system

Abstract

The system according to this embodiment aims to constantly monitor the health status and behavior of pets and to easily record and share the daily lives of pets. [Solution] The system according to the embodiment comprises a recording unit, a checking unit, a generation unit, and a posting unit. The recording unit records the pet's movements. The checking unit analyzes the data recorded by the recording unit and checks the pet's health status. The generation unit automatically generates a blog post based on the data obtained by the checking unit. The posting unit automatically posts the blog post generated by the generation unit to social media.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to constantly grasp the health status and behavior of a pet, and there is a problem that means for easily recording and sharing the daily life of the pet are insufficient.

[0005] The system according to the embodiment aims to constantly grasp the health status and behavior of a pet and easily record and share the daily life of the pet.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, a checking unit, a generation unit, and a posting unit. The recording unit records the pet's movements. The checking unit analyzes the data recorded by the recording unit and checks the pet's health status. The generation unit automatically generates a blog post based on the data obtained by the checking unit. The posting unit automatically posts the blog post generated by the generation unit to social media. [Effects of the Invention]

[0007] The system according to this embodiment allows for constant monitoring of a pet's health and behavior, and makes it easy to record and share the pet's daily life. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pet management system according to an embodiment of the present invention is a system for constantly monitoring a pet's health and behavior, and for easily recording and sharing a pet's daily life. This pet management system uses a pet camera and behavioral analytics, with AI analyzing video and recording the pet's movements. Next, the AI ​​analyzes heart rate and activity data from a pet health monitor device to check the pet's health. Furthermore, the AI ​​graphs the pet's daily activities, automatically generates a concise blog post, and automatically posts it to a social networking platform. This mechanism enables consistent health management of pets, facilitating information sharing and reducing the burden on pet owners. It also promotes interaction through pets and encourages active information exchange within the community. For example, the pet management system uses a pet camera and behavioral analytics, with AI analyzing video. In this process, the AI ​​records the pet's movements in detail, understanding its favorite toys and movement patterns. For example, it collects data such as what kind of movements the pet makes and at what times of day it is most active. This allows for understanding the pet's behavior patterns and early detection of changes in its health. Next, the pet management system uses AI to analyze heart rate and activity data from pet health monitoring devices. For example, if a pet's heart rate is higher than normal, it may indicate stress or illness. By monitoring this data in real time and checking the pet's health status, illnesses and behavioral problems can be detected early, allowing for appropriate care. Furthermore, the pet management system uses AI to graph the pet's daily activities and automatically generate concise blog posts. For example, it graphs what the pet did and what actions it took at what times of day, and uses this to create a blog post. This blog post is automatically posted to existing social media platforms. This allows pet owners to easily record their pet's daily life and share it on social media. This enables consistent health management of pets. By reflecting behavior and health status in real time, the system can maintain the pet's comfort and health. It also facilitates information sharing and reduces the burden on pet owners. Easily recording and sharing pets' daily lives on social media reduces the burden on pet owners. Furthermore, it promotes interaction through pets and encourages active information exchange within the community.For example, by sharing information about pet health and behavior, owners can exchange information with other pet owners. In this way, a system that integrates pet health management and information sharing is realized. As a result, the pet management system can constantly monitor pet health and behavior, and easily record and share pets' daily lives.

[0029] The pet management system according to this embodiment comprises a recording unit, a checking unit, a generation unit, and a posting unit. The recording unit records the pet's movements. For example, the recording unit records the pet's movements using a pet camera. The recording unit can record the pet's behavior in detail and understand the pet's favorite toys and movement patterns. The checking unit analyzes the data recorded by the recording unit and checks the pet's health status. For example, the checking unit analyzes heart rate and activity data from a pet health monitor device. If the pet's heart rate is higher than normal, the checking unit can analyze that it may be a sign of stress or illness. The generation unit automatically generates blog posts based on the data obtained by the checking unit. For example, the generation unit graphs the pet's daily activities and automatically generates a concise blog post. The generation unit can graph the pet's activities to make them visually easy to understand. The posting unit automatically posts the blog post generated by the generation unit to social networking services (SNS). For example, the posting unit automatically posts the generated blog post to an existing SNS platform. The posting function reduces the burden on pet owners by automatically posting blog articles. As a result, the pet management system according to this embodiment can efficiently manage pet health and share information by recording pet activity, checking health status, and automatically generating and posting blog articles to social media.

[0030] The recording unit records the pet's movements. For example, it uses a pet camera to record the pet's movements. Specifically, the pet camera captures high-resolution video in real time, recording the pet's behavior in detail. The camera is equipped with a motion sensor to track the pet's movements and can automatically adjust its orientation each time the pet moves. This ensures that the pet's movements are recorded without fail, no matter where it is. The recording unit also saves the pet's behavior data to the cloud, making it accessible at any time. This data includes playtime, mealtimes, and resting times, and analyzing this data helps understand the pet's preferred toys and movement patterns. Furthermore, the recording unit can collect not only audio and video recordings of the pet's behavior but also environmental data such as temperature and humidity. This provides information to maintain a comfortable environment for the pet. The recording unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the checking and generation units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the recording unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The monitoring unit analyzes data recorded by the recording unit to check the pet's health status. For example, the monitoring unit analyzes heart rate and activity data from a pet health monitor device. Specifically, the pet health monitor device is attached to the pet's collar or harness and collects biometric data such as heart rate, respiratory rate, activity level, and sleep patterns in real time. This data is transmitted to the monitoring unit via wireless communication and analyzed by a dedicated analysis algorithm. The monitoring unit can analyze whether a higher-than-normal heart rate indicates stress or illness. For example, if a sudden increase in heart rate is observed, the pet may be experiencing some kind of stress, and an alert is sent to the owner to draw their attention. Also, if the activity level is lower than normal, the pet may be feeling unwell or fatigued, and the monitoring unit can instruct the owner to get adequate rest. Furthermore, the monitoring unit can also analyze long-term health trends by utilizing past data and statistical information. For example, based on past data, it can predict fluctuations in the pet's health status under specific seasons or environmental conditions and plan measures for future health management. Furthermore, the monitoring unit uses an anomaly detection algorithm to detect unusual patterns or abnormal data, enabling it to issue warnings early. This allows the monitoring unit to not only monitor health status in real time but also to handle long-term health management and anomaly detection, improving reliability and safety for maintaining pet health.

