system

Abstract

We provide the system. [Solution] Means for collecting biometric information, A means for transmitting collected biological information to an information processing device, An information processing device includes means for analyzing the biological information to detect abnormalities in health status, Means for generating individual care plans based on detected abnormalities, A means of notifying the user of the generated care plan, A means of monitoring changes in biological information in real time, A system that includes means for quickly sending notifications when an anomaly is detected.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the changes in the lifestyles of livestock and pets, the importance of animal health management has been increasing. However, it is not always easy for pet owners to appropriately grasp the health status of animals and provide timely and appropriate care. Especially in modern busy society, it is often a practical and economic burden for pet owners to continuously monitor the health indicators of animals daily. Furthermore, there may be a lack of specialized knowledge and resources for early detection and appropriate handling of animal health abnormalities. The object of this invention is to provide a method for reducing such burdens on pet owners and more efficiently and effectively managing the health status of animals.

Means for Solving the Problems

[0005] This invention provides a system that has means for continuously and automatically collecting health data using sensors and devices attached to animals. In addition, it includes means for transmitting the collected data to a central processing unit and analyzing the data using machine learning algorithms. This enables the detection of abnormalities in the animal's health in real time and notifies the owner as needed. Furthermore, the central processing unit automatically generates an appropriate care plan for the detected abnormalities and provides it to the user, making it possible for owners without specialized knowledge to protect their animals' health. Moreover, by providing means for responding to additional questions and requests from the user, it can respond to user needs more flexibly.

[0006] "Means for collecting animal health data" refers to devices and processes that use sensors and devices to continuously acquire physiological and behavioral data from animals.

[0007] "Means for transmission to a central processing unit" refers to technologies for efficiently transmitting collected data to a device that performs centralized data processing, either wirelessly or via wired communication.

[0008] "Means for detecting abnormal health conditions" refer to algorithms and models that identify abnormal fluctuations related to an animal's health in real time by comparing them with past data patterns.

[0009] "Means for generating animal care plans" refers to a function that generates processes and recommendations for appropriate treatment and care for animals based on abnormal events or specific health conditions.

[0010] "Means of notifying the user" refers to communication methods or interfaces for quickly and reliably transmitting generated care plans and health information to the animal owner. [Brief explanation of the drawing]

[0011] [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 the data processing device and 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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] The 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.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] One embodiment of this invention is a system for monitoring the health status of animals, detecting health abnormalities early, and providing appropriate care plans. This system mainly consists of a terminal part including sensors attached to animals, a server part for processing data, and a user interface part for providing information.

[0033] First, the terminal is equipped with various sensors to collect animal health data, and it collects physiological data such as heart rate, body temperature, and activity level in real time. This data is transmitted wirelessly to a central server via the internet or other networks.

[0034] Next, the server stores the received data and manages a separate data stream for each animal. The managed data is analyzed by a model using a pre-trained machine learning algorithm, and if an abnormality in health status is detected, the change is quickly identified. For example, if a sudden change in heart rate deviates from the normal activity level and an abnormality flag is set, the server processes that information immediately.

[0035] Furthermore, the server generates a care plan for the animal based on the analysis results. This care plan may include suggestions for specific dietary restrictions or recommendations for rest. The generated plan is then converted into a format that can be directly communicated to the user, providing the owner with attribute-specific information via smartphone or computer.

[0036] Finally, pet owners can receive notifications and view detailed health reports and suggested care plans through a dedicated application. Furthermore, if they have additional questions or need advice, they can receive further support through the interactive interface within the application.

[0037] For example, if the heart rate is above normal, the server generates a notification recommending "avoiding strenuous exercise and providing a safe and calm environment," encouraging the user to take appropriate action. In this way, the system provides a means to support the maintenance of animal health.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device acquires physiological data (e.g., heart rate, body temperature, activity level) in real time from sensors attached to the animal. The acquired data is temporarily stored on the device.

[0041] Step 2:

[0042] The terminal collects data at regular intervals, packets it, and sends it to the server via the internet or a dedicated network using a secure communication protocol.

[0043] Step 3:

[0044] The server stores the received data in a database for analysis. This data is organized by animal and made accessible for subsequent processing.

[0045] Step 4:

[0046] The server uses accumulated data to run a pre-trained machine learning model and detect abnormalities in the animals' health in real time. When an abnormality is detected, a flag corresponding to that abnormality is set.

[0047] Step 5:

[0048] The server automatically generates an optimal care plan based on anomaly flags and analysis results. The care plan includes information on recommended behaviors and treatments and is customized to take into account the animal's current health condition.

[0049] Step 6:

[0050] The server generates a care plan, converts it into a notification format, and sends it to the user's smartphone app via push notification.

[0051] Step 7:

[0052] Users receive notifications on their smartphones and can view care plan details within the app. If necessary, users can also send additional questions to the server via the app.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] There is a need to monitor animals' health in real time, detect abnormalities early, and quickly provide appropriate care plans. However, conventional systems can be time-consuming to analyze data and provide countermeasures, which hinders pet owners from responding quickly.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for accumulating animal physiological data and analyzing it using a generation AI model, means for generating a care plan when an abnormality in the animal's health is detected based on the analyzed physiological data, and means for notifying the user of the generated care plan via a communication terminal. This enables immediate assessment of the animal's health status and prompt and appropriate action in the event of an abnormality.

[0058] An "animal" is a living organism whose physiological data, such as heart rate and body temperature, is monitored.

[0059] "Physiological data" refers to data such as heart rate, body temperature, and activity level collected to assess the health status of animals.

[0060] "Wireless communication" is a technology that uses radio waves or other means to transmit and receive data, and is a means of transmitting information via the internet and other networks.

[0061] An "information processing device" is a computer system that manages and analyzes data, and is a device that performs processing using a generative AI model.

[0062] A "generative AI model" is an artificial intelligence model that analyzes data based on pre-trained algorithms to perform pattern recognition and anomaly detection.

[0063] A "care plan" is a plan that includes the necessary care and suggestions for an animal based on its health condition, and is a specific action plan that is generated when an abnormality is detected.

[0064] A "communication terminal" is a device used by a user to receive and confirm information, and includes smartphones and computers.

[0065] "Users" refer to animal owners or caregivers who receive notifications and take appropriate action based on the information.

[0066] To implement this invention, a device for acquiring health data is attached to an animal. Specifically, sensors that measure heart rate, body temperature, and activity level are attached to the animal, and a terminal is used to acquire data in real time. This terminal transmits the collected data to a server, which is an information processing device, via wireless communication technology such as Bluetooth or Wi-Fi.

[0067] The server properly manages physiological data transmitted from multiple animals and stores it in a database. During this process, the server analyzes the data using a generative AI model. This generative AI model, built with software such as Python and Tensorflow®, determines whether there are any abnormalities in the animals' health by comparing them to normal health patterns.

[0068] If an anomaly is detected, the server automatically generates a care plan. This plan includes suggestions tailored to the user's needs, such as specific dietary restrictions or activity limitations. This generated care plan is promptly notified to the user via a communication terminal. The user can receive this notification and confirm specific actions through their smartphone or computer device.

[0069] Furthermore, users can use a dedicated application to view detailed health reports of their animals and ask additional questions as needed. This allows for quick and appropriate responses to any health abnormalities the animals exhibit.

[0070] As a concrete example, when a dog shows a higher-than-normal heart rate, the server generates a suggestion such as "We recommend resting in a calm environment" and notifies the user. An example of a prompt to be input to the generating AI model is, "If an abnormality is detected, what kind of care plan should be proposed?"

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The device uses sensors attached to the animal to collect physiological data such as heart rate, body temperature, and activity level in real time. This input data is transmitted wirelessly to a server via Bluetooth or Wi-Fi. Specifically, this involves the sensors continuously monitoring the animal's condition and updating the data every second.

[0074] Step 2:

[0075] The server receives physiological data transmitted from the terminal and stores it in a database based on the individual animal's information. The server then uses generative AI models, such as Python or TensorFlow, to analyze this received data. This analysis process compares the current physiological data (the input) with the stored normal pattern data, and if an anomaly is detected, the result is output as a flag. The server checks and analyzes new data every minute.

[0076] Step 3:

[0077] When the server detects an anomaly based on the analysis results, it uses a generated AI model to create a care plan. Based on the analysis flags, a specific plan is generated, such as "Rest is recommended due to high heart rate." This generated care plan is then converted into a notification format and output.

[0078] Step 4:

[0079] The server sends the care plan, converted into a notification format, to the communication terminal, providing the user with warnings and suggested actions. The process involves the server sending a push notification to the user's smartphone or computer, displaying the message within seconds.

[0080] Step 5:

[0081] Based on the received notification, the user opens a dedicated application to view a detailed health report. This action allows them to easily understand the animal's current health status and the proposed care plan. Furthermore, if necessary, they can enter prompts such as "How should I respond?" within the application to receive additional advice. In terms of operation, touching the notification instantly launches the app and displays the information.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] The challenge lies in monitoring the health status of the elderly and sick patients in real time, enabling swift and accurate responses when abnormalities occur. Furthermore, making it easy for users to ask additional questions or make requests is another crucial issue that needs to be addressed.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes means for analyzing biometric information to detect abnormalities in health status, means for generating individualized care plans based on the detected abnormalities, and means for monitoring changes in biometric information in real time. This enables continuous monitoring of the health status of elderly people and patients, prompt notification in the event of an abnormality, and the presentation of appropriate care plans.

[0087] "Biometric information" refers to information such as heart rate, body temperature, and activity level that indicates the health status of an organism.

[0088] An "information processing device" is a device that analyzes collected biological information, detects abnormalities, and provides necessary information.

[0089] An "abnormality" refers to a state that deviates from normal health conditions or behavioral patterns, and indicates a health condition that requires prompt attention.

[0090] A "care plan" is a plan that recommends specific measures or actions based on the abnormalities that have been detected.

