long-term health trends by referring to past health data. This allows for a more accurate analysis of the current health status by referring to past health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0045] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit may use different analysis methods for dogs and cats. It can also use different analysis methods for young pets and elderly pets. Furthermore, the analysis unit may use analysis methods specific to particular breeds. For example, the analysis unit may use different analysis methods for dogs and cats. It can also use different analysis methods for young pets and elderly pets. It can also use analysis methods specific to particular breeds. This allows for improved analysis accuracy by applying analysis methods appropriate to the type and age of the pet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet type and age data into a generating AI and have the generating AI execute the application of different analysis methods.

[0046] The analysis unit can estimate the pet's emotions and appropriately adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can display detailed analysis results. If the pet is stressed, the analysis unit can also display concise analysis results. Furthermore, if the pet is excited, the analysis unit can display visually easy-to-understand analysis results. For example, if the pet is relaxed, the analysis unit can display detailed analysis results. If the pet is stressed, the analysis unit can also display concise analysis results. If the pet is excited, the analysis unit can also display visually easy-to-understand analysis results. By adjusting the display method of the analysis results according to the pet's emotions, it is possible to provide analysis results that are easier to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0047] The analysis unit can improve the accuracy of its analysis by referring to data on the pet's living environment during the analysis. For example, the analysis unit can improve the accuracy by referring to indoor temperature and humidity data. The analysis unit can also improve the accuracy by referring to outdoor weather data. Furthermore, the analysis unit can improve the accuracy by referring to the noise level of the pet's living environment. For example, the analysis unit can improve the accuracy by referring to indoor temperature and humidity data. The analysis unit can also improve the accuracy by referring to outdoor weather data. The analysis unit can also improve the accuracy by referring to the noise level of the pet's living environment. In this way, the accuracy of the analysis can be improved by referring to living environment data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using 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 improvement of the analysis accuracy.

[0048] The analysis unit can optimize its analysis algorithm by incorporating feedback from pet owners during analysis. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also improve the way the analysis results are displayed by incorporating the owner's opinions. Furthermore, the analysis unit can improve the accuracy of the analysis by reflecting the owner's feedback. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also improve the way the analysis results are displayed by incorporating the owner's opinions. The analysis unit can also improve the accuracy of the analysis by reflecting the owner's feedback. In this way, by incorporating feedback from pet owners, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0049] The notification unit can estimate the pet's emotions and appropriately adjust the notification content based on the estimated emotions. For example, if the pet is relaxed, the notification unit can provide detailed notification content. It can also provide concise notification content if the pet is stressed. Furthermore, if the pet is excited, the notification unit can provide visually easy-to-understand notification content. This allows for more appropriate notifications by adjusting the notification content according to the pet's emotions. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input pet emotion data into a generating AI and have the generating AI adjust the notification content.

[0050] The notification unit can propose the optimal course of action by referring to the pet's past health data when it sends a notification. For example, the notification unit can suggest the optimal diet based on past health data. It can also suggest increasing or decreasing exercise based on past exercise data. Furthermore, it can suggest methods of health management based on past excretion data. For example, the notification unit can suggest the optimal diet based on past health data. It can also suggest increasing or decreasing exercise based on past exercise data. It can also suggest methods of health management based on past excretion data. In this way, by referring to past health data, it can propose the optimal course of action. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past health data into a generating AI and have the generating AI execute the proposal of the optimal course of action.

[0051] The notification unit can customize the notification method according to the pet owner's lifestyle when a notification is sent. For example, if the owner is busy, the notification unit can provide a concise notification. If the owner is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the owner is away from home, the notification unit can send a notification to their mobile device. This allows for more effective notifications by providing notification methods tailored to the owner's lifestyle. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's lifestyle data into a generating AI and have the generating AI customize the notification method.

[0052] The notification unit can propose the most appropriate course of action when it sends a notification, taking into account the geographical location of the pet owner. For example, if the owner is at home, the notification unit can propose a course of action that can be taken at home. If the owner is out, the notification unit can also suggest a nearby veterinary hospital. Furthermore, if the owner is traveling, the notification unit can also suggest a course of action at the travel destination. By taking into account the owner's geographical location, the notification unit can propose a more appropriate course of action. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's geographical location into a generating AI and have the generating AI propose the most appropriate course of action.

[0053] The notification unit can analyze the pet owner's social media activity when sending notifications and customize the notification content accordingly. For example, if the owner posts about their pet's health on social media, the notification unit will provide relevant notifications. The notification unit can also provide visually easy-to-understand notifications if the owner frequently posts photos of their pet on social media. Furthermore, if the owner shares their pet's activities on social media, the notification unit can provide notifications related to those activities. For example, if the owner posts about their pet's health on social media, the notification unit will provide relevant notifications. The notification unit can also provide visually easy-to-understand notifications if the owner frequently posts photos of their pet on social media. The notification unit can also provide notifications related to those activities if the owner shares their pet's activities on social media. This allows the notification unit to provide more relevant notifications by analyzing the owner's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the owner's social media activity data into a generating AI, which can then customize the notification content.

[0054] The recording unit can estimate the pet's emotions and appropriately adjust the timing of video recording based on the estimated emotions. For example, if the pet is relaxed, the recording unit will perform normal video recording. The recording unit can also increase the frequency of video recording if the pet is stressed. Furthermore, the recording unit can adjust the timing of video recording if the pet is excited. For example, if the pet is relaxed, the recording unit will perform normal video recording. The recording unit can also increase the frequency of video recording if the pet is stressed. The recording unit can also adjust the timing of video recording if the pet is excited. By adjusting the timing of video recording according to the pet's emotions, more important footage can be recorded. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet emotion data into a generating AI and have the generating AI perform the adjustment of the video recording timing.