[0032] The generation unit automatically generates blog posts based on data obtained by the checking unit. For example, the generation unit graphs a pet's daily activities and automatically generates a concise blog post. Specifically, the generation unit analyzes the data provided by the checking unit and visualizes the pet's activity level, meal times, rest times, etc., in an easy-to-understand graph. This allows pet owners to grasp their pet's daily activities at a glance. Based on the graphed data, the generation unit automatically generates a concise blog post about the pet's daily events and health condition. For example, it can generate a blog post with content such as, "Today, my pet was playing energetically. Its heart rate was stable, and its health condition is good." The generation unit uses natural language generation technology to convert data into text and create blog posts in an easy-to-read format. Furthermore, the generation unit can also attach photos and videos to blog posts. For example, it can insert photos and videos of the pet taken by the recording unit into the blog post, providing visually appealing content. In this way, the generation unit not only makes the pet's activities visually easy to understand but also provides interesting content for pet owners. The generation unit can also customize the content of blog posts. For example, it can change the tone and style of the article according to the owner's preferences. This allows the generation unit to provide blog posts tailored to the owner's needs, enabling efficient pet health management and information sharing.

[0033] The posting unit automatically posts blog articles generated by the generation unit to social media. For example, the posting unit automatically posts generated blog articles to existing social media platforms. Specifically, the posting unit receives blog articles provided by the generation unit and automatically posts them to the social media accounts specified by the pet owner. The posting unit uses the API of the social media platform to automate article posting. This saves pet owners the trouble of manually posting articles. The posting unit can also set a posting schedule for articles. For example, by scheduling articles to be posted at a fixed time every day, pet information can be shared regularly. Furthermore, the posting unit can customize the content of the articles. For example, hashtags can be added to articles, or specific users can be tagged. This allows the posting unit to increase the reach of articles and deliver pet information to more users. The posting unit can also monitor reactions after posting. For example, it collects the number of likes and comments on articles and provides feedback to pet owners. This allows pet owners to understand what kind of content is popular and improve future posts. The posting function can also post to multiple social media platforms simultaneously. For example, by posting an article to multiple platforms at once, information can be delivered to a wider range of users. This allows the posting function to reduce the burden on pet owners and facilitate the efficient sharing of pet information.

[0034] The recording unit can record the pet's movements using a pet camera. For example, the recording unit can record the pet's movements in detail using a pet camera. By recording the pet's behavior in detail, the recording unit can understand the pet's preferred toys and movement patterns. The recording unit can record the pet's movements in more detail by adjusting the resolution and field of view of the pet camera. This allows for detailed recording of the pet's movements using a pet camera.

[0035] The monitoring unit can analyze heart rate and activity data from pet health monitoring devices. For example, the monitoring unit can use a pet health monitoring device to analyze the pet's heart rate and activity data in detail. If the pet's heart rate is higher than normal, the monitoring unit can analyze whether it may be a sign of stress or illness. The monitoring unit can monitor the pet's activity data in real time and check its health status. This allows for a detailed check of the pet's health status using a pet health monitoring device.

[0036] The generation unit can graph a pet's daily activities and automatically generate concise blog posts. For example, the generation unit graphs a pet's daily activities and automatically generates blog posts based on that graph. The generation unit can graph pet activities to make them visually easy to understand. The generation unit can analyze pet activity data and highlight important events and behaviors. As a result, graphing a pet's daily activities makes blog posts visually easier to understand.

[0037] The posting function can automatically post generated blog articles to existing social networking services (SNS) platforms. For example, the posting function automatically posts generated blog articles to existing SNS platforms. By automatically posting blog articles, the posting function can reduce the burden on pet owners. The posting function can automatically convert blog articles to the optimal posting format for each SNS platform. This reduces the burden on pet owners by automatically posting blog articles.

[0038] The monitoring unit can analyze whether a pet's heart rate is higher than normal, potentially indicating stress or illness. For example, it can analyze whether a pet's heart rate is higher than normal, potentially indicating stress or illness. The monitoring unit can monitor the pet's heart rate in real time and issue an alert if an abnormality is detected. The monitoring unit can analyze the fluctuations in the pet's heart rate in detail to check its health status. This allows for early detection of signs of stress or illness by analyzing the pet's heart rate.

[0039] The recording unit can analyze the pet's behavior patterns in real time during recording and issue alerts if abnormal behavior is detected. For example, if the pet moves in an unusual way, the recording unit will issue an alert to notify the owner. The recording unit can also issue an alert if the pet remains motionless for a long period of time, as this is considered abnormal behavior. The recording unit can also issue an alert if the pet stays in a specific area for a long period of time, as this is considered abnormal behavior. This allows for the detection of abnormal behavior in real time and prompt notification to the owner by issuing alerts.

[0040] The recording unit can automatically identify the pet's activity area during recording and focus on recording behavior in specific areas. For example, it can focus on recording areas where the pet frequently plays, areas where the pet eats, and areas where the pet rests. The recording unit can automatically identify the pet's activity area and record behavior in specific areas in detail. This allows for a detailed understanding of the pet's behavior patterns by focusing on recording behavior in specific areas.

[0041] The recording unit can measure the pet's body temperature non-contact during recording and record any abnormalities. For example, if the pet's body temperature is high, the recording unit can record it as an abnormality and notify the owner. If the pet's body temperature is low, the recording unit can record it as an abnormality and notify the owner. If the pet's body temperature changes rapidly, the recording unit can record it as an abnormality and notify the owner. This allows for early detection of abnormalities by measuring the pet's body temperature non-contact.

[0042] The recording unit can analyze the pet's vocalizations during recording and record specific vocalization patterns. For example, if the pet makes a specific vocalization, the recording unit will record that pattern. If the pet makes an abnormal vocalization, the recording unit will record that pattern and notify the owner. The recording unit can also record the frequency of the pet's vocalizations and notify if there is an abnormality. This allows for the early detection of abnormal vocalizations by analyzing the pet's vocalizations.

