System

Abstract

An object of a system according to an embodiment is to appropriately monitor a health condition of a pet and determine the necessity of a hospital visit at an early stage.SOLUTION: A system includes a video acquisition unit, an analysis unit, a determination unit, and a notification unit. The video acquisition part acquires video data of a pet camera. The analysis part analyzes the video data of the pet camera acquired by the video acquisition part. The determination unit determines the necessity of a hospital visit on the basis of the result analyzed by the analysis unit. The notification unit notifies the user of the necessity of a hospital visit determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to properly monitor a pet's health and determine the need for early medical visits.

[0005] The system according to the embodiment aims to appropriately monitor the health condition of a pet and determine the need for early medical visits. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, an analysis unit, a determination unit, and a notification unit. The video acquisition unit acquires video data from a pet camera. The analysis unit analyzes the video data from the pet camera acquired by the video acquisition unit. The determination unit determines the need for a hospital visit based on the results of the analysis by the analysis unit. The notification unit notifies the user of the need for a hospital visit determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately monitor the health condition of a pet and determine the need for early medical treatment. [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 monitoring system according to an embodiment of the present invention uses video data from a pet camera to monitor the health of an animal, and a generating AI compares the data with case data to determine whether a visit to the hospital is necessary and notifies the user. This allows for early visits to the hospital, which can be expensive for veterinary care, and reduces veterinary medical expenses.

[0029] The pet health monitoring system according to the embodiment includes a video acquisition unit, an analysis unit, a determination unit, and a notification unit. The video acquisition unit acquires video data from a pet camera. For example, it records the pet's behavior, such as how the pet moves around the house, whether the pet is eating, and whether the pet is resting. The video acquisition unit can also collect the video data from the pet camera in real time. Furthermore, the video acquisition unit can adjust the resolution and frame rate of the pet camera to acquire more detailed video data. The analysis unit analyzes the video data from the pet camera acquired by the video acquisition unit. For example, the generation AI analyzes the pet's behavior, posture, facial expressions, etc. to determine whether there are any abnormalities. The generation AI can also compare the pet's video data with case data to detect signs of abnormalities. Furthermore, the generation AI can learn the pet's behavioral patterns and detect abnormal behavior early. The determination unit determines whether the pet needs to visit a vet based on the results of the analysis by the analysis unit. For example, if the pet exhibits abnormal behavior or if an abnormality matching the case data is detected, the determination unit determines that the pet needs to visit a vet. The determination unit can also comprehensively evaluate the pet's health condition and determine the need for hospital visits. Furthermore, the determination unit can also determine the need for hospital visits by taking into account past hospital visit history and treatment results. The notification unit notifies the user of the need for hospital visits determined by the determination unit. For example, the notification can be made via a smartphone app, email, or messaging service. Furthermore, the notification unit can also provide a detailed report of the pet's health condition when notifying the user of the need for hospital visits. Furthermore, the notification unit can also allow the user to select a notification method according to their preferences. Thus, the pet health monitoring system according to the embodiment monitors the pet's health condition, determines the need for hospital visits, and notifies the user, thereby encouraging early hospital visits and reducing veterinary medical expenses.

[0030] The image acquisition unit can scan handwritten answers and convert them into digital data. For example, the image acquisition unit reads handwritten answers using a scanner and saves them as image data. It then converts the image data into text data using OCR technology. The image acquisition unit can also photograph handwritten answers using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The image acquisition unit can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0031] The video acquisition unit has a function to automatically tag specific pet behaviors, which can include eating, excretion, and sleeping. The video acquisition unit, for example, uses a video data analysis algorithm to automatically detect and tag the pet's eating behavior. This allows the frequency and amount of food eaten to be recorded and health status to be monitored. The video acquisition unit also incorporates video analysis technology to detect excretion behavior and automatically tags the pet when it uses the litter box. This allows for understanding excretion patterns and early detection of abnormalities. The video acquisition unit also analyzes sleeping behavior and creates a system that automatically tags the pet's resting times. This allows for monitoring sleep duration and quality and evaluating health status. This allows for detailed monitoring of behavioral patterns by automatically tagging specific pet behaviors.

[0032] The video acquisition unit can use a drone to collect outdoor pet behavior data. For example, the video acquisition unit can be equipped with a camera on the drone, which captures images of the pet playing outdoors in real time. This allows for the collection of outdoor behavior data and the monitoring of the pet's health. The video acquisition unit can also set the drone to automatic tracking mode and develop a system that constantly tracks the pet as it moves. This allows for the collection of behavior data over a wide area. The video acquisition unit can also equip the drone with a GPS function, which can acquire the pet's location information in real time. This allows the range of outdoor behavior and movement patterns to be understood and the health condition to be evaluated. This allows for the collection of outdoor pet behavior data to enable the monitoring of a wider range of health conditions.

[0033] The video acquisition unit can link multiple pet cameras and integrate and analyze video data from different angles. For example, the video acquisition unit can install multiple pet cameras in a home and build a system that simultaneously collects video data from different angles. This allows for multi-angle monitoring of pet behavior. The video acquisition unit also introduces technology that integrates video data collected from multiple cameras and generates a 3D model. This allows for three-dimensional analysis of pet movements. The video acquisition unit also develops a system that integrates video data from different angles in real time and analyzes pet behavior in detail. This enables early detection of abnormal behavior. As a result, linking multiple pet cameras makes it possible to monitor pet behavior from multiple angles.

[0034] The analysis unit uses the generative AI to learn the pet's behavioral patterns over the long term, enabling early detection of abnormalities. For example, the analysis unit has the generative AI learn the pet's long-term behavioral data and model normal behavioral patterns. This allows for early detection of abnormal behavior. The analysis unit also builds a system that analyzes changes in the pet's health based on the long-term behavioral data. For example, it detects changes in behavioral patterns and identifies signs of abnormality. The analysis unit also uses the generative AI to continuously learn the pet's behavioral patterns and builds a predictive model for abnormal behavior. This allows for early detection of abnormalities. By learning the pet's behavioral patterns over the long term, this allows for early detection of abnormalities.

