system

Abstract

The system according to this embodiment aims to monitor the pet's physical condition in real time and detect abnormalities early. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a storage unit. The collection unit collects data using a camera or sensor to monitor the pet's condition. The analysis unit analyzes the data collected by the collection unit and checks the pet's physical condition. The notification unit notifies the owner if an abnormality is detected by the analysis unit. The storage unit stores the data collected by the collection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to monitor changes in a pet's physical condition in real time and detect abnormalities early.

[0005] The system according to this embodiment aims to monitor the pet's physical condition in real time and detect abnormalities early. [Means for solving the problem]

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, and a storage unit. The data collection unit collects data using a camera or sensor to monitor the pet's condition. The analysis unit analyzes the data collected by the data collection unit to check the pet's health. The notification unit notifies the owner if an abnormality is detected by the analysis unit. The storage unit stores the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the pet's physical condition in real time and detect abnormalities early. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pet health management system according to an embodiment of the present invention is a system that uses multimodal AI to check the health of pets and supports pet owners in managing their pets' health. This pet health management system monitors the pet's condition 24 hours a day using cameras and sensors, and the multimodal AI analyzes the monitoring data to check the pet's health. If an abnormality is detected, the owner is notified. The monitoring data is also stored in the cloud and provided to the pet industry on a subscription basis. For example, if a pet licks a foreign object, the AI ​​detects this behavior and notifies the owner. Also, if the pet's movements are different from usual, the AI ​​detects the abnormality and notifies the owner. This allows owners to respond quickly to changes in their pet's health. Furthermore, pet hotels and veterinarians can use this system to monitor pets 24 hours a day. This streamlines pet health management and reduces the burden of work. This system not only supports pet health management but also contributes to the efficiency of the entire pet industry. By accumulating monitoring data and providing it to the pet industry on a subscription basis, industry-wide knowledge is accumulated, improving pet health management. This allows pet owners to respond quickly to changes in their pet's health and manage their pet's health efficiently. Furthermore, it can reduce the workload of pet hotels and veterinarians, contributing to increased efficiency throughout the pet industry.

[0029] A pet health management system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a storage unit. The collection unit collects data using a camera or sensor for monitoring the pet's condition. The collection unit can monitor the pet's condition in detail using, for example, an infrared camera, a temperature sensor, a motion sensor, etc. For example, the collection unit can monitor the pet's body temperature using an infrared camera and detect changes in the pet's body temperature using a temperature sensor. The collection unit can also monitor the pet's movements and detect abnormal movements using a motion sensor. The analysis unit analyzes the data collected by the collection unit to check the pet's health. The analysis unit can check the pet's health in detail using, for example, a combination of techniques such as image analysis, audio analysis, and motion analysis. For example, the analysis unit can analyze the pet's facial expressions and movements using image analysis, and the pet's cries and breathing sounds using audio analysis. The analysis unit can also analyze the pet's movement patterns using motion analysis to detect abnormal movements. The notification unit notifies the owner when an abnormality is detected by the analysis unit. The notification unit can quickly notify the owner using, for example, email, SMS, app notification, etc. For example, the notification unit can send an email to the owner to provide detailed information when an abnormality is detected. It can also send emergency notifications using SMS. The storage unit stores the data collected by the collection unit. For example, the storage unit can store the monitoring data in the cloud and protect it by encrypting it. For example, the storage unit can regularly back up the monitoring data to prevent data loss. The storage unit can also automatically classify the monitoring data and store it in an easy-to-search format. As a result, the pet health management system according to the embodiment can support pet health management by monitoring the pet's health 24 hours a day and notifying the owner when an abnormality is detected.

[0030] The data collection unit can monitor the pet's condition using an infrared camera, temperature sensor, or motion sensor. For example, the data collection unit can monitor the pet's body temperature using an infrared camera. For instance, the data collection unit can monitor the pet's body temperature in real time using an infrared camera and detect abnormal changes in body temperature. The data collection unit can also monitor the pet's body temperature using a temperature sensor. For example, the data collection unit can periodically measure the pet's body temperature using a temperature sensor and detect abnormal changes in body temperature. The data collection unit can also monitor the pet's movement using a motion sensor. For example, the data collection unit can monitor the pet's movement patterns using a motion sensor and detect abnormal movement. In this way, the data collection unit can monitor the pet's condition in detail by using a variety of sensors.

[0031] The analysis unit can check a pet's health by combining image analysis, audio analysis, and motion analysis. For example, the analysis unit can use image analysis to analyze a pet's facial expressions and movements. For instance, it can use image analysis to analyze a pet's facial expressions and detect abnormal expressions. The analysis unit can also use audio analysis to analyze a pet's barks and breathing sounds. For example, it can use audio analysis to analyze a pet's barks and detect abnormal barks. Furthermore, the analysis unit can use motion analysis to analyze a pet's movement patterns. For example, it can use motion analysis to analyze a pet's movement patterns and detect abnormal movements. As a result, the analysis unit can more accurately check a pet's health by combining multiple analysis methods.

[0032] The notification unit can notify the owner if an abnormality is detected. For example, the notification unit can send an email to the owner when an abnormality is detected. For example, the notification unit can send an email to the owner when an abnormality is detected and provide detailed information. The notification unit can also send urgent notifications using SMS. For example, the notification unit can send an SMS to the owner when an abnormality is detected and urge them to take prompt action. The notification unit can also notify the owner using app notifications. For example, the notification unit can send an app notification to the owner when an abnormality is detected and provide detailed information. This allows the notification unit to quickly notify the owner when an abnormality is detected, enabling early response.

[0033] The storage unit can store monitoring data in the cloud and protect it by encryption. For example, the storage unit can store monitoring data in the cloud to prevent data loss. The storage unit can also encrypt and protect the monitoring data. For example, the storage unit can encrypt the monitoring data to protect it from unauthorized access. Furthermore, the storage unit can periodically back up the monitoring data. For example, the storage unit can periodically back up the monitoring data to prevent data loss. In this way, the storage unit can prevent data leaks by securely storing and protecting the monitoring data.

[0034] The storage unit can periodically back up the monitoring data. For example, the storage unit can periodically back up the monitoring data. For example, the storage unit can back up the monitoring data daily to prevent data loss. The storage unit can also back up the monitoring data weekly. For example, the storage unit can back up the monitoring data weekly to prevent data loss. The storage unit can also back up the monitoring data monthly. For example, the storage unit can back up the monitoring data monthly to prevent data loss. In this way, the storage unit can perform regular backups to prevent data loss.