[0043] The monitoring unit can detect abnormalities by comparing the current pet's health data with past data during the monitoring process and issue an alert. For example, the unit can issue an alert if the pet's heart rate is abnormally high compared to past data. The unit can also issue an alert if the pet's activity level is abnormally low compared to past data. The unit can also issue an alert if the pet's body temperature is abnormally high compared to past data. In this way, abnormalities can be detected early and alerts can be issued by comparing them with past health data.

[0044] The checking unit can analyze the pet's eating patterns during the check and evaluate their relationship to its health status. For example, the checking unit can evaluate whether the amount of food the pet eats affects its health status. The checking unit can evaluate whether the timing of the pet's meals affects its health status. The checking unit can evaluate whether the type of food the pet eats affects its health status. In this way, by analyzing the pet's eating patterns, the relationship to its health status can be evaluated.

[0045] The checking unit can analyze the pet's sleep patterns during the check and evaluate their correlation with its health status. For example, the checking unit can evaluate whether the pet's sleep duration affects its health status. The checking unit can evaluate whether the quality of the pet's sleep affects its health status. The checking unit can evaluate whether the timing of the pet's sleep affects its health status. In this way, by analyzing the pet's sleep patterns, the correlation with its health status can be evaluated.

[0046] The checking unit can analyze the pet's exercise level during the check and evaluate its relationship to its health status. For example, the checking unit can evaluate whether the pet's exercise level affects its health status. The checking unit can evaluate whether the type of exercise affects its health status. The checking unit can evaluate whether the frequency of exercise affects its health status. In this way, by analyzing the pet's exercise level, the relationship to its health status can be evaluated.

[0047] The generation unit can analyze pet activity data in detail during generation and highlight specific events. For example, it can highlight an event when a pet plays with a new toy. It can highlight an event when a pet successfully performs a specific trick. It can highlight an event when a pet visits a specific place. This makes blog posts more engaging by highlighting specific events.

[0048] The generation unit can graph a pet's behavior patterns during generation, creating blog posts in a visually easy-to-understand format. For example, the generation unit can graph a pet's daily activities and insert them into a blog post. It can also graph a pet's eating patterns and insert them into a blog post. It can also graph a pet's exercise levels and insert them into a blog post. By graphing the pet's behavior patterns, the blog posts become visually easier to understand.

[0049] The generation unit can classify pet activity data by time of day during generation and create daily reports. For example, the generation unit can classify a pet's daily activities by time of day and create daily reports. The generation unit can classify a pet's feeding patterns by time of day and create daily reports. The generation unit can classify a pet's exercise levels by time of day and create daily reports. In this way, by classifying pet activity data by time of day, daily reports can be created.

[0050] The generation unit can compare a pet's activity data with other pets during generation and perform a relative evaluation. For example, the generation unit can compare a pet's exercise level with other pets and perform a relative evaluation. The generation unit can compare a pet's food intake with other pets and perform a relative evaluation. The generation unit can compare a pet's sleep time with other pets and perform a relative evaluation. In this way, a relative evaluation can be performed by comparing a pet's activity data with that of other pets.

[0051] The posting function can automatically convert posts to the optimal format for each social media platform at the time of posting. For example, it can optimize images for a first social media platform, optimize text for a second social media platform, and optimize short text and images for a third social media platform. This maximizes the effectiveness of posts by automatically converting them to the optimal format for each social media platform.

[0052] The posting function can automatically generate and add hashtags related to the post content at the time of posting. For example, the posting function can automatically generate relevant hashtags based on the pet's activities. The posting function can automatically generate relevant hashtags based on the pet's health condition. The posting function can automatically generate relevant hashtags based on the pet's behavioral patterns. By automatically generating hashtags related to the post content, the reach of the post is expanded.

[0053] The posting function can automatically select and add images and videos related to the content of a post when it is submitted. For example, the posting function can automatically select relevant images and videos based on the pet's activities. The posting function can automatically select relevant images and videos based on the pet's health condition. The posting function can automatically select relevant images and videos based on the pet's behavioral patterns. This automatically selects images and videos related to the content of the post, thereby improving the appeal of the post.

[0054] The posting function can refer to past posts related to the content of the post when it is submitted, and suggest highly relevant content. For example, the posting function can refer to relevant past posts based on the pet's activities. The posting function can refer to relevant past posts based on the pet's health condition. The posting function can refer to relevant past posts based on the pet's behavioral patterns. This allows the posting function to suggest highly relevant content by referring to past posts related to the content of the post.

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

[0056] The recording unit can analyze the pet's behavior patterns and record the frequency of repetition of specific behaviors when recording the pet's actions. For example, if a pet repeats a specific behavior during a specific time period, the recording unit can record that behavior pattern and notify the owner. Similarly, if a pet repeats a specific behavior in a specific location, the recording unit can record that behavior pattern and notify the owner. Furthermore, if a pet repeats a specific behavior under specific circumstances, the recording unit can record that behavior pattern and notify the owner. This allows for a detailed understanding of the pet's behavior patterns, which can be used to detect abnormal behavior early and improve behavioral patterns.

[0057] The monitoring unit can periodically measure the pet's weight and record weight fluctuations when checking the pet's health. For example, a sudden increase in a pet's weight may indicate excessive eating or lack of exercise. Conversely, a sudden decrease in a pet's weight may indicate illness or stress. Furthermore, if a pet's weight remains stable over a certain period, it may indicate good health. This allows for a detailed understanding of the pet's weight fluctuations and early detection of changes in its health.

[0058] The generation unit can graph a pet's daily activity, classifying the pet's activity data by time of day and highlighting peak activity periods. For example, by highlighting the time of day when the pet is most active, owners can quickly grasp their pet's activity patterns. Similarly, by highlighting the time of day when the pet is most relaxed, owners can understand their pet's rest periods. Furthermore, if a pet repeats a specific behavior at a specific time, this behavior pattern can be highlighted on the graph, allowing owners to understand their pet's behavior patterns in detail. This makes pet activity data visually easy to understand, enabling owners to easily grasp their pet's activity patterns.

