System

Abstract

An object of a system according to an embodiment is to monitor a health condition of a pet in real time and detect an abnormality early.SOLUTION: A system includes a behavior data collection part, a health information collection part, an analysis part, an abnormality detection part, a notification part, and a community support part. The behavior data collection unit collects behavior data of the pet. The health information collection unit collects health information of the pet. The analysis unit analyzes the data collected by the behavior data collection unit and the health information collection unit. The abnormality detection unit detects an abnormality based on the data analyzed by the analysis unit. The notification unit notifies the owner of the abnormality detected by the abnormality detection unit. The community support unit supports a user community.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to monitor a pet's health condition in real time and detect abnormalities early.

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

[0006] The system according to the embodiment includes a behavioral data collection unit, a health information collection unit, an analysis unit, an abnormality detection unit, a notification unit, and a community support unit. The behavioral data collection unit collects behavioral data of the pet. The health information collection unit collects health information of the pet. The analysis unit analyzes the data collected by the behavioral data collection unit and the health information collection unit. The abnormality detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the owner of abnormalities detected by the abnormality detection unit. The community support unit supports the user community. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the health condition of a pet in real time and detect abnormalities at an early stage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A pet health monitoring system according to an embodiment of the present invention is a system that centrally manages pet behavior data, health information, and communication with veterinarians, and provides pet owners with real-time information on their pet's health status. This allows the pet health monitoring system to grasp the pet's health status in real time, detect abnormalities early, and support the user community.

[0029] A pet health monitoring system according to an embodiment includes a behavioral data collection unit, a health information collection unit, an analysis unit, an abnormality detection unit, a notification unit, and a community support unit. The behavioral data collection unit collects behavioral data of the pet. For example, a sensor is attached to the pet's collar to collect walking patterns and activity levels. The behavioral data collection unit can also monitor the pet's eating behavior and sleeping patterns. For example, it measures the pet's food intake and collects the data. The health information collection unit collects health information of the pet. For example, it measures the pet's body temperature and heart rate and collects the data. The health information collection unit can also monitor the pet's respiratory rate and blood pressure. For example, it measures the pet's respiratory rate with a sensor and collects the data. The analysis unit analyzes the data collected by the behavioral data collection unit and the health information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health condition. The analysis unit can also analyze the data using a machine learning algorithm. For example, it learns the pet's behavioral patterns and detects abnormalities. The abnormality detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, the system may detect abnormalities in a pet's behavioral patterns and notify the owner. The abnormality detection unit may also detect abnormal values ​​in health information. For example, an abnormality is detected if a pet's body temperature is higher than normal. The notification unit notifies the owner of the abnormality detected by the abnormality detection unit. For example, it may send a push notification to a smartphone. The notification unit may also send an email notification. For example, if an abnormality is detected, it may notify the owner by email. The community support unit supports the user community. For example, it may provide a forum for sharing questions and experiences about pet health. The community support unit may also support interactions between users. For example, it may provide appropriate answers to inquiries about pet health. In this way, the pet health monitoring system can grasp the health status of pets in real time, detect abnormalities early, and support the user community.

[0030] The behavioral data collection unit collects pet behavioral data in real time, and the generation AI analyzes it on the spot, allowing for instantaneous evaluation of the pet's health condition. The behavioral data collection unit, for example, attaches a sensor to the pet's collar to collect walking patterns and activity levels in real time. For example, behavioral data is collected using a smart collar attached to the pet's collar. The generation AI instantly analyzes the collected data and evaluates the pet's health condition. For example, if the generation AI analyzes the pet's behavioral data and detects an abnormal behavioral pattern, it immediately notifies the owner. The generation AI can also send the data collected in real time to the cloud and perform analysis on the cloud. For example, the generation AI analyzes the data on the cloud and evaluates the pet's health condition. This allows for real-time evaluation of the pet's health condition.

[0031] The health information collection unit simultaneously collects biometric data such as the pet's body temperature and heart rate, allowing the generation AI to comprehensively analyze this data. For example, the health information collection unit uses a non-contact thermometer to measure the pet's body temperature. For example, it measures the pet's temperature while it is sleeping and sends the data to the generation AI. The health information collection unit also attaches a heart rate sensor to the pet's collar to measure its heart rate. For example, it monitors the pet's heart rate while it is exercising and sends an alert if an abnormality is detected. The generation AI comprehensively analyzes the body temperature and heart rate data to evaluate the pet's health. For example, if an increase in body temperature and heart rate is detected simultaneously, it suggests the possibility of a fever. This allows for a more detailed assessment of the pet's health.

[0032] The behavioral data collection unit uses wearable devices and smart collars, and the generation AI can acquire data from these devices. The behavioral data collection unit collects behavioral data using, for example, a smart collar attached to a pet's collar. For example, a smart collar equipped with GPS functionality tracks the pet's movement route. The behavioral data collection unit also attaches a wearable device to the pet to monitor activity levels and sleep patterns. For example, an activity tracker attached to the pet's paw measures walking distance and exercise volume. The generation AI analyzes the collected data and evaluates the pet's health. For example, it notifies the owner if insufficient or excessive exercise is detected. This allows data to be collected using wearable devices and smart collars.

[0033] The analysis unit can develop a generative AI that learns the behavioral patterns of various pets to accommodate different types of pets. For example, the analysis unit develops a generative AI that learns the behavioral patterns of dogs and analyzes dog-specific behavioral data. For example, the health condition is evaluated based on the dog's walking patterns and food intake. The analysis unit also develops a generative AI that learns cat behavioral patterns and analyzes cat-specific behavioral data. For example, the health condition is evaluated based on the cat's play time and sleeping time. The analysis unit also develops a generative AI that learns bird behavioral patterns and analyzes bird-specific behavioral data. For example, the health condition is evaluated based on the bird's flight pattern and frequency of chirping. This makes it possible to accommodate different types of pets.

[0034] The anomaly detection unit analyzes pet behavior data and, when an abnormality is detected, can suggest specific countermeasures to the owner. For example, the generation AI analyzes pet behavior data and, when an abnormality is detected, suggests specific countermeasures to the owner. For example, if a pet does not eat, it suggests changing its diet or taking it to the veterinarian. Furthermore, when an abnormality is detected, the generation AI suggests the optimal countermeasure based on past data. For example, if a pet has exhibited a similar abnormality in the past, it will refer to the countermeasures used at that time. Furthermore, the anomaly detection unit allows the generation AI to estimate the cause of the abnormality and suggest countermeasures based on that. For example, if a pet is not getting enough exercise, it will provide specific advice on increasing the amount of exercise. This makes it possible to suggest specific countermeasures when an abnormality is detected.

