system

Abstract

The system according to this embodiment aims to analyze the health status and emotions of pets in real time and provide feedback to their owners. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an alert unit, and a reporting unit. The collection unit collects data such as the pet's behavior, barking, appetite, and excretion. The analysis unit analyzes the data collected by the collection unit and infers the pet's health condition and emotions. The generation unit generates an individual care plan based on the health condition and emotions inferred by the analysis unit. The alert unit sends an alert to the owner if an abnormality is detected based on the care plan generated by the generation unit. The reporting unit provides periodic reports on the health condition based on the care plan generated by the generation unit.

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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, there is a problem that it is difficult to accurately grasp the health condition and emotions of a pet, and it is difficult to detect abnormalities at an early stage.

[0005] The system according to the embodiment aims to analyze the health condition and emotions of a pet in real time and provide feedback to the owner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, an alert unit, and a reporting unit. The data collection unit collects data such as the pet's behavior, vocalizations, appetite, and excretion. The analysis unit analyzes the data collected by the data collection unit and infers the pet's health status and emotions. The generation unit generates an individualized care plan based on the health status and emotions inferred by the analysis unit. The alert unit sends an alert to the owner if an abnormality is detected based on the care plan generated by the generation unit. The reporting unit provides periodic health status reports based on the care plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the pet's health and emotions in real time and provide feedback to the owner. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 receiving 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 receiving 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 next-generation pet health and emotion monitoring system according to an embodiment of the present invention is a system that uses generative AI to analyze the health status and emotions of pets in real time and provide feedback to the owner. This system collects data such as the pet's behavior, vocalizations, appetite, and excretion, and the generative AI analyzes this data to predict the pet's health status and emotions. For example, it can detect changes in the pet's vocalizations or abnormal behavior and send an alert to the owner. It also provides regular health status reports to support preventative care. Furthermore, the generative AI proposes a preventative care plan based on the pet's health data. This allows owners to take appropriate action before their pet's health deteriorates. For example, it analyzes the pet's behavior data, and if an abnormality is detected, it sends an alert to the owner to encourage early action. It also provides individualized care plans according to the pet's health status, improving the pet's well-being. The system of the present invention can be implemented at low cost and scalably by utilizing smartphones. No additional equipment purchase is required, minimizing initial investment and enabling global deployment. In addition, the generative AI learns from data each time a user uses the service, improving diagnostic accuracy. This allows for a deeper understanding of pet behavior patterns and improves owner satisfaction. Furthermore, the generative AI can provide health data through collaboration with pet insurance companies and veterinarians. This enables the development of pet insurance products based on individual health risks and diagnostic support tools for veterinarians, securing data licensing revenue. In addition, the generative AI can sell pet supplies and supplements recommended based on the pet's health and emotions, generating revenue through e-commerce. This invention not only improves the health and well-being of pets but also strengthens the bond with owners, reduces costs, and improves efficiency. It also aims for sustainable growth through the creation of new value through data utilization and global market expansion. Furthermore, by collaborating with the entire pet care industry and building an ecosystem, it aims to become the standard for pet care. As a result, the next-generation pet health and emotion monitoring system can analyze the pet's health and emotions in real time and provide feedback to owners.

[0029] The next-generation pet health and emotion monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, an alert unit, and a reporting unit. The data collection unit collects data such as the pet's behavior, vocalizations, appetite, and excretion. The data collection unit can collect pet behavior data using, for example, a smartphone or sensors. The data collection unit can record the pet's vocalizations and collect the resulting audio data. The data collection unit can observe the pet's appetite and record the amount and frequency of meals. The data collection unit can monitor the pet's excretion and record the state of urination and defecation. The analysis unit analyzes the data collected by the data collection unit and infers the pet's health status and emotions. The analysis unit can, for example, use a generation AI to analyze the pet's behavior patterns and infer its health status. The analysis unit can, for example, use a generation AI to analyze the pet's vocalizations and infer its emotions. The analysis unit can, for example, use a generation AI to analyze the pet's appetite data and infer its health status. The generation unit generates individual care plans based on the health status and emotions inferred by the analysis unit. For example, the generation unit can use generation AI to generate a meal plan tailored to the pet's health status. For example, the generation unit can use generation AI to generate an exercise plan tailored to the pet's emotions. For example, the generation unit can use generation AI to set the frequency of health checks tailored to the pet's health status. The alert unit sends an alert to the owner when an abnormality is detected based on the care plan generated by the generation unit. For example, the alert unit can send a notification to the smartphone when an abnormality is detected in the pet's health status. For example, the alert unit can notify the owner by email when an abnormality is detected in the pet's emotions. For example, the alert unit can send an alert to the owner via an app when an abnormality is detected in the pet's behavior. The reporting unit provides regular health status reports based on the care plan generated by the generation unit. For example, the reporting unit can provide weekly reports on the pet's health status. For example, the reporting unit can provide monthly reports on the pet's emotional state.The reporting section can, for example, provide an annual report on the pet's behavioral patterns. This allows the next-generation pet health and emotion monitoring system according to the embodiment to analyze the pet's health and emotions in real time and provide feedback to the owner.

[0030] The data collection unit collects data on pet behavior, vocalizations, appetite, and excretion. For example, the unit can collect pet behavioral data using a smartphone or sensors. Specifically, it can use accelerometers and gyroscopes attached to the pet's collar or harness to record the pet's movements and activity level in detail. This allows for understanding how much exercise the pet is getting and what behavioral patterns it exhibits. The unit can also record the pet's vocalizations and collect the resulting audio data. This audio data provides important clues for inferring the pet's emotions and health. Furthermore, the unit can observe the pet's appetite and record the amount and frequency of meals. The amount and frequency of meals are important indicators of health, allowing for early detection of any abnormalities. Finally, the unit can monitor the pet's excretion and record the state of urination and defecation. The state of excretion is an important indicator of digestive system health, allowing for early intervention of any abnormalities. In this way, the unit can collect diverse data on pets and monitor their health and emotions in detail. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to infer the pet's health and emotions. For example, the analysis unit can use generative AI to analyze the pet's behavioral patterns and infer its health. Specifically, the generative AI detects abnormal behavior that differs from normal behavioral patterns based on the pet's behavioral data. For example, if a pet's activity level is lower than usual, there may be a health problem. The analysis unit can also use generative AI to analyze the pet's vocalizations and infer its emotions. The generative AI uses speech recognition technology to analyze the characteristics of the vocalizations and infer whether the pet is stressed, happy, or anxious. The analysis unit can also use generative AI to analyze the pet's appetite data and infer its health. Changes in the amount and frequency of meals are important indicators of changes in health, and the generative AI analyzes this data to detect abnormalities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the pet's health and emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term health assessments and trend analyses. For example, based on past data, it can predict fluctuations in health status under specific seasons or environmental conditions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The generation unit generates individualized care plans based on the health status and emotions inferred by the analysis unit. For example, the generation unit can use generation AI to generate a meal plan tailored to the pet's health status. Specifically, it creates a meal plan that considers the optimal nutritional balance based on data such as the pet's weight, age, activity level, and health status. For example, the generation unit can use generation AI to generate an exercise plan tailored to the pet's emotions. If the pet is stressed, it will suggest exercise and play plans that will help them relax. For example, the generation unit can use generation AI to set the frequency of health checks according to the pet's health status. For example, if there is a specific health risk, increasing the frequency of regular health checks will allow for early detection and treatment of problems. In this way, the generation unit can provide care plans tailored to the individual needs of pets and support improvements in their health status and emotions. Furthermore, the generation unit can continuously monitor the effectiveness of the care plan and modify the plan as needed. For example, if the pet's health status does not improve, it will review the meal plan and exercise plan and take more effective measures. The generation unit can also propose feasible plans that take into account the owner's lifestyle and the pet's characteristics. This allows the generator to provide a comprehensive care plan to maintain the health and well-being of pets, thereby reducing the burden on pet owners.

