System

Abstract

To provide a system which monitors physical conditions of a pet in real time and detects an abnormality in its early stage in order to take appropriate measures.SOLUTION: A system comprises a sensor part, a collection part, an analysis part, a communication part, and an introduction part. The sensor part measures a heart rate and temperature of a pet. The collection part collects data measured by the sensor part. The analysis part analyzes the data collected by the collection part and evaluates the physical conditions of the pet. If an abnormality is detected by the analysis part, the notification part notifies the owner of the pet of it. The introduction part introduces an animal hospital on the basis of the information notified by the notification part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to monitor a pet's physical condition in real time, detect abnormalities early, and take appropriate action.

[0005] The system according to the embodiment aims to monitor the physical condition of a pet in real time, detect abnormalities at an early stage, and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, a collection unit, an analysis unit, a notification unit, and a referral unit. The sensor unit measures the pet's heart rate and body temperature. The collection unit collects data measured by the sensor unit. The analysis unit analyzes the data collected by the collection unit and evaluates the pet's physical condition. The notification unit notifies the pet owner if an abnormality is detected by the analysis unit. The referral unit refers the pet owner to a veterinary clinic based on the information notified by the notification unit. Effect of the Invention

[0007] The system according to the embodiment can monitor the physical condition of a pet in real time, detect abnormalities early, and take appropriate measures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The pet monitoring system according to the embodiment of the present invention is a system that attaches sensors to the cage or body of a pet, collects information such as heart rate and body temperature, and monitors the physical condition of the pet. In this system, the sensor measures the pet's heart rate and body temperature in real time, and the AI ​​analyzes the data. The AI ​​evaluates the pet's physical condition based on the collected data, and notifies the owner when an abnormality is detected. The notification includes information on possible illnesses and a referral to the most suitable veterinary clinic. For example, if the pet's heart rate is higher than normal, the AI ​​evaluates the possibility of stress or illness and notifies the owner. In addition, if the body temperature is abnormally high, the AI ​​evaluates the possibility of fever and refers the owner to an appropriate veterinary clinic. This system allows the pet's health to be constantly monitored and abnormalities to be detected early. For example, the types of sensors include a heart rate sensor attached to a collar and a body temperature sensor installed in a cage. These sensors measure the pet's heart rate and body temperature in real time. The AI ​​analysis algorithm uses a machine learning algorithm for abnormality detection. The normal range is learned based on past data, and if data exceeding that range is detected, it is determined to be abnormal. The notification method can be a real-time notification via a smartphone app. Notification can also be sent by email. This allows the pet monitoring system to constantly monitor the health of your pet and detect any abnormalities at an early stage.

[0029] The pet monitoring system according to the embodiment includes a sensor unit, a collection unit, an analysis unit, a notification unit, and an introduction unit. The sensor unit measures the heart rate and body temperature of the pet. For example, the sensor unit can include a heart rate sensor attached to a collar. The sensor unit can also include a body temperature sensor installed in a cage. The collection unit collects data measured by the sensor unit. For example, the collection unit can collect heart rate and body temperature data in real time. The analysis unit analyzes the data collected by the collection unit and evaluates the physical condition of the pet. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns a normal range based on past data, and determines that an abnormality exists when data exceeding the normal range is detected. The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify in real time through a smartphone app. Also, the notification unit can notify by email. The introduction unit introduces the most suitable veterinary clinic based on the information notified by the notification unit. For example, the introduction unit can select the most suitable veterinary clinic according to the symptoms of the pet and introduce it to the owner. As a result, the pet monitoring system of the embodiment can monitor a pet's heart rate and body temperature in real time, notify the owner when an abnormality is detected, and refer the owner to the most suitable veterinary clinic.

[0030] The sensor unit measures the pet's heart rate and body temperature. For example, the sensor unit can include a heart rate sensor attached to a collar. This heart rate sensor is attached to the pet's neck, so the pet's heart rate can be constantly monitored. The heart rate sensor accurately measures the heart rate using optical or electrical technology and transmits the data to the collection unit in real time. The sensor unit can also include a body temperature sensor installed in the cage. The body temperature sensor measures the pet's body temperature while the pet is in the cage and detects changes in the environment and the pet's physical condition. The body temperature sensor can measure the body temperature non-contact using infrared technology, so data can be collected without stressing the pet. Furthermore, the sensor unit can also include an acceleration sensor or a gyro sensor for monitoring the pet's activity level and sleep pattern. This allows the pet's movement level and resting state to be grasped in detail, enabling a comprehensive evaluation of the pet's health condition. By combining these various sensors, the sensor unit comprehensively monitors the pet's health condition and contributes to early detection of abnormalities.

[0031] The collection unit collects data measured by the sensor unit. For example, the collection unit can collect data on heart rate and body temperature in real time. The collection unit receives data transmitted from the sensor unit and stores it in a central database. The collection unit has a high-speed communication protocol and a large-capacity storage in order to efficiently receive and store data. Furthermore, the collection unit has a function to detect duplication or loss of data and to request retransmission as necessary in order to ensure the integrity and reliability of the data. The collection unit centrally manages data from the sensor unit and makes it accessible to the analysis unit and notification unit. For example, the collection unit manages the data as time-series data by adding a time stamp to the data and recording the time of occurrence of each data point. This allows the analysis unit to compare past data with current data to detect abnormalities and analyze trends. The collection unit can flexibly set the frequency and range of data collection, thereby achieving optimal data collection according to the pet's condition and environment.

[0032] The analysis unit analyzes the data collected by the collection unit and evaluates the physical condition of the pet. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns the normal range based on past data, and judges it to be abnormal when data exceeding that range is detected. Specifically, the analysis unit performs time series analysis of the heart rate and body temperature data to detect abnormal patterns and sudden changes. For example, if the heart rate increases suddenly or the body temperature is abnormally high, the analysis unit judges this to be abnormal and notifies the notification unit. The analysis unit can also analyze the activity level and sleep pattern of the pet to perform a comprehensive evaluation of the health condition. For example, if the pet is less active or sleeps less than usual, the analysis unit judges this to be a sign of abnormality and alerts the owner. Furthermore, the analysis unit can provide information that is useful not only for abnormality detection but also for long-term health management and prevention. For example, the analysis unit analyzes the trends in the pet's health condition and predicts future risks. This allows the owner to take measures early and provide appropriate care to maintain the health of the pet.

[0033] The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify in real time through a smartphone app. The notification unit has an interface for receiving abnormality detection information from the analysis unit and promptly notifying the owner. The notification unit uses the push notification function of the smartphone to immediately send a notification to the owner when an abnormality is detected. The notification unit can also notify by email. Email notifications can include detailed information and can also be used as records for the owner to check later. Furthermore, the notification unit can use voice calls and SMS to more reliably deliver notifications to the owner in an emergency. The notification unit can customize the notification content and select an appropriate notification method depending on the type and urgency of the abnormality. For example, in the case of a minor abnormality, only a push notification is sent, and in the case of a serious abnormality, a voice call and SMS are also used. This allows the notification unit to quickly and reliably notify the owner of the abnormality and encourage them to take appropriate action.

[0034] The introduction unit introduces the most suitable veterinary hospital based on the information notified by the notification unit. For example, the introduction unit can select the most suitable veterinary hospital according to the symptoms of the pet and introduce it to the owner. The introduction unit has a database of veterinary hospitals and manages information such as the specialty, consultation hours, and location of each hospital. The introduction unit searches for the most suitable veterinary hospital based on the symptoms and condition of the pet provided by the analysis unit and introduces it to the owner. For example, if the pet's heart rate is abnormally high, it preferentially introduces a veterinary hospital that specializes in heart. In addition, the introduction unit can select the most accessible hospital taking into account the owner's current location and means of transportation. The introduction unit provides the owner with detailed information about the veterinary hospital to be introduced, and also guides the owner on the reservation procedure and emergency contact information. Furthermore, the introduction unit can continuously improve the accuracy of the introduction based on past introduction history and owner feedback. This allows the introduction unit to provide prompt and appropriate medical support to the owner and support the health management of the pet.

[0035] The sensor unit may include a heart rate sensor attached to a collar. The heart rate sensor is attached to, for example, the collar of the pet. The heart rate sensor can measure the pet's heart rate in real time. For example, the heart rate sensor can measure the heart rate using an optical sensor. The heart rate sensor can also measure the heart rate using an electrical sensor. Furthermore, the heart rate sensor can also measure the heart rate using an ultrasonic sensor. This allows the pet's heart rate to be accurately measured by the heart rate sensor attached to the collar.

[0036] The sensor unit may include a body temperature sensor that is installed in the cage. The body temperature sensor is installed, for example, in the pet's cage. The body temperature sensor can measure the pet's body temperature in real time. For example, the body temperature sensor can measure the body temperature using an infrared sensor. The body temperature sensor can also measure the body temperature using a contact sensor. Furthermore, the body temperature sensor can measure the body temperature using a thermistor. This allows the pet's body temperature to be accurately measured by the body temperature sensor installed in the cage.