[0035] The anomaly detection unit can identify abnormal patterns by comparing data with past data and infer the cause of the abnormality. For example, the generation AI compares data on a pet's behavior with past data to identify abnormal patterns. For example, if a pet exhibits abnormal behavior at a specific time of day, the generation AI analyzes that pattern. After identifying the abnormal pattern, the generation AI infers the cause of the abnormality. For example, if a pet exhibits abnormal behavior after eating a specific meal, the generation AI infers that the meal is the cause. Furthermore, the anomaly detection unit collects additional data to analyze the abnormal pattern and identify the cause of the abnormality. For example, data on the pet's environment and lifestyle is collected to identify the cause of the abnormality. This allows the generation AI to identify abnormal patterns and infer the cause.

[0036] The anomaly detection unit can compare a pet's behavioral data with data from other pets and find common abnormalities. For example, the generation AI compares a pet's behavioral data with data from other pets and finds common abnormalities. For example, if pets of the same species exhibit similar abnormalities, the common points are analyzed. In addition, the anomaly detection unit analyzes large data sets to find common abnormalities. For example, it collects data from multiple pets and identifies abnormal patterns. Furthermore, after the generation AI finds common abnormalities, the anomaly detection unit uses that information to suggest specific countermeasures to the owner. For example, it provides a common countermeasure to owners of pets that exhibit the same abnormality. This makes it possible to find common abnormalities.

[0037] The notification unit can send an abnormality detection alert to the owner's smartphone or smartwatch in real time. For example, when an abnormality is detected, the generation AI sends an alert to the owner's smartphone in real time. For example, if a pet exhibits abnormal behavior, the notification unit will immediately notify the owner. The notification unit also uses the smartwatch to send an abnormality detection alert in real time. For example, if an abnormality is detected in the pet's health condition, the smartwatch will notify the owner by vibration or sound. Furthermore, the generation AI sends an alert via the cloud so that the owner can check for abnormalities even when they are out. For example, if a pet exhibits abnormal behavior at home, the notification unit will notify the owner while they are out. This allows an abnormality detection alert to be sent in real time.

[0038] The analysis unit can automatically organize the pet's health information and generate a report that is easy to understand for the veterinarian. For example, the generation AI in the analysis unit automatically organizes the pet's health information and generates a report that is easy to understand for the veterinarian. For example, it compiles data such as the pet's body temperature, heart rate, and activity level into graphs and tables. The analysis unit also organizes the health information chronologically, allowing the veterinarian to grasp changes in the pet's health at a glance. For example, it generates a report that summarizes health data for the past month by day. Furthermore, the generation AI in the analysis unit generates a detailed report including the results of any abnormalities detected and sends it to the veterinarian. For example, the report may include the date and time when the abnormality was detected and its cause. This allows the pet's health information to be provided to the veterinarian in an easy-to-understand report.

[0039] The analysis unit analyzes the communication history with the veterinarian and can make predictions based on past diagnostic results and advice. For example, the generation AI analyzes the communication history with the veterinarian and makes predictions based on past diagnostic results and advice. For example, it predicts future changes in the pet's health condition based on past diagnostic results. The analysis unit also analyzes advice from the veterinarian and provides information useful for managing the pet's health. For example, it suggests improvements to the pet's diet and exercise based on past advice. Furthermore, the analysis unit predicts the pet's health condition based on the generation AI's communication history with the veterinarian and suggests specific measures to the owner. For example, it provides preventive measures for predicted health risks. This makes it possible to make predictions based on past diagnostic results and advice.

[0040] The communication unit can enable communication with a veterinarian not only via text chat but also via video call or voice call. For example, the communication unit enables communication with a veterinarian not only via text chat but also via video call. For example, a pet owner can consult with a veterinarian while directly showing the pet's condition. The communication unit also enables communication with a veterinarian via voice call. For example, a pet owner can verbally explain the pet's symptoms and receive advice from the veterinarian. Furthermore, the communication unit provides a communication platform that integrates text chat, video call, and voice call, allowing owners to contact a veterinarian in the most appropriate way depending on the situation. This allows for a variety of ways to communicate with a veterinarian.

[0041] The communication unit allows pet owners to share their communication history with their veterinarian with other pet owners, allowing them to find solutions to common problems. For example, the communication unit shares communication history with their veterinarian with other pet owners to find solutions to common problems. For example, owners of pets with the same symptoms can exchange information. The communication unit also uses the generative AI to suggest solutions to common problems based on the shared communication history. For example, if multiple owners receive the same advice, that advice is provided to other owners as well. Furthermore, the communication unit anonymizes communication history with their veterinarian and shares it with other owners, protecting privacy while sharing information. For example, sharing history with the pet's name and personal information removed. This allows pet owners to share their communication history with their veterinarian and find solutions to common problems.

[0042] The community support unit can analyze conversations within a user community, identify common interests and problems, and provide related information. For example, the generation AI analyzes conversations within a user community and identifies common interests and problems. For example, if there are many questions about pet diets, the community support unit can provide related articles and advice based on that information. The community support unit also analyzes the conversation history within the community and suggests solutions to common problems. For example, if multiple users have the same problem, the community support unit can provide a solution to that problem. Furthermore, the generation AI automatically provides related information based on conversations within the user community. For example, if there are many questions about pet health, the community support unit can provide an answer to that question. This allows the community support unit to analyze conversations within a user community, identify common interests and problems, and provide related information.

[0043] The community support unit can enable conversations within the user community to be conducted not only with text but also with images and videos. For example, the community support unit enables conversations within the user community to be conducted not only with text but also with images. For example, conversations can be conducted while sharing photos of pets. The community support unit also uses videos to conduct conversations within the user community. For example, a user can share a video of their pet's behavior and receive advice from other users. Furthermore, the community support unit provides a communication platform that integrates text, images, and videos, allowing users to converse in the most appropriate way depending on the situation. This enables conversations within the user community to be conducted not only with text but also with images and videos.