[0091] "User" refers to an individual or organization that receives health information through this system and is responsible for taking appropriate action.

[0092] "Real-time" refers to the ability to monitor the status almost instantaneously and respond immediately to changes.

[0093] A "notification" is information sent to a user when an anomaly is detected, and it is a means of prompting a quick response.

[0094] As a form of implementing this invention, a server is first used as the information processing device. The server runs on a cloud platform and functions as infrastructure for data communication, storage, and analysis. The server implements a program that receives biometric information and analyzes it in real time. This analysis is performed using data analysis libraries such as Pandas and Scikit-learn, using Python or R. As a result, the server detects anomalies and generates a care plan based on those anomalies.

[0095] Next, wearable devices are used as terminals. These terminals are equipped with sensors to monitor biometric information such as heart rate and body temperature. These sensors use a small computer such as a Raspberry Pi to acquire biometric information, and then transmit the data to a server via Bluetooth or Wi-Fi.

[0096] Furthermore, users receive notifications and care plans sent from the server via a dedicated smartphone application. This application is developed using React Native and Flutter®, providing an intuitive user interface. When an anomaly is detected, users can receive notifications promptly.

[0097] For example, if an elderly person's heart rate exceeds a predetermined threshold, the server generates a notification such as, "Your heart rate has reached an abnormal level. We recommend prompt attention," and sends it to the user's smartphone. This allows the user to take appropriate action.

[0098] Furthermore, by utilizing a generative AI model, prompt messages can be easily customized. For example, by entering a prompt message such as, "Create a plan to monitor the health information of elderly individuals and notify them in case of abnormalities," the server will perform appropriate analysis and generate a corresponding care plan.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The device collects biometric information from sensors. Inputs include heart rate and body temperature data. This data is processed on a small computer and converted into a digital format. This data is then transmitted to a server via Bluetooth or Wi-Fi.

[0102] Step 2:

[0103] The server temporarily stores biometric information received from the terminal in storage. The input is biometric information from the terminal, and the output is the stored dataset. Data cleaning is performed to prepare this dataset for analysis.

[0104] Step 3:

[0105] The server performs analysis using a machine learning model based on the formatted data. The input is a cleaned dataset, and the output is a health status assessment result. For data processing, an anomaly detection algorithm is executed using the Python Scikit-learn library.

[0106] Step 4:

[0107] The server generates a care plan if it detects an anomaly from the analysis results. The input is the health status assessment result, and the output is a specific care plan. Based on the generated AI model, the care content is designed based on the prompt messages.

[0108] Step 5:

[0109] The server notifies the user of the generated care plan. The input is the care plan, and the output is a push notification to the smartphone. The user receives the notification from the server in real time and is prompted to take the necessary action.

[0110] Step 6:

[0111] Users receive notifications and view detailed information through a smartphone application. Input is the notification from the server, and output is the user's action. Users can review care plans and provide additional instructions within the application.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides an advanced pet health management system that, in addition to conventional systems for collecting and analyzing animal health and behavioral data, also takes into account the user's emotions. Specifically, it is designed to improve the overall user experience by incorporating an emotion engine that recognizes the user's emotions and enables appropriate interaction based on those emotions into conventional technology that monitors the animal's health status and provides a corresponding care plan.

[0114] First, the device collects health data from the animals. This involves using sensors attached to the animals to obtain physiological data such as heart rate, body temperature, and activity level. This data is sent to and received by a server at regular intervals.

[0115] Next, the server analyzes the received data to detect abnormalities in the animal's health. A machine learning model is used, and deviations from the normal state are flagged as abnormal. Based on these results, a customized care plan for the animal is automatically generated.

[0116] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes the user's emotions. When a user accesses data through the system, emotion data is collected and analyzed through the camera of a smartphone or PC, voice input, etc. The emotion engine recognizes the user's emotional state, such as stress, relief, excitement, etc., and adjusts the content and method of notifications according to these emotions.

[0117] For example, if the server detects that a user is experiencing stress, it may deliver system notifications in a more calming tone or include additional reassuring information. Conversely, for users who are feeling reassured, it can provide normal notifications and recommend actions.

[0118] In this way, the present invention realizes personalized interactions for individual users through a system that combines animal health management information with the user's emotional state. This not only makes animal care more efficient and effective, but also provides a mechanism that is more familiar and supportive to the user.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device uses sensors attached to the animal to collect health data such as heart rate, body temperature, and activity level in real time. The collected data is compiled into batches at regular intervals, preparing it for the next step.

[0122] Step 2:

[0123] The terminal transmits the prepared data wirelessly over the internet via a security protocol to the server. It is crucial that the data transmission is secure and efficient.

[0124] Step 3:

[0125] The server stores the received data in a database in the central processing system. In the database, the data is organized into animal profiles, making it easily accessible for subsequent analysis.

[0126] Step 4:

[0127] The server executes machine learning algorithms based on accumulated data to analyze anomalies and patterns related to the animals' health. If an anomaly is detected, an anomaly flag is set, and it is treated as an early warning.

[0128] Step 5:

[0129] The server generates an appropriate care plan for the animal based on the analysis results. The generated care plan includes specific instructions to optimize the animal's health, such as exercise restrictions and special diet plans tailored to the animal's condition on that day.

[0130] Step 6:

[0131] The server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes facial expression data and voice data obtained from smartphone or PC cameras to identify the user's mental state.

[0132] Step 7:

[0133] The server adjusts notification content and communication style based on the user's emotional state. For example, it might provide details in a calm tone or include additional information to reassure users who are feeling stressed.

[0134] Step 8:

[0135] Users receive tailored care plans and notifications, and review the content through a smartphone app. They can submit feedback and request additional support within the app as needed. This feedback is used by the system to improve future care plans.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] Conventional animal health management systems can monitor an animal's health and propose care plans, but they lack personalized responses that take into account the user's emotional state. Such systems have problems in reducing the user's mental burden and responding appropriately to emotional changes such as stress.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for analyzing animal physiological data to detect abnormalities in health, means for acquiring user emotional information, and means for adjusting notification content based on the acquired emotional information. This enables not only the provision of animal health management information but also personalized interaction that takes into account the user's emotional state.

[0141] "Animal physiological data" refers to data that indicates the internal state of an animal's body, such as heart rate, body temperature, and activity level.

[0142] An "information processing device" is a central computer device that analyzes received data and performs anomaly detection and generates care plans.

[0143] "Means for detecting abnormalities in health status" refer to devices or methods that have the function of identifying deviations from the normal state by analyzing physiological data.

[0144] A "care plan" is a plan that outlines specific care and treatment options based on the animal's health condition.

[0145] "User emotional information" refers to data that indicates the user's emotional state, such as stress levels and feelings of security, which is analyzed from information such as audio and images.

[0146] "Means for adjusting notification content" refers to devices or methods that have the function of changing the expression and tone of messages and alerts from the system according to the user's emotional state.

[0147] A "tailored notification" is a message whose content has been processed based on the user's emotional state in order to optimize the interaction with the user.

[0148] This system comprehensively monitors the animal's health and the user's emotional state, providing individually personalized interactions. This section describes the specific implementation method of the system.

[0149] The device collects physiological data from animals using sensors attached to them (e.g., heart rate monitor, temperature sensor, accelerometer). This physiological data includes heart rate, body temperature, and activity level, and is collected at regular intervals. The data is then encrypted and transmitted to the server via Bluetooth or Wi-Fi. The SSL / TLS protocol is used for communication to ensure secure data transmission.

[0150] The server analyzes the received physiological data. A machine learning model is employed to perform anomaly detection, identifying deviations from normal conditions. If an anomaly is detected, a personalized care plan is automatically generated for the animal based on its specific characteristics. The generative AI model suggests appropriate care methods for the animal's health condition. This process enables animal caregivers to take quick and effective action.

[0151] Next, data necessary to understand the user's emotional state is collected. When a user accesses the system, emotional data is collected using the camera on their smartphone or PC, as well as the voice input function. This information is analyzed through facial recognition technology and voice analysis algorithms to identify emotional states such as stress, reassurance, and excitement.

[0152] The server adjusts notification content based on analyzed sentiment data. For example, when a user is stressed, the notification can be made gentler and include information that enhances their sense of security. For users who feel secure, information can be provided in the usual notification format, and the next action can be recommended.

[0153] As a concrete example, consider the prompt message: "If the dog's heart rate is above normal, generate a notification message indicating that the user is stressed." This allows the system to optimize the animal's healthcare information according to the user's emotions, providing personalized care.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The device collects physiological data from sensors attached to the animal. The inputs here are the animal's heart rate, body temperature, and activity level, as captured by the sensors. This data is recorded at regular intervals and temporarily stored as hourly data to obtain output. This data, measured in real-time by the sensors, serves as fundamental data for understanding the animal's health status.

[0157] Step 2:

[0158] The terminal encrypts the collected physiological data and sends it to the server. The input is the animal's physiological data recorded in the previous step. This data is encrypted using the SSL / TLS protocol and transmitted via a secure and high-speed communication method (Bluetooth or Wi-Fi) to achieve output. This step aims to ensure the reliability and security of the data.

[0159] Step 3:

[0160] The server analyzes the received physiological data. The input is encrypted physiological data received from the terminal. Using a machine learning model, the server analyzes the data and determines abnormalities by detecting patterns that are different from the norm. The output generates whether or not there is an abnormality and details thereof, providing basic information for monitoring the animal's health status.

[0161] Step 4:

[0162] The server generates a customized care plan based on the detected anomalies. Here, the anomaly information obtained in step 3 is used as input. Using the generated AI model, the optimal care methods (such as diet, exercise, and diagnostic suggestions) corresponding to the animal's health condition are formulated. This results in output that instructs the user on specific intervention methods.

[0163] Step 5:

[0164] Users access care plans and animal health data via their smartphones or PCs. During this process, the user's device collects emotional information through its camera and microphone. The user's facial expressions and voice are used as input, and based on this, emotional analysis is performed to output the user's emotional state (stress level, sense of security, etc.).

