System

Abstract

An object of the system according to the embodiment is to accurately grasp the health condition and emotion of the pet and provide appropriate advice to the owner.SOLUTION: A system according to an embodiment includes a data collection unit, a data transmission unit, a data analysis unit, an emotion estimation unit, and an advice providing unit. The data collection part collects data of pulse, body temperature and the number of steps by using a sensor attached to a collar of the pet. The data transmission unit transmits the data collected by the data collection unit to the server. The data analysis part analyzes the data transmitted by the data transmission part and determines the weight and excretion action of the pet. The emotion estimator reads the emotion of the pet based on the data analyzed by the data analyzer. The advice providing unit provides advice to the owner based on the feeling read by the feeling estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to accurately grasp a pet's health condition and emotions and provide appropriate advice.

[0005] The system according to the embodiment aims to accurately grasp the health condition and emotions of pets and provide appropriate advice to their owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data transmission unit, a data analysis unit, an emotion estimation unit, and an advice provision unit. The data collection unit collects pulse, body temperature, and step count data using a sensor attached to the pet's collar. The data transmission unit transmits the data collected by the data collection unit to a server. The data analysis unit analyzes the data transmitted by the data transmission unit and determines the pet's weight and excretory behavior. The emotion estimation unit reads the pet's emotion based on the data analyzed by the data analysis unit. The advice provision unit provides advice to the owner based on the emotion read by the emotion estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the health condition and emotions of a pet and provide appropriate advice to the owner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A pet care system according to an embodiment of the present invention is a system that grasps the health and emotional state of a pet in real time and provides appropriate advice to the owner. As a result, the pet care system is able to grasp the health and emotional state of a pet in real time and provide appropriate advice to the owner.

[0029] The pet care system according to the embodiment includes a data collection unit, a data transmission unit, a data analysis unit, an emotion estimation unit, and an advice provision unit. The data collection unit collects pulse, body temperature, and step count data using a sensor attached to the pet's collar. For example, a sensor built into the collar measures the pet's pulse in real time and transmits the data to a server. A body temperature sensor periodically measures the pet's body temperature and issues an alert if an abnormality is detected. A pedometer is also built in, allowing the pet's activity level to be monitored. The data transmission unit transmits the data collected by the data collection unit to a server. For example, the data can be transmitted using Wi-Fi or Bluetooth. Alternatively, the data can be transmitted using cellular communication. The data analysis unit analyzes the data transmitted by the data transmission unit and determines the pet's weight and excretory behavior. For example, the data on the pulse and body temperature can be analyzed using a machine learning algorithm to evaluate the pet's health. The excretory behavior data can be analyzed to determine the health of the pet's digestive system. The emotion estimation unit interprets the pet's emotion based on the data analyzed by the data analysis unit. For example, if the pulse rate suddenly rises, it is determined that the pet may be excited. Also, if the body temperature is high, it is analyzed that the pet may be feeling stressed. The advice providing unit provides advice to the owner based on the emotions read by the emotion estimation unit. For example, if the pet is feeling stressed, advice is given on how to create a relaxing environment. Also, if an abnormality is found in the pet's health condition, information on necessary medications and hospitals, and dietary management methods are suggested. As a result, the pet care system according to the embodiment can grasp the health condition and emotional state of the pet in real time and provide appropriate advice to the owner.

[0030] The data collection unit can measure the electrical resistance of a pet's skin and estimate whether it is experiencing stress. For example, the data collection unit uses a sensor built into a collar to measure the electrical resistance of a pet's skin in real time and send the data to a server. For example, it analyzes changes in electrical resistance to estimate the pet's stress level. It also develops an algorithm to evaluate a pet's stress level based on the electrical resistance data. For example, it determines that stress is high when electrical resistance exceeds a certain range. It also integrates the electrical resistance measurement data with other biological data to build a system that comprehensively evaluates a pet's stress level. For example, it combines and analyzes data on pulse rate and body temperature. This makes it possible to grasp a pet's stress level in real time.

[0031] The data collection unit can analyze a pet's movement patterns and detect whether or not there is any abnormal behavior. For example, the data collection unit uses the GPS function built into the collar to track the pet's movement patterns in real time and send the data to a server. For example, an algorithm to detect abnormal movement patterns can be developed. A system can also be built that analyzes a pet's movement patterns based on GPS data and detects abnormal behavior. For example, an alert can be issued if the pet goes beyond its normal movement range. A system can also be developed that integrates movement pattern data with other biological data to comprehensively evaluate the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to detect abnormal pet behavior in real time.

[0032] The data collection unit can collect video from the pet's perspective and perform behavioral analysis. The data collection unit, for example, uses a camera built into a collar to collect video from the pet's perspective in real time and transmits the data to a server. For example, it performs video analysis to analyze the pet's behavioral patterns. It also develops algorithms to analyze the pet's behavioral patterns based on the video data. For example, it uses video analysis technology to detect specific movements and actions. It also integrates the video data with other biometric data to build a system that comprehensively evaluates the pet's health condition. For example, it combines and analyzes data such as pulse rate and body temperature. This makes it possible to perform behavioral analysis based on video from the pet's perspective.

[0033] The data collection unit can detect subtle changes in a pet's movements and evaluate whether or not it is in good health. The data collection unit, for example, uses a vibration sensor built into a collar to detect subtle changes in a pet's movements in real time and transmits the data to a server. For example, it analyzes movement patterns and evaluates the health condition. In addition, an algorithm for evaluating a pet's health condition is developed based on the vibration sensor data. For example, specific movement changes are used as an indicator of health condition. In addition, the vibration sensor data is integrated with other biometric data to build a system for comprehensively evaluating a pet's health condition. For example, it is analyzed in combination with pulse and body temperature data. This makes it possible to evaluate the health condition based on subtle changes in a pet's movements.