[0044] In the community support department, the generation AI can provide an automatic translation function so that users from different regions and cultural areas can interact with each other. For example, the generation AI can provide an automatic translation function so that users from different regions and cultural areas can interact with each other. For example, it can translate a conversation posted in Japanese into English. The community support department can also use the automatic translation function to enable users who speak different languages ​​to interact in real time. For example, it can answer questions in English in Japanese. Furthermore, the community support department can provide an automatic translation function so that users from different cultural areas can interact with each other. For example, it can perform translations that take cultural background into consideration. This makes it possible to provide an automatic translation function so that users from different regions and cultural areas can interact with each other.

[0045] The analysis unit can analyze a user's usage history and propose the optimal subscription plan. For example, the generation AI in the analysis unit analyzes a user's usage history and proposes the optimal subscription plan. For example, it customizes the plan based on frequently used features. The analysis unit also develops an algorithm to propose the optimal plan to the user based on the usage history. For example, it analyzes past usage data and proposes a plan that meets the user's needs. Furthermore, the generation AI in the analysis unit analyzes the user's usage history in real time and dynamically proposes the optimal subscription plan. For example, it adjusts the plan according to changes in usage patterns. This makes it possible to analyze a user's usage history and propose the optimal subscription plan.

[0046] The notification unit can encourage continued use by sending a reminder to the user when it is time for subscription renewal. For example, the generation AI sends a reminder to the user when it is time for subscription renewal. For example, the notification unit sends a notification one week before the renewal deadline. Furthermore, when sending the reminder, the generation AI emphasizes the benefits of continued use based on the user's usage history. For example, the notification unit presents specific benefits based on past usage data. Furthermore, the notification unit optimizes the timing at which the generation AI sends reminders to encourage continued use by the user. For example, the notification is sent during the time period when the user is most active. This allows reminders to be sent when it is time for subscription renewal to encourage continued use.

[0047] The community support department can provide additional services (e.g., pet insurance or discounts on health food) that are useful for pet health management in the subscription plan. For example, the community support department can add pet insurance to the subscription plan, allowing users to use services that are useful for pet health management. For example, the community support department can provide insurance that covers pet medical expenses. The community support department can also include discounts on health food in the subscription plan, allowing users to purchase food to maintain their pet's health at a discounted price. For example, the community support department can provide a specific brand of health food at a discounted price. Furthermore, the community support department can improve user satisfaction by incorporating additional services into the subscription plan. For example, the community support department can provide information and tools that are useful for pet health management. This allows the community support department to provide additional services (e.g., pet insurance or discounts on health food) that are useful for pet health management in the subscription plan.

[0048] The community support unit can provide community access at different levels (e.g., basic, premium) to provide services according to the user's needs. For example, the community support unit can provide community access at a basic level and a premium level to provide services according to the user's needs. For example, at the premium level, expert advice can be received. The community support unit can also provide different levels of community access, allowing the user to select a plan that suits them. For example, the basic level provides general information, while the premium level provides detailed information. Furthermore, the community support unit dynamically adjusts the level of community access according to the user's usage. For example, premium level access can be provided to users who use the service frequently. This allows community access to be provided at different levels to provide services according to the user's needs.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can learn the pet's preferences and habits based on the pet's behavioral data and suggest toys and food that suit the pet's preferences to the owner. For example, if the pet likes to play with a particular type of toy, it can suggest a new toy similar to that toy. Also, if the pet likes a particular food, it can suggest new recipes or products related to that food. Furthermore, the analysis unit can detect changes in the pet's preferences and habits based on the pet's behavioral data and provide appropriate advice to the owner. For example, if the pet stops playing with a toy that it previously liked, it can suggest a new toy. This makes it possible to make suggestions based on the pet's preferences and habits.

[0051] The notification unit can send reminders to the owner about pet health management based on the pet behavior data. For example, it can send reminders when the pet's regular health check or vaccination time is approaching. The notification unit can also detect lack of or excessive exercise based on the pet behavior data and send reminders to maintain an appropriate amount of exercise. Furthermore, the notification unit can monitor the pet's diet and water intake and send reminders at appropriate times. For example, if the pet is not drinking enough water, it can send a reminder to encourage the pet to drink more water. In this way, it is possible to send reminders about pet health management.

[0052] The analysis unit can evaluate the pet's sociability based on the pet's behavioral data and provide the owner with advice to improve the pet's sociability. For example, the analysis unit can analyze how the pet interacts with other pets and people and evaluate the pet's sociability. The analysis unit can also provide specific advice to improve the pet's sociability based on the pet's behavioral data. For example, if the pet spends less time playing with other pets, the analysis unit can provide advice to promote pet interaction. Furthermore, the analysis unit can monitor changes in the pet's sociability based on the pet's behavioral data and provide the owner with appropriate advice. For example, if the pet spends more time playing with other pets than before, the analysis unit can evaluate the change and report it to the owner. This makes it possible to evaluate the pet's sociability and provide advice to improve it.

[0053] The analysis unit can evaluate the pet's athletic ability based on the pet's behavioral data and provide the owner with a training plan to improve the pet's athletic ability. For example, the analysis unit can analyze the pet's walking pattern and activity level to evaluate the athletic ability. The analysis unit can also provide a specific training plan to improve the pet's athletic ability based on the pet's behavioral data. For example, if the pet is not getting enough exercise, the analysis unit can provide a training plan to maintain an appropriate amount of exercise. Furthermore, the analysis unit can monitor changes in the pet's athletic ability and provide the owner with appropriate advice. For example, if the pet's athletic ability improves, the analysis unit can evaluate the change and report it to the owner. This makes it possible to evaluate the pet's athletic ability and provide a training plan to improve it.

[0054] The analysis unit can evaluate the pet's eating patterns based on the pet's behavioral data and provide the owner with advice regarding the pet's dietary management. For example, it can analyze the pet's food amount and frequency to evaluate the eating pattern. The analysis unit can also provide specific advice regarding the pet's dietary management based on the pet's behavioral data. For example, if the pet is not eating, it can provide advice on changing the pet's diet or adjusting the timing of meals. Furthermore, the analysis unit can monitor changes in the pet's eating patterns and provide the owner with appropriate advice. For example, if the pet's eating volume decreases, it can identify the cause and suggest countermeasures. This makes it possible to evaluate the pet's eating patterns and provide advice regarding dietary management.