[0165] Step 6:

[0166] The server analyzes emotional data and adjusts the notification content according to the user's emotional state. In this step, the emotional state obtained in step 5 is used as input. The emotion engine analyzes this and generates an adjusted output, such as changing the notification to a calmer one if the user is stressed.

[0167] Step 7:

[0168] The server sends a tailored notification to the user. Here, the notification content generated in step 6 is entered and sent to the user's device via push notification or email. This allows the user to understand the animal's care situation and receive emotionally appropriate support.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] In modern society, many households keep pets, and properly managing their health is crucial. However, conventional health management systems rely solely on the animal's physiological data and do not consider the user's psychological state or emotions. As a result, it is difficult to provide flexible interactions that respond to the user's emotions or to present appropriate care plans, leading to challenges in pet health management.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for collecting physiological data of animals, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device to detect abnormalities in the animal's health, means for generating an animal care plan based on the abnormalities, means for notifying the user of the generated care plan, and means for recognizing the user's emotions and adjusting the content and method of notification based on those emotions. This enables interaction that responds to the user's emotional state and provides effective animal health management.

[0174] "Animal physiological data" refers to physiological information that indicates the health status of an animal, such as its heart rate, body temperature, and activity level.

[0175] An "information processing device" is a computer system that has the function of analyzing collected data and detecting anomalies.

[0176] "Abnormal health status" refers to physiological indicators or behaviors of an animal that deviate from a normal state of health.

[0177] A "care plan" is a plan for the care and management of an animal, formulated based on abnormalities in the animal's health condition.

[0178] "Users" refers to people who care for and manage the health of animals.

[0179] "Means of recognizing emotions" refers to technologies that use cameras and voice input to analyze a user's psychological state and understand their emotions.

[0180] "Means for adjusting the content and method of notifications" refers to system functions that change the tone and delivery method of notification messages according to the user's emotional state.

[0181] The pet health management system of the present invention aims to understand the health status of animals and generate appropriate care plans by collecting and analyzing their physiological data. Furthermore, it provides personalized interaction by recognizing the user's emotional state and dynamically adjusting the content and method of notifications. Specific embodiments are described below.

[0182] First, the device collects physiological data such as heart rate, body temperature, and activity level through sensors attached to the animal. This device can utilize platforms such as Raspberry Pi or NVIDIA Jetson. The collected data is transmitted wirelessly to a central information processing unit.

[0183] The server (information processing device) runs a program developed using Python to analyze the collected physiological data. The analysis utilizes machine learning libraries such as TensorFlow and PyTorch to detect data points that deviate from the normal range as health abnormalities. Based on these abnormalities, the system automatically generates an animal care plan and notifies the user.

[0184] On the other hand, emotional data is collected from users through their cameras and microphones and analyzed using Google Cloud's Natural Language API and Microsoft Azure's Emotion Recognition API. This allows the server to understand the user's emotional state and adjust the content and presentation of notifications in the generated care plan. For example, if the system determines that the user is stressed, it will use a calmer tone for notifications and add advice to help them relax.

[0185] As a concrete example, consider a scenario where the owner is at home on a holiday, and the server detects that the pet's resting heart rate is abnormal. If the server perceives that the user is experiencing some stress, it will send a notification saying, "Thank you for your hard work. It seems your pet needs a little rest. Try giving them a gentle hug. I'm sure your pet will relax."

[0186] An example of a prompt statement is as follows:

[0187] "Check on the pet's condition and generate a comforting message for users who are experiencing stress."

[0188] "Adjust care plan notifications flexibly to match the emotions the user is feeling."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The device collects physiological data from sensors attached to the animal. At this stage, it acquires data such as heart rate, body temperature, and activity level from the sensors. This input data represents the animal's real-time state, and this data is transmitted to the server wirelessly as output.

[0192] Step 2:

[0193] The server analyzes the received physiological data. A Python-based program applies a TensorFlow or PyTorch machine learning model to the input physiological data to detect deviations from the normal range. The output here is the result of determining whether or not an abnormality is present.

[0194] Step 3:

[0195] The server automatically generates an animal care plan based on detected anomaly data. Using the anomaly detection results as input, it creates an individual plan by fitting them to a predefined care plan template. The output is a specific action list representing the care plan.

[0196] Step 4:

[0197] Users provide emotional data through the camera and microphone of their smartphone or computer. This input is sent to the server as image and audio data.

[0198] Step 5:

[0199] The server analyzes the user's emotional data using an emotion recognition API. Specifically, it processes input data using services from Google Cloud and Microsoft Azure to determine emotional states such as stress, reassurance, and excitement. The output is the user's current emotional state.

[0200] Step 6:

[0201] The server takes the user's emotional state into consideration and adjusts the content and method of notifications accordingly. Using the emotional assessment results as input, it customizes the tone and message content of the care plan and notifies the user in an appropriate format. The output is a notification message adjusted according to the emotional state.

[0202] Step 7:

[0203] The user performs animal care based on the notification message received. Here, specific actions are taken according to the care plan notified by the server. The input is the pre-arranged notification message, and the output is the actual care action.

[0204] 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.

[0205] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0211] 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.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0213] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] 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.

[0215] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The 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.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] One embodiment of this invention is a system for monitoring the health status of animals, detecting health abnormalities early, and providing appropriate care plans. This system mainly consists of a terminal part including sensors attached to animals, a server part for processing data, and a user interface part for providing information.

[0221] First, the terminal is equipped with various sensors to collect animal health data, and it collects physiological data such as heart rate, body temperature, and activity level in real time. This data is transmitted wirelessly to a central server via the internet or other networks.

[0222] Next, the server stores the received data and manages a separate data stream for each animal. The managed data is analyzed by a model using a pre-trained machine learning algorithm, and if an abnormality in health status is detected, the change is quickly identified. For example, if a sudden change in heart rate deviates from the normal activity level and an abnormality flag is set, the server processes that information immediately.

[0223] Furthermore, the server generates a care plan for the animal based on the analysis results. This care plan may include suggestions for specific dietary restrictions or recommendations for rest. The generated plan is then converted into a format that can be directly communicated to the user, providing the owner with attribute-specific information via smartphone or computer.

[0224] Finally, pet owners can receive notifications and view detailed health reports and suggested care plans through a dedicated application. Furthermore, if they have additional questions or need advice, they can receive further support through the interactive interface within the application.

[0225] For example, if the heart rate is above normal, the server generates a notification recommending "avoiding strenuous exercise and providing a safe and calm environment," encouraging the user to take appropriate action. In this way, the system provides a means to support the maintenance of animal health.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The device acquires physiological data (e.g., heart rate, body temperature, activity level) in real time from sensors attached to the animal. The acquired data is temporarily stored on the device.

[0229] Step 2:

[0230] The terminal collects data at regular intervals, packets it, and sends it to the server via the internet or a dedicated network using a secure communication protocol.

[0231] Step 3:

[0232] The server stores the received data in a database for analysis. This data is organized by animal and made accessible for subsequent processing.

[0233] Step 4:

[0234] The server uses accumulated data to run a pre-trained machine learning model and detect abnormalities in the animals' health in real time. When an abnormality is detected, a flag corresponding to that abnormality is set.

[0235] Step 5:

[0236] The server automatically generates an optimal care plan based on anomaly flags and analysis results. The care plan includes information on recommended behaviors and treatments and is customized to take into account the animal's current health condition.

[0237] Step 6:

[0238] The server converts the generated care plan into a notification format and sends it to the user's smartphone app via push notification.

[0239] Step 7:

[0240] Users receive notifications on their smartphones and can view care plan details within the app. If necessary, users can also send additional questions to the server via the app.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] There is a need to monitor animals' health in real time, detect abnormalities early, and quickly provide appropriate care plans. However, conventional systems can be time-consuming to analyze data and provide countermeasures, which hinders pet owners from responding quickly.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for accumulating animal physiological data and analyzing it using a generation AI model, means for generating a care plan when an abnormality in the animal's health is detected based on the analyzed physiological data, and means for notifying the user of the generated care plan via a communication terminal. This enables immediate assessment of the animal's health status and prompt and appropriate action in the event of an abnormality.

[0246] An "animal" is a living organism whose physiological data, such as heart rate and body temperature, is monitored.

[0247] "Physiological data" refers to data such as heart rate, body temperature, and activity level collected to assess the health status of animals.

[0248] "Wireless communication" is a technology that uses radio waves or other means to transmit and receive data, and is a means of transmitting information via the internet and other networks.

[0249] An "information processing device" is a computer system that manages and analyzes data, and is a device that performs processing using a generative AI model.

[0250] A "generative AI model" is an artificial intelligence model that analyzes data based on pre-trained algorithms to perform pattern recognition and anomaly detection.

[0251] A "care plan" is a plan that includes the necessary care and suggestions for an animal based on its health condition, and is a specific action plan that is generated when an abnormality is detected.

[0252] A "communication terminal" is a device used by a user to receive and confirm information, and includes smartphones and computers.

[0253] "Users" refer to animal owners or caregivers who receive notifications and take appropriate action based on the information.

[0254] To implement this invention, a device for acquiring health data is attached to an animal. Specifically, sensors that measure heart rate, body temperature, and activity level are attached to the animal, and a terminal is used to acquire data in real time. This terminal transmits the collected data to a server, which is an information processing device, via wireless communication technology such as Bluetooth or Wi-Fi.

[0255] The server properly manages physiological data sent from multiple animals and stores it in a database. During this process, the server analyzes the data using a generative AI model. This generative AI model, built with software such as Python and TensorFlow, determines whether there are any abnormalities in the animals' health by comparing them to normal health patterns.

[0256] If an anomaly is detected, the server automatically generates a care plan. This plan includes suggestions tailored to the user's needs, such as specific dietary restrictions or activity limitations. This generated care plan is promptly notified to the user via a communication terminal. The user can receive this notification and confirm specific actions through their smartphone or computer device.