[0055] The recording unit can improve recording accuracy by referring to past behavioral data of the pet during video recording. For example, the recording unit can record current behavior more accurately based on past behavioral data. The recording unit can also detect abnormal behavior from past data and compare it with current data. Furthermore, the recording unit can record long-term behavioral trends by referring to past behavioral data. For example, the recording unit can record current behavior more accurately based on past behavioral data. The recording unit can also detect abnormal behavior from past data and compare it with current data. The recording unit can also record long-term behavioral trends by referring to past behavioral data. This allows for more accurate recording of current behavior by referring to past behavioral data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past behavioral data into a generating AI and have the generating AI perform the improvement of recording accuracy.

[0056] The recording unit can appropriately adjust the recording frequency according to the pet's activity level when recording video. For example, the recording unit can increase the recording frequency when the pet is actively moving around. It can also decrease the recording frequency when the pet is resting. Furthermore, if the pet is active at night, the recording unit can adjust the recording frequency at night. For example, the recording unit can increase the recording frequency when the pet is actively moving around. It can also decrease the recording frequency when the pet is resting. If the pet is active at night, the recording unit can also adjust the recording frequency at night. This allows for efficient video recording by adjusting the recording frequency according to the pet's activity level. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet activity level data into a generating AI and have the generating AI adjust the recording frequency.

[0057] The recording unit can estimate the pet's emotions and appropriately determine the priority of video recordings based on the estimated emotions. For example, if the pet is stressed, the recording unit will prioritize recording stress-related behaviors. The recording unit can also prioritize recording normal behaviors if the pet is relaxed. Furthermore, if the pet is excited, the recording unit can prioritize recording exercise-related behaviors. For example, if the pet is stressed, the recording unit will prioritize recording stress-related behaviors. The recording unit can also prioritize recording normal behaviors if the pet is relaxed. The recording unit can also prioritize recording exercise-related behaviors if the pet is excited. This allows important footage to be recorded preferentially by determining the priority of video recordings according to the pet's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet emotion data into a generating AI and have the generating AI perform the determination of video recording priorities.

[0058] The recording unit can prioritize recording highly relevant footage by considering the pet's living environment information during video recording. For example, if a pet spends a lot of time indoors, the recording unit will prioritize recording indoor activities. Similarly, if a pet spends a lot of time outdoors, the recording unit can prioritize recording outdoor activities. Furthermore, the recording unit can also prioritize recording activities related to changes in body temperature by considering the temperature and humidity of the living environment. This allows for the efficient recording of highly relevant footage by considering the living environment information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the pet's living environment information into a generating AI and have the generating AI determine the priority of highly relevant footage.

[0059] The recording unit can appropriately adjust the recording timing when recording video, taking into account the pet owner's schedule. For example, the recording unit can record the pet's behavior during times when the owner is away. It can also record the pet's exercise during the times when the owner takes it for a walk. Furthermore, the recording unit can record the pet's eating during the times when the owner feeds it. This allows for efficient video recording by taking the owner's schedule into consideration. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the owner's schedule data into a generating AI and have the generating AI adjust the recording timing.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can collect data not only on the pet's food intake, exercise level, and excretion, but also on its sleep patterns. For example, it can record the pet's sleep duration, sleep quality, and number of nighttime awakenings. It can also detect the pet's movements during sleep and issue an alert if abnormal movements are detected. Furthermore, the data collection unit can monitor the pet's sleep environment (temperature, humidity, noise level) and collect data to provide an optimal sleep environment. This allows owners to understand their pet's sleep patterns and use this information for health management.

[0062] The analysis unit can analyze collected data by referencing not only the pet's past health data but also data from other pets of the same species. For example, the analysis unit can compare the health status of pets based on data from pets of the same dog or cat breed. It can also refer to data from pets of the same age group to provide age-appropriate health management advice. Furthermore, the analysis unit can suggest health management tailored to the living environment based on data from pets living in the same environment (indoor or outdoor). This enables more accurate health management.

[0063] The data collection unit can collect not only data on pet food intake, exercise levels, and excretion, but also pet weight data. For example, the unit can regularly measure pet weight and record weight fluctuations. Furthermore, based on the pet's weight data, the unit can suggest appropriate food and exercise levels. In addition, based on the pet's weight data, the unit can assess the risk of obesity or being underweight and provide health management advice. This allows for more effective health management through pet weight management.

[0064] The analysis unit can consider a pet's genetic information when analyzing its health data. For example, the analysis unit can assess genetic health risks based on the pet's genetic information. It can also predict the risk of specific diseases and suggest preventative measures based on genetic information. Furthermore, the analysis unit can provide advice on appropriate diet and exercise for pets based on genetic information. This enables more personalized health management by utilizing genetic information.

[0065] The data collection unit can collect not only data on pet food intake, exercise levels, and excretion, but also pet weight data. For example, the unit can regularly measure pet weight and record weight fluctuations. Furthermore, based on the pet's weight data, the unit can suggest appropriate food and exercise levels. In addition, based on the pet's weight data, the unit can assess the risk of obesity or being underweight and provide health management advice. This allows for more effective health management through pet weight management.

