System

Abstract

An object of a system according to an embodiment is to grasp a health condition of a pet in real time and provide appropriate care.SOLUTION: A system includes a collection unit, an analysis unit, a notification unit, and a provision unit. The collection unit is configured to collect health data of a companion animal. The analysis unit analyzes the data collected by the collection unit and detects an abnormality. The notification part notifies the owner based on the abnormality detected by the analysis part. The providing unit provides advice on care or diet based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to monitor a pet's health in real time and provide appropriate care.

[0005] The system according to the embodiment aims to grasp the health condition of a pet in real time and provide appropriate care. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a provision unit. The collection unit collects health data of the pet. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit notifies the owner based on the abnormalities detected by the analysis unit. The provision unit provides care or dietary advice based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the health condition of a pet in real time and provide appropriate care. [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 management system according to an embodiment of the present invention monitors a pet's health status in real time and provides appropriate care. The pet health management system uses temperature sensors, heart rate sensors, and activity sensors attached to the pet to collect health data, which is then analyzed by AI. If an abnormality is detected, the owner is notified, and the AI ​​provides appropriate care and dietary advice based on the pet's health status. For example, if a pet's body temperature is higher than normal, the AI ​​suggests cooling methods to the owner. If a pet's activity level decreases, the AI ​​warns the owner about lack of exercise and suggests an appropriate exercise plan. The AI ​​also provides dietary advice and reminders for regular health checks. This allows owners to monitor their pet's health status in real time and provide appropriate care. For example, it can detect abnormalities early and take appropriate measures before the pet becomes ill. It is also possible to accumulate pet health data over a long period of time and track changes in health status. This is expected to lead to more effective pet health management and extend the pet's lifespan. This allows the pet health management system to monitor a pet's health status in real time and provide appropriate care. For example, it will be possible to detect abnormalities early on before pets become ill and take appropriate measures. It will also be possible to accumulate pet health data over a long period of time and track changes in health status. This will enable more effective health management of pets and is expected to extend their lifespans.

[0029] A pet health management system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a provision unit. The collection unit collects health data of the pet. The pet's health data includes, but is not limited to, body temperature, heart rate, and activity level. The collection unit measures the pet's body temperature using, for example, a body temperature sensor. The collection unit can also measure the pet's heart rate using a heart rate sensor. The collection unit can also measure the pet's activity level using an activity level sensor. For example, the body temperature sensor measures body temperature using a contact sensor or a non-contact sensor. The heart rate sensor measures heart rate using an optical sensor or an electrical sensor. The activity level sensor measures activity level using an acceleration sensor or a gyro sensor. The analysis unit analyzes the data collected by the collection unit to detect abnormalities. Examples of abnormalities include, but are not limited to, elevated body temperature and abnormal heart rate. The analysis unit detects abnormalities by comparing the data with past data. The analysis unit can also analyze data using AI to detect abnormalities. For example, the analysis unit stores data from the past year in cloud storage and builds a predictive model based on the past data. The notification unit notifies the owner based on the abnormality detected by the analysis unit. Examples of notifications include, but are not limited to, notifications to a smartphone or email notifications. For example, the notification unit sends a push notification to the smartphone. The notification unit can also send emails. The provision unit provides appropriate care and dietary advice based on the data analyzed by the analysis unit. Examples of care and dietary advice include, but are not limited to, cooling methods, exercise plans, and dietary plans. For example, the provision unit suggests cooling methods when the pet's body temperature is higher than normal. The provision unit can also suggest exercise plans when the pet's activity level is decreasing. The provision unit can also provide dietary advice and reminders for regular health checks. For example, the provision unit suggests cooling methods when the pet's body temperature is high. The provision unit suggests exercise plans when the pet's activity level is decreasing. The provision unit provides dietary advice and reminders for regular health checks.As a result, the pet health management system according to the embodiment can grasp the health condition of a pet in real time and provide appropriate care. For example, it can detect abnormalities early before a pet becomes ill and take appropriate measures. It can also accumulate health data of a pet over a long period of time and track changes in the health condition. This is expected to enable more effective health management of pets and extend the lifespan of pets.

[0030] The collection unit includes a body temperature sensor, a heart rate sensor, and an activity sensor. The body temperature sensor measures body temperature using, for example, a contact sensor or a non-contact sensor. For example, a contact sensor measures body temperature by directly contacting the pet's skin. A non-contact sensor measures the pet's body temperature using infrared rays. The heart rate sensor measures heart rate using, for example, an optical sensor or an electrical sensor. For example, an optical sensor measures heart rate by irradiating light onto the pet's skin and measuring reflected light. An electrical sensor measures heart rate by attaching electrodes to the pet's skin and taking an electrocardiogram. The activity sensor measures activity using, for example, an acceleration sensor or a gyro sensor. For example, an acceleration sensor measures activity by detecting the pet's movements. A gyro sensor measures activity by detecting the pet's rotational movement. This allows the collection unit to collect pet health data from multiple angles. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a body temperature sensor, a heart rate sensor, and an activity sensor into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can detect anomalies by comparing with past data. For example, the analysis unit stores data from the past year in cloud storage and builds a predictive model based on the past data. For example, the analysis unit compares past data with current data to detect anomalies. The analysis unit can also analyze data using AI to detect anomalies. For example, the analysis unit detects anomalies using a machine learning model. A machine learning model can learn from large amounts of data and identify abnormal patterns. This allows the analysis unit to detect anomalies early by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past data and current data into a generation AI and have the generation AI detect anomalies.

[0032] The providing unit can suggest a cooling method when the pet's body temperature rises above normal. The providing unit suggests a cooling method when the pet's body temperature is higher than normal, for example. For example, the providing unit suggests a method of using a cooling sheet. The providing unit can also suggest a method of using cooling gel. The providing unit can also suggest a method of using a cooling fan. For example, the providing unit suggests a method of laying a cooling sheet on the pet's bed. The providing unit suggests a method of applying cooling gel to the pet's body. The providing unit suggests a method of installing a cooling fan near the pet. In this way, the providing unit can suggest an appropriate cooling method when the pet's body temperature is high. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's body temperature data into the generating AI and cause the generating AI to suggest a cooling method.

[0033] The providing unit can propose an exercise plan when the pet's activity level is decreasing. The providing unit proposes an exercise plan when the pet's activity level is decreasing, for example. For example, the providing unit proposes a plan to increase the number of walks. The providing unit can also propose a plan to increase indoor play. The providing unit can also propose a plan to use exercise equipment. For example, the providing unit proposes a plan to increase the number of walks from two to three times a day. The providing unit proposes a plan to increase indoor ball play. The providing unit proposes a plan to use a pet treadmill. In this way, the providing unit can propose an appropriate exercise plan when the pet's activity level is decreasing. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's activity level data into the generating AI and cause the generating AI to execute the exercise plan proposal.

[0034] The providing unit can provide dietary advice or reminders for regular health checks. The providing unit, for example, provides dietary advice. For example, the providing unit can suggest a nutritionally balanced meal plan. The providing unit can also suggest a meal plan that manages calorie intake. The providing unit can also suggest a meal plan that supplements specific nutrients. For example, the providing unit can suggest a meal plan that restricts calories based on the pet's weight. The providing unit can suggest a meal plan that supplements vitamins and minerals. The providing unit can suggest a meal plan that includes specific nutrients based on the pet's health condition. The providing unit, for example, provides reminders for regular health checks. For example, the providing unit can send reminders for regular health checks. The providing unit can also send reminders for vaccinations. The providing unit can also send reminders for administering heartworm preventative medication. For example, the providing unit can send reminders for monthly health checks. The providing unit can send reminders for annual vaccinations. The providing unit can send reminders for monthly heartworm preventative medication. This allows the providing unit to more effectively manage the health of the pet. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the pet's health data into the generating AI and cause the generating AI to provide dietary advice and health check reminders.

[0035] The analysis unit can store data for a long period of time using cloud storage and build a predictive model based on past data. The analysis unit, for example, stores data for a long period of time using cloud storage. For example, the analysis unit stores data using cloud storage such as AWS (registered trademark) or Google (registered trademark) Cloud. The analysis unit also builds a predictive model based on past data. For example, the analysis unit builds a predictive model using a machine learning model. The machine learning model can learn from large amounts of data and predict future health risks. This allows the analysis unit to track changes in the pet's health condition over a long period of time and predict future health risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data stored in cloud storage into a generation AI and cause the generation AI to build a predictive model.

[0036] The collection unit can estimate the pet's emotion and adjust the frequency of health data collection based on the estimated pet's emotion. The collection unit, for example, estimates the pet's emotion. For example, the collection unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The collection unit also adjusts the frequency of health data collection based on the estimated pet's emotion. For example, if the pet is stressed, the collection unit increases the collection frequency to collect more detailed data. If the pet is relaxed, the collection unit can also reduce the collection frequency to reduce the burden of data collection. If the pet is excited, the collection unit can set the collection frequency to a medium level to focus on collecting activity data. For example, the collection unit analyzes the pet's behavioral data to estimate the emotion. The collection unit analyzes the pet's facial expression data to estimate the emotion. The collection unit adjusts the collection frequency based on the pet's emotion. This allows the collection unit to collect more accurate data by adjusting the frequency of data collection according to the pet's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input behavioral data of a pet into the generation AI and cause the generation AI to estimate emotions and adjust the collection frequency.

[0037] The collection unit can automatically adjust the sensitivity of the sensor according to the pet's activity environment. The collection unit automatically adjusts the sensitivity of the sensor according to the pet's activity environment, for example. For example, when the pet is indoors, the collection unit sets the sensor sensitivity low to detect even minute movements. Furthermore, when the pet is outdoors, the collection unit can set the sensor sensitivity high to detect only large movements. Furthermore, when the pet is in a specific area, the collection unit can automatically apply a sensitivity setting appropriate for that area. For example, the collection unit acquires the pet's location information and identifies the activity environment. The collection unit adjusts the sensor sensitivity according to the activity environment. The collection unit uses an algorithm that automatically adjusts the sensor sensitivity. This allows the collection unit to adjust the sensor sensitivity according to the pet's activity environment, thereby collecting more accurate data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the pet's location information to the generation AI and cause the generation AI to adjust the sensor sensitivity.

[0038] The collection unit can detect specific behaviors of the pet and prioritize collecting data corresponding to those behaviors. The collection unit, for example, detects specific behaviors of the pet. For example, the collection unit detects behaviors such as eating, sleeping, and exercise. The collection unit also prioritizes collecting data corresponding to those behaviors. For example, if the pet is eating, the collection unit prioritizes collecting the heart rate and body temperature while the pet is eating. If the pet is sleeping, the collection unit can also prioritize collecting the quality of sleep and body temperature. If the pet is exercising, the collection unit can also prioritize collecting the activity level and heart rate. For example, the collection unit uses a sensor that detects the pet's behavior. The collection unit uses an algorithm that prioritizes collecting data corresponding to the behavior. The collection unit analyzes the behavioral data and selects the data to prioritize collection. As a result, the collection unit prioritizes collecting data corresponding to the pet's specific behaviors, thereby obtaining more detailed health data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pet behavior data into the generation AI and have the generation AI select the data to be collected preferentially.