[0034] The data collection unit can measure the humidity on the pet's body surface and estimate the stress level using an emotion estimation function. The data collection unit, for example, uses a sensor built into the collar to measure the humidity on the pet's body surface in real time and transmits the data to a server. For example, it analyzes changes in humidity and estimates the stress level. It also develops an algorithm to evaluate the pet's stress level based on the humidity data. For example, it determines that stress is high when humidity exceeds a certain range. It also integrates the humidity measurement data with other biological data to build a system that comprehensively evaluates the pet's stress level. For example, it combines it with pulse and body temperature data and analyzes it. This makes it possible to estimate the stress level based on the humidity on the pet's body surface.

[0035] Toilet sensors can evaluate a pet's health condition by analyzing images of the color or shape of excrement. For example, a toilet sensor uses a camera built into the toilet to capture images of the color and shape of excrement in real time and send the data to a server. For example, image analysis is performed to evaluate the health condition. An algorithm is also developed that analyzes the color and shape of excrement based on image data. For example, changes in specific color or shape are used as indicators of health condition. Image data is also integrated with other biometric data to build a system that comprehensively evaluates a pet's health condition. For example, it is combined with pulse and body temperature data for analysis. This makes it possible to evaluate the health condition based on the color and shape of excrement.

[0036] Toilet sensors can detect the smell of excrement and analyze whether it is an abnormal smell. For example, toilet sensors use an odor sensor built into the toilet to detect the smell of excrement in real time and send the data to a server. For example, they analyze the components of the smell and detect abnormalities. Furthermore, algorithms can be developed based on the smell data to analyze abnormal excrement smells. For example, changes in specific components can be used as indicators of abnormalities. Furthermore, the smell data can be integrated with other biological data to build a system that comprehensively evaluates the health of pets. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to detect abnormalities based on the smell of excrement.

[0037] Toilet sensors can measure the temperature of excrement and assess whether the pet is healthy. For example, a toilet sensor built into the toilet measures the temperature of excrement in real time and sends the data to a server. For example, temperature changes can be analyzed to assess the pet's health. We also develop algorithms that analyze the temperature of excrement based on the temperature data. For example, specific temperature changes can be used as an indicator of health. We also build systems that integrate temperature data with other biological data to comprehensively assess the pet's health. For example, we analyze the data in combination with pulse and body temperature data. This makes it possible to assess the pet's health based on the temperature of the excrement.

[0038] Toilet sensors can chemically analyze the components of excrement and assess whether the pet is healthy. For example, a toilet sensor could use a built-in chemical sensor to analyze the components of excrement in real time and send the data to a server. For example, changes in the components could be analyzed to assess the pet's health. An algorithm could also be developed to analyze the components of excrement based on the component data. For example, changes in specific components could be used as an indicator of health. The component data could also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it could be combined with pulse and body temperature data for analysis. This would allow the health of the pet to be assessed based on the components of the excrement.

[0039] Toilet sensors can collect excrement sounds and perform analysis using generative AI. For example, toilet sensors use a microphone built into the toilet to collect excrement sounds in real time and send the data to a server. For example, they perform frequency analysis of the sound to evaluate health status. They also develop algorithms to analyze excrement sounds based on the sound data. For example, they use changes in specific frequencies and volume as indicators of health status. They also integrate sound data with other biological data to build a system that comprehensively evaluates the health status of pets. For example, they analyze the data in combination with pulse and body temperature data. This makes it possible to perform analysis based on the sound of excrement.

[0040] Toilet sensors can measure the amount of excrement and use emotion estimation functions to estimate stress levels. For example, toilet sensors use a weight sensor built into the toilet to measure the amount of excrement in real time and send the data to a server. For example, changes in the amount can be analyzed to estimate stress levels. We also develop algorithms to analyze the amount of excrement based on the amount data. For example, specific changes in the amount can be used as an indicator of stress. We also integrate the amount data with other biological data to build a system that comprehensively evaluates pet stress levels. For example, we can analyze it in combination with pulse and body temperature data. This makes it possible to estimate stress levels based on the amount of excrement.

[0041] The data transmission unit can improve data security by using encryption technology when transmitting data. The data transmission unit, for example, builds a system that uses encryption technology when transmitting data to strengthen data security. For example, it encrypts data using AES encryption. It also uses encryption technology to protect the data transmission path. For example, it encrypts data transmission using the SSL / TLS protocol. It also develops a system that encrypts and decrypts data in real time. For example, it encrypts data when transmitting and decrypts it when receiving it. This strengthens security when transmitting data.

[0042] The data analysis unit can introduce an algorithm that compares data with past data to detect abnormalities during data analysis. The data analysis unit, for example, develops an algorithm that compares data with past data during data analysis to detect abnormalities. For example, it compares past pulse and body temperature data with current data to detect abnormalities. It also uses an anomaly detection algorithm to build a system that monitors pet health in real time. For example, it issues an alert if an abnormality is detected. It also integrates past data and current data to build a database for detecting abnormalities. For example, it sets a reference value for detecting abnormalities and performs analysis based on that. This makes it possible to detect abnormalities by comparing with past data.

[0043] The data analysis unit learns the pet's behavioral patterns during data analysis and can detect whether or not the behavior is abnormal in real time. The data analysis unit, for example, develops an algorithm that learns the pet's behavioral patterns during data analysis. For example, it learns normal behavioral patterns and detects abnormal behavior. It also builds a system that detects abnormal behavior in real time based on behavioral pattern data. For example, it issues an alert when abnormal behavior is detected. It also develops a system that integrates behavioral pattern data with other biological data to comprehensively evaluate the pet's health. For example, it combines and analyzes data on pulse rate and body temperature. This makes it possible to learn the pet's behavioral patterns and detect abnormal behavior in real time.

[0044] The data transmission unit synchronizes data between different devices when transmitting data, and can integrate data from multiple pets. The data transmission unit, for example, builds a system that synchronizes data between different devices when transmitting data. For example, it integrates data from multiple pets into a single server. It also develops a data synchronization algorithm to integrate data from different devices in real time. For example, it synchronizes data from collars and toilets. It also builds a system that integrates data from multiple pets and comprehensively evaluates their health. For example, it compares data from multiple pets and identifies common health problems. This allows data synchronization between different devices and integration of data from multiple pets.