[0037] The analysis unit can detect anomalies using a machine learning algorithm. The machine learning algorithm analyzes, for example, data on a pet's heart rate and body temperature. The machine learning algorithm learns a normal range based on past data, and determines that anomalies exist when data exceeding the normal range is detected. For example, the machine learning algorithm can detect anomalies using K-means clustering. The machine learning algorithm can also detect anomalies using a support vector machine (SVM). Furthermore, the machine learning algorithm can detect anomalies using deep learning. As a result, the accuracy of anomaly detection is improved by using the machine learning algorithm.

[0038] The notification unit can notify the owner in real time through a smartphone app. The notification unit notifies the owner, for example, through a smartphone app. The notification unit can notify the owner when an abnormality is detected in real time. For example, the notification unit can notify the owner using a push notification. The notification unit can also notify the owner using a notification function within the app. Furthermore, the notification unit can display abnormality information on the dashboard of the app. This allows the owner to respond quickly by notifying the owner in real time through the smartphone app.

[0039] The notification unit can notify the owner by email. For example, the notification unit notifies the owner by email. The notification unit can notify the owner by email when an abnormality is detected. For example, the notification unit can automatically send an email including abnormality information. The notification unit can also customize the content of the email. Furthermore, the notification unit can set the timing of sending the email. In this way, notifying the owner by email makes it easier for the owner to check for abnormalities.

[0040] The sensor unit can adjust the measurement frequency of the heart rate and body temperature based on the behavioral data of the pet. The sensor unit, for example, collects behavioral data of the pet and adjusts the measurement frequency of the heart rate and body temperature. The sensor unit can estimate the emotion of the pet and adjust the measurement frequency based on the emotion. For example, when the pet is relaxed, the measurement frequency of the heart rate and body temperature can be set low. Also, when the pet is excited, the measurement frequency of the heart rate and body temperature can be set high. Furthermore, when the pet is stressed, the measurement frequency of the heart rate and body temperature can be set to a medium level. In this way, more accurate data can be collected by adjusting the measurement frequency according to the emotion of the pet.

[0041] The sensor unit can evaluate the physical condition by combining the activity amount data, the heart rate and the body temperature data with an additional acceleration sensor that measures the activity amount of the pet. The sensor unit includes, for example, an acceleration sensor that measures the activity amount of the pet. The acceleration sensor can measure the movement of the pet in real time. The sensor unit evaluates the physical condition by combining the activity amount data, the heart rate and the body temperature data. For example, when the pet is actively moving, the heart rate and the body temperature data are combined with the activity amount data to evaluate the physical condition. In addition, when the pet is stationary, the heart rate and the body temperature data can be combined with the activity amount data to evaluate the physical condition. Furthermore, when the pet is sleeping, the heart rate and the body temperature data can be combined with the activity amount data to evaluate the physical condition. In this way, the physical condition of the pet can be more accurately evaluated by combining the activity amount data.

[0042] The sensor unit can add a sensor for measuring the respiration rate of the pet, and evaluate the physical condition by combining the respiration rate data with the heart rate and body temperature data. The sensor unit includes, for example, a sensor for measuring the respiration rate of the pet. The respiration rate sensor can measure the respiration rate of the pet in real time. The sensor unit evaluates the physical condition by combining the respiration rate data with the heart rate and body temperature data. For example, if the respiration rate of the pet is higher than normal, the heart rate and body temperature data are combined for evaluation. In addition, if the respiration rate of the pet is lower than normal, the heart rate and body temperature data can be combined for evaluation. Furthermore, if the respiration rate of the pet is irregular, the heart rate and body temperature data can be combined for evaluation. This allows the physical condition of the pet to be evaluated more accurately by combining the respiration rate data.

[0043] The sensor unit can adjust the sensitivity of the sensor based on the behavioral data of the pet. The sensor unit, for example, collects behavioral data of the pet and adjusts the sensitivity of the sensor. The sensor unit can estimate the emotion of the pet and adjust the sensitivity of the sensor based on the emotion. For example, when the pet is relaxed, the sensitivity of the sensor can be set low. Also, when the pet is excited, the sensitivity of the sensor can be set high. Furthermore, when the pet is stressed, the sensitivity of the sensor can be set to a medium level. In this way, more accurate data can be collected by adjusting the sensitivity of the sensor according to the emotion of the pet.

[0044] The sensor unit can evaluate the physical condition by combining the food amount data, the heart rate data, and the body temperature data with an additional sensor that measures the amount of food eaten by the pet. The food amount sensor can measure the amount of food eaten by the pet in real time. The sensor unit evaluates the physical condition by combining the food amount data, the heart rate data, and the body temperature data. For example, if the pet eats more than usual, the heart rate data and the body temperature data are combined for evaluation. In addition, if the pet eats less than usual, the heart rate data and the body temperature data can be combined for evaluation. Furthermore, if the pet eats irregularly, the heart rate data and the body temperature data can be combined for evaluation. In this way, the physical condition of the pet can be more accurately evaluated by combining the food amount data.

[0045] The sensor unit can add a sensor that measures the sleep state of the pet, and evaluate the physical condition by combining the sleep data with the heart rate and body temperature data. The sensor unit includes, for example, a sensor that measures the sleep state of the pet. The sleep state sensor can measure the sleep state of the pet in real time. The sensor unit evaluates the physical condition by combining the sleep data with the heart rate and body temperature data. For example, if the pet's sleep time is longer than usual, the heart rate and body temperature data are combined for evaluation. In addition, if the pet's sleep time is shorter than usual, the heart rate and body temperature data can be combined for evaluation. Furthermore, if the pet's sleep is irregular, the heart rate and body temperature data can be combined for evaluation. This allows the pet's physical condition to be evaluated more accurately by combining the sleep data.

[0046] The collection unit can adjust the timing of data collection based on the behavioral data of the pet. For example, the collection unit collects behavioral data of the pet and adjusts the timing of data collection. The collection unit can estimate the emotions of the pet and adjust the timing of data collection based on the emotions. For example, when the pet is relaxed, the frequency of data collection can be set low. Also, when the pet is excited, the frequency of data collection can be set high. Furthermore, when the pet is stressed, the frequency of data collection can be set to a medium level. In this way, more accurate data can be collected by adjusting the timing of data collection according to the emotions of the pet.

[0047] The collection unit can add a function of evaluating the reliability of data and filter out low-reliability data. For example, the collection unit evaluates the reliability of data and filters out low-reliability data. The collection unit can evaluate the reliability of data and automatically exclude low-reliability data. For example, the collection unit evaluates the reliability of data and collects only high-reliability data. The collection unit can also evaluate the reliability of data and manually check low-reliability data. This improves the accuracy of data by filtering out low-reliability data.

[0048] The collection unit can add a function of automatically detecting outliers in the data and excluding the outliers. For example, the collection unit automatically detects outliers in the data and excludes the outliers. The collection unit can automatically detect outliers in the data and exclude the outliers. For example, the collection unit automatically detects outliers in the data and manually checks the outliers. The collection unit can also automatically detect outliers in the data and correct the outliers. This improves the accuracy of the data by excluding the outliers.

[0049] The collection unit can determine the priority of data to be collected based on the behavioral data of the pet. For example, the collection unit collects the behavioral data of the pet and determines the priority of data to be collected. The collection unit can estimate the emotion of the pet and determine the priority of data to be collected based on the emotion. For example, when the pet is relaxed, the heart rate data can be collected with priority. Also, when the pet is excited, the body temperature data can be collected with priority. Furthermore, when the pet is stressed, the respiratory rate data can be collected with priority. In this way, important data can be collected with priority by determining the priority of data according to the emotion of the pet.

[0050] The collection unit can add a function for setting a data retention period and automatically delete old data. The collection unit, for example, sets a data retention period and automatically deletes old data. The collection unit can set a data retention period and automatically delete data that has passed the retention period. For example, the collection unit sets a data retention period and manually deletes data that has passed the retention period. The collection unit can also set a data retention period and archive data that has passed the retention period. This makes it easier to manage data by automatically deleting old data.

[0051] The collection unit can add a data backup function to prevent data loss. The collection unit, for example, periodically backs up data to prevent data loss. The collection unit can periodically back up data to prevent data loss. For example, the collection unit manually backs up data to prevent data loss. The collection unit can also store the data backup in the cloud to prevent data loss. In this way, the data backup function can prevent data loss.

[0052] The analysis unit can adjust the analysis algorithm based on the behavioral data of the pet. The analysis unit, for example, collects behavioral data of the pet and adjusts the analysis algorithm. The analysis unit can estimate the emotions of the pet and adjust the analysis algorithm based on the emotions. For example, when the pet is relaxed, the sensitivity of the analysis algorithm can be set low. Also, when the pet is excited, the sensitivity of the analysis algorithm can be set high. Furthermore, when the pet is stressed, the sensitivity of the analysis algorithm can be set to a medium level. This improves the analysis accuracy by adjusting the analysis algorithm according to the emotions of the pet.