[0039] When collecting pet health data, the collection unit can complement the data in cooperation with the owner's smartphone or other devices. For example, when collecting pet health data, the collection unit complements the data in cooperation with the owner's smartphone or other devices. For example, the collection unit acquires GPS data from the owner's smartphone and complements the pet's location information. The collection unit can also acquire heart rate data from the owner's smartwatch and compare it with the pet's heart rate data. The collection unit can also acquire room temperature data from the owner's smart home device and associate it with the pet's body temperature data. For example, the collection unit acquires location information in cooperation with the owner's smartphone. The collection unit acquires heart rate data in cooperation with the owner's smartwatch. The collection unit acquires room temperature data in cooperation with the owner's smart home device. This allows the collection unit to collect more detailed health data by cooperating with the owner's devices. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data obtained from the owner's device into the generation AI and have the generation AI complete the data.

[0040] The collection unit can estimate the pet's emotion and select the type of data to collect based on the estimated pet's emotion. The collection unit, for example, estimates the pet's emotion. For example, the collection unit estimates the pet's emotion using behavioral analysis and facial expression analysis. The collection unit also selects the type of data to collect based on the estimated pet's emotion. For example, if the pet is stressed, the collection unit prioritizes collecting heart rate and body temperature. If the pet is relaxed, the collection unit can also prioritize collecting activity level and sleep data. If the pet is excited, the collection unit can also prioritize collecting movement data and respiratory rate. For example, the collection unit analyzes the pet's behavioral data to estimate the emotion. The collection unit analyzes the pet's facial expression data to estimate the emotion. The collection unit selects the type of data to collect based on the pet's emotion. In this way, the collection unit can collect more appropriate data by selecting the type of data to collect according to the pet's emotion. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input behavioral data of a pet into the generation AI and have the generation AI estimate emotions and select data to collect.

[0041] The collection unit can acquire location information of the pet and optimize collection of health data based on the location information. The collection unit, for example, acquires location information of the pet. For example, the collection unit acquires location information of the pet using GPS data. The collection unit also optimizes collection of health data based on the location information. For example, the collection unit prioritizes collection of activity data when the pet is outdoors. The collection unit can also prioritize collection of heart rate and body temperature data when the pet is indoors. The collection unit can also collect data appropriate for a specific area when the pet is in that area. For example, the collection unit acquires location information of the pet and identifies the activity environment. The collection unit selects the type of data to collect depending on the activity environment. The collection unit uses an algorithm that optimizes data collection based on the location information. As a result, the collection unit can collect more accurate data by optimizing data collection based on the pet's location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input location information of the pet to a generation AI and cause the generation AI to optimize data collection.

[0042] When collecting pet health data, the collection unit can start data collection when triggered by the owner's voice or behavior. For example, when collecting pet health data, the collection unit starts data collection when triggered by the owner's voice or behavior. For example, when the owner calls the pet's name, the collection unit starts collecting heart rate and body temperature data. The collection unit can also start collecting activity level and body temperature data when the owner touches the pet. The collection unit can also start data collection when the owner issues a specific command to the pet. For example, the collection unit detects the owner's voice using voice recognition technology. The collection unit detects the owner's behavior using a behavior detection algorithm. The collection unit starts data collection when triggered by the owner's voice or behavior. This allows the collection unit to collect data at more appropriate times by starting data collection when triggered by the owner's voice or behavior. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the owner's voice and behavioral data into the generation AI and cause the generation AI to start collecting data.

[0043] When collecting health data of a pet, the collection unit can simultaneously collect interaction data with other pets. For example, when collecting health data of a pet, the collection unit simultaneously collects interaction data with other pets. For example, the collection unit collects activity level and heart rate data when the pet is playing with other pets. The collection unit can also collect mealtime data when the pet is eating with other pets. The collection unit can also collect sleep data when the pet is sleeping with other pets. For example, the collection unit uses a sensor that detects the pet's behavior. The collection unit uses an algorithm that collects interaction data. The collection unit collects interaction data with other pets. By collecting interaction data with other pets, the collection unit can thereby understand the health condition of the pet in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input behavioral data of the pet to a generation AI and cause the generation AI to collect interaction data.

[0044] The analysis unit can estimate the pet's emotions and adjust the anomaly detection threshold based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions. For example, the analysis unit estimates the pet's emotions using behavioral analysis and facial expression analysis. The analysis unit also adjusts the anomaly detection threshold based on the estimated pet's emotions. For example, if the pet is stressed, the analysis unit sets the anomaly detection threshold low to detect an abnormality early. If the pet is relaxed, the analysis unit can set the anomaly detection threshold high to prevent excessive alerts. If the pet is excited, the analysis unit can set the anomaly detection threshold to a medium level to detect an abnormality at an appropriate time. For example, the analysis unit analyzes the pet's behavioral data to estimate the emotions. The analysis unit analyzes the pet's facial expression data to estimate the emotions. The analysis unit adjusts the anomaly detection threshold based on the pet's emotions. In this way, the analysis unit can adjust the anomaly detection threshold according to the pet's emotions, thereby enabling more appropriate anomaly detection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input pet behavior data to the generation AI and cause the generation AI to estimate emotions and adjust anomaly detection thresholds.

[0045] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the influence of seasons and weather. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the influence of seasons and weather. For example, the analysis unit sets a lower threshold for detecting an increase in body temperature as an abnormality in summer. The analysis unit can also set a lower threshold for detecting a decrease in activity level as an abnormality in winter. The analysis unit can also set a higher threshold for detecting a decrease in activity level as an abnormality on rainy days. For example, the analysis unit acquires seasonal and weather data and adjusts the abnormality detection threshold. The analysis unit uses an algorithm for detecting abnormalities by taking into account the influence of seasons and weather. The analysis unit detects abnormalities by taking into account the influence of seasons and weather. This enables the analysis unit to detect abnormalities more accurately by taking into account the influence of seasons and weather. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input seasonal and weather data into the generation AI and cause the generation AI to adjust the abnormality detection threshold.

[0046] When analyzing the pet's health data, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed. For example, when analyzing the pet's health data, the analysis unit applies an anomaly detection algorithm according to the pet's age and breed. For example, the analysis unit applies an algorithm that detects a decrease in activity level as an abnormality to young pets. The analysis unit can also apply an algorithm that detects heart rate fluctuations as an abnormality to elderly pets. The analysis unit can also apply an anomaly detection algorithm that takes into account health risks specific to a specific breed. For example, the analysis unit acquires the pet's age data and applies an anomaly detection algorithm according to the age. The analysis unit acquires the pet's breed data and applies an anomaly detection algorithm according to the breed. The analysis unit applies an anomaly detection algorithm according to the age and breed. This enables the analysis unit to apply an anomaly detection algorithm according to the pet's age and breed, thereby enabling more appropriate anomaly detection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's age data and breed data to the generation AI and cause the generation AI to apply an anomaly detection algorithm.

[0047] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking the pet's past medical history into account. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking the pet's past medical history into account. For example, if the pet has previously suffered from heart disease, the analysis unit can detect fluctuations in heart rate as an abnormality. Furthermore, if the pet has previously suffered from arthritis, the analysis unit can detect a decrease in activity level as an abnormality. Furthermore, if the pet has previously suffered from digestive problems, the analysis unit can detect fluctuations in food intake as an abnormality. For example, the analysis unit acquires the pet's past medical history data and reflects it in abnormality detection. The analysis unit uses an algorithm that detects abnormalities by taking the pet's past medical history into account. The analysis unit detects abnormalities by taking the pet's past medical history into account. This enables the analysis unit to more appropriately detect abnormalities by taking the pet's past medical history into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past medical history data into the generation AI and cause the generation AI to perform an analysis for abnormality detection.

[0048] The analysis unit can estimate the pet's emotions and determine the priority of abnormality detection based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions. For example, the analysis unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The analysis unit also determines the priority of abnormality detection based on the estimated pet's emotions. For example, the analysis unit prioritizes detecting abnormalities in heart rate when the pet is stressed. The analysis unit can also prioritize detecting abnormalities in activity level when the pet is relaxed. The analysis unit can also prioritize detecting abnormalities in body temperature when the pet is excited. For example, the analysis unit analyzes behavioral data of the pet to estimate the emotions. The analysis unit analyzes facial expression data of the pet to estimate the emotions. The analysis unit determines the priority of abnormality detection based on the pet's emotions. As a result, the analysis unit can prioritize detecting more important abnormalities by determining the priority of abnormality detection according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input pet behavior data into the generation AI and have the generation AI perform emotion estimation and determine priorities for anomaly detection.

[0049] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's dietary content and calorie intake. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the pet's dietary content and calorie intake. For example, if the pet is eating excessively, the analysis unit detects weight gain as an abnormality. Furthermore, if the pet is not eating, the analysis unit can also detect weight loss as an abnormality. Furthermore, if the pet is consuming an excessive amount of a specific nutrient, the analysis unit can detect the effects of this as an abnormality. For example, the analysis unit acquires the pet's dietary data and calculates the calorie intake. The analysis unit uses an algorithm to detect abnormalities based on the calorie intake. The analysis unit detects abnormalities by taking into account the dietary content and calorie intake. This enables the analysis unit to more accurately detect abnormalities by taking into account the pet's dietary content and calorie intake. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's dietary data into the generation AI and cause the generation AI to perform an analysis to detect abnormalities.

[0050] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's amount of exercise and activity pattern. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the pet's amount of exercise and activity pattern. For example, if the pet is exercising less than usual, the analysis unit detects insufficient exercise as an abnormality. Furthermore, if the pet is exercising more than usual, the analysis unit can detect excessive exercise as an abnormality. Furthermore, if the pet's activity pattern changes suddenly, the analysis unit can detect the change as an abnormality. For example, the analysis unit acquires the pet's exercise data and calculates the amount of exercise. The analysis unit uses an algorithm that detects abnormalities based on the amount of exercise and activity pattern. The analysis unit detects abnormalities by taking into account the amount of exercise and activity pattern. This enables the analysis unit to detect abnormalities more accurately by taking into account the pet's amount of exercise and activity pattern. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's exercise data to a generation AI and have the generation AI perform an analysis to detect abnormalities.

[0051] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking the pet's sleep patterns into consideration. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking the pet's sleep patterns into consideration. For example, if the pet sleeps for a shorter period of time than usual, the analysis unit detects insufficient sleep as an abnormality. Furthermore, if the pet sleeps for a longer period of time than usual, the analysis unit can detect excessive sleep as an abnormality. Furthermore, if the pet sleeps for a longer period of time than usual, the analysis unit can detect a sudden change in the pet's sleep pattern as an abnormality. For example, the analysis unit acquires the pet's sleep data and calculates the sleep time. The analysis unit uses an algorithm that detects abnormalities based on the sleep patterns. The analysis unit detects abnormalities by taking the sleep patterns into consideration. This enables the analysis unit to detect abnormalities more accurately by taking the pet's sleep patterns into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's sleep data to a generation AI and cause the generation AI to perform an analysis to detect abnormalities.

