system

Abstract

The system according to the embodiment aims to analyze pet behavior and environmental data, detect abnormalities, and take appropriate measures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, an operation unit, and a suggestion unit. The collection unit collects behavioral or environmental data of a pet. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit notifies the owner based on the abnormality detected by the analysis unit. The operation unit operates the smart lock based on the results notified by the notification unit. The suggestion unit makes health management suggestions based on the results detected by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to monitor pet behavior and environmental data in real time, detect abnormalities, and take appropriate action.

[0005] The system according to the embodiment aims to analyze pet behavior and environmental data, detect abnormalities, and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, an operation unit, and a suggestion unit. The collection unit collects behavioral or environmental data of a pet. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit notifies the owner based on the abnormality detected by the analysis unit. The operation unit operates the smart lock based on the results notified by the notification unit. The suggestion unit makes health management suggestions based on the results detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze pet behavior and environmental data, detect abnormalities, and take appropriate action. [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 care system according to an embodiment of the present invention is an AI system specialized for pet care during business trips or outings. This pet care system collects data on the pet's daily behavior, temperature, and water and litter box usage, and visualizes it as a heat map. If an abnormality is detected, the system notifies the owner via an app and automatically opens and closes the pet's room door using a smart lock to create a comfortable environment for the pet. Furthermore, as a function useful for health management, the system detects lack of exercise and loss of appetite and suggests play and meals. In this way, a new style of connection between pets and their owners is proposed. For example, the pet care system includes a collection unit that collects data on the pet's behavior and environment. The collection unit collects data on the pet's behavior, temperature, and water and litter box usage. Next, an analysis unit that analyzes the collected data and detects abnormalities is included. The analysis unit analyzes the collected data and detects abnormalities. Furthermore, a notification unit is included that notifies the owner when an abnormality is detected. The notification unit notifies the owner when an abnormality is detected. Next, an operation unit is included that operates the smart lock. The operation unit operates the smart lock to open and close the room door. Finally, a suggestion unit is provided to make health management suggestions. The suggestion unit detects lack of exercise and loss of appetite and makes suggestions for play and meals. This enables the pet care system to collect data on pet behavior and environment, detect and notify of abnormalities, operate the smart lock, and make health management suggestions.

[0029] A pet care system according to an embodiment includes a collection unit, an analysis unit, a notification unit, an operation unit, and a suggestion unit. The collection unit collects data on pet behavior, temperature, and water and toilet usage. The collection unit collects data using, for example, a camera or a sensor for monitoring pet behavior. The collection unit can also measure indoor temperature using a temperature sensor. The collection unit can also use a water volume sensor for measuring water consumption and a toilet sensor for counting the number of times the toilet is used. For example, the collection unit monitors pet behavior in real time and collects data using a camera. The collection unit can also periodically measure indoor temperature using a temperature sensor and collect data. The collection unit can also measure pet water consumption using a water volume sensor and collect data. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. For example, the analysis unit analyzes pet behavior patterns based on the collected data and detects abnormalities. The analysis unit can also analyze temperature data and detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the toilet is used to detect abnormalities. For example, the analysis unit can analyze a pet's behavioral patterns to detect abnormal behavior. The analysis unit can also analyze temperature data to detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the toilet is used to detect abnormalities. The notification unit notifies the owner when an abnormality is detected. The notification unit can send a notification to the owner, for example, via a smartphone app. The notification unit can also send a notification via email or SMS. The notification unit can also notify the owner by voice via a smart speaker. For example, the notification unit can notify the owner of an abnormality via a smartphone app. The notification unit can also notify the owner of an abnormality via email or SMS. The notification unit can also notify the owner of an abnormality by voice via a smart speaker. The operation unit operates the smart lock to open and close the room door. The operation unit can operate the smart lock, for example, via Bluetooth (registered trademark) or Wi-Fi. The operation unit can also operate the smart lock via a smartphone app.Furthermore, the operation unit can operate the smart lock through voice commands. For example, the operation unit can operate the smart lock through Bluetooth. The operation unit can also operate the smart lock through a smartphone app. The operation unit can also operate the smart lock through voice commands. The suggestion unit detects lack of exercise or loss of appetite and makes suggestions for play or meals. For example, the suggestion unit analyzes the amount of exercise the pet receives and detects lack of exercise. The suggestion unit can also analyze the amount of food the pet receives and detects loss of appetite. Furthermore, the suggestion unit makes suggestions for play or meals when it detects lack of exercise or loss of appetite. For example, the suggestion unit analyzes the amount of exercise the pet receives and detects lack of exercise. The suggestion unit can also analyze the amount of food the pet receives and detects loss of appetite. The suggestion unit makes suggestions for play or meals when it detects lack of exercise or loss of appetite. As a result, the pet care system according to the embodiment can collect data on the pet's behavior and environment, detect and notify abnormalities, operate the smart lock, and make health management suggestions.

[0030] The collection unit can collect data on pet behavior, temperature, and water or toilet usage. The collection unit collects data using, for example, a camera or sensor for monitoring pet behavior. For example, the collection unit monitors pet behavior in real time using a camera and collects data. The collection unit can also measure indoor temperature using a temperature sensor. For example, the collection unit can periodically measure indoor temperature using a temperature sensor and collect data. The collection unit can also use a water volume sensor for measuring water consumption or a toilet sensor for counting the number of toilet uses. For example, the collection unit can measure pet water consumption using a water volume sensor and collect data. In this way, detailed data can be obtained by collecting data on pet behavior, temperature, and water or toilet usage. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input pet behavior data acquired by a camera to a generation AI and have the generation AI analyze the behavior data.

[0031] The analysis unit can analyze the collected data and detect abnormalities. For example, the analysis unit analyzes the behavioral patterns of a pet based on the collected data and detects abnormalities. For example, the analysis unit analyzes the behavioral patterns of a pet and detects abnormal behavior. The analysis unit can also analyze temperature data and detect abnormal temperature changes. For example, the analysis unit can analyze temperature data and detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the toilet is used and detect abnormalities. For example, the analysis unit can analyze water consumption and the number of times the toilet is used and detect abnormalities. In this way, by analyzing the collected data and detecting abnormalities, abnormalities in the pet can be discovered early. 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 collected data to a generation AI and cause the generation AI to detect abnormalities.

[0032] The notification unit can notify the owner when an abnormality is detected. The notification unit sends a notification to the owner, for example, via a smartphone app. For example, the notification unit notifies the owner of the abnormality via the smartphone app. The notification unit can also send a notification via email or SMS. For example, the notification unit can notify the owner of the abnormality via email or SMS. The notification unit can also notify the owner by voice via a smart speaker. For example, the notification unit can notify the owner of the abnormality by voice via a smart speaker. This allows for a prompt response by notifying the owner when an abnormality is detected. 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 content of the abnormality notification to a generation AI and cause the generation AI to generate a notification.

[0033] The operation unit can operate the smart lock to open and close the room door. The operation unit, for example, operates the smart lock via Bluetooth or Wi-Fi. For example, the operation unit operates the smart lock via Bluetooth. The operation unit can also operate the smart lock via a smartphone app. For example, the operation unit can operate the smart lock via a smartphone app. The operation unit can also operate the smart lock via a voice command. For example, the operation unit can also operate the smart lock via a voice command. This allows the pet's environment to be improved by operating the smart lock to open and close the room door. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the operation content of the smart lock to a generation AI and have the generation AI generate the operation.