[0035] The collection unit can estimate the pet's emotions and adjust the monitoring frequency based on the estimated pet's emotions. For example, if the pet is feeling stressed, the collection unit increases the monitoring frequency to collect detailed data. For example, if the pet is feeling stressed, the collection unit can set the monitoring frequency to every hour to collect detailed data. Furthermore, if the pet is relaxed, the collection unit can decrease the monitoring frequency to collect the minimum necessary data. For example, if the pet is relaxed, the collection unit can set the monitoring frequency to once a day to collect the minimum necessary data. Furthermore, if the pet is excited, the collection unit can set the monitoring frequency to a medium level to record the behavioral pattern in detail. For example, if the pet is excited, the collection unit can set the monitoring frequency to every three hours to record the behavioral pattern in detail. This allows the collection unit to adjust the monitoring frequency according to the pet's emotions, thereby enabling more appropriate data collection.

[0036] The collection unit can start collecting detailed data when it detects a specific behavioral pattern of the pet. For example, if the pet behaves abnormally, the collection unit increases the camera resolution to record detailed video. For example, if the pet behaves abnormally, the collection unit can set the camera resolution to 4K to record detailed video. Furthermore, if the pet frequently licks a specific spot, the collection unit can record the temperature and humidity of that spot in detail. For example, if the pet frequently licks a specific spot, the collection unit can record the temperature of that spot every hour and the humidity every hour. Furthermore, if the pet makes an abnormal sound, the collection unit can increase the sensitivity of the audio sensor to collect detailed audio data. For example, if the pet makes an abnormal sound, the collection unit can set the sensitivity of the audio sensor to maximum and collect detailed audio data. In this way, the collection unit can collect detailed data when it detects a specific behavioral pattern, thereby enabling early detection of abnormalities.

[0037] The data collection unit can automatically switch between different sensors depending on the pet's health condition. For example, if the pet's body temperature is high, the data collection unit will prioritize collecting data from the temperature sensor. For example, if the pet's body temperature is high, the data collection unit can use the temperature sensor to collect detailed body temperature data. The data collection unit can also prioritize collecting data from the motion sensor if the pet is not moving much. For example, if the pet is not moving much, the data collection unit can use the motion sensor to collect detailed motion data. The data collection unit can also prioritize collecting data from the sound sensor if the pet is breathing heavily. For example, if the pet is breathing heavily, the data collection unit can use the sound sensor to collect detailed respiratory sound data. This allows the data collection unit to use the most appropriate sensor according to the pet's health condition, enabling more accurate data collection.

[0038] The data collection unit can estimate the pet's emotions and select the types of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit can prioritize collecting heart rate and respiratory rate data. For example, if the pet is stressed, the data collection unit can collect heart rate data and respiratory rate data in detail. The data collection unit can also prioritize collecting body temperature and movement data if the pet is relaxed. For example, if the pet is relaxed, the data collection unit can collect body temperature data and movement data in detail. The data collection unit can also prioritize collecting sounds and movements if the pet is excited. For example, if the pet is excited, the data collection unit can collect sound data and movement data in detail. This allows the data collection unit to select the types of data to collect according to the pet's emotions, enabling more appropriate data collection.

[0039] The collection unit can simultaneously collect environmental data around the pet. For example, the collection unit can periodically record the temperature around the pet and analyze factors for changes in the pet's physical condition. For example, the collection unit can record the temperature around the pet every hour and analyze factors for changes in the pet's physical condition. The collection unit can also periodically record the humidity around the pet and analyze factors for changes in the pet's physical condition. For example, the collection unit can record the humidity around the pet every hour and analyze factors for changes in the pet's physical condition. The collection unit can also periodically record the volume around the pet and analyze factors for stress. For example, the collection unit can record the volume around the pet every hour and analyze factors for stress. In this way, the collection unit can more easily analyze factors for changes in the pet's physical condition by collecting environmental data around the pet.

[0040] The collection unit can monitor the pet's food intake and water intake and collect data. The collection unit, for example, can periodically record the pet's food intake and analyze factors for changes in physical condition. For example, the collection unit can record the pet's food intake daily and analyze factors for changes in physical condition. The collection unit can also periodically record the pet's water intake and analyze factors for changes in physical condition. For example, the collection unit can record the pet's water intake daily and analyze factors for changes in physical condition. The collection unit can also periodically record the pet's mealtimes and analyze factors for changes in physical condition. For example, the collection unit can record the pet's mealtimes daily and analyze factors for changes in physical condition. In this way, the collection unit can more easily analyze factors for changes in physical condition by monitoring the pet's food intake and water intake.

[0041] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated pet's emotions. For example, if the pet is feeling stressed, the analysis unit prioritizes analyzing stress-related data. For example, if the pet is feeling stressed, the analysis unit can prioritize analyzing stress-related data and detect abnormalities. The analysis unit can also prioritize analyzing relaxation-related data when the pet is relaxed. For example, if the pet is relaxed, the analysis unit can prioritize analyzing relaxation-related data and detect abnormalities. The analysis unit can also prioritize analyzing excitement-related data when the pet is excited. For example, if the pet is excited, the analysis unit can prioritize analyzing excitement-related data and detect abnormalities. This allows the analysis unit to adjust the analysis algorithm according to the pet's emotions, enabling more accurate analysis.

[0042] The analysis unit can detect abnormalities by comparing with the pet's past health data. For example, the analysis unit can detect abnormal body temperature changes by comparing with the pet's past body temperature data. For example, the analysis unit can detect abnormal body temperature changes by comparing with the pet's past body temperature data. The analysis unit can also detect abnormal movement patterns by comparing with the pet's past movement data. For example, the analysis unit can detect abnormal movement patterns by comparing with the pet's past movement data. The analysis unit can also detect abnormal eating patterns by comparing with the pet's past dietary data. For example, the analysis unit can detect abnormal eating patterns by comparing with the pet's past dietary data. In this way, the analysis unit can detect abnormalities early by comparing with past data.

[0043] The analysis unit can learn the pet's behavior patterns and detect abnormal behavior in real time. For example, the analysis unit can learn the pet's normal movement patterns and detect abnormal movements in real time. The analysis unit can also learn the pet's normal eating patterns and detect abnormal eating behavior in real time. For example, the analysis unit can learn the pet's normal eating patterns and detect abnormal eating behavior in real time. The analysis unit can also learn the pet's normal vocal patterns and detect abnormal vocalizations in real time. For example, the analysis unit can learn the pet's normal vocalization patterns and detect abnormal vocalizations in real time. In this way, the analysis unit can detect abnormal behavior in real time by learning behavior patterns.

[0044] The analysis unit can estimate the pet's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can prioritize displaying stress-related analysis results. Similarly, if the pet is relaxed, the analysis unit can prioritize displaying relaxation-related analysis results. Similarly, if the pet is excited, the analysis unit can prioritize displaying excitement-related analysis results. In this way, the analysis unit can prioritize important information by determining the priority of the analysis results according to the pet's emotions.