[0053] The analysis unit can add a data preprocessing function for improving the accuracy of anomaly detection. The analysis unit can add, for example, a data preprocessing function to improve the accuracy of anomaly detection. The analysis unit can add, for example, a data preprocessing function to remove noise from data. The analysis unit can also add a data preprocessing function to complement missing values ​​in data. Thereby, the accuracy of anomaly detection is improved by adding the data preprocessing function.

[0054] The analysis unit can add a function for visualizing the results of anomaly detection, and display them in an easy-to-understand manner for the owner. For example, the analysis unit can display the results of anomaly detection in a graph, making it easy for the owner to understand. The analysis unit can display the results of anomaly detection in a graph, making it easy for the owner to understand. For example, the analysis unit can display the results of anomaly detection in a chart, making it easy for the owner to understand. The analysis unit can also display the results of anomaly detection in text, making it easy for the owner to understand. In this way, by visualizing the results of anomaly detection, it becomes easier for the owner to understand the abnormality.

[0055] The analysis unit can adjust the display method of the analysis results based on the behavioral data of the pet. The analysis unit, for example, collects behavioral data of the pet and adjusts the display method of the analysis results. The analysis unit can estimate the emotions of the pet and adjust the display method of the analysis results based on the emotions. For example, when the pet is relaxed, detailed analysis results can be displayed. Also, when the pet is excited, concise analysis results can be displayed. Furthermore, when the pet is stressed, analysis results that are visually easy to understand can be displayed. In this way, by adjusting the display method of the analysis results according to the emotions of the pet, the owner can easily understand the analysis results.

[0056] The analysis unit can add a function of comparing the results of anomaly detection with data of other pets and evaluate the degree of the abnormality. The analysis unit, for example, compares the results of anomaly detection with data of other pets and evaluates the degree of the abnormality. The analysis unit can compare the results of anomaly detection with data of other pets and evaluate the degree of the abnormality. For example, the analysis unit compares the results of anomaly detection with data of other pets and identifies the cause of the abnormality. The analysis unit can also compare the results of anomaly detection with data of other pets and propose measures to address the abnormality. This makes it easier to evaluate the degree of the abnormality by comparing with data of other pets.

[0057] The analysis unit can add a function to compare the results of anomaly detection with past data and analyze anomaly trends. For example, the analysis unit compares the results of anomaly detection with past data and analyzes anomaly trends. The analysis unit can compare the results of anomaly detection with past data and analyze anomaly trends. For example, the analysis unit compares the results of anomaly detection with past data and identifies the cause of the anomaly. The analysis unit can also compare the results of anomaly detection with past data and propose measures to deal with the anomaly. This makes it easier to analyze anomaly trends by comparing with past data.

[0058] The notification unit can adjust the content of the notification based on the behavioral data of the pet. For example, the notification unit collects behavioral data of the pet and adjusts the content of the notification. The notification unit can estimate the emotion of the pet and adjust the content of the notification based on the emotion. For example, when the pet is relaxed, detailed notification content can be provided. Also, when the pet is excited, concise notification content can be provided. Furthermore, when the pet is stressed, visually easy-to-understand notification content can be provided. In this way, by adjusting the content of the notification according to the emotion of the pet, the owner can easily understand the notification content.

[0059] The notification unit can add a function for setting the urgency of the notification and change the notification method according to the urgency. For example, the notification unit sets the urgency of the notification and changes the notification method according to the urgency. The notification unit can set the urgency of the notification and change the notification method according to the urgency. For example, if the urgency is high, the notification unit immediately notifies the user by using a push notification on a smartphone. Also, if the urgency is medium, the notification unit can notify the user by email. Furthermore, if the urgency is low, the notification unit can display the notification in a notification center within the app. This allows the owner to respond quickly by changing the notification method according to the urgency of the notification.

[0060] The notification unit can add a function of saving the notification history, allowing past notification contents to be referred to. The notification unit, for example, saves the notification history, allowing past notification contents to be referred to. The notification unit can save the notification history, allowing past notification contents to be referred to. For example, the notification unit can save the notification history, displaying past notification contents in a list. The notification unit can also save the notification history, allowing specific notification contents to be searched for. Furthermore, the notification unit can save the notification history, and display the notification contents in chronological order. In this way, saving the notification history makes it easier to refer to past notification contents.

[0061] The notification unit can adjust the timing of the notification based on the behavioral data of the pet. For example, the notification unit collects behavioral data of the pet and adjusts the timing of the notification. The notification unit can estimate the emotion of the pet and adjust the timing of the notification based on the emotion. For example, if the pet is relaxed, the timing of the notification can be delayed. Also, if the pet is excited, the timing of the notification can be advanced. Furthermore, if the pet is stressed, the timing of the notification can be set to a medium level. In this way, the timing of the notification can be adjusted according to the emotion of the pet, allowing the owner to receive the notification at an appropriate time.

[0062] The notification unit can add a notification customization function and change the notification content according to the owner's preferences. The notification unit can add, for example, a notification customization function and change the notification content according to the owner's preferences. The notification unit can add a notification customization function and change the notification content according to the owner's preferences. For example, if the owner desires detailed notification, the notification unit can provide detailed notification content. Also, if the owner desires concise notification, the notification unit can provide concise notification content. Furthermore, if the owner desires visual notification, the notification unit can provide notification content including graphs and charts. This allows the owner to receive notification content according to their preferences using the notification customization function.

[0063] The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language. The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language, for example. The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language. For example, the notification unit automatically sets the notification language based on the language setting of the owner's device. The notification unit can also provide a language switching function when the owner uses multiple languages. Furthermore, the notification unit can provide notifications in a specific language when the owner selects that language. This allows the owner to receive notifications in their desired language by using the notification language selection function.

[0064] The introduction unit can adjust the selection criteria for the veterinary clinic to be introduced based on the behavioral data of the pet. For example, the introduction unit collects the behavioral data of the pet and adjusts the selection criteria for the veterinary clinic to be introduced. The introduction unit can estimate the emotion of the pet and adjust the selection criteria for the veterinary clinic to be introduced based on the emotion. For example, if the pet is relaxed, a regular veterinary clinic can be introduced. Also, if the pet is excited, a veterinary clinic that can handle emergencies can be introduced. Furthermore, if the pet is stressed, a veterinary clinic that specializes in stress reduction can be introduced. In this way, the selection criteria for the veterinary clinic can be adjusted according to the emotion of the pet, so that the most suitable veterinary clinic can be introduced.

[0065] The introduction unit can add a function of collecting evaluation information on veterinary clinics and preferentially introducing hospitals with high ratings. The introduction unit can, for example, collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. The introduction unit can collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. For example, the introduction unit can collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. The introduction unit can also collect evaluation information on veterinary clinics and exclude hospitals with low ratings. Furthermore, the introduction unit can collect evaluation information on veterinary clinics and display highly rated hospitals in a ranking format. As a result, preferentially introducing highly rated hospitals makes it easier for pet owners to choose a reliable hospital.

[0066] The introduction unit can add a function of selecting the most suitable hospital based on the medical department and specialty of the veterinary hospital. The introduction unit selects the most suitable hospital based on, for example, the medical department and specialty of the veterinary hospital. The introduction unit can select the most suitable hospital based on the medical department and specialty of the veterinary hospital. For example, the introduction unit selects the most suitable hospital based on the medical department of the veterinary hospital. The introduction unit can also select the most suitable hospital based on the specialty of the veterinary hospital. Furthermore, the introduction unit can select the most suitable hospital by combining the medical department and specialty of the veterinary hospital. In this way, by selecting the most suitable hospital based on the medical department and specialty, it is possible to introduce a hospital suitable for the symptoms of the pet.

[0067] The introduction unit can determine the priority of veterinary clinics to be introduced based on the behavioral data of the pet. The introduction unit, for example, collects behavioral data of the pet and determines the priority of veterinary clinics to be introduced. The introduction unit can estimate the emotions of the pet and determine the priority of veterinary clinics to be introduced based on the emotions. For example, if the pet is relaxed, a regular veterinary clinic can be preferentially introduced. Also, if the pet is excited, a veterinary clinic that can handle emergencies can be preferentially introduced. Furthermore, if the pet is stressed, a veterinary clinic that specializes in stress reduction can be preferentially introduced. In this way, the priority of veterinary clinics can be determined according to the emotions of the pet, so that the most suitable clinic can be introduced quickly.

[0068] The introduction unit can add a function of checking the reservation status of veterinary clinics and introducing hospitals where it is easy to make an appointment. The introduction unit can check the reservation status of veterinary clinics and introduce hospitals where it is easy to make an appointment. For example, the introduction unit can check the reservation status of veterinary clinics and introduce hospitals where it is easy to make an appointment. The introduction unit can also check the reservation status of veterinary clinics and exclude hospitals where it is difficult to make an appointment. Furthermore, the introduction unit can check the reservation status of veterinary clinics and display hospitals where it is easy to make an appointment in a ranking format. In this way, by introducing hospitals where it is easy to make an appointment, pet owners can quickly make an appointment.