[0033] The alert unit sends alerts to pet owners when an abnormality is detected based on the care plan generated by the generation unit. For example, the alert unit can send notifications to a smartphone when an abnormality is detected in the pet's health. Specifically, it sends an immediate notification when an abnormality is observed in the pet's health, such as a sudden decrease in the pet's activity level or a decrease in appetite. The alert unit can also notify pet owners via email when an abnormality is detected in the pet's emotions. If the pet is experiencing stress or persistent anxiety, it prompts the owner to take appropriate action. For example, the alert unit can send alerts to pet owners via an app when an abnormality is detected in the pet's behavior. For example, if the pet exhibits unusual behavior, it prompts the owner to investigate the cause of that behavior. This allows the alert unit to detect abnormalities in the pet's health or emotions early and prompt pet owners to take prompt action. Furthermore, the alert unit can customize the content and frequency of alerts. For example, it can adjust the frequency and content of notifications according to the owner's preferences, reducing stress caused by excessive notifications. Furthermore, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the alert unit to quickly and reliably notify pet owners of abnormalities and support appropriate actions to maintain the health and well-being of their pets.

[0034] The reporting unit provides regular health status reports based on the care plans generated by the generation unit. For example, the reporting unit can provide weekly reports on the pet's health status. Specifically, it creates and provides weekly health reports to pet owners based on data such as the pet's activity level, food intake, and excretion status. For example, the reporting unit can provide monthly reports on the pet's emotional state. It analyzes the pet's vocalizations and behavioral patterns and reports on emotional fluctuations on a monthly basis. For example, the reporting unit can provide annual reports on the pet's behavioral patterns. Based on long-term data, it reports on changes in the pet's behavioral patterns and health status on an annual basis, providing pet owners with comprehensive health management guidelines. This allows the reporting unit to regularly monitor the pet's health status and emotional fluctuations and provide continuous feedback to pet owners. Furthermore, the reporting unit can customize the content of the reports. For example, it can create reports that highlight specific data or indicators according to the pet owner's interests. The reporting unit can also visually show improvements or deteriorations in the pet's health status by comparing it with past data. This allows pet owners to quickly understand their pet's health status and take appropriate action. Furthermore, the reporting department can choose how reports are delivered. For example, reports can be provided via email, in-app notifications, or printed reports, according to the owner's preference. This enables the reporting department to continuously report on the pet's health status and emotional changes to owners, supporting comprehensive health management.

[0035] The data collection unit can collect data on pet behavior, vocalizations, appetite, and excretion using smartphones and sensors. For example, the data collection unit can collect pet behavior data using a smartphone application. For example, the data collection unit can detect pet movement using an accelerometer and collect data. For example, the data collection unit can measure pet body temperature using a temperature sensor and collect data. This allows for detailed collection of pet behavior and health status using smartphones and sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a smartphone application into a generating AI and have the generating AI perform the collection of pet behavior data.

[0036] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can increase the frequency of data collection to collect more detailed information. For example, if the pet is relaxed, the data collection unit can decrease the frequency of data collection to reduce the pet's burden. For example, if the pet is excited, the data collection unit can focus on collecting specific behaviors. This allows for the collection of detailed information while reducing the pet's burden by adjusting the frequency of data collection according to the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI adjust the frequency of data collection.

[0037] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, if a pet was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. For example, if a pet frequently performed a specific behavior in the past, the data collection unit can prioritize collecting data related to that behavior. For example, based on a pet's past health condition, the data collection unit can focus on collecting specific health indicators. This allows the optimal data collection method to be selected by analyzing the pet's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data of pets into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is in poor health, the data collection unit can collect data more frequently to gather detailed information. For example, if the pet is active, the data collection unit can focus on collecting data related to exercise volume and activity patterns. For example, if the pet is resting, the data collection unit can reduce the burden on the pet by limiting data collection. This allows for efficient collection of necessary information by filtering data based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet health data into a generating AI and have the generating AI perform data filtering.

[0039] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can prioritize collecting data related to its health status. For example, if the pet is excited, the data collection unit can prioritize collecting data related to its behavioral patterns. This allows for the priority collection of important data by determining the priority of data to collect based on the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI determine the data priority.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the pet's geographical location information during data collection. For example, if the pet is in a specific location, the data collection unit can prioritize the collection of data related to that location. For example, if the pet is on the move, the data collection unit can prioritize the collection of data related to the travel route. For example, if the pet is staying in a specific area for an extended period, the data collection unit can collect data related to that area. In this way, by considering the pet's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the social media activity of pet owners and collect relevant data during data collection. For example, if an owner posts about their pet's health on social media, the data collection unit can collect data based on that information. For example, if an owner posts about their pet's behavior, the data collection unit can collect data related to that behavior. For example, if an owner posts about their pet's food, the data collection unit can collect data related to that food. This allows for the efficient collection of relevant data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the pet is relaxed, the analysis unit can prioritize data related to its health status in its analysis. For example, if the pet is excited, the analysis unit can prioritize data related to its behavioral patterns in its analysis. By adjusting the analysis algorithm based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the analysis algorithm.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health condition during the analysis. For example, if the pet's health condition is poor, the analysis unit can perform a detailed analysis to detect abnormalities early. For example, if the pet's health condition is good, the analysis unit can perform a simpler analysis to support daily health management. For example, if the pet's health condition is unknown, the analysis unit can perform a standard analysis to understand its health condition. This allows for efficient analysis of necessary information by adjusting the level of detail of the analysis based on the importance of the pet's health condition. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input pet health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, the analysis unit can apply different analysis algorithms to dogs and cats, performing analysis according to their respective characteristics. For example, the analysis unit can apply different analysis algorithms to young pets and elderly pets, performing analysis according to age. For example, the analysis unit can apply analysis algorithms specialized for specific breeds, performing analysis according to the characteristics of each breed. By applying different analysis algorithms according to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet type and age data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can highlight health-related data. For example, if the pet is excited, the analysis unit can highlight behavioral pattern data. By adjusting the display method of the analysis results based on the pet's emotions, it becomes possible to display the results in a way that is easy for the owner to understand. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the pet's emotional data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0046] The analysis unit can determine the priority of analysis based on when the pet's behavioral data was submitted. For example, the analysis unit can prioritize the analysis of the most recent behavioral data to understand the pet's real-time health status. For example, the analysis unit can analyze past behavioral data to understand long-term changes in health status. For example, the analysis unit can prioritize the analysis of data before and after a specific event (e.g., a hospital visit) to understand the impact of the event. This allows for real-time understanding of the pet's health status by determining the priority of analysis based on when the pet's behavioral data was submitted. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet behavioral data into a generative AI and have the generative AI determine the priority of analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis process. For example, the analysis unit can update its analysis algorithm by referring to the latest veterinary research. For example, the analysis unit can support the early detection of diseases by referring to literature on specific pet diseases. For example, the analysis unit can improve the accuracy of behavioral analysis by referring to research on pet behavioral patterns. Thus, the accuracy of the analysis is improved by referring to relevant pet literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input pet-related literature data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0048] The generation unit can estimate the pet's emotions and adjust the care plan based on the estimated emotions. For example, if the pet is stressed, the generation unit can generate a relaxing care plan. For example, if the pet is relaxed, the generation unit can generate a care plan for maintaining health. For example, if the pet is excited, the generation unit can generate a care plan to help the pet release energy. By adjusting the care plan based on the pet's emotions, the optimal care plan for the pet can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input pet emotion data into a generation AI and have the generation AI adjust the care plan.