[0045] The analysis unit can analyze not only the pet's physical condition but also its stress level and happiness level. For example, the analysis unit can analyze the pet's heart rate and respiratory rate to assess its stress level. For example, the analysis unit can analyze the pet's heart rate data to assess its stress level. The analysis unit can also analyze the pet's movements and sounds to assess its happiness level. For example, the analysis unit can analyze the pet's movement data to assess its happiness level. The analysis unit can also analyze the pet's food and water intake to assess its physical condition and stress level. For example, the analysis unit can analyze the pet's food data to assess its physical condition and stress level. As a result, the analysis unit can analyze not only the pet's physical condition but also its stress level and happiness level, enabling comprehensive health management.

[0046] The analysis unit can automatically refer to relevant past data when it detects abnormal behavior in a pet. For example, when it detects an abnormal change in body temperature, it can automatically refer to past body temperature data. For example, when it detects an abnormal change in body temperature, it can automatically refer to past body temperature data and identify the cause of the abnormality. The analysis unit can also automatically refer to past movement data when it detects an abnormal movement pattern. For example, when it detects an abnormal movement pattern, it can automatically refer to past movement data and identify the cause of the abnormality. The analysis unit can also automatically refer to past feeding data when it detects abnormal eating behavior. For example, when it detects abnormal eating behavior, it can automatically refer to past feeding data and identify the cause of the abnormality. As a result, the analysis unit can perform more accurate analysis by referring to past data when it detects abnormal behavior.

[0047] The notification unit can select different notification methods depending on the type of abnormality. For example, in the case of a minor abnormality, the notification unit sends an app notification. For example, if a minor abnormality is detected, the notification unit can send an app notification to inform the owner of the situation. The notification unit can also send an SMS in the case of a moderate abnormality. For example, if a moderate abnormality is detected, the notification unit can send an SMS to urge the owner to take a prompt action. The notification unit can also send an email and an app notification simultaneously in the case of a severe abnormality. For example, if a severe abnormality is detected, the notification unit can send an email and an app notification simultaneously to urge the owner to take an emergency action. This allows the notification unit to select the optimal notification method depending on the type of abnormality, enabling a prompt and appropriate response.

[0048] The notification unit can suggest specific countermeasures to the owner when abnormal behavior of the pet is detected. For example, the notification unit can suggest a cooling method when an abnormal change in body temperature is detected. For example, the notification unit can suggest a cooling method for lowering the pet's body temperature when an abnormal change in body temperature is detected. The notification unit can also suggest a resting method when an abnormal movement pattern is detected. For example, the notification unit can suggest a method for resting the pet when an abnormal movement pattern is detected. The notification unit can also suggest a method for changing the pet's diet when abnormal eating behavior is detected. For example, the notification unit can suggest a method for changing the pet's diet when abnormal eating behavior is detected. In this way, the notification unit can suggest a specific countermeasure when abnormal behavior is detected, allowing the owner to respond quickly.

[0049] The notification unit can also notify the owner's family and pet hotel staff when abnormal behavior of the pet is detected. For example, the notification unit notifies the owner's family when an abnormal change in body temperature is detected. For example, the notification unit can notify the owner's family when an abnormal change in body temperature is detected and prompt them to take a prompt action. The notification unit can also notify pet hotel staff when an abnormal movement pattern is detected. For example, the notification unit can notify pet hotel staff when an abnormal movement pattern is detected and prompt them to take a prompt action. The notification unit can also notify the owner's family and pet hotel staff when abnormal eating behavior is detected. For example, the notification unit can notify the owner's family and pet hotel staff when abnormal eating behavior is detected and prompt them to take a prompt action. In this way, the notification unit can notify multiple parties involved when abnormal behavior is detected, enabling a prompt response.

[0050] The notification unit can automatically contact a relevant veterinarian when it detects abnormal behavior of a pet. The notification unit can automatically contact a veterinarian when it detects, for example, an abnormal change in body temperature. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal change in body temperature and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects an abnormal movement pattern. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal movement pattern and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects abnormal eating behavior. For example, the notification unit can automatically contact a veterinarian when it detects abnormal eating behavior and prompt professional treatment. In this way, the notification unit can automatically contact a veterinarian when it detects abnormal behavior, thereby enabling prompt professional treatment.

[0051] The storage unit can automatically classify the pet's health data and store it in an easily searchable format. The storage unit, for example, automatically classifies the pet's body temperature data and stores it in an easily searchable format. For example, the storage unit can classify the pet's body temperature data by date and store it in an easily searchable format. The storage unit can also automatically classify the pet's movement data and store it in an easily searchable format. For example, the storage unit can classify the pet's movement data by time and store it in an easily searchable format. The storage unit can also automatically classify the pet's diet data and store it in an easily searchable format. For example, the storage unit can classify the pet's diet data by type and store it in an easily searchable format. In this way, the storage unit can automatically classify the health data and store it in an easily searchable format, thereby enabling the required data to be obtained quickly.

[0052] When storing pet data, the storage unit can apply different encryption levels depending on the importance of the data. For example, the storage unit can apply a high encryption level to important health data. For example, the storage unit can apply AES-256 encryption to the pet's important health data to ensure high security. The storage unit can also apply a medium encryption level to general behavioral data. For example, the storage unit can apply AES-128 encryption to the pet's general behavioral data to ensure appropriate security. The storage unit can also apply a low encryption level to temporary data. For example, the storage unit can apply SHA-256 encryption to the pet's temporary data to ensure basic security. In this way, the storage unit can ensure data security by applying an encryption level depending on the importance of the data.

[0053] When storing pet data, the storage unit can link some of the data with a pet health management app. For example, the storage unit can link the pet's body temperature data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's body temperature data to the health management app, allowing the owner to check the body temperature data through the app. The storage unit can also link the pet's movement data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's movement data to the health management app, allowing the owner to check the movement data through the app. The storage unit can also link the pet's diet data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's diet data to the health management app, allowing the owner to check the diet data through the app. In this way, the storage unit can link with the health management app to allow the owner to easily check the data.

[0054] When storing pet data, the storage unit can share a portion of the data with research institutions related to pet health management. For example, the storage unit can share pet body temperature data with research institutions to help with health management research. For example, the storage unit can transmit pet body temperature data to research institutions to help with health management research. The storage unit can also share pet movement data with research institutions to help with health management research. For example, the storage unit can transmit pet movement data to research institutions to help with health management research. The storage unit can also share pet diet data with research institutions to help with health management research. For example, the storage unit can transmit pet diet data to research institutions to help with health management research. In this way, the storage unit can share data with research institutions to help with research on pet health management.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When analyzing the pet's physical condition data, the analysis unit can compare it with the pet's past health data to detect abnormalities. For example, the analysis unit can compare the pet's past body temperature data with its current body temperature data to detect abnormal changes in body temperature. The analysis unit can also compare the pet's past movement data with its current movement data to detect abnormal movement patterns. Furthermore, the analysis unit can compare the pet's past dietary data with its current dietary data to detect abnormal dietary patterns. This allows the analysis unit to detect abnormalities early by comparing it with past data.