[0257] Furthermore, users can use a dedicated application to view detailed health reports of their animals and ask additional questions as needed. This allows for quick and appropriate responses to any health abnormalities the animals exhibit.

[0258] As a concrete example, when a dog shows a higher-than-normal heart rate, the server generates a suggestion such as "We recommend resting in a calm environment" and notifies the user. An example of a prompt to be input to the generating AI model is, "If an abnormality is detected, what kind of care plan should be proposed?"

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The device uses sensors attached to the animal to collect physiological data such as heart rate, body temperature, and activity level in real time. This input data is transmitted wirelessly to a server via Bluetooth or Wi-Fi. Specifically, this involves the sensors continuously monitoring the animal's condition and updating the data every second.

[0262] Step 2:

[0263] The server receives physiological data transmitted from the terminal and stores it in a database based on the individual animal's information. The server then uses generative AI models, such as Python or TensorFlow, to analyze this received data. This analysis process compares the current physiological data (the input) with the stored normal pattern data, and if an anomaly is detected, the result is output as a flag. The server checks and analyzes new data every minute.

[0264] Step 3:

[0265] When the server detects an anomaly based on the analysis results, it uses a generated AI model to create a care plan. Based on the analysis flags, a specific plan is generated, such as "Rest is recommended due to high heart rate." This generated care plan is then converted into a notification format and output.

[0266] Step 4:

[0267] The server sends the care plan, converted into a notification format, to the communication terminal, providing the user with warnings and suggested actions. The process involves the server sending a push notification to the user's smartphone or computer, displaying the message within seconds.

[0268] Step 5:

[0269] Based on the received notification, the user opens a dedicated application to view a detailed health report. This action allows them to easily understand the animal's current health status and the proposed care plan. Furthermore, if necessary, they can enter prompts such as "How should I respond?" within the application to receive additional advice. In terms of operation, touching the notification instantly launches the app and displays the information.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] The challenge lies in monitoring the health status of the elderly and sick patients in real time, enabling swift and accurate responses when abnormalities occur. Furthermore, making it easy for users to ask additional questions or make requests is another crucial issue that needs to be addressed.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes means for analyzing biometric information to detect abnormalities in health status, means for generating individualized care plans based on the detected abnormalities, and means for monitoring changes in biometric information in real time. This enables continuous monitoring of the health status of elderly people and patients, prompt notification in the event of an abnormality, and the presentation of appropriate care plans.

[0275] "Biometric information" refers to information such as heart rate, body temperature, and activity level that indicates the health status of an organism.

[0276] An "information processing device" is a device that analyzes collected biological information, detects abnormalities, and provides necessary information.

[0277] An "abnormality" refers to a state that deviates from normal health conditions or behavioral patterns, and indicates a health condition that requires prompt attention.

[0278] A "care plan" is a plan that recommends specific measures or actions based on the abnormalities that have been detected.

[0279] "User" refers to an individual or organization that receives health information through this system and is responsible for taking appropriate action.

[0280] "Real-time" refers to the ability to monitor the status almost instantaneously and respond immediately to changes.

[0281] A "notification" is information sent to a user when an anomaly is detected, and it is a means of prompting a quick response.

[0282] As a form for implementing this invention, first, a server is used as the information processing device. The server is executed on a cloud platform and functions as an infrastructure for data communication, storage, and analysis. The server implements a program that receives biometric information and analyzes that information in real time. This analysis is performed using libraries for data analysis such as Python and R, for example, Pandas and Scikit-learn. Thereby, the server detects abnormalities and generates a care plan based on those abnormalities.

[0283] Next, as the terminal, a wearable device is used. The terminal is equipped with sensors for monitoring biometric information such as heart rate and body temperature. These sensors use a small computer such as a Raspberry Pi to acquire biometric information and then transmit the data to the server via Bluetooth or Wi-Fi.

[0284] Furthermore, the user receives notifications and care plans sent from the server via a dedicated smartphone application. This application is developed using React Native or Flutter and provides a user interface that can be intuitively operated. When an abnormality is detected, the user can receive a notification promptly.

[0285] As a specific example, when the heart rate of an elderly person exceeds a prescribed threshold value, the server generates a notification with content such as "The heart rate has reached an abnormal value. Prompt confirmation is recommended." and sends it to the user's smartphone. Thereby, the user can take appropriate actions.

[0286] Furthermore, by using a generative AI model, it is possible to make it easier to customize prompt sentences. For example, by inputting a prompt sentence such as "Create a plan to monitor the health information of the elderly and notify when there is an abnormality.", the server performs appropriate analysis and generates a corresponding care plan.

[0287] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0288] Step 1:

[0289] The device collects biometric information from sensors. Inputs include heart rate and body temperature data. This data is processed on a small computer and converted into a digital format. This data is then transmitted to a server via Bluetooth or Wi-Fi.

[0290] Step 2:

[0291] The server temporarily stores biometric information received from the terminal in storage. The input is biometric information from the terminal, and the output is the stored dataset. Data cleaning is performed to prepare this dataset for analysis.

[0292] Step 3:

[0293] The server performs analysis using a machine learning model based on the formatted data. The input is a cleaned dataset, and the output is a health status assessment result. For data processing, an anomaly detection algorithm is executed using the Python Scikit-learn library.

[0294] Step 4:

[0295] The server generates a care plan if it detects an anomaly from the analysis results. The input is the health status assessment result, and the output is a specific care plan. Based on the generated AI model, the care content is designed based on the prompt messages.

[0296] Step 5:

[0297] The server notifies the user of the generated care plan. The input is the care plan, and the output is a push notification to the smartphone. The user receives the notification from the server in real time and is prompted to take the necessary action.

[0298] Step 6:

[0299] Users receive notifications and view detailed information through a smartphone application. Input is the notification from the server, and output is the user's action. Users can review care plans and provide additional instructions within the application.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention provides an advanced pet health management system that, in addition to conventional systems for collecting and analyzing animal health and behavioral data, also takes into account the user's emotions. Specifically, it is designed to improve the overall user experience by incorporating an emotion engine that recognizes the user's emotions and enables appropriate interaction based on those emotions into conventional technology that monitors the animal's health status and provides a corresponding care plan.

[0302] First, the device collects health data from the animals. This involves using sensors attached to the animals to obtain physiological data such as heart rate, body temperature, and activity level. This data is sent to and received by the server at regular intervals.

[0303] Next, the server analyzes the received data to detect abnormalities in the animal's health. A machine learning model is used, and deviations from the normal state are flagged as abnormal. Based on these results, a customized care plan for the animal is automatically generated.

[0304] As a distinctive aspect of the present invention, an emotion engine for recognizing the emotions of users is incorporated into the server. When a user accesses data via the system, emotion data is collected and analyzed through cameras on smartphones or PCs, voice input, etc. The emotion engine recognizes the user's emotional state, such as stress, sense of security, excitement, etc., and adjusts the content and method of notifications according to these emotions.

[0305] As a specific example, when the user is recognized as feeling stressed, the server provides notifications from the system in a more calming tone or includes additional reassuring information. Also, for users who are feeling at ease, normal notifications can be made and action recommendations can be provided.

[0306] In this way, through a system that combines animal health management information and the user's emotional state, the present invention realizes personalized interactions for individual users. This not only enables more efficient and effective animal care but also provides a mechanism that is more familiar and reassuring for the user.

[0307] The processing flow will be described below.

[0308] Step 1:

[0309] The terminal uses sensors attached to the animal to collect health data such as heart rate, body temperature, activity level, etc. in real time. The collected data is grouped as a batch at regular intervals and prepared for the next step.

[0310] Step 2:

[0311] The terminal transmits the prepared data to the server via the Internet through a security protocol using wireless communication. It is important that the data is transmitted securely and efficiently.

[0312] Step 3:

[0313] The server stores the received data in a database in the central processing system. In the database, the data is organized into animal profiles, making it easily accessible for subsequent analysis.

[0314] Step 4:

[0315] The server executes machine learning algorithms based on accumulated data to analyze anomalies and patterns related to the animals' health. If an anomaly is detected, an anomaly flag is set, and it is treated as an early warning.

[0316] Step 5:

[0317] The server generates an appropriate care plan for the animal based on the analysis results. The generated care plan includes specific instructions to optimize the animal's health, such as exercise restrictions and special diet plans tailored to the animal's condition on that day.

[0318] Step 6:

[0319] The server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes facial expression data and voice data obtained from smartphone or PC cameras to identify the user's mental state.

[0320] Step 7:

[0321] The server adjusts notification content and communication style based on the user's emotional state. For example, it might provide details in a calm tone or include additional information to reassure users who are feeling stressed.

[0322] Step 8:

[0323] Users receive tailored care plans and notifications, and review the content through a smartphone app. They can submit feedback and request additional support within the app as needed. This feedback is used by the system to improve future care plans.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] Conventional animal health management systems can monitor an animal's health and propose care plans, but they lack personalized responses that take into account the user's emotional state. Such systems have problems in reducing the user's mental burden and responding appropriately to emotional changes such as stress.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes means for analyzing animal physiological data to detect abnormalities in health, means for acquiring user emotional information, and means for adjusting notification content based on the acquired emotional information. This enables not only the provision of animal health management information but also personalized interaction that takes into account the user's emotional state.

[0329] "Animal physiological data" refers to data that indicates the internal state of an animal's body, such as heart rate, body temperature, and activity level.

[0330] An "information processing device" is a central computer device that analyzes received data and performs anomaly detection and generates care plans.

[0331] "Means for detecting abnormalities in health status" refer to devices or methods that have the function of identifying deviations from the normal state by analyzing physiological data.

[0332] A "care plan" is a plan that outlines specific care and treatment options based on the animal's health condition.

[0333] "User emotional information" refers to data that indicates the user's emotional state, such as stress levels and feelings of security, which is analyzed from information such as audio and images.