[0035] The analysis unit can detect changes in a pet's weight and body shape by analyzing the video data. For example, the analysis unit develops an algorithm to estimate a pet's weight from video data and monitors weight changes. This allows for early detection of weight gain or loss. The analysis unit also introduces video analysis technology to analyze changes in body shape and builds a system to detect abnormalities when a pet's body shape changes. The analysis unit also develops a system that simultaneously analyzes changes in a pet's weight and body shape based on video data. This enables a comprehensive evaluation of the pet's health condition. This allows for detailed monitoring of the pet's health condition by detecting changes in the pet's weight and body shape.

[0036] The analysis unit can integrate case data from different types of pets and perform analysis according to type. The analysis unit collects case data from different types of pets, such as dogs, cats, and birds, and builds an integrated database. This makes it possible to perform analysis according to type. The analysis unit also trains the generation AI to learn case data from different types of pets and develops an algorithm to identify abnormal behavior for each type. The analysis unit also builds a system to evaluate the health status of each type of pet based on the case data from different types of pets. This makes it possible to provide appropriate medical advice according to type. By integrating case data from different types of pets, analysis according to type becomes possible.

[0037] The analysis unit can integrate the results of the video data analysis with other health data to perform a comprehensive health assessment. The analysis unit, for example, integrates the results of the video data analysis with food records to build a system that comprehensively evaluates the health condition of a pet. For example, the health condition is evaluated based on the amount and frequency of meals. The analysis unit also integrates the results of the video data analysis with exercise data to develop a system that evaluates the activity level of a pet. This detects signs of insufficient or excessive exercise. The analysis unit also integrates the video data, food records, and exercise data to build a system that comprehensively evaluates the health condition of a pet. This enables more accurate health assessment. This enables comprehensive health assessment by integrating the results of the video data analysis with other health data.

[0038] The judgment unit can take past hospital visit history and treatment results into consideration when the generating AI determines the need for hospital visits. For example, the judgment unit has the generating AI learn past hospital visit history data and build a system that takes this into consideration when determining the need for hospital visits. This makes it possible to make appropriate decisions based on past treatment results. The judgment unit also integrates hospital visit history and treatment results to develop an algorithm that allows the generating AI to determine the need for hospital visits. For example, it evaluates the need for hospital visits based on the effectiveness of past treatments. The judgment unit also has the generating AI learn past hospital visit history and treatment results and build a system that comprehensively determines the need for hospital visits. This enables more accurate judgments. By taking past hospital visit history and treatment results into consideration, more accurate decisions can be made regarding hospital visits.

[0039] The judgment unit can perform risk assessment according to the pet's age and species when determining the need for vet visits. For example, the judgment unit has the generation AI learn the pet's age data and build a system that performs risk assessment according to age. This makes it possible to make appropriate vet visit decisions according to age. The judgment unit also develops an algorithm that performs risk assessment for each species based on the pet's species data. For example, it performs risk assessment according to species such as dogs and cats. The judgment unit also integrates age and species data and builds a system that performs risk assessment when the generation AI determines the need for vet visits. This enables more accurate vet visit decisions. This enables more accurate vet visit decisions by performing risk assessment according to the pet's age and species.

[0040] The judgment unit can take into account the congestion status and operating hours of local veterinary clinics when determining the need for a visit. For example, the judgment unit collects congestion status data of local veterinary clinics and builds a system that takes this into consideration when determining the need for a visit. This makes it possible to visit the clinic without overcrowding. The judgment unit also develops an algorithm that determines the need for a visit based on data on the clinic's operating hours. For example, it evaluates whether a visit is possible during operating hours. The judgment unit also integrates data on congestion status and operating hours and builds a system that the generation AI takes into consideration when determining the need for a visit. This allows it to suggest the optimal timing for a visit. This makes it possible to suggest the optimal timing for a visit by taking into account the congestion status and operating hours of local veterinary clinics.

[0041] The judgment unit takes into account the pet owner's schedule when determining the need for vet visits and can suggest the optimal timing for visits. For example, the judgment unit collects the owner's schedule data and builds a system that takes this into consideration when determining the need for vet visits. This makes it possible for vet visits to be tailored to the owner's convenience. The judgment unit also integrates the owner's schedule with the pet's health condition and develops an algorithm that allows the generative AI to suggest the optimal timing for vet visits. For example, it suggests visiting the pet when the owner is free. The judgment unit also builds a system that determines the need for vet visits based on the owner's schedule data. This makes it possible for vet visits to be tailored to the owner's convenience. By taking the owner's schedule into consideration, it makes it possible for vet visits to be tailored to the owner's convenience.

[0042] The notification unit can provide a detailed report on the pet's health condition when notifying the user of the need for a vet visit. The notification unit, for example, builds a system that generates a detailed report on the pet's health condition when notifying the user of the need for a vet visit and provides it to the user. For example, a report including details of abnormal behavior and analysis results is provided. The notification unit also develops an algorithm that automatically generates a health condition report and attaches it when notifying the user of the need for a vet visit. This allows the user to understand the pet's health condition in detail. The notification unit also builds a system that provides a detailed report on the pet's health condition when notifying the user of the need for a vet visit. This provides the user with information to make an appropriate decision. By providing a detailed report on the pet's health condition, it is possible to provide the user with information to make an appropriate decision.