[0055] The analysis unit can evaluate the pet's sleep pattern based on the pet's behavioral data and provide the owner with advice to improve the pet's sleeping environment. For example, the analysis unit can analyze the pet's sleep duration and sleep quality and evaluate the sleep pattern. The analysis unit can also provide specific advice to improve the pet's sleeping environment based on the pet's behavioral data. For example, if the pet wakes up frequently at night, the analysis unit can provide advice to provide a quiet environment. Furthermore, the analysis unit can monitor changes in the pet's sleep pattern and provide appropriate advice to the owner. For example, if the pet's sleep time decreases, the analysis unit can identify the cause and suggest countermeasures. This makes it possible to evaluate the pet's sleep pattern and provide advice to improve the sleeping environment.

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

[0057] Step 1: The behavioral data collection unit collects data on the pet's behavior. For example, a sensor is attached to the pet's collar to collect data on its walking patterns and activity levels. It also monitors the pet's eating behavior and sleeping patterns and collects data. Step 2: The health information collection unit collects health information about the pet. For example, it measures the pet's body temperature and heart rate and collects data. It also monitors the pet's respiratory rate and blood pressure and collects data. Step 3: The analysis unit analyzes the data collected by the behavioral data collection unit and the health information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health status. It also analyzes the data using machine learning algorithms to learn the pet's behavioral patterns and detect abnormalities. Step 4: The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. For example, it detects anomalies in the pet's behavioral patterns and detects abnormal values ​​in the health information. Step 5: The notification unit notifies the owner of the abnormality detected by the abnormality detection unit, for example, by sending a push notification to a smartphone or by email. Step 6: The community support department supports the user community, for example by providing a forum for users to share their questions and experiences about pet health and supporting interactions between users.

[0058] (Example 2) A pet health monitoring system according to an embodiment of the present invention is a system that centrally manages pet behavior data, health information, and communication with veterinarians, and provides pet owners with real-time information on their pet's health status. This allows the pet health monitoring system to grasp the pet's health status in real time, detect abnormalities early, and support the user community.

[0059] A pet health monitoring system according to an embodiment includes a behavioral data collection unit, a health information collection unit, an analysis unit, an abnormality detection unit, a notification unit, and a community support unit. The behavioral data collection unit collects behavioral data of the pet. For example, a sensor is attached to the pet's collar to collect walking patterns and activity levels. The behavioral data collection unit can also monitor the pet's eating behavior and sleeping patterns. For example, it measures the pet's food intake and collects the data. The health information collection unit collects health information of the pet. For example, it measures the pet's body temperature and heart rate and collects the data. The health information collection unit can also monitor the pet's respiratory rate and blood pressure. For example, it measures the pet's respiratory rate with a sensor and collects the data. The analysis unit analyzes the data collected by the behavioral data collection unit and the health information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health condition. The analysis unit can also analyze the data using a machine learning algorithm. For example, it learns the pet's behavioral patterns and detects abnormalities. The abnormality detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, the system may detect abnormalities in a pet's behavioral patterns and notify the owner. The abnormality detection unit may also detect abnormal values ​​in health information. For example, an abnormality is detected if a pet's body temperature is higher than normal. The notification unit notifies the owner of the abnormality detected by the abnormality detection unit. For example, it may send a push notification to a smartphone. The notification unit may also send an email notification. For example, if an abnormality is detected, it may notify the owner by email. The community support unit supports the user community. For example, it may provide a forum for sharing questions and experiences about pet health. The community support unit may also support interactions between users. For example, it may provide appropriate answers to inquiries about pet health. In this way, the pet health monitoring system can grasp the health status of pets in real time, detect abnormalities early, and support the user community.

[0060] The behavioral data collection unit collects pet behavioral data in real time, and the generation AI analyzes it on the spot, allowing for instantaneous evaluation of the pet's health condition. The behavioral data collection unit, for example, attaches a sensor to the pet's collar to collect walking patterns and activity levels in real time. For example, behavioral data is collected using a smart collar attached to the pet's collar. The generation AI instantly analyzes the collected data and evaluates the pet's health condition. For example, if the generation AI analyzes the pet's behavioral data and detects an abnormal behavioral pattern, it immediately notifies the owner. The generation AI can also send the data collected in real time to the cloud and perform analysis on the cloud. For example, the generation AI analyzes the data on the cloud and evaluates the pet's health condition. This allows for real-time evaluation of the pet's health condition.

[0061] The health information collection unit simultaneously collects biometric data such as the pet's body temperature and heart rate, allowing the generation AI to comprehensively analyze this data. For example, the health information collection unit uses a non-contact thermometer to measure the pet's body temperature. For example, it measures the pet's temperature while it is sleeping and sends the data to the generation AI. The health information collection unit also attaches a heart rate sensor to the pet's collar to measure its heart rate. For example, it monitors the pet's heart rate while it is exercising and sends an alert if an abnormality is detected. The generation AI comprehensively analyzes the body temperature and heart rate data to evaluate the pet's health. For example, if an increase in body temperature and heart rate is detected simultaneously, it suggests the possibility of a fever. This allows for a more detailed assessment of the pet's health.

[0062] The analysis unit can use the emotion estimation function to estimate the pet's emotional state from the pet's behavioral data and detect signs of stress or anxiety. The analysis unit, for example, analyzes the pet's behavioral data and estimates the pet's emotional state using an emotion estimation algorithm. For example, frequent barking and restless movements are detected as signs of stress. The analysis unit also uses the emotion estimation function to detect signs of anxiety from the pet's behavioral data. For example, if the pet shows behavior of avoiding a particular place, it suggests anxiety about that place. Furthermore, the analysis unit monitors the pet's emotional state in real time and notifies the owner if signs of stress or anxiety are detected. For example, if the pet shows anxious behavior for a long period of time, it sends an alert to the owner. This allows the pet's emotional state to be understood and signs of stress or anxiety to be detected early.

[0063] The behavioral data collection unit uses wearable devices and smart collars, and the generation AI can acquire data from these devices. The behavioral data collection unit collects behavioral data using, for example, a smart collar attached to a pet's collar. For example, a smart collar equipped with GPS functionality tracks the pet's movement route. The behavioral data collection unit also attaches a wearable device to the pet to monitor activity levels and sleep patterns. For example, an activity tracker attached to the pet's paw measures walking distance and exercise volume. The generation AI analyzes the collected data and evaluates the pet's health. For example, it notifies the owner if insufficient or excessive exercise is detected. This allows data to be collected using wearable devices and smart collars.