[0059] The posting function can automatically generate and add hashtags related to the content of a post when automatically posting the generated blog article to social media. For example, by automatically generating relevant hashtags based on a pet's activities, the reach of the post can be expanded. Also, by automatically generating relevant hashtags based on a pet's health condition, health-related information can be shared. Furthermore, by automatically generating relevant hashtags based on a pet's behavioral patterns, information exchange with other pet owners can be facilitated. In this way, by automatically generating hashtags related to the content of the post, the effectiveness of the post can be maximized and information sharing can be promoted.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The recording unit records the pet's movements. For example, a pet camera is used to record the pet's movements and behavior in detail. This allows you to understand the pet's favorite toys and movement patterns. Step 2: The checking unit analyzes the data recorded by the recording unit to check the pet's health. For example, it analyzes heart rate and activity data from the pet health monitoring device and analyzes whether a higher-than-normal heart rate may indicate stress or illness. Step 3: The generation unit automatically generates blog posts based on the data obtained by the checking unit. For example, it graphs the pet's daily activities and automatically generates a concise blog post. This makes the pet's activities visually easy to understand. Step 4: The posting unit automatically posts the blog articles generated by the generation unit to social media. For example, it automatically posts the generated blog articles to existing social media platforms. This reduces the burden on pet owners.

[0062] (Example of form 2) The pet management system according to an embodiment of the present invention is a system for constantly monitoring a pet's health and behavior, and for easily recording and sharing a pet's daily life. This pet management system uses a pet camera and behavioral analytics, with AI analyzing video and recording the pet's movements. Next, the AI ​​analyzes heart rate and activity data from a pet health monitor device to check the pet's health. Furthermore, the AI ​​graphs the pet's daily activities, automatically generates a concise blog post, and automatically posts it to a social networking platform. This mechanism enables consistent health management of pets, facilitating information sharing and reducing the burden on pet owners. It also promotes interaction through pets and encourages active information exchange within the community. For example, the pet management system uses a pet camera and behavioral analytics, with AI analyzing video. In this process, the AI ​​records the pet's movements in detail, understanding its favorite toys and movement patterns. For example, it collects data such as what kind of movements the pet makes and at what times of day it is most active. This allows for understanding the pet's behavior patterns and early detection of changes in its health. Next, the pet management system uses AI to analyze heart rate and activity data from pet health monitoring devices. For example, if a pet's heart rate is higher than normal, it may indicate stress or illness. By monitoring this data in real time and checking the pet's health status, illnesses and behavioral problems can be detected early, allowing for appropriate care. Furthermore, the pet management system uses AI to graph the pet's daily activities and automatically generate concise blog posts. For example, it graphs what the pet did and what actions it took at what times of day, and uses this to create a blog post. This blog post is automatically posted to existing social media platforms. This allows pet owners to easily record their pet's daily life and share it on social media. This enables consistent health management of pets. By reflecting behavior and health status in real time, the system can maintain the pet's comfort and health. It also facilitates information sharing and reduces the burden on pet owners. Easily recording and sharing pets' daily lives on social media reduces the burden on pet owners. Furthermore, it promotes interaction through pets and encourages active information exchange within the community.For example, by sharing information about pet health and behavior, owners can exchange information with other pet owners. In this way, a system that integrates pet health management and information sharing is realized. As a result, the pet management system can constantly monitor pet health and behavior, and easily record and share pets' daily lives.

[0063] The pet management system according to this embodiment comprises a recording unit, a checking unit, a generation unit, and a posting unit. The recording unit records the pet's movements. For example, the recording unit records the pet's movements using a pet camera. The recording unit can record the pet's behavior in detail and understand the pet's favorite toys and movement patterns. The checking unit analyzes the data recorded by the recording unit and checks the pet's health status. For example, the checking unit analyzes heart rate and activity data from a pet health monitor device. If the pet's heart rate is higher than normal, the checking unit can analyze that it may be a sign of stress or illness. The generation unit automatically generates blog posts based on the data obtained by the checking unit. For example, the generation unit graphs the pet's daily activities and automatically generates a concise blog post. The generation unit can graph the pet's activities to make them visually easy to understand. The posting unit automatically posts the blog post generated by the generation unit to social networking services (SNS). For example, the posting unit automatically posts the generated blog post to an existing SNS platform. The posting function reduces the burden on pet owners by automatically posting blog articles. As a result, the pet management system according to this embodiment can efficiently manage pet health and share information by recording pet activity, checking health status, and automatically generating and posting blog articles to social media.

[0064] The recording unit records the pet's movements. For example, it uses a pet camera to record the pet's movements. Specifically, the pet camera captures high-resolution video in real time, recording the pet's behavior in detail. The camera is equipped with a motion sensor to track the pet's movements and can automatically adjust its orientation each time the pet moves. This ensures that the pet's movements are recorded without fail, no matter where it is. The recording unit also saves the pet's behavior data to the cloud, making it accessible at any time. This data includes playtime, mealtimes, and resting times, and analyzing this data helps understand the pet's preferred toys and movement patterns. Furthermore, the recording unit can collect not only audio and video recordings of the pet's behavior but also environmental data such as temperature and humidity. This provides information to maintain a comfortable environment for the pet. The recording unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the checking and generation units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the recording unit to collect data efficiently and effectively, improving the overall performance of the system.

[0065] The monitoring unit analyzes data recorded by the recording unit to check the pet's health status. For example, the monitoring unit analyzes heart rate and activity data from a pet health monitor device. Specifically, the pet health monitor device is attached to the pet's collar or harness and collects biometric data such as heart rate, respiratory rate, activity level, and sleep patterns in real time. This data is transmitted to the monitoring unit via wireless communication and analyzed by a dedicated analysis algorithm. The monitoring unit can analyze whether a higher-than-normal heart rate indicates stress or illness. For example, if a sudden increase in heart rate is observed, the pet may be experiencing some kind of stress, and an alert is sent to the owner to draw their attention. Also, if the activity level is lower than normal, the pet may be feeling unwell or fatigued, and the monitoring unit can instruct the owner to get adequate rest. Furthermore, the monitoring unit can also analyze long-term health trends by utilizing past data and statistical information. For example, based on past data, it can predict fluctuations in the pet's health status under specific seasons or environmental conditions and plan measures for future health management. Furthermore, the monitoring unit uses an anomaly detection algorithm to detect unusual patterns or abnormal data, enabling it to issue warnings early. This allows the monitoring unit to not only monitor health status in real time but also to handle long-term health management and anomaly detection, improving reliability and safety for maintaining pet health.