[0045] The data analysis unit can compare data from different pets during data analysis and identify whether there are any common health issues. The data analysis unit, for example, develops an algorithm for comparing data from different pets during data analysis. For example, it compares pulse and body temperature data from multiple pets to identify common health issues. It also builds a system for identifying common health issues based on data from different pets. For example, it integrates and analyzes data from pets with the same symptoms. It also builds a database for comparing pet data and identifying common health issues. For example, it aggregates data from pets with specific symptoms or abnormalities. This makes it possible to compare data from different pets and identify common health issues.

[0046] The advice providing unit can generate individually customized advice based on the pet's past health data. The advice providing unit, for example, builds a system that generates individually customized advice based on the pet's past health data when providing advice. For example, optimal advice is provided based on past data. Also, an algorithm is developed that analyzes the pet's past health data and generates individually customized advice. For example, advice is provided according to a specific health condition. Also, a system is built that integrates the past health data with other biometric data, comprehensively evaluates the pet's health condition, and provides individually customized advice. For example, it combines and analyzes pulse and body temperature data. This makes it possible to generate individually customized advice based on the pet's past health data.

[0047] The advice providing unit can propose optimal environmental improvement measures based on the pet's living environment. The advice providing unit, for example, builds a system that takes the pet's living environment into consideration when providing advice and proposes optimal environmental improvement measures. For example, it provides optimal improvement measures based on living environment data. It also develops an algorithm that analyzes the pet's living environment data and proposes optimal environmental improvement measures. For example, it provides improvement measures according to specific environmental conditions. It also builds a system that integrates the living environment data with other biometric data, comprehensively evaluates the pet's health condition, and proposes optimal environmental improvement measures. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to propose optimal environmental improvement measures taking the pet's living environment into consideration.

[0048] The advice providing unit can analyze the pet's dietary history and propose an optimal meal plan. The advice providing unit, for example, analyzes the pet's dietary history when providing advice and builds a system that proposes an optimal meal plan. For example, an optimal meal plan is provided based on dietary history data. Also, an algorithm is developed that analyzes the pet's dietary history data and proposes an optimal meal plan. For example, a meal plan is proposed based on the intake of specific nutrients. Also, a system is built that integrates dietary history data with other biometric data to comprehensively evaluate the pet's health condition and propose an optimal meal plan. For example, it is analyzed in combination with pulse and body temperature data. This makes it possible to propose an optimal meal plan based on the pet's dietary history.

[0049] The advice providing unit can compare data of different pets and provide advice for common health problems. The advice providing unit, for example, builds a system that compares data of different pets when providing advice and provides advice for common health problems. For example, it identifies common health problems based on data of multiple pets. It also develops an algorithm that provides advice for common health problems based on data of different pets. For example, it provides advice for pets with the same symptoms. It also builds a database for comparing pet data and providing advice for common health problems. For example, it aggregates data of pets with specific symptoms or abnormalities. This makes it possible to compare data of different pets and provide advice for common health problems.

[0050] The advice providing unit can propose an optimal care plan based on the lifestyle of the pet owner. For example, the advice providing unit builds a system that takes the lifestyle of the pet owner into consideration when providing advice and proposes the optimal care plan. For example, it provides an optimal care plan based on data on the owner's lifestyle patterns. It also develops an algorithm that analyzes the owner's lifestyle data and proposes the optimal care plan. For example, it provides a care plan that suits a specific lifestyle pattern. It also builds a system that integrates lifestyle data with other biometric data, comprehensively evaluates the pet's health condition, and proposes the optimal care plan. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to propose the optimal care plan taking the lifestyle of the pet owner into consideration.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data collection unit can measure the electrical resistance of a pet's skin and estimate whether it is experiencing stress. For example, a sensor built into the collar measures the electrical resistance of a pet's skin in real time and sends the data to a server. Changes in electrical resistance are analyzed to estimate the pet's stress level. An algorithm is also developed to evaluate the pet's stress level based on the electrical resistance data. For example, if the electrical resistance exceeds a certain range, it is determined that stress is high. The electrical resistance measurement data is also integrated with other biological data to build a system that comprehensively evaluates the pet's stress level. For example, it is combined with pulse and body temperature data and analyzed. This makes it possible to grasp the pet's stress level in real time.

[0053] The data collection unit can analyze a pet's movement patterns and detect whether or not they are behaving abnormally. For example, the GPS function built into the collar can be used to track a pet's movement patterns in real time and send the data to a server. An algorithm to detect abnormal movement patterns will be developed. A system will also be built that analyzes a pet's movement patterns based on GPS data and detects abnormal behavior. For example, an alert will be issued if the pet goes beyond its normal movement range. A system will also be developed that integrates movement pattern data with other biological data to comprehensively evaluate a pet's health. For example, it will be combined with pulse and body temperature data for analysis. This will enable abnormal pet behavior to be detected in real time.

[0054] The data collection unit can collect video from the pet's perspective and perform behavioral analysis. For example, a camera built into the collar can be used to collect video from the pet's perspective in real time and send the data to a server. Video analysis is then performed to analyze the pet's behavioral patterns. An algorithm can also be developed to analyze the pet's behavioral patterns based on the video data. For example, video analysis technology can be used to detect specific movements and actions. The video data can also be integrated with other biometric data to build a system that comprehensively evaluates the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This allows behavioral analysis to be performed based on video from the pet's perspective.

[0055] The data collection unit can detect subtle changes in a pet's movements and assess its health status. For example, a vibration sensor built into the collar can be used to detect subtle changes in a pet's movements in real time and send the data to a server. The movement patterns are analyzed to assess the pet's health status. An algorithm for assessing a pet's health status can also be developed based on the vibration sensor data. For example, specific changes in movement can be used as an indicator of health status. The vibration sensor data can also be integrated with other biometric data to build a system for comprehensively assessing a pet's health status. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to assess a pet's health status based on subtle changes in a pet's movements.

[0056] Toilet sensors can perform image analysis of the color or shape of excrement to assess health status. For example, a camera built into the toilet can be used to capture images of the color and shape of excrement in real time and send the data to a server. Image analysis is then performed to assess health status. We will also develop algorithms that analyze the color and shape of excrement based on image data. For example, changes in specific colors or shapes can be used as indicators of health status. We will also integrate image data with other biometric data to build a system that comprehensively assesses pet health status. For example, we will analyze the data in combination with pulse and body temperature data. This will make it possible to assess health status based on the color and shape of excrement.