[0043] The notification unit can customize the notification content and enable the user to select a notification method according to their preferences. The notification unit, for example, customizes the notification content and builds a system that allows the user to select a notification method according to their preferences. For example, the notification unit provides options such as voice notification, text notification, and image notification. The notification unit also develops an algorithm that automatically selects a notification method according to the user's preferences. For example, the notification unit suggests the optimal notification method based on past notification history. The notification unit also customizes the notification content and builds a system that allows the user to select a notification method according to their preferences. This allows the user to receive notifications in the method that is most convenient for them. This allows the user to receive notifications in the method that is most convenient for them by customizing the notification content and enabling the user to select a notification method according to their preferences.

[0044] The notification unit can make the notification content multilingual and accommodate users who speak different languages. The notification unit, for example, builds a system that makes the notification content multilingual and accommodates users who speak different languages. For example, notifications are provided in multiple languages, such as English, French, and Chinese. The notification unit also develops a multilingual notification system that allows users to select their preferred language. This makes it possible to accommodate users who speak different languages. The notification unit also develops an algorithm that automatically translates the notification content and provides notifications in different languages. This makes it possible to accommodate users who speak different languages. By making the notification content multilingual, it is possible to accommodate users who speak different languages.

[0045] The notification unit can provide the notification content together with an action plan according to the pet's health condition. The notification unit, for example, builds a system that includes an action plan according to the pet's health condition in the notification content. For example, specific advice such as dietary changes or recommended exercise is provided. The notification unit also develops an algorithm that automatically generates an action plan according to the health condition and includes it in the notification content. This allows the user to take specific measures. The notification unit also builds a system that includes an action plan according to the pet's health condition in the notification content. This provides information for the user to take appropriate measures. This allows the user to take specific measures by providing an action plan according to the pet's health condition.

[0046] The system periodically monitors the health of pets and recommends preventive care, thereby reducing medical costs. For example, the system may periodically monitor a pet's health and, if an abnormality is detected, recommend preventive care. This allows for early countermeasures to be taken. The system may also conduct regular health checks and develop algorithms that recommend preventive care. For example, the system may suggest regular exercise or a review of diet. The system may also build a system that recommends preventive care based on health monitoring data. This prevents serious illnesses and reduces medical costs. This allows for regular monitoring of a pet's health and recommends preventive care, thereby reducing medical costs.

[0047] The system analyzes historical animal medical expenses and suggests cost-effective treatments. For example, the system collects historical data on animal medical expenses and builds a system that suggests cost-effective treatments. For example, it suggests the optimal treatment based on past treatment costs and treatment effects. The system also analyzes medical expense history and develops an algorithm that automatically suggests cost-effective treatments. This allows users to select the optimal treatment. The system also builds a system that suggests cost-effective treatments based on historical data on animal medical expenses. This allows users to receive effective treatment while keeping medical expenses down. This makes it possible to keep medical expenses down by analyzing historical animal medical expenses and suggesting cost-effective treatments.

[0048] The system builds a community for sharing information about reducing veterinary medical costs with other pet owners. The system, for example, builds an online community for sharing information about reducing veterinary medical costs. For example, it provides a forum where pet owners can exchange information. The system also develops a system for sharing success stories and advice about reducing veterinary medical costs within the community. This provides information that other pet owners can use as a reference. The system also builds a community for sharing information about reducing veterinary medical costs, allowing pet owners to work together to find ways to reduce medical costs. By building a community for sharing information about reducing veterinary medical costs, pet owners can work together to find ways to reduce medical costs.

[0049] The system provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. The system, for example, builds a system that provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. For example, the information is provided through the insurance company's website or app. The system also works with pet insurance companies to develop a platform for sharing information on reducing veterinary medical expenses. This allows insureds to obtain information on reducing medical expenses. The system also builds a system that provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. This allows insureds to obtain information on reducing medical expenses. By providing information on reducing veterinary medical expenses in cooperation with pet insurance companies, insureds can obtain information on reducing medical expenses.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The pet health monitoring system can also learn your pet's preferences and habits based on their behavioral data and provide the optimal environment for them. For example, if your pet likes to rest in a particular place, it will suggest providing a comfortable bed in that location. If your pet is active at a certain time of day, it will provide toys tailored to that time. Furthermore, if your pet likes a certain food, it will recommend providing that food regularly. This will help optimize your pet's living environment and maintain its health.

[0052] The pet health monitoring system can also evaluate a pet's socialization based on its behavioral data and encourage interaction with other pets. For example, if a pet likes to play with other pets, it can suggest partnering with nearby pet owners to hold social events. If a pet feels lonely, it can recommend using a pet sitter or pet hotel. Furthermore, if a pet tends to get along with a particular pet, it can suggest regular interaction with that pet. This can improve a pet's socialization and maintain its mental health.

[0053] The pet health monitoring system can also evaluate your pet's exercise volume based on its behavioral data and provide an appropriate exercise plan. For example, if your pet is not getting enough exercise, it can suggest increasing daily walks and playtime. If your pet is overexerting, it can recommend increasing rest time. Furthermore, it can provide exercise plans tailored to your pet's age and physical condition to help maintain its health. This allows you to properly manage your pet's exercise volume and optimize its health.

[0054] The pet health monitoring system can also evaluate your pet's eating patterns based on your pet's behavioral data and provide an appropriate meal plan. For example, if your pet tends to eat at a certain time of day, it will suggest providing meals at that time. Also, if your pet has a preference for a certain food, it can recommend providing that food regularly. Furthermore, it can provide a meal plan based on your pet's age and physical condition to maintain its health. This allows you to properly manage your pet's eating patterns and optimize its health.