[0052] The notification unit can estimate the pet's emotion and adjust the urgency of the notification based on the estimated pet's emotion. The notification unit, for example, estimates the pet's emotion. For example, the notification unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The notification unit also adjusts the urgency of the notification based on the estimated pet's emotion. For example, the notification unit sends a high-urgency notification to the owner if the pet is stressed. The notification unit can also send a low-urgency notification to the owner if the pet is relaxed. The notification unit can also send a medium-urgency notification to the owner if the pet is excited. For example, the notification unit analyzes the pet's behavioral data to estimate the emotion. The notification unit analyzes the pet's facial expression data to estimate the emotion. The notification unit adjusts the urgency of the notification based on the pet's emotion. This allows the notification unit to adjust the urgency of the notification according to the pet's emotion, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input pet behavior data into the generation AI and have the generation AI estimate emotions and adjust the urgency of notifications.

[0053] When an abnormality is detected, the notification unit can select a notification method according to the owner's current situation. For example, when an abnormality is detected, the notification unit selects a notification method according to the owner's current situation. For example, when the owner is at work, the notification unit prioritizes sending email or app notifications. Furthermore, when the owner is out, the notification unit can also prioritize sending SMS or phone notifications. Furthermore, when the owner is at home, the notification unit can send a notification through a smart home device. For example, the notification unit acquires the owner's location information and identifies the current situation. The notification unit acquires the owner's activity status and selects a notification method. The notification unit selects a notification method according to the owner's current situation. This enables the notification unit to select a notification method according to the owner's situation, thereby enabling more effective notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input the owner's location information and activity status data into the generation AI and have the generation AI select the notification method.

[0054] When an abnormality is detected, the notification unit can generate optimal notification content by referring to the owner's past response history. For example, when an abnormality is detected, the notification unit generates optimal notification content by referring to the owner's past response history. For example, the notification unit generates detailed notification content for an abnormality that the owner responded to quickly in the past. The notification unit can also generate concise notification content for an abnormality that the owner responded slowly in the past. The notification unit can also generate notification content including a specific method by which the owner responded in the past. For example, the notification unit acquires the owner's past notification history and analyzes the response history. The notification unit uses an algorithm that generates notification content based on the past response history. The notification unit generates optimal notification content by referring to the past response history. As a result, the notification unit can generate more appropriate notification content by referring to the owner's past response history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the owner's past notification history data into the generation AI and have the generation AI generate the notification content.

[0055] The notification unit can provide a notification including the current location information of the pet when an abnormality is detected. For example, when an abnormality is detected, the notification unit provides a notification including the current location information of the pet. For example, when the pet is outdoors, the notification unit sends a notification to the owner including the current location information. Furthermore, when the pet is indoors, the notification unit can also send a notification to the owner including the location information of the room. Furthermore, when the pet is in a specific area, the notification unit can also send a notification to the owner including detailed location information of the area. For example, the notification unit acquires the pet's location information and includes it in the notification content. The notification unit uses an algorithm that generates the notification content based on the location information. The notification unit provides a notification including the pet's current location information. As a result, the notification unit includes the pet's current location information, allowing the owner to respond quickly. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pet's location information data to a generation AI and cause the generation AI to generate the notification content.

[0056] The notification unit can estimate the pet's emotion and customize the notification content based on the estimated pet's emotion. The notification unit, for example, estimates the pet's emotion. For example, the notification unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The notification unit also customizes the notification content based on the estimated pet's emotion. For example, the notification unit generates notification content with a high level of urgency when the pet is stressed. The notification unit can also generate notification content with a low level of urgency when the pet is relaxed. The notification unit can also generate notification content with a medium level of urgency when the pet is excited. For example, the notification unit analyzes the pet's behavioral data to estimate the emotion. The notification unit analyzes the pet's facial expression data to estimate the emotion. The notification unit customizes the notification content based on the pet's emotion. This allows the notification unit to customize the notification content according to the pet's emotion, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input pet behavior data into the generation AI and have the generation AI estimate emotions and customize the notification content.

[0057] The notification unit can send a notification to the owner's smartwatch or other wearable device when an abnormality is detected. For example, the notification unit sends a notification to the owner's smartwatch or other wearable device when an abnormality is detected. For example, the notification unit can send a vibration notification to the owner's smartwatch to notify the abnormality. The notification unit can also send a notification to the owner's fitness tracker to notify the abnormality. The notification unit can also send a visual notification to the owner's smartglasses to notify the abnormality. For example, the notification unit can work with the owner's smartwatch to send a notification. The notification unit can work with the owner's fitness tracker to send a notification. The notification unit can also work with the owner's smartglasses to send a notification. This allows the notification unit to send a notification to the owner's wearable device, enabling a prompt response. Some or all of the above-mentioned processing by the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input data for linking with the owner's wearable device into the generation AI and cause the generation AI to send a notification.

[0058] When an abnormality is detected, the notification unit can simultaneously send a notification to the owner's family and the pet sitter. For example, when an abnormality is detected, the notification unit simultaneously sends a notification to the owner's family and the pet sitter. For example, the notification unit sends an email notification to all of the owner's family members to notify them of the abnormality. The notification unit can also send an SMS notification to the pet sitter to notify them of the abnormality. The notification unit can also send an app notification to the owner's family and the pet sitter to notify them of the abnormality. For example, the notification unit obtains contact information for the owner's family members and sends the notification. The notification unit obtains contact information for the pet sitter and sends the notification. The notification unit simultaneously sends notifications to the family members and the pet sitter. This allows the notification unit to send notifications to the owner's family and the pet sitter, enabling a prompt response. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input contact information for the family members and the pet sitter into the generation AI and cause the generation AI to send the notification.

[0059] When an abnormality is detected, the notification unit can provide a notification including a graph and statistical information of the pet's health data. For example, when an abnormality is detected, the notification unit provides a notification including a graph and statistical information of the pet's health data. For example, when an abnormality is detected, the notification unit transmits a notification including a graph of the pet's heart rate. When an abnormality is detected, the notification unit can also transmit a notification including statistical information of the pet's body temperature. When an abnormality is detected, the notification unit can also transmit a notification including a graph of the pet's activity level. For example, the notification unit acquires the pet's health data and generates graphs and statistical information. The notification unit uses an algorithm to generate a notification including graphs and statistical information. The notification unit provides a notification including graphs and statistical information of the pet's health data. By including the graphs and statistical information of the pet's health data, the notification unit can allow the owner to obtain more detailed information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pet's health data to a generation AI and cause the generation AI to generate graphs and statistical information and send a notification.

[0060] The providing unit can estimate the pet's emotions and adjust care or diet advice based on the estimated pet emotions. The providing unit, for example, estimates the pet's emotions. For example, the providing unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The providing unit also adjusts the care or diet advice based on the estimated pet emotions. For example, if the pet is stressed, the providing unit suggests a care method that will help the pet relax. If the pet is relaxed, the providing unit can also suggest a normal care method. If the pet is excited, the providing unit can also suggest a care method to calm the pet. For example, the providing unit analyzes the pet's behavioral data to estimate the emotions. The providing unit analyzes the pet's facial expression data to estimate the emotions. The providing unit adjusts the care or diet advice based on the pet's emotions. This allows the providing unit to adjust the care or diet advice according to the pet's emotions, enabling more appropriate care. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input behavioral data of a pet into the generating AI and have the generating AI estimate emotions and adjust care and dietary advice.

[0061] The providing unit can provide seasonal care and diet advice based on the pet's health condition. The providing unit provides seasonal care and diet advice based on, for example, the pet's health condition. For example, the providing unit can suggest a care method to lower the pet's body temperature in the summer. The providing unit can also suggest a care method to maintain the pet's body temperature in the winter. The providing unit can also suggest a care method to prevent allergies in the spring. For example, the providing unit acquires pet health data and generates seasonal care and diet advice. The providing unit uses an algorithm to provide seasonal care and diet advice. The providing unit provides seasonal care and diet advice based on the pet's health condition. As a result, the providing unit can maintain the pet's health by providing seasonal care and diet advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input pet health data to a generation AI and cause the generation AI to generate seasonal care and diet advice.

[0062] The providing unit can provide care and diet advice according to the age and breed of the pet based on the health condition of the pet. The providing unit provides care and diet advice according to the age and breed of the pet based on, for example, the health condition of the pet. For example, the providing unit provides diet advice to promote growth to a young pet. The providing unit can also provide care methods to maintain joint health to an elderly pet. The providing unit can also provide care methods for a specific breed that take into account health risks specific to that breed. For example, the providing unit acquires age data of the pet and generates care and diet advice according to the age. The providing unit acquires breed data of the pet and generates care and diet advice according to the breed. The providing unit provides care and diet advice according to the age and breed based on the health condition of the pet. This enables more appropriate care by providing care and diet advice according to the age and breed of the pet. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the pet's age data and breed data into the generation AI and cause the generation AI to generate care and dietary advice.

[0063] The providing unit can provide care and diet advice that takes into account the pet's past medical history based on the pet's health condition. The providing unit provides care and diet advice that takes into account the pet's past medical history based on the pet's health condition, for example. For example, if the pet has previously suffered from heart disease, the providing unit can provide heart-friendly diet advice. Furthermore, if the pet has previously suffered from arthritis, the providing unit can provide care methods to maintain joint health. Furthermore, if the pet has previously suffered from digestive problems, the providing unit can provide digestive-friendly diet advice. For example, the providing unit acquires data on the pet's past medical history and generates care and diet advice based on the medical history. The providing unit uses an algorithm that provides care and diet advice that takes into account the pet's past medical history. The providing unit provides care and diet advice that takes into account the pet's past medical history based on the pet's health condition. This enables the providing unit to provide care and diet advice that takes into account the pet's past medical history, thereby enabling more appropriate care. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provider can input the pet's past medical history data into the generation AI and have the generation AI generate care and dietary advice.

[0064] The providing unit can estimate the pet's emotions and determine the priority of care or dietary advice based on the estimated pet's emotions. The providing unit, for example, estimates the pet's emotions. For example, the providing unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The providing unit also determines the priority of care or dietary advice based on the estimated pet's emotions. For example, if the pet is stressed, the providing unit can preferentially suggest a care method that helps the pet relax. If the pet is relaxed, the providing unit can also preferentially suggest a normal care method. If the pet is excited, the providing unit can also preferentially suggest a care method that calms the pet. For example, the providing unit analyzes the pet's behavioral data to estimate the emotions. The providing unit analyzes the pet's facial expression data to estimate the emotions. The providing unit determines the priority of care or dietary advice based on the pet's emotions. In this way, the providing unit can prioritize the provision of more important care by determining the priority of care or dietary advice according to the pet's emotions. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input behavioral data of a pet to the generating AI and cause the generating AI to estimate emotions and determine priorities for care and dietary advice.