[0066] The generation unit automatically generates blog posts based on data obtained by the checking unit. For example, the generation unit graphs a pet's daily activities and automatically generates a concise blog post. Specifically, the generation unit analyzes the data provided by the checking unit and visualizes the pet's activity level, meal times, rest times, etc., in an easy-to-understand graph. This allows pet owners to grasp their pet's daily activities at a glance. Based on the graphed data, the generation unit automatically generates a concise blog post about the pet's daily events and health condition. For example, it can generate a blog post with content such as, "Today, my pet was playing energetically. Its heart rate was stable, and its health condition is good." The generation unit uses natural language generation technology to convert data into text and create blog posts in an easy-to-read format. Furthermore, the generation unit can also attach photos and videos to blog posts. For example, it can insert photos and videos of the pet taken by the recording unit into the blog post, providing visually appealing content. In this way, the generation unit not only makes the pet's activities visually easy to understand but also provides interesting content for pet owners. The generation unit can also customize the content of blog posts. For example, it can change the tone and style of the article according to the owner's preferences. This allows the generation unit to provide blog posts tailored to the owner's needs, enabling efficient pet health management and information sharing.

[0067] The posting unit automatically posts blog articles generated by the generation unit to social media. For example, the posting unit automatically posts generated blog articles to existing social media platforms. Specifically, the posting unit receives blog articles provided by the generation unit and automatically posts them to the social media accounts specified by the pet owner. The posting unit uses the API of the social media platform to automate article posting. This saves pet owners the trouble of manually posting articles. The posting unit can also set a posting schedule for articles. For example, by scheduling articles to be posted at a fixed time every day, pet information can be shared regularly. Furthermore, the posting unit can customize the content of the articles. For example, hashtags can be added to articles, or specific users can be tagged. This allows the posting unit to increase the reach of articles and deliver pet information to more users. The posting unit can also monitor reactions after posting. For example, it collects the number of likes and comments on articles and provides feedback to pet owners. This allows pet owners to understand what kind of content is popular and improve future posts. The posting function can also post to multiple social media platforms simultaneously. For example, by posting an article to multiple platforms at once, information can be delivered to a wider range of users. This allows the posting function to reduce the burden on pet owners and facilitate the efficient sharing of pet information.

[0068] The recording unit can record the pet's movements using a pet camera. For example, the recording unit can record the pet's movements in detail using a pet camera. By recording the pet's behavior in detail, the recording unit can understand the pet's preferred toys and movement patterns. The recording unit can record the pet's movements in more detail by adjusting the resolution and field of view of the pet camera. This allows for detailed recording of the pet's movements using a pet camera.

[0069] The monitoring unit can analyze heart rate and activity data from pet health monitoring devices. For example, the monitoring unit can use a pet health monitoring device to analyze the pet's heart rate and activity data in detail. If the pet's heart rate is higher than normal, the monitoring unit can analyze whether it may be a sign of stress or illness. The monitoring unit can monitor the pet's activity data in real time and check its health status. This allows for a detailed check of the pet's health status using a pet health monitoring device.

[0070] The generation unit can graph a pet's daily activities and automatically generate concise blog posts. For example, the generation unit graphs a pet's daily activities and automatically generates blog posts based on that graph. The generation unit can graph pet activities to make them visually easy to understand. The generation unit can analyze pet activity data and highlight important events and behaviors. As a result, graphing a pet's daily activities makes blog posts visually easier to understand.

[0071] The posting function can automatically post generated blog articles to existing social networking services (SNS) platforms. For example, the posting function automatically posts generated blog articles to existing SNS platforms. By automatically posting blog articles, the posting function can reduce the burden on pet owners. The posting function can automatically convert blog articles to the optimal posting format for each SNS platform. This reduces the burden on pet owners by automatically posting blog articles.

[0072] The monitoring unit can analyze whether a pet's heart rate is higher than normal, potentially indicating stress or illness. For example, it can analyze whether a pet's heart rate is higher than normal, potentially indicating stress or illness. The monitoring unit can monitor the pet's heart rate in real time and issue an alert if an abnormality is detected. The monitoring unit can analyze the fluctuations in the pet's heart rate in detail to check its health status. This allows for early detection of signs of stress or illness by analyzing the pet's heart rate.

[0073] The recording unit can estimate the pet's emotions and adjust the frame rate of the recorded video based on the estimated emotions. For example, if the pet is excited, the recording unit can increase the frame rate to record detailed movements. If the pet is relaxed, the recording unit can lower the frame rate to record for a longer period. If the pet is stressed, the recording unit can adjust the frame rate to identify the cause of the stress. This allows for the recording of detailed movements by adjusting the video frame rate according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The recording unit can analyze the pet's behavior patterns in real time during recording and issue alerts if abnormal behavior is detected. For example, if the pet moves in an unusual way, the recording unit will issue an alert to notify the owner. The recording unit can also issue an alert if the pet remains motionless for a long period of time, as this is considered abnormal behavior. The recording unit can also issue an alert if the pet stays in a specific area for a long period of time, as this is considered abnormal behavior. This allows for the detection of abnormal behavior in real time and prompt notification to the owner by issuing alerts.

[0075] The recording unit can automatically identify the pet's activity area during recording and focus on recording behavior in specific areas. For example, it can focus on recording areas where the pet frequently plays, areas where the pet eats, and areas where the pet rests. The recording unit can automatically identify the pet's activity area and record behavior in specific areas in detail. This allows for a detailed understanding of the pet's behavior patterns by focusing on recording behavior in specific areas.