[0055] The pet health monitoring system can also evaluate a pet's stress level based on the pet's behavioral data and provide stress reduction measures. For example, if a pet feels stressed in a particular situation, it can provide advice on how to avoid that situation. It can also make suggestions on how to create a relaxing environment for the pet. It can also provide play and exercise plans to reduce the pet's stress level and maintain its health. This allows you to properly manage your pet's stress level and optimize its health.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The video acquisition unit acquires video data from the pet camera. For example, it records the pet's behavior, such as how the pet moves around the house, whether it is eating, whether it is resting, etc. The video acquisition unit can also collect video data from the pet camera in real time. Furthermore, the video acquisition unit can adjust the resolution and frame rate of the pet camera to acquire more detailed video data. Step 2: The analysis unit analyzes the video data from the pet camera acquired by the video acquisition unit. For example, the generation AI analyzes the pet's behavior, posture, facial expressions, etc. to determine whether there are any abnormalities. The generation AI can also compare the pet's video data with case data to detect signs of abnormalities. Furthermore, the generation AI can learn the pet's behavioral patterns and detect abnormal behavior early on. Step 3: The determination unit determines whether a visit to the hospital is necessary based on the results of the analysis by the analysis unit. For example, if the pet exhibits abnormal behavior or if an abnormality matching the case data is detected, it determines whether a visit to the hospital is necessary. The determination unit can also comprehensively evaluate the pet's health condition and determine whether a visit to the hospital is necessary. Furthermore, the determination unit can also determine whether a visit to the hospital is necessary by taking into account past visit history and treatment results. Step 4: The notification unit notifies the user of the need for a vet visit determined by the determination unit. For example, the notification may be sent via a smartphone app, email, or messaging service. The notification unit may also provide a detailed report of the pet's health status when notifying the user of the need for a vet visit. Furthermore, the notification unit may allow the user to select the notification method according to their preferences.

[0058] (Example 2) The pet health monitoring system according to an embodiment of the present invention uses video data from a pet camera to monitor the health of an animal, and a generating AI compares the data with case data to determine whether a visit to the hospital is necessary and notifies the user. This allows for early visits to the hospital, which can be expensive for veterinary care, and reduces veterinary medical expenses.

[0059] The pet health monitoring system according to the embodiment includes a video acquisition unit, an analysis unit, a determination unit, and a notification unit. The video acquisition unit acquires video data from a pet camera. For example, it records the pet's behavior, such as how the pet moves around the house, whether the pet is eating, and whether the pet is resting. The video acquisition unit can also collect the video data from the pet camera in real time. Furthermore, the video acquisition unit can adjust the resolution and frame rate of the pet camera to acquire more detailed video data. The analysis unit analyzes the video data from the pet camera acquired by the video acquisition unit. For example, the generation AI analyzes the pet's behavior, posture, facial expressions, etc. to determine whether there are any abnormalities. The generation AI can also compare the pet's video data with case data to detect signs of abnormalities. Furthermore, the generation AI can learn the pet's behavioral patterns and detect abnormal behavior early. The determination unit determines whether the pet needs to visit a vet based on the results of the analysis by the analysis unit. For example, if the pet exhibits abnormal behavior or if an abnormality matching the case data is detected, the determination unit determines that the pet needs to visit a vet. The determination unit can also comprehensively evaluate the pet's health condition and determine the need for hospital visits. Furthermore, the determination unit can also determine the need for hospital visits by taking into account past hospital visit history and treatment results. The notification unit notifies the user of the need for hospital visits determined by the determination unit. For example, the notification can be made via a smartphone app, email, or messaging service. Furthermore, the notification unit can also provide a detailed report of the pet's health condition when notifying the user of the need for hospital visits. Furthermore, the notification unit can also allow the user to select a notification method according to their preferences. Thus, the pet health monitoring system according to the embodiment monitors the pet's health condition, determines the need for hospital visits, and notifies the user, thereby encouraging early hospital visits and reducing veterinary medical expenses.

[0060] The image acquisition unit can scan handwritten answers and convert them into digital data. For example, the image acquisition unit reads handwritten answers using a scanner and saves them as image data. It then converts the image data into text data using OCR technology. The image acquisition unit can also photograph handwritten answers using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The image acquisition unit can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0061] The video acquisition unit has a function to automatically tag specific pet behaviors, which can include eating, excretion, and sleeping. The video acquisition unit, for example, uses a video data analysis algorithm to automatically detect and tag the pet's eating behavior. This allows the frequency and amount of food eaten to be recorded and health status to be monitored. The video acquisition unit also incorporates video analysis technology to detect excretion behavior and automatically tags the pet when it uses the litter box. This allows for understanding excretion patterns and early detection of abnormalities. The video acquisition unit also analyzes sleeping behavior and creates a system that automatically tags the pet's resting times. This allows for monitoring sleep duration and quality and evaluating health status. This allows for detailed monitoring of behavioral patterns by automatically tagging specific pet behaviors.

[0062] The video acquisition unit can use the emotion estimation function to estimate the emotional state from the pet's facial expressions and movements and collect the data. The video acquisition unit, for example, analyzes the pet's facial expressions from video data and develops an algorithm to estimate the emotional state. For example, it detects facial expressions of joy and sadness and collects that data. The video acquisition unit also analyzes the pet's movements and builds a system to estimate the emotional state. For example, it estimates emotions from the way the pet wags its tail or moves its ears and collects that data. The video acquisition unit also combines facial expression analysis and movement analysis to develop a system that more accurately estimates the pet's emotional state. This allows the collection of emotional data to be used in evaluating the pet's health. This makes it possible to monitor the pet's health in detail by estimating the pet's emotional state and collecting that data.

[0063] The video acquisition unit can use a drone to collect outdoor pet behavior data. For example, the video acquisition unit can be equipped with a camera on the drone, which captures images of the pet playing outdoors in real time. This allows for the collection of outdoor behavior data and the monitoring of the pet's health. The video acquisition unit can also set the drone to automatic tracking mode and develop a system that constantly tracks the pet as it moves. This allows for the collection of behavior data over a wide area. The video acquisition unit can also equip the drone with a GPS function, which can acquire the pet's location information in real time. This allows the range of outdoor behavior and movement patterns to be understood and the health condition to be evaluated. This allows for the collection of outdoor pet behavior data to enable the monitoring of a wider range of health conditions.