[0057] The notification unit can notify the owner's family and pet hotel staff when it detects abnormal behavior of the pet. For example, when it detects an abnormal change in body temperature, the notification unit can notify the owner's family and prompt them to take action quickly. The notification unit can also notify pet hotel staff when it detects an abnormal movement pattern. Furthermore, the notification unit can also notify the owner's family and pet hotel staff when it detects abnormal eating behavior. In this way, the notification unit can notify multiple parties when it detects abnormal behavior, enabling a quick response.

[0058] When storing pet data, the storage unit can link some of the data with a pet health management app. For example, the storage unit can send the pet's body temperature data to the health management app, allowing the owner to check the body temperature data through the app. The storage unit can also send the pet's movement data to the health management app, allowing the owner to check the movement data through the app. The storage unit can also send the pet's diet data to the health management app, allowing the owner to check the diet data through the app. In this way, the storage unit can link with the health management app, allowing the owner to easily check the data.

[0059] The analysis unit can learn the behavioral patterns of a pet and detect abnormal behavior in real time. For example, the analysis unit can learn the normal movement patterns of a pet and detect abnormal movements in real time. The analysis unit can also learn the normal eating patterns of a pet and detect abnormal eating behavior in real time. Furthermore, the analysis unit can learn the normal sound patterns of a pet and detect abnormal sounds in real time. In this way, the analysis unit can detect abnormal behavior in real time by learning behavioral patterns.

[0060] The notification unit can automatically contact a relevant veterinarian when it detects abnormal behavior of a pet. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal change in body temperature and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects an abnormal movement pattern. Furthermore, the notification unit can automatically contact a veterinarian when it detects abnormal eating behavior. In this way, the notification unit can automatically contact a veterinarian when it detects abnormal behavior, allowing for prompt professional treatment.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects data using a camera or sensor for monitoring the pet's condition. For example, an infrared camera, a temperature sensor, a motion sensor, etc. can be used to monitor the pet's condition in detail. An infrared camera can be used to monitor the pet's body temperature, and a temperature sensor can be used to detect changes in the pet's body temperature. A motion sensor can also be used to monitor the pet's movements and detect abnormal movements. Step 2: The analysis unit analyzes the data collected by the collection unit and checks the pet's physical condition. For example, the pet's physical condition can be checked in detail by combining techniques such as image analysis, audio analysis, and movement analysis. Image analysis can be used to analyze the pet's facial expressions and movements, and audio analysis can be used to analyze the pet's cries and breathing sounds. Movement analysis can also be used to analyze the pet's movement patterns and detect abnormal behavior. Step 3: The notification unit notifies the owner if an abnormality is detected by the analysis unit. For example, the owner can be notified promptly using methods such as email, SMS, or app notification. When an abnormality is detected, an email can be sent to the owner to provide detailed information. Emergency notifications can also be sent using SMS. Step 4: The storage unit stores the data collected by the collection unit. For example, the monitoring data can be stored in the cloud and protected by encryption. The monitoring data can be backed up regularly to prevent data loss. The monitoring data can also be automatically categorized and stored in a format that is easy to search.

[0063] (Example 2) A pet health management system according to an embodiment of the present invention uses multimodal AI to monitor pet health and support pet owners in managing their pets' health. This system monitors pets 24 hours a day using cameras and sensors, and multimodal AI analyzes the monitoring data to monitor the pet's health. If an abnormality is detected, the owner is notified. The monitoring data is stored in the cloud and provided to the pet industry via subscription. For example, if a pet licks a foreign object, the AI ​​detects this behavior and notifies the owner. Furthermore, if the pet's behavior is unusual, the AI ​​detects the abnormality and notifies the owner. This allows owners to respond quickly to changes in their pet's health. Furthermore, pet hotels and veterinarians can use this system to monitor pets 24 hours a day, thereby streamlining pet health management and reducing their workload. This system not only supports pet health management but also contributes to the efficiency of the entire pet industry. By accumulating monitoring data and providing it to the pet industry via subscription, industry-wide knowledge is accumulated and pet health management is improved. This allows pet owners to respond quickly to changes in their pet's health, allowing for efficient health management of pets. It also reduces the workload of pet hotels and veterinarians, contributing to the efficiency of the entire pet industry.

[0064] A pet health management system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a storage unit. The collection unit collects data using a camera or sensor for monitoring the pet's condition. The collection unit can monitor the pet's condition in detail using, for example, an infrared camera, a temperature sensor, a motion sensor, etc. For example, the collection unit can monitor the pet's body temperature using an infrared camera and detect changes in the pet's body temperature using a temperature sensor. The collection unit can also monitor the pet's movements and detect abnormal movements using a motion sensor. The analysis unit analyzes the data collected by the collection unit to check the pet's health. The analysis unit can check the pet's health in detail using, for example, a combination of techniques such as image analysis, audio analysis, and motion analysis. For example, the analysis unit can analyze the pet's facial expressions and movements using image analysis, and the pet's cries and breathing sounds using audio analysis. The analysis unit can also analyze the pet's movement patterns using motion analysis to detect abnormal movements. The notification unit notifies the owner when an abnormality is detected by the analysis unit. The notification unit can quickly notify the owner using, for example, email, SMS, app notification, etc. For example, the notification unit can send an email to the owner to provide detailed information when an abnormality is detected. It can also send emergency notifications using SMS. The storage unit stores the data collected by the collection unit. For example, the storage unit can store the monitoring data in the cloud and protect it by encrypting it. For example, the storage unit can regularly back up the monitoring data to prevent data loss. The storage unit can also automatically classify the monitoring data and store it in an easy-to-search format. As a result, the pet health management system according to the embodiment can support pet health management by monitoring the pet's health 24 hours a day and notifying the owner when an abnormality is detected.

[0065] The collection unit can monitor the pet's condition using an infrared camera, a temperature sensor, or a motion sensor. The collection unit can, for example, monitor the pet's body temperature using an infrared camera. For example, the collection unit can monitor the pet's body temperature in real time using the infrared camera and detect abnormal body temperature changes. The collection unit can also monitor the pet's body temperature using a temperature sensor. For example, the collection unit can periodically measure the pet's body temperature using a temperature sensor and detect abnormal body temperature changes. The collection unit can also monitor the pet's movements using a motion sensor. For example, the collection unit can monitor the pet's movement patterns using a motion sensor and detect abnormal movements. In this way, the collection unit can monitor the pet's condition in detail by using a variety of sensors.

[0066] The analysis unit can check the physical condition of a pet by combining image analysis, audio analysis, and movement analysis. The analysis unit, for example, uses image analysis to analyze the facial expressions and movements of a pet. For example, the analysis unit can use image analysis to analyze the facial expressions of a pet and detect abnormal facial expressions. The analysis unit can also use audio analysis to analyze the cries and breathing sounds of a pet. For example, the analysis unit can use audio analysis to analyze the cries of a pet and detect abnormal cries. The analysis unit can also analyze the movement patterns of a pet using movement analysis. For example, the analysis unit can use movement analysis to analyze the movement patterns of a pet and detect abnormal movements. In this way, the analysis unit can more accurately check the physical condition of a pet by combining multiple analysis methods.