[0334] "Means for adjusting notification content" refers to devices or methods that have the function of changing the expression and tone of messages and alerts from the system according to the user's emotional state.

[0335] A "tailored notification" is a message whose content has been processed based on the user's emotional state in order to optimize the interaction with the user.

[0336] This system comprehensively monitors the animal's health and the user's emotional state, providing individually personalized interactions. This section describes the specific implementation method of the system.

[0337] The device collects physiological data from animals using sensors attached to them (e.g., heart rate monitor, temperature sensor, accelerometer). This physiological data includes heart rate, body temperature, and activity level, and is collected at regular intervals. The data is then encrypted and transmitted to the server via Bluetooth or Wi-Fi. The SSL / TLS protocol is used for communication to ensure secure data transmission.

[0338] The server analyzes the received physiological data. A machine learning model is employed to perform anomaly detection, identifying deviations from normal conditions. If an anomaly is detected, a personalized care plan is automatically generated for the animal based on its specific characteristics. The generative AI model suggests appropriate care methods for the animal's health condition. This process enables animal caregivers to take quick and effective action.

[0339] Next, data necessary to understand the user's emotional state is collected. When a user accesses the system, emotional data is collected using the camera on their smartphone or PC, as well as the voice input function. This information is analyzed through facial recognition technology and voice analysis algorithms to identify emotional states such as stress, reassurance, and excitement.

[0340] The server adjusts notification content based on analyzed sentiment data. For example, when a user is stressed, the notification can be made gentler and include information that enhances their sense of security. For users who feel secure, information can be provided in the usual notification format, and the next action can be recommended.

[0341] As a concrete example, consider the prompt message: "If the dog's heart rate is above normal, generate a notification message indicating that the user is stressed." This allows the system to optimize the animal's healthcare information according to the user's emotions, providing personalized care.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The device collects physiological data from sensors attached to the animal. The inputs here are the animal's heart rate, body temperature, and activity level, as captured by the sensors. This data is recorded at regular intervals and temporarily stored as hourly data to obtain output. This data, measured in real-time by the sensors, serves as fundamental data for understanding the animal's health status.

[0345] Step 2:

[0346] The terminal encrypts the collected physiological data and sends it to the server. The input is the animal's physiological data recorded in the previous step. This data is encrypted using the SSL / TLS protocol and transmitted via a secure and high-speed communication method (Bluetooth or Wi-Fi) to achieve output. This step aims to ensure the reliability and security of the data.

[0347] Step 3:

[0348] The server analyzes the received physiological data. The input is encrypted physiological data received from the terminal. Using a machine learning model, the server analyzes the data and determines abnormalities by detecting patterns that are different from the norm. The output generates whether or not there is an abnormality and details thereof, providing basic information for monitoring the animal's health status.

[0349] Step 4:

[0350] The server generates a customized care plan based on the detected anomalies. Here, the anomaly information obtained in step 3 is used as input. Using the generated AI model, the optimal care methods (such as diet, exercise, and diagnostic suggestions) corresponding to the animal's health condition are formulated. This results in output that instructs the user on specific intervention methods.

[0351] Step 5:

[0352] Users access care plans and animal health data via their smartphones or PCs. During this process, the user's device collects emotional information through its camera and microphone. The user's facial expressions and voice are used as input, and based on this, emotional analysis is performed to output the user's emotional state (stress level, sense of security, etc.).

[0353] Step 6:

[0354] The server analyzes emotional data and adjusts the notification content according to the user's emotional state. In this step, the emotional state obtained in step 5 is used as input. The emotion engine analyzes this and generates an adjusted output, such as changing the notification to a calmer one if the user is stressed.

[0355] Step 7:

[0356] The server sends a tailored notification to the user. Here, the notification content generated in step 6 is entered and sent to the user's device via push notification or email. This allows the user to understand the animal's care situation and receive emotionally appropriate support.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0359] In modern society, many households keep pets, and properly managing their health is crucial. However, conventional health management systems rely solely on the animal's physiological data and do not consider the user's psychological state or emotions. As a result, it is difficult to provide flexible interactions that respond to the user's emotions or to present appropriate care plans, leading to challenges in pet health management.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes means for collecting physiological data of animals, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device to detect abnormalities in the animal's health, means for generating an animal care plan based on the abnormalities, means for notifying the user of the generated care plan, and means for recognizing the user's emotions and adjusting the content and method of notification based on those emotions. This enables interaction that responds to the user's emotional state and provides effective animal health management.

[0362] "Animal physiological data" refers to physiological information that indicates the health status of an animal, such as its heart rate, body temperature, and activity level.

[0363] An "information processing device" is a computer system that has the function of analyzing collected data and detecting anomalies.

[0364] "Abnormal health status" refers to physiological indicators or behaviors of an animal that deviate from a normal state of health.

[0365] A "care plan" is a plan for the care and management of an animal, formulated based on abnormalities in the animal's health condition.

[0366] "Users" refers to people who care for and manage the health of animals.

[0367] "Means of recognizing emotions" refers to technologies that use cameras and voice input to analyze a user's psychological state and understand their emotions.

[0368] "Means for adjusting the content and method of notifications" refers to system functions that change the tone and delivery method of notification messages according to the user's emotional state.

[0369] The pet health management system of the present invention aims to understand the health status of animals and generate appropriate care plans by collecting and analyzing their physiological data. Furthermore, it provides personalized interaction by recognizing the user's emotional state and dynamically adjusting the content and method of notifications. Specific embodiments are described below.

[0370] First, the device collects physiological data such as heart rate, body temperature, and activity level through sensors attached to the animal. This device can utilize platforms such as Raspberry Pi or NVIDIA Jetson. The collected data is transmitted wirelessly to a central information processing unit.

[0371] The server (information processing device) runs a program developed using Python to analyze the collected physiological data. The analysis utilizes machine learning libraries such as TensorFlow and PyTorch to detect data points that deviate from the normal range as health abnormalities. Based on these abnormalities, the system automatically generates an animal care plan and notifies the user.

[0372] On the other hand, emotional data is collected from users through their cameras and microphones and analyzed using Google Cloud's Natural Language API and Microsoft Azure's Emotion Recognition API. This allows the server to understand the user's emotional state and adjust the content and presentation of notifications in the generated care plan. For example, if the system determines that the user is stressed, it will use a calmer tone for notifications and add advice to help them relax.

[0373] As a concrete example, consider a scenario where the owner is at home on a holiday, and the server detects that the pet's resting heart rate is abnormal. If the server perceives that the user is experiencing some stress, it will send a notification saying, "Thank you for your hard work. It seems your pet needs a little rest. Try giving them a gentle hug. I'm sure your pet will relax."

[0374] An example of a prompt statement is as follows:

[0375] "Check on the pet's condition and generate a comforting message for users who are experiencing stress."

[0376] "Adjust care plan notifications flexibly to match the emotions the user is feeling."

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The device collects physiological data from sensors attached to the animal. At this stage, it acquires data such as heart rate, body temperature, and activity level from the sensors. This input data represents the animal's real-time state, and this data is transmitted to the server wirelessly as output.

[0380] Step 2:

[0381] The server analyzes the received physiological data. A Python-based program applies a TensorFlow or PyTorch machine learning model to the input physiological data to detect deviations from the normal range. The output here is the result of determining whether or not an abnormality is present.

[0382] Step 3:

[0383] The server automatically generates an animal care plan based on detected anomaly data. Using the anomaly detection results as input, it creates an individual plan by fitting them to a predefined care plan template. The output is a specific action list representing the care plan.

[0384] Step 4:

[0385] Users provide emotional data through the camera and microphone of their smartphone or computer. This input is sent to the server as image and audio data.

[0386] Step 5:

[0387] The server analyzes the user's emotional data using an emotion recognition API. Specifically, it processes input data using services from Google Cloud and Microsoft Azure to determine emotional states such as stress, reassurance, and excitement. The output is the user's current emotional state.

[0388] Step 6:

[0389] The server takes the user's emotional state into consideration and adjusts the content and method of notifications accordingly. Using the emotional assessment results as input, it customizes the tone and message content of the care plan and notifies the user in an appropriate format. The output is a notification message adjusted according to the emotional state.

[0390] Step 7:

[0391] The user performs animal care based on the notification message received. Here, specific actions are taken according to the care plan notified by the server. The input is the pre-arranged notification message, and the output is the actual care action.

[0392] 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.

[0393] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0394] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0399] 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.

[0400] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0401] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0402] 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.

[0403] 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.

[0404] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0405] The 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.

[0406] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0407] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0408] One embodiment of this invention is a system for monitoring the health status of animals, detecting health abnormalities early, and providing appropriate care plans. This system mainly consists of a terminal part including sensors attached to animals, a server part for processing data, and a user interface part for providing information.

[0409] First, the terminal is equipped with various sensors to collect animal health data, and it collects physiological data such as heart rate, body temperature, and activity level in real time. This data is transmitted wirelessly to a central server via the internet or other networks.

[0410] Next, the server stores the received data and manages a separate data stream for each animal. The managed data is analyzed by a model using a pre-trained machine learning algorithm, and if an abnormality in health status is detected, the change is quickly identified. For example, if a sudden change in heart rate deviates from the normal activity level and an abnormality flag is set, the server processes that information immediately.

[0411] Furthermore, the server generates a care plan for the animal based on the analysis results. This care plan may include suggestions for specific dietary restrictions or recommendations for rest. The generated plan is then converted into a format that can be directly communicated to the user, providing the owner with attribute-specific information via smartphone or computer.

[0412] Finally, pet owners can receive notifications and view detailed health reports and suggested care plans through a dedicated application. Furthermore, if they have additional questions or need advice, they can receive further support through the interactive interface within the application.

[0413] For example, if the heart rate is above normal, the server generates a notification recommending "avoiding strenuous exercise and providing a safe and calm environment," encouraging the user to take appropriate action. In this way, the system provides a means to support the maintenance of animal health.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The device acquires physiological data (e.g., heart rate, body temperature, activity level) in real time from sensors attached to the animal. The acquired data is temporarily stored on the device.