[0069] The introduction unit can add a function of providing location information of veterinary clinics and introducing hospitals close to the owner's residence. The introduction unit, for example, provides location information of veterinary clinics and introduces hospitals close to the owner's residence. The introduction unit can provide location information of veterinary clinics and introduce hospitals close to the owner's residence. For example, the introduction unit can provide location information of veterinary clinics and introduce hospitals close to the owner's residence. The introduction unit can also provide location information of veterinary clinics and exclude hospitals far from the owner's residence. Furthermore, the introduction unit can provide location information of veterinary clinics and display hospitals close to the owner's residence in a ranking format. In this way, by introducing hospitals close to the owner's residence, the owner can quickly access hospitals.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The pet monitoring system may further include a sensor for measuring the amount of food eaten by the pet. The food amount sensor measures the amount of food eaten by the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the pet's physical condition by combining the food amount data with the heart rate and body temperature data. For example, if the pet eats more than usual, the heart rate and body temperature data may be combined for evaluation. Also, if the pet eats less than usual, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the pet eats irregularly, the heart rate and body temperature data may be combined for evaluation. In this way, the pet's physical condition can be more accurately evaluated by combining the food amount data.

[0072] The pet monitoring system may further include a sensor for measuring the sleep state of the pet. The sleep state sensor measures the sleep state of the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the physical condition of the pet by combining the sleep data with the heart rate and body temperature data. For example, if the pet's sleep time is longer than usual, the heart rate and body temperature data may be combined for evaluation. Also, if the pet's sleep time is shorter than usual, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the pet's sleep is irregular, the heart rate and body temperature data may be combined for evaluation. This allows the pet's physical condition to be more accurately evaluated by combining the sleep data.

[0073] The pet monitoring system may further include a sensor for measuring the respiration rate of the pet. The respiration rate sensor measures the respiration rate of the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the physical condition of the pet by combining the respiration rate data with the heart rate and body temperature data. For example, if the respiration rate of the pet is higher than normal, the heart rate and body temperature data may be combined for evaluation. Also, if the respiration rate of the pet is lower than normal, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the respiration rate of the pet is irregular, the heart rate and body temperature data may be combined for evaluation. This allows the physical condition of the pet to be evaluated more accurately by combining the respiration rate data.

[0074] The process flow of the first embodiment will be briefly described below.

[0075] Step 1: The sensor unit measures the pet's heart rate and body temperature. For example, the sensor unit may include a heart rate sensor attached to a collar and a body temperature sensor installed in a cage. Step 2: The collecting unit collects the data measured by the sensor unit. For example, the collecting unit can collect data on heart rate and body temperature in real time. Step 3: The analysis unit analyzes the data collected by the collection unit and evaluates the pet's physical condition. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns the normal range based on past data, and if data exceeding that range is detected, it is determined to be abnormal. Step 4: The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify the owner in real time via a smartphone app. The notification unit can also notify the owner by email. Step 5: The referral unit refers the pet owner to the most suitable veterinary clinic based on the information notified by the notification unit. For example, the referral unit can select the most suitable veterinary clinic based on the pet's symptoms and refer the pet owner to the best veterinary clinic.

[0076] (Example 2) The pet monitoring system according to the embodiment of the present invention is a system that attaches sensors to the cage or body of a pet, collects information such as heart rate and body temperature, and monitors the physical condition of the pet. In this system, the sensor measures the pet's heart rate and body temperature in real time, and the AI ​​analyzes the data. The AI ​​evaluates the pet's physical condition based on the collected data, and notifies the owner when an abnormality is detected. The notification includes information on possible illnesses and a referral to the most suitable veterinary clinic. For example, if the pet's heart rate is higher than normal, the AI ​​evaluates the possibility of stress or illness and notifies the owner. In addition, if the body temperature is abnormally high, the AI ​​evaluates the possibility of fever and refers the owner to an appropriate veterinary clinic. This system allows the pet's health to be constantly monitored and abnormalities to be detected early. For example, the types of sensors include a heart rate sensor attached to a collar and a body temperature sensor installed in a cage. These sensors measure the pet's heart rate and body temperature in real time. The AI ​​analysis algorithm uses a machine learning algorithm for abnormality detection. The normal range is learned based on past data, and if data exceeding that range is detected, it is determined to be abnormal. The notification method can be a real-time notification via a smartphone app. Notification can also be sent by email. This allows the pet monitoring system to constantly monitor the health of your pet and detect any abnormalities at an early stage.

[0077] The pet monitoring system according to the embodiment includes a sensor unit, a collection unit, an analysis unit, a notification unit, and an introduction unit. The sensor unit measures the heart rate and body temperature of the pet. For example, the sensor unit can include a heart rate sensor attached to a collar. The sensor unit can also include a body temperature sensor installed in a cage. The collection unit collects data measured by the sensor unit. For example, the collection unit can collect heart rate and body temperature data in real time. The analysis unit analyzes the data collected by the collection unit and evaluates the physical condition of the pet. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns a normal range based on past data, and determines that an abnormality exists when data exceeding the normal range is detected. The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify in real time through a smartphone app. Also, the notification unit can notify by email. The introduction unit introduces the most suitable veterinary clinic based on the information notified by the notification unit. For example, the introduction unit can select the most suitable veterinary clinic according to the symptoms of the pet and introduce it to the owner. As a result, the pet monitoring system of the embodiment can monitor a pet's heart rate and body temperature in real time, notify the owner when an abnormality is detected, and refer the owner to the most suitable veterinary clinic.

[0078] The sensor unit measures the pet's heart rate and body temperature. For example, the sensor unit can include a heart rate sensor attached to a collar. This heart rate sensor is attached to the pet's neck, so the pet's heart rate can be constantly monitored. The heart rate sensor accurately measures the heart rate using optical or electrical technology and transmits the data to the collection unit in real time. The sensor unit can also include a body temperature sensor installed in the cage. The body temperature sensor measures the pet's body temperature while the pet is in the cage and detects changes in the environment and the pet's physical condition. The body temperature sensor can measure the body temperature non-contact using infrared technology, so data can be collected without stressing the pet. Furthermore, the sensor unit can also include an acceleration sensor or a gyro sensor for monitoring the pet's activity level and sleep pattern. This allows the pet's movement level and resting state to be grasped in detail, enabling a comprehensive evaluation of the pet's health condition. By combining these various sensors, the sensor unit comprehensively monitors the pet's health condition and contributes to early detection of abnormalities.

[0079] The collection unit collects data measured by the sensor unit. For example, the collection unit can collect data on heart rate and body temperature in real time. The collection unit receives data transmitted from the sensor unit and stores it in a central database. The collection unit has a high-speed communication protocol and a large-capacity storage in order to efficiently receive and store data. Furthermore, the collection unit has a function to detect duplication or loss of data and to request retransmission as necessary in order to ensure the integrity and reliability of the data. The collection unit centrally manages data from the sensor unit and makes it accessible to the analysis unit and notification unit. For example, the collection unit manages the data as time-series data by adding a time stamp to the data and recording the time of occurrence of each data point. This allows the analysis unit to compare past data with current data to detect abnormalities and analyze trends. The collection unit can flexibly set the frequency and range of data collection, thereby achieving optimal data collection according to the pet's condition and environment.

[0080] The analysis unit analyzes the data collected by the collection unit and evaluates the physical condition of the pet. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns the normal range based on past data, and judges it to be abnormal when data exceeding that range is detected. Specifically, the analysis unit performs time series analysis of the heart rate and body temperature data to detect abnormal patterns and sudden changes. For example, if the heart rate increases suddenly or the body temperature is abnormally high, the analysis unit judges this to be abnormal and notifies the notification unit. The analysis unit can also analyze the activity level and sleep pattern of the pet to perform a comprehensive evaluation of the health condition. For example, if the pet is less active or sleeps less than usual, the analysis unit judges this to be a sign of abnormality and alerts the owner. Furthermore, the analysis unit can provide information that is useful not only for abnormality detection but also for long-term health management and prevention. For example, the analysis unit analyzes the trends in the pet's health condition and predicts future risks. This allows the owner to take measures early and provide appropriate care to maintain the health of the pet.

[0081] The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify in real time through a smartphone app. The notification unit has an interface for receiving abnormality detection information from the analysis unit and promptly notifying the owner. The notification unit uses the push notification function of the smartphone to immediately send a notification to the owner when an abnormality is detected. The notification unit can also notify by email. Email notifications can include detailed information and can also be used as records for the owner to check later. Furthermore, the notification unit can use voice calls and SMS to more reliably deliver notifications to the owner in an emergency. The notification unit can customize the notification content and select an appropriate notification method depending on the type and urgency of the abnormality. For example, in the case of a minor abnormality, only a push notification is sent, and in the case of a serious abnormality, a voice call and SMS are also used. This allows the notification unit to quickly and reliably notify the owner of the abnormality and encourage them to take appropriate action.