[0066] The analysis unit can consider a pet's genetic information when analyzing its health data. For example, the analysis unit can assess genetic health risks based on the pet's genetic information. It can also predict the risk of specific diseases and suggest preventative measures based on genetic information. Furthermore, the analysis unit can provide advice on appropriate diet and exercise for pets based on genetic information. This enables more personalized health management by utilizing genetic information.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects data on the pet's food intake, exercise level, and excretion. Specifically, it records the number of meals, amount, and type of meals; the number of steps taken during exercise; the duration of exercise; the type of exercise; and the number of excretions, amount, and duration. Step 2: The analysis unit analyzes the data collected by the collection unit to check the pet's health status. Specifically, it analyzes weight, body temperature, and behavioral patterns, and determines that there is a concern about the pet's health if there are abnormal data patterns or if certain thresholds are exceeded. Step 3: The notification unit, based on the data analyzed by the analysis unit, will alert users to take necessary actions if there are concerns about their health status. Specifically, it will alert users about dietary adjustments, changes in exercise, communication methods, contacting hospitals, and specific care methods. Step 4: The recording unit records daily video data and detects changes in movement and injuries. Specifically, it collects data using cameras attached to the leash, sensors attached to the collar and harness, and AI analyzes this data to detect changes in movement and injuries. It can also encrypt and store the data.

[0069] (Example of form 2) The pet health management system according to an embodiment of the present invention is a system for managing pet health using AI. This pet health management system accumulates data on the pet's food intake, exercise level, and excretion, and checks its health status using AI. For example, it uses AI to collect data on the speed of the pet's walks, the amount of food (water intake), and changes in the color and amount of excrement. If there are concerns about the pet's health, it alerts the user to take necessary action. For example, this could involve adjusting the pet's diet, increasing or decreasing exercise, improving communication, or taking the pet to the vet. Next, it records video data of the pet's daily activities (indoor and outdoor walks) to detect changes in movement and injuries early. For example, even if a pet has a habit of hiding injuries when with family, videos of its movements and sleep (or lack thereof) when the family is away can be used to manage injuries, illnesses, and stress. Specifically, a chip is attached to the collar or other part of the pet to record indoor behavior on video and detect changes in behavior and movements. In addition, outdoor exercise levels are recorded using a harness (exercise level and body temperature), and a camera is attached to the leash to record movement. This allows for the accumulation of information that makes it easier to explain the pet's condition to a veterinarian. This system makes it possible to generate information from the "voices" of pets that cannot speak. This system can help extend the healthy lifespan of pets and enable early detection of illnesses and injuries. It can also help prevent obesity in pets that live indoors. The target audience is people who consider pets such as dogs and cats as family members, and this system is effective because there are limitations to manually recording information. As a result, the pet health management system can efficiently manage the health status of pets and take necessary actions quickly.

[0070] The pet health management system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, and a recording unit. The data collection unit collects data on the pet's food intake, exercise level, and excretion. For example, the data collection unit records the number of meals, amount, and type of food to measure the pet's food intake. The data collection unit can also record the number of steps, exercise time, and type of exercise to measure the pet's exercise level. Furthermore, the data collection unit can also record the number of excretions, amount, and time to measure excretion data. For example, the data collection unit records the number of meals to measure the pet's food intake. The data collection unit can also record the number of steps to measure the pet's exercise level. The data collection unit can also record the number of excretions to measure the pet's excretion data. The analysis unit analyzes the data collected by the data collection unit and checks the pet's health status. For example, the analysis unit analyzes the pet's weight, body temperature, and behavioral patterns based on the collected data. The analysis unit can also determine that there is a concern about the pet's health status if there is an abnormal data pattern or if certain thresholds are exceeded. Furthermore, the analysis unit can also evaluate the pet's health status based on the collected data. For example, the analysis unit can analyze the pet's weight based on the collected data. The analysis unit can also analyze the pet's body temperature based on the collected data. The analysis unit can also analyze the pet's behavioral patterns based on the collected data. The notification unit alerts the pet to take necessary action if there are concerns about its health status, based on the data analyzed by the analysis unit. For example, the notification unit can alert the pet to adjust its diet, increase or decrease exercise, improve communication, or take it to the vet. The notification unit can also alert the pet to contact a veterinarian if there are concerns about its health status. In addition, the notification unit can alert the pet to specific care methods if there are concerns about its health status. For example, the notification unit can alert the pet to adjust its diet if there are concerns about its health status. The notification unit can also alert the pet to increase or decrease exercise if there are concerns about its health status. The notification unit can also alert the pet to improve communication if there are concerns about its health status. The recording unit records everyday video data and detects changes in movement and injuries.The recording unit can, for example, record video using a camera attached to the leash, and the AI ​​analyzes the video to detect changes in movement or injuries. The recording unit can also collect data using sensors attached to the collar or harness, and the AI ​​analyzes this data to detect changes in movement or injuries. Furthermore, the recording unit can encrypt and store the data. For example, the recording unit can record video using a camera attached to the leash. The recording unit can also collect data using sensors attached to the collar. The recording unit can also collect data using sensors attached to the harness. This allows the pet health management system according to this embodiment to efficiently manage the pet's health status and take necessary actions quickly.