[0067] The notification unit can notify the owner when an abnormality is detected. For example, the notification unit can send an email to the owner when an abnormality is detected. For example, the notification unit can send an email to the owner when an abnormality is detected and provide detailed information. The notification unit can also send an emergency notification using SMS. For example, the notification unit can send an SMS to the owner when an abnormality is detected and urge the owner to take a prompt action. The notification unit can also notify the owner using an app notification. For example, the notification unit can send an app notification to the owner when an abnormality is detected and provide detailed information. This allows the notification unit to quickly notify the owner when an abnormality is detected, enabling an early action to be taken.

[0068] The storage unit can store the monitoring data in the cloud and encrypt and protect it. The storage unit, for example, stores the monitoring data in the cloud. For example, the storage unit can store the monitoring data in the cloud and prevent data loss. The storage unit can also encrypt and protect the monitoring data. For example, the storage unit can encrypt the monitoring data and protect it from unauthorized access. Furthermore, the storage unit can also regularly back up the monitoring data. For example, the storage unit can regularly back up the monitoring data and prevent data loss. In this way, the storage unit can safely store and protect the monitoring data, thereby preventing data leakage.

[0069] The storage unit can periodically back up the monitoring data. The storage unit, for example, periodically backs up the monitoring data. For example, the storage unit can back up the monitoring data daily to prevent data loss. The storage unit can also periodically back up the monitoring data. For example, the storage unit can back up the monitoring data weekly to prevent data loss. The storage unit can also periodically back up the monitoring data monthly. For example, the storage unit can back up the monitoring data monthly to prevent data loss. This allows the storage unit to periodically perform backups to prevent data loss.

[0070] The collection unit can estimate the pet's emotions and adjust the monitoring frequency based on the estimated pet's emotions. For example, if the pet is feeling stressed, the collection unit increases the monitoring frequency to collect detailed data. For example, if the pet is feeling stressed, the collection unit can set the monitoring frequency to every hour to collect detailed data. Furthermore, if the pet is relaxed, the collection unit can decrease the monitoring frequency to collect the minimum necessary data. For example, if the pet is relaxed, the collection unit can set the monitoring frequency to once a day to collect the minimum necessary data. Furthermore, if the pet is excited, the collection unit can set the monitoring frequency to a medium level to record the behavioral pattern in detail. For example, if the pet is excited, the collection unit can set the monitoring frequency to every three hours to record the behavioral pattern in detail. This allows the collection unit to adjust the monitoring frequency according to the pet's emotions, thereby enabling more appropriate data collection.

[0071] The collection unit can start collecting detailed data when it detects a specific behavioral pattern of the pet. For example, if the pet behaves abnormally, the collection unit increases the camera resolution to record detailed video. For example, if the pet behaves abnormally, the collection unit can set the camera resolution to 4K to record detailed video. Furthermore, if the pet frequently licks a specific spot, the collection unit can record the temperature and humidity of that spot in detail. For example, if the pet frequently licks a specific spot, the collection unit can record the temperature of that spot every hour and the humidity every hour. Furthermore, if the pet makes an abnormal sound, the collection unit can increase the sensitivity of the audio sensor to collect detailed audio data. For example, if the pet makes an abnormal sound, the collection unit can set the sensitivity of the audio sensor to maximum and collect detailed audio data. In this way, the collection unit can collect detailed data when it detects a specific behavioral pattern, thereby enabling early detection of abnormalities.

[0072] The collection unit can automatically switch between different sensors depending on the health condition of the pet. For example, if the pet's body temperature is high, the collection unit prioritizes collecting data from the temperature sensor. For example, if the pet's body temperature is high, the collection unit can use the temperature sensor to collect detailed body temperature data. Furthermore, if the pet is not moving much, the collection unit can also prioritize collecting data from the motion sensor. For example, if the pet is not moving much, the collection unit can use the motion sensor to collect detailed movement data. Furthermore, if the pet is breathing heavily, the collection unit can also prioritize collecting data from the audio sensor. For example, if the pet is breathing heavily, the collection unit can use the audio sensor to collect detailed breathing sound data. This allows the collection unit to use the optimal sensor depending on the pet's health condition, enabling more accurate data collection.

[0073] The collection unit can estimate the pet's emotions and select the type of data to collect based on the estimated pet's emotions. For example, when the pet is stressed, the collection unit prioritizes collection of heart rate and respiratory rate. For example, when the pet is stressed, the collection unit can collect detailed heart rate data and detailed respiratory rate data. The collection unit can also prioritize collection of body temperature and movement when the pet is relaxed. For example, when the pet is relaxed, the collection unit can collect detailed body temperature data and detailed movement data. The collection unit can also prioritize collection of voice and movement when the pet is excited. For example, when the pet is excited, the collection unit can collect detailed voice data and detailed movement data. This allows the collection unit to select the type of data to collect according to the pet's emotions, thereby enabling more appropriate data collection.

[0074] The collection unit can simultaneously collect environmental data around the pet. For example, the collection unit can periodically record the temperature around the pet and analyze factors for changes in the pet's physical condition. For example, the collection unit can record the temperature around the pet every hour and analyze factors for changes in the pet's physical condition. The collection unit can also periodically record the humidity around the pet and analyze factors for changes in the pet's physical condition. For example, the collection unit can record the humidity around the pet every hour and analyze factors for changes in the pet's physical condition. The collection unit can also periodically record the volume around the pet and analyze factors for stress. For example, the collection unit can record the volume around the pet every hour and analyze factors for stress. In this way, the collection unit can more easily analyze factors for changes in the pet's physical condition by collecting environmental data around the pet.

[0075] The collection unit can monitor the pet's food intake and water intake and collect data. The collection unit, for example, can periodically record the pet's food intake and analyze factors for changes in physical condition. For example, the collection unit can record the pet's food intake daily and analyze factors for changes in physical condition. The collection unit can also periodically record the pet's water intake and analyze factors for changes in physical condition. For example, the collection unit can record the pet's water intake daily and analyze factors for changes in physical condition. The collection unit can also periodically record the pet's mealtimes and analyze factors for changes in physical condition. For example, the collection unit can record the pet's mealtimes daily and analyze factors for changes in physical condition. In this way, the collection unit can more easily analyze factors for changes in physical condition by monitoring the pet's food intake and water intake.

[0076] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated pet's emotions. For example, if the pet is feeling stressed, the analysis unit prioritizes analyzing stress-related data. For example, if the pet is feeling stressed, the analysis unit can prioritize analyzing stress-related data and detect abnormalities. The analysis unit can also prioritize analyzing relaxation-related data when the pet is relaxed. For example, if the pet is relaxed, the analysis unit can prioritize analyzing relaxation-related data and detect abnormalities. The analysis unit can also prioritize analyzing excitement-related data when the pet is excited. For example, if the pet is excited, the analysis unit can prioritize analyzing excitement-related data and detect abnormalities. This allows the analysis unit to adjust the analysis algorithm according to the pet's emotions, enabling more accurate analysis.