[0082] The introduction unit introduces the most suitable veterinary hospital based on the information notified by the notification unit. For example, the introduction unit can select the most suitable veterinary hospital according to the symptoms of the pet and introduce it to the owner. The introduction unit has a database of veterinary hospitals and manages information such as the specialty, consultation hours, and location of each hospital. The introduction unit searches for the most suitable veterinary hospital based on the symptoms and condition of the pet provided by the analysis unit and introduces it to the owner. For example, if the pet's heart rate is abnormally high, it preferentially introduces a veterinary hospital that specializes in heart. In addition, the introduction unit can select the most accessible hospital taking into account the owner's current location and means of transportation. The introduction unit provides the owner with detailed information about the veterinary hospital to be introduced, and also guides the owner on the reservation procedure and emergency contact information. Furthermore, the introduction unit can continuously improve the accuracy of the introduction based on past introduction history and owner feedback. This allows the introduction unit to provide prompt and appropriate medical support to the owner and support the health management of the pet.

[0083] The sensor unit may include a heart rate sensor attached to a collar. The heart rate sensor is attached to, for example, the collar of the pet. The heart rate sensor can measure the pet's heart rate in real time. For example, the heart rate sensor can measure the heart rate using an optical sensor. The heart rate sensor can also measure the heart rate using an electrical sensor. Furthermore, the heart rate sensor can also measure the heart rate using an ultrasonic sensor. This allows the pet's heart rate to be accurately measured by the heart rate sensor attached to the collar.

[0084] The sensor unit may include a body temperature sensor that is installed in the cage. The body temperature sensor is installed, for example, in the pet's cage. The body temperature sensor can measure the pet's body temperature in real time. For example, the body temperature sensor can measure the body temperature using an infrared sensor. The body temperature sensor can also measure the body temperature using a contact sensor. Furthermore, the body temperature sensor can measure the body temperature using a thermistor. This allows the pet's body temperature to be accurately measured by the body temperature sensor installed in the cage.

[0085] The analysis unit can detect anomalies using a machine learning algorithm. The machine learning algorithm analyzes, for example, data on a pet's heart rate and body temperature. The machine learning algorithm learns a normal range based on past data, and determines that anomalies exist when data exceeding the normal range is detected. For example, the machine learning algorithm can detect anomalies using K-means clustering. The machine learning algorithm can also detect anomalies using a support vector machine (SVM). Furthermore, the machine learning algorithm can detect anomalies using deep learning. As a result, the accuracy of anomaly detection is improved by using the machine learning algorithm.

[0086] The notification unit can notify the owner in real time through a smartphone app. The notification unit notifies the owner, for example, through a smartphone app. The notification unit can notify the owner when an abnormality is detected in real time. For example, the notification unit can notify the owner using a push notification. The notification unit can also notify the owner using a notification function within the app. Furthermore, the notification unit can display abnormality information on the dashboard of the app. This allows the owner to respond quickly by notifying the owner in real time through the smartphone app.

[0087] The notification unit can notify the owner by email. For example, the notification unit notifies the owner by email. The notification unit can notify the owner by email when an abnormality is detected. For example, the notification unit can automatically send an email including abnormality information. The notification unit can also customize the content of the email. Furthermore, the notification unit can set the timing of sending the email. In this way, notifying the owner by email makes it easier for the owner to check for abnormalities.

[0088] The sensor unit can adjust the measurement frequency of the heart rate and body temperature based on the behavioral data of the pet. The sensor unit, for example, collects behavioral data of the pet and adjusts the measurement frequency of the heart rate and body temperature. The sensor unit can estimate the emotion of the pet and adjust the measurement frequency based on the emotion. For example, when the pet is relaxed, the measurement frequency of the heart rate and body temperature can be set low. Also, when the pet is excited, the measurement frequency of the heart rate and body temperature can be set high. Furthermore, when the pet is stressed, the measurement frequency of the heart rate and body temperature can be set to a medium level. In this way, more accurate data can be collected by adjusting the measurement frequency according to the emotion of the pet.

[0089] The sensor unit can evaluate the physical condition by combining the activity amount data, the heart rate and the body temperature data with an additional acceleration sensor that measures the activity amount of the pet. The sensor unit includes, for example, an acceleration sensor that measures the activity amount of the pet. The acceleration sensor can measure the movement of the pet in real time. The sensor unit evaluates the physical condition by combining the activity amount data, the heart rate and the body temperature data. For example, when the pet is actively moving, the heart rate and the body temperature data are combined with the activity amount data to evaluate the physical condition. In addition, when the pet is stationary, the heart rate and the body temperature data can be combined with the activity amount data to evaluate the physical condition. Furthermore, when the pet is sleeping, the heart rate and the body temperature data can be combined with the activity amount data to evaluate the physical condition. In this way, the physical condition of the pet can be more accurately evaluated by combining the activity amount data.

[0090] The sensor unit can add a sensor for measuring the respiration rate of the pet, and evaluate the physical condition by combining the respiration rate data with the heart rate and body temperature data. The sensor unit includes, for example, a sensor for measuring the respiration rate of the pet. The respiration rate sensor can measure the respiration rate of the pet in real time. The sensor unit evaluates the physical condition by combining the respiration rate data with the heart rate and body temperature data. For example, if the respiration rate of the pet is higher than normal, the heart rate and body temperature data are combined for evaluation. In addition, if the respiration rate of the pet is lower than normal, the heart rate and body temperature data can be combined for evaluation. Furthermore, if the respiration rate of the pet is irregular, the heart rate and body temperature data can be combined for evaluation. This allows the physical condition of the pet to be evaluated more accurately by combining the respiration rate data.

[0091] The sensor unit can adjust the sensitivity of the sensor based on the behavioral data of the pet. The sensor unit, for example, collects behavioral data of the pet and adjusts the sensitivity of the sensor. The sensor unit can estimate the emotion of the pet and adjust the sensitivity of the sensor based on the emotion. For example, when the pet is relaxed, the sensitivity of the sensor can be set low. Also, when the pet is excited, the sensitivity of the sensor can be set high. Furthermore, when the pet is stressed, the sensitivity of the sensor can be set to a medium level. In this way, more accurate data can be collected by adjusting the sensitivity of the sensor according to the emotion of the pet.

[0092] The sensor unit can evaluate the physical condition by combining the food amount data, the heart rate data, and the body temperature data with an additional sensor that measures the amount of food eaten by the pet. The food amount sensor can measure the amount of food eaten by the pet in real time. The sensor unit evaluates the physical condition by combining the food amount data, the heart rate data, and the body temperature data. For example, if the pet eats more than usual, the heart rate data and the body temperature data are combined for evaluation. In addition, if the pet eats less than usual, the heart rate data and the body temperature data can be combined for evaluation. Furthermore, if the pet eats irregularly, the heart rate data and the body temperature data can be combined for evaluation. In this way, the physical condition of the pet can be more accurately evaluated by combining the food amount data.

[0093] The sensor unit can add a sensor that measures the sleep state of the pet, and evaluate the physical condition by combining the sleep data with the heart rate and body temperature data. The sensor unit includes, for example, a sensor that measures the sleep state of the pet. The sleep state sensor can measure the sleep state of the pet in real time. The sensor unit evaluates the physical condition by combining the sleep data with the heart rate and body temperature data. For example, if the pet's sleep time is longer than usual, the heart rate and body temperature data are combined for evaluation. In addition, if the pet's sleep time is shorter than usual, the heart rate and body temperature data can be combined for evaluation. Furthermore, if the pet's sleep is irregular, the heart rate and body temperature data can be combined for evaluation. This allows the pet's physical condition to be evaluated more accurately by combining the sleep data.

[0094] The collection unit can adjust the timing of data collection based on the behavioral data of the pet. For example, the collection unit collects behavioral data of the pet and adjusts the timing of data collection. The collection unit can estimate the emotions of the pet and adjust the timing of data collection based on the emotions. For example, when the pet is relaxed, the frequency of data collection can be set low. Also, when the pet is excited, the frequency of data collection can be set high. Furthermore, when the pet is stressed, the frequency of data collection can be set to a medium level. In this way, more accurate data can be collected by adjusting the timing of data collection according to the emotions of the pet.

[0095] The collection unit can add a function of evaluating the reliability of data and filter out low-reliability data. For example, the collection unit evaluates the reliability of data and filters out low-reliability data. The collection unit can evaluate the reliability of data and automatically exclude low-reliability data. For example, the collection unit evaluates the reliability of data and collects only high-reliability data. The collection unit can also evaluate the reliability of data and manually check low-reliability data. This improves the accuracy of data by filtering out low-reliability data.

[0096] The collection unit can add a function of automatically detecting outliers in the data and excluding the outliers. For example, the collection unit automatically detects outliers in the data and excludes the outliers. The collection unit can automatically detect outliers in the data and exclude the outliers. For example, the collection unit automatically detects outliers in the data and manually checks the outliers. The collection unit can also automatically detect outliers in the data and correct the outliers. This improves the accuracy of the data by excluding the outliers.