[0064] The video acquisition unit can link multiple pet cameras and integrate and analyze video data from different angles. For example, the video acquisition unit can install multiple pet cameras in a home and build a system that simultaneously collects video data from different angles. This allows for multi-angle monitoring of pet behavior. The video acquisition unit also introduces technology that integrates video data collected from multiple cameras and generates a 3D model. This allows for three-dimensional analysis of pet movements. The video acquisition unit also develops a system that integrates video data from different angles in real time and analyzes pet behavior in detail. This enables early detection of abnormal behavior. As a result, linking multiple pet cameras makes it possible to monitor pet behavior from multiple angles.

[0065] The video acquisition unit uses the emotion estimation function to provide audio guidance according to the pet's emotional state, thereby reducing the pet's stress. For example, the video acquisition unit uses the emotion estimation function to build a system that plays music with a relaxing effect when the pet is feeling stressed. This reduces the pet's stress. The video acquisition unit also adds a function that analyzes the pet's emotional state and records and plays back the owner's voice that gives a sense of security. This reduces the pet's anxiety. The video acquisition unit also develops a system that provides environmental sounds (e.g., the sound of waves, birds chirping) that the pet can relax to, based on the emotion estimation data. This reduces the pet's stress. This makes it possible to reduce the pet's stress by providing audio guidance according to the pet's emotional state.

[0066] The analysis unit uses the generative AI to learn the pet's behavioral patterns over the long term, enabling early detection of abnormalities. For example, the analysis unit has the generative AI learn the pet's long-term behavioral data and model normal behavioral patterns. This allows for early detection of abnormal behavior. The analysis unit also builds a system that analyzes changes in the pet's health based on the long-term behavioral data. For example, it detects changes in behavioral patterns and identifies signs of abnormality. The analysis unit also uses the generative AI to continuously learn the pet's behavioral patterns and builds a predictive model for abnormal behavior. This allows for early detection of abnormalities. By learning the pet's behavioral patterns over the long term, this allows for early detection of abnormalities.

[0067] The analysis unit can detect changes in a pet's weight and body shape by analyzing the video data. For example, the analysis unit develops an algorithm to estimate a pet's weight from video data and monitors weight changes. This allows for early detection of weight gain or loss. The analysis unit also introduces video analysis technology to analyze changes in body shape and builds a system to detect abnormalities when a pet's body shape changes. The analysis unit also develops a system that simultaneously analyzes changes in a pet's weight and body shape based on video data. This enables a comprehensive evaluation of the pet's health condition. This allows for detailed monitoring of the pet's health condition by detecting changes in the pet's weight and body shape.

[0068] The analysis unit can use the emotion estimation function to compare the pet's emotional state with case data and detect emotional abnormalities. For example, the analysis unit uses the emotion estimation function to build a system that analyzes the pet's emotional state and compares it with case data. This allows for early detection of emotional abnormalities. The analysis unit also develops an algorithm that identifies signs of abnormal behavior based on the pet's emotional state. For example, it detects signs of stress or anxiety. The analysis unit also integrates the emotion estimation data with case data to build a system that comprehensively evaluates emotional abnormalities. This allows for early detection of emotional abnormalities. This makes it possible to compare the pet's emotional state with case data and detect emotional abnormalities early.

[0069] The analysis unit can integrate case data from different types of pets and perform analysis according to type. The analysis unit collects case data from different types of pets, such as dogs, cats, and birds, and builds an integrated database. This makes it possible to perform analysis according to type. The analysis unit also trains the generation AI to learn case data from different types of pets and develops an algorithm to identify abnormal behavior for each type. The analysis unit also builds a system to evaluate the health status of each type of pet based on the case data from different types of pets. This makes it possible to provide appropriate medical advice according to type. By integrating case data from different types of pets, analysis according to type becomes possible.

[0070] The analysis unit can integrate the results of the video data analysis with other health data to perform a comprehensive health assessment. The analysis unit, for example, integrates the results of the video data analysis with food records to build a system that comprehensively evaluates the health condition of a pet. For example, the health condition is evaluated based on the amount and frequency of meals. The analysis unit also integrates the results of the video data analysis with exercise data to develop a system that evaluates the activity level of a pet. This detects signs of insufficient or excessive exercise. The analysis unit also integrates the video data, food records, and exercise data to build a system that comprehensively evaluates the health condition of a pet. This enables more accurate health assessment. This enables comprehensive health assessment by integrating the results of the video data analysis with other health data.

[0071] The analysis unit can use the emotion estimation function to provide health advice based on the pet's emotional state. The analysis unit, for example, uses the emotion estimation function to analyze the pet's emotional state and builds a system that provides health advice based on the results. For example, advice is given to reduce stress. The analysis unit also develops an algorithm that provides appropriate diet and exercise advice based on the pet's emotional state. This makes it possible to manage health according to the emotional state. The analysis unit also builds a system that provides advice on relaxation methods and play methods according to the pet's emotional state based on the emotion estimation data. This allows for comprehensive management of the pet's health condition. As a result, health advice based on the pet's emotional state can be provided, making health management more effective.

[0072] The judgment unit can take past hospital visit history and treatment results into consideration when the generating AI determines the need for hospital visits. For example, the judgment unit has the generating AI learn past hospital visit history data and build a system that takes this into consideration when determining the need for hospital visits. This makes it possible to make appropriate decisions based on past treatment results. The judgment unit also integrates hospital visit history and treatment results to develop an algorithm that allows the generating AI to determine the need for hospital visits. For example, it evaluates the need for hospital visits based on the effectiveness of past treatments. The judgment unit also has the generating AI learn past hospital visit history and treatment results and build a system that comprehensively determines the need for hospital visits. This enables more accurate judgments. By taking past hospital visit history and treatment results into consideration, more accurate decisions can be made regarding hospital visits.