[0077] The analysis unit can detect abnormalities by comparing with the pet's past health data. For example, the analysis unit can detect abnormal body temperature changes by comparing with the pet's past body temperature data. For example, the analysis unit can detect abnormal body temperature changes by comparing with the pet's past body temperature data. The analysis unit can also detect abnormal movement patterns by comparing with the pet's past movement data. For example, the analysis unit can detect abnormal movement patterns by comparing with the pet's past movement data. The analysis unit can also detect abnormal eating patterns by comparing with the pet's past dietary data. For example, the analysis unit can detect abnormal eating patterns by comparing with the pet's past dietary data. In this way, the analysis unit can detect abnormalities early by comparing with past data.

[0078] The analysis unit can learn the behavioral patterns of the pet and detect abnormal behavior in real time. The analysis unit, for example, learns the normal movement patterns of the pet and detects abnormal movements in real time. For example, the analysis unit can learn the normal movement patterns of the pet and detect abnormal movements in real time. The analysis unit can also learn the normal eating patterns of the pet and detect abnormal eating behavior in real time. For example, the analysis unit can learn the normal eating patterns of the pet and detect abnormal eating behavior in real time. The analysis unit can also learn the normal sound patterns of the pet and detect abnormal sounds in real time. For example, the analysis unit can learn the normal sound patterns of the pet and detect abnormal sounds in real time. In this way, the analysis unit can detect abnormal behavior in real time by learning behavioral patterns.

[0079] The analysis unit can estimate the pet's emotions and determine the priority of analysis results based on the estimated pet's emotions. For example, if the pet is feeling stressed, the analysis unit can prioritize and display stress-related analysis results. For example, if the pet is feeling stressed, the analysis unit can prioritize and display stress-related analysis results. Furthermore, if the pet is relaxed, the analysis unit can prioritize and display relaxation-related analysis results. For example, if the pet is relaxed, the analysis unit can prioritize and display relaxation-related analysis results. Furthermore, if the pet is excited, the analysis unit can prioritize and display excitement-related analysis results. For example, if the pet is excited, the analysis unit can prioritize and display excitement-related analysis results. In this way, the analysis unit can prioritize and provide important information by determining the priority of analysis results according to the pet's emotions.

[0080] The analysis unit can analyze not only the physical condition of a pet, but also its stress level and happiness level. The analysis unit, for example, analyzes the pet's heart rate and respiratory rate to evaluate the stress level. For example, the analysis unit can analyze the pet's heart rate data to evaluate the stress level. The analysis unit can also analyze the pet's movements and sounds to evaluate the happiness level. For example, the analysis unit can analyze the pet's movement data to evaluate the happiness level. The analysis unit can also analyze the pet's food and water intake to evaluate the physical condition and stress level. For example, the analysis unit can analyze the pet's diet data to evaluate the physical condition and stress level. This allows the analysis unit to analyze not only the pet's physical condition, but also its stress level and happiness level, enabling comprehensive health management.

[0081] The analysis unit can automatically refer to related past data when it detects abnormal behavior of a pet. For example, the analysis unit automatically refers to past body temperature data when it detects an abnormal change in body temperature. For example, the analysis unit can automatically refer to past body temperature data when it detects an abnormal change in body temperature to identify the cause of the abnormality. The analysis unit can also automatically refer to past movement data when it detects an abnormal movement pattern. For example, the analysis unit can automatically refer to past movement data when it detects an abnormal movement pattern to identify the cause of the abnormality. The analysis unit can also automatically refer to past diet data when it detects abnormal eating behavior. For example, the analysis unit can automatically refer to past diet data when it detects abnormal eating behavior to identify the cause of the abnormality. In this way, the analysis unit can perform a more accurate analysis by referring to past data when it detects abnormal behavior.

[0082] The notification unit can estimate the owner's emotions and adjust the content and timing of the notification based on the estimated owner's emotions. For example, if the owner is feeling stressed, the notification unit can send a concise and to-the-point notification. For example, if the owner is feeling stressed, the notification unit can send a concise and to-the-point notification to encourage a quick response. Furthermore, if the owner is relaxed, the notification unit can send a notification including detailed information. For example, if the owner is relaxed, the notification unit can send a notification including detailed information to explain the situation in detail. Furthermore, if the owner is in a hurry, the notification unit can send a notification that allows a quick response. For example, if the owner is in a hurry, the notification unit can send a notification that allows a quick response to encourage an immediate action. This allows the notification unit to adjust the content and timing of the notification according to the owner's emotions, enabling more appropriate notifications.

[0083] The notification unit can select different notification methods depending on the type of abnormality. For example, in the case of a minor abnormality, the notification unit sends an app notification. For example, if a minor abnormality is detected, the notification unit can send an app notification to inform the owner of the situation. The notification unit can also send an SMS in the case of a moderate abnormality. For example, if a moderate abnormality is detected, the notification unit can send an SMS to urge the owner to take a prompt action. The notification unit can also send an email and an app notification simultaneously in the case of a severe abnormality. For example, if a severe abnormality is detected, the notification unit can send an email and an app notification simultaneously to urge the owner to take an emergency action. This allows the notification unit to select the optimal notification method depending on the type of abnormality, enabling a prompt and appropriate response.

[0084] The notification unit can suggest specific countermeasures to the owner when abnormal behavior of the pet is detected. For example, the notification unit can suggest a cooling method when an abnormal change in body temperature is detected. For example, the notification unit can suggest a cooling method for lowering the pet's body temperature when an abnormal change in body temperature is detected. The notification unit can also suggest a resting method when an abnormal movement pattern is detected. For example, the notification unit can suggest a method for resting the pet when an abnormal movement pattern is detected. The notification unit can also suggest a method for changing the pet's diet when abnormal eating behavior is detected. For example, the notification unit can suggest a method for changing the pet's diet when abnormal eating behavior is detected. In this way, the notification unit can suggest a specific countermeasure when abnormal behavior is detected, allowing the owner to respond quickly.

[0085] The notification unit can estimate the owner's emotions and determine the priority of notifications based on the estimated owner's emotions. For example, if the owner is feeling stressed, the notification unit can prioritize sending important notifications. For example, if the owner is feeling stressed, the notification unit can prioritize sending important notifications to prompt the owner to take a prompt action. The notification unit can also prioritize sending detailed notifications if the owner is relaxed. For example, if the owner is relaxed, the notification unit can prioritize sending detailed notifications to explain the situation in detail. The notification unit can also prioritize sending notifications that can be quickly responded to if the owner is in a hurry. For example, if the owner is in a hurry, the notification unit can prioritize sending notifications that can be quickly responded to to prompt the owner to take immediate action. In this way, the notification unit can prioritize sending important notifications by determining the priority of notifications according to the owner's emotions.