[0049] The generation unit can analyze the pet's past health data to select the optimal care plan when generating a care plan. For example, the generation unit can generate a preventative care plan based on the pet's past health status. For example, the generation unit can generate a care plan that emphasizes the prevention of a specific disease based on the pet's past medical history. For example, the generation unit can generate a care plan aimed at improving the pet's behavior based on the pet's past behavioral patterns. This allows for the selection of the optimal care plan by analyzing the pet's past health data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the pet's past health data into a generation AI and have the generation AI select the optimal care plan.

[0050] The generation unit can customize the care plan based on the pet's current living situation when generating the care plan. For example, if the pet is kept indoors, the generation unit can generate a care plan that emphasizes indoor exercise. If the pet is kept outdoors, the generation unit can generate a care plan that emphasizes outdoor activities. If the pet is kept in a multi-pet household, the generation unit can generate a care plan that takes into account the relationships with other pets. By customizing the care plan based on the pet's current living situation, the generation unit can provide the pet with the most suitable care plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the pet's current living situation into the generation AI and have the generation AI perform the care plan customization.

[0051] The generation unit can estimate the pet's emotions and determine the priority of the care plan based on the estimated emotions. For example, if the pet is stressed, the generation unit can generate a care plan that prioritizes stress reduction. For example, if the pet is relaxed, the generation unit can generate a care plan that prioritizes health maintenance. For example, if the pet is excited, the generation unit can generate a care plan that prioritizes energy release. In this way, by determining the priority of the care plan based on the pet's emotions, the optimal care plan for the pet can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input pet emotion data into a generation AI and have the generation AI perform the determination of the care plan priority.

[0052] The generation unit can select the optimal care plan by considering the pet's geographical location information when generating a care plan. For example, if the pet lives in an urban area, the generation unit can generate a care plan suitable for the urban environment. For example, if the pet lives in a rural area, the generation unit can generate a care plan suitable for the natural environment. For example, if the pet is traveling, the generation unit can generate a care plan suitable for the environment of the travel destination. In this way, the optimal care plan can be provided by considering the pet's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the pet's geographical location information into the generation AI and have the generation AI select the optimal care plan.

[0053] The generation unit can analyze the pet owner's social media activity when generating a care plan and propose a care plan based on that analysis. For example, if the owner posts about the pet's health on social media, the generation unit can generate a care plan based on that information. For example, if the owner posts about the pet's behavior, the generation unit can generate a care plan related to that behavior. For example, if the owner posts about the pet's diet, the generation unit can generate a care plan related to that diet. This allows the system to provide the most suitable care plan for the pet by analyzing the owner's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the owner's social media activity data into a generation AI and have the generation AI propose a care plan.

[0054] The alert unit can estimate the pet's emotions and adjust the timing of alert transmission based on the estimated emotions. For example, the alert unit can send an alert immediately if the pet is stressed. For example, the alert unit can send alerts periodically if the pet is relaxed. For example, the alert unit can send an alert when a specific behavior is observed if the pet is excited. This allows alerts to be sent at the appropriate time by adjusting the timing of alert transmission based on the pet's emotions. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input pet emotion data into a generating AI and have the generating AI perform the adjustment of the alert transmission timing.

[0055] The alert unit can analyze past abnormal data of the pet to select the optimal alert method when sending an alert. For example, if the pet has exhibited a specific abnormal behavior in the past, the alert unit can send an alert when that behavior recurs. For example, based on data from when the pet's health deteriorated in the past, the alert unit can send an alert when similar signs are observed. For example, the alert unit can analyze past abnormal data of the pet and send a preventative alert before an abnormality occurs. This allows the optimal alert method to be selected by analyzing the pet's past abnormal data. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input past abnormal data of the pet into a generating AI and have the generating AI select the optimal alert method.

[0056] The alert unit can estimate the pet's emotions and determine the priority of alerts based on the estimated emotions. For example, if the pet is stressed, the alert unit will prioritize sending stress-related alerts. For example, if the pet is relaxed, the alert unit can prioritize sending alerts related to its health status. For example, if the pet is excited, the alert unit can prioritize sending alerts related to its behavioral patterns. This ensures that important alerts are sent preferentially by prioritizing them based on the pet's emotions. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input pet emotion data into a generating AI and have the generating AI determine the priority of alerts.

[0057] The alert unit can select the optimal alert method by considering the pet's geographical location when sending an alert. For example, if the pet is in a specific location, the alert unit can prioritize sending alerts related to that location. For example, if the pet is on the move, the alert unit can send alerts related to the travel route. For example, if the pet is staying in a specific area for an extended period, the alert unit can send alerts related to that area. This allows the system to select the optimal alert method by considering the pet's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the pet's geographical location information into a generating AI and have the generating AI select the optimal alert method.

[0058] The reporting unit can estimate the pet's emotions and adjust the report content based on the estimated emotions. For example, if the pet is stressed, the reporting unit can generate a report that highlights stress-related data. For example, if the pet is relaxed, the reporting unit can generate a report that highlights data related to its health status. For example, if the pet is excited, the reporting unit can generate a report that highlights data related to its behavioral patterns. By adjusting the report content based on the pet's emotions, the reporting unit can provide a report that is easy for pet owners to understand. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input pet emotion data into a generating AI and have the generating AI adjust the report content.

[0059] The reporting unit can analyze the pet's past health data to select the most suitable report content when generating a report. For example, the reporting unit can generate a report on preventative care based on the pet's past health status. For example, the reporting unit can generate a report emphasizing the prevention of a specific disease based on the pet's past medical history. For example, the reporting unit can generate a report aimed at improving the pet's behavior based on its past behavioral patterns. In this way, the optimal report content can be selected by analyzing the pet's past health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the pet's past health data into a generating AI and have the generating AI select the optimal report content.

[0060] The reporting unit can customize reports based on the pet's current living situation when generating them. For example, if the pet is kept indoors, the reporting unit can generate a report on indoor health management. If the pet is kept outdoors, the reporting unit can generate a report on outdoor health management. If the pet is kept in a multi-pet household, the reporting unit can generate a report that takes into account the relationships between pets. By customizing reports based on the pet's current living situation, the reporting unit can provide useful information to pet owners. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data on the pet's current living situation into a generating AI and have the generating AI perform the report customization.