[0073] The judgment unit can perform risk assessment according to the pet's age and species when determining the need for vet visits. For example, the judgment unit has the generation AI learn the pet's age data and build a system that performs risk assessment according to age. This makes it possible to make appropriate vet visit decisions according to age. The judgment unit also develops an algorithm that performs risk assessment for each species based on the pet's species data. For example, it performs risk assessment according to species such as dogs and cats. The judgment unit also integrates age and species data and builds a system that performs risk assessment when the generation AI determines the need for vet visits. This enables more accurate vet visit decisions. This enables more accurate vet visit decisions by performing risk assessment according to the pet's age and species.

[0074] The judgment unit can use the emotion estimation function to recommend a visit to the vet if the pet's emotional state is deteriorating. The judgment unit, for example, uses the emotion estimation function to analyze the pet's emotional state and build a system that recommends a visit to the vet if the emotional state is deteriorating. For example, a visit to the vet is recommended if stress or anxiety is increasing. The judgment unit also develops an algorithm that identifies signs of abnormal behavior based on the pet's emotional state and determines the need for a visit to the vet. This enables early detection of emotional abnormalities and recommendation of a visit to the vet. The judgment unit also builds a system that recommends a visit to the vet if the pet's emotional state is deteriorating based on the emotion estimation data. This allows early detection of emotional abnormalities and appropriate medical treatment. As a result, by recommending a visit to the vet even if the pet's emotional state is deteriorating, early detection of emotional abnormalities and appropriate medical treatment are possible.

[0075] The judgment unit can take into account the congestion status and operating hours of local veterinary clinics when determining the need for a visit. For example, the judgment unit collects congestion status data of local veterinary clinics and builds a system that takes this into consideration when determining the need for a visit. This makes it possible to visit the clinic without overcrowding. The judgment unit also develops an algorithm that determines the need for a visit based on data on the clinic's operating hours. For example, it evaluates whether a visit is possible during operating hours. The judgment unit also integrates data on congestion status and operating hours and builds a system that the generation AI takes into consideration when determining the need for a visit. This allows it to suggest the optimal timing for a visit. This makes it possible to suggest the optimal timing for a visit by taking into account the congestion status and operating hours of local veterinary clinics.

[0076] The judgment unit takes into account the pet owner's schedule when determining the need for vet visits and can suggest the optimal timing for visits. For example, the judgment unit collects the owner's schedule data and builds a system that takes this into consideration when determining the need for vet visits. This makes it possible for vet visits to be tailored to the owner's convenience. The judgment unit also integrates the owner's schedule with the pet's health condition and develops an algorithm that allows the generative AI to suggest the optimal timing for vet visits. For example, it suggests visiting the pet when the owner is free. The judgment unit also builds a system that determines the need for vet visits based on the owner's schedule data. This makes it possible for vet visits to be tailored to the owner's convenience. By taking the owner's schedule into consideration, it makes it possible for vet visits to be tailored to the owner's convenience.

[0077] The judgment unit uses the emotion estimation function to suggest relaxation methods based on the pet's emotional state, thereby reducing stress before a visit to the hospital. The judgment unit, for example, uses the emotion estimation function to analyze the pet's emotional state and build a system that suggests ways to relax before a visit to the hospital. For example, music with a relaxing effect is played. The judgment unit also develops an algorithm that provides a relaxing environment before a visit to the hospital based on the pet's emotional state. For example, the owner's voice that gives a sense of security is played. The judgment unit also builds a system that suggests relaxation methods according to the pet's emotional state based on the emotion estimation data. This reduces stress before a visit to the hospital. By suggesting relaxation methods based on the pet's emotional state, stress before a visit to the hospital can be reduced.

[0078] The notification unit can provide a detailed report on the pet's health condition when notifying the user of the need for a vet visit. The notification unit, for example, builds a system that generates a detailed report on the pet's health condition when notifying the user of the need for a vet visit and provides it to the user. For example, a report including details of abnormal behavior and analysis results is provided. The notification unit also develops an algorithm that automatically generates a health condition report and attaches it when notifying the user of the need for a vet visit. This allows the user to understand the pet's health condition in detail. The notification unit also builds a system that provides a detailed report on the pet's health condition when notifying the user of the need for a vet visit. This provides the user with information to make an appropriate decision. By providing a detailed report on the pet's health condition, it is possible to provide the user with information to make an appropriate decision.

[0079] The notification unit can customize the notification content and enable the user to select a notification method according to their preferences. The notification unit, for example, customizes the notification content and builds a system that allows the user to select a notification method according to their preferences. For example, the notification unit provides options such as voice notification, text notification, and image notification. The notification unit also develops an algorithm that automatically selects a notification method according to the user's preferences. For example, the notification unit suggests the optimal notification method based on past notification history. The notification unit also customizes the notification content and builds a system that allows the user to select a notification method according to their preferences. This allows the user to receive notifications in the method that is most convenient for them. This allows the user to receive notifications in the method that is most convenient for them by customizing the notification content and enabling the user to select a notification method according to their preferences.

[0080] The notification unit uses the emotion estimation function to adjust the notification content according to the user's emotional state, thereby reducing stress. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state and build a system to adjust the notification content. For example, if the user is feeling stressed, the notification unit notifies the user in kind words. The notification unit also develops an algorithm to customize the notification content based on the user's emotional state. This makes it possible to provide appropriate notifications according to the user's emotional state. The notification unit also builds a system to adjust the notification content according to the user's emotional state based on the emotion estimation data. This reduces the user's stress. As a result, the user's stress can be reduced by adjusting the notification content according to the user's emotional state.