[0071] The data collection unit collects data on the pet's food intake, exercise level, and excretion. Specifically, to measure the pet's food intake, it records the frequency, amount, and type of meals. For example, a weight sensor attached to the pet's food bowl can accurately measure the amount of food, and the type of food can be automatically recognized using a barcode scanner or RFID tag. The data collection unit can also record steps, exercise time, and type of exercise to measure exercise level. Accelerometers and gyroscopes attached to the pet's collar or harness can be used to track the pet's movements in detail and identify the type of exercise (e.g., walking, playing, running). Furthermore, the data collection unit can record the frequency, amount, and time of excretion to measure excretion data. For example, a sensor installed in the pet's litter box measures the amount of excrement and records the time of excretion. This allows the data collection unit to collect diverse data for detailed monitoring of the pet's health. The collected data is transmitted to a central database using wireless communication technology (e.g., Wi-Fi, Bluetooth) and updated in real time. This allows the collection unit to continuously monitor the pet's health and respond quickly if any abnormalities occur.

[0072] The analysis unit analyzes the data collected by the data collection unit to check the pet's health status. Specifically, it analyzes the pet's weight, body temperature, and behavioral patterns based on the collected data. For example, a pet's weight can be estimated by combining data on food intake and exercise. Body temperature is measured using temperature sensors attached to collars or harnesses. Behavioral patterns can be understood by analyzing data from acceleration sensors and gyroscope sensors to grasp the pet's activity level and rest time. The analysis unit uses AI to analyze this data in real time and can determine if there are health concerns if abnormal data patterns or certain thresholds are exceeded. For example, if a pet's weight increases or decreases rapidly, its body temperature exceeds the normal range, or abnormal behavioral patterns are observed, the analysis unit can detect this and suggest appropriate actions. Furthermore, the analysis unit can also use historical data and statistical information to evaluate long-term health trends and predict future health risks. This allows the analysis unit to comprehensively evaluate the pet's health status, detect problems early, and take countermeasures.

[0073] The notification unit, based on data analyzed by the analysis unit, alerts owners to necessary actions if there are concerns about their pet's health. Specifically, it alerts owners to actions such as adjusting their pet's diet, increasing or decreasing exercise, improving communication, and taking their pet to the vet. For example, if a pet's weight is increasing, the notification unit can alert owners to reduce their pet's food intake. If a pet is not getting enough exercise, the notification unit can alert owners to increase exercise. Furthermore, if an abnormality is detected in the pet's behavioral patterns, the notification unit can alert owners to contact a veterinarian. The notification unit can quickly transmit information to pet owners via smartphone apps, email, SMS, etc. In addition, the notification unit can customize the content of alerts, allowing owners to adjust the notification method and frequency according to their preferences. This enables the notification unit to quickly provide appropriate actions based on the pet's health condition, allowing pet owners to efficiently manage their pet's health.

[0074] The recording unit records daily video data and detects changes in movement and injuries. Specifically, it records video using a camera attached to the leash, and AI analyzes the video to detect changes in movement and injuries. For example, if a pet is walking differently than usual or frequently licking a particular body part, the AI ​​can detect this and flag it as an abnormality. The recording unit can also collect data using sensors attached to the collar or harness, and the AI ​​can analyze this data to detect changes in movement and injuries. For example, by analyzing data from acceleration sensors and gyroscope sensors, it can understand the pet's movement patterns in detail and issue an alert if abnormal movement is detected. Furthermore, the recording unit can encrypt and store the data. This ensures data security while protecting the pet's privacy. The recording unit can also store the collected data on a cloud server, allowing the owner to access it at any time. This allows the recording unit to record the pet's health in detail and respond quickly if an abnormality occurs.

[0075] The data collection unit can collect data using sensors attached to the collar and sensors attached to the harness. For example, the data collection unit can collect the pet's location information using a GPS sensor attached to the collar. The data collection unit can also collect the pet's activity level using an accelerometer attached to the collar. Furthermore, the data collection unit can collect the pet's heart rate using a heart rate sensor attached to the harness. For example, the data collection unit can collect the pet's location information using a GPS sensor attached to the collar. The data collection unit can also collect the pet's activity level using an accelerometer attached to the collar. The data collection unit can also collect the pet's heart rate using a heart rate sensor attached to the harness. This allows for accurate collection of pet data using sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the sensors attached to the collar into a generating AI and have the generating AI perform data analysis.

[0076] The recording unit records video using a camera attached to the leash, and AI can analyze the video to detect changes in movement and injuries. For example, the recording unit records video of the pet using a high-resolution camera attached to the leash. The recording unit can also use AI to analyze the recorded video and detect changes in the pet's movement. Furthermore, the recording unit can use AI to analyze the recorded video and detect injuries to the pet. For example, the recording unit records video of the pet using a high-resolution camera attached to the leash. The recording unit can also use AI to analyze the recorded video and detect changes in the pet's movement. The recording unit can also use AI to analyze the recorded video and detect injuries to the pet. This allows for early detection of changes in the pet's movement and injuries by analyzing the video. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input video data acquired by a camera attached to the leash into a generating AI and have the generating AI perform the analysis of the video data.

[0077] The recording unit can encrypt and securely store data. For example, the recording unit can encrypt data using AES encryption. Alternatively, the recording unit can encrypt data using RSA encryption. Furthermore, the recording unit can use dedicated hardware for encrypting and storing data. For example, the recording unit can encrypt data using AES encryption. The recording unit can also encrypt data using RSA encryption. The recording unit can also use dedicated hardware for encrypting and storing data. This ensures the security of pet data through data encryption. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data into a generating AI and have the generating AI perform data encryption.

[0078] The data collection unit can estimate the pet's emotions and appropriately adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data on food intake and excretion when the pet is relaxed. It can also prioritize collecting data on exercise levels when the pet is excited. Furthermore, the data collection unit can temporarily suspend data collection when the pet is stressed and resume it later. This allows for the collection of more accurate data by adjusting the timing of data collection according to the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's emotional data into a generating AI and have the generating AI perform the adjustment of data collection timing based on emotions.