[0034] The suggestion unit can detect lack of exercise or loss of appetite and suggest activities or meals. The suggestion unit, for example, analyzes the amount of exercise the pet receives and detects lack of exercise. For example, the suggestion unit can analyze the amount of exercise the pet receives and detect lack of exercise. The suggestion unit can also analyze the amount of food the pet eats and detect loss of appetite. For example, the suggestion unit can analyze the amount of food the pet eats and detect loss of appetite. The suggestion unit can also suggest activities or meals when it detects lack of exercise or loss of appetite. For example, the suggestion unit can suggest activities suitable for the pet when it detects lack of exercise. The suggestion unit can also suggest meals suitable for the pet when it detects loss of appetite. This makes it possible to manage the health of the pet by detecting lack of exercise or loss of appetite and suggesting activities or meals. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the amount of exercise and food the pet receives into a generation AI and cause the generation AI to generate suggestions.

[0035] The collection unit can estimate the pet's emotions and adjust the behavioral data collection frequency based on the estimated pet emotions. The collection unit, for example, estimates the pet's emotions and adjusts the behavioral data collection frequency based on the estimated pet emotions. For example, when the pet is excited, the collection unit increases the collection frequency to acquire detailed behavioral data. Furthermore, when the pet is relaxed, the collection unit can decrease the collection frequency to acquire the minimum necessary data. Furthermore, when the pet is stressed, the collection unit can set the collection frequency to a medium level to acquire data for identifying the cause of stress. This allows for more appropriate data collection by adjusting the behavioral data collection frequency based on the pet's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the collection frequency.

[0036] The collection unit can analyze the pet's past behavioral patterns and select an appropriate timing for collecting data. The collection unit, for example, analyzes the pet's past behavioral patterns and selects the optimal timing for collecting data. For example, the collection unit identifies time periods when the pet is active and concentrates data collection during those time periods. The collection unit can also collect data by avoiding time periods when the pet is resting. The collection unit can also adjust the timing for collecting data taking into account the pet's meal and toilet times. In this way, the optimal timing for collecting data can be selected by analyzing the pet's past behavioral patterns. 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 pet's past behavioral data into the generation AI and cause the generation AI to select the timing for collecting data.

[0037] The collection unit can customize the type of data to be collected based on the pet's health condition and age at the time of collection. The collection unit customizes the type of data to be collected based on, for example, the pet's health condition and age. For example, for an elderly pet, the collection unit may prioritize collecting data related to the pet's health condition. For a young pet, the collection unit may also focus on collecting data on the amount of exercise and play. For a pet in poor health, the collection unit may also collect detailed data on food and water intake. This enables more appropriate data collection by customizing the type of data to be collected based on the pet's health condition and age. 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 may cause the generation AI to customize data collection based on the pet's health condition and age.

[0038] The collection unit can estimate the pet's emotions and determine the priority of data to be collected based on the estimated pet emotions. The collection unit, for example, estimates the pet's emotions and determines the priority of data to be collected based on the estimated pet emotions. For example, if the pet is excited, the collection unit can prioritize collecting data on the amount of exercise and play. Also, if the pet is relaxed, the collection unit can prioritize collecting data on rest and sleep. Also, if the pet is stressed, the collection unit can prioritize collecting data to identify the cause of stress. In this way, by determining the priority of data to be collected based on the pet's emotions, more important data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the pet's emotion data to the generation AI and have the generation AI determine the priority of the data.

[0039] The collection unit can collect data taking into account the pet's living environment and the owner's lifestyle when collecting data. The collection unit, for example, collects data taking into account the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the collection unit collects data on indoor temperature and humidity. The collection unit can also collect data to reduce the pet's sense of loneliness when the owner is often away from home. The collection unit can also collect data on outdoor temperature and weather when the pet spends a lot of time outdoors. This allows for more appropriate data collection by collecting data taking into account the pet's living environment and the owner's lifestyle. 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 cause the generation AI to collect data based on the pet's living environment and the owner's lifestyle.

[0040] The collection unit can analyze the social media activity of the pet at the time of collection and collect related data. The collection unit, for example, analyzes the social media activity of the pet and collects related data. For example, the collection unit collects information on how often the pet interacts with other pets to obtain sociability data. If the pet is photographed frequently in a particular location, the collection unit can also collect data on the location. If the pet is more active during a particular time period, the collection unit can also collect data on that time period. In this way, related data can be collected by analyzing the social media activity of the pet. 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 social media activity data of the pet to the generation AI and cause the generation AI to collect related data.

[0041] The analysis unit can estimate the pet's emotions and adjust the anomaly detection criteria based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions and adjusts the anomaly detection criteria based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit relaxes the anomaly detection criteria to prevent false detection. Furthermore, if the pet is relaxed, the analysis unit can tighten the anomaly detection criteria to perform accurate detection. Furthermore, if the pet is stressed, the analysis unit can set the anomaly detection criteria to a moderate level to perform appropriate detection. This allows for more accurate anomaly detection by adjusting the anomaly detection criteria based on the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the anomaly detection criteria.

[0042] During analysis, the analysis unit can improve the accuracy of abnormality detection by referring to the pet's past health data. The analysis unit can improve the accuracy of abnormality detection by referring to the pet's past health data, for example. For example, the analysis unit can identify abnormality patterns based on the pet's past health data. The analysis unit can also adjust the abnormality detection algorithm by referring to the pet's past health data. The analysis unit can also analyze the pet's past health data and perform early abnormality detection. In this way, by referring to the pet's past health data, the accuracy of abnormality detection can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the pet's past health data into the generation AI and cause the generation AI to improve the accuracy of abnormality detection.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. The analysis unit applies different analysis algorithms depending on, for example, the type and age of the pet. For example, the analysis unit applies an analysis algorithm that emphasizes activity level to young pets. The analysis unit can also apply an analysis algorithm that emphasizes health status to elderly pets. The analysis unit can also apply an analysis algorithm specialized for a specific type of pet. This allows for more appropriate analysis by applying different analysis algorithms depending on the type and age of the pet. 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 cause the generation AI to apply an analysis algorithm based on the type and age of the pet.

[0044] The analysis unit can estimate the pet's emotions and adjust the order in which the anomaly detection results are displayed based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions and adjusts the order in which the anomaly detection results are displayed based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit can prioritize displaying important anomalies. Furthermore, if the pet is relaxed, the analysis unit can sequentially display detailed anomaly information. Furthermore, if the pet is stressed, the analysis unit can prioritize displaying stress-related anomalies. In this way, by adjusting the order in which the anomaly detection results are displayed based on the pet's emotions, important information can be prioritized. 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 emotion data to the generation AI and cause the generation AI to adjust the display order.

[0045] During analysis, the analysis unit can detect anomalies by taking into account climate data of the pet's residential area. The analysis unit, for example, performs anomaly detection by taking into account climate data of the pet's residential area. For example, in hot areas, the analysis unit performs anomaly detection by taking into account the risk of heatstroke. In cold areas, the analysis unit can also perform anomaly detection by taking into account the risk of hypothermia. In humid areas, the analysis unit can also detect anomalies related to humidity. This enables more appropriate anomaly detection by taking into account climate data of the pet's residential area. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input climate data of the pet's residential area to the generation AI and cause the generation AI to perform anomaly detection.