[0064] The analysis unit can develop a generative AI that learns the behavioral patterns of various pets to accommodate different types of pets. For example, the analysis unit develops a generative AI that learns the behavioral patterns of dogs and analyzes dog-specific behavioral data. For example, the health condition is evaluated based on the dog's walking patterns and food intake. The analysis unit also develops a generative AI that learns cat behavioral patterns and analyzes cat-specific behavioral data. For example, the health condition is evaluated based on the cat's play time and sleeping time. The analysis unit also develops a generative AI that learns bird behavioral patterns and analyzes bird-specific behavioral data. For example, the health condition is evaluated based on the bird's flight pattern and frequency of chirping. This makes it possible to accommodate different types of pets.

[0065] The analysis unit can use the emotion estimation function to estimate the owner's emotional state from the pet's behavioral data and analyze the relationship between the pet and the owner. The analysis unit, for example, analyzes the pet's behavioral data and estimates the owner's emotional state. For example, if the pet behaves in a way that shows affection toward the owner, it estimates that the owner's emotional state is positive. The analysis unit also uses the emotion estimation function to analyze the relationship between the pet and the owner. For example, if the pet senses the owner's stress and exhibits restless behavior, it estimates that the owner's emotional state is negative. Furthermore, the analysis unit monitors the relationship between the pet and the owner in real time and analyzes changes in the emotional state. For example, if the pet spends more time playing with the owner, it suggests that the owner's emotional state is changing to a positive state. This allows the relationship between the pet and the owner to be analyzed and the emotional state to be understood.

[0066] The anomaly detection unit analyzes pet behavior data and, when an abnormality is detected, can suggest specific countermeasures to the owner. For example, the generation AI analyzes pet behavior data and, when an abnormality is detected, suggests specific countermeasures to the owner. For example, if a pet does not eat, it suggests changing its diet or taking it to the veterinarian. Furthermore, when an abnormality is detected, the generation AI suggests the optimal countermeasure based on past data. For example, if a pet has exhibited a similar abnormality in the past, it will refer to the countermeasures used at that time. Furthermore, the anomaly detection unit allows the generation AI to estimate the cause of the abnormality and suggest countermeasures based on that. For example, if a pet is not getting enough exercise, it will provide specific advice on increasing the amount of exercise. This makes it possible to suggest specific countermeasures when an abnormality is detected.

[0067] The anomaly detection unit can identify abnormal patterns by comparing data with past data and infer the cause of the abnormality. For example, the generation AI compares data on a pet's behavior with past data to identify abnormal patterns. For example, if a pet exhibits abnormal behavior at a specific time of day, the generation AI analyzes that pattern. After identifying the abnormal pattern, the generation AI infers the cause of the abnormality. For example, if a pet exhibits abnormal behavior after eating a specific meal, the generation AI infers that the meal is the cause. Furthermore, the anomaly detection unit collects additional data to analyze the abnormal pattern and identify the cause of the abnormality. For example, data on the pet's environment and lifestyle is collected to identify the cause of the abnormality. This allows the generation AI to identify abnormal patterns and infer the cause.

[0068] The anomaly detection unit can compare a pet's behavioral data with data from other pets and find common abnormalities. For example, the generation AI compares a pet's behavioral data with data from other pets and finds common abnormalities. For example, if pets of the same species exhibit similar abnormalities, the common points are analyzed. In addition, the anomaly detection unit analyzes large data sets to find common abnormalities. For example, it collects data from multiple pets and identifies abnormal patterns. Furthermore, after the generation AI finds common abnormalities, the anomaly detection unit uses that information to suggest specific countermeasures to the owner. For example, it provides a common countermeasure to owners of pets that exhibit the same abnormality. This makes it possible to find common abnormalities.

[0069] The notification unit can send an abnormality detection alert to the owner's smartphone or smartwatch in real time. For example, when an abnormality is detected, the generation AI sends an alert to the owner's smartphone in real time. For example, if a pet exhibits abnormal behavior, the notification unit will immediately notify the owner. The notification unit also uses the smartwatch to send an abnormality detection alert in real time. For example, if an abnormality is detected in the pet's health condition, the smartwatch will notify the owner by vibration or sound. Furthermore, the generation AI sends an alert via the cloud so that the owner can check for abnormalities even when they are out. For example, if a pet exhibits abnormal behavior at home, the notification unit will notify the owner while they are out. This allows an abnormality detection alert to be sent in real time.

[0070] The notification unit can use the emotion estimation function to generate a message that minimizes the emotional impact of the anomaly detection alert on the owner. For example, the notification unit uses the emotion estimation function to evaluate the emotional impact of the anomaly detection alert on the owner. For example, the notification unit estimates the likelihood that the alert will cause anxiety to the owner. The notification unit also generates an alert message taking into account the owner's emotional state. For example, if the owner is feeling anxious, the notification unit sends a message to reassure the owner. Furthermore, the notification unit uses the emotion estimation function to adjust the content of the alert message to minimize the emotional impact of the owner. For example, the notification unit adds positive elements to the alert message. This makes it possible to generate a message that minimizes the emotional impact of the anomaly detection alert on the owner.

[0071] The analysis unit can automatically organize the pet's health information and generate a report that is easy to understand for the veterinarian. For example, the generation AI in the analysis unit automatically organizes the pet's health information and generates a report that is easy to understand for the veterinarian. For example, it compiles data such as the pet's body temperature, heart rate, and activity level into graphs and tables. The analysis unit also organizes the health information chronologically, allowing the veterinarian to grasp changes in the pet's health at a glance. For example, it generates a report that summarizes health data for the past month by day. Furthermore, the generation AI in the analysis unit generates a detailed report including the results of any abnormalities detected and sends it to the veterinarian. For example, the report may include the date and time when the abnormality was detected and its cause. This allows the pet's health information to be provided to the veterinarian in an easy-to-understand report.

[0072] The analysis unit analyzes the communication history with the veterinarian and can make predictions based on past diagnostic results and advice. For example, the generation AI analyzes the communication history with the veterinarian and makes predictions based on past diagnostic results and advice. For example, it predicts future changes in the pet's health condition based on past diagnostic results. The analysis unit also analyzes advice from the veterinarian and provides information useful for managing the pet's health. For example, it suggests improvements to the pet's diet and exercise based on past advice. Furthermore, the analysis unit predicts the pet's health condition based on the generation AI's communication history with the veterinarian and suggests specific measures to the owner. For example, it provides preventive measures for predicted health risks. This makes it possible to make predictions based on past diagnostic results and advice.