[0057] Toilet sensors can detect the smell of excrement and analyze whether it is an abnormal smell. For example, an odor sensor built into the toilet can be used to detect the smell of excrement in real time and send the data to a server. The components of the odor can be analyzed to detect abnormalities. Furthermore, an algorithm can be developed to analyze abnormal excrement odors based on the odor data. For example, changes in specific components can be used as an indicator of abnormalities. Furthermore, the odor data can be integrated with other biological data to build a system that comprehensively evaluates the health of pets. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to detect abnormalities based on the odor of excrement.

[0058] Toilet sensors can measure the temperature of excrement and assess whether the pet is healthy. For example, a temperature sensor built into the toilet can be used to measure the temperature of excrement in real time and send the data to a server. Temperature changes can be analyzed to assess the pet's health. An algorithm can also be developed to analyze the temperature of excrement based on the temperature data. For example, specific temperature changes can be used as an indicator of health. Temperature data can also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to assess the pet's health based on the temperature of the excrement.

[0059] Toilet sensors can chemically analyze the components of excrement and assess whether the pet is healthy. For example, a chemical sensor built into the toilet could be used to analyze the components of excrement in real time and send the data to a server. Changes in the components could be analyzed to assess the pet's health. An algorithm could also be developed to analyze the components of excrement based on the component data. For example, changes in specific components could be used as an indicator of health. The component data could also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it could be combined with pulse and body temperature data for analysis. This would allow the health of the pet to be assessed based on the components of excrement.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit uses a sensor attached to your pet's collar to collect pulse, body temperature, and step count data. For example, a sensor built into the collar measures your pet's pulse in real time and sends that data to a server. In addition, a body temperature sensor periodically measures your pet's temperature and issues an alert if there is an abnormality. A pedometer is also built in, allowing you to keep track of your pet's exercise level. Step 2: The data transmission unit transmits the data collected by the data collection unit to the server. For example, the data may be transmitted using Wi-Fi or Bluetooth. Alternatively, the data may be transmitted using cellular communication. Step 3: The data analysis unit analyzes the data sent by the data transmission unit and determines the pet's weight and excretory behavior. For example, it uses a machine learning algorithm to analyze pulse and body temperature data to evaluate the pet's health. It also analyzes excretory behavior data to understand the health of the pet's digestive system. Step 4: The emotion estimation unit reads the pet's emotions based on the data analyzed by the data analysis unit. For example, if the pulse rate suddenly increases, it may determine that the pet is excited. If the body temperature is high, it may analyze that the pet is feeling stressed. Step 5: The advice provider provides advice to the owner based on the emotions detected by the emotion estimation unit. For example, if the pet is feeling stressed, the system will provide advice on creating a relaxing environment for the pet. If any abnormalities are detected in the pet's health, the system will suggest necessary medications, hospital information, and dietary management methods.

[0062] (Example 2) A pet care system according to an embodiment of the present invention is a system that grasps the health and emotional state of a pet in real time and provides appropriate advice to the owner. As a result, the pet care system is able to grasp the health and emotional state of a pet in real time and provide appropriate advice to the owner.

[0063] The pet care system according to the embodiment includes a data collection unit, a data transmission unit, a data analysis unit, an emotion estimation unit, and an advice provision unit. The data collection unit collects pulse, body temperature, and step count data using a sensor attached to the pet's collar. For example, a sensor built into the collar measures the pet's pulse in real time and transmits the data to a server. A body temperature sensor periodically measures the pet's body temperature and issues an alert if an abnormality is detected. A pedometer is also built in, allowing the pet's activity level to be monitored. The data transmission unit transmits the data collected by the data collection unit to a server. For example, the data can be transmitted using Wi-Fi or Bluetooth. Alternatively, the data can be transmitted using cellular communication. The data analysis unit analyzes the data transmitted by the data transmission unit and determines the pet's weight and excretory behavior. For example, the data on the pulse and body temperature can be analyzed using a machine learning algorithm to evaluate the pet's health. The excretory behavior data can be analyzed to determine the health of the pet's digestive system. The emotion estimation unit interprets the pet's emotion based on the data analyzed by the data analysis unit. For example, if the pulse rate suddenly rises, it is determined that the pet may be excited. Also, if the body temperature is high, it is analyzed that the pet may be feeling stressed. The advice providing unit provides advice to the owner based on the emotions read by the emotion estimation unit. For example, if the pet is feeling stressed, advice is given on how to create a relaxing environment. Also, if an abnormality is found in the pet's health condition, information on necessary medications and hospitals, and dietary management methods are suggested. As a result, the pet care system according to the embodiment can grasp the health condition and emotional state of the pet in real time and provide appropriate advice to the owner.

[0064] The data collection unit can measure the electrical resistance of a pet's skin and estimate whether it is experiencing stress. For example, the data collection unit uses a sensor built into a collar to measure the electrical resistance of a pet's skin in real time and send the data to a server. For example, it analyzes changes in electrical resistance to estimate the pet's stress level. It also develops an algorithm to evaluate a pet's stress level based on the electrical resistance data. For example, it determines that stress is high when electrical resistance exceeds a certain range. It also integrates the electrical resistance measurement data with other biological data to build a system that comprehensively evaluates a pet's stress level. For example, it combines and analyzes data on pulse rate and body temperature. This makes it possible to grasp a pet's stress level in real time.

[0065] The data collection unit can analyze a pet's movement patterns and detect whether or not there is any abnormal behavior. For example, the data collection unit uses the GPS function built into the collar to track the pet's movement patterns in real time and send the data to a server. For example, an algorithm to detect abnormal movement patterns can be developed. A system can also be built that analyzes a pet's movement patterns based on GPS data and detects abnormal behavior. For example, an alert can be issued if the pet goes beyond its normal movement range. A system can also be developed that integrates movement pattern data with other biological data to comprehensively evaluate the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to detect abnormal pet behavior in real time.