[0046] The analysis unit can improve the accuracy of the analysis by referring to pet-related literature during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to pet-related literature. For example, the analysis unit performs the analysis by referring to the latest research papers on pet health. The analysis unit can also identify abnormal patterns based on literature on pet behavior. The analysis unit can also adjust the analysis algorithm by referring to literature specialized for the type of pet. In this way, the accuracy of the analysis can be improved by referring to pet-related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input pet-related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality when issuing a notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the abnormality when issuing a notification. For example, in the case of a serious abnormality, the notification unit provides a notification including detailed information. In addition, in the case of a minor abnormality, the notification unit can provide a brief notification. In addition, in the case of a moderate abnormality, the notification unit can provide a notification with appropriate level of detail. In this way, by adjusting the level of detail of the notification based on the importance of the abnormality, important information can be given priority in 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 importance data of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0048] The notification unit can apply different notification methods depending on the category of the abnormality when making a notification. For example, the notification unit can apply different notification methods depending on the category of the abnormality when making a notification. For example, in the case of a health-related abnormality, the notification unit can provide a notification including a detailed explanation. In addition, in the case of a behavior-related abnormality, the notification unit can provide a brief notification. In addition, in the case of an environmental abnormality, the notification unit can provide a notification including appropriate countermeasures. In this way, by applying different notification methods depending on the category of the abnormality, more appropriate notification is possible. 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 abnormality category data to the generation AI and cause the generation AI to apply the notification method.

[0049] The notification unit can select an appropriate notification method by taking into account the owner's current location information when sending a notification. For example, the notification unit selects an appropriate notification method by taking into account the owner's current location information when sending a notification. For example, if the owner is at home, the notification unit sends a notification through a smart speaker. If the owner is out, the notification unit can also send a notification to a smartphone. If the owner is driving, the notification unit can also prioritize voice notification. This makes it possible to select a more appropriate notification method by taking into account the owner's current location information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the owner's location information data into the generation AI and have the generation AI select the notification method.

[0050] The notification unit can analyze the owner's social media activity and provide relevant notifications at the time of notification. The notification unit can, for example, analyze the owner's social media activity and provide relevant notifications. For example, if the owner posts about their pet on social media, the notification unit can provide notifications related to the content of those posts. Furthermore, if the owner uses social media during a specific time period, the notification unit can provide notifications during that time period. Furthermore, if the owner shares information about their pet's health on social media, the notification unit can provide notifications based on that information. In this way, relevant notifications can be provided by analyzing the owner's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the owner's social media activity data into a generation AI and cause the generation AI to generate relevant notifications.

[0051] The operation unit can estimate the pet's emotions and adjust the operation timing of the smart lock based on the estimated pet's emotions. The operation unit, for example, estimates the pet's emotions and adjusts the operation timing of the smart lock based on the estimated pet's emotions. For example, if the pet is excited, the operation unit delays the operation of the smart lock to ensure safety. The operation unit can also immediately operate the smart lock if the pet is relaxed. The operation unit can also operate the smart lock at a moderate timing if the pet is stressed. This allows for more appropriate operation by adjusting the operation timing of the smart lock based on the pet's emotions. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can input the pet's emotion data into the generation AI and have the generation AI adjust the operation timing.

[0052] When operating, the operation unit can select an appropriate operation method by referring to the pet's past behavioral data. The operation unit selects an appropriate operation method by referring to the pet's past behavioral data, for example. For example, if the pet has wanted to open the door at a specific time in the past, the operation unit operates the smart lock at that time. Also, if the pet has wanted to open the door in a specific situation in the past, the operation unit can operate the smart lock according to that situation. The operation unit can also analyze the pet's past behavioral data and select the optimal operation timing. In this way, the optimal operation method can be selected by referring to the pet's past behavioral data. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's past behavioral data into the generation AI and have the generation AI select an operation method.

[0053] The operation unit can customize the operation content based on the pet's current health condition during operation. The operation unit customizes the operation content based on the pet's current health condition, for example. For example, if the pet is in good health, the operation unit performs a normal operation. Furthermore, if the pet is in poor health, the operation unit can open and close the door slowly to avoid putting strain on the pet. Furthermore, if the pet is tired, the operation unit can perform an operation that minimizes opening and closing of the door. This allows for more appropriate operation by customizing the operation content based on the pet's current health condition. Some or all of the above-described processing in the operation unit may be performed using, or without, AI, for example. For example, the operation unit can input the pet's health condition data into the generation AI and have the generation AI customize the operation content.

[0054] The operation unit can estimate the pet's emotions and determine the operation priority of the smart lock based on the estimated pet's emotions. The operation unit, for example, estimates the pet's emotions and determines the operation priority of the smart lock based on the estimated pet's emotions. For example, if the pet is excited, the operation unit prioritizes the operation of the smart lock over other operations. Also, if the pet is relaxed, the operation unit can prioritize other operations and postpone the operation of the smart lock. Also, if the pet is stressed, the operation unit can set the operation of the smart lock to a medium priority. This enables more appropriate operation by determining the operation priority of the smart lock based on the pet's emotions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's emotion data into the generation AI and have the generation AI determine the operation priority.

[0055] The operation unit can perform operations taking into consideration the pet's living environment and the owner's lifestyle. The operation unit performs operations taking into consideration, for example, the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the operation unit operates the smart lock taking into consideration the indoor temperature and humidity. The operation unit can also operate the smart lock to reduce the pet's sense of loneliness when the owner is often away. The operation unit can also operate the smart lock taking into consideration the outside temperature and weather when the pet spends a lot of time outdoors. This enables more appropriate operations by taking into consideration the pet's living environment and the owner's lifestyle. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input data on the pet's living environment and the owner's lifestyle into the generation AI and have the generation AI determine the operation content.

[0056] When operated, the operation unit can analyze the social media activity of the pet and perform a related operation. The operation unit, for example, analyzes the social media activity of the pet and performs a related operation. For example, the operation unit analyzes how often the pet interacts with other pets and operates the smart lock at that timing. Furthermore, if the pet is photographed frequently at a specific location, the operation unit can perform an operation related to that location. Furthermore, if the pet is active during a specific time period, the operation unit can operate the smart lock at that time period. In this way, related operations can be performed by analyzing the pet's social media activity. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's social media activity data into the generation AI and have the generation AI execute a decision on a related operation.

[0057] The suggestion unit can estimate the pet's emotions and adjust the way the suggestions are expressed based on the estimated pet's emotions. The suggestion unit, for example, estimates the pet's emotions and adjusts the way the suggestions are expressed based on the estimated pet's emotions. For example, if the pet is excited, the suggestion unit can prioritize suggesting play. Also, if the pet is relaxed, the suggestion unit can prioritize suggesting meals. Also, if the pet is stressed, the suggestion unit can make suggestions to reduce stress. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the pet's emotions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet emotion data into a generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0058] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the pet's health condition. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the pet's health condition. For example, if the pet is in good health, the suggestion unit makes a concise suggestion. Furthermore, if the pet is in poor health, the suggestion unit can make a detailed suggestion and provide specific measures. Furthermore, if the pet is tired, the suggestion unit can make a suggestion encouraging the pet to rest. In this way, adjusting the level of detail of the suggestion based on the pet's health condition enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet health condition data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0059] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the type and age of the pet. The suggestion unit applies different suggestion algorithms depending on, for example, the type and age of the pet. For example, the suggestion unit makes suggestions to increase the amount of exercise for young pets. The suggestion unit can also make suggestions to maintain health for elderly pets. The suggestion unit can also make suggestions specific to a specific type of pet. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0060] The suggestion unit can estimate the pet's emotions and determine the priority of suggestions based on the estimated pet's emotions. The suggestion unit can, for example, estimate the pet's emotions and determine the priority of suggestions based on the estimated pet's emotions. For example, if the pet is excited, the suggestion unit can prioritize play suggestions. Also, if the pet is relaxed, the suggestion unit can prioritize meal suggestions. Also, if the pet is stressed, the suggestion unit can prioritize stress reduction suggestions. Thus, by determining the priority of suggestions based on the pet's emotions, more appropriate suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet emotion data to the generation AI and cause the generation AI to determine the priority of suggestions.