[0417] Step 2:

[0418] The terminal collects data at regular intervals, packets it, and sends it to the server via the internet or a dedicated network using a secure communication protocol.

[0419] Step 3:

[0420] The server stores the received data in a database for analysis. This data is organized by animal and made accessible for subsequent processing.

[0421] Step 4:

[0422] The server uses accumulated data to run a pre-trained machine learning model and detect abnormalities in the animals' health in real time. When an abnormality is detected, a flag corresponding to that abnormality is set.

[0423] Step 5:

[0424] The server automatically generates an optimal care plan based on anomaly flags and analysis results. The care plan includes information on recommended behaviors and treatments and is customized to take into account the animal's current health condition.

[0425] Step 6:

[0426] The server converts the generated care plan into a notification format and sends it to the user's smartphone app via push notification.

[0427] Step 7:

[0428] Users receive notifications on their smartphones and can view care plan details within the app. If necessary, users can also send additional questions to the server via the app.

[0429] (Example 1)

[0430] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0431] There is a need to monitor animals' health in real time, detect abnormalities early, and quickly provide appropriate care plans. However, conventional systems can be time-consuming to analyze data and provide countermeasures, which hinders pet owners from responding quickly.

[0432] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0433] In this invention, the server includes means for accumulating animal physiological data and analyzing it using a generation AI model, means for generating a care plan when an abnormality in the animal's health is detected based on the analyzed physiological data, and means for notifying the user of the generated care plan via a communication terminal. This enables immediate assessment of the animal's health status and prompt and appropriate action in the event of an abnormality.

[0434] An "animal" is a living organism whose physiological data, such as heart rate and body temperature, is monitored.

[0435] "Physiological data" refers to data such as heart rate, body temperature, and activity level collected to assess the health status of animals.

[0436] "Wireless communication" is a technology that uses radio waves or other means to transmit and receive data, and is a means of transmitting information via the internet and other networks.

[0437] An "information processing device" is a computer system that manages and analyzes data, and is a device that performs processing using a generative AI model.

[0438] A "generative AI model" is an artificial intelligence model that analyzes data based on pre-trained algorithms to perform pattern recognition and anomaly detection.

[0439] A "care plan" is a plan that includes the necessary care and suggestions for an animal based on its health condition, and is a specific action plan that is generated when an abnormality is detected.

[0440] A "communication terminal" is a device used by a user to receive and confirm information, and includes smartphones and computers.

[0441] "Users" refer to animal owners or caregivers who receive notifications and take appropriate action based on the information.

[0442] To implement this invention, a device for acquiring health data is attached to an animal. Specifically, sensors that measure heart rate, body temperature, and activity level are attached to the animal, and a terminal is used to acquire data in real time. This terminal transmits the collected data to a server, which is an information processing device, via wireless communication technology such as Bluetooth or Wi-Fi.

[0443] The server properly manages physiological data sent from multiple animals and stores it in a database. During this process, the server analyzes the data using a generative AI model. This generative AI model, built with software such as Python and TensorFlow, determines whether there are any abnormalities in the animals' health by comparing them to normal health patterns.

[0444] If an anomaly is detected, the server automatically generates a care plan. This plan includes suggestions tailored to the user's needs, such as specific dietary restrictions or activity limitations. This generated care plan is promptly notified to the user via a communication terminal. The user can receive this notification and confirm specific actions through their smartphone or computer device.

[0445] Furthermore, users can use a dedicated application to view detailed health reports of their animals and ask additional questions as needed. This allows for quick and appropriate responses to any health abnormalities the animals exhibit.

[0446] As a concrete example, when a dog shows a higher-than-normal heart rate, the server generates a suggestion such as "We recommend resting in a calm environment" and notifies the user. An example of a prompt to be input to the generating AI model is, "If an abnormality is detected, what kind of care plan should be proposed?"

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The device uses sensors attached to the animal to collect physiological data such as heart rate, body temperature, and activity level in real time. This input data is transmitted wirelessly to a server via Bluetooth or Wi-Fi. Specifically, this involves the sensors continuously monitoring the animal's condition and updating the data every second.

[0450] Step 2:

[0451] The server receives physiological data transmitted from the terminal and stores it in a database based on the individual animal's information. The server then uses generative AI models, such as Python or TensorFlow, to analyze this received data. This analysis process compares the current physiological data (the input) with the stored normal pattern data, and if an anomaly is detected, the result is output as a flag. The server checks and analyzes new data every minute.

[0452] Step 3:

[0453] When the server detects an anomaly based on the analysis results, it uses a generated AI model to create a care plan. Based on the analysis flags, a specific plan is generated, such as "Rest is recommended due to high heart rate." This generated care plan is then converted into a notification format and output.

[0454] Step 4:

[0455] The server sends the care plan, converted into a notification format, to the communication terminal, providing the user with warnings and suggested actions. The process involves the server sending a push notification to the user's smartphone or computer, displaying the message within seconds.

[0456] Step 5:

[0457] Based on the received notification, the user opens a dedicated application to view a detailed health report. This action allows them to easily understand the animal's current health status and the proposed care plan. Furthermore, if necessary, they can enter prompts such as "How should I respond?" within the application to receive additional advice. In terms of operation, touching the notification instantly launches the app and displays the information.

[0458] (Application Example 1)

[0459] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] The challenge lies in monitoring the health status of the elderly and sick patients in real time, enabling swift and accurate responses when abnormalities occur. Furthermore, making it easy for users to ask additional questions or make requests is another crucial issue that needs to be addressed.

[0461] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0462] In this invention, the server includes means for analyzing biometric information to detect abnormalities in health status, means for generating individualized care plans based on the detected abnormalities, and means for monitoring changes in biometric information in real time. This enables continuous monitoring of the health status of elderly people and patients, prompt notification in the event of an abnormality, and the presentation of appropriate care plans.

[0463] "Biometric information" refers to information such as heart rate, body temperature, and activity level that indicates the health status of an organism.

[0464] An "information processing device" is a device that analyzes collected biological information, detects abnormalities, and provides necessary information.

[0465] An "abnormality" refers to a state that deviates from normal health conditions or behavioral patterns, and indicates a health condition that requires prompt attention.

[0466] A "care plan" is a plan that recommends specific measures or actions based on the abnormalities that have been detected.

[0467] "User" refers to an individual or organization that receives health information through this system and is responsible for taking appropriate action.

[0468] "Real-time" refers to the ability to monitor the status almost instantaneously and respond immediately to changes.

[0469] A "notification" is information sent to a user when an anomaly is detected, and it is a means of prompting a quick response.

[0470] As a form of implementing this invention, a server is first used as the information processing device. The server runs on a cloud platform and functions as infrastructure for data communication, storage, and analysis. The server implements a program that receives biometric information and analyzes it in real time. This analysis is performed using data analysis libraries such as Pandas and Scikit-learn, using Python or R. As a result, the server detects anomalies and generates a care plan based on those anomalies.

[0471] Next, wearable devices are used as terminals. These terminals are equipped with sensors to monitor biometric information such as heart rate and body temperature. These sensors use a small computer such as a Raspberry Pi to acquire biometric information, and then transmit the data to a server via Bluetooth or Wi-Fi.

[0472] Furthermore, users receive notifications and care plans sent from the server via a dedicated smartphone application. This application is developed using React Native and Flutter, providing an intuitive user interface. When an anomaly is detected, users can receive notifications immediately.

[0473] For example, if an elderly person's heart rate exceeds a predetermined threshold, the server generates a notification such as, "Your heart rate has reached an abnormal level. We recommend prompt attention," and sends it to the user's smartphone. This allows the user to take appropriate action.

[0474] Furthermore, by utilizing a generative AI model, prompt messages can be easily customized. For example, by entering a prompt message such as, "Create a plan to monitor the health information of elderly individuals and notify them in case of abnormalities," the server will perform appropriate analysis and generate a corresponding care plan.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The device collects biometric information from sensors. Inputs include heart rate and body temperature data. This data is processed on a small computer and converted into a digital format. This data is then transmitted to a server via Bluetooth or Wi-Fi.

[0478] Step 2:

[0479] The server temporarily stores biometric information received from the terminal in storage. The input is biometric information from the terminal, and the output is the stored dataset. Data cleaning is performed to prepare this dataset for analysis.

[0480] Step 3:

[0481] The server performs analysis using a machine learning model based on the formatted data. The input is a cleaned dataset, and the output is a health status assessment result. For data processing, an anomaly detection algorithm is executed using the Python Scikit-learn library.

[0482] Step 4:

[0483] The server generates a care plan if it detects an anomaly from the analysis results. The input is the health status assessment result, and the output is a specific care plan. Based on the generated AI model, the care content is designed based on the prompt messages.

[0484] Step 5:

[0485] The server notifies the user of the generated care plan. The input is the care plan, and the output is a push notification to the smartphone. The user receives the notification from the server in real time and is prompted to take the necessary action.

[0486] Step 6:

[0487] Users receive notifications and view detailed information through a smartphone application. Input is the notification from the server, and output is the user's action. Users can review care plans and provide additional instructions within the application.

[0488] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0489] This invention provides an advanced pet health management system that, in addition to conventional systems for collecting and analyzing animal health and behavioral data, also takes into account the user's emotions. Specifically, it is designed to improve the overall user experience by incorporating an emotion engine that recognizes the user's emotions and enables appropriate interaction based on those emotions into conventional technology that monitors the animal's health status and provides a corresponding care plan.

[0490] First, the device collects health data from the animals. This involves using sensors attached to the animals to obtain physiological data such as heart rate, body temperature, and activity level. This data is sent to and received by the server at regular intervals.

[0491] Next, the server analyzes the received data to detect abnormalities in the animal's health. A machine learning model is used, and deviations from the normal state are flagged as abnormal. Based on these results, a customized care plan for the animal is automatically generated.