[0066] The data collection unit can collect the pet's cries and analyze its emotional state using the generation AI. For example, the data collection unit uses a microphone built into the collar to collect the pet's cries in real time and send the data to a server. For example, it performs frequency analysis of the cries to estimate the emotional state. It also develops algorithms to analyze the pet's emotional state based on the cries data. For example, it uses changes in specific frequencies and volume as indicators of emotion. It also integrates the cries data with other biometric data to build a system that comprehensively evaluates the pet's emotional state. For example, it combines and analyzes data on pulse and body temperature. This makes it possible to analyze emotions from the pet's cries.

[0067] The data collection unit can collect video from the pet's perspective and perform behavioral analysis. The data collection unit, for example, uses a camera built into a collar to collect video from the pet's perspective in real time and transmits the data to a server. For example, it performs video analysis to analyze the pet's behavioral patterns. It also develops algorithms to analyze the pet's behavioral patterns based on the video data. For example, it uses video analysis technology to detect specific movements and actions. It also integrates the video data with other biometric data to build a system that comprehensively evaluates the pet's health condition. For example, it combines and analyzes data such as pulse rate and body temperature. This makes it possible to perform behavioral analysis based on video from the pet's perspective.

[0068] The data collection unit can detect subtle changes in a pet's movements and evaluate whether or not it is in good health. The data collection unit, for example, uses a vibration sensor built into a collar to detect subtle changes in a pet's movements in real time and transmits the data to a server. For example, it analyzes movement patterns and evaluates the health condition. In addition, an algorithm for evaluating a pet's health condition is developed based on the vibration sensor data. For example, specific movement changes are used as an indicator of health condition. In addition, the vibration sensor data is integrated with other biometric data to build a system for comprehensively evaluating a pet's health condition. For example, it is analyzed in combination with pulse and body temperature data. This makes it possible to evaluate the health condition based on subtle changes in a pet's movements.

[0069] The data collection unit can measure the humidity on the pet's body surface and estimate the stress level using an emotion estimation function. The data collection unit, for example, uses a sensor built into the collar to measure the humidity on the pet's body surface in real time and transmits the data to a server. For example, it analyzes changes in humidity and estimates the stress level. It also develops an algorithm to evaluate the pet's stress level based on the humidity data. For example, it determines that stress is high when humidity exceeds a certain range. It also integrates the humidity measurement data with other biological data to build a system that comprehensively evaluates the pet's stress level. For example, it combines it with pulse and body temperature data and analyzes it. This makes it possible to estimate the stress level based on the humidity on the pet's body surface.

[0070] Toilet sensors can evaluate a pet's health condition by analyzing images of the color or shape of excrement. For example, a toilet sensor uses a camera built into the toilet to capture images of the color and shape of excrement in real time and send the data to a server. For example, image analysis is performed to evaluate the health condition. An algorithm is also developed that analyzes the color and shape of excrement based on image data. For example, changes in specific color or shape are used as indicators of health condition. Image data is also integrated with other biometric data to build a system that comprehensively evaluates a pet's health condition. For example, it is combined with pulse and body temperature data for analysis. This makes it possible to evaluate the health condition based on the color and shape of excrement.

[0071] Toilet sensors can detect the smell of excrement and analyze whether it is an abnormal smell. For example, toilet sensors use an odor sensor built into the toilet to detect the smell of excrement in real time and send the data to a server. For example, they analyze the components of the smell and detect abnormalities. Furthermore, algorithms can be developed based on the smell data to analyze abnormal excrement smells. For example, changes in specific components can be used as indicators of abnormalities. Furthermore, the smell data can be integrated with other biological data to build a system that comprehensively evaluates the health of pets. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to detect abnormalities based on the smell of excrement.

[0072] Toilet sensors can measure the temperature of excrement and assess whether the pet is healthy. For example, a toilet sensor built into the toilet measures the temperature of excrement in real time and sends the data to a server. For example, temperature changes can be analyzed to assess the pet's health. We also develop algorithms that analyze the temperature of excrement based on the temperature data. For example, specific temperature changes can be used as an indicator of health. We also build systems that integrate temperature data with other biological data to comprehensively assess the pet's health. For example, we analyze the data in combination with pulse and body temperature data. This makes it possible to assess the pet's health based on the temperature of the excrement.

[0073] Toilet sensors can chemically analyze the components of excrement and assess whether the pet is healthy. For example, a toilet sensor could use a built-in chemical sensor to analyze the components of excrement in real time and send the data to a server. For example, changes in the components could be analyzed to assess the pet's health. An algorithm could also be developed to analyze the components of excrement based on the component data. For example, changes in specific components could be used as an indicator of health. The component data could also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it could be combined with pulse and body temperature data for analysis. This would allow the health of the pet to be assessed based on the components of the excrement.

[0074] Toilet sensors can collect excrement sounds and perform analysis using generative AI. For example, toilet sensors use a microphone built into the toilet to collect excrement sounds in real time and send the data to a server. For example, they perform frequency analysis of the sound to evaluate health status. They also develop algorithms to analyze excrement sounds based on the sound data. For example, they use changes in specific frequencies and volume as indicators of health status. They also integrate sound data with other biological data to build a system that comprehensively evaluates the health status of pets. For example, they analyze the data in combination with pulse and body temperature data. This makes it possible to perform analysis based on the sound of excrement.

[0075] Toilet sensors can measure the amount of excrement and use emotion estimation functions to estimate stress levels. For example, toilet sensors use a weight sensor built into the toilet to measure the amount of excrement in real time and send the data to a server. For example, changes in the amount can be analyzed to estimate stress levels. We also develop algorithms to analyze the amount of excrement based on the amount data. For example, specific changes in the amount can be used as an indicator of stress. We also integrate the amount data with other biological data to build a system that comprehensively evaluates pet stress levels. For example, we can analyze it in combination with pulse and body temperature data. This makes it possible to estimate stress levels based on the amount of excrement.