[0079] The data collection unit can analyze the pet's past health data and select the optimal sensor placement. For example, based on past data, the data collection unit can place sensors in areas where the pet's body temperature fluctuates significantly. The data collection unit can also improve the accuracy of data collection by adjusting the harness position based on past activity data. Furthermore, the data collection unit can place sensors in positions that allow for accurate measurement of the color and amount of excrement, referencing past excretion data. For example, based on past data, the data collection unit can place sensors in areas where the pet's body temperature fluctuates significantly. The data collection unit can also improve the accuracy of data collection by adjusting the harness position based on past activity data. The data collection unit can also place sensors in positions that allow for accurate measurement of the color and amount of excrement, referencing past excretion data. This allows for improved data collection accuracy by selecting the optimal sensor placement based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal sensor placement.

[0080] The data collection unit can appropriately adjust the data collection frequency based on the pet's activity level during data collection. For example, the data collection unit can increase the data collection frequency when the pet is active. It can also decrease the data collection frequency when the pet is resting. Furthermore, if the pet is active at night, the data collection unit can adjust the data collection frequency at night. For example, the data collection unit can increase the data collection frequency when the pet is active. It can also decrease the data collection frequency when the pet is resting. If the pet is active at night, the data collection unit can also adjust the data collection frequency at night. This allows for efficient data collection by adjusting the collection frequency according to the pet's activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet activity level data into a generating AI and have the generating AI adjust the collection frequency.

[0081] The data collection unit can estimate the pet's emotions and appropriately determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting data on excrement. If the pet is relaxed, the data collection unit can also prioritize collecting data on food intake. Furthermore, if the pet is excited, the data collection unit can also prioritize collecting data on exercise level. In this way, by prioritizing data according to the pet's emotions, important data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI perform the data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the pet's living environment information during data collection. For example, if a pet spends a lot of time indoors, the data collection unit will prioritize the collection of indoor activity data. Similarly, if a pet spends a lot of time outdoors, the data collection unit can prioritize the collection of outdoor exercise data. Furthermore, the data collection unit can prioritize the collection of body temperature data by considering the temperature and humidity of the living environment. This allows for the efficient collection of highly relevant data by considering the living environment information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's living environment information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0083] The data collection unit can appropriately adjust the timing of data collection, taking into account the pet owner's schedule. For example, the data collection unit can collect pet behavior data during times when the owner is away. It can also collect exercise data during times when the owner takes the pet for a walk. Furthermore, it can collect food intake data during times when the owner feeds the pet. For example, the data collection unit can collect pet behavior data during times when the owner is away. It can also collect exercise data during times when the owner takes the pet for a walk. It can also collect food intake data during times when the owner feeds the pet. This enables efficient data collection by taking the owner's schedule into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's schedule data into a generating AI and have the generating AI adjust the timing of data collection.

[0084] The analysis unit can estimate the pet's emotions and appropriately adjust the analysis algorithm based on the estimated emotions. For example, if the pet is relaxed, the analysis unit uses the normal analysis algorithm. If the pet is stressed, the analysis unit can also use an algorithm that emphasizes stress-related data. Furthermore, if the pet is excited, the analysis unit can also use an algorithm that emphasizes activity level data. This allows for improved analysis accuracy by adjusting the analysis algorithm according to the pet's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's emotional data into a generating AI and have the generating AI adjust the analysis algorithm.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the pet's past health data during the analysis process. For example, the analysis unit can more accurately analyze the current health status based on past health data. The analysis unit can also detect outliers from past data and compare them with current data. Furthermore, the analysis unit can analyze long-term health trends by referring to past health data. For example, the analysis unit can more accurately analyze the current health status based on past health data. The analysis unit can also detect outliers from past data and compare them with current data. The analysis unit can also analyze long-term health trends by referring to past health data. This allows for a more accurate analysis of the current health status by referring to past health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0086] The analysis unit can apply different analysis methods depending on the type and age of the pet during analysis. For example, the analysis unit may use different analysis methods for dogs and cats. It can also use different analysis methods for young pets and elderly pets. Furthermore, the analysis unit may use analysis methods specific to particular breeds. For example, the analysis unit may use different analysis methods for dogs and cats. It can also use different analysis methods for young pets and elderly pets. It can also use analysis methods specific to particular breeds. This allows for improved analysis accuracy by applying analysis methods appropriate to the type and age of the pet. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet type and age data into a generating AI and have the generating AI execute the application of different analysis methods.

[0087] The analysis unit can estimate the pet's emotions and appropriately adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can display detailed analysis results. If the pet is stressed, the analysis unit can also display concise analysis results. Furthermore, if the pet is excited, the analysis unit can display visually easy-to-understand analysis results. For example, if the pet is relaxed, the analysis unit can display detailed analysis results. If the pet is stressed, the analysis unit can also display concise analysis results. If the pet is excited, the analysis unit can also display visually easy-to-understand analysis results. By adjusting the display method of the analysis results according to the pet's emotions, it is possible to provide analysis results that are easier to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0088] The analysis unit can improve the accuracy of its analysis by referring to data on the pet's living environment during the analysis. For example, the analysis unit can improve the accuracy by referring to indoor temperature and humidity data. The analysis unit can also improve the accuracy by referring to outdoor weather data. Furthermore, the analysis unit can improve the accuracy by referring to the noise level of the pet's living environment. For example, the analysis unit can improve the accuracy by referring to indoor temperature and humidity data. The analysis unit can also improve the accuracy by referring to outdoor weather data. The analysis unit can also improve the accuracy by referring to the noise level of the pet's living environment. In this way, the accuracy of the analysis can be improved by referring to living environment data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using 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 improvement of the analysis accuracy.