[0065] The providing unit can provide an exercise plan according to the amount of exercise and activity pattern of the pet based on the pet's health condition. The providing unit provides an exercise plan according to the amount of exercise and activity pattern of the pet based on, for example, the pet's health condition. For example, the providing unit proposes an appropriate exercise plan if the pet is not exercising enough. The providing unit can also propose a plan to adjust the amount of exercise if the pet is exercising excessively. The providing unit can also provide an optimal exercise plan based on the pet's activity pattern. For example, the providing unit acquires the pet's exercise data and generates an exercise plan. The providing unit uses an algorithm to provide an exercise plan based on the amount of exercise and activity pattern. The providing unit provides an exercise plan according to the amount of exercise and activity pattern based on the pet's health condition. In this way, the providing unit can promote appropriate exercise by providing an exercise plan according to the pet's amount of exercise and activity pattern. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's exercise data into a generation AI and cause the generation AI to generate an exercise plan.

[0066] The provider unit can provide a meal plan based on the pet's dietary content and calorie intake based on the pet's health condition. The provider unit provides a meal plan based on the pet's dietary content and calorie intake based on the pet's health condition, for example. For example, the provider unit can suggest a calorie-restricted meal plan if the pet is eating too much. The provider unit can also suggest a nutritionally balanced meal plan if the pet is not eating enough. The provider unit can also provide an optimal meal plan based on the pet's health condition. For example, the provider unit acquires the pet's dietary data and calculates the calorie intake. The provider unit uses an algorithm to provide a meal plan based on the calorie intake. The provider unit provides a meal plan based on the pet's health condition based on the dietary content and calorie intake. This enables the provider unit to provide a meal plan based on the pet's dietary content and calorie intake, thereby enabling appropriate nutritional management. Some or all of the above-described processing by the provider unit may be performed using, for example, AI, or may be performed without AI. For example, the provider unit can input the pet's dietary data into a generation AI and cause the generation AI to generate a meal plan.

[0067] The providing unit can provide a care plan according to the pet's sleep pattern based on the pet's health condition. The providing unit provides a care plan according to the pet's sleep pattern based on, for example, the pet's health condition. For example, if the pet is sleep-deprived, the providing unit can suggest a relaxing care plan. Furthermore, if the pet is sleeping excessively, the providing unit can suggest an active care plan. Furthermore, the providing unit can provide an optimal care plan based on the pet's sleep pattern. For example, the providing unit acquires the pet's sleep data and analyzes the sleep pattern. The providing unit uses an algorithm to provide a care plan based on the sleep pattern. The providing unit provides a care plan according to the sleep pattern based on the pet's health condition. As a result, the providing unit provides a care plan according to the pet's sleep pattern, enabling appropriate sleep management. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's sleep data into a generation AI and cause the generation AI to generate a care plan.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] When analyzing the pet's health data, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed. For example, the analysis unit can apply an algorithm that detects a decrease in activity level as an abnormality to young pets. The analysis unit can also apply an algorithm that detects heart rate fluctuations as an abnormality to elderly pets. The analysis unit can also apply an anomaly detection algorithm that takes into account the health risks specific to a specific breed. For example, the analysis unit can acquire the pet's age data and apply an anomaly detection algorithm according to the age. The analysis unit can acquire the pet's breed data and apply an anomaly detection algorithm according to the breed. The analysis unit can apply an anomaly detection algorithm according to the age and breed. In this way, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed, enabling more appropriate anomaly detection. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's age data and breed data to the generation AI and cause the generation AI to apply an anomaly detection algorithm.

[0070] The providing unit can provide seasonal care and diet advice based on the pet's health condition. For example, the providing unit can suggest a care method to lower the pet's body temperature in the summer. The providing unit can also suggest a care method to maintain the pet's body temperature in the winter. The providing unit can also suggest a care method to prevent allergies in the spring. For example, the providing unit acquires the pet's health data and generates seasonal care and diet advice. The providing unit uses an algorithm to provide seasonal care and diet advice. The providing unit provides seasonal care and diet advice based on the pet's health condition. In this way, the providing unit can maintain the pet's health by providing seasonal care and diet advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's health data into a generating AI and cause the generating AI to generate seasonal care and diet advice.

[0071] When an abnormality is detected, the notification unit can select a notification method according to the owner's current situation. For example, if the owner is at work, the notification unit can prioritize sending email or app notifications. Furthermore, if the owner is out, the notification unit can also prioritize sending SMS or phone notifications. Furthermore, if the owner is at home, the notification unit can send a notification via a smart home device. For example, the notification unit acquires the owner's location information and identifies the current situation. The notification unit acquires the owner's activity status and selects a notification method. The notification unit selects a notification method according to the owner's current situation. This enables the notification unit to select a notification method according to the owner's situation, thereby enabling more effective notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the owner's location information and activity status data to the generation AI and have the generation AI select a notification method.

[0072] The providing unit can provide care and diet advice tailored to the age and breed of the pet based on the pet's health condition. For example, the providing unit can provide diet advice to promote growth for young pets. The providing unit can also provide care methods to maintain joint health for elderly pets. The providing unit can also provide care methods for specific breeds that take into account health risks specific to that breed. For example, the providing unit can acquire age data of the pet and generate care and diet advice tailored to the age. The providing unit can acquire breed data of the pet and generate care and diet advice tailored to the breed. The providing unit can provide care and diet advice tailored to the age and breed based on the pet's health condition. This allows the providing unit to provide care and diet advice tailored to the age and breed of the pet, enabling more appropriate care. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the pet's age data and breed data into a generation AI and cause the generation AI to generate care and diet advice.

[0073] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the influence of seasons and weather. For example, the analysis unit can set a lower threshold for detecting an increase in body temperature as an abnormality in summer. The analysis unit can also set a lower threshold for detecting a decrease in activity level as an abnormality in winter. The analysis unit can also set a higher threshold for detecting a decrease in activity level as an abnormality on rainy days. For example, the analysis unit acquires seasonal and weather data and adjusts the abnormality detection threshold. The analysis unit uses an algorithm for detecting abnormalities that takes into account the influence of seasons and weather. The analysis unit detects abnormalities by taking into account the influence of seasons and weather. This enables the analysis unit to detect abnormalities more accurately by taking into account the influence of seasons and weather. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input seasonal and weather data into the generation AI and cause the generation AI to adjust the abnormality detection threshold.

[0074] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's past medical history. For example, if the pet has previously suffered from heart disease, the analysis unit can detect fluctuations in heart rate as abnormal. Furthermore, if the pet has previously suffered from arthritis, the analysis unit can also detect a decrease in activity level as abnormal. Furthermore, if the pet has previously suffered from digestive problems, the analysis unit can detect fluctuations in food intake as abnormal. For example, the analysis unit acquires the pet's past medical history data and reflects it in abnormality detection. The analysis unit uses an algorithm that takes into account the pet's past medical history to detect abnormalities. The analysis unit detects abnormalities by taking into account the pet's past medical history. This enables the analysis unit to more appropriately detect abnormalities by taking into account the pet's past medical history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past medical history data into the generation AI and have the generation AI perform an analysis to detect abnormalities.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects the pet's health data. The pet's health data includes body temperature, heart rate, activity level, etc. The collection unit measures this data using a body temperature sensor, a heart rate sensor, and an activity level sensor. For example, the body temperature sensor measures body temperature using a contact sensor or a non-contact sensor, the heart rate sensor measures heart rate using an optical sensor or an electrical sensor, and the activity level sensor measures activity level using an acceleration sensor or a gyro sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and detects abnormalities. Abnormalities include elevated body temperature and abnormal heart rate. In addition to detecting abnormalities by comparing the data with past data, the analysis unit can also analyze the data using AI to detect abnormalities. For example, data from the past year can be stored in cloud storage and a predictive model can be built. Step 3: The notification unit notifies the owner based on the abnormality detected by the analysis unit. The notification may be a push notification to a smartphone or an email notification. For example, the notification unit may send a push notification to a smartphone and also an email. Step 4: The provider provides appropriate care and dietary advice based on the data analyzed by the analyzer. Care and dietary advice may include cooling methods, exercise plans, and meal plans. For example, if the pet's body temperature is higher than normal, the provider suggests cooling methods, and if the pet's activity level is decreasing, the provider suggests an exercise plan. The provider also provides dietary advice and reminders for regular health checks.

[0077] (Example 2) A pet health management system according to an embodiment of the present invention monitors a pet's health status in real time and provides appropriate care. The pet health management system uses temperature sensors, heart rate sensors, and activity sensors attached to the pet to collect health data, which is then analyzed by AI. If an abnormality is detected, the owner is notified, and the AI ​​provides appropriate care and dietary advice based on the pet's health status. For example, if a pet's body temperature is higher than normal, the AI ​​suggests cooling methods to the owner. If a pet's activity level decreases, the AI ​​warns the owner about lack of exercise and suggests an appropriate exercise plan. The AI ​​also provides dietary advice and reminders for regular health checks. This allows owners to monitor their pet's health status in real time and provide appropriate care. For example, it can detect abnormalities early and take appropriate measures before the pet becomes ill. It is also possible to accumulate pet health data over a long period of time and track changes in health status. This is expected to lead to more effective pet health management and extend the pet's lifespan. This allows the pet health management system to monitor a pet's health status in real time and provide appropriate care. For example, it will be possible to detect abnormalities early on before pets become ill and take appropriate measures. It will also be possible to accumulate pet health data over a long period of time and track changes in health status. This will enable more effective health management of pets and is expected to extend their lifespans.

[0078] A pet health management system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a provision unit. The collection unit collects health data of the pet. The pet's health data includes, but is not limited to, body temperature, heart rate, and activity level. The collection unit measures the pet's body temperature using, for example, a body temperature sensor. The collection unit can also measure the pet's heart rate using a heart rate sensor. The collection unit can also measure the pet's activity level using an activity level sensor. For example, the body temperature sensor measures body temperature using a contact sensor or a non-contact sensor. The heart rate sensor measures heart rate using an optical sensor or an electrical sensor. The activity level sensor measures activity level using an acceleration sensor or a gyro sensor. The analysis unit analyzes the data collected by the collection unit to detect abnormalities. Examples of abnormalities include, but are not limited to, elevated body temperature and abnormal heart rate. The analysis unit detects abnormalities by comparing the data with past data. The analysis unit can also analyze data using AI to detect abnormalities. For example, the analysis unit stores data from the past year in cloud storage and builds a predictive model based on the past data. The notification unit notifies the owner based on the abnormality detected by the analysis unit. Examples of notifications include, but are not limited to, notifications to a smartphone or email notifications. For example, the notification unit sends a push notification to the smartphone. The notification unit can also send emails. The provision unit provides appropriate care and dietary advice based on the data analyzed by the analysis unit. Examples of care and dietary advice include, but are not limited to, cooling methods, exercise plans, and dietary plans. For example, the provision unit suggests cooling methods when the pet's body temperature is higher than normal. The provision unit can also suggest exercise plans when the pet's activity level is decreasing. The provision unit can also provide dietary advice and reminders for regular health checks. For example, the provision unit suggests cooling methods when the pet's body temperature is high. The provision unit suggests exercise plans when the pet's activity level is decreasing. The provision unit provides dietary advice and reminders for regular health checks.As a result, the pet health management system according to the embodiment can grasp the health condition of a pet in real time and provide appropriate care. For example, it can detect abnormalities early before a pet becomes ill and take appropriate measures. It can also accumulate health data of a pet over a long period of time and track changes in the health condition. This is expected to enable more effective health management of pets and extend the lifespan of pets.