[0076] The data transmission unit can improve data security by using encryption technology when transmitting data. The data transmission unit, for example, builds a system that uses encryption technology when transmitting data to strengthen data security. For example, it encrypts data using AES encryption. It also uses encryption technology to protect the data transmission path. For example, it encrypts data transmission using the SSL / TLS protocol. It also develops a system that encrypts and decrypts data in real time. For example, it encrypts data when transmitting and decrypts it when receiving it. This strengthens security when transmitting data.

[0077] The data analysis unit can introduce an algorithm that compares data with past data to detect abnormalities during data analysis. The data analysis unit, for example, develops an algorithm that compares data with past data during data analysis to detect abnormalities. For example, it compares past pulse and body temperature data with current data to detect abnormalities. It also uses an anomaly detection algorithm to build a system that monitors pet health in real time. For example, it issues an alert if an abnormality is detected. It also integrates past data and current data to build a database for detecting abnormalities. For example, it sets a reference value for detecting abnormalities and performs analysis based on that. This makes it possible to detect abnormalities by comparing with past data.

[0078] The data analysis unit learns the pet's behavioral patterns during data analysis and can detect whether or not the behavior is abnormal in real time. The data analysis unit, for example, develops an algorithm that learns the pet's behavioral patterns during data analysis. For example, it learns normal behavioral patterns and detects abnormal behavior. It also builds a system that detects abnormal behavior in real time based on behavioral pattern data. For example, it issues an alert when abnormal behavior is detected. It also develops a system that integrates behavioral pattern data with other biological data to comprehensively evaluate the pet's health. For example, it combines and analyzes data on pulse rate and body temperature. This makes it possible to learn the pet's behavioral patterns and detect abnormal behavior in real time.

[0079] The data transmission unit synchronizes data between different devices when transmitting data, and can integrate data from multiple pets. The data transmission unit, for example, builds a system that synchronizes data between different devices when transmitting data. For example, it integrates data from multiple pets into a single server. It also develops a data synchronization algorithm to integrate data from different devices in real time. For example, it synchronizes data from collars and toilets. It also builds a system that integrates data from multiple pets and comprehensively evaluates their health. For example, it compares data from multiple pets and identifies common health problems. This allows data synchronization between different devices and integration of data from multiple pets.

[0080] The data analysis unit can compare data from different pets during data analysis and identify whether there are any common health issues. The data analysis unit, for example, develops an algorithm for comparing data from different pets during data analysis. For example, it compares pulse and body temperature data from multiple pets to identify common health issues. It also builds a system for identifying common health issues based on data from different pets. For example, it integrates and analyzes data from pets with the same symptoms. It also builds a database for comparing pet data and identifying common health issues. For example, it aggregates data from pets with specific symptoms or abnormalities. This makes it possible to compare data from different pets and identify common health issues.

[0081] The data analysis unit can use the emotion estimation function during data analysis to analyze the emotional state of the pet in real time. The data analysis unit, for example, uses the emotion estimation function during data analysis to build a system that analyzes the emotional state of the pet in real time. For example, the emotional state is estimated based on pulse rate and body temperature data. An emotion estimation algorithm is also developed to analyze the emotional state of the pet in real time. For example, changes in pulse rate are used as an indicator of emotion. The emotion estimation data is also integrated with other biometric data to build a system that comprehensively evaluates the emotional state of the pet. For example, the emotion estimation data is analyzed in combination with pulse rate and body temperature data. This allows the emotion estimation function to analyze the emotional state of the pet in real time.

[0082] The emotion estimation unit can perform frequency analysis of the pet's cries and estimate whether or not it represents an emotional state. The emotion estimation unit, for example, builds a system that performs frequency analysis of the pet's cries when reading emotions. For example, it estimates the emotional state based on the frequency data of the cries. It also develops an algorithm that analyzes the pet's emotional state based on the frequency data of the cries. For example, it uses changes in specific frequencies as an indicator of emotions. It also builds a system that integrates the frequency data of the cries with other biological data to comprehensively evaluate the pet's emotional state. For example, it analyzes it in combination with pulse and body temperature data. This makes it possible to estimate the emotional state based on the frequency analysis of the pet's cries.

[0083] The emotion estimation unit can analyze the eye movements of a pet and estimate whether or not it is in a certain emotional state. The emotion estimation unit, for example, builds a system that analyzes the eye movements of a pet in real time when reading emotions. For example, it estimates the emotional state based on eye movement data. It also develops an algorithm that analyzes the emotional state of a pet based on the eye movement data. For example, it uses changes in specific movements as an indicator of emotion. It also builds a system that integrates the eye movement data with other biometric data to comprehensively evaluate the emotional state of a pet. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to estimate the emotional state based on the pet's eye movements.

[0084] The emotion estimation unit can analyze the patterns of a pet's body movements and estimate whether or not they represent an emotional state. The emotion estimation unit, for example, builds a system that analyzes the patterns of a pet's body movements in real time when reading emotions. For example, the emotional state is estimated based on movement pattern data. Also, an algorithm is developed that analyzes the pet's emotional state based on the body movement pattern data. For example, specific changes in movement are used as indicators of emotions. Also, a system is built that integrates the body movement pattern data with other biometric data to comprehensively evaluate the pet's emotional state. For example, it is analyzed in combination with pulse and body temperature data. This makes it possible to estimate the emotional state based on the pet's body movement patterns.

[0085] The emotion estimation unit can analyze the movements of a pet's tail and estimate whether or not it reflects an emotional state. The emotion estimation unit, for example, builds a system that analyzes the movements of a pet's tail in real time when reading emotions. For example, it estimates the emotional state based on tail movement data. It also develops an algorithm that analyzes the emotional state of a pet based on tail movement data. For example, it uses changes in specific movements as an indicator of emotion. It also builds a system that integrates tail movement data with other biometric data to comprehensively evaluate the emotional state of a pet. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to estimate the emotional state based on a pet's tail movement.