[0086] The notification unit can also notify the owner's family and pet hotel staff when abnormal behavior of the pet is detected. For example, the notification unit notifies the owner's family when an abnormal change in body temperature is detected. For example, the notification unit can notify the owner's family when an abnormal change in body temperature is detected and prompt them to take a prompt action. The notification unit can also notify pet hotel staff when an abnormal movement pattern is detected. For example, the notification unit can notify pet hotel staff when an abnormal movement pattern is detected and prompt them to take a prompt action. The notification unit can also notify the owner's family and pet hotel staff when abnormal eating behavior is detected. For example, the notification unit can notify the owner's family and pet hotel staff when abnormal eating behavior is detected and prompt them to take a prompt action. In this way, the notification unit can notify multiple parties involved when abnormal behavior is detected, enabling a prompt response.

[0087] The notification unit can automatically contact a relevant veterinarian when it detects abnormal behavior of a pet. The notification unit can automatically contact a veterinarian when it detects, for example, an abnormal change in body temperature. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal change in body temperature and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects an abnormal movement pattern. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal movement pattern and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects abnormal eating behavior. For example, the notification unit can automatically contact a veterinarian when it detects abnormal eating behavior and prompt professional treatment. In this way, the notification unit can automatically contact a veterinarian when it detects abnormal behavior, thereby enabling prompt professional treatment.

[0088] The storage unit can estimate the owner's emotions and adjust the data storage period based on the estimated owner's emotions. For example, if the owner is feeling stressed, the storage unit can prioritize short-term data storage. For example, if the owner is feeling stressed, the storage unit can set the data storage period to one week and prioritize short-term data storage. The storage unit can also prioritize long-term data storage if the owner is relaxed. For example, if the owner is relaxed, the storage unit can set the data storage period to one year and prioritize long-term data storage. The storage unit can also prioritize minimum necessary data storage if the owner is in a hurry. For example, if the owner is in a hurry, the storage unit can set the data storage period to one day and prioritize minimum necessary data storage. In this way, the storage unit can adjust the data storage period according to the owner's emotions, thereby appropriately storing necessary data.

[0089] The storage unit can automatically classify the pet's health data and store it in an easily searchable format. The storage unit, for example, automatically classifies the pet's body temperature data and stores it in an easily searchable format. For example, the storage unit can classify the pet's body temperature data by date and store it in an easily searchable format. The storage unit can also automatically classify the pet's movement data and store it in an easily searchable format. For example, the storage unit can classify the pet's movement data by time and store it in an easily searchable format. The storage unit can also automatically classify the pet's diet data and store it in an easily searchable format. For example, the storage unit can classify the pet's diet data by type and store it in an easily searchable format. In this way, the storage unit can automatically classify the health data and store it in an easily searchable format, thereby enabling the required data to be obtained quickly.

[0090] When storing pet data, the storage unit can apply different encryption levels depending on the importance of the data. For example, the storage unit can apply a high encryption level to important health data. For example, the storage unit can apply AES-256 encryption to the pet's important health data to ensure high security. The storage unit can also apply a medium encryption level to general behavioral data. For example, the storage unit can apply AES-128 encryption to the pet's general behavioral data to ensure appropriate security. The storage unit can also apply a low encryption level to temporary data. For example, the storage unit can apply SHA-256 encryption to the pet's temporary data to ensure basic security. In this way, the storage unit can ensure data security by applying an encryption level depending on the importance of the data.

[0091] The storage unit can estimate the owner's emotions and adjust the data backup frequency based on the estimated owner's emotions. For example, if the owner is feeling stressed, the storage unit can back up data more frequently. For example, if the owner is feeling stressed, the storage unit can set the data backup frequency to every hour and back up data more frequently. Furthermore, if the owner is relaxed, the storage unit can back up data at a normal frequency. For example, if the owner is relaxed, the storage unit can set the data backup frequency to once a day and back up data at a normal frequency. Furthermore, if the owner is in a hurry, the storage unit can back up only the minimum necessary data. For example, if the owner is in a hurry, the storage unit can set the data backup frequency to once a week and back up the minimum necessary data. In this way, the storage unit can adjust the data backup frequency according to the owner's emotions, thereby appropriately backing up necessary data.

[0092] When storing pet data, the storage unit can link some of the data with a pet health management app. For example, the storage unit can link the pet's body temperature data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's body temperature data to the health management app, allowing the owner to check the body temperature data through the app. The storage unit can also link the pet's movement data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's movement data to the health management app, allowing the owner to check the movement data through the app. The storage unit can also link the pet's diet data with the health management app to allow the owner to easily check the data. For example, the storage unit can send the pet's diet data to the health management app, allowing the owner to check the diet data through the app. In this way, the storage unit can link with the health management app to allow the owner to easily check the data.

[0093] When storing pet data, the storage unit can share a portion of the data with research institutions related to pet health management. For example, the storage unit can share pet body temperature data with research institutions to help with health management research. For example, the storage unit can transmit pet body temperature data to research institutions to help with health management research. The storage unit can also share pet movement data with research institutions to help with health management research. For example, the storage unit can transmit pet movement data to research institutions to help with health management research. The storage unit can also share pet diet data with research institutions to help with health management research. For example, the storage unit can transmit pet diet data to research institutions to help with health management research. In this way, the storage unit can share data with research institutions to help with research on pet health management. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and storage unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the pet's condition using the camera 42 and sensors of the smart device 14 and collects data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to check the pet's physical condition. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the owner if an abnormality is detected. The storage unit, for example, stores the monitoring data in the database 24 of the data processing device 12 and backs it up to the cloud. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and storage 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 collection unit monitors the pet's condition using the camera 42 and sensors of the smart glasses 214 and collects data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to check the pet's physical condition. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the owner if an abnormality is detected. The storage unit, for example, stores the monitoring data in the database 24 of the data processing device 12 and backs it up to the cloud. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and storage unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit monitors the pet's condition using the camera 42 and sensors of the headset-type terminal 314 and collects data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to check the pet's physical condition. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and notifies the owner if an abnormality is detected. The storage unit, for example, stores the monitoring data in the database 24 of the data processing device 12 and backs it up to the cloud. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and storage unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the pet's condition using the camera 42 and sensors of the robot 414 and collects data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to check the pet's physical condition. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the owner if an abnormality is detected. The storage unit, for example, stores the monitoring data in the database 24 of the data processing device 12 and backs it up to the cloud.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] When analyzing the pet's physical condition data, the analysis unit can compare it with the pet's past health data to detect abnormalities. For example, the analysis unit can compare the pet's past body temperature data with its current body temperature data to detect abnormal changes in body temperature. The analysis unit can also compare the pet's past movement data with its current movement data to detect abnormal movement patterns. Furthermore, the analysis unit can compare the pet's past dietary data with its current dietary data to detect abnormal dietary patterns. This allows the analysis unit to detect abnormalities early by comparing it with past data.