[0097] The collection unit can determine the priority of data to be collected based on the behavioral data of the pet. For example, the collection unit collects the behavioral data of the pet and determines the priority of data to be collected. The collection unit can estimate the emotion of the pet and determine the priority of data to be collected based on the emotion. For example, when the pet is relaxed, the heart rate data can be collected with priority. Also, when the pet is excited, the body temperature data can be collected with priority. Furthermore, when the pet is stressed, the respiratory rate data can be collected with priority. In this way, important data can be collected with priority by determining the priority of data according to the emotion of the pet.

[0098] The collection unit can add a function for setting a data retention period and automatically delete old data. The collection unit, for example, sets a data retention period and automatically deletes old data. The collection unit can set a data retention period and automatically delete data that has passed the retention period. For example, the collection unit sets a data retention period and manually deletes data that has passed the retention period. The collection unit can also set a data retention period and archive data that has passed the retention period. This makes it easier to manage data by automatically deleting old data.

[0099] The collection unit can add a data backup function to prevent data loss. The collection unit, for example, periodically backs up data to prevent data loss. The collection unit can periodically back up data to prevent data loss. For example, the collection unit manually backs up data to prevent data loss. The collection unit can also store the data backup in the cloud to prevent data loss. In this way, the data backup function can prevent data loss.

[0100] The analysis unit can adjust the analysis algorithm based on the behavioral data of the pet. The analysis unit, for example, collects behavioral data of the pet and adjusts the analysis algorithm. The analysis unit can estimate the emotions of the pet and adjust the analysis algorithm based on the emotions. For example, when the pet is relaxed, the sensitivity of the analysis algorithm can be set low. Also, when the pet is excited, the sensitivity of the analysis algorithm can be set high. Furthermore, when the pet is stressed, the sensitivity of the analysis algorithm can be set to a medium level. This improves the analysis accuracy by adjusting the analysis algorithm according to the emotions of the pet.

[0101] The analysis unit can add a data preprocessing function for improving the accuracy of anomaly detection. The analysis unit can add, for example, a data preprocessing function to improve the accuracy of anomaly detection. The analysis unit can add, for example, a data preprocessing function to remove noise from data. The analysis unit can also add a data preprocessing function to complement missing values ​​in data. Thereby, the accuracy of anomaly detection is improved by adding the data preprocessing function.

[0102] The analysis unit can add a function for visualizing the results of anomaly detection, and display them in an easy-to-understand manner for the owner. For example, the analysis unit can display the results of anomaly detection in a graph, making it easy for the owner to understand. The analysis unit can display the results of anomaly detection in a graph, making it easy for the owner to understand. For example, the analysis unit can display the results of anomaly detection in a chart, making it easy for the owner to understand. The analysis unit can also display the results of anomaly detection in text, making it easy for the owner to understand. In this way, by visualizing the results of anomaly detection, it becomes easier for the owner to understand the abnormality.

[0103] The analysis unit can adjust the display method of the analysis results based on the behavioral data of the pet. The analysis unit, for example, collects behavioral data of the pet and adjusts the display method of the analysis results. The analysis unit can estimate the emotions of the pet and adjust the display method of the analysis results based on the emotions. For example, when the pet is relaxed, detailed analysis results can be displayed. Also, when the pet is excited, concise analysis results can be displayed. Furthermore, when the pet is stressed, analysis results that are visually easy to understand can be displayed. In this way, by adjusting the display method of the analysis results according to the emotions of the pet, the owner can easily understand the analysis results.

[0104] The analysis unit can add a function of comparing the results of anomaly detection with data of other pets and evaluate the degree of the abnormality. The analysis unit, for example, compares the results of anomaly detection with data of other pets and evaluates the degree of the abnormality. The analysis unit can compare the results of anomaly detection with data of other pets and evaluate the degree of the abnormality. For example, the analysis unit compares the results of anomaly detection with data of other pets and identifies the cause of the abnormality. The analysis unit can also compare the results of anomaly detection with data of other pets and propose measures to address the abnormality. This makes it easier to evaluate the degree of the abnormality by comparing with data of other pets.

[0105] The analysis unit can add a function to compare the results of anomaly detection with past data and analyze anomaly trends. For example, the analysis unit compares the results of anomaly detection with past data and analyzes anomaly trends. The analysis unit can compare the results of anomaly detection with past data and analyze anomaly trends. For example, the analysis unit compares the results of anomaly detection with past data and identifies the cause of the anomaly. The analysis unit can also compare the results of anomaly detection with past data and propose measures to deal with the anomaly. This makes it easier to analyze anomaly trends by comparing with past data.

[0106] The notification unit can adjust the content of the notification based on the behavioral data of the pet. For example, the notification unit collects behavioral data of the pet and adjusts the content of the notification. The notification unit can estimate the emotion of the pet and adjust the content of the notification based on the emotion. For example, when the pet is relaxed, detailed notification content can be provided. Also, when the pet is excited, concise notification content can be provided. Furthermore, when the pet is stressed, visually easy-to-understand notification content can be provided. In this way, by adjusting the content of the notification according to the emotion of the pet, the owner can easily understand the notification content.

[0107] The notification unit can add a function for setting the urgency of the notification and change the notification method according to the urgency. For example, the notification unit sets the urgency of the notification and changes the notification method according to the urgency. The notification unit can set the urgency of the notification and change the notification method according to the urgency. For example, if the urgency is high, the notification unit immediately notifies the user by using a push notification on a smartphone. Also, if the urgency is medium, the notification unit can notify the user by email. Furthermore, if the urgency is low, the notification unit can display the notification in a notification center within the app. This allows the owner to respond quickly by changing the notification method according to the urgency of the notification.

[0108] The notification unit can add a function of saving the notification history, allowing past notification contents to be referred to. The notification unit, for example, saves the notification history, allowing past notification contents to be referred to. The notification unit can save the notification history, allowing past notification contents to be referred to. For example, the notification unit can save the notification history, displaying past notification contents in a list. The notification unit can also save the notification history, allowing specific notification contents to be searched for. Furthermore, the notification unit can save the notification history, and display the notification contents in chronological order. In this way, saving the notification history makes it easier to refer to past notification contents.

[0109] The notification unit can adjust the timing of the notification based on the behavioral data of the pet. For example, the notification unit collects behavioral data of the pet and adjusts the timing of the notification. The notification unit can estimate the emotion of the pet and adjust the timing of the notification based on the emotion. For example, if the pet is relaxed, the timing of the notification can be delayed. Also, if the pet is excited, the timing of the notification can be advanced. Furthermore, if the pet is stressed, the timing of the notification can be set to a medium level. In this way, the timing of the notification can be adjusted according to the emotion of the pet, allowing the owner to receive the notification at an appropriate time.

[0110] The notification unit can add a notification customization function and change the notification content according to the owner's preferences. The notification unit can add, for example, a notification customization function and change the notification content according to the owner's preferences. The notification unit can add a notification customization function and change the notification content according to the owner's preferences. For example, if the owner desires detailed notification, the notification unit can provide detailed notification content. Also, if the owner desires concise notification, the notification unit can provide concise notification content. Furthermore, if the owner desires visual notification, the notification unit can provide notification content including graphs and charts. This allows the owner to receive notification content according to their preferences using the notification customization function.

[0111] The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language. The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language, for example. The notification unit can provide notifications in multiple languages ​​by adding a function for selecting a notification language. For example, the notification unit automatically sets the notification language based on the language setting of the owner's device. The notification unit can also provide a language switching function when the owner uses multiple languages. Furthermore, the notification unit can provide notifications in a specific language when the owner selects that language. This allows the owner to receive notifications in their desired language by using the notification language selection function.

[0112] The introduction unit can adjust the selection criteria for the veterinary clinic to be introduced based on the behavioral data of the pet. For example, the introduction unit collects the behavioral data of the pet and adjusts the selection criteria for the veterinary clinic to be introduced. The introduction unit can estimate the emotion of the pet and adjust the selection criteria for the veterinary clinic to be introduced based on the emotion. For example, if the pet is relaxed, a regular veterinary clinic can be introduced. Also, if the pet is excited, a veterinary clinic that can handle emergencies can be introduced. Furthermore, if the pet is stressed, a veterinary clinic that specializes in stress reduction can be introduced. In this way, the selection criteria for the veterinary clinic can be adjusted according to the emotion of the pet, so that the most suitable veterinary clinic can be introduced.

[0113] The introduction unit can add a function of collecting evaluation information on veterinary clinics and preferentially introducing hospitals with high ratings. The introduction unit can, for example, collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. The introduction unit can collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. For example, the introduction unit can collect evaluation information on veterinary clinics and preferentially introduce hospitals with high ratings. The introduction unit can also collect evaluation information on veterinary clinics and exclude hospitals with low ratings. Furthermore, the introduction unit can collect evaluation information on veterinary clinics and display highly rated hospitals in a ranking format. As a result, preferentially introducing highly rated hospitals makes it easier for pet owners to choose a reliable hospital.