[0061] When making a suggestion, the suggestion unit can take into consideration the pet's living environment and the owner's lifestyle. The suggestion unit makes a suggestion, for example, taking into consideration the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the suggestion unit can suggest indoor play activities. Furthermore, if the owner is often away from home, the suggestion unit can make suggestions to reduce the pet's sense of loneliness. Furthermore, if the pet spends a lot of time outdoors, the suggestion unit can suggest outdoor play activities. This enables more appropriate suggestions to be made by taking into consideration the pet's living environment and the owner's lifestyle. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the pet's living environment and the owner's lifestyle into the generation AI and have the generation AI determine the content of the suggestion.

[0062] When making a suggestion, the suggestion unit can analyze the social media activity of the pet and make a related suggestion. The suggestion unit, for example, analyzes the social media activity of the pet and makes a related suggestion. For example, the suggestion unit analyzes the frequency with which the pet interacts with other pets and makes suggestions for play at that timing. Furthermore, if the pet is photographed frequently in a particular location, the suggestion unit can make suggestions related to that location. Furthermore, if the pet is active during a particular time period, the suggestion unit can make suggestions related to that time period. In this way, relevant suggestions can be made by analyzing the pet's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the pet's social media activity data into a generation AI and cause the generation AI to generate related suggestions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The pet care system may further include a learning unit that learns the pet's preferences based on the pet's behavioral data. The learning unit may, for example, learn the pet's tendency to prefer certain activities or foods, and provide that information to the suggestion unit. This allows the suggestion unit to suggest activities or foods based on the pet's preferences. The learning unit may also collect the pet's behavioral data over a long period of time and learn changes in preferences depending on the season or time of day. Furthermore, the learning unit may reflect changes in the pet's preferences in real time and provide the latest information to the suggestion unit.

[0065] The pet care system may further include a prediction unit that predicts the health status of the pet based on the pet's behavioral data. The prediction unit, for example, analyzes the pet's behavioral patterns and environmental data to predict future health risks. This allows the suggestion unit to suggest preventive measures according to the pet's health risks. The prediction unit may also notify the owner if the pet's health risk is high. Furthermore, the prediction unit may provide the suggestion unit with specific measures to maintain the pet's health.

[0066] The pet care system may further include an optimization unit that optimizes the amount of exercise of the pet based on the behavioral data of the pet. The optimization unit, for example, analyzes the amount of exercise of the pet and calculates an appropriate amount of exercise. This allows the suggestion unit to suggest activities that correspond to the amount of exercise of the pet. The optimization unit may also notify the owner if the amount of exercise of the pet is insufficient. Furthermore, the optimization unit may provide the suggestion unit with specific measures to increase the amount of exercise of the pet.

[0067] The pet care system may further include a diet optimization unit that optimizes the pet's eating pattern based on the pet's behavioral data. The diet optimization unit, for example, analyzes the pet's food intake and meal times to calculate an appropriate eating pattern. This allows the suggestion unit to suggest a diet that matches the pet's eating pattern. The diet optimization unit may also notify the owner if the pet's food intake is insufficient. The diet optimization unit may also provide the suggestion unit with specific measures to increase the pet's food intake.

[0068] The pet care system may further include a sleep optimization unit that optimizes the pet's sleep pattern based on the pet's behavioral data. The sleep optimization unit, for example, analyzes the pet's sleep duration and sleep quality and calculates an appropriate sleep pattern. This allows the suggestion unit to suggest rest according to the pet's sleep pattern. The sleep optimization unit may also notify the owner if the pet is not getting enough sleep. Furthermore, the sleep optimization unit may provide the suggestion unit with specific measures to improve the pet's sleep quality.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects data on pet behavior, temperature, and water and toilet usage. For example, data is collected using cameras and sensors for monitoring pet behavior, temperature sensors, water level sensors, and toilet sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and detects abnormalities, such as the pet's behavioral patterns, temperature data, water consumption, and toilet usage count. Step 3: The notification unit notifies the owner based on the abnormality detected by the analysis unit, for example, via a smartphone app, email, SMS, or smart speaker. Step 4: The operation unit operates the smart lock based on the notification result from the notification unit, for example, via Bluetooth, Wi-Fi, a smartphone app, or voice command. Step 5: The suggestion unit makes health management suggestions based on the results detected by the analysis unit. For example, it detects lack of exercise or loss of appetite and makes suggestions about play and meals.

[0071] (Example 2) A pet care system according to an embodiment of the present invention is an AI system specialized for pet care during business trips or outings. This pet care system collects data on the pet's daily behavior, temperature, and water and litter box usage, and visualizes it as a heat map. If an abnormality is detected, the system notifies the owner via an app and automatically opens and closes the pet's room door using a smart lock to create a comfortable environment for the pet. Furthermore, as a function useful for health management, the system detects lack of exercise and loss of appetite and suggests play and meals. In this way, a new style of connection between pets and their owners is proposed. For example, the pet care system includes a collection unit that collects data on the pet's behavior and environment. The collection unit collects data on the pet's behavior, temperature, and water and litter box usage. Next, an analysis unit that analyzes the collected data and detects abnormalities is included. The analysis unit analyzes the collected data and detects abnormalities. Furthermore, a notification unit is included that notifies the owner when an abnormality is detected. The notification unit notifies the owner when an abnormality is detected. Next, an operation unit is included that operates the smart lock. The operation unit operates the smart lock to open and close the room door. Finally, a suggestion unit is provided to make health management suggestions. The suggestion unit detects lack of exercise and loss of appetite and makes suggestions for play and meals. This enables the pet care system to collect data on pet behavior and environment, detect and notify of abnormalities, operate the smart lock, and make health management suggestions.

[0072] A pet care system according to an embodiment includes a collection unit, an analysis unit, a notification unit, an operation unit, and a suggestion unit. The collection unit collects data on pet behavior, temperature, and water and toilet usage. The collection unit collects data using, for example, a camera or a sensor for monitoring pet behavior. The collection unit can also measure indoor temperature using a temperature sensor. The collection unit can also use a water volume sensor for measuring water consumption and a toilet sensor for counting the number of times the toilet is used. For example, the collection unit monitors pet behavior in real time and collects data using a camera. The collection unit can also periodically measure indoor temperature using a temperature sensor and collect data. The collection unit can also measure pet water consumption using a water volume sensor and collect data. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. For example, the analysis unit analyzes pet behavior patterns based on the collected data and detects abnormalities. The analysis unit can also analyze temperature data and detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the pet uses the toilet to detect abnormalities. For example, the analysis unit can analyze a pet's behavioral patterns to detect abnormal behavior. The analysis unit can also analyze temperature data to detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the pet uses the toilet to detect abnormalities. The notification unit notifies the pet owner when an abnormality is detected. The notification unit can send a notification to the pet owner, for example, via a smartphone app. The notification unit can also send a notification via email or SMS. The notification unit can also notify the pet owner by voice via a smart speaker. For example, the notification unit can notify the pet owner of an abnormality via a smartphone app. The notification unit can also notify the pet owner of an abnormality via email or SMS. The notification unit can also notify the pet owner of an abnormality by voice via a smart speaker. The operation unit operates the smart lock to open and close the room door. The operation unit can operate the smart lock, for example, via Bluetooth or Wi-Fi. The operation unit can also operate the smart lock via a smartphone app.Furthermore, the operation unit can operate the smart lock through voice commands. For example, the operation unit can operate the smart lock through Bluetooth. The operation unit can also operate the smart lock through a smartphone app. The operation unit can also operate the smart lock through voice commands. The suggestion unit detects lack of exercise or loss of appetite and makes suggestions for play or meals. For example, the suggestion unit analyzes the amount of exercise the pet receives and detects lack of exercise. The suggestion unit can also analyze the amount of food the pet receives and detects loss of appetite. Furthermore, the suggestion unit makes suggestions for play or meals when it detects lack of exercise or loss of appetite. For example, the suggestion unit analyzes the amount of exercise the pet receives and detects lack of exercise. The suggestion unit can also analyze the amount of food the pet receives and detects loss of appetite. The suggestion unit makes suggestions for play or meals when it detects lack of exercise or loss of appetite. As a result, the pet care system according to the embodiment can collect data on the pet's behavior and environment, detect and notify abnormalities, operate the smart lock, and make health management suggestions.