[0089] The analysis unit can optimize its analysis algorithm by incorporating feedback from pet owners during analysis. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also improve the way the analysis results are displayed by incorporating the owner's opinions. Furthermore, the analysis unit can improve the accuracy of the analysis by reflecting the owner's feedback. For example, the analysis unit adjusts the analysis algorithm based on the owner's feedback. The analysis unit can also improve the way the analysis results are displayed by incorporating the owner's opinions. The analysis unit can also improve the accuracy of the analysis by reflecting the owner's feedback. In this way, by incorporating feedback from pet owners, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input owner feedback data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0090] The notification unit can estimate the pet's emotions and appropriately adjust the notification content based on the estimated emotions. For example, if the pet is relaxed, the notification unit can provide detailed notification content. It can also provide concise notification content if the pet is stressed. Furthermore, if the pet is excited, the notification unit can provide visually easy-to-understand notification content. This allows for more appropriate notifications by adjusting the notification content according to the pet's emotions. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input pet emotion data into a generating AI and have the generating AI adjust the notification content.

[0091] The notification unit can propose the optimal course of action by referring to the pet's past health data when it sends a notification. For example, the notification unit can suggest the optimal diet based on past health data. It can also suggest increasing or decreasing exercise based on past exercise data. Furthermore, it can suggest methods of health management based on past excretion data. For example, the notification unit can suggest the optimal diet based on past health data. It can also suggest increasing or decreasing exercise based on past exercise data. It can also suggest methods of health management based on past excretion data. In this way, by referring to past health data, it can propose the optimal course of action. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past health data into a generating AI and have the generating AI execute the proposal of the optimal course of action.

[0092] The notification unit can customize the notification method according to the pet owner's lifestyle when a notification is sent. For example, if the owner is busy, the notification unit can provide a concise notification. If the owner is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the owner is away from home, the notification unit can send a notification to their mobile device. This allows for more effective notifications by providing notification methods tailored to the owner's lifestyle. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's lifestyle data into a generating AI and have the generating AI customize the notification method.

[0093] The notification unit can propose the most appropriate course of action when it sends a notification, taking into account the geographical location of the pet owner. For example, if the owner is at home, the notification unit can propose a course of action that can be taken at home. If the owner is out, the notification unit can also suggest a nearby veterinary hospital. Furthermore, if the owner is traveling, the notification unit can also suggest a course of action at the travel destination. By taking into account the owner's geographical location, the notification unit can propose a more appropriate course of action. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the owner's geographical location into a generating AI and have the generating AI propose the most appropriate course of action.

[0094] The notification unit can analyze the pet owner's social media activity when sending notifications and customize the notification content accordingly. For example, if the owner posts about their pet's health on social media, the notification unit will provide relevant notifications. The notification unit can also provide visually easy-to-understand notifications if the owner frequently posts photos of their pet on social media. Furthermore, if the owner shares their pet's activities on social media, the notification unit can provide notifications related to those activities. For example, if the owner posts about their pet's health on social media, the notification unit will provide relevant notifications. The notification unit can also provide visually easy-to-understand notifications if the owner frequently posts photos of their pet on social media. The notification unit can also provide notifications related to those activities if the owner shares their pet's activities on social media. This allows the notification unit to provide more relevant notifications by analyzing the owner's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the owner's social media activity data into a generating AI, which can then customize the notification content.

[0095] The recording unit can estimate the pet's emotions and appropriately adjust the timing of video recording based on the estimated emotions. For example, if the pet is relaxed, the recording unit will perform normal video recording. The recording unit can also increase the frequency of video recording if the pet is stressed. Furthermore, the recording unit can adjust the timing of video recording if the pet is excited. For example, if the pet is relaxed, the recording unit will perform normal video recording. The recording unit can also increase the frequency of video recording if the pet is stressed. The recording unit can also adjust the timing of video recording if the pet is excited. By adjusting the timing of video recording according to the pet's emotions, more important footage can be recorded. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet emotion data into a generating AI and have the generating AI perform the adjustment of the video recording timing.

[0096] The recording unit can improve recording accuracy by referring to past behavioral data of the pet during video recording. For example, the recording unit can record current behavior more accurately based on past behavioral data. The recording unit can also detect abnormal behavior from past data and compare it with current data. Furthermore, the recording unit can record long-term behavioral trends by referring to past behavioral data. For example, the recording unit can record current behavior more accurately based on past behavioral data. The recording unit can also detect abnormal behavior from past data and compare it with current data. The recording unit can also record long-term behavioral trends by referring to past behavioral data. This allows for more accurate recording of current behavior by referring to past behavioral data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past behavioral data into a generating AI and have the generating AI perform the improvement of recording accuracy.

[0097] The recording unit can appropriately adjust the recording frequency according to the pet's activity level when recording video. For example, the recording unit can increase the recording frequency when the pet is actively moving around. It can also decrease the recording frequency when the pet is resting. Furthermore, if the pet is active at night, the recording unit can adjust the recording frequency at night. For example, the recording unit can increase the recording frequency when the pet is actively moving around. It can also decrease the recording frequency when the pet is resting. If the pet is active at night, the recording unit can also adjust the recording frequency at night. This allows for efficient video recording by adjusting the recording frequency according to the pet's activity level. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet activity level data into a generating AI and have the generating AI adjust the recording frequency.