[0086] The emotion estimation unit can analyze the emotional state of a pet in real time using the emotion estimation function. The emotion estimation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of a pet in real time. For example, the emotional state is estimated based on pulse rate and body temperature data. An emotion estimation algorithm is also developed to analyze the emotional state of a pet in real time. For example, changes in pulse rate are used as an indicator of emotion. The emotion estimation data is also integrated with other biometric data to build a system that comprehensively evaluates the emotional state of a pet. For example, the data is analyzed in combination with pulse rate and body temperature data. This makes it possible to use the emotion estimation function to analyze the emotional state of a pet in real time.

[0087] The advice providing unit can generate individually customized advice based on the pet's past health data. The advice providing unit, for example, builds a system that generates individually customized advice based on the pet's past health data when providing advice. For example, optimal advice is provided based on past data. Also, an algorithm is developed that analyzes the pet's past health data and generates individually customized advice. For example, advice is provided according to a specific health condition. Also, a system is built that integrates the past health data with other biometric data, comprehensively evaluates the pet's health condition, and provides individually customized advice. For example, it combines and analyzes pulse and body temperature data. This makes it possible to generate individually customized advice based on the pet's past health data.

[0088] The advice providing unit can propose optimal environmental improvement measures based on the pet's living environment. The advice providing unit, for example, builds a system that takes the pet's living environment into consideration when providing advice and proposes optimal environmental improvement measures. For example, it provides optimal improvement measures based on living environment data. It also develops an algorithm that analyzes the pet's living environment data and proposes optimal environmental improvement measures. For example, it provides improvement measures according to specific environmental conditions. It also builds a system that integrates the living environment data with other biometric data, comprehensively evaluates the pet's health condition, and proposes optimal environmental improvement measures. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to propose optimal environmental improvement measures taking the pet's living environment into consideration.

[0089] The advice providing unit can analyze the pet's dietary history and propose an optimal meal plan. The advice providing unit, for example, analyzes the pet's dietary history when providing advice and builds a system that proposes an optimal meal plan. For example, an optimal meal plan is provided based on dietary history data. Also, an algorithm is developed that analyzes the pet's dietary history data and proposes an optimal meal plan. For example, a meal plan is proposed based on the intake of specific nutrients. Also, a system is built that integrates dietary history data with other biometric data to comprehensively evaluate the pet's health condition and propose an optimal meal plan. For example, it is analyzed in combination with pulse and body temperature data. This makes it possible to propose an optimal meal plan based on the pet's dietary history.

[0090] The advice providing unit can compare data of different pets and provide advice for common health problems. The advice providing unit, for example, builds a system that compares data of different pets when providing advice and provides advice for common health problems. For example, it identifies common health problems based on data of multiple pets. It also develops an algorithm that provides advice for common health problems based on data of different pets. For example, it provides advice for pets with the same symptoms. It also builds a database for comparing pet data and providing advice for common health problems. For example, it aggregates data of pets with specific symptoms or abnormalities. This makes it possible to compare data of different pets and provide advice for common health problems.

[0091] The advice providing unit can propose an optimal care plan based on the lifestyle of the pet owner. For example, the advice providing unit builds a system that takes the lifestyle of the pet owner into consideration when providing advice and proposes the optimal care plan. For example, it provides an optimal care plan based on data on the owner's lifestyle patterns. It also develops an algorithm that analyzes the owner's lifestyle data and proposes the optimal care plan. For example, it provides a care plan that suits a specific lifestyle pattern. It also builds a system that integrates lifestyle data with other biometric data, comprehensively evaluates the pet's health condition, and proposes the optimal care plan. For example, it analyzes the data in combination with pulse and body temperature data. This makes it possible to propose the optimal care plan taking the lifestyle of the pet owner into consideration.

[0092] The advice providing unit can use the emotion estimation function to provide advice in real time according to the emotional state of the pet. The advice providing unit, for example, uses the emotion estimation function to build a system that provides advice in real time according to the emotional state of the pet. For example, the emotional state is estimated based on pulse rate and body temperature data and advice is provided. An emotion estimation algorithm is also developed to provide advice in real time according to the emotional state of the pet. For example, changes in pulse rate are used as an indicator of emotion. Furthermore, the emotion estimation data is integrated with other biometric data to comprehensively evaluate the emotional state of the pet and build a system that provides advice. For example, the emotion estimation data is combined with pulse rate and body temperature data for analysis. This makes it possible to use the emotion estimation function to provide advice in real time according to the emotional state of the pet.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The data collection unit can measure the electrical resistance of a pet's skin and estimate whether it is experiencing stress. For example, a sensor built into the collar measures the electrical resistance of a pet's skin in real time and sends the data to a server. Changes in electrical resistance are analyzed to estimate the pet's stress level. An algorithm is also developed to evaluate the pet's stress level based on the electrical resistance data. For example, if the electrical resistance exceeds a certain range, it is determined that stress is high. The electrical resistance measurement data is also integrated with other biological data to build a system that comprehensively evaluates the pet's stress level. For example, it is combined with pulse and body temperature data and analyzed. This makes it possible to grasp the pet's stress level in real time.

[0095] The data collection unit can analyze a pet's movement patterns and detect whether or not they are behaving abnormally. For example, the GPS function built into the collar can be used to track a pet's movement patterns in real time and send the data to a server. An algorithm to detect abnormal movement patterns will be developed. A system will also be built that analyzes a pet's movement patterns based on GPS data and detects abnormal behavior. For example, an alert will be issued if the pet goes beyond its normal movement range. A system will also be developed that integrates movement pattern data with other biological data to comprehensively evaluate a pet's health. For example, it will be combined with pulse and body temperature data for analysis. This will enable abnormal pet behavior to be detected in real time.

[0096] The data collection unit can collect the pet's cries and analyze its emotional state using the generation AI. For example, a microphone built into the collar can be used to collect the pet's cries in real time and send the data to a server. The frequency of the cries is analyzed to estimate the emotional state. An algorithm can also be developed to analyze the pet's emotional state based on the cries data. For example, changes in specific frequencies and volume can be used as indicators of emotions. The cries data can also be integrated with other biometric data to build a system that comprehensively evaluates the pet's emotional state. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to analyze emotions from the pet's cries.