[0061] The reporting unit can estimate the pet's emotions and determine the priority of reports based on the estimated emotions. For example, if the pet is stressed, the reporting unit will prioritize generating reports related to stress reduction. For example, if the pet is relaxed, the reporting unit will prioritize generating reports related to maintaining health. For example, if the pet is excited, the reporting unit will prioritize generating reports related to improving behavior. This allows for the priority provision of important information by prioritizing reports based on the pet's emotions. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input pet emotion data into a generating AI and have the generating AI determine the report priorities.

[0062] The reporting unit can select the most suitable report content by considering the pet's geographical location information when generating a report. For example, if the pet lives in an urban area, the reporting unit can generate a report on health management suitable for the urban environment. For example, if the pet lives in a rural area, the reporting unit can generate a report on health management suitable for the natural environment. For example, if the pet is traveling, the reporting unit can generate a report on health management suitable for the environment of the travel destination. In this way, the reporting unit can provide the most suitable report content by considering the pet's geographical location information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the pet's geographical location information into a generation AI and have the generation AI select the most suitable report content.

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

[0064] The next-generation pet health and emotion monitoring system can analyze a pet's health and emotions in real time and provide feedback to the owner. Furthermore, the system can incorporate new elements to collect pet behavioral data and estimate the pet's health and emotions. For example, sensors can be added to collect pet behavioral data, allowing for detailed understanding of the pet's movements and location. Additionally, voice recognition technology can be introduced to analyze pet vocalizations, enabling more accurate estimation of the pet's emotions. Furthermore, smart feeders and smart litter boxes can be implemented to collect data on the pet's appetite and excretion, allowing for comprehensive monitoring of the pet's health. This enables a more detailed understanding of the pet's health and emotions, providing appropriate feedback to the owner.

[0065] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can introduce wearable devices to measure pet body temperature and heart rate, allowing for detailed monitoring of pet health. They can also incorporate machine learning algorithms to analyze pet behavioral data, enabling more accurate estimation of pet health and emotions. Furthermore, by storing pet health data in the cloud and sharing it with veterinarians and pet insurance companies, they can support pet health management. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0066] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can include smart feeders to collect pet food data, allowing for detailed monitoring of pet appetite and nutritional status. They can also include smart litter boxes to collect pet excretion data, enabling monitoring of digestive system health. Furthermore, they can incorporate deep learning algorithms to analyze pet behavior data, leading to more accurate estimations of pet health and emotions. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0067] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can include smart beds to collect pet sleep data and monitor sleep patterns in detail. They can also include fitness trackers to collect pet exercise data and monitor pet exercise levels. Furthermore, they can incorporate big data analytics technology to analyze pet health data, allowing for more accurate estimation of pet health and emotions. This enables a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0068] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can introduce smart collars to monitor pet skin condition, allowing for a detailed understanding of the pet's skin health. They can also incorporate smart scales to collect pet weight data, supporting pet weight management. Furthermore, they can utilize cloud computing technology to analyze pet health data, enabling more accurate estimation of pet health and emotions. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data on the pet's behavior, vocalizations, appetite, and excretion. For example, it collects pet behavior data using a smartphone or sensors, records vocalizations, and collects the resulting audio data. Furthermore, it observes the pet's appetite, records the amount and frequency of meals, monitors excretion, and records the state of urination and defecation. Step 2: The analysis unit analyzes the data collected by the collection unit to infer the pet's health and emotions. For example, it uses a generative AI to analyze the pet's behavior patterns, vocalizations, and appetite data to infer its health and emotions. Step 3: The generation unit generates an individualized care plan based on the health status and emotions inferred by the analysis unit. For example, the generation AI is used to set a meal plan, exercise plan, and frequency of health checks according to the pet's health status. Step 4: The alert unit sends an alert to the pet owner if an abnormality is detected based on the care plan generated by the generation unit. For example, if an abnormality is detected in the pet's health, emotions, or behavior, a notification is sent via smartphone, email, or app. Step 5: The reporting unit provides regular health status reports based on the care plan generated by the generation unit. For example, it provides weekly reports on the pet's health status, monthly reports on its emotional state, and annual reports on its behavioral patterns.

[0071] (Example of form 2) The next-generation pet health and emotion monitoring system according to an embodiment of the present invention is a system that uses generative AI to analyze the health status and emotions of pets in real time and provide feedback to the owner. This system collects data such as the pet's behavior, vocalizations, appetite, and excretion, and the generative AI analyzes this data to predict the pet's health status and emotions. For example, it can detect changes in the pet's vocalizations or abnormal behavior and send an alert to the owner. It also provides regular health status reports to support preventative care. Furthermore, the generative AI proposes a preventative care plan based on the pet's health data. This allows owners to take appropriate action before their pet's health deteriorates. For example, it analyzes the pet's behavior data, and if an abnormality is detected, it sends an alert to the owner to encourage early action. It also provides individualized care plans according to the pet's health status, improving the pet's well-being. The system of the present invention can be implemented at low cost and scalably by utilizing smartphones. No additional equipment purchase is required, minimizing initial investment and enabling global deployment. In addition, the generative AI learns from data each time a user uses the service, improving diagnostic accuracy. This allows for a deeper understanding of pet behavior patterns and improves owner satisfaction. Furthermore, the generative AI can provide health data through collaboration with pet insurance companies and veterinarians. This enables the development of pet insurance products based on individual health risks and diagnostic support tools for veterinarians, securing data licensing revenue. In addition, the generative AI can sell pet supplies and supplements recommended based on the pet's health and emotions, generating revenue through e-commerce. This invention not only improves the health and well-being of pets but also strengthens the bond with owners, reduces costs, and improves efficiency. It also aims for sustainable growth through the creation of new value through data utilization and global market expansion. Furthermore, by collaborating with the entire pet care industry and building an ecosystem, it aims to become the standard for pet care. As a result, the next-generation pet health and emotion monitoring system can analyze the pet's health and emotions in real time and provide feedback to owners.

[0072] The next-generation pet health and emotion monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, an alert unit, and a reporting unit. The data collection unit collects data such as the pet's behavior, vocalizations, appetite, and excretion. The data collection unit can collect pet behavior data using, for example, a smartphone or sensors. The data collection unit can record the pet's vocalizations and collect the resulting audio data. The data collection unit can observe the pet's appetite and record the amount and frequency of meals. The data collection unit can monitor the pet's excretion and record the state of urination and defecation. The analysis unit analyzes the data collected by the data collection unit and infers the pet's health status and emotions. The analysis unit can, for example, use a generation AI to analyze the pet's behavior patterns and infer its health status. The analysis unit can, for example, use a generation AI to analyze the pet's vocalizations and infer its emotions. The analysis unit can, for example, use a generation AI to analyze the pet's appetite data and infer its health status. The generation unit generates individual care plans based on the health status and emotions inferred by the analysis unit. For example, the generation unit can use generation AI to generate a meal plan tailored to the pet's health status. For example, the generation unit can use generation AI to generate an exercise plan tailored to the pet's emotions. For example, the generation unit can use generation AI to set the frequency of health checks tailored to the pet's health status. The alert unit sends an alert to the owner when an abnormality is detected based on the care plan generated by the generation unit. For example, the alert unit can send a notification to the smartphone when an abnormality is detected in the pet's health status. For example, the alert unit can notify the owner by email when an abnormality is detected in the pet's emotions. For example, the alert unit can send an alert to the owner via an app when an abnormality is detected in the pet's behavior. The reporting unit provides regular health status reports based on the care plan generated by the generation unit. For example, the reporting unit can provide weekly reports on the pet's health status. For example, the reporting unit can provide monthly reports on the pet's emotional state.The reporting section can, for example, provide an annual report on the pet's behavioral patterns. This allows the next-generation pet health and emotion monitoring system according to the embodiment to analyze the pet's health and emotions in real time and provide feedback to the owner.