[0079] The collection unit includes a body temperature sensor, a heart rate sensor, and an activity sensor. The body temperature sensor measures body temperature using, for example, a contact sensor or a non-contact sensor. For example, a contact sensor measures body temperature by directly contacting the pet's skin. A non-contact sensor measures the pet's body temperature using infrared rays. The heart rate sensor measures heart rate using, for example, an optical sensor or an electrical sensor. For example, an optical sensor measures heart rate by irradiating light onto the pet's skin and measuring reflected light. An electrical sensor measures heart rate by attaching electrodes to the pet's skin and taking an electrocardiogram. The activity sensor measures activity using, for example, an acceleration sensor or a gyro sensor. For example, an acceleration sensor measures activity by detecting the pet's movements. A gyro sensor measures activity by detecting the pet's rotational movement. This allows the collection unit to collect pet health data from multiple angles. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a body temperature sensor, a heart rate sensor, and an activity sensor into the generation AI and have the generation AI analyze the data.

[0080] The analysis unit can detect anomalies by comparing with past data. For example, the analysis unit stores data from the past year in cloud storage and builds a predictive model based on the past data. For example, the analysis unit compares past data with current data to detect anomalies. The analysis unit can also analyze data using AI to detect anomalies. For example, the analysis unit detects anomalies using a machine learning model. A machine learning model can learn from large amounts of data and identify abnormal patterns. This allows the analysis unit to detect anomalies early by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past data and current data into a generation AI and have the generation AI detect anomalies.

[0081] The providing unit can suggest a cooling method when the pet's body temperature rises above normal. The providing unit suggests a cooling method when the pet's body temperature is higher than normal, for example. For example, the providing unit suggests a method of using a cooling sheet. The providing unit can also suggest a method of using cooling gel. The providing unit can also suggest a method of using a cooling fan. For example, the providing unit suggests a method of laying a cooling sheet on the pet's bed. The providing unit suggests a method of applying cooling gel to the pet's body. The providing unit suggests a method of installing a cooling fan near the pet. In this way, the providing unit can suggest an appropriate cooling method when the pet's body temperature is high. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's body temperature data into the generating AI and cause the generating AI to suggest a cooling method.

[0082] The providing unit can propose an exercise plan when the pet's activity level is decreasing. The providing unit proposes an exercise plan when the pet's activity level is decreasing, for example. For example, the providing unit proposes a plan to increase the number of walks. The providing unit can also propose a plan to increase indoor play. The providing unit can also propose a plan to use exercise equipment. For example, the providing unit proposes a plan to increase the number of walks from two to three times a day. The providing unit proposes a plan to increase indoor ball play. The providing unit proposes a plan to use a pet treadmill. In this way, the providing unit can propose an appropriate exercise plan when the pet's activity level is decreasing. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's activity level data into the generating AI and cause the generating AI to execute the exercise plan proposal.

[0083] The providing unit can provide dietary advice or reminders for regular health checks. The providing unit, for example, provides dietary advice. For example, the providing unit can suggest a nutritionally balanced meal plan. The providing unit can also suggest a meal plan that manages calorie intake. The providing unit can also suggest a meal plan that supplements specific nutrients. For example, the providing unit can suggest a meal plan that restricts calories based on the pet's weight. The providing unit can suggest a meal plan that supplements vitamins and minerals. The providing unit can suggest a meal plan that includes specific nutrients based on the pet's health condition. The providing unit, for example, provides reminders for regular health checks. For example, the providing unit can send reminders for regular health checks. The providing unit can also send reminders for vaccinations. The providing unit can also send reminders for administering heartworm preventative medication. For example, the providing unit can send reminders for monthly health checks. The providing unit can send reminders for annual vaccinations. The providing unit can send reminders for monthly heartworm preventative medication. This allows the providing unit to more effectively manage the health of the pet. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the pet's health data into the generating AI and cause the generating AI to provide dietary advice and health check reminders.

[0084] The analysis unit can store data for a long period of time using cloud storage and build a predictive model based on past data. The analysis unit, for example, stores data for a long period of time using cloud storage. For example, the analysis unit stores data using cloud storage such as AWS or Google Cloud. The analysis unit also builds a predictive model based on past data. For example, the analysis unit builds a predictive model using a machine learning model. The machine learning model can learn from large amounts of data and predict future health risks. This allows the analysis unit to track changes in the pet's health condition over a long period of time and predict future health risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data stored in cloud storage into a generation AI and have the generation AI build a predictive model.

[0085] The collection unit can estimate the pet's emotion and adjust the frequency of health data collection based on the estimated pet's emotion. The collection unit, for example, estimates the pet's emotion. For example, the collection unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The collection unit also adjusts the frequency of health data collection based on the estimated pet's emotion. For example, if the pet is stressed, the collection unit increases the collection frequency to collect more detailed data. If the pet is relaxed, the collection unit can also reduce the collection frequency to reduce the burden of data collection. If the pet is excited, the collection unit can set the collection frequency to a medium level to focus on collecting activity data. For example, the collection unit analyzes the pet's behavioral data to estimate the emotion. The collection unit analyzes the pet's facial expression data to estimate the emotion. The collection unit adjusts the collection frequency based on the pet's emotion. This allows the collection unit to collect more accurate data by adjusting the frequency of data collection according to the pet's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input behavioral data of a pet into the generation AI and cause the generation AI to estimate emotions and adjust the collection frequency.

[0086] The collection unit can automatically adjust the sensitivity of the sensor according to the pet's activity environment. The collection unit automatically adjusts the sensitivity of the sensor according to the pet's activity environment, for example. For example, when the pet is indoors, the collection unit sets the sensor sensitivity low to detect even minute movements. Furthermore, when the pet is outdoors, the collection unit can set the sensor sensitivity high to detect only large movements. Furthermore, when the pet is in a specific area, the collection unit can automatically apply a sensitivity setting appropriate for that area. For example, the collection unit acquires the pet's location information and identifies the activity environment. The collection unit adjusts the sensor sensitivity according to the activity environment. The collection unit uses an algorithm that automatically adjusts the sensor sensitivity. This allows the collection unit to adjust the sensor sensitivity according to the pet's activity environment, thereby collecting more accurate data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the pet's location information to the generation AI and cause the generation AI to adjust the sensor sensitivity.

[0087] The collection unit can detect specific behaviors of the pet and prioritize collecting data corresponding to those behaviors. The collection unit, for example, detects specific behaviors of the pet. For example, the collection unit detects behaviors such as eating, sleeping, and exercise. The collection unit also prioritizes collecting data corresponding to those behaviors. For example, if the pet is eating, the collection unit prioritizes collecting the heart rate and body temperature while the pet is eating. If the pet is sleeping, the collection unit can also prioritize collecting the quality of sleep and body temperature. If the pet is exercising, the collection unit can also prioritize collecting the activity level and heart rate. For example, the collection unit uses a sensor that detects the pet's behavior. The collection unit uses an algorithm that prioritizes collecting data corresponding to the behavior. The collection unit analyzes the behavioral data and selects the data to prioritize collection. As a result, the collection unit prioritizes collecting data corresponding to the pet's specific behaviors, thereby obtaining more detailed health data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pet behavior data into the generation AI and have the generation AI select the data to be collected preferentially.

[0088] When collecting pet health data, the collection unit can complement the data in cooperation with the owner's smartphone or other devices. For example, when collecting pet health data, the collection unit complements the data in cooperation with the owner's smartphone or other devices. For example, the collection unit acquires GPS data from the owner's smartphone and complements the pet's location information. The collection unit can also acquire heart rate data from the owner's smartwatch and compare it with the pet's heart rate data. The collection unit can also acquire room temperature data from the owner's smart home device and associate it with the pet's body temperature data. For example, the collection unit acquires location information in cooperation with the owner's smartphone. The collection unit acquires heart rate data in cooperation with the owner's smartwatch. The collection unit acquires room temperature data in cooperation with the owner's smart home device. This allows the collection unit to collect more detailed health data by cooperating with the owner's devices. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data obtained from the owner's device into the generation AI and have the generation AI complete the data.

[0089] The collection unit can estimate the pet's emotion and select the type of data to collect based on the estimated pet's emotion. The collection unit, for example, estimates the pet's emotion. For example, the collection unit estimates the pet's emotion using behavioral analysis and facial expression analysis. The collection unit also selects the type of data to collect based on the estimated pet's emotion. For example, if the pet is stressed, the collection unit prioritizes collecting heart rate and body temperature. If the pet is relaxed, the collection unit can also prioritize collecting activity level and sleep data. If the pet is excited, the collection unit can also prioritize collecting movement data and respiratory rate. For example, the collection unit analyzes the pet's behavioral data to estimate the emotion. The collection unit analyzes the pet's facial expression data to estimate the emotion. The collection unit selects the type of data to collect based on the pet's emotion. In this way, the collection unit can collect more appropriate data by selecting the type of data to collect according to the pet's emotion. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input behavioral data of a pet into the generation AI and have the generation AI estimate emotions and select data to collect.

[0090] The collection unit can acquire location information of the pet and optimize collection of health data based on the location information. The collection unit, for example, acquires location information of the pet. For example, the collection unit acquires location information of the pet using GPS data. The collection unit also optimizes collection of health data based on the location information. For example, the collection unit prioritizes collection of activity data when the pet is outdoors. The collection unit can also prioritize collection of heart rate and body temperature data when the pet is indoors. The collection unit can also collect data appropriate for a specific area when the pet is in that area. For example, the collection unit acquires location information of the pet and identifies the activity environment. The collection unit selects the type of data to collect depending on the activity environment. The collection unit uses an algorithm that optimizes data collection based on the location information. As a result, the collection unit can collect more accurate data by optimizing data collection based on the pet's location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input location information of the pet to a generation AI and cause the generation AI to optimize data collection.

[0091] When collecting pet health data, the collection unit can start data collection when triggered by the owner's voice or behavior. For example, when collecting pet health data, the collection unit starts data collection when triggered by the owner's voice or behavior. For example, when the owner calls the pet's name, the collection unit starts collecting heart rate and body temperature data. The collection unit can also start collecting activity level and body temperature data when the owner touches the pet. The collection unit can also start data collection when the owner issues a specific command to the pet. For example, the collection unit detects the owner's voice using voice recognition technology. The collection unit detects the owner's behavior using a behavior detection algorithm. The collection unit starts data collection when triggered by the owner's voice or behavior. This allows the collection unit to collect data at more appropriate times by starting data collection when triggered by the owner's voice or behavior. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the owner's voice and behavioral data into the generation AI and cause the generation AI to start collecting data.