[0073] The collection unit can collect data on pet behavior, temperature, and water or toilet usage. The collection unit collects data using, for example, a camera or sensor for monitoring pet behavior. For example, the collection unit monitors pet behavior in real time using a camera and collects data. The collection unit can also measure indoor temperature using a temperature sensor. For example, the collection unit can periodically measure indoor temperature using a temperature sensor and collect data. The collection unit can also use a water volume sensor for measuring water consumption or a toilet sensor for counting the number of toilet uses. For example, the collection unit can measure pet water consumption using a water volume sensor and collect data. In this way, detailed data can be obtained by collecting data on pet behavior, temperature, and water or toilet usage. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input pet behavior data acquired by a camera to a generation AI and have the generation AI analyze the behavior data.

[0074] The analysis unit can analyze the collected data and detect abnormalities. For example, the analysis unit analyzes the behavioral patterns of a pet based on the collected data and detects abnormalities. For example, the analysis unit analyzes the behavioral patterns of a pet and detects abnormal behavior. The analysis unit can also analyze temperature data and detect abnormal temperature changes. For example, the analysis unit can analyze temperature data and detect abnormal temperature changes. The analysis unit can also analyze water consumption and the number of times the toilet is used and detect abnormalities. For example, the analysis unit can analyze water consumption and the number of times the toilet is used and detect abnormalities. In this way, by analyzing the collected data and detecting abnormalities, abnormalities in the pet can be discovered early. 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 collected data to a generation AI and cause the generation AI to detect abnormalities.

[0075] The notification unit can notify the owner when an abnormality is detected. The notification unit sends a notification to the owner, for example, via a smartphone app. For example, the notification unit notifies the owner of the abnormality via the smartphone app. The notification unit can also send a notification via email or SMS. For example, the notification unit can notify the owner of the abnormality via email or SMS. The notification unit can also notify the owner by voice via a smart speaker. For example, the notification unit can notify the owner of the abnormality by voice via a smart speaker. This allows for a prompt response by notifying the owner when an abnormality is detected. 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 content of the abnormality notification to a generation AI and cause the generation AI to generate a notification.

[0076] The operation unit can operate the smart lock to open and close the room door. The operation unit, for example, operates the smart lock via Bluetooth or Wi-Fi. For example, the operation unit operates the smart lock via Bluetooth. The operation unit can also operate the smart lock via a smartphone app. For example, the operation unit can operate the smart lock via a smartphone app. The operation unit can also operate the smart lock via a voice command. For example, the operation unit can also operate the smart lock via a voice command. This allows the pet's environment to be improved by operating the smart lock to open and close the room door. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the operation content of the smart lock to a generation AI and have the generation AI generate the operation.

[0077] The suggestion unit can detect lack of exercise or loss of appetite and suggest activities or meals. The suggestion unit, for example, analyzes the amount of exercise the pet receives and detects lack of exercise. For example, the suggestion unit can analyze the amount of exercise the pet receives and detect lack of exercise. The suggestion unit can also analyze the amount of food the pet eats and detect loss of appetite. For example, the suggestion unit can analyze the amount of food the pet eats and detect loss of appetite. The suggestion unit can also suggest activities or meals when it detects lack of exercise or loss of appetite. For example, the suggestion unit can suggest activities suitable for the pet when it detects lack of exercise. The suggestion unit can also suggest meals suitable for the pet when it detects loss of appetite. This makes it possible to manage the health of the pet by detecting lack of exercise or loss of appetite and suggesting activities or meals. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the amount of exercise and food the pet receives into a generation AI and cause the generation AI to generate suggestions.

[0078] The collection unit can estimate the pet's emotions and adjust the behavioral data collection frequency based on the estimated pet emotions. The collection unit, for example, estimates the pet's emotions and adjusts the behavioral data collection frequency based on the estimated pet emotions. For example, when the pet is excited, the collection unit increases the collection frequency to acquire detailed behavioral data. Furthermore, when the pet is relaxed, the collection unit can decrease the collection frequency to acquire the minimum necessary data. Furthermore, when the pet is stressed, the collection unit can set the collection frequency to a medium level to acquire data for identifying the cause of stress. This allows for more appropriate data collection by adjusting the behavioral data collection frequency based on the pet's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the collection frequency.

[0079] The collection unit can analyze the pet's past behavioral patterns and select an appropriate timing for collecting data. The collection unit, for example, analyzes the pet's past behavioral patterns and selects the optimal timing for collecting data. For example, the collection unit identifies time periods when the pet is active and concentrates data collection during those time periods. The collection unit can also collect data by avoiding time periods when the pet is resting. The collection unit can also adjust the timing for collecting data taking into account the pet's meal and toilet times. In this way, the optimal timing for collecting data can be selected by analyzing the pet's past behavioral patterns. 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 pet's past behavioral data into the generation AI and cause the generation AI to select the timing for collecting data.

[0080] The collection unit can customize the type of data to be collected based on the pet's health condition and age at the time of collection. The collection unit customizes the type of data to be collected based on, for example, the pet's health condition and age. For example, for an elderly pet, the collection unit may prioritize collecting data related to the pet's health condition. For a young pet, the collection unit may also focus on collecting data on the amount of exercise and play. For a pet in poor health, the collection unit may also collect detailed data on food and water intake. This enables more appropriate data collection by customizing the type of data to be collected based on the pet's health condition and age. 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 may cause the generation AI to customize data collection based on the pet's health condition and age.

[0081] The collection unit can estimate the pet's emotions and determine the priority of data to be collected based on the estimated pet emotions. The collection unit, for example, estimates the pet's emotions and determines the priority of data to be collected based on the estimated pet emotions. For example, if the pet is excited, the collection unit can prioritize collecting data on the amount of exercise and play. Also, if the pet is relaxed, the collection unit can prioritize collecting data on rest and sleep. Also, if the pet is stressed, the collection unit can prioritize collecting data to identify the cause of stress. In this way, by determining the priority of data to be collected based on the pet's emotions, more important data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the pet's emotion data to the generation AI and have the generation AI determine the priority of the data.

[0082] The collection unit can collect data taking into account the pet's living environment and the owner's lifestyle when collecting data. The collection unit, for example, collects data taking into account the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the collection unit collects data on indoor temperature and humidity. The collection unit can also collect data to reduce the pet's sense of loneliness when the owner is often away from home. The collection unit can also collect data on outdoor temperature and weather when the pet spends a lot of time outdoors. This allows for more appropriate data collection by collecting data taking into account the pet's living environment and the owner's lifestyle. 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 cause the generation AI to collect data based on the pet's living environment and the owner's lifestyle.