[0073] The analysis unit can use the emotion estimation function to grasp the owner's emotional state while communicating with the veterinarian and provide appropriate support. The analysis unit, for example, uses the emotion estimation function to grasp the owner's emotional state in real time while communicating with the veterinarian. For example, if the owner is feeling anxious, it detects that emotion. The analysis unit also supports communication with the veterinarian taking the owner's emotional state into consideration. For example, if the owner is feeling anxious, it provides a message to reassure the owner. Furthermore, the analysis unit uses the emotion estimation function to monitor the owner's emotional state while communicating with the veterinarian and provide appropriate support. For example, if the owner is feeling stressed, it provides advice to relax. This makes it possible to grasp the owner's emotional state while communicating with the veterinarian and provide appropriate support.

[0074] The communication unit can enable communication with a veterinarian not only via text chat but also via video call or voice call. For example, the communication unit enables communication with a veterinarian not only via text chat but also via video call. For example, a pet owner can consult with a veterinarian while directly showing the pet's condition. The communication unit also enables communication with a veterinarian via voice call. For example, a pet owner can verbally explain the pet's symptoms and receive advice from the veterinarian. Furthermore, the communication unit provides a communication platform that integrates text chat, video call, and voice call, allowing owners to contact a veterinarian in the most appropriate way depending on the situation. This allows for a variety of ways to communicate with a veterinarian.

[0075] The communication unit allows pet owners to share their communication history with their veterinarian with other pet owners, allowing them to find solutions to common problems. For example, the communication unit shares communication history with their veterinarian with other pet owners to find solutions to common problems. For example, owners of pets with the same symptoms can exchange information. The communication unit also uses the generative AI to suggest solutions to common problems based on the shared communication history. For example, if multiple owners receive the same advice, that advice is provided to other owners as well. Furthermore, the communication unit anonymizes communication history with their veterinarian and shares it with other owners, protecting privacy while sharing information. For example, sharing history with the pet's name and personal information removed. This allows pet owners to share their communication history with their veterinarian and find solutions to common problems.

[0076] The analysis unit uses the emotion estimation function to understand the emotional state of the pet during communication with the veterinarian, which can be useful for diagnosis. For example, the analysis unit uses the emotion estimation function to understand the emotional state of the pet in real time during communication with the veterinarian. For example, if the pet is feeling anxious, it detects that emotion. The analysis unit also takes the emotional state of the pet into consideration and provides it as reference information for the veterinarian when making a diagnosis. For example, if the pet is feeling stressed, it conveys that information to the veterinarian. Furthermore, the analysis unit uses the emotion estimation function to monitor the emotional state of the pet during communication with the veterinarian, which can be useful for diagnosis. For example, if the pet is relaxed, it provides that information to the veterinarian. This makes it possible to understand the emotional state of the pet during communication with the veterinarian, which can be useful for diagnosis.

[0077] The community support unit can analyze conversations within a user community, identify common interests and problems, and provide related information. For example, the generation AI analyzes conversations within a user community and identifies common interests and problems. For example, if there are many questions about pet diets, the community support unit can provide related articles and advice based on that information. The community support unit also analyzes the conversation history within the community and suggests solutions to common problems. For example, if multiple users have the same problem, the community support unit can provide a solution to that problem. Furthermore, the generation AI automatically provides related information based on conversations within the user community. For example, if there are many questions about pet health, the community support unit can provide an answer to that question. This allows the community support unit to analyze conversations within a user community, identify common interests and problems, and provide related information.

[0078] The community support unit can use the emotion estimation function to understand the user's emotional state from conversations within the user community and provide appropriate support. For example, the community support unit uses the emotion estimation function to understand the user's emotional state in real time from conversations within the user community. For example, if the user is feeling anxious, the community support unit detects that emotion. The community support unit also supports conversations within the community taking the user's emotional state into consideration. For example, if the user is feeling anxious, the community support unit provides a message to reassure the user. Furthermore, the community support unit uses the emotion estimation function to monitor the user's emotional state from conversations within the user community and provide appropriate support. For example, if the user is feeling stressed, the community support unit provides advice on how to relax. In this way, the community support unit can understand the user's emotional state from conversations within the user community and provide appropriate support.

[0079] The community support unit can enable conversations within the user community to be conducted not only with text but also with images and videos. For example, the community support unit enables conversations within the user community to be conducted not only with text but also with images. For example, conversations can be conducted while sharing photos of pets. The community support unit also uses videos to conduct conversations within the user community. For example, a user can share a video of their pet's behavior and receive advice from other users. Furthermore, the community support unit provides a communication platform that integrates text, images, and videos, allowing users to converse in the most appropriate way depending on the situation. This enables conversations within the user community to be conducted not only with text but also with images and videos.

[0080] In the community support department, the generation AI can provide an automatic translation function so that users from different regions and cultural areas can interact with each other. For example, the generation AI can provide an automatic translation function so that users from different regions and cultural areas can interact with each other. For example, it can translate a conversation posted in Japanese into English. The community support department can also use the automatic translation function to enable users who speak different languages ​​to interact in real time. For example, it can answer questions in English in Japanese. Furthermore, the community support department can provide an automatic translation function so that users from different cultural areas can interact with each other. For example, it can perform translations that take cultural background into consideration. This makes it possible to provide an automatic translation function so that users from different regions and cultural areas can interact with each other.

[0081] The community support unit can use the emotion estimation function to make suggestions for eliciting positive emotions from conversations within the user community. For example, the community support unit uses the emotion estimation function to make suggestions for eliciting positive emotions from conversations within the user community. For example, the community support unit encourages the user to express gratitude. The community support unit also supports conversations for eliciting positive emotions by taking into account the emotional state of the user. For example, the community support unit provides a message that makes the user feel joy. Furthermore, the community support unit uses the emotion estimation function to make suggestions for eliciting positive emotions from conversations within the user community. For example, the community support unit encourages the user to encourage other users. This makes it possible to make suggestions for eliciting positive emotions from conversations within the user community.