[0076] The recording unit can estimate the pet's emotions and adjust the resolution of the recorded video based on the estimated emotions. For example, if the pet is excited, the recording unit can record at high resolution to capture detailed movements. If the pet is relaxed, the recording unit can record at low resolution for longer periods. If the pet is stressed, the recording unit can adjust the resolution to identify the cause of the stress. This allows for the recording of detailed movements by adjusting the video resolution according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The recording unit can measure the pet's body temperature non-contact during recording and record any abnormalities. For example, if the pet's body temperature is high, the recording unit can record it as an abnormality and notify the owner. If the pet's body temperature is low, the recording unit can record it as an abnormality and notify the owner. If the pet's body temperature changes rapidly, the recording unit can record it as an abnormality and notify the owner. This allows for early detection of abnormalities by measuring the pet's body temperature non-contact.

[0078] The recording unit can analyze the pet's vocalizations during recording and record specific vocalization patterns. For example, if the pet makes a specific vocalization, the recording unit will record that pattern. If the pet makes an abnormal vocalization, the recording unit will record that pattern and notify the owner. The recording unit can also record the frequency of the pet's vocalizations and notify if there is an abnormality. This allows for the early detection of abnormal vocalizations by analyzing the pet's vocalizations.

[0079] The checking unit can estimate the pet's emotions and adjust the frequency of health checks based on the estimated emotions. For example, if the pet is stressed, the checking unit can increase the frequency of health checks. If the pet is relaxed, the checking unit can decrease the frequency of health checks. If the pet is excited, the checking unit can adjust the frequency of health checks. This allows health checks to be performed at the appropriate time by adjusting the frequency according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The monitoring unit can detect abnormalities by comparing the current pet's health data with past data during the monitoring process and issue an alert. For example, the unit can issue an alert if the pet's heart rate is abnormally high compared to past data. The unit can also issue an alert if the pet's activity level is abnormally low compared to past data. The unit can also issue an alert if the pet's body temperature is abnormally high compared to past data. In this way, abnormalities can be detected early and alerts can be issued by comparing them with past health data.

[0081] The checking unit can analyze the pet's eating patterns during the check and evaluate their relationship to its health status. For example, the checking unit can evaluate whether the amount of food the pet eats affects its health status. The checking unit can evaluate whether the timing of the pet's meals affects its health status. The checking unit can evaluate whether the type of food the pet eats affects its health status. In this way, by analyzing the pet's eating patterns, the relationship to its health status can be evaluated.

[0082] The checking unit can estimate the pet's emotions and adjust the health check items based on the estimated emotions. For example, if the pet is stressed, the checking unit can add stress check items. If the pet is relaxed, the checking unit can reduce the health check items. If the pet is excited, the checking unit can adjust the health check items. In this way, an appropriate health check can be performed by adjusting the health check items according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The checking unit can analyze the pet's sleep patterns during the check and evaluate their correlation with its health status. For example, the checking unit can evaluate whether the pet's sleep duration affects its health status. The checking unit can evaluate whether the quality of the pet's sleep affects its health status. The checking unit can evaluate whether the timing of the pet's sleep affects its health status. In this way, by analyzing the pet's sleep patterns, the correlation with its health status can be evaluated.

[0084] The checking unit can analyze the pet's exercise level during the check and evaluate its relationship to its health status. For example, the checking unit can evaluate whether the pet's exercise level affects its health status. The checking unit can evaluate whether the type of exercise affects its health status. The checking unit can evaluate whether the frequency of exercise affects its health status. In this way, by analyzing the pet's exercise level, the relationship to its health status can be evaluated.

[0085] The generation unit can estimate the pet's emotions and adjust the tone of the blog post based on the estimated emotions. For example, if the pet is relaxed, the generation unit can generate a blog post in a calm tone. If the pet is excited, the generation unit can generate a blog post in an energetic tone. If the pet is stressed, the generation unit can generate a blog post in an alert tone. In this way, by adjusting the tone of the blog post according to the pet's emotions, it is possible to generate a blog post with an appropriate tone. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The generation unit can analyze pet activity data in detail during generation and highlight specific events. For example, it can highlight an event when a pet plays with a new toy. It can highlight an event when a pet successfully performs a specific trick. It can highlight an event when a pet visits a specific place. This makes blog posts more engaging by highlighting specific events.

[0087] The generation unit can graph a pet's behavior patterns during generation, creating blog posts in a visually easy-to-understand format. For example, the generation unit can graph a pet's daily activities and insert them into a blog post. It can also graph a pet's eating patterns and insert them into a blog post. It can also graph a pet's exercise levels and insert them into a blog post. By graphing the pet's behavior patterns, the blog posts become visually easier to understand.

[0088] The generation unit can estimate the pet's emotions and adjust the content of the blog post based on the estimated emotions. For example, if the pet is relaxed, the generation unit can generate a blog post with calm content. If the pet is excited, the generation unit can generate a blog post with lively content. If the pet is stressed, the generation unit can generate a blog post with attention-grabbing content. In this way, by adjusting the content of the blog post according to the pet's emotions, it is possible to generate blog posts with appropriate content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The generation unit can classify pet activity data by time of day during generation and create daily reports. For example, the generation unit can classify a pet's daily activities by time of day and create daily reports. The generation unit can classify a pet's feeding patterns by time of day and create daily reports. The generation unit can classify a pet's exercise levels by time of day and create daily reports. In this way, by classifying pet activity data by time of day, daily reports can be created.

[0090] The generation unit can compare a pet's activity data with other pets during generation and perform a relative evaluation. For example, the generation unit can compare a pet's exercise level with other pets and perform a relative evaluation. The generation unit can compare a pet's food intake with other pets and perform a relative evaluation. The generation unit can compare a pet's sleep time with other pets and perform a relative evaluation. In this way, a relative evaluation can be performed by comparing a pet's activity data with that of other pets.

[0091] The posting function can estimate the pet's emotions and adjust the posting timing based on the estimated emotions. For example, if the pet is relaxed, the posting function will post at a calm time. If the pet is excited, the posting function can post at an active time. If the pet is stressed, the posting function can post at a time to draw attention. In this way, by adjusting the posting timing according to the pet's emotions, posting can be done at the appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The posting function can automatically convert posts to the optimal format for each social media platform at the time of posting. For example, it can optimize images for a first social media platform, optimize text for a second social media platform, and optimize short text and images for a third social media platform. This maximizes the effectiveness of posts by automatically converting them to the optimal format for each social media platform.