[0081] The notification unit can make the notification content multilingual and accommodate users who speak different languages. The notification unit, for example, builds a system that makes the notification content multilingual and accommodates users who speak different languages. For example, notifications are provided in multiple languages, such as English, French, and Chinese. The notification unit also develops a multilingual notification system that allows users to select their preferred language. This makes it possible to accommodate users who speak different languages. The notification unit also develops an algorithm that automatically translates the notification content and provides notifications in different languages. This makes it possible to accommodate users who speak different languages. By making the notification content multilingual, it is possible to accommodate users who speak different languages.

[0082] The notification unit can provide the notification content together with an action plan according to the pet's health condition. The notification unit, for example, builds a system that includes an action plan according to the pet's health condition in the notification content. For example, specific advice such as dietary changes or recommended exercise is provided. The notification unit also develops an algorithm that automatically generates an action plan according to the health condition and includes it in the notification content. This allows the user to take specific measures. The notification unit also builds a system that includes an action plan according to the pet's health condition in the notification content. This provides information for the user to take appropriate measures. This allows the user to take specific measures by providing an action plan according to the pet's health condition.

[0083] The notification unit can use the emotion estimation function to include an encouraging message based on the user's emotional state in the notification. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state and build a system that includes an encouraging message in the notification. For example, if the user is feeling anxious, encouraging words are added. The notification unit also develops an algorithm that automatically generates an encouraging message based on the user's emotional state. This makes it possible to provide an appropriate message according to the user's emotional state. The notification unit also builds a system that includes an encouraging message in the notification based on the emotion estimation data. This reduces the user's stress. By including an encouraging message based on the user's emotional state in the notification, the user's stress can be reduced.

[0084] The system periodically monitors the health of pets and recommends preventive care, thereby reducing medical costs. For example, the system may periodically monitor a pet's health and, if an abnormality is detected, recommend preventive care. This allows for early countermeasures to be taken. The system may also conduct regular health checks and develop algorithms that recommend preventive care. For example, the system may suggest regular exercise or a review of diet. The system may also build a system that recommends preventive care based on health monitoring data. This prevents serious illnesses and reduces medical costs. This allows for regular monitoring of a pet's health and recommends preventive care, thereby reducing medical costs.

[0085] The system analyzes historical animal medical expenses and suggests cost-effective treatments. For example, the system collects historical data on animal medical expenses and builds a system that suggests cost-effective treatments. For example, it suggests the optimal treatment based on past treatment costs and treatment effects. The system also analyzes medical expense history and develops an algorithm that automatically suggests cost-effective treatments. This allows users to select the optimal treatment. The system also builds a system that suggests cost-effective treatments based on historical data on animal medical expenses. This allows users to receive effective treatment while keeping medical expenses down. This makes it possible to keep medical expenses down by analyzing historical animal medical expenses and suggesting cost-effective treatments.

[0086] The system uses an emotion estimation function to strengthen preventive care when a pet's emotional state is good. For example, the system uses the emotion estimation function to build a system that strengthens preventive care when a pet's emotional state is good. For example, health checks are performed when the emotional state is stable. The system also develops an algorithm that strengthens preventive care based on the pet's emotional state. This allows appropriate care to be provided when the emotional state is good. The system also builds a system that strengthens preventive care when a pet's emotional state is good based on the emotion estimation data. This allows the pet to maintain its health and reduce medical costs. By strengthening preventive care when the pet's emotional state is good, the pet can maintain its health and reduce medical costs.

[0087] The system builds a community for sharing information about reducing veterinary medical costs with other pet owners. The system, for example, builds an online community for sharing information about reducing veterinary medical costs. For example, it provides a forum where pet owners can exchange information. The system also develops a system for sharing success stories and advice about reducing veterinary medical costs within the community. This provides information that other pet owners can use as a reference. The system also builds a community for sharing information about reducing veterinary medical costs, allowing pet owners to work together to find ways to reduce medical costs. By building a community for sharing information about reducing veterinary medical costs, pet owners can work together to find ways to reduce medical costs.

[0088] The system provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. The system, for example, builds a system that provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. For example, the information is provided through the insurance company's website or app. The system also works with pet insurance companies to develop a platform for sharing information on reducing veterinary medical expenses. This allows insureds to obtain information on reducing medical expenses. The system also builds a system that provides information on reducing veterinary medical expenses in cooperation with pet insurance companies. This allows insureds to obtain information on reducing medical expenses. By providing information on reducing veterinary medical expenses in cooperation with pet insurance companies, insureds can obtain information on reducing medical expenses.

[0089] The system uses an emotion estimation function to propose a cost-effective care plan based on the emotional state of a pet. For example, the system uses the emotion estimation function to analyze the emotional state of a pet and build a system that proposes a cost-effective care plan. For example, appropriate care is provided when the emotional state is stable. The system also develops an algorithm that proposes a cost-effective care plan based on the emotional state of a pet. This provides appropriate care according to the emotional state. The system also builds a system that proposes a cost-effective care plan based on the emotional state of a pet based on the emotion estimation data. This maintains health and reduces medical costs. By proposing a cost-effective care plan based on the emotional state of a pet, it is possible to maintain health and reduce medical costs.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The pet health monitoring system can also learn your pet's preferences and habits based on their behavioral data and provide the optimal environment for them. For example, if your pet likes to rest in a particular place, it will suggest providing a comfortable bed in that location. If your pet is active at a certain time of day, it will provide toys tailored to that time. Furthermore, if your pet likes a certain food, it will recommend providing that food regularly. This will help optimize your pet's living environment and maintain its health.

[0092] The pet health monitoring system can also evaluate a pet's socialization based on its behavioral data and encourage interaction with other pets. For example, if a pet likes to play with other pets, it can suggest partnering with nearby pet owners to hold social events. If a pet feels lonely, it can recommend using a pet sitter or pet hotel. Furthermore, if a pet tends to get along with a particular pet, it can suggest regular interaction with that pet. This can improve a pet's socialization and maintain its mental health.