[0082] The analysis unit can analyze a user's usage history and propose the optimal subscription plan. For example, the generation AI in the analysis unit analyzes a user's usage history and proposes the optimal subscription plan. For example, it customizes the plan based on frequently used features. The analysis unit also develops an algorithm to propose the optimal plan to the user based on the usage history. For example, it analyzes past usage data and proposes a plan that meets the user's needs. Furthermore, the generation AI in the analysis unit analyzes the user's usage history in real time and dynamically proposes the optimal subscription plan. For example, it adjusts the plan according to changes in usage patterns. This makes it possible to analyze a user's usage history and propose the optimal subscription plan.

[0083] The notification unit can encourage continued use by sending a reminder to the user when it is time for subscription renewal. For example, the generation AI sends a reminder to the user when it is time for subscription renewal. For example, the notification unit sends a notification one week before the renewal deadline. Furthermore, when sending the reminder, the generation AI emphasizes the benefits of continued use based on the user's usage history. For example, the notification unit presents specific benefits based on past usage data. Furthermore, the notification unit optimizes the timing at which the generation AI sends reminders to encourage continued use by the user. For example, the notification is sent during the time period when the user is most active. This allows reminders to be sent when it is time for subscription renewal to encourage continued use.

[0084] The analysis unit can use the emotion estimation function to evaluate user satisfaction based on subscription usage status and suggest areas for improvement. The analysis unit, for example, uses the emotion estimation function to evaluate user satisfaction based on subscription usage status. For example, it estimates satisfaction based on usage frequency and usage time. The analysis unit also suggests improvements to the subscription plan based on the results of the evaluation of user satisfaction. For example, it makes suggestions to improve functions that are used less frequently. Furthermore, the analysis unit uses the emotion estimation function to monitor user satisfaction in real time and continuously suggest areas for improvement. For example, it adjusts the plan according to the user's emotional state. This makes it possible to evaluate user satisfaction based on subscription usage status and suggest areas for improvement.

[0085] The community support department can provide additional services (e.g., pet insurance or discounts on health food) that are useful for pet health management in the subscription plan. For example, the community support department can add pet insurance to the subscription plan, allowing users to use services that are useful for pet health management. For example, the community support department can provide insurance that covers pet medical expenses. The community support department can also include discounts on health food in the subscription plan, allowing users to purchase food to maintain their pet's health at a discounted price. For example, the community support department can provide a specific brand of health food at a discounted price. Furthermore, the community support department can improve user satisfaction by incorporating additional services into the subscription plan. For example, the community support department can provide information and tools that are useful for pet health management. This allows the community support department to provide additional services (e.g., pet insurance or discounts on health food) that are useful for pet health management in the subscription plan.

[0086] The community support unit can provide community access at different levels (e.g., basic, premium) to provide services according to the user's needs. For example, the community support unit can provide community access at a basic level and a premium level to provide services according to the user's needs. For example, at the premium level, expert advice can be received. The community support unit can also provide different levels of community access, allowing the user to select a plan that suits them. For example, the basic level provides general information, while the premium level provides detailed information. Furthermore, the community support unit dynamically adjusts the level of community access according to the user's usage. For example, premium level access can be provided to users who use the service frequently. This allows community access to be provided at different levels to provide services according to the user's needs.

[0087] The analysis unit uses the emotion estimation function to understand the user's emotional state from the subscription usage status and can provide appropriate support. The analysis unit, for example, uses the emotion estimation function to understand the user's emotional state in real time from the subscription usage status. For example, the emotional state is estimated based on usage frequency and usage time. The analysis unit also takes the user's emotional state into consideration and suggests improvements to the subscription plan. For example, it makes suggestions to improve functions that are used less frequently. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide appropriate support. For example, if the user is dissatisfied, the cause is identified and a remedy is suggested. This makes it possible to understand the user's emotional state from the subscription usage status and provide appropriate support.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can learn the pet's preferences and habits based on the pet's behavioral data and suggest toys and food that suit the pet's preferences to the owner. For example, if the pet likes to play with a particular type of toy, it can suggest a new toy similar to that toy. Also, if the pet likes a particular food, it can suggest new recipes or products related to that food. Furthermore, the analysis unit can detect changes in the pet's preferences and habits based on the pet's behavioral data and provide appropriate advice to the owner. For example, if the pet stops playing with a toy that it previously liked, it can suggest a new toy. This makes it possible to make suggestions based on the pet's preferences and habits.

[0090] The notification unit can send reminders to the owner about pet health management based on the pet behavior data. For example, it can send reminders when the pet's regular health check or vaccination time is approaching. The notification unit can also detect lack of or excessive exercise based on the pet behavior data and send reminders to maintain an appropriate amount of exercise. Furthermore, the notification unit can monitor the pet's diet and water intake and send reminders at appropriate times. For example, if the pet is not drinking enough water, it can send a reminder to encourage the pet to drink more water. In this way, it is possible to send reminders about pet health management.

[0091] The analysis unit can evaluate the pet's sociability based on the pet's behavioral data and provide the owner with advice to improve the pet's sociability. For example, the analysis unit can analyze how the pet interacts with other pets and people and evaluate the pet's sociability. The analysis unit can also provide specific advice to improve the pet's sociability based on the pet's behavioral data. For example, if the pet spends less time playing with other pets, the analysis unit can provide advice to promote pet interaction. Furthermore, the analysis unit can monitor changes in the pet's sociability based on the pet's behavioral data and provide the owner with appropriate advice. For example, if the pet spends more time playing with other pets than before, the analysis unit can evaluate the change and report it to the owner. This makes it possible to evaluate the pet's sociability and provide advice to improve it.

[0092] The analysis unit can use the emotion estimation function to estimate the pet's happiness level from the pet's behavioral data and provide the owner with advice to improve the pet's happiness level. For example, if the pet plays frequently, the behavior can be evaluated as an indicator of happiness level. The analysis unit can also use the emotion estimation function to monitor the pet's happiness level in real time from the pet's behavioral data and provide the owner with appropriate advice. For example, if the pet has not played for a long time, advice to increase play time can be provided. Furthermore, the analysis unit can also provide specific advice to improve the pet's happiness level. For example, if the pet likes to play in a particular place, advice to increase play time in that place can be provided. In this way, the pet's happiness level can be estimated and advice to improve it can be provided.