[0093] The posting function can automatically generate and add hashtags related to the post content at the time of posting. For example, the posting function can automatically generate relevant hashtags based on the pet's activities. The posting function can automatically generate relevant hashtags based on the pet's health condition. The posting function can automatically generate relevant hashtags based on the pet's behavioral patterns. By automatically generating hashtags related to the post content, the reach of the post is expanded.

[0094] The posting function can estimate the pet's emotions and adjust the posting frequency based on the estimated emotions. For example, if the pet is relaxed, the posting function can reduce the posting frequency. If the pet is excited, the posting function can increase the posting frequency. If the pet is stressed, the posting function can adjust the posting frequency. This allows for posting at an appropriate frequency by adjusting the posting frequency according to the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The posting function can automatically select and add images and videos related to the content of a post when it is submitted. For example, the posting function can automatically select relevant images and videos based on the pet's activities. The posting function can automatically select relevant images and videos based on the pet's health condition. The posting function can automatically select relevant images and videos based on the pet's behavioral patterns. This automatically selects images and videos related to the content of the post, thereby improving the appeal of the post.

[0096] The posting function can refer to past posts related to the content of the post when it is submitted, and suggest highly relevant content. For example, the posting function can refer to relevant past posts based on the pet's activities. The posting function can refer to relevant past posts based on the pet's health condition. The posting function can refer to relevant past posts based on the pet's behavioral patterns. This allows the posting function to suggest highly relevant content by referring to past posts related to the content of the post.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The recording unit can analyze the pet's behavior patterns and record the frequency of repetition of specific behaviors when recording the pet's actions. For example, if a pet repeats a specific behavior during a specific time period, the recording unit can record that behavior pattern and notify the owner. Similarly, if a pet repeats a specific behavior in a specific location, the recording unit can record that behavior pattern and notify the owner. Furthermore, if a pet repeats a specific behavior under specific circumstances, the recording unit can record that behavior pattern and notify the owner. This allows for a detailed understanding of the pet's behavior patterns, which can be used to detect abnormal behavior early and improve behavioral patterns.

[0099] The monitoring unit can periodically measure the pet's weight and record weight fluctuations when checking the pet's health. For example, a sudden increase in a pet's weight may indicate excessive eating or lack of exercise. Conversely, a sudden decrease in a pet's weight may indicate illness or stress. Furthermore, if a pet's weight remains stable over a certain period, it may indicate good health. This allows for a detailed understanding of the pet's weight fluctuations and early detection of changes in its health.

[0100] The generation unit can graph a pet's daily activity, classifying the pet's activity data by time of day and highlighting peak activity periods. For example, by highlighting the time of day when the pet is most active, owners can quickly grasp their pet's activity patterns. Similarly, by highlighting the time of day when the pet is most relaxed, owners can understand their pet's rest periods. Furthermore, if a pet repeats a specific behavior at a specific time, this behavior pattern can be highlighted on the graph, allowing owners to understand their pet's behavior patterns in detail. This makes pet activity data visually easy to understand, enabling owners to easily grasp their pet's activity patterns.

[0101] The posting function can automatically generate and add hashtags related to the content of a post when automatically posting the generated blog article to social media. For example, by automatically generating relevant hashtags based on a pet's activities, the reach of the post can be expanded. Also, by automatically generating relevant hashtags based on a pet's health condition, health-related information can be shared. Furthermore, by automatically generating relevant hashtags based on a pet's behavioral patterns, information exchange with other pet owners can be facilitated. In this way, by automatically generating hashtags related to the content of the post, the effectiveness of the post can be maximized and information sharing can be promoted.

[0102] The recording unit can estimate the pet's emotions and adjust the frame rate of the recorded video based on the estimated emotions. For example, if the pet is excited, the frame rate can be increased to record detailed movements. Conversely, if the pet is relaxed, the frame rate can be lowered to record for a longer period. Furthermore, if the pet is stressed, the frame rate can be adjusted to identify the cause of the stress. In this way, by adjusting the frame rate of the video according to the pet's emotions, detailed movements can be recorded.

[0103] The monitoring unit can estimate the pet's emotions and adjust the frequency of health checks based on the estimated emotions. For example, if the pet is stressed, the frequency of health checks can be increased. Conversely, if the pet is relaxed, the frequency of health checks can be decreased. Furthermore, if the pet is excited, the frequency of health checks can be adjusted. This allows for health checks to be performed at the appropriate time by adjusting the frequency according to the pet's emotions.

[0104] The generation unit can estimate the pet's emotions and adjust the tone of the blog post based on the estimated emotions. For example, if the pet is relaxed, the blog post can be generated in a calm tone. If the pet is excited, the blog post can be generated in an energetic tone. Furthermore, if the pet is stressed, the blog post can be generated in an alert tone. In this way, by adjusting the tone of the blog post according to the pet's emotions, it is possible to generate a blog post with an appropriate tone.

[0105] The posting function can estimate the pet's emotions and adjust the posting timing based on that estimation. For example, if the pet is relaxed, it can post at a calm time. If the pet is excited, it can post at an active time. Furthermore, if the pet is stressed, it can post at a time to draw attention. This allows for posting at the appropriate time by adjusting the posting timing according to the pet's emotions.

[0106] The recording unit can estimate the pet's emotions and adjust the resolution of the recorded video based on the estimated emotions. For example, if the pet is excited, it can record in high resolution to capture detailed movements. If the pet is relaxed, it can record in low resolution for longer periods. Furthermore, if the pet is stressed, the resolution can be adjusted to identify the cause of the stress. This allows for the recording of detailed movements by adjusting the video resolution according to the pet's emotions.