[0114] The introduction unit can add a function of selecting the most suitable hospital based on the medical department and specialty of the veterinary hospital. The introduction unit selects the most suitable hospital based on, for example, the medical department and specialty of the veterinary hospital. The introduction unit can select the most suitable hospital based on the medical department and specialty of the veterinary hospital. For example, the introduction unit selects the most suitable hospital based on the medical department of the veterinary hospital. The introduction unit can also select the most suitable hospital based on the specialty of the veterinary hospital. Furthermore, the introduction unit can select the most suitable hospital by combining the medical department and specialty of the veterinary hospital. In this way, by selecting the most suitable hospital based on the medical department and specialty, it is possible to introduce a hospital suitable for the symptoms of the pet.

[0115] The introduction unit can determine the priority of veterinary clinics to be introduced based on the behavioral data of the pet. The introduction unit, for example, collects behavioral data of the pet and determines the priority of veterinary clinics to be introduced. The introduction unit can estimate the emotions of the pet and determine the priority of veterinary clinics to be introduced based on the emotions. For example, if the pet is relaxed, a regular veterinary clinic can be preferentially introduced. Also, if the pet is excited, a veterinary clinic that can handle emergencies can be preferentially introduced. Furthermore, if the pet is stressed, a veterinary clinic that specializes in stress reduction can be preferentially introduced. In this way, the priority of veterinary clinics can be determined according to the emotions of the pet, so that the most suitable clinic can be introduced quickly.

[0116] The introduction unit can add a function of checking the reservation status of veterinary clinics and introducing hospitals where it is easy to make an appointment. The introduction unit can check the reservation status of veterinary clinics and introduce hospitals where it is easy to make an appointment. For example, the introduction unit can check the reservation status of veterinary clinics and introduce hospitals where it is easy to make an appointment. The introduction unit can also check the reservation status of veterinary clinics and exclude hospitals where it is difficult to make an appointment. Furthermore, the introduction unit can check the reservation status of veterinary clinics and display hospitals where it is easy to make an appointment in a ranking format. In this way, by introducing hospitals where it is easy to make an appointment, pet owners can quickly make an appointment.

[0117] The introduction unit can add a function of providing location information of veterinary clinics and introducing hospitals close to the owner's residence. The introduction unit, for example, provides location information of veterinary clinics and introduces hospitals close to the owner's residence. The introduction unit can provide location information of veterinary clinics and introduce hospitals close to the owner's residence. For example, the introduction unit can provide location information of veterinary clinics and introduce hospitals close to the owner's residence. The introduction unit can also provide location information of veterinary clinics and exclude hospitals far from the owner's residence. Furthermore, the introduction unit can provide location information of veterinary clinics and display hospitals close to the owner's residence in a ranking format. In this way, by introducing hospitals close to the owner's residence, the owner can quickly access hospitals.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The pet monitoring system may further include a sensor for measuring the amount of food eaten by the pet. The food amount sensor measures the amount of food eaten by the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the pet's physical condition by combining the food amount data with the heart rate and body temperature data. For example, if the pet eats more than usual, the heart rate and body temperature data may be combined for evaluation. Also, if the pet eats less than usual, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the pet eats irregularly, the heart rate and body temperature data may be combined for evaluation. In this way, the pet's physical condition can be more accurately evaluated by combining the food amount data.

[0120] The pet monitoring system can further include a function of adjusting the sensitivity of the sensor based on the behavioral data of the pet. The sensor unit collects the behavioral data of the pet and estimates the emotions. For example, when the pet is relaxed, the sensitivity of the sensor is set to low. Also, when the pet is excited, the sensitivity of the sensor can be set to high. Furthermore, when the pet is stressed, the sensitivity of the sensor can be set to medium. In this way, more accurate data can be collected by adjusting the sensitivity of the sensor according to the emotions of the pet.

[0121] The pet monitoring system may further include a sensor for measuring the sleep state of the pet. The sleep state sensor measures the sleep state of the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the physical condition of the pet by combining the sleep data with the heart rate and body temperature data. For example, if the pet's sleep time is longer than usual, the heart rate and body temperature data may be combined for evaluation. Also, if the pet's sleep time is shorter than usual, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the pet's sleep is irregular, the heart rate and body temperature data may be combined for evaluation. This allows the pet's physical condition to be more accurately evaluated by combining the sleep data.

[0122] The pet monitoring system may further include a sensor for measuring the respiration rate of the pet. The respiration rate sensor measures the respiration rate of the pet in real time and transmits the data to the collection unit. The analysis unit may evaluate the physical condition of the pet by combining the respiration rate data with the heart rate and body temperature data. For example, if the respiration rate of the pet is higher than normal, the heart rate and body temperature data may be combined for evaluation. Also, if the respiration rate of the pet is lower than normal, the heart rate and body temperature data may be combined for evaluation. Furthermore, if the respiration rate of the pet is irregular, the heart rate and body temperature data may be combined for evaluation. This allows the physical condition of the pet to be evaluated more accurately by combining the respiration rate data.

[0123] The pet monitoring system can further include a function of adjusting the timing of data collection based on the behavioral data of the pet. The collection unit collects the behavioral data of the pet and estimates the emotions. For example, if the pet is relaxed, the frequency of data collection can be set low. Also, if the pet is excited, the frequency of data collection can be set high. Furthermore, if the pet is stressed, the frequency of data collection can be set to a medium level. This allows more accurate data to be collected by adjusting the timing of data collection according to the emotions of the pet.

[0124] The pet monitoring system can further include a function of adjusting the content of the notification based on the behavioral data of the pet. The notification unit collects the behavioral data of the pet and estimates the emotion. For example, if the pet is relaxed, detailed notification content can be provided. Also, if the pet is excited, concise notification content can be provided. Furthermore, if the pet is stressed, visually easy-to-understand notification content can be provided. In this way, by adjusting the content of the notification according to the emotion of the pet, the owner can easily understand the notification content.

[0125] The pet monitoring system can further include a function of adjusting the timing of the notification based on the behavioral data of the pet. The notification unit collects the behavioral data of the pet and estimates the emotion. For example, if the pet is relaxed, the timing of the notification can be delayed. Also, if the pet is excited, the timing of the notification can be advanced. Furthermore, if the pet is stressed, the timing of the notification can be set to a medium level. In this way, the timing of the notification can be adjusted according to the emotion of the pet, allowing the owner to receive the notification at an appropriate time.

[0126] The pet monitoring system can further include a function of adjusting the analysis algorithm based on the behavioral data of the pet. The analysis unit collects the behavioral data of the pet and estimates the emotions. For example, if the pet is relaxed, the sensitivity of the analysis algorithm can be set low. Also, if the pet is excited, the sensitivity of the analysis algorithm can be set high. Furthermore, if the pet is stressed, the sensitivity of the analysis algorithm can be set to a medium level. This improves the analysis accuracy by adjusting the analysis algorithm according to the pet's emotions.

[0127] The pet monitoring system can further include a function of determining the priority of data to be collected based on the behavioral data of the pet. The collection unit collects the behavioral data of the pet and estimates the emotions. For example, if the pet is relaxed, the collection of heart rate data can be prioritized. Also, if the pet is excited, the collection of body temperature data can be prioritized. Furthermore, if the pet is stressed, the collection of respiratory rate data can be prioritized. In this way, important data can be collected preferentially by determining the priority of data according to the emotions of the pet.

[0128] The pet monitoring system can further include a function for adjusting the selection criteria for the veterinary clinic to be introduced based on the behavioral data of the pet. The introduction unit collects the behavioral data of the pet and estimates the emotions. For example, if the pet is relaxed, it can introduce a regular veterinary clinic. Also, if the pet is excited, it can introduce a veterinary clinic that can handle emergencies. Furthermore, if the pet is stressed, it can introduce a veterinary clinic that specializes in stress reduction. In this way, it is possible to introduce the most suitable veterinary clinic by adjusting the selection criteria for the veterinary clinic according to the pet's emotions.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The sensor unit measures the pet's heart rate and body temperature. For example, the sensor unit may include a heart rate sensor attached to a collar and a body temperature sensor installed in a cage. Step 2: The collecting unit collects the data measured by the sensor unit. For example, the collecting unit can collect data on heart rate and body temperature in real time. Step 3: The analysis unit analyzes the data collected by the collection unit and evaluates the pet's physical condition. For example, the analysis unit can detect abnormalities using a machine learning algorithm. The machine learning algorithm learns the normal range based on past data, and if data exceeding that range is detected, it is determined to be abnormal. Step 4: The notification unit notifies the owner when an abnormality is detected by the analysis unit. For example, the notification unit can notify the owner in real time via a smartphone app. The notification unit can also notify the owner by email. Step 5: The referral unit refers the pet owner to the most suitable veterinary clinic based on the information notified by the notification unit. For example, the referral unit can select the most suitable veterinary clinic based on the pet's symptoms and refer the pet owner to the best veterinary clinic.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the sensor unit, collection unit, analysis unit, notification unit, and introduction unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the sensor unit includes a heart rate sensor attached to a collar as part of the smart device 14 and a body temperature sensor installed in a cage. The collection unit is realized by the control unit 46A of the smart device 14 and collects heart rate and body temperature data in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a machine learning algorithm. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the owner through a smartphone app or email. The introduction unit is realized by the specific processing unit 290 of the data processing device 12 and selects the most suitable veterinary hospital according to the symptoms of the pet and introduces it to the owner. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] 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 performed in a secure state.