[0092] When collecting health data of a pet, the collection unit can simultaneously collect interaction data with other pets. For example, when collecting health data of a pet, the collection unit simultaneously collects interaction data with other pets. For example, the collection unit collects activity level and heart rate data when the pet is playing with other pets. The collection unit can also collect mealtime data when the pet is eating with other pets. The collection unit can also collect sleep data when the pet is sleeping with other pets. For example, the collection unit uses a sensor that detects the pet's behavior. The collection unit uses an algorithm that collects interaction data. The collection unit collects interaction data with other pets. By collecting interaction data with other pets, the collection unit can thereby understand the health condition of the pet in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input behavioral data of the pet to a generation AI and cause the generation AI to collect interaction data.

[0093] The analysis unit can estimate the pet's emotions and adjust the anomaly detection threshold based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions. For example, the analysis unit estimates the pet's emotions using behavioral analysis and facial expression analysis. The analysis unit also adjusts the anomaly detection threshold based on the estimated pet's emotions. For example, if the pet is stressed, the analysis unit sets the anomaly detection threshold low to detect an abnormality early. If the pet is relaxed, the analysis unit can set the anomaly detection threshold high to prevent excessive alerts. If the pet is excited, the analysis unit can set the anomaly detection threshold to a medium level to detect an abnormality at an appropriate time. For example, the analysis unit analyzes the pet's behavioral data to estimate the emotions. The analysis unit analyzes the pet's facial expression data to estimate the emotions. The analysis unit adjusts the anomaly detection threshold based on the pet's emotions. In this way, the analysis unit can adjust the anomaly detection threshold according to the pet's emotions, thereby enabling more appropriate anomaly detection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input pet behavior data to the generation AI and cause the generation AI to estimate emotions and adjust anomaly detection thresholds.

[0094] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the influence of seasons and weather. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the influence of seasons and weather. For example, the analysis unit sets a lower threshold for detecting an increase in body temperature as an abnormality in summer. The analysis unit can also set a lower threshold for detecting a decrease in activity level as an abnormality in winter. The analysis unit can also set a higher threshold for detecting a decrease in activity level as an abnormality on rainy days. For example, the analysis unit acquires seasonal and weather data and adjusts the abnormality detection threshold. The analysis unit uses an algorithm for detecting abnormalities by taking into account the influence of seasons and weather. The analysis unit detects abnormalities by taking into account the influence of seasons and weather. This enables the analysis unit to detect abnormalities more accurately by taking into account the influence of seasons and weather. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input seasonal and weather data into the generation AI and cause the generation AI to adjust the abnormality detection threshold.

[0095] When analyzing the pet's health data, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed. For example, when analyzing the pet's health data, the analysis unit applies an anomaly detection algorithm according to the pet's age and breed. For example, the analysis unit applies an algorithm that detects a decrease in activity level as an abnormality to young pets. The analysis unit can also apply an algorithm that detects heart rate fluctuations as an abnormality to elderly pets. The analysis unit can also apply an anomaly detection algorithm that takes into account health risks specific to a specific breed. For example, the analysis unit acquires the pet's age data and applies an anomaly detection algorithm according to the age. The analysis unit acquires the pet's breed data and applies an anomaly detection algorithm according to the breed. The analysis unit applies an anomaly detection algorithm according to the age and breed. This enables the analysis unit to apply an anomaly detection algorithm according to the pet's age and breed, thereby enabling more appropriate anomaly detection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's age data and breed data to the generation AI and cause the generation AI to apply an anomaly detection algorithm.

[0096] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking the pet's past medical history into account. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking the pet's past medical history into account. For example, if the pet has previously suffered from heart disease, the analysis unit can detect fluctuations in heart rate as an abnormality. Furthermore, if the pet has previously suffered from arthritis, the analysis unit can detect a decrease in activity level as an abnormality. Furthermore, if the pet has previously suffered from digestive problems, the analysis unit can detect fluctuations in food intake as an abnormality. For example, the analysis unit acquires the pet's past medical history data and reflects it in abnormality detection. The analysis unit uses an algorithm that detects abnormalities by taking the pet's past medical history into account. The analysis unit detects abnormalities by taking the pet's past medical history into account. This enables the analysis unit to more appropriately detect abnormalities by taking the pet's past medical history into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past medical history data into the generation AI and cause the generation AI to perform an analysis for abnormality detection.

[0097] The analysis unit can estimate the pet's emotions and determine the priority of abnormality detection based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions. For example, the analysis unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The analysis unit also determines the priority of abnormality detection based on the estimated pet's emotions. For example, the analysis unit prioritizes detecting abnormalities in heart rate when the pet is stressed. The analysis unit can also prioritize detecting abnormalities in activity level when the pet is relaxed. The analysis unit can also prioritize detecting abnormalities in body temperature when the pet is excited. For example, the analysis unit analyzes behavioral data of the pet to estimate the emotions. The analysis unit analyzes facial expression data of the pet to estimate the emotions. The analysis unit determines the priority of abnormality detection based on the pet's emotions. As a result, the analysis unit can prioritize detecting more important abnormalities by determining the priority of abnormality detection according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input pet behavior data into the generation AI and have the generation AI perform emotion estimation and determine priorities for anomaly detection.

[0098] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's dietary content and calorie intake. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the pet's dietary content and calorie intake. For example, if the pet is eating excessively, the analysis unit detects weight gain as an abnormality. Furthermore, if the pet is not eating, the analysis unit can also detect weight loss as an abnormality. Furthermore, if the pet is consuming an excessive amount of a specific nutrient, the analysis unit can detect the effects of this as an abnormality. For example, the analysis unit acquires the pet's dietary data and calculates the calorie intake. The analysis unit uses an algorithm to detect abnormalities based on the calorie intake. The analysis unit detects abnormalities by taking into account the dietary content and calorie intake. This enables the analysis unit to more accurately detect abnormalities by taking into account the pet's dietary content and calorie intake. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's dietary data into the generation AI and cause the generation AI to perform an analysis to detect abnormalities.

[0099] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's amount of exercise and activity pattern. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking into account the pet's amount of exercise and activity pattern. For example, if the pet is exercising less than usual, the analysis unit detects insufficient exercise as an abnormality. Furthermore, if the pet is exercising more than usual, the analysis unit can detect excessive exercise as an abnormality. Furthermore, if the pet's activity pattern changes suddenly, the analysis unit can detect the change as an abnormality. For example, the analysis unit acquires the pet's exercise data and calculates the amount of exercise. The analysis unit uses an algorithm that detects abnormalities based on the amount of exercise and activity pattern. The analysis unit detects abnormalities by taking into account the amount of exercise and activity pattern. This enables the analysis unit to detect abnormalities more accurately by taking into account the pet's amount of exercise and activity pattern. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's exercise data to a generation AI and have the generation AI perform an analysis to detect abnormalities.

[0100] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking the pet's sleep patterns into consideration. For example, when analyzing the pet's health data, the analysis unit detects abnormalities by taking the pet's sleep patterns into consideration. For example, if the pet sleeps for a shorter period of time than usual, the analysis unit detects insufficient sleep as an abnormality. Furthermore, if the pet sleeps for a longer period of time than usual, the analysis unit can detect excessive sleep as an abnormality. Furthermore, if the pet sleeps for a longer period of time than usual, the analysis unit can detect a sudden change in the pet's sleep pattern as an abnormality. For example, the analysis unit acquires the pet's sleep data and calculates the sleep time. The analysis unit uses an algorithm that detects abnormalities based on the sleep patterns. The analysis unit detects abnormalities by taking the sleep patterns into consideration. This enables the analysis unit to detect abnormalities more accurately by taking the pet's sleep patterns into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the pet's sleep data to a generation AI and cause the generation AI to perform an analysis to detect abnormalities.

[0101] The notification unit can estimate the pet's emotion and adjust the urgency of the notification based on the estimated pet's emotion. The notification unit, for example, estimates the pet's emotion. For example, the notification unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The notification unit also adjusts the urgency of the notification based on the estimated pet's emotion. For example, the notification unit sends a high-urgency notification to the owner if the pet is stressed. The notification unit can also send a low-urgency notification to the owner if the pet is relaxed. The notification unit can also send a medium-urgency notification to the owner if the pet is excited. For example, the notification unit analyzes the pet's behavioral data to estimate the emotion. The notification unit analyzes the pet's facial expression data to estimate the emotion. The notification unit adjusts the urgency of the notification based on the pet's emotion. This allows the notification unit to adjust the urgency of the notification according to the pet's emotion, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input pet behavior data into the generation AI and have the generation AI estimate emotions and adjust the urgency of notifications.

[0102] When an abnormality is detected, the notification unit can select a notification method according to the owner's current situation. For example, when an abnormality is detected, the notification unit selects a notification method according to the owner's current situation. For example, when the owner is at work, the notification unit prioritizes sending email or app notifications. Furthermore, when the owner is out, the notification unit can also prioritize sending SMS or phone notifications. Furthermore, when the owner is at home, the notification unit can send a notification through a smart home device. For example, the notification unit acquires the owner's location information and identifies the current situation. The notification unit acquires the owner's activity status and selects a notification method. The notification unit selects a notification method according to the owner's current situation. This enables the notification unit to select a notification method according to the owner's situation, thereby enabling more effective notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input the owner's location information and activity status data into the generation AI and have the generation AI select the notification method.

[0103] When an abnormality is detected, the notification unit can generate optimal notification content by referring to the owner's past response history. For example, when an abnormality is detected, the notification unit generates optimal notification content by referring to the owner's past response history. For example, the notification unit generates detailed notification content for an abnormality that the owner responded to quickly in the past. The notification unit can also generate concise notification content for an abnormality that the owner responded slowly in the past. The notification unit can also generate notification content including a specific method by which the owner responded in the past. For example, the notification unit acquires the owner's past notification history and analyzes the response history. The notification unit uses an algorithm that generates notification content based on the past response history. The notification unit generates optimal notification content by referring to the past response history. As a result, the notification unit can generate more appropriate notification content by referring to the owner's past response history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the owner's past notification history data into the generation AI and have the generation AI generate the notification content.

[0104] The notification unit can provide a notification including the current location information of the pet when an abnormality is detected. For example, when an abnormality is detected, the notification unit provides a notification including the current location information of the pet. For example, when the pet is outdoors, the notification unit sends a notification to the owner including the current location information. Furthermore, when the pet is indoors, the notification unit can also send a notification to the owner including the location information of the room. Furthermore, when the pet is in a specific area, the notification unit can also send a notification to the owner including detailed location information of the area. For example, the notification unit acquires the pet's location information and includes it in the notification content. The notification unit uses an algorithm that generates the notification content based on the location information. The notification unit provides a notification including the pet's current location information. As a result, the notification unit includes the pet's current location information, allowing the owner to respond quickly. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pet's location information data to a generation AI and cause the generation AI to generate the notification content.