[0073] The data collection unit collects data on pet behavior, vocalizations, appetite, and excretion. For example, the unit can collect pet behavioral data using a smartphone or sensors. Specifically, it can use accelerometers and gyroscopes attached to the pet's collar or harness to record the pet's movements and activity level in detail. This allows for understanding how much exercise the pet is getting and what behavioral patterns it exhibits. The unit can also record the pet's vocalizations and collect the resulting audio data. This audio data provides important clues for inferring the pet's emotions and health. Furthermore, the unit can observe the pet's appetite and record the amount and frequency of meals. The amount and frequency of meals are important indicators of health, allowing for early detection of any abnormalities. Finally, the unit can monitor the pet's excretion and record the state of urination and defecation. The state of excretion is an important indicator of digestive system health, allowing for early intervention of any abnormalities. In this way, the unit can collect diverse data on pets and monitor their health and emotions in detail. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis unit analyzes the data collected by the collection unit to infer the pet's health and emotions. For example, the analysis unit can use generative AI to analyze the pet's behavioral patterns and infer its health. Specifically, the generative AI detects abnormal behavior that differs from normal behavioral patterns based on the pet's behavioral data. For example, if a pet's activity level is lower than usual, there may be a health problem. The analysis unit can also use generative AI to analyze the pet's vocalizations and infer its emotions. The generative AI uses speech recognition technology to analyze the characteristics of the vocalizations and infer whether the pet is stressed, happy, or anxious. The analysis unit can also use generative AI to analyze the pet's appetite data and infer its health. Changes in the amount and frequency of meals are important indicators of changes in health, and the generative AI analyzes this data to detect abnormalities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the pet's health and emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term health assessments and trend analyses. For example, based on past data, it can predict fluctuations in health status under specific seasons or environmental conditions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0075] The generation unit generates individualized care plans based on the health status and emotions inferred by the analysis unit. For example, the generation unit can use generation AI to generate a meal plan tailored to the pet's health status. Specifically, it creates a meal plan that considers the optimal nutritional balance based on data such as the pet's weight, age, activity level, and health status. For example, the generation unit can use generation AI to generate an exercise plan tailored to the pet's emotions. If the pet is stressed, it will suggest exercise and play plans that will help them relax. For example, the generation unit can use generation AI to set the frequency of health checks according to the pet's health status. For example, if there is a specific health risk, increasing the frequency of regular health checks will allow for early detection and treatment of problems. In this way, the generation unit can provide care plans tailored to the individual needs of pets and support improvements in their health status and emotions. Furthermore, the generation unit can continuously monitor the effectiveness of the care plan and modify the plan as needed. For example, if the pet's health status does not improve, it will review the meal plan and exercise plan and take more effective measures. The generation unit can also propose feasible plans that take into account the owner's lifestyle and the pet's characteristics. This allows the generator to provide a comprehensive care plan to maintain the health and well-being of pets, thereby reducing the burden on pet owners.

[0076] The alert unit sends alerts to pet owners when an abnormality is detected based on the care plan generated by the generation unit. For example, the alert unit can send notifications to a smartphone when an abnormality is detected in the pet's health. Specifically, it sends an immediate notification when an abnormality is observed in the pet's health, such as a sudden decrease in the pet's activity level or a decrease in appetite. The alert unit can also notify pet owners via email when an abnormality is detected in the pet's emotions. If the pet is experiencing stress or persistent anxiety, it prompts the owner to take appropriate action. For example, the alert unit can send alerts to pet owners via an app when an abnormality is detected in the pet's behavior. For example, if the pet exhibits unusual behavior, it prompts the owner to investigate the cause of that behavior. This allows the alert unit to detect abnormalities in the pet's health or emotions early and prompt pet owners to take prompt action. Furthermore, the alert unit can customize the content and frequency of alerts. For example, it can adjust the frequency and content of notifications according to the owner's preferences, reducing stress caused by excessive notifications. Furthermore, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the alert unit to quickly and reliably notify pet owners of abnormalities and support appropriate actions to maintain the health and well-being of their pets.

[0077] The reporting unit provides regular health status reports based on the care plans generated by the generation unit. For example, the reporting unit can provide weekly reports on the pet's health status. Specifically, it creates and provides weekly health reports to pet owners based on data such as the pet's activity level, food intake, and excretion status. For example, the reporting unit can provide monthly reports on the pet's emotional state. It analyzes the pet's vocalizations and behavioral patterns and reports on emotional fluctuations on a monthly basis. For example, the reporting unit can provide annual reports on the pet's behavioral patterns. Based on long-term data, it reports on changes in the pet's behavioral patterns and health status on an annual basis, providing pet owners with comprehensive health management guidelines. This allows the reporting unit to regularly monitor the pet's health status and emotional fluctuations and provide continuous feedback to pet owners. Furthermore, the reporting unit can customize the content of the reports. For example, it can create reports that highlight specific data or indicators according to the pet owner's interests. The reporting unit can also visually show improvements or deteriorations in the pet's health status by comparing it with past data. This allows pet owners to quickly understand their pet's health status and take appropriate action. Furthermore, the reporting department can choose how reports are delivered. For example, reports can be provided via email, in-app notifications, or printed reports, according to the owner's preference. This enables the reporting department to continuously report on the pet's health status and emotional changes to owners, supporting comprehensive health management.

[0078] The data collection unit can collect data on pet behavior, vocalizations, appetite, and excretion using smartphones and sensors. For example, the data collection unit can collect pet behavior data using a smartphone application. For example, the data collection unit can detect pet movement using an accelerometer and collect data. For example, the data collection unit can measure pet body temperature using a temperature sensor and collect data. This allows for detailed collection of pet behavior and health status using smartphones and sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a smartphone application into a generating AI and have the generating AI perform the collection of pet behavior data.

[0079] The data collection unit can estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the pet is stressed, the data collection unit can increase the frequency of data collection to collect more detailed information. For example, if the pet is relaxed, the data collection unit can decrease the frequency of data collection to reduce the pet's burden. For example, if the pet is excited, the data collection unit can focus on collecting specific behaviors. This allows for the collection of detailed information while reducing the pet's burden by adjusting the frequency of data collection according to the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI adjust the frequency of data collection.