[0083] The collection unit can analyze the social media activity of the pet at the time of collection and collect related data. The collection unit, for example, analyzes the social media activity of the pet and collects related data. For example, the collection unit collects information on how often the pet interacts with other pets to obtain sociability data. If the pet is photographed frequently in a particular location, the collection unit can also collect data on the location. If the pet is more active during a particular time period, the collection unit can also collect data on that time period. In this way, related data can be collected by analyzing the social media activity of the pet. 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 social media activity data of the pet to the generation AI and cause the generation AI to collect related data.

[0084] The analysis unit can estimate the pet's emotions and adjust the anomaly detection criteria based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions and adjusts the anomaly detection criteria based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit relaxes the anomaly detection criteria to prevent false detection. Furthermore, if the pet is relaxed, the analysis unit can tighten the anomaly detection criteria to perform accurate detection. Furthermore, if the pet is stressed, the analysis unit can set the anomaly detection criteria to a moderate level to perform appropriate detection. This allows for more accurate anomaly detection by adjusting the anomaly detection criteria based on the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the anomaly detection criteria.

[0085] During analysis, the analysis unit can improve the accuracy of abnormality detection by referring to the pet's past health data. The analysis unit can improve the accuracy of abnormality detection by referring to the pet's past health data, for example. For example, the analysis unit can identify abnormality patterns based on the pet's past health data. The analysis unit can also adjust the abnormality detection algorithm by referring to the pet's past health data. The analysis unit can also analyze the pet's past health data and perform early abnormality detection. In this way, by referring to the pet's past health data, the accuracy of abnormality detection can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the pet's past health data into the generation AI and cause the generation AI to improve the accuracy of abnormality detection.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. The analysis unit applies different analysis algorithms depending on, for example, the type and age of the pet. For example, the analysis unit applies an analysis algorithm that emphasizes activity level to young pets. The analysis unit can also apply an analysis algorithm that emphasizes health status to elderly pets. The analysis unit can also apply an analysis algorithm specialized for a specific type of pet. This allows for more appropriate analysis by applying different analysis algorithms depending on the type and age of the pet. 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 cause the generation AI to apply an analysis algorithm based on the type and age of the pet.

[0087] The analysis unit can estimate the pet's emotions and adjust the order in which the anomaly detection results are displayed based on the estimated pet's emotions. The analysis unit, for example, estimates the pet's emotions and adjusts the order in which the anomaly detection results are displayed based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit can prioritize displaying important anomalies. Furthermore, if the pet is relaxed, the analysis unit can sequentially display detailed anomaly information. Furthermore, if the pet is stressed, the analysis unit can prioritize displaying stress-related anomalies. In this way, by adjusting the order in which the anomaly detection results are displayed based on the pet's emotions, important information can be prioritized. 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 emotion data to the generation AI and cause the generation AI to adjust the display order.

[0088] During analysis, the analysis unit can detect anomalies by taking into account climate data of the pet's residential area. The analysis unit, for example, performs anomaly detection by taking into account climate data of the pet's residential area. For example, in hot areas, the analysis unit performs anomaly detection by taking into account the risk of heatstroke. In cold areas, the analysis unit can also perform anomaly detection by taking into account the risk of hypothermia. In humid areas, the analysis unit can also detect anomalies related to humidity. This enables more appropriate anomaly detection by taking into account climate data of the pet's residential area. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input climate data of the pet's residential area to the generation AI and cause the generation AI to perform anomaly detection.

[0089] The analysis unit can improve the accuracy of the analysis by referring to pet-related literature during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to pet-related literature. For example, the analysis unit performs the analysis by referring to the latest research papers on pet health. The analysis unit can also identify abnormal patterns based on literature on pet behavior. The analysis unit can also adjust the analysis algorithm by referring to literature specialized for the type of pet. In this way, the accuracy of the analysis can be improved by referring to pet-related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input pet-related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] The notification unit can estimate the owner's emotions and adjust the notification expression method based on the estimated owner's emotions. The notification unit, for example, estimates the owner's emotions and adjusts the notification expression method based on the estimated owner's emotions. For example, if the owner is nervous, the notification unit can provide a notification using calm expression. If the owner is relaxed, the notification unit can also provide a notification including detailed information. If the owner is in a hurry, the notification unit can also provide a concise and quick notification. This allows for more appropriate notification by adjusting the notification expression method based on the owner's emotions. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the owner's emotion data into a generation AI and cause the generation AI to adjust the notification expression method.

[0091] The notification unit can adjust the level of detail of the notification based on the importance of the abnormality when issuing a notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the abnormality when issuing a notification. For example, in the case of a serious abnormality, the notification unit provides a notification including detailed information. In addition, in the case of a minor abnormality, the notification unit can provide a brief notification. In addition, in the case of a moderate abnormality, the notification unit can provide a notification with appropriate level of detail. In this way, by adjusting the level of detail of the notification based on the importance of the abnormality, important information can be given priority in 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 importance data of the abnormality to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0092] The notification unit can apply different notification methods depending on the category of the abnormality when making a notification. For example, the notification unit can apply different notification methods depending on the category of the abnormality when making a notification. For example, in the case of a health-related abnormality, the notification unit can provide a notification including a detailed explanation. In addition, in the case of a behavior-related abnormality, the notification unit can provide a brief notification. In addition, in the case of an environmental abnormality, the notification unit can provide a notification including appropriate countermeasures. In this way, by applying different notification methods depending on the category of the abnormality, more appropriate notification is possible. 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 abnormality category data to the generation AI and cause the generation AI to apply the notification method.

[0093] The notification unit can estimate the owner's emotions and adjust the timing of the notification based on the estimated owner's emotions. The notification unit, for example, estimates the owner's emotions and adjusts the timing of the notification based on the estimated owner's emotions. For example, if the owner is busy, the notification unit delays and sends the notification later. The notification unit can also send the notification immediately if the owner is relaxed. The notification unit can also prioritize sending important notifications if the owner is in a hurry. In this way, by adjusting the timing of the notification based on the owner's emotions, it is possible to send the notification at a more appropriate time. 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 emotion data to the generation AI and cause the generation AI to adjust the timing of the notification.

[0094] The notification unit can select an appropriate notification method by taking into account the owner's current location information when sending a notification. For example, the notification unit selects an appropriate notification method by taking into account the owner's current location information when sending a notification. For example, if the owner is at home, the notification unit sends a notification through a smart speaker. If the owner is out, the notification unit can also send a notification to a smartphone. If the owner is driving, the notification unit can also prioritize voice notification. This makes it possible to select a more appropriate notification method by taking into account the owner's current location information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the owner's location information data into the generation AI and have the generation AI select the notification method.

[0095] The notification unit can analyze the owner's social media activity and provide relevant notifications at the time of notification. The notification unit can, for example, analyze the owner's social media activity and provide relevant notifications. For example, if the owner posts about their pet on social media, the notification unit can provide notifications related to the content of those posts. Furthermore, if the owner uses social media during a specific time period, the notification unit can provide notifications during that time period. Furthermore, if the owner shares information about their pet's health on social media, the notification unit can provide notifications based on that information. In this way, relevant notifications can be provided by analyzing the owner's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the owner's social media activity data into a generation AI and cause the generation AI to generate relevant notifications.