[0105] The notification unit can estimate the pet's emotion and customize the notification content based on the estimated pet's emotion. The notification unit, for example, estimates the pet's emotion. For example, the notification unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The notification unit also customizes the notification content based on the estimated pet's emotion. For example, the notification unit generates notification content with a high level of urgency when the pet is stressed. The notification unit can also generate notification content with a low level of urgency when the pet is relaxed. The notification unit can also generate notification content with a medium level of urgency when the pet is excited. For example, the notification unit analyzes the pet's behavioral data to estimate the emotion. The notification unit analyzes the pet's facial expression data to estimate the emotion. The notification unit customizes the notification content based on the pet's emotion. This allows the notification unit to customize the notification content according to the pet's emotion, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can input pet behavior data into the generation AI and have the generation AI estimate emotions and customize the notification content.

[0106] The notification unit can send a notification to the owner's smartwatch or other wearable device when an abnormality is detected. For example, the notification unit sends a notification to the owner's smartwatch or other wearable device when an abnormality is detected. For example, the notification unit can send a vibration notification to the owner's smartwatch to notify the abnormality. The notification unit can also send a notification to the owner's fitness tracker to notify the abnormality. The notification unit can also send a visual notification to the owner's smartglasses to notify the abnormality. For example, the notification unit can work with the owner's smartwatch to send a notification. The notification unit can work with the owner's fitness tracker to send a notification. The notification unit can also work with the owner's smartglasses to send a notification. This allows the notification unit to send a notification to the owner's wearable device, enabling a prompt response. Some or all of the above-mentioned processing by the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input data for linking with the owner's wearable device into the generation AI and cause the generation AI to send a notification.

[0107] When an abnormality is detected, the notification unit can simultaneously send a notification to the owner's family and the pet sitter. For example, when an abnormality is detected, the notification unit simultaneously sends a notification to the owner's family and the pet sitter. For example, the notification unit sends an email notification to all of the owner's family members to notify them of the abnormality. The notification unit can also send an SMS notification to the pet sitter to notify them of the abnormality. The notification unit can also send an app notification to the owner's family and the pet sitter to notify them of the abnormality. For example, the notification unit obtains contact information for the owner's family members and sends the notification. The notification unit obtains contact information for the pet sitter and sends the notification. The notification unit simultaneously sends notifications to the family members and the pet sitter. This allows the notification unit to send notifications to the owner's family and the pet sitter, enabling a prompt response. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input contact information for the family members and the pet sitter into the generation AI and cause the generation AI to send the notification.

[0108] When an abnormality is detected, the notification unit can provide a notification including a graph and statistical information of the pet's health data. For example, when an abnormality is detected, the notification unit provides a notification including a graph and statistical information of the pet's health data. For example, when an abnormality is detected, the notification unit transmits a notification including a graph of the pet's heart rate. When an abnormality is detected, the notification unit can also transmit a notification including statistical information of the pet's body temperature. When an abnormality is detected, the notification unit can also transmit a notification including a graph of the pet's activity level. For example, the notification unit acquires the pet's health data and generates graphs and statistical information. The notification unit uses an algorithm to generate a notification including graphs and statistical information. The notification unit provides a notification including graphs and statistical information of the pet's health data. By including the graphs and statistical information of the pet's health data, the notification unit can allow the owner to obtain more detailed information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pet's health data to a generation AI and cause the generation AI to generate graphs and statistical information and send a notification.

[0109] The providing unit can estimate the pet's emotions and adjust care or diet advice based on the estimated pet emotions. The providing unit, for example, estimates the pet's emotions. For example, the providing unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The providing unit also adjusts the care or diet advice based on the estimated pet emotions. For example, if the pet is stressed, the providing unit suggests a care method that will help the pet relax. If the pet is relaxed, the providing unit can also suggest a normal care method. If the pet is excited, the providing unit can also suggest a care method to calm the pet. For example, the providing unit analyzes the pet's behavioral data to estimate the emotions. The providing unit analyzes the pet's facial expression data to estimate the emotions. The providing unit adjusts the care or diet advice based on the pet's emotions. This allows the providing unit to adjust the care or diet advice according to the pet's emotions, enabling more appropriate care. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input behavioral data of a pet into the generating AI and have the generating AI estimate emotions and adjust care and dietary advice.

[0110] The providing unit can provide seasonal care and diet advice based on the pet's health condition. The providing unit provides seasonal care and diet advice based on, for example, the pet's health condition. For example, the providing unit can suggest a care method to lower the pet's body temperature in the summer. The providing unit can also suggest a care method to maintain the pet's body temperature in the winter. The providing unit can also suggest a care method to prevent allergies in the spring. For example, the providing unit acquires pet health data and generates seasonal care and diet advice. The providing unit uses an algorithm to provide seasonal care and diet advice. The providing unit provides seasonal care and diet advice based on the pet's health condition. As a result, the providing unit can maintain the pet's health by providing seasonal care and diet advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input pet health data to a generation AI and cause the generation AI to generate seasonal care and diet advice.

[0111] The providing unit can provide care and diet advice according to the age and breed of the pet based on the health condition of the pet. The providing unit provides care and diet advice according to the age and breed of the pet based on, for example, the health condition of the pet. For example, the providing unit provides diet advice to promote growth to a young pet. The providing unit can also provide care methods to maintain joint health to an elderly pet. The providing unit can also provide care methods for a specific breed that take into account health risks specific to that breed. For example, the providing unit acquires age data of the pet and generates care and diet advice according to the age. The providing unit acquires breed data of the pet and generates care and diet advice according to the breed. The providing unit provides care and diet advice according to the age and breed based on the health condition of the pet. This enables more appropriate care by providing care and diet advice according to the age and breed of the pet. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the pet's age data and breed data into the generation AI and cause the generation AI to generate care and dietary advice.

[0112] The providing unit can provide care and diet advice that takes into account the pet's past medical history based on the pet's health condition. The providing unit provides care and diet advice that takes into account the pet's past medical history based on the pet's health condition, for example. For example, if the pet has previously suffered from heart disease, the providing unit can provide heart-friendly diet advice. Furthermore, if the pet has previously suffered from arthritis, the providing unit can provide care methods to maintain joint health. Furthermore, if the pet has previously suffered from digestive problems, the providing unit can provide digestive-friendly diet advice. For example, the providing unit acquires data on the pet's past medical history and generates care and diet advice based on the medical history. The providing unit uses an algorithm that provides care and diet advice that takes into account the pet's past medical history. The providing unit provides care and diet advice that takes into account the pet's past medical history based on the pet's health condition. This enables the providing unit to provide care and diet advice that takes into account the pet's past medical history, thereby enabling more appropriate care. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provider can input the pet's past medical history data into the generation AI and have the generation AI generate care and dietary advice.

[0113] The providing unit can estimate the pet's emotions and determine the priority of care or dietary advice based on the estimated pet's emotions. The providing unit, for example, estimates the pet's emotions. For example, the providing unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The providing unit also determines the priority of care or dietary advice based on the estimated pet's emotions. For example, if the pet is stressed, the providing unit can preferentially suggest a care method that helps the pet relax. If the pet is relaxed, the providing unit can also preferentially suggest a normal care method. If the pet is excited, the providing unit can also preferentially suggest a care method that calms the pet. For example, the providing unit analyzes the pet's behavioral data to estimate the emotions. The providing unit analyzes the pet's facial expression data to estimate the emotions. The providing unit determines the priority of care or dietary advice based on the pet's emotions. In this way, the providing unit can prioritize the provision of more important care by determining the priority of care or dietary advice according to the pet's emotions. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input behavioral data of a pet to the generating AI and cause the generating AI to estimate emotions and determine priorities for care and dietary advice.

[0114] The providing unit can provide an exercise plan according to the amount of exercise and activity pattern of the pet based on the pet's health condition. The providing unit provides an exercise plan according to the amount of exercise and activity pattern of the pet based on, for example, the pet's health condition. For example, the providing unit proposes an appropriate exercise plan if the pet is not exercising enough. The providing unit can also propose a plan to adjust the amount of exercise if the pet is exercising excessively. The providing unit can also provide an optimal exercise plan based on the pet's activity pattern. For example, the providing unit acquires the pet's exercise data and generates an exercise plan. The providing unit uses an algorithm to provide an exercise plan based on the amount of exercise and activity pattern. The providing unit provides an exercise plan according to the amount of exercise and activity pattern based on the pet's health condition. In this way, the providing unit can promote appropriate exercise by providing an exercise plan according to the pet's amount of exercise and activity pattern. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's exercise data into a generation AI and cause the generation AI to generate an exercise plan.

[0115] The provider unit can provide a meal plan based on the pet's dietary content and calorie intake based on the pet's health condition. The provider unit provides a meal plan based on the pet's dietary content and calorie intake based on the pet's health condition, for example. For example, the provider unit can suggest a calorie-restricted meal plan if the pet is eating too much. The provider unit can also suggest a nutritionally balanced meal plan if the pet is not eating enough. The provider unit can also provide an optimal meal plan based on the pet's health condition. For example, the provider unit acquires the pet's dietary data and calculates the calorie intake. The provider unit uses an algorithm to provide a meal plan based on the calorie intake. The provider unit provides a meal plan based on the pet's health condition based on the dietary content and calorie intake. This enables the provider unit to provide a meal plan based on the pet's dietary content and calorie intake, thereby enabling appropriate nutritional management. Some or all of the above-described processing by the provider unit may be performed using, for example, AI, or may be performed without AI. For example, the provider unit can input the pet's dietary data into a generation AI and cause the generation AI to generate a meal plan.

[0116] The providing unit can provide a care plan according to the pet's sleep pattern based on the pet's health condition. The providing unit provides a care plan according to the pet's sleep pattern based on, for example, the pet's health condition. For example, if the pet is sleep-deprived, the providing unit can suggest a relaxing care plan. Furthermore, if the pet is sleeping excessively, the providing unit can suggest an active care plan. Furthermore, the providing unit can provide an optimal care plan based on the pet's sleep pattern. For example, the providing unit acquires the pet's sleep data and analyzes the sleep pattern. The providing unit uses an algorithm to provide a care plan based on the sleep pattern. The providing unit provides a care plan according to the sleep pattern based on the pet's health condition. As a result, the providing unit provides a care plan according to the pet's sleep pattern, enabling appropriate sleep management. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's sleep data into a generation AI and cause the generation AI to generate a care plan. === Hard Collateral 1-1 === For example, the collection unit can collect health data of the pet using a body temperature sensor, a heart rate sensor, and an activity sensor of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit can send a notification to the owner using the control unit 46A of the smart device 14. The provision unit provides appropriate care and dietary advice based on the data analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === For example, the collection unit can collect pet health data using a body temperature sensor, a heart rate sensor, and an activity sensor of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit can send notifications to the owner using the control unit 46A of the smart glasses 214. The provision unit provides appropriate care and dietary advice based on the data analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === For example, the collection unit can collect health data of the pet using a body temperature sensor, a heart rate sensor, and an activity amount sensor of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit can send a notification to the owner using the control unit 46A of the headset terminal 314. The provision unit provides appropriate care and dietary advice based on the data analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === For example, the collection unit can collect health data of the pet using a body temperature sensor, a heart rate sensor, and an activity amount sensor of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit can send a notification to the owner using the control unit 46A of the robot 414. The provision unit provides appropriate care and dietary advice based on the data analyzed by the specific processing unit 290 of the data processing device 12.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] When analyzing the pet's health data, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed. For example, the analysis unit can apply an algorithm that detects a decrease in activity level as an abnormality to young pets. The analysis unit can also apply an algorithm that detects heart rate fluctuations as an abnormality to elderly pets. The analysis unit can also apply an anomaly detection algorithm that takes into account the health risks specific to a specific breed. For example, the analysis unit can acquire the pet's age data and apply an anomaly detection algorithm according to the age. The analysis unit can acquire the pet's breed data and apply an anomaly detection algorithm according to the breed. The analysis unit can apply an anomaly detection algorithm according to the age and breed. In this way, the analysis unit can apply an anomaly detection algorithm according to the pet's age and breed, enabling more appropriate anomaly detection. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's age data and breed data to the generation AI and cause the generation AI to apply an anomaly detection algorithm.