[0080] The data collection unit can analyze past behavioral data of pets and select the optimal data collection method. For example, if a pet was active during a specific time period in the past, the data collection unit can concentrate data collection during that time period. For example, if a pet frequently performed a specific behavior in the past, the data collection unit can prioritize collecting data related to that behavior. For example, based on a pet's past health condition, the data collection unit can focus on collecting specific health indicators. This allows the optimal data collection method to be selected by analyzing the pet's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data of pets into a generating AI and have the generating AI select the optimal data collection method.

[0081] The data collection unit can filter data based on the pet's current health status and activity level during data collection. For example, if the pet is in poor health, the data collection unit can collect data more frequently to gather detailed information. For example, if the pet is active, the data collection unit can focus on collecting data related to exercise volume and activity patterns. For example, if the pet is resting, the data collection unit can reduce the burden on the pet by limiting data collection. This allows for efficient collection of necessary information by filtering data based on the pet's current health status and activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet health data into a generating AI and have the generating AI perform data filtering.

[0082] The data collection unit can estimate the pet's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the pet is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the pet is relaxed, the data collection unit can prioritize collecting data related to its health status. For example, if the pet is excited, the data collection unit can prioritize collecting data related to its behavioral patterns. This allows for the priority collection of important data by determining the priority of data to collect based on the pet's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input pet emotion data into a generating AI and have the generating AI determine the data priority.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the pet's geographical location information during data collection. For example, if the pet is in a specific location, the data collection unit can prioritize the collection of data related to that location. For example, if the pet is on the move, the data collection unit can prioritize the collection of data related to the travel route. For example, if the pet is staying in a specific area for an extended period, the data collection unit can collect data related to that area. In this way, by considering the pet's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze the social media activity of pet owners and collect relevant data during data collection. For example, if an owner posts about their pet's health on social media, the data collection unit can collect data based on that information. For example, if an owner posts about their pet's behavior, the data collection unit can collect data related to that behavior. For example, if an owner posts about their pet's food, the data collection unit can collect data related to that food. This allows for the efficient collection of relevant data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0085] The analysis unit can estimate the pet's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the pet is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the pet is relaxed, the analysis unit can prioritize data related to its health status in its analysis. For example, if the pet is excited, the analysis unit can prioritize data related to its behavioral patterns in its analysis. By adjusting the analysis algorithm based on the pet's emotions, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the pet's emotional data into a generative AI and have the generative AI adjust the analysis algorithm.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the pet's health condition during the analysis. For example, if the pet's health condition is poor, the analysis unit can perform a detailed analysis to detect abnormalities early. For example, if the pet's health condition is good, the analysis unit can perform a simpler analysis to support daily health management. For example, if the pet's health condition is unknown, the analysis unit can perform a standard analysis to understand its health condition. This allows for efficient analysis of necessary information by adjusting the level of detail of the analysis based on the importance of the pet's health condition. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input pet health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, the analysis unit can apply different analysis algorithms to dogs and cats, performing analysis according to their respective characteristics. For example, the analysis unit can apply different analysis algorithms to young pets and elderly pets, performing analysis according to age. For example, the analysis unit can apply analysis algorithms specialized for specific breeds, performing analysis according to the characteristics of each breed. By applying different analysis algorithms according to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet type and age data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0088] The analysis unit can estimate the pet's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pet is stressed, the analysis unit can highlight stress-related data. For example, if the pet is relaxed, the analysis unit can highlight health-related data. For example, if the pet is excited, the analysis unit can highlight behavioral pattern data. By adjusting the display method of the analysis results based on the pet's emotions, it becomes possible to display the results in a way that is easy for the owner to understand. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the pet's emotional data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0089] The analysis unit can determine the priority of analysis based on when the pet's behavioral data was submitted. For example, the analysis unit can prioritize the analysis of the most recent behavioral data to understand the pet's real-time health status. For example, the analysis unit can analyze past behavioral data to understand long-term changes in health status. For example, the analysis unit can prioritize the analysis of data before and after a specific event (e.g., a hospital visit) to understand the impact of the event. This allows for real-time understanding of the pet's health status by determining the priority of analysis based on when the pet's behavioral data was submitted. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet behavioral data into a generative AI and have the generative AI determine the priority of analysis.

[0090] The analysis unit can improve the accuracy of its analysis by referring to relevant pet literature during the analysis process. For example, the analysis unit can update its analysis algorithm by referring to the latest veterinary research. For example, the analysis unit can support the early detection of diseases by referring to literature on specific pet diseases. For example, the analysis unit can improve the accuracy of behavioral analysis by referring to research on pet behavioral patterns. Thus, the accuracy of the analysis is improved by referring to relevant pet literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input pet-related literature data into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0091] The generation unit can estimate the pet's emotions and adjust the care plan based on the estimated emotions. For example, if the pet is stressed, the generation unit can generate a relaxing care plan. For example, if the pet is relaxed, the generation unit can generate a care plan for maintaining health. For example, if the pet is excited, the generation unit can generate a care plan to help the pet release energy. By adjusting the care plan based on the pet's emotions, the optimal care plan for the pet can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input pet emotion data into a generation AI and have the generation AI adjust the care plan.

[0092] The generation unit can analyze the pet's past health data to select the optimal care plan when generating a care plan. For example, the generation unit can generate a preventative care plan based on the pet's past health status. For example, the generation unit can generate a care plan that emphasizes the prevention of a specific disease based on the pet's past medical history. For example, the generation unit can generate a care plan aimed at improving the pet's behavior based on the pet's past behavioral patterns. This allows for the selection of the optimal care plan by analyzing the pet's past health data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the pet's past health data into a generation AI and have the generation AI select the optimal care plan.

[0093] The generation unit can customize the care plan based on the pet's current living situation when generating the care plan. For example, if the pet is kept indoors, the generation unit can generate a care plan that emphasizes indoor exercise. If the pet is kept outdoors, the generation unit can generate a care plan that emphasizes outdoor activities. If the pet is kept in a multi-pet household, the generation unit can generate a care plan that takes into account the relationships with other pets. By customizing the care plan based on the pet's current living situation, the generation unit can provide the pet with the most suitable care plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the pet's current living situation into the generation AI and have the generation AI perform the care plan customization.

[0094] The generation unit can estimate the pet's emotions and determine the priority of the care plan based on the estimated emotions. For example, if the pet is stressed, the generation unit can generate a care plan that prioritizes stress reduction. For example, if the pet is relaxed, the generation unit can generate a care plan that prioritizes health maintenance. For example, if the pet is excited, the generation unit can generate a care plan that prioritizes energy release. In this way, by determining the priority of the care plan based on the pet's emotions, the optimal care plan for the pet can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input pet emotion data into a generation AI and have the generation AI perform the determination of the care plan priority.

[0095] The generation unit can select the optimal care plan by considering the pet's geographical location information when generating a care plan. For example, if the pet lives in an urban area, the generation unit can generate a care plan suitable for the urban environment. For example, if the pet lives in a rural area, the generation unit can generate a care plan suitable for the natural environment. For example, if the pet is traveling, the generation unit can generate a care plan suitable for the environment of the travel destination. In this way, the optimal care plan can be provided by considering the pet's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the pet's geographical location information into the generation AI and have the generation AI select the optimal care plan.