[0096] The operation unit can estimate the pet's emotions and adjust the operation timing of the smart lock based on the estimated pet's emotions. The operation unit, for example, estimates the pet's emotions and adjusts the operation timing of the smart lock based on the estimated pet's emotions. For example, if the pet is excited, the operation unit delays the operation of the smart lock to ensure safety. The operation unit can also immediately operate the smart lock if the pet is relaxed. The operation unit can also operate the smart lock at a moderate timing if the pet is stressed. This allows for more appropriate operation by adjusting the operation timing of the smart lock based on the pet's emotions. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can input the pet's emotion data into the generation AI and have the generation AI adjust the operation timing.

[0097] When operating, the operation unit can select an appropriate operation method by referring to the pet's past behavioral data. The operation unit selects an appropriate operation method by referring to the pet's past behavioral data, for example. For example, if the pet has wanted to open the door at a specific time in the past, the operation unit operates the smart lock at that time. Also, if the pet has wanted to open the door in a specific situation in the past, the operation unit can operate the smart lock according to that situation. The operation unit can also analyze the pet's past behavioral data and select the optimal operation timing. In this way, the optimal operation method can be selected by referring to the pet's past behavioral data. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's past behavioral data into the generation AI and have the generation AI select an operation method.

[0098] The operation unit can customize the operation content based on the pet's current health condition during operation. The operation unit customizes the operation content based on the pet's current health condition, for example. For example, if the pet is in good health, the operation unit performs a normal operation. Furthermore, if the pet is in poor health, the operation unit can open and close the door slowly to avoid putting strain on the pet. Furthermore, if the pet is tired, the operation unit can perform an operation that minimizes opening and closing of the door. This allows for more appropriate operation by customizing the operation content based on the pet's current health condition. Some or all of the above-described processing in the operation unit may be performed using, or without, AI, for example. For example, the operation unit can input the pet's health condition data into the generation AI and have the generation AI customize the operation content.

[0099] The operation unit can estimate the pet's emotions and determine the operation priority of the smart lock based on the estimated pet's emotions. The operation unit, for example, estimates the pet's emotions and determines the operation priority of the smart lock based on the estimated pet's emotions. For example, if the pet is excited, the operation unit prioritizes the operation of the smart lock over other operations. Also, if the pet is relaxed, the operation unit can prioritize other operations and postpone the operation of the smart lock. Also, if the pet is stressed, the operation unit can set the operation of the smart lock to a medium priority. This enables more appropriate operation by determining the operation priority of the smart lock based on the pet's emotions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's emotion data into the generation AI and have the generation AI determine the operation priority.

[0100] The operation unit can perform operations taking into consideration the pet's living environment and the owner's lifestyle. The operation unit performs operations taking into consideration, for example, the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the operation unit operates the smart lock taking into consideration the indoor temperature and humidity. The operation unit can also operate the smart lock to reduce the pet's sense of loneliness when the owner is often away. The operation unit can also operate the smart lock taking into consideration the outside temperature and weather when the pet spends a lot of time outdoors. This enables more appropriate operations by taking into consideration the pet's living environment and the owner's lifestyle. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input data on the pet's living environment and the owner's lifestyle into the generation AI and have the generation AI determine the operation content.

[0101] When operated, the operation unit can analyze the social media activity of the pet and perform a related operation. The operation unit, for example, analyzes the social media activity of the pet and performs a related operation. For example, the operation unit analyzes how often the pet interacts with other pets and operates the smart lock at that timing. Furthermore, if the pet is photographed frequently at a specific location, the operation unit can perform an operation related to that location. Furthermore, if the pet is active during a specific time period, the operation unit can operate the smart lock at that time period. In this way, related operations can be performed by analyzing the pet's social media activity. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the pet's social media activity data into the generation AI and have the generation AI execute a decision on a related operation.

[0102] The suggestion unit can estimate the pet's emotions and adjust the way the suggestions are expressed based on the estimated pet's emotions. The suggestion unit, for example, estimates the pet's emotions and adjusts the way the suggestions are expressed based on the estimated pet's emotions. For example, if the pet is excited, the suggestion unit can prioritize suggesting play. Also, if the pet is relaxed, the suggestion unit can prioritize suggesting meals. Also, if the pet is stressed, the suggestion unit can make suggestions to reduce stress. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the pet's emotions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet emotion data into a generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0103] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the pet's health condition. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the pet's health condition. For example, if the pet is in good health, the suggestion unit makes a concise suggestion. Furthermore, if the pet is in poor health, the suggestion unit can make a detailed suggestion and provide specific measures. Furthermore, if the pet is tired, the suggestion unit can make a suggestion encouraging the pet to rest. In this way, adjusting the level of detail of the suggestion based on the pet's health condition enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet health condition data into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0104] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the type and age of the pet. The suggestion unit applies different suggestion algorithms depending on, for example, the type and age of the pet. For example, the suggestion unit makes suggestions to increase the amount of exercise for young pets. The suggestion unit can also make suggestions to maintain health for elderly pets. The suggestion unit can also make suggestions specific to a specific type of pet. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0105] The suggestion unit can estimate the pet's emotions and determine the priority of suggestions based on the estimated pet's emotions. The suggestion unit can, for example, estimate the pet's emotions and determine the priority of suggestions based on the estimated pet's emotions. For example, if the pet is excited, the suggestion unit can prioritize play suggestions. Also, if the pet is relaxed, the suggestion unit can prioritize meal suggestions. Also, if the pet is stressed, the suggestion unit can prioritize stress reduction suggestions. Thus, by determining the priority of suggestions based on the pet's emotions, more appropriate suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input pet emotion data to the generation AI and cause the generation AI to determine the priority of suggestions.

[0106] When making a suggestion, the suggestion unit can take into consideration the pet's living environment and the owner's lifestyle. The suggestion unit makes a suggestion, for example, taking into consideration the pet's living environment and the owner's lifestyle. For example, if the pet spends a lot of time indoors, the suggestion unit can suggest indoor play activities. Furthermore, if the owner is often away from home, the suggestion unit can make suggestions to reduce the pet's sense of loneliness. Furthermore, if the pet spends a lot of time outdoors, the suggestion unit can suggest outdoor play activities. This enables more appropriate suggestions to be made by taking into consideration the pet's living environment and the owner's lifestyle. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the pet's living environment and the owner's lifestyle into the generation AI and have the generation AI determine the content of the suggestion.