[0119] The providing unit can provide seasonal care and diet advice based on the pet's health condition. For example, the providing unit can suggest a care method to lower the pet's body temperature in the summer. The providing unit can also suggest a care method to maintain the pet's body temperature in the winter. The providing unit can also suggest a care method to prevent allergies in the spring. For example, the providing unit acquires the pet's health data and generates seasonal care and diet advice. The providing unit uses an algorithm to provide seasonal care and diet advice. The providing unit provides seasonal care and diet advice based on the pet's health condition. In this way, the providing unit can maintain the pet's health by providing seasonal care and diet advice. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's health data into a generating AI and cause the generating AI to generate seasonal care and diet advice.

[0120] When an abnormality is detected, the notification unit can select a notification method according to the owner's current situation. For example, if the owner is at work, the notification unit can prioritize sending email or app notifications. Furthermore, if the owner is out, the notification unit can also prioritize sending SMS or phone notifications. Furthermore, if the owner is at home, the notification unit can send a notification via a smart home device. For example, the notification unit acquires the owner's location information and identifies the current situation. The notification unit acquires the owner's activity status and selects a notification method. The notification unit selects a notification method according to the owner's current situation. This enables the notification unit to select a notification method according to the owner's situation, thereby enabling more effective notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the owner's location information and activity status data to the generation AI and have the generation AI select a notification method.

[0121] The collection unit can estimate the pet's emotion and adjust the frequency of health data collection based on the estimated pet's emotion. For example, the collection unit estimates the pet's emotion. For example, the collection unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The collection unit also adjusts the frequency of health data collection based on the estimated pet's emotion. For example, if the pet is stressed, the collection unit increases the collection frequency to collect more detailed data. If the pet is relaxed, the collection unit can also reduce the collection frequency to reduce the burden of data collection. If the pet is excited, the collection unit can set the collection frequency to a medium level to focus on collecting activity data. For example, the collection unit analyzes the pet's behavioral data to estimate the emotion. The collection unit analyzes the pet's facial expression data to estimate the emotion. The collection unit adjusts the collection frequency based on the pet's emotion. This allows the collection unit to collect more accurate data by adjusting the frequency of data collection according to the pet's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input behavioral data of a pet into the generation AI and cause the generation AI to estimate emotions and adjust the collection frequency.

[0122] The providing unit can estimate the pet's emotions and adjust care or diet advice based on the estimated pet emotions. For example, the providing unit estimates the pet's emotions. For example, the providing unit estimates the pet's emotions using behavioral analysis or facial expression analysis. The providing unit also adjusts the care or diet advice based on the estimated pet emotions. For example, if the pet is stressed, the providing unit suggests a care method that will help the pet relax. If the pet is relaxed, the providing unit can also suggest a normal care method. If the pet is excited, the providing unit can also suggest a care method to calm the pet. For example, the providing unit analyzes the pet's behavioral data to estimate the emotions. The providing unit analyzes the pet's facial expression data to estimate the emotions. The providing unit adjusts the care or diet advice based on the pet's emotions. This allows the providing unit to adjust the care or diet advice according to the pet's emotions, enabling more appropriate care. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input behavioral data of a pet into the generating AI and have the generating AI estimate emotions and adjust care and dietary advice.

[0123] The analysis unit can estimate the pet's emotions and adjust the anomaly detection threshold based on the estimated pet's emotions. For example, the analysis unit estimates the pet's emotions using behavioral analysis and facial expression analysis. The analysis unit also adjusts the anomaly detection threshold based on the estimated pet's emotions. For example, if the pet is stressed, the analysis unit sets the anomaly detection threshold low to detect an abnormality early. If the pet is relaxed, the analysis unit can set the anomaly detection threshold high to prevent excessive alerts. If the pet is excited, the analysis unit can set the anomaly detection threshold to a medium level to detect an abnormality at an appropriate time. For example, the analysis unit analyzes the pet's behavioral data to estimate the emotions. The analysis unit analyzes the pet's facial expression data to estimate the emotions. The analysis unit adjusts the anomaly detection threshold based on the pet's emotions. This allows the analysis unit to adjust the anomaly detection threshold according to the pet's emotions, thereby enabling more appropriate anomaly detection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input pet behavior data to the generation AI and cause the generation AI to estimate emotions and adjust anomaly detection thresholds.

[0124] The notification unit can estimate the pet's emotion and adjust the urgency of the notification based on the estimated pet's emotion. For example, the notification unit estimates the pet's emotion. For example, the notification unit estimates the pet's emotion using behavioral analysis or facial expression analysis. The notification unit also adjusts the urgency of the notification based on the estimated pet's emotion. For example, the notification unit sends a high-urgency notification to the owner if the pet is stressed. The notification unit can also send a low-urgency notification to the owner if the pet is relaxed. The notification unit can also send a medium-urgency notification to the owner if the pet is excited. For example, the notification unit analyzes the pet's behavioral data to estimate the emotion. The notification unit analyzes the pet's facial expression data to estimate the emotion. The notification unit adjusts the urgency of the notification based on the pet's emotion. This allows the notification unit to adjust the urgency of the notification according to the pet's emotion, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input pet behavior data into the generation AI and have the generation AI estimate emotions and adjust the urgency of notifications.

[0125] The providing unit can provide care and diet advice tailored to the age and breed of the pet based on the pet's health condition. For example, the providing unit can provide diet advice to promote growth for young pets. The providing unit can also provide care methods to maintain joint health for elderly pets. The providing unit can also provide care methods for specific breeds that take into account health risks specific to that breed. For example, the providing unit can acquire age data of the pet and generate care and diet advice tailored to the age. The providing unit can acquire breed data of the pet and generate care and diet advice tailored to the breed. The providing unit can provide care and diet advice tailored to the age and breed based on the pet's health condition. This allows the providing unit to provide care and diet advice tailored to the age and breed of the pet, enabling more appropriate care. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the pet's age data and breed data into a generation AI and cause the generation AI to generate care and diet advice.

[0126] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the influence of seasons and weather. For example, the analysis unit can set a lower threshold for detecting an increase in body temperature as an abnormality in summer. The analysis unit can also set a lower threshold for detecting a decrease in activity level as an abnormality in winter. The analysis unit can also set a higher threshold for detecting a decrease in activity level as an abnormality on rainy days. For example, the analysis unit acquires seasonal and weather data and adjusts the abnormality detection threshold. The analysis unit uses an algorithm for detecting abnormalities that takes into account the influence of seasons and weather. The analysis unit detects abnormalities by taking into account the influence of seasons and weather. This enables the analysis unit to detect abnormalities more accurately by taking into account the influence of seasons and weather. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input seasonal and weather data into the generation AI and cause the generation AI to adjust the abnormality detection threshold.

[0127] When analyzing the pet's health data, the analysis unit can detect abnormalities by taking into account the pet's past medical history. For example, if the pet has previously suffered from heart disease, the analysis unit can detect fluctuations in heart rate as abnormal. Furthermore, if the pet has previously suffered from arthritis, the analysis unit can also detect a decrease in activity level as abnormal. Furthermore, if the pet has previously suffered from digestive problems, the analysis unit can detect fluctuations in food intake as abnormal. For example, the analysis unit acquires the pet's past medical history data and reflects it in abnormality detection. The analysis unit uses an algorithm that takes into account the pet's past medical history to detect abnormalities. The analysis unit detects abnormalities by taking into account the pet's past medical history. This enables the analysis unit to more appropriately detect abnormalities by taking into account the pet's past medical history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past medical history data into the generation AI and have the generation AI perform an analysis to detect abnormalities.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects the pet's health data. The pet's health data includes body temperature, heart rate, activity level, etc. The collection unit measures this data using a body temperature sensor, a heart rate sensor, and an activity level sensor. For example, the body temperature sensor measures body temperature using a contact sensor or a non-contact sensor, the heart rate sensor measures heart rate using an optical sensor or an electrical sensor, and the activity level sensor measures activity level using an acceleration sensor or a gyro sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and detects abnormalities. Abnormalities include elevated body temperature and abnormal heart rate. In addition to detecting abnormalities by comparing the data with past data, the analysis unit can also analyze the data using AI to detect abnormalities. For example, data from the past year can be stored in cloud storage and a predictive model can be built. Step 3: The notification unit notifies the owner based on the abnormality detected by the analysis unit. The notification may be a push notification to a smartphone or an email notification. For example, the notification unit may send a push notification to a smartphone and also an email. Step 4: The provider provides appropriate care and dietary advice based on the data analyzed by the analyzer. Care and dietary advice may include cooling methods, exercise plans, and meal plans. For example, if the pet's body temperature is higher than normal, the provider suggests cooling methods, and if the pet's activity level is decreasing, the provider suggests an exercise plan. The provider also provides dietary advice and reminders for regular health checks.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[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 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.

[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 (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).

[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] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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 collection unit that collects pet health data; an analysis unit that analyzes the data collected by the collection unit and detects abnormalities; a notification unit that notifies the owner based on the abnormality detected by the analysis unit; a providing unit that provides care or dietary advice based on the data analyzed by the analyzing unit. A system characterized by:

2. The collecting unit Equipped with a body temperature sensor, heart rate sensor, and activity sensor 2. The system of claim 1.

3. The analysis unit Compare with past data to detect anomalies 2. The system of claim 1.

4. The providing unit Suggest cooling methods if your pet's temperature rises above normal 2. The system of claim 1.

5. The providing unit Suggest an exercise plan if your pet is less active 2. The system of claim 1.

6. The providing unit Providing dietary advice or reminders for regular health checks 2. The system of claim 1.

7. The analysis unit Use cloud storage to store data for long periods and build predictive models based on historical data 2. The system of claim 1.

8. The collecting unit Estimate the pet's emotions and adjust the frequency of health data collection based on the estimated pet's emotions.

2. The system of claim 1.

9. The collecting unit Automatically adjusts sensor sensitivity according to your pet's activity environment 2. The system of claim 1.

10. The collecting unit Detect specific pet behaviors and prioritize data collection based on those behaviors 2. The system of claim 1.

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