[0093] The pet health monitoring system can also evaluate your pet's exercise volume based on its behavioral data and provide an appropriate exercise plan. For example, if your pet is not getting enough exercise, it can suggest increasing daily walks and playtime. If your pet is overexerting, it can recommend increasing rest time. Furthermore, it can provide exercise plans tailored to your pet's age and physical condition to help maintain its health. This allows you to properly manage your pet's exercise volume and optimize its health.

[0094] The pet health monitoring system can also evaluate your pet's eating patterns based on your pet's behavioral data and provide an appropriate meal plan. For example, if your pet tends to eat at a certain time of day, it will suggest providing meals at that time. Also, if your pet has a preference for a certain food, it can recommend providing that food regularly. Furthermore, it can provide a meal plan based on your pet's age and physical condition to maintain its health. This allows you to properly manage your pet's eating patterns and optimize its health.

[0095] The pet health monitoring system can also evaluate a pet's stress level based on the pet's behavioral data and provide stress reduction measures. For example, if a pet feels stressed in a particular situation, it can provide advice on how to avoid that situation. It can also make suggestions on how to create a relaxing environment for the pet. It can also provide play and exercise plans to reduce the pet's stress level and maintain its health. This allows you to properly manage your pet's stress level and optimize its health.

[0096] The pet health monitoring system can also provide a care plan tailored to the pet's emotional state based on the pet's emotional state. For example, if the pet is feeling anxious, it can suggest playing relaxing music. If the pet is happy, it can provide a play or exercise plan to maintain that emotional state. Furthermore, it can adjust the pet's diet and environment according to the pet's emotional state to maintain its health. This allows the pet's emotional state to be properly managed and its health to be optimized.

[0097] The pet health monitoring system can also provide toys that correspond to the pet's emotional state based on the pet's emotional state. For example, if the pet is excited, toys that help the pet release its energy can be suggested. If the pet is relaxed, quiet toys can be provided to help the pet maintain that state. Furthermore, toys that correspond to the pet's emotional state can be periodically updated to maintain the pet's health. This allows the pet's emotional state to be properly managed and its health to be optimized.

[0098] The pet health monitoring system can also adjust the environment based on the pet's emotional state. For example, if the pet is feeling stressed, the system can suggest ways to keep the environment quiet. If the pet is relaxed, the system can adjust the environment to maintain that state. Furthermore, the system can adjust the temperature and lighting according to the pet's emotional state to maintain its health. This allows the pet's emotional state to be properly managed and its health to be optimized.

[0099] The pet health monitoring system can also provide communication methods tailored to the pet's emotional state based on the pet's emotional state. For example, if the pet is feeling anxious, it can suggest playing back a recording of the owner's voice. If the pet is happy, it can offer a video call to share the pet's emotions. Furthermore, the communication methods tailored to the pet's emotional state can be regularly updated to maintain the pet's health. This allows the pet's emotional state to be properly managed and its health to be optimized.

[0100] The pet health monitoring system can also provide health advice based on the pet's emotional state. For example, if the pet is feeling stressed, advice to reduce stress can be provided. If the pet is relaxed, health advice to maintain that state can be provided. Furthermore, advice on diet and exercise can be provided based on the pet's emotional state to maintain its health. This allows the pet's emotional state to be properly managed and its health to be optimized.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The video acquisition unit acquires video data from the pet camera. For example, it records the pet's behavior, such as how the pet moves around the house, whether it is eating, whether it is resting, etc. The video acquisition unit can also collect video data from the pet camera in real time. Furthermore, the video acquisition unit can adjust the resolution and frame rate of the pet camera to acquire more detailed video data. Step 2: The analysis unit analyzes the video data from the pet camera acquired by the video acquisition unit. For example, the generation AI analyzes the pet's behavior, posture, facial expressions, etc. to determine whether there are any abnormalities. The generation AI can also compare the pet's video data with case data to detect signs of abnormalities. Furthermore, the generation AI can learn the pet's behavioral patterns and detect abnormal behavior early on. Step 3: The determination unit determines whether a visit to the hospital is necessary based on the results of the analysis by the analysis unit. For example, if the pet exhibits abnormal behavior or if an abnormality matching the case data is detected, it determines whether a visit to the hospital is necessary. The determination unit can also comprehensively evaluate the pet's health condition and determine whether a visit to the hospital is necessary. Furthermore, the determination unit can also determine whether a visit to the hospital is necessary by taking into account past visit history and treatment results. Step 4: The notification unit notifies the user of the need for a vet visit determined by the determination unit. For example, the notification may be sent via a smartphone app, email, or messaging service. The notification unit may also provide a detailed report of the pet's health status when notifying the user of the need for a vet visit. Furthermore, the notification unit may allow the user to select the notification method according to their preferences.

[0103] 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.

[0104] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 video acquisition unit for acquiring video data from a pet camera; an analysis unit that analyzes the pet camera video data acquired by the video acquisition unit; a determination unit that determines the need for hospital visits based on the results of the analysis by the analysis unit; a notification unit that notifies the user of the need for hospital visits determined by the determination unit. A system characterized by:

2. The image acquisition unit Adding temperature and heart rate sensors to pet cameras to collect biological data along with video data 2. The system of claim 1.

3. The image acquisition unit It has the ability to automatically tag specific pet behaviors, including eating, toileting, and sleeping.

2. The system of claim 1.

4. The image acquisition unit Collect data on pets' emotional states based on their facial expressions and movements 2. The system of claim 1.

5. The image acquisition unit Using drones to collect data on outdoor pet behavior 2. The system of claim 1.

6. The image acquisition unit Linking multiple pet cameras and integrating and analyzing video data from different angles 2. The system of claim 1.

7. The image acquisition unit Provides audio guidance according to the pet's emotional state to reduce stress 2. The system of claim 1.

8. The analysis unit Using generative AI, it learns pet behavior patterns over the long term, enabling early detection of abnormalities.

2. The system of claim 1.

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