[0096] The generation unit can analyze the pet owner's social media activity when generating a care plan and propose a care plan based on that analysis. For example, if the owner posts about the pet's health on social media, the generation unit can generate a care plan based on that information. For example, if the owner posts about the pet's behavior, the generation unit can generate a care plan related to that behavior. For example, if the owner posts about the pet's diet, the generation unit can generate a care plan related to that diet. This allows the system to provide the most suitable care plan for the pet by analyzing the owner's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the owner's social media activity data into a generation AI and have the generation AI propose a care plan.

[0097] The alert unit can estimate the pet's emotions and adjust the timing of alert transmission based on the estimated emotions. For example, the alert unit can send an alert immediately if the pet is stressed. For example, the alert unit can send alerts periodically if the pet is relaxed. For example, the alert unit can send an alert when a specific behavior is observed if the pet is excited. This allows alerts to be sent at the appropriate time by adjusting the timing of alert transmission based on the pet's emotions. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input pet emotion data into a generating AI and have the generating AI perform the adjustment of the alert transmission timing.

[0098] The alert unit can analyze past abnormal data of the pet to select the optimal alert method when sending an alert. For example, if the pet has exhibited a specific abnormal behavior in the past, the alert unit can send an alert when that behavior recurs. For example, based on data from when the pet's health deteriorated in the past, the alert unit can send an alert when similar signs are observed. For example, the alert unit can analyze past abnormal data of the pet and send a preventative alert before an abnormality occurs. This allows the optimal alert method to be selected by analyzing the pet's past abnormal data. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input past abnormal data of the pet into a generating AI and have the generating AI select the optimal alert method.

[0099] The alert unit can estimate the pet's emotions and determine the priority of alerts based on the estimated emotions. For example, if the pet is stressed, the alert unit will prioritize sending stress-related alerts. For example, if the pet is relaxed, the alert unit can prioritize sending alerts related to its health status. For example, if the pet is excited, the alert unit can prioritize sending alerts related to its behavioral patterns. This ensures that important alerts are sent preferentially by prioritizing them based on the pet's emotions. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input pet emotion data into a generating AI and have the generating AI determine the priority of alerts.

[0100] The alert unit can select the optimal alert method by considering the pet's geographical location when sending an alert. For example, if the pet is in a specific location, the alert unit can prioritize sending alerts related to that location. For example, if the pet is on the move, the alert unit can send alerts related to the travel route. For example, if the pet is staying in a specific area for an extended period, the alert unit can send alerts related to that area. This allows the system to select the optimal alert method by considering the pet's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the pet's geographical location information into a generating AI and have the generating AI select the optimal alert method.

[0101] The reporting unit can estimate the pet's emotions and adjust the report content based on the estimated emotions. For example, if the pet is stressed, the reporting unit can generate a report that highlights stress-related data. For example, if the pet is relaxed, the reporting unit can generate a report that highlights data related to its health status. For example, if the pet is excited, the reporting unit can generate a report that highlights data related to its behavioral patterns. By adjusting the report content based on the pet's emotions, the reporting unit can provide a report that is easy for pet owners to understand. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input pet emotion data into a generating AI and have the generating AI adjust the report content.

[0102] The reporting unit can analyze the pet's past health data to select the most suitable report content when generating a report. For example, the reporting unit can generate a report on preventative care based on the pet's past health status. For example, the reporting unit can generate a report emphasizing the prevention of a specific disease based on the pet's past medical history. For example, the reporting unit can generate a report aimed at improving the pet's behavior based on its past behavioral patterns. In this way, the optimal report content can be selected by analyzing the pet's past health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the pet's past health data into a generating AI and have the generating AI select the optimal report content.

[0103] The reporting unit can customize reports based on the pet's current living situation when generating them. For example, if the pet is kept indoors, the reporting unit can generate a report on indoor health management. If the pet is kept outdoors, the reporting unit can generate a report on outdoor health management. If the pet is kept in a multi-pet household, the reporting unit can generate a report that takes into account the relationships between pets. By customizing reports based on the pet's current living situation, the reporting unit can provide useful information to pet owners. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data on the pet's current living situation into a generating AI and have the generating AI perform the report customization.

[0104] The reporting unit can estimate the pet's emotions and determine the priority of reports based on the estimated emotions. For example, if the pet is stressed, the reporting unit will prioritize generating reports related to stress reduction. For example, if the pet is relaxed, the reporting unit will prioritize generating reports related to maintaining health. For example, if the pet is excited, the reporting unit will prioritize generating reports related to improving behavior. This allows for the priority provision of important information by prioritizing reports based on the pet's emotions. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input pet emotion data into a generating AI and have the generating AI determine the report priorities.

[0105] The reporting unit can select the most suitable report content by considering the pet's geographical location information when generating a report. For example, if the pet lives in an urban area, the reporting unit can generate a report on health management suitable for the urban environment. For example, if the pet lives in a rural area, the reporting unit can generate a report on health management suitable for the natural environment. For example, if the pet is traveling, the reporting unit can generate a report on health management suitable for the environment of the travel destination. In this way, the reporting unit can provide the most suitable report content by considering the pet's geographical location information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the pet's geographical location information into a generation AI and have the generation AI select the most suitable report content.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The next-generation pet health and emotion monitoring system can analyze a pet's health and emotions in real time and provide feedback to the owner. Furthermore, the system can incorporate new elements to collect pet behavioral data and estimate the pet's health and emotions. For example, sensors can be added to collect pet behavioral data, allowing for detailed understanding of the pet's movements and location. Additionally, voice recognition technology can be introduced to analyze pet vocalizations, enabling more accurate estimation of the pet's emotions. Furthermore, smart feeders and smart litter boxes can be implemented to collect data on the pet's appetite and excretion, allowing for comprehensive monitoring of the pet's health. This enables a more detailed understanding of the pet's health and emotions, providing appropriate feedback to the owner.

[0108] The next-generation pet health and emotion monitoring system can estimate a pet's emotions and predict its behavior based on those emotions. For example, if a pet is stressed, it can recommend actions to reduce stress. If a pet is relaxed, it can recommend actions to maintain that relaxation. If a pet is excited, it can recommend actions to release energy. By recommending appropriate actions based on a pet's emotions, this system can improve the pet's health and well-being.

[0109] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can introduce wearable devices to measure pet body temperature and heart rate, allowing for detailed monitoring of pet health. They can also incorporate machine learning algorithms to analyze pet behavioral data, enabling more accurate estimation of pet health and emotions. Furthermore, by storing pet health data in the cloud and sharing it with veterinarians and pet insurance companies, they can support pet health management. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0110] The next-generation pet health and emotion monitoring system can estimate a pet's emotions and predict its health based on those emotions. For example, if a pet is stressed, it can predict the impact of stress on its health and suggest preventative care. If a pet is relaxed, it can highlight the positive impact of relaxation on its health and suggest care to maintain its health. If a pet is excited, it can predict the impact of excitement on its health and suggest appropriate coping strategies. In this way, by predicting a pet's health based on its emotions and suggesting appropriate care to the owner, the system can improve the pet's health.