[0142] Fig. 4 shows an example of 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the sensor unit, the collection unit, the analysis unit, the notification unit, and the introduction unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit includes a heart rate sensor attached to a collar as part of the smart glasses 214 and a body temperature sensor installed in a cage. The collection unit is realized by the control unit 46A of the smart glasses 214 and collects heart rate and body temperature data in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a machine learning algorithm. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the owner through a smartphone app or email. The introduction unit is realized by the specific processing unit 290 of the data processing device 12 and selects the most suitable veterinary hospital according to the symptoms of the pet and introduces it to the owner. The correspondence between each unit and the device and the control unit is not limited to the above example, and various changes are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0154] 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.

[0155] 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 the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0157] 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 performed in a secure state.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 identification processing unit 290 using these models.

[0162] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] 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 voice indicating a user input for 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.

[0164] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements including the sensor unit, the collection unit, the analysis unit, the notification unit, and the introduction unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the sensor unit includes a heart rate sensor attached to a collar as part of the headset type terminal 314 and a body temperature sensor installed in a cage. The collection unit is realized by the control unit 46A of the headset type terminal 314 and collects heart rate and body temperature data in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a machine learning algorithm. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the owner through a smartphone app or email. The introduction unit is realized by the specific processing unit 290 of the data processing device 12 and selects the most suitable veterinary hospital according to the symptoms of the pet and introduces it to the owner. The correspondence between each unit and the device and the control unit is not limited to the above example, and various changes are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0170] 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.

[0171] 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 the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] 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 performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0179] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] 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 a voice indicating a user input for 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.

[0181] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0183] Each of the multiple elements including the sensor unit, the collection unit, the analysis unit, the notification unit, and the introduction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit includes a heart rate sensor attached to a collar as part of the robot 414 and a body temperature sensor installed in a cage. The collection unit is realized by the control unit 46A of the robot 414 and collects data on heart rate and body temperature in real time. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a machine learning algorithm. The notification unit is realized by the control unit 46A of the robot 414 and notifies the owner through a smartphone app or email. The introduction unit is realized by the specific processing unit 290 of the data processing device 12 and selects the most suitable veterinary hospital according to the symptoms of the pet and introduces it to the owner. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0191] 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.

[0192] 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 Universal Serial Bus (USB) 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.

[0193] In addition, 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 upon request from the data processing device 12.

[0194] 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.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0197] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) A sensor unit that measures your pet's heart rate and body temperature, A collection unit that collects data measured by the sensor unit; an analysis unit that analyzes the data collected by the collection unit and evaluates the physical condition of the pet; A notification unit that notifies the owner when an abnormality is detected by the analysis unit; An introduction unit that introduces veterinary hospitals based on the information notified by the notification unit. A system characterized by: (Appendix 2) The sensor unit includes: Equipped with a heart rate sensor that can be attached to a collar 2. The system of claim 1, (Appendix 3) The sensor unit includes: Equipped with a body temperature sensor to be installed in the cage 2. The system of claim 1, (Appendix 4) The analysis unit is Use machine learning algorithms to detect anomalies 2. The system of claim 1, (Appendix 5) The notification unit is Real-time notifications via smartphone app 2. The system of claim 1, (Appendix 6) The notification unit is Notify by email 2. The system of claim 1, (Appendix 7) The sensor unit includes: Adjust the frequency of heart rate and temperature measurements based on your pet's behavior data 2. The system of claim 1, (Appendix 8) The sensor unit includes: An acceleration sensor has been added to measure the pet's activity level, and the activity data can be combined with heart rate and body temperature data to assess the pet's health. 2. The system of claim 1, (Appendix 9) The sensor unit includes: Add a sensor to measure your pet's breathing rate and combine the breathing rate data with heart rate and temperature data to assess their health. 2. The system of claim 1, (Appendix 10) The sensor unit includes: Adjust sensor sensitivity based on pet behavior data 2. The system of claim 1, (Appendix 11) The sensor unit includes: Add a sensor to measure the amount of food your pet eats, and evaluate its health by combining food intake data with heart rate and body temperature data. 2. The system of claim 1, (Appendix 12) The sensor unit includes: Add a sensor to measure your pet's sleep state and evaluate its health condition by combining sleep data with heart rate and body temperature data. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Adjust the timing of data collection based on pet behavior data 2. The system of claim 1, (Appendix 14) The collecting unit includes: Add a function to evaluate the reliability of data and filter out unreliable data 2. The system of claim 1, (Appendix 15) The collecting unit includes: Automatically detect outliers in your data and add the ability to filter out outliers 2. The system of claim 1, (Appendix 16) The collecting unit includes: Prioritize data collection based on pet behavior data 2. The system of claim 1, (Appendix 17) The collecting unit includes: Added the ability to set the data retention period and automatically delete old data 2. The system of claim 1, (Appendix 18) The collecting unit includes: Added data backup function to prevent data loss 2. The system of claim 1, (Appendix 19) The analysis unit is Adjust the analysis algorithm based on your pet's behavior data 2. The system of claim 1, (Appendix 20) The analysis unit is Add data preprocessing functions to improve the accuracy of anomaly detection 2. The system of claim 1, (Appendix 21) The analysis unit is Added a function to visualize the results of anomaly detection, and display them in an easy-to-understand manner to pet owners 2. The system of claim 1, (Appendix 22) The analysis unit is Adjust how analytics results are displayed based on your pet's behavior data 2. The system of claim 1, (Appendix 23) The analysis unit is Add a function to compare the results of anomaly detection with data from other pets to evaluate the severity of the anomaly. 2. The system of claim 1, (Appendix 24) The analysis unit is Add a function to compare anomaly detection results with past data to analyze anomaly trends. 2. The system of claim 1, (Appendix 25) The notification unit is Tailor your notifications based on your pet's behavior data 2. The system of claim 1, (Appendix 26) The notification unit is Added the ability to set the urgency of notifications, and change the notification method depending on the urgency. 2. The system of claim 1, (Appendix 27) The notification unit is Added a function to save notification history so that past notification contents can be referenced. 2. The system of claim 1, (Appendix 28) The notification unit is Adjust notification timing based on pet behavior data 2. The system of claim 1, (Appendix 29) The notification unit is Added notification customization function to change notification content according to the owner's preferences 2. The system of claim 1, (Appendix 30) The notification unit is Added the ability to select notification language, and notifications will be available in multiple languages. 2. The system of claim 1, (Appendix 31) The introduction unit is Adjusting the selection criteria for veterinary clinics to refer patients to based on pet behavior data 2. The system of claim 1, (Appendix 32) The introduction unit is Add a function to collect evaluation information on veterinary clinics and prioritize the introduction of highly rated clinics 2. The system of claim 1, (Appendix 33) The introduction unit is Add a feature to select the best veterinary clinic based on the medical specialty or field of expertise of the clinic. 2. The system of claim 1, (Appendix 34) The introduction unit is Prioritize veterinary referrals based on pet behavior data 2. The system of claim 1, (Appendix 35) The introduction unit is Add a function to check the reservation status of veterinary clinics and introduce clinics where it is easy to make reservations 2. The system of claim 1, (Appendix 36) The introduction unit is Add a feature to provide location information for veterinary clinics and introduce clinics close to pet owners' residences. 2. The system of claim 1, [Explanation of symbols]

[0203] 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 sensor unit that measures your pet's heart rate and body temperature, A collection unit that collects data measured by the sensor unit; an analysis unit that analyzes the data collected by the collection unit and evaluates the physical condition of the pet; A notification unit that notifies the owner when an abnormality is detected by the analysis unit; An introduction unit that introduces veterinary hospitals based on the information notified by the notification unit. A system characterized by:

2. The sensor unit includes: Equipped with a heart rate sensor that can be attached to a collar 2. The system of claim 1 .

3. The sensor unit includes: Equipped with a body temperature sensor to be installed in the cage 2. The system of claim 1 .

4. The analysis unit is Use machine learning algorithms to detect anomalies 2. The system of claim 1 .

5. The notification unit is Real-time notifications via smartphone app 2. The system of claim 1 .

6. The notification unit is Notify by email 2. The system of claim 1 .

7. The sensor unit includes: Adjust the frequency of heart rate and temperature measurements based on your pet's behavior data 2. The system of claim 1 .

8. The sensor unit includes: An acceleration sensor is added to measure the pet's activity level, and the activity data is combined with heart rate and body temperature data to evaluate the pet's health.

2. The system of claim 1 .

9. The sensor unit includes: Add a sensor to measure your pet's breathing rate and combine the breathing rate data with heart rate and temperature data to assess their health.

2. The system of claim 1 .

10. The sensor unit includes: Adjust sensor sensitivity based on pet behavior data 2. The system of claim 1 .

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