[0098] The recording unit can estimate the pet's emotions and appropriately determine the priority of video recordings based on the estimated emotions. For example, if the pet is stressed, the recording unit will prioritize recording stress-related behaviors. The recording unit can also prioritize recording normal behaviors if the pet is relaxed. Furthermore, if the pet is excited, the recording unit can prioritize recording exercise-related behaviors. For example, if the pet is stressed, the recording unit will prioritize recording stress-related behaviors. The recording unit can also prioritize recording normal behaviors if the pet is relaxed. The recording unit can also prioritize recording exercise-related behaviors if the pet is excited. This allows important footage to be recorded preferentially by determining the priority of video recordings according to the pet's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input pet emotion data into a generating AI and have the generating AI perform the determination of video recording priorities.

[0099] The recording unit can prioritize recording highly relevant footage by considering the pet's living environment information during video recording. For example, if a pet spends a lot of time indoors, the recording unit will prioritize recording indoor activities. Similarly, if a pet spends a lot of time outdoors, the recording unit can prioritize recording outdoor activities. Furthermore, the recording unit can also prioritize recording activities related to changes in body temperature by considering the temperature and humidity of the living environment. This allows for the efficient recording of highly relevant footage by considering the living environment information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the pet's living environment information into a generating AI and have the generating AI determine the priority of highly relevant footage.

[0100] The recording unit can appropriately adjust the recording timing when recording video, taking into account the pet owner's schedule. For example, the recording unit can record the pet's behavior during times when the owner is away. It can also record the pet's exercise during the times when the owner takes it for a walk. Furthermore, the recording unit can record the pet's eating during the times when the owner feeds it. This allows for efficient video recording by taking the owner's schedule into consideration. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the owner's schedule data into a generating AI and have the generating AI adjust the recording timing.

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

[0102] The data collection unit can collect data not only on the pet's food intake, exercise level, and excretion, but also on its sleep patterns. For example, it can record the pet's sleep duration, sleep quality, and number of nighttime awakenings. It can also detect the pet's movements during sleep and issue an alert if abnormal movements are detected. Furthermore, the data collection unit can monitor the pet's sleep environment (temperature, humidity, noise level) and collect data to provide an optimal sleep environment. This allows owners to understand their pet's sleep patterns and use this information for health management.

[0103] The analysis unit can analyze collected data by referencing not only the pet's past health data but also data from other pets of the same species. For example, the analysis unit can compare the health status of pets based on data from pets of the same dog or cat breed. It can also refer to data from pets of the same age group to provide age-appropriate health management advice. Furthermore, the analysis unit can suggest health management tailored to the living environment based on data from pets living in the same environment (indoor or outdoor). This enables more accurate health management.

[0104] The notification unit not only provides owners with health management advice based on their pet's health status, but can also estimate the pet's emotions and provide emotion-based advice. For example, if the notification unit is stressed, it will provide advice on stress reduction. It can also provide advice on maintaining relaxation if the pet is relaxed. Furthermore, if the pet is excited, it can provide advice on calming them down. This enables appropriate health management tailored to the pet's emotions.

[0105] The recording unit can not only record pet behavioral data but also pet voice data. For example, the recording unit can record pet barks and meows and perform voice analysis. Furthermore, based on the pet's voice data, the recording unit can detect abnormal barks and meows and issue alerts. In addition, based on the pet's voice data, the recording unit can estimate emotions and suggest appropriate responses. This allows for more detailed health management by utilizing pet voice data.

[0106] The data collection unit can collect not only data on pet food intake, exercise levels, and excretion, but also pet weight data. For example, the unit can regularly measure pet weight and record weight fluctuations. Furthermore, based on the pet's weight data, the unit can suggest appropriate food and exercise levels. In addition, based on the pet's weight data, the unit can assess the risk of obesity or being underweight and provide health management advice. This allows for more effective health management through pet weight management.

[0107] The analysis unit can consider a pet's genetic information when analyzing its health data. For example, the analysis unit can assess genetic health risks based on the pet's genetic information. It can also predict the risk of specific diseases and suggest preventative measures based on genetic information. Furthermore, the analysis unit can provide advice on appropriate diet and exercise for pets based on genetic information. This enables more personalized health management by utilizing genetic information.

[0108] The notification unit not only provides owners with health management advice based on their pet's health status, but can also estimate the pet's emotions and provide emotion-based advice. For example, if the notification unit is stressed, it will provide advice on stress reduction. It can also provide advice on maintaining relaxation if the pet is relaxed. Furthermore, if the pet is excited, it can provide advice on calming them down. This enables appropriate health management tailored to the pet's emotions.

[0109] The recording unit can not only record pet behavioral data but also pet voice data. For example, the recording unit can record pet barks and meows and perform voice analysis. Furthermore, based on the pet's voice data, the recording unit can detect abnormal barks and meows and issue alerts. In addition, based on the pet's voice data, the recording unit can estimate emotions and suggest appropriate responses. This allows for more detailed health management by utilizing pet voice data.

[0110] The data collection unit can collect not only data on pet food intake, exercise levels, and excretion, but also pet weight data. For example, the unit can regularly measure pet weight and record weight fluctuations. Furthermore, based on the pet's weight data, the unit can suggest appropriate food and exercise levels. In addition, based on the pet's weight data, the unit can assess the risk of obesity or being underweight and provide health management advice. This allows for more effective health management through pet weight management.