[0107] When making a suggestion, the suggestion unit can analyze the social media activity of the pet and make a related suggestion. The suggestion unit, for example, analyzes the social media activity of the pet and makes a related suggestion. For example, the suggestion unit analyzes the frequency with which the pet interacts with other pets and makes suggestions for play at that timing. Furthermore, if the pet is photographed frequently in a particular location, the suggestion unit can make suggestions related to that location. Furthermore, if the pet is active during a particular time period, the suggestion unit can make suggestions related to that time period. In this way, relevant suggestions can be made by analyzing the pet's social media activity. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the pet's social media activity data into a generation AI and cause the generation AI to generate related suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, notification unit, operation unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the pet's behavior and environment using the camera 42 and sensors of the smart device 14. For example, the analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and detects abnormalities. For example, the notification unit sends a notification to the owner via the control unit 46A of the smart device 14. For example, the operation unit operates a smart lock using the control unit 46A of the smart device 14 to open and close a room door. For example, the suggestion unit detects lack of exercise or loss of appetite by the specific processing unit 290 of the data processing device 12 and makes suggestions about play and meals. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, operation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects behavioral and environmental data of the pet using the camera 42 and sensors of the smart glasses 214. For example, the analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and detects abnormalities. For example, the notification unit sends a notification to the owner via the control unit 46A of the smart glasses 214. For example, the operation unit operates a smart lock using the control unit 46A of the smart glasses 214 to open and close a room door. For example, the suggestion unit detects lack of exercise or loss of appetite by the specific processing unit 290 of the data processing device 12 and makes suggestions about play and meals. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, notification unit, operation unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral and environmental data of the pet using the camera 42 and sensors of the headset-type terminal 314. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit sends a notification to the owner via, for example, the control unit 46A of the headset-type terminal 314. The operation unit operates a smart lock using, for example, the control unit 46A of the headset-type terminal 314 to open and close a room door. The suggestion unit detects lack of exercise or loss of appetite by, for example, the specific processing unit 290 of the data processing device 12 and makes suggestions about play and meals. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, notification unit, operation unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral and environmental data of the pet using the camera 42 and sensors of the robot 414. For example, the analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and detects abnormalities. For example, the notification unit sends a notification to the owner via the control unit 46A of the robot 414. For example, the operation unit operates a smart lock using the control unit 46A of the robot 414 to open and close a room door. For example, the suggestion unit detects lack of exercise or loss of appetite using the specific processing unit 290 of the data processing device 12 and makes suggestions about play and meals.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The pet care system may further include a learning unit that learns the pet's preferences based on the pet's behavioral data. The learning unit may, for example, learn the pet's tendency to prefer certain activities or foods, and provide that information to the suggestion unit. This allows the suggestion unit to suggest activities or foods based on the pet's preferences. The learning unit may also collect the pet's behavioral data over a long period of time and learn changes in preferences depending on the season or time of day. Furthermore, the learning unit may reflect changes in the pet's preferences in real time and provide the latest information to the suggestion unit.

[0110] The pet care system may further include an evaluation unit that evaluates the pet's stress level based on the pet's behavioral data. The evaluation unit may, for example, analyze the pet's behavioral patterns and environmental data to quantify the stress level. This allows the suggestion unit to suggest activities and meals according to the pet's stress level. The evaluation unit may also notify the owner if the pet's stress level is high. The evaluation unit may also provide the suggestion unit with specific measures to reduce the pet's stress level.

[0111] The pet care system may further include a sociability evaluation unit that evaluates the sociability of the pet based on the pet's behavioral data. The sociability evaluation unit, for example, evaluates the degree to which the pet interacts with other pets and quantifies the level of sociability. This allows the suggestion unit to suggest play and interactions according to the pet's sociability. The sociability evaluation unit may also notify the owner if the pet feels lonely. Furthermore, the sociability evaluation unit may provide the suggestion unit with specific measures to improve the pet's sociability.

[0112] The pet care system may further include a prediction unit that predicts the health status of the pet based on the pet's behavioral data. The prediction unit, for example, analyzes the pet's behavioral patterns and environmental data to predict future health risks. This allows the suggestion unit to suggest preventive measures according to the pet's health risks. The prediction unit may also notify the owner if the pet's health risk is high. Furthermore, the prediction unit may provide the suggestion unit with specific measures to maintain the pet's health.

[0113] The pet care system may further include an optimization unit that optimizes the amount of exercise of the pet based on the behavioral data of the pet. The optimization unit, for example, analyzes the amount of exercise of the pet and calculates an appropriate amount of exercise. This allows the suggestion unit to suggest activities that correspond to the amount of exercise of the pet. The optimization unit may also notify the owner if the amount of exercise of the pet is insufficient. Furthermore, the optimization unit may provide the suggestion unit with specific measures to increase the amount of exercise of the pet.

[0114] The pet care system may further include a diet optimization unit that optimizes the pet's eating pattern based on the pet's behavioral data. The diet optimization unit, for example, analyzes the pet's food intake and meal times to calculate an appropriate eating pattern. This allows the suggestion unit to suggest a diet that matches the pet's eating pattern. The diet optimization unit may also notify the owner if the pet's food intake is insufficient. The diet optimization unit may also provide the suggestion unit with specific measures to increase the pet's food intake.

[0115] The pet care system may further include a sleep optimization unit that optimizes the pet's sleep pattern based on the pet's behavioral data. The sleep optimization unit, for example, analyzes the pet's sleep duration and sleep quality and calculates an appropriate sleep pattern. This allows the suggestion unit to suggest rest according to the pet's sleep pattern. The sleep optimization unit may also notify the owner if the pet is not getting enough sleep. Furthermore, the sleep optimization unit may provide the suggestion unit with specific measures to improve the pet's sleep quality.

[0116] The pet care system may further include an emotion estimation unit that estimates the pet's emotion based on the pet's behavioral data and suggests measures to reduce the pet's stress based on the estimated emotion. The emotion estimation unit, for example, analyzes the pet's behavioral patterns and environmental data to estimate the pet's emotion. This allows the suggestion unit to suggest stress reduction measures according to the pet's emotion. The emotion estimation unit may also notify the pet's owner if the pet's emotion is unstable. The emotion estimation unit may also provide the suggestion unit with specific measures to stabilize the pet's emotion.

[0117] The pet care system may further include a loneliness reduction unit that proposes measures to reduce the pet's sense of loneliness based on the pet's behavioral data. The loneliness reduction unit, for example, analyzes the amount of time the pet spends alone and quantifies the sense of loneliness. This allows the suggestion unit to propose interactions and play activities that correspond to the pet's sense of loneliness. The loneliness reduction unit may also notify the owner if the pet's sense of loneliness is high. Furthermore, the loneliness reduction unit may provide the suggestion unit with specific measures to reduce the pet's sense of loneliness.

[0118] The pet care system may further include a happiness evaluation unit that evaluates the happiness of the pet based on the pet's behavioral data. The happiness evaluation unit may, for example, analyze the pet's behavioral patterns and environmental data to quantify the happiness level. This allows the suggestion unit to suggest activities and meals according to the pet's happiness level. The happiness evaluation unit may also notify the owner if the pet's happiness level is low. Furthermore, the happiness evaluation unit may provide the suggestion unit with specific measures to improve the pet's happiness level.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data on pet behavior, temperature, and water and toilet usage. For example, data is collected using cameras and sensors for monitoring pet behavior, temperature sensors, water level sensors, and toilet sensors. Step 2: The analysis unit analyzes the data collected by the collection unit and detects abnormalities, such as the pet's behavioral patterns, temperature data, water consumption, and toilet usage count. Step 3: The notification unit notifies the owner based on the abnormality detected by the analysis unit, for example, via a smartphone app, email, SMS, or smart speaker. Step 4: The operation unit operates the smart lock based on the notification result from the notification unit, for example, via Bluetooth, Wi-Fi, a smartphone app, or voice command. Step 5: The suggestion unit makes health management suggestions based on the results detected by the analysis unit. For example, it detects lack of exercise or loss of appetite and makes suggestions about play and meals.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 for collecting behavioral or environmental data of a pet; 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; an operation unit that operates the smart lock based on the result notified by the notification unit; a suggestion unit that makes health management suggestions based on the results detected by the analysis unit. A system characterized by:

2. The collecting unit Collect pet behavior or temperature, water or litter box usage The system of claim 1 .

3. The analysis unit Analyzing collected data and detecting anomalies The system of claim 1 .

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

5. The operation unit includes: Operate the smart lock to open and close the room door The system of claim 1 .

6. The proposal unit Detects lack of exercise or appetite and suggests play or meals The system of claim 1 .

7. The collecting unit Estimate the pet's emotions and adjust the frequency of behavioral data collection based on the estimated pet's emotions. The system of claim 1 .

8. The collecting unit Analyze your pet's past behavioral patterns and select the appropriate timing for data collection The system of claim 1 .

Images