[0107] The checking unit can estimate the pet's emotions and adjust the health check items based on the estimated emotions. For example, if the pet is stressed, stress check items can be added. Conversely, if the pet is relaxed, health check items can be reduced. Furthermore, if the pet is excited, health check items can be adjusted. In this way, by adjusting the health check items according to the pet's emotions, an appropriate health check can be performed.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The recording unit records the pet's movements. For example, a pet camera is used to record the pet's movements and behavior in detail. This allows you to understand the pet's favorite toys and movement patterns. Step 2: The checking unit analyzes the data recorded by the recording unit to check the pet's health. For example, it analyzes heart rate and activity data from the pet health monitoring device and analyzes whether a higher-than-normal heart rate may indicate stress or illness. Step 3: The generation unit automatically generates blog posts based on the data obtained by the checking unit. For example, it graphs the pet's daily activities and automatically generates a concise blog post. This makes the pet's activities visually easy to understand. Step 4: The posting unit automatically posts the blog articles generated by the generation unit to social media. For example, it automatically posts the generated blog articles to existing social media platforms. This reduces the burden on pet owners.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the recording unit, checking unit, generation unit, and posting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the pet's movements using the camera 42 of the smart device 14, and the control unit 46A records the pet's behavior in detail. The checking unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes heart rate and activity data from the pet health monitor device. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and graphs the pet's daily activities and automatically generates a blog post. The posting unit is implemented in the specific processing unit 46A of the smart device 14, and automatically posts the generated blog post to social networking services (SNS). The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the recording unit, checking unit, generation unit, and posting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the pet's movements using the camera 42 of the smart glasses 214, and the control unit 46A records the pet's behavior in detail. The checking unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes heart rate and activity data from a pet health monitor device. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and graphs the pet's daily activities and automatically generates a blog post. The posting unit is implemented in the specific processing unit 46A of the smart glasses 214, and automatically posts the generated blog post to social media. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the recording unit, checking unit, generation unit, and posting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the pet's movements using the camera 42 of the headset terminal 314, and the control unit 46A records the pet's behavior in detail. The checking unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes heart rate and activity data from the pet health monitor device. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and graphs the pet's daily activities and automatically generates a blog post. The posting unit is implemented in the control unit 46A of the headset terminal 314, and automatically posts the generated blog post to SNS. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the recording unit, checking unit, generation unit, and posting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit records the pet's movements using the camera 42 of the robot 414, and the control unit 46A records the pet's behavior in detail. The checking unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes heart rate and activity data from a pet health monitor device. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and graphs the pet's daily activities and automatically generates a blog post. The posting unit is implemented in the control unit 46A of the robot 414, and automatically posts the generated blog post to social media. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A recording unit that records the movements of pets, A checking unit analyzes the data recorded by the recording unit and checks the health status of the pet, A generation unit that automatically generates blog posts based on the data obtained by the aforementioned checking unit, The system includes a posting unit that automatically posts blog articles generated by the generation unit to social media. A system characterized by the following features. (Note 2) The aforementioned recording unit is Use a pet camera to record your pet's movements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned checking unit is Analyze heart rate and activity data from pet health monitoring devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Graph your pet's daily activities and automatically generate concise blog posts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned submission section, Automatically post generated blog articles to existing social media platforms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned checking unit is Analyzing whether a pet's heart rate is higher than normal may indicate stress or illness. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is It estimates the pet's emotions and adjusts the frame rate of the recorded video based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is During recording, the system analyzes the pet's behavior patterns in real time and issues an alert if abnormal behavior is detected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is During recording, the system automatically identifies the pet's activity area and focuses on recording its behavior in that specific area. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is It estimates the pet's emotions and adjusts the resolution of the recorded video based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, the pet's body temperature is measured non-contact, and any abnormalities are recorded. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is During recording, the system analyzes the pet's vocalizations and records specific vocalization patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned checking unit is Estimate your pet's emotions and adjust the frequency of health checks based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is During the check, the system detects abnormalities by comparing them with the pet's past health data and issues an alert. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is During the check-up, we analyze the pet's eating patterns and evaluate their correlation with its health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is The system estimates the pet's emotions and adjusts the health check items based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is During the check-up, we analyze the pet's sleep patterns and assess their correlation with their health status. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned checking unit is During the check-up, we analyze the pet's activity level and evaluate its correlation with their health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the pet's emotions and adjust the tone of blog posts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the pet's activity data is analyzed in detail, highlighting specific events. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the pet's behavioral patterns are graphed, and blog posts are created in a visually easy-to-understand format. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is We estimate the pet's emotions and adjust the blog post content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, pet activity data is categorized by time of day, and daily reports are created. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, pet activity data is compared with other pets to perform a relative evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned submission section, We estimate the pet's emotions and adjust the timing of posts based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned submission section, When posting, the system automatically converts the post to the optimal format for each social media platform. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned submission section, When you post, hashtags related to the post content will be automatically generated and added to your post. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned submission section, We estimate the pet's emotions and adjust the posting frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned submission section, When you post, images and videos related to the post content will be automatically selected and added to the post. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned submission section, When you post, the system will refer to past posts related to your post and suggest highly relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit that records the movements of pets, A checking unit analyzes the data recorded by the recording unit and checks the health status of the pet, A generation unit that automatically generates blog posts based on the data obtained by the aforementioned checking unit, The system includes a posting unit that automatically posts blog articles generated by the generation unit to social media. A system characterized by the following features.

2. The aforementioned recording unit is Use a pet camera to record your pet's movements. The system according to feature 1.

3. The aforementioned checking unit is Analyze heart rate and activity data from pet health monitoring devices. The system according to feature 1.

4. The generating unit is Graph your pet's daily activities and automatically generate concise blog posts. The system according to feature 1.

5. The aforementioned submission section, Automatically post generated blog articles to existing social media platforms. The system according to feature 1.

6. The aforementioned checking unit is Analyzing whether a pet's heart rate is higher than normal may indicate stress or illness. The system according to feature 1.

7. The aforementioned recording unit is It estimates the pet's emotions and adjusts the frame rate of the recorded video based on the estimated emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the system analyzes the pet's behavior patterns in real time and issues an alert if abnormal behavior is detected. The system according to feature 1.

9. The aforementioned recording unit is During recording, the system automatically identifies the pet's activity area and focuses on recording its behavior in that specific area. The system according to feature 1.

10. The aforementioned recording unit is It estimates the pet's emotions and adjusts the resolution of the recorded video based on the estimated emotions. The system according to feature 1.

Images