[0096] The notification unit can notify the owner's family and pet hotel staff when it detects abnormal behavior of the pet. For example, when it detects an abnormal change in body temperature, the notification unit can notify the owner's family and prompt them to take action quickly. The notification unit can also notify pet hotel staff when it detects an abnormal movement pattern. Furthermore, the notification unit can also notify the owner's family and pet hotel staff when it detects abnormal eating behavior. In this way, the notification unit can notify multiple parties when it detects abnormal behavior, enabling a quick response.

[0097] When storing pet data, the storage unit can link some of the data with a pet health management app. For example, the storage unit can send the pet's body temperature data to the health management app, allowing the owner to check the body temperature data through the app. The storage unit can also send the pet's movement data to the health management app, allowing the owner to check the movement data through the app. The storage unit can also send the pet's diet data to the health management app, allowing the owner to check the diet data through the app. In this way, the storage unit can link with the health management app, allowing the owner to easily check the data.

[0098] The analysis unit can learn the behavioral patterns of a pet and detect abnormal behavior in real time. For example, the analysis unit can learn the normal movement patterns of a pet and detect abnormal movements in real time. The analysis unit can also learn the normal eating patterns of a pet and detect abnormal eating behavior in real time. Furthermore, the analysis unit can learn the normal sound patterns of a pet and detect abnormal sounds in real time. In this way, the analysis unit can detect abnormal behavior in real time by learning behavioral patterns.

[0099] The notification unit can automatically contact a relevant veterinarian when it detects abnormal behavior of a pet. For example, the notification unit can automatically contact a veterinarian when it detects an abnormal change in body temperature and prompt professional treatment. The notification unit can also automatically contact a veterinarian when it detects an abnormal movement pattern. Furthermore, the notification unit can automatically contact a veterinarian when it detects abnormal eating behavior. In this way, the notification unit can automatically contact a veterinarian when it detects abnormal behavior, allowing for prompt professional treatment.

[0100] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated pet's emotions. For example, if the pet is feeling stressed, the analysis unit can prioritize analyzing stress-related data and detect abnormalities. Also, if the pet is relaxed, the analysis unit can prioritize analyzing relaxation-related data. Furthermore, if the pet is excited, the analysis unit can prioritize analyzing excitement-related data. This allows the analysis unit to adjust the analysis algorithm according to the pet's emotions, enabling more accurate analysis.

[0101] The notification unit can estimate the owner's emotions and adjust the content and timing of the notification based on the estimated owner's emotions. For example, if the owner is feeling stressed, the notification unit can send a concise and to-the-point notification to prompt the owner to take action quickly. If the owner is relaxed, the notification unit can send a notification including detailed information to explain the situation in detail. Furthermore, if the owner is in a hurry, the notification unit can send a notification that allows the owner to take action quickly and prompt the owner to take action immediately. This allows the notification unit to adjust the content and timing of the notification according to the owner's emotions, enabling more appropriate notifications.

[0102] The storage unit can estimate the owner's emotions and adjust the data storage period based on the estimated owner's emotions. For example, if the owner is feeling stressed, the storage unit can set the data storage period to one week, prioritizing short-term data storage. If the owner is relaxed, the storage unit can set the data storage period to one year, prioritizing long-term data storage. Furthermore, if the owner is in a hurry, the storage unit can set the data storage period to one day, prioritizing minimum data storage. In this way, the storage unit can appropriately store necessary data by adjusting the data storage period according to the owner's emotions.

[0103] The storage unit can estimate the owner's emotions and adjust the data backup frequency based on the estimated owner's emotions. For example, if the owner is feeling stressed, the storage unit can set the data backup frequency to every hour to back up data frequently. If the owner is relaxed, the storage unit can set the data backup frequency to once a day to back up data at a normal frequency. If the owner is in a hurry, the storage unit can set the data backup frequency to once a week to back up the minimum amount of data necessary. In this way, the storage unit can appropriately back up necessary data by adjusting the data backup frequency according to the owner's emotions.

[0104] The analysis unit can estimate the pet's emotions and prioritize the analysis results based on the estimated pet's emotions. For example, if the pet is feeling stressed, the analysis unit can prioritize and display stress-related analysis results. Also, if the pet is relaxed, the analysis unit can prioritize and display relaxation-related analysis results. Furthermore, if the pet is excited, the analysis unit can prioritize and display excitement-related analysis results. In this way, the analysis unit can prioritize and provide important information by prioritizing the analysis results according to the pet's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection unit collects data using a camera or sensor for monitoring the pet's condition. For example, an infrared camera, a temperature sensor, a motion sensor, etc. can be used to monitor the pet's condition in detail. An infrared camera can be used to monitor the pet's body temperature, and a temperature sensor can be used to detect changes in the pet's body temperature. A motion sensor can also be used to monitor the pet's movements and detect abnormal movements. Step 2: The analysis unit analyzes the data collected by the collection unit and checks the pet's physical condition. For example, the pet's physical condition can be checked in detail by combining techniques such as image analysis, audio analysis, and movement analysis. Image analysis can be used to analyze the pet's facial expressions and movements, and audio analysis can be used to analyze the pet's cries and breathing sounds. Movement analysis can also be used to analyze the pet's movement patterns and detect abnormal behavior. Step 3: The notification unit notifies the owner if an abnormality is detected by the analysis unit. For example, the owner can be notified promptly using methods such as email, SMS, or app notification. When an abnormality is detected, an email can be sent to the owner to provide detailed information. Emergency notifications can also be sent using SMS. Step 4: The storage unit stores the data collected by the collection unit. For example, the monitoring data can be stored in the cloud and protected by encryption. The monitoring data can be backed up regularly to prevent data loss. The monitoring data can also be automatically categorized and stored in a format that is easy to search.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] 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.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] 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.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects data using a camera or sensor to monitor the pet's condition, The data collected by the aforementioned collection unit is analyzed by an analysis unit to check the pet's health condition, A notification unit that notifies the owner if an abnormality is detected by the aforementioned analysis unit, The collection unit comprises a storage unit for storing the data collected by the collection unit. A system characterized by:

2. The collecting unit Monitor your pet using an infrared camera or temperature sensor and motion sensor. The system of claim 1 .

3. The analysis unit Check your pet's health by combining image analysis, audio analysis, and motion analysis. The system of claim 1 .

4. The notification unit Notify the owner if an abnormality is detected The system of claim 1 .

5. The storage unit Store monitoring data in the cloud and encrypt and protect it. The system of claim 1 .

6. The storage unit Regularly back up monitoring data. The system of claim 1 .

7. The collecting unit It estimates the pet's emotions and adjusts the monitoring frequency based on the estimated emotions. The system of claim 1 .

8. The collecting unit When a specific behavioral pattern of your pet is detected, detailed data collection will begin. The system of claim 1 .

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