[0111] The analysis unit can consider a pet's genetic information when analyzing its health data. For example, the analysis unit can assess genetic health risks based on the pet's genetic information. It can also predict the risk of specific diseases and suggest preventative measures based on genetic information. Furthermore, the analysis unit can provide advice on appropriate diet and exercise for pets based on genetic information. This enables more personalized health management by utilizing genetic information.

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

[0113] Step 1: The data collection unit collects data on the pet's food intake, exercise level, and excretion. Specifically, it records the number of meals, amount, and type of meals; the number of steps taken during exercise; the duration of exercise; the type of exercise; and the number of excretions, amount, and duration. Step 2: The analysis unit analyzes the data collected by the collection unit to check the pet's health status. Specifically, it analyzes weight, body temperature, and behavioral patterns, and determines that there is a concern about the pet's health if there are abnormal data patterns or if certain thresholds are exceeded. Step 3: The notification unit, based on the data analyzed by the analysis unit, will alert users to take necessary actions if there are concerns about their health status. Specifically, it will alert users about dietary adjustments, changes in exercise, communication methods, contacting hospitals, and specific care methods. Step 4: The recording unit records daily video data and detects changes in movement and injuries. Specifically, it collects data using cameras attached to the leash, sensors attached to the collar and harness, and AI analyzes this data to detect changes in movement and injuries. It can also encrypt and store the data.

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

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include 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.

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

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the pet's food intake, exercise level, and excretion using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to check the pet's health status. The notification unit is implemented in the control unit 46A of the smart device 14, for example, and notifies an alert if there is a concern about the pet's health status. The recording unit records daily video data using the camera 42 and sensors of the smart device 14, for example, and detects changes in movement and injuries. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

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

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on the pet's food intake, exercise level, and excretion using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to check the pet's health status. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, which sends an alert if there is a concern about the pet's health status. The recording unit records daily video data using the camera 42 and sensors of the smart glasses 214, for example, to detect changes in movement or injuries. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

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

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the pet's food intake, exercise level, and excretion using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to check the pet's health status. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314, which sends an alert if there are concerns about the pet's health status. The recording unit records daily video data using the camera 42 and sensors of the headset terminal 314, for example, to detect changes in movement or injuries. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

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

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and recording unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on the pet's food intake, exercise level, and excretion using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to check the pet's health status. The notification unit is implemented, for example, by the control unit 46A of the robot 414, which sends an alert if there is a concern about the pet's health status. The recording unit records daily video data using the camera 42 and sensors of the robot 414, and detects changes in movement or injuries. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0185] (Note 1) A data collection unit that collects data on pets' food intake, exercise levels, and excretion, The data collected by the aforementioned collection unit is analyzed by an analysis unit to check the health status of the pet, Based on the data analyzed by the aforementioned analysis unit, a notification unit alerts the user to take necessary action if there are concerns about their health status. It includes a recording unit that records daily video data and detects changes in movement and injuries. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected using sensors attached to the collar and sensors attached to the harness. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recording unit is A camera attached to the lead records video, and AI analyzes the video to detect changes in movement and injuries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recording unit is Encrypt and securely store data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the pet's emotions and adjusts the timing of data collection appropriately based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the pet's past health data to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, adjust the collection frequency appropriately based on the pet's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Estimate the pet's emotions and appropriately prioritize the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the timing of collection should be appropriately adjusted to take into account the pet owner's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the pet's emotions and appropriately adjusts the analysis algorithm based on the estimated emotions of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the pet's past health data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the pet's emotions and appropriately adjusts the display method of the analysis results based on the estimated emotions of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we refer to data on the pet's living environment to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, we incorporate feedback from pet owners to optimize the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, It estimates the pet's emotions and adjusts the notification content appropriately based on the estimated emotions of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When a notification is sent, the system will refer to the pet's past health data to suggest the most appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When a notification is sent, the notification method can be customized according to the pet owner's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When notifying, the system will suggest the most appropriate course of action, taking into account the pet owner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending notifications, the system analyzes the pet owner's social media activity to appropriately customize the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recording unit is The system estimates the pet's emotions and adjusts the timing of video recordings appropriately based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recording unit is When recording video, the accuracy of the recording is improved by referring to the pet's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recording unit is When recording video, adjust the recording frequency appropriately according to the pet's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is It estimates the pet's emotions and appropriately determines the priority of video recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is When recording video, the system prioritizes recording highly relevant footage, taking into account information about the pet's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is When recording video, adjust the recording timing appropriately, taking into account the pet owner's schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects data on pets' food intake, exercise levels, and excretion, The data collected by the aforementioned collection unit is analyzed by an analysis unit to check the health status of the pet, Based on the data analyzed by the aforementioned analysis unit, a notification unit alerts the user to take necessary action if there are concerns about their health status. It includes a recording unit that records daily video data and detects changes in movement and injuries. A system characterized by the following features.

2. The aforementioned collection unit is Data is collected using sensors attached to the collar and sensors attached to the harness. The system according to feature 1.

3. The recording unit is, A camera attached to the lead records video, and AI analyzes the video to detect changes in movement and injuries. The system according to feature 1.

4. The recording unit is, Encrypt and securely store data. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the pet's emotions and adjusts the timing of data collection appropriately based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the pet's past health data to select the optimal sensor placement. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, adjust the collection frequency appropriately based on the pet's activity level. The system according to feature 1.

8. The aforementioned collection unit is Estimate the pet's emotions and appropriately prioritize the data to collect based on the estimated emotions. The system according to feature 1.

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