[0093] The analysis unit can evaluate the pet's athletic ability based on the pet's behavioral data and provide the owner with a training plan to improve the pet's athletic ability. For example, the analysis unit can analyze the pet's walking pattern and activity level to evaluate the athletic ability. The analysis unit can also provide a specific training plan to improve the pet's athletic ability based on the pet's behavioral data. For example, if the pet is not getting enough exercise, the analysis unit can provide a training plan to maintain an appropriate amount of exercise. Furthermore, the analysis unit can monitor changes in the pet's athletic ability and provide the owner with appropriate advice. For example, if the pet's athletic ability improves, the analysis unit can evaluate the change and report it to the owner. This makes it possible to evaluate the pet's athletic ability and provide a training plan to improve it.

[0094] The analysis unit can use the emotion estimation function to estimate a pet's stress level from the pet's behavioral data and provide the owner with advice to reduce the pet's stress. For example, if the pet barks frequently, the analysis unit can evaluate this behavior as an indicator of stress. The analysis unit can also use the emotion estimation function to monitor the pet's stress level in real time from the pet's behavioral data and provide the owner with appropriate advice. For example, if the pet exhibits restless behavior for a long period of time, the analysis unit can provide advice to help the pet relax. Furthermore, the analysis unit can provide specific advice to reduce the pet's stress. For example, if the pet feels stressed in a particular place, the analysis unit can provide advice to avoid that place. This makes it possible to estimate the pet's stress level and provide advice to reduce it.

[0095] The analysis unit can evaluate the pet's eating patterns based on the pet's behavioral data and provide the owner with advice regarding the pet's dietary management. For example, it can analyze the pet's food amount and frequency to evaluate the eating pattern. The analysis unit can also provide specific advice regarding the pet's dietary management based on the pet's behavioral data. For example, if the pet is not eating, it can provide advice on changing the pet's diet or adjusting the timing of meals. Furthermore, the analysis unit can monitor changes in the pet's eating patterns and provide the owner with appropriate advice. For example, if the pet's eating volume decreases, it can identify the cause and suggest countermeasures. This makes it possible to evaluate the pet's eating patterns and provide advice regarding dietary management.

[0096] The analysis unit can use the emotion estimation function to estimate a pet's play preferences from the pet's behavioral data and provide the owner with advice to enrich their pet's play. For example, if a pet often plays with a particular type of toy, the analysis unit can suggest a new toy similar to that toy. The analysis unit can also use the emotion estimation function to monitor a pet's play preferences in real time from the pet's behavioral data and provide the owner with appropriate advice. For example, if a pet shows no interest in a new toy, the analysis unit can suggest a different type of toy. Furthermore, the analysis unit can provide specific advice to enrich the pet's play. For example, if a pet likes to play in a particular place, the analysis unit can provide advice to increase play in that place. In this way, the analysis unit can estimate a pet's play preferences and provide advice to enrich their play.

[0097] The analysis unit can evaluate the pet's sleep pattern based on the pet's behavioral data and provide the owner with advice to improve the pet's sleeping environment. For example, the analysis unit can analyze the pet's sleep duration and sleep quality and evaluate the sleep pattern. The analysis unit can also provide specific advice to improve the pet's sleeping environment based on the pet's behavioral data. For example, if the pet wakes up frequently at night, the analysis unit can provide advice to provide a quiet environment. Furthermore, the analysis unit can monitor changes in the pet's sleep pattern and provide appropriate advice to the owner. For example, if the pet's sleep time decreases, the analysis unit can identify the cause and suggest countermeasures. This makes it possible to evaluate the pet's sleep pattern and provide advice to improve the sleeping environment.

[0098] The analysis unit can use the emotion estimation function to estimate the emotional state of a pet from the pet's behavioral data and provide the owner with advice to improve the pet's emotional state. For example, if the pet barks frequently, the behavior can be evaluated as an indicator of anxiety. The analysis unit can also use the emotion estimation function to monitor the pet's emotional state from the pet's behavioral data in real time and provide the owner with appropriate advice. For example, if the pet exhibits restless behavior for a long period of time, advice to help the pet relax can be provided. Furthermore, the analysis unit can provide specific advice to improve the pet's emotional state. For example, if the pet feels anxious in a particular place, advice to avoid that place can be provided. This makes it possible to estimate the pet's emotional state and provide advice to improve it.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The behavioral data collection unit collects data on the pet's behavior. For example, a sensor is attached to the pet's collar to collect data on its walking patterns and activity levels. It also monitors the pet's eating behavior and sleeping patterns and collects data. Step 2: The health information collection unit collects health information about the pet. For example, it measures the pet's body temperature and heart rate and collects data. It also monitors the pet's respiratory rate and blood pressure and collects data. Step 3: The analysis unit analyzes the data collected by the behavioral data collection unit and the health information collection unit. For example, it performs statistical analysis of the data to evaluate the pet's health status. It also analyzes the data using machine learning algorithms to learn the pet's behavioral patterns and detect abnormalities. Step 4: The anomaly detection unit detects anomalies based on the data analyzed by the analysis unit. For example, it detects anomalies in the pet's behavioral patterns and detects abnormal values ​​in the health information. Step 5: The notification unit notifies the owner of the abnormality detected by the abnormality detection unit, for example, by sending a push notification to a smartphone or by email. Step 6: The community support department supports the user community, for example by providing a forum for users to share their questions and experiences about pet health and supporting interactions between users.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0145] 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 behavioral data collection unit that collects behavioral data of pets; a health information collection department that collects pet health information; an analysis unit that analyzes the data collected by the behavioral data collection unit and the health information collection unit; an anomaly detection unit that detects an anomaly based on the data analyzed by the analysis unit; a notification unit that notifies an owner of an abnormality detected by the abnormality detection unit; a community support unit that supports a user community; A system characterized by:

2. The behavioral data collection unit Pet behavior data is collected in real time, and the AI ​​analyzes it on the spot to instantly assess the pet's health.

2. The system of claim 1.

3. The health information collection unit: Biometric data such as pet body temperature and heart rate are also collected at the same time, and the generative AI analyzes this data comprehensively.

2. The system of claim 1.

4. The analysis unit Inferring the emotional state of the pet from the behavioral data of the pet and detecting signs of stress or anxiety 2. The system of claim 1.

5. The behavioral data collection unit Using wearable devices and smart collars, generative AI will capture data from these devices.

2. The system of claim 1.

6. The analysis unit Developing a generative AI that learns the behavioral patterns of various pets to accommodate different types of pets 2. The system of claim 1.

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