[0492] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes the user's emotions. When a user accesses data through the system, emotion data is collected and analyzed through the camera of a smartphone or PC, voice input, etc. The emotion engine recognizes the user's emotional state, such as stress, relief, excitement, etc., and adjusts the content and method of notifications according to these emotions.

[0493] For example, if the server detects that a user is experiencing stress, it may deliver system notifications in a more calming tone or include additional reassuring information. Conversely, for users who are feeling reassured, it can provide normal notifications and recommend actions.

[0494] In this way, the present invention realizes personalized interactions for individual users through a system that combines animal health management information with the user's emotional state. This not only makes animal care more efficient and effective, but also provides a mechanism that is more familiar and supportive to the user.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device uses sensors attached to the animal to collect health data such as heart rate, body temperature, and activity level in real time. The collected data is compiled into batches at regular intervals, preparing it for the next step.

[0498] Step 2:

[0499] The terminal transmits the prepared data wirelessly over the internet via a security protocol to the server. It is crucial that the data transmission is secure and efficient.

[0500] Step 3:

[0501] The server stores the received data in a database in the central processing system. In the database, the data is organized into animal profiles, making it easily accessible for subsequent analysis.

[0502] Step 4:

[0503] The server executes machine learning algorithms based on accumulated data to analyze anomalies and patterns related to the animals' health. If an anomaly is detected, an anomaly flag is set, and it is treated as an early warning.

[0504] Step 5:

[0505] The server generates an appropriate care plan for the animal based on the analysis results. The generated care plan includes specific instructions to optimize the animal's health, such as exercise restrictions and special diet plans tailored to the animal's condition on that day.

[0506] Step 6:

[0507] The server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes facial expression data and voice data obtained from smartphone or PC cameras to identify the user's mental state.

[0508] Step 7:

[0509] The server adjusts notification content and communication style based on the user's emotional state. For example, it might provide details in a calm tone or include additional information to reassure users who are feeling stressed.

[0510] Step 8:

[0511] Users receive tailored care plans and notifications, and review the content through a smartphone app. They can submit feedback and request additional support within the app as needed. This feedback is used by the system to improve future care plans.

[0512] (Example 2)

[0513] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Conventional animal health management systems can monitor an animal's health and propose care plans, but they lack personalized responses that take into account the user's emotional state. Such systems have problems in reducing the user's mental burden and responding appropriately to emotional changes such as stress.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0516] In this invention, the server includes means for analyzing animal physiological data to detect abnormalities in health, means for acquiring user emotional information, and means for adjusting notification content based on the acquired emotional information. This enables not only the provision of animal health management information but also personalized interaction that takes into account the user's emotional state.

[0517] "Animal physiological data" refers to data that indicates the internal state of an animal's body, such as heart rate, body temperature, and activity level.

[0518] An "information processing device" is a central computer device that analyzes received data and performs anomaly detection and generates care plans.

[0519] "Means for detecting abnormalities in health status" refer to devices or methods that have the function of identifying deviations from the normal state by analyzing physiological data.

[0520] A "care plan" is a plan that outlines specific care and treatment options based on the animal's health condition.

[0521] "User emotional information" refers to data that indicates the user's emotional state, such as stress levels and feelings of security, which is analyzed from information such as audio and images.

[0522] "Means for adjusting notification content" refers to devices or methods that have the function of changing the expression and tone of messages and alerts from the system according to the user's emotional state.

[0523] A "tailored notification" is a message whose content has been processed based on the user's emotional state in order to optimize the interaction with the user.

[0524] This system comprehensively monitors the animal's health and the user's emotional state, providing individually personalized interactions. This section describes the specific implementation method of the system.

[0525] The device collects physiological data from animals using sensors attached to them (e.g., heart rate monitor, temperature sensor, accelerometer). This physiological data includes heart rate, body temperature, and activity level, and is collected at regular intervals. The data is then encrypted and transmitted to the server via Bluetooth or Wi-Fi. The SSL / TLS protocol is used for communication to ensure secure data transmission.

[0526] The server analyzes the received physiological data. A machine learning model is employed to perform anomaly detection, identifying deviations from normal conditions. If an anomaly is detected, a personalized care plan is automatically generated for the animal based on its specific characteristics. The generative AI model suggests appropriate care methods for the animal's health condition. This process enables animal caregivers to take quick and effective action.

[0527] Next, data necessary to understand the user's emotional state is collected. When a user accesses the system, emotional data is collected using the camera on their smartphone or PC, as well as the voice input function. This information is analyzed through facial recognition technology and voice analysis algorithms to identify emotional states such as stress, reassurance, and excitement.

[0528] The server adjusts notification content based on analyzed sentiment data. For example, when a user is stressed, the notification can be made gentler and include information that enhances their sense of security. For users who feel secure, information can be provided in the usual notification format, and the next action can be recommended.

[0529] As a concrete example, consider the prompt message: "If the dog's heart rate is above normal, generate a notification message indicating that the user is stressed." This allows the system to optimize the animal's healthcare information according to the user's emotions, providing personalized care.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The device collects physiological data from sensors attached to the animal. The inputs here are the animal's heart rate, body temperature, and activity level, as captured by the sensors. This data is recorded at regular intervals and temporarily stored as hourly data to obtain output. This data, measured in real-time by the sensors, serves as fundamental data for understanding the animal's health status.

[0533] Step 2:

[0534] The terminal encrypts the collected physiological data and sends it to the server. The input is the animal's physiological data recorded in the previous step. This data is encrypted using the SSL / TLS protocol and transmitted via a secure and high-speed communication method (Bluetooth or Wi-Fi) to achieve output. This step aims to ensure the reliability and security of the data.

[0535] Step 3:

[0536] The server analyzes the received physiological data. The input is encrypted physiological data received from the terminal. Using a machine learning model, the server analyzes the data and determines abnormalities by detecting patterns that are different from the norm. The output generates whether or not there is an abnormality and details thereof, providing basic information for monitoring the animal's health status.

[0537] Step 4:

[0538] The server generates a customized care plan based on the detected anomalies. Here, the anomaly information obtained in step 3 is used as input. Using the generated AI model, the optimal care methods (such as diet, exercise, and diagnostic suggestions) corresponding to the animal's health condition are formulated. This results in output that instructs the user on specific intervention methods.

[0539] Step 5:

[0540] Users access care plans and animal health data via their smartphones or PCs. During this process, the user's device collects emotional information through its camera and microphone. The user's facial expressions and voice are used as input, and based on this, emotional analysis is performed to output the user's emotional state (stress level, sense of security, etc.).

[0541] Step 6:

[0542] The server analyzes emotional data and adjusts the notification content according to the user's emotional state. In this step, the emotional state obtained in step 5 is used as input. The emotion engine analyzes this and generates an adjusted output, such as changing the notification to a calmer one if the user is stressed.

[0543] Step 7:

[0544] The server sends a tailored notification to the user. Here, the notification content generated in step 6 is entered and sent to the user's device via push notification or email. This allows the user to understand the animal's care situation and receive emotionally appropriate support.

[0545] (Application Example 2)

[0546] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] In modern society, many households keep pets, and properly managing their health is crucial. However, conventional health management systems rely solely on the animal's physiological data and do not consider the user's psychological state or emotions. As a result, it is difficult to provide flexible interactions that respond to the user's emotions or to present appropriate care plans, leading to challenges in pet health management.

[0548] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0549] In this invention, the server includes means for collecting physiological data of animals, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device to detect abnormalities in the animal's health, means for generating an animal care plan based on the abnormalities, means for notifying the user of the generated care plan, and means for recognizing the user's emotions and adjusting the content and method of notification based on those emotions. This enables interaction that responds to the user's emotional state and provides effective animal health management.

[0550] "Animal physiological data" refers to physiological information that indicates the health status of an animal, such as its heart rate, body temperature, and activity level.

[0551] An "information processing device" is a computer system that has the function of analyzing collected data and detecting anomalies.

[0552] "Abnormal health status" refers to physiological indicators or behaviors of an animal that deviate from a normal state of health.

[0553] A "care plan" is a plan for the care and management of an animal, formulated based on abnormalities in the animal's health condition.

[0554] "Users" refers to people who care for and manage the health of animals.

[0555] "Means of recognizing emotions" refers to technologies that use cameras and voice input to analyze a user's psychological state and understand their emotions.

[0556] "Means for adjusting the content and method of notifications" refers to system functions that change the tone and delivery method of notification messages according to the user's emotional state.

[0557] The pet health management system of the present invention aims to understand the health status of animals and generate appropriate care plans by collecting and analyzing their physiological data. Furthermore, it provides personalized interaction by recognizing the user's emotional state and dynamically adjusting the content and method of notifications. Specific embodiments are described below.

[0558] First, the device collects physiological data such as heart rate, body temperature, and activity level through sensors attached to the animal. This device can utilize platforms such as Raspberry Pi or NVIDIA Jetson. The collected data is transmitted wirelessly to a central information processing unit.

[0559] The server (information processing device) runs a program developed using Python to analyze the collected physiological data. The analysis utilizes machine learning libraries such as TensorFlow and PyTorch to detect data points that deviate from the normal range as health abnormalities. Based on these abnormalities, the system automatically generates an animal care plan and notifies the user.

[0560] On the other hand, emotional data is collected from users through their cameras and microphones and analyzed using Google Cloud's Natural Language API and Microsoft Azure's Emotion Recognition API. This allows the server to understand the user's emotional state and adjust the content and presentation of notifications in the generated care plan. For example, if the system determines that the user is stressed, it will use a calmer tone for notifications and add advice to help them relax.

[0561] As a concrete example, consider a scenario where the owner is at home on a holiday, and the server detects that the pet's resting heart rate is abnormal. If the server perceives that the user is experiencing some stress, it will send a notification saying, "Thank you for your hard work. It seems your pet needs a little rest. Try giving them a gentle hug. I'm sure your pet will relax."