[0111] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can include smart feeders to collect pet food data, allowing for detailed monitoring of pet appetite and nutritional status. They can also include smart litter boxes to collect pet excretion data, enabling monitoring of digestive system health. Furthermore, they can incorporate deep learning algorithms to analyze pet behavior data, leading to more accurate estimations of pet health and emotions. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0112] The next-generation pet health and emotion monitoring system can estimate a pet's emotions and adjust its behavior based on those emotions. For example, if a pet is stressed, it can adjust the environment to reduce stress. If a pet is relaxed, it can provide an environment that maintains that relaxation. If a pet is excited, it can provide play or exercise to help them release energy. By providing an appropriate environment and behavior based on the pet's emotions, it can improve the pet's health and well-being.

[0113] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can include smart beds to collect pet sleep data and monitor sleep patterns in detail. They can also include fitness trackers to collect pet exercise data and monitor pet exercise levels. Furthermore, they can incorporate big data analytics technology to analyze pet health data, allowing for more accurate estimation of pet health and emotions. This enables a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0114] The next-generation pet health and emotion monitoring system can estimate a pet's emotions and suggest actions to improve the pet's health based on those emotions. For example, if a pet is stressed, it can suggest relaxation methods to reduce stress. If a pet is relaxed, it can suggest care methods to maintain that relaxation. If a pet is excited, it can suggest exercise methods to release energy. By suggesting appropriate actions based on the pet's emotions, it can improve the pet's health and well-being.

[0115] Next-generation pet health and emotion monitoring systems can incorporate new elements for analyzing pet health and emotions. For example, they can introduce smart collars to monitor pet skin condition, allowing for a detailed understanding of the pet's skin health. They can also incorporate smart scales to collect pet weight data, supporting pet weight management. Furthermore, they can utilize cloud computing technology to analyze pet health data, enabling more accurate estimation of pet health and emotions. This allows for a comprehensive analysis of pet health and emotions, providing appropriate feedback to pet owners.

[0116] The next-generation pet health and emotion monitoring system can estimate a pet's emotions and suggest actions to maintain the pet's health based on those emotions. For example, if a pet is stressed, it can suggest environmental adjustments to reduce stress. If a pet is relaxed, it can suggest care methods to maintain that relaxation. If a pet is excited, it can suggest exercise methods to release energy. By suggesting appropriate actions based on the pet's emotions, it can improve the pet's health and well-being.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects data on the pet's behavior, vocalizations, appetite, and excretion. For example, it collects pet behavior data using a smartphone or sensors, records vocalizations, and collects the resulting audio data. Furthermore, it observes the pet's appetite, records the amount and frequency of meals, monitors excretion, and records the state of urination and defecation. Step 2: The analysis unit analyzes the data collected by the collection unit to infer the pet's health and emotions. For example, it uses a generative AI to analyze the pet's behavior patterns, vocalizations, and appetite data to infer its health and emotions. Step 3: The generation unit generates an individualized care plan based on the health status and emotions inferred by the analysis unit. For example, the generation AI is used to set a meal plan, exercise plan, and frequency of health checks according to the pet's health status. Step 4: The alert unit sends an alert to the pet owner if an abnormality is detected based on the care plan generated by the generation unit. For example, if an abnormality is detected in the pet's health, emotions, or behavior, a notification is sent via smartphone, email, or app. Step 5: The reporting unit provides regular health status reports based on the care plan generated by the generation unit. For example, it provides weekly reports on the pet's health status, monthly reports on its emotional state, and annual reports on its behavioral patterns.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, alert unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the pet's behavior and vocalizations using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A records the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data using a generation AI to infer the pet's health status and emotions. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an individualized care plan based on the analysis results. The alert unit is implemented in the specific processing unit 46A of the smart device 14, for example, and sends a notification to the owner when an abnormality is detected. The reporting unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and periodically generates a health status report and provides it to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, alert unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the pet's behavior and vocalizations, and the control unit 46A records the data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses a generation AI to analyze the collected data and infer the pet's health status and emotions. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and generates an individualized care plan based on the analysis results. The alert unit is implemented in the control unit 46A of the smart glasses 214, for example, and sends a notification to the owner when an abnormality is detected. The reporting unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and periodically generates a health status report and provides it to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, alert unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the pet's behavior and vocalizations, and the control unit 46A records the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses a generation AI to analyze the collected data and infer the pet's health status and emotions. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an individualized care plan based on the analysis results. The alert unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, and sends a notification to the owner when an abnormality is detected. The reporting unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and periodically generates a health status report and provides it to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, alert unit, and reporting unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the pet's behavior and vocalizations, and the control unit 46A records the data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to infer the pet's health status and emotions. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates an individualized care plan based on the analysis results. The alert unit is implemented, for example, by the control unit 46A of the robot 414, which sends a notification to the owner when an abnormality is detected. The reporting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which periodically generates a health status report and provides it to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A data collection unit that collects data on pet behavior, vocalizations, appetite, excretion, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit that infers the pet's health condition and emotions, A generation unit that generates an individualized care plan based on the health status and emotions inferred by the analysis unit, An alert unit sends an alert to the owner when an abnormality is detected based on the care plan generated by the generation unit, The system includes a reporting unit that periodically provides health status reports based on the care plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Smartphones and sensors are used to collect data on pet behavior, vocalizations, appetite, and excretion. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting data, filtering is performed based on the pet's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the pet's emotions and prioritize the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, we analyze the social media activity of pet owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the pet's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the pet's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the pet's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the pet's behavioral data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we refer to relevant pet-related literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the pet's emotions and adjusts the care plan based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a care plan, the system analyzes the pet's past health data to select the most suitable care plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a care plan, customize it based on the pet's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the pet's emotions and prioritizes care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a care plan, the optimal care plan is selected by considering the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a care plan, we analyze the pet owner's social media activity to propose a suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, It estimates the pet's emotions and adjusts the timing of alerts based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, When sending an alert, the system analyzes past abnormal data of the pet to select the most suitable alert method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, It estimates the pet's emotions and prioritizes alerts based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When sending an alert, the system will select the most appropriate alert method, taking into account the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report section is, The system estimates the pet's emotions and adjusts the report content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report section is, When generating a report, the system analyzes the pet's past health data to select the most suitable report content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report section is, When generating a report, customize the report based on the pet's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report section is, The system estimates the pet's emotions and prioritizes reports based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned report section is, When generating a report, the report content is selected to be optimal, taking into account the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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 data collection unit that collects data on pet behavior, vocalizations, appetite, excretion, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit that infers the pet's health condition and emotions, A generation unit that generates an individualized care plan based on the health status and emotions inferred by the analysis unit, An alert unit sends an alert to the owner when an abnormality is detected based on the care plan generated by the generation unit, The system includes a reporting unit that periodically provides health status reports based on the care plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Smartphones and sensors are used to collect data on pet behavior, vocalizations, appetite, and excretion. The system according to feature 1.

3. The aforementioned collection unit is We estimate the pet's emotions and adjust the frequency of data collection based on the estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze past pet behavior data to select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting data, filtering is performed based on the pet's current health status and activity level. The system according to feature 1.

6. The aforementioned collection unit is We estimate the pet's emotions and prioritize the data to collect based on the estimated emotions. The system according to feature 1.

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