[0097] The data collection unit can collect video from the pet's perspective and perform behavioral analysis. For example, a camera built into the collar can be used to collect video from the pet's perspective in real time and send the data to a server. Video analysis is then performed to analyze the pet's behavioral patterns. An algorithm can also be developed to analyze the pet's behavioral patterns based on the video data. For example, video analysis technology can be used to detect specific movements and actions. The video data can also be integrated with other biometric data to build a system that comprehensively evaluates the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This allows behavioral analysis to be performed based on video from the pet's perspective.

[0098] The data collection unit can detect subtle changes in a pet's movements and assess its health status. For example, a vibration sensor built into the collar can be used to detect subtle changes in a pet's movements in real time and send the data to a server. The movement patterns are analyzed to assess the pet's health status. An algorithm for assessing a pet's health status can also be developed based on the vibration sensor data. For example, specific changes in movement can be used as an indicator of health status. The vibration sensor data can also be integrated with other biometric data to build a system for comprehensively assessing a pet's health status. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to assess a pet's health status based on subtle changes in a pet's movements.

[0099] The data collection unit can measure the humidity on the pet's body surface and use the emotion estimation function to estimate the stress level. For example, a sensor built into the collar can be used to measure the humidity on the pet's body surface in real time and send the data to a server. Changes in humidity can be analyzed to estimate the stress level. An algorithm can also be developed to evaluate the pet's stress level based on the humidity data. For example, if the humidity exceeds a certain range, it can be determined that the pet is highly stressed. The humidity measurement data can also be integrated with other biological data to build a system that comprehensively evaluates the pet's stress level. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to estimate the stress level based on the humidity on the pet's body surface.

[0100] Toilet sensors can perform image analysis of the color or shape of excrement to assess health status. For example, a camera built into the toilet can be used to capture images of the color and shape of excrement in real time and send the data to a server. Image analysis is then performed to assess health status. We will also develop algorithms that analyze the color and shape of excrement based on image data. For example, changes in specific colors or shapes can be used as indicators of health status. We will also integrate image data with other biometric data to build a system that comprehensively assesses pet health status. For example, we will analyze the data in combination with pulse and body temperature data. This will make it possible to assess health status based on the color and shape of excrement.

[0101] Toilet sensors can detect the smell of excrement and analyze whether it is an abnormal smell. For example, an odor sensor built into the toilet can be used to detect the smell of excrement in real time and send the data to a server. The components of the odor can be analyzed to detect abnormalities. Furthermore, an algorithm can be developed to analyze abnormal excrement odors based on the odor data. For example, changes in specific components can be used as an indicator of abnormalities. Furthermore, the odor data can be integrated with other biological data to build a system that comprehensively evaluates the health of pets. For example, it can be combined with pulse and body temperature data and analyzed. This makes it possible to detect abnormalities based on the odor of excrement.

[0102] Toilet sensors can measure the temperature of excrement and assess whether the pet is healthy. For example, a temperature sensor built into the toilet can be used to measure the temperature of excrement in real time and send the data to a server. Temperature changes can be analyzed to assess the pet's health. An algorithm can also be developed to analyze the temperature of excrement based on the temperature data. For example, specific temperature changes can be used as an indicator of health. Temperature data can also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it can be combined with pulse and body temperature data for analysis. This makes it possible to assess the pet's health based on the temperature of the excrement.

[0103] Toilet sensors can chemically analyze the components of excrement and assess whether the pet is healthy. For example, a chemical sensor built into the toilet could be used to analyze the components of excrement in real time and send the data to a server. Changes in the components could be analyzed to assess the pet's health. An algorithm could also be developed to analyze the components of excrement based on the component data. For example, changes in specific components could be used as an indicator of health. The component data could also be integrated with other biological data to build a system that comprehensively assesses the pet's health. For example, it could be combined with pulse and body temperature data for analysis. This would allow the health of the pet to be assessed based on the components of excrement.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The data collection unit uses a sensor attached to your pet's collar to collect pulse, body temperature, and step count data. For example, a sensor built into the collar measures your pet's pulse in real time and sends that data to a server. In addition, a body temperature sensor periodically measures your pet's temperature and issues an alert if there is an abnormality. A pedometer is also built in, allowing you to keep track of your pet's exercise level. Step 2: The data transmission unit transmits the data collected by the data collection unit to the server. For example, the data may be transmitted using Wi-Fi or Bluetooth. Alternatively, the data may be transmitted using cellular communication. Step 3: The data analysis unit analyzes the data sent by the data transmission unit and determines the pet's weight and excretory behavior. For example, it uses a machine learning algorithm to analyze pulse and body temperature data to evaluate the pet's health. It also analyzes excretory behavior data to understand the health of the pet's digestive system. Step 4: The emotion estimation unit reads the pet's emotions based on the data analyzed by the data analysis unit. For example, if the pulse rate suddenly increases, it may determine that the pet is excited. If the body temperature is high, it may analyze that the pet is feeling stressed. Step 5: The advice provider provides advice to the owner based on the emotions detected by the emotion estimation unit. For example, if the pet is feeling stressed, the system will provide advice on creating a relaxing environment for the pet. If any abnormalities are detected in the pet's health, the system will suggest necessary medications, hospital information, and dietary management methods.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data on pulse, body temperature, and number of steps using a sensor attached to a collar of the pet; a data transmission unit that transmits the data collected by the data collection unit to a server; a data analysis unit that analyzes the data transmitted by the data transmission unit and determines the weight and excretory behavior of the pet; an emotion estimation unit that reads the emotion of the pet based on the data analyzed by the data analysis unit; an advice providing unit that provides advice to the owner based on the emotion read by the emotion estimation unit. A system characterized by:

2. The data collection unit Measure the electrical resistance of the pet's skin to estimate its stress level.

2. The system of claim 1.

3. The data collection unit Analyzing the movement pattern of the pet and detecting whether the behavior is abnormal 2. The system of claim 1.

4. The data collection unit Collect the pet's cries and analyze its emotional state using a generative AI.

2. The system of claim 1.

5. The data collection unit Collect footage from the pet's point of view and perform behavioral analysis 2. The system of claim 1.

6. The data collection unit Detecting minute changes in the pet's movements and assessing its health status 2. The system of claim 1.

Images