[0562] An example of a prompt statement is as follows:

[0563] "Check on the pet's condition and generate a comforting message for users who are experiencing stress."

[0564] "Adjust care plan notifications flexibly to match the emotions the user is feeling."

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The device collects physiological data from sensors attached to the animal. At this stage, it acquires data such as heart rate, body temperature, and activity level from the sensors. This input data represents the animal's real-time state, and this data is transmitted to the server wirelessly as output.

[0568] Step 2:

[0569] The server analyzes the received physiological data. A Python-based program applies a TensorFlow or PyTorch machine learning model to the input physiological data to detect deviations from the normal range. The output here is the result of determining whether or not an abnormality is present.

[0570] Step 3:

[0571] The server automatically generates an animal care plan based on detected anomaly data. Using the anomaly detection results as input, it creates an individual plan by fitting them to a predefined care plan template. The output is a specific action list representing the care plan.

[0572] Step 4:

[0573] Users provide emotional data through the camera and microphone of their smartphone or computer. This input is sent to the server as image and audio data.

[0574] Step 5:

[0575] The server analyzes the user's emotional data using an emotion recognition API. Specifically, it processes input data using services from Google Cloud and Microsoft Azure to determine emotional states such as stress, reassurance, and excitement. The output is the user's current emotional state.

[0576] Step 6:

[0577] The server takes the user's emotional state into consideration and adjusts the content and method of notifications accordingly. Using the emotional assessment results as input, it customizes the tone and message content of the care plan and notifies the user in an appropriate format. The output is a notification message adjusted according to the emotional state.

[0578] Step 7:

[0579] The user performs animal care based on the notification message received. Here, specific actions are taken according to the care plan notified by the server. The input is the pre-arranged notification message, and the output is the actual care action.

[0580] 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.

[0581] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0582] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0587] 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.

[0588] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0589] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0590] 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.

[0591] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0592] 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.

[0593] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0594] The 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.

[0595] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0596] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] One embodiment of this invention is a system for monitoring the health status of animals, detecting health abnormalities early, and providing appropriate care plans. This system mainly consists of a terminal part including sensors attached to animals, a server part for processing data, and a user interface part for providing information.

[0598] First, the terminal is equipped with various sensors to collect animal health data, and it collects physiological data such as heart rate, body temperature, and activity level in real time. This data is transmitted wirelessly to a central server via the internet or other networks.

[0599] Next, the server stores the received data and manages a separate data stream for each animal. The managed data is analyzed by a model using a pre-trained machine learning algorithm, and if an abnormality in health status is detected, the change is quickly identified. For example, if a sudden change in heart rate deviates from the normal activity level and an abnormality flag is set, the server processes that information immediately.

[0600] Furthermore, the server generates a care plan for the animal based on the analysis results. This care plan may include suggestions for specific dietary restrictions or recommendations for rest. The generated plan is then converted into a format that can be directly communicated to the user, providing the owner with attribute-specific information via smartphone or computer.

[0601] Finally, pet owners can receive notifications and view detailed health reports and suggested care plans through a dedicated application. Furthermore, if they have additional questions or need advice, they can receive further support through the interactive interface within the application.

[0602] For example, if the heart rate is above normal, the server generates a notification recommending "avoiding strenuous exercise and providing a safe and calm environment," encouraging the user to take appropriate action. In this way, the system provides a means to support the maintenance of animal health.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The device acquires physiological data (e.g., heart rate, body temperature, activity level) in real time from sensors attached to the animal. The acquired data is temporarily stored on the device.

[0606] Step 2:

[0607] The terminal collects data at regular intervals, packets it, and sends it to the server via the internet or a dedicated network using a secure communication protocol.

[0608] Step 3:

[0609] The server stores the received data in a database for analysis. This data is organized by animal and made accessible for subsequent processing.

[0610] Step 4:

[0611] The server uses accumulated data to run a pre-trained machine learning model and detect abnormalities in the animals' health in real time. When an abnormality is detected, a flag corresponding to that abnormality is set.

[0612] Step 5:

[0613] The server automatically generates an optimal care plan based on anomaly flags and analysis results. The care plan includes information on recommended behaviors and treatments and is customized to take into account the animal's current health condition.

[0614] Step 6:

[0615] The server converts the generated care plan into a notification format and sends it to the user's smartphone app via push notification.

[0616] Step 7:

[0617] Users receive notifications on their smartphones and can view care plan details within the app. If necessary, users can also send additional questions to the server via the app.

[0618] (Example 1)

[0619] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] There is a need to monitor animals' health in real time, detect abnormalities early, and quickly provide appropriate care plans. However, conventional systems can be time-consuming to analyze data and provide countermeasures, which hinders pet owners from responding quickly.

[0621] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0622] In this invention, the server includes means for accumulating animal physiological data and analyzing it using a generation AI model, means for generating a care plan when an abnormality in the animal's health is detected based on the analyzed physiological data, and means for notifying the user of the generated care plan via a communication terminal. This enables immediate assessment of the animal's health status and prompt and appropriate action in the event of an abnormality.

[0623] An "animal" is a living organism whose physiological data, such as heart rate and body temperature, is monitored.

[0624] "Physiological data" refers to data such as heart rate, body temperature, and activity level collected to assess the health status of animals.

[0625] "Wireless communication" is a technology that uses radio waves or other means to transmit and receive data, and is a means of transmitting information via the internet and other networks.

[0626] An "information processing device" is a computer system that manages and analyzes data, and is a device that performs processing using a generative AI model.

[0627] A "generative AI model" is an artificial intelligence model that analyzes data based on pre-trained algorithms to perform pattern recognition and anomaly detection.

[0628] A "care plan" is a plan that includes the necessary care and suggestions for an animal based on its health condition, and is a specific action plan that is generated when an abnormality is detected.

[0629] A "communication terminal" is a device used by a user to receive and confirm information, and includes smartphones and computers.

[0630] "Users" refer to animal owners or caregivers who receive notifications and take appropriate action based on the information.

[0631] To implement this invention, a device for acquiring health data is attached to an animal. Specifically, sensors that measure heart rate, body temperature, and activity level are attached to the animal, and a terminal is used to acquire data in real time. This terminal transmits the collected data to a server, which is an information processing device, via wireless communication technology such as Bluetooth or Wi-Fi.

[0632] The server properly manages physiological data sent from multiple animals and stores it in a database. During this process, the server analyzes the data using a generative AI model. This generative AI model, built with software such as Python and TensorFlow, determines whether there are any abnormalities in the animals' health by comparing them to normal health patterns.

[0633] If an anomaly is detected, the server automatically generates a care plan. This plan includes suggestions tailored to the user's needs, such as specific dietary restrictions or activity limitations. This generated care plan is promptly notified to the user via a communication terminal. The user can receive this notification and confirm specific actions through their smartphone or computer device.

[0634] Furthermore, users can use a dedicated application to view detailed health reports of their animals and ask additional questions as needed. This allows for quick and appropriate responses to any health abnormalities the animals exhibit.

[0635] As a concrete example, when a dog shows a higher-than-normal heart rate, the server generates a suggestion such as "We recommend resting in a calm environment" and notifies the user. An example of a prompt to be input to the generating AI model is, "If an abnormality is detected, what kind of care plan should be proposed?"

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The device uses sensors attached to the animal to collect physiological data such as heart rate, body temperature, and activity level in real time. This input data is transmitted wirelessly to a server via Bluetooth or Wi-Fi. Specifically, this involves the sensors continuously monitoring the animal's condition and updating the data every second.

[0639] Step 2:

[0640] The server receives physiological data transmitted from the terminal and stores it in a database based on the individual animal's information. The server then uses generative AI models, such as Python or TensorFlow, to analyze this received data. This analysis process compares the current physiological data (the input) with the stored normal pattern data, and if an anomaly is detected, the result is output as a flag. The server checks and analyzes new data every minute.

[0641] Step 3:

[0642] When the server detects an anomaly based on the analysis results, it uses a generated AI model to create a care plan. Based on the analysis flags, a specific plan is generated, such as "Rest is recommended due to high heart rate." This generated care plan is then converted into a notification format and output.

[0643] Step 4:

[0644] The server sends the care plan, converted into a notification format, to the communication terminal, providing the user with warnings and suggested actions. The process involves the server sending a push notification to the user's smartphone or computer, displaying the message within seconds.

[0645] Step 5:

[0646] Based on the received notification, the user opens a dedicated application to view a detailed health report. This action allows them to easily understand the animal's current health status and the proposed care plan. Furthermore, if necessary, they can enter prompts such as "How should I respond?" within the application to receive additional advice. In terms of operation, touching the notification instantly launches the app and displays the information.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] The challenge lies in monitoring the health status of the elderly and sick patients in real time, enabling swift and accurate responses when abnormalities occur. Furthermore, making it easy for users to ask additional questions or make requests is another crucial issue that needs to be addressed.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] In this invention, the server includes means for analyzing biometric information to detect abnormalities in health status, means for generating individualized care plans based on the detected abnormalities, and means for monitoring changes in biometric information in real time. This enables continuous monitoring of the health status of elderly people and patients, prompt notification in the event of an abnormality, and the presentation of appropriate care plans.

[0652] "Biometric information" refers to information such as heart rate, body temperature, and activity level that indicates the health status of an organism.

[0653] An "information processing device" is a device that analyzes collected biological information, detects abnormalities, and provides necessary information.

[0654] An "abnormality" refers to a state that deviates from normal health conditions or behavioral patterns, and indicates a health condition that requires prompt attention.

[0655] A "care plan" is a plan that recommends specific measures or actions based on the abnormalities that have been detected.

[0656] "User" refers to an individual or organization that receives health information through this system and is responsible for taking appropriate action.

[0657] "Real-time" refers to the ability to monitor the status almost instantaneously and respond immediately to changes.

[0658] A "notification" is information sent to a user when an anomaly is detected, and it is a means of prompting a quick response.

[0659] As a form of implementing this invention, a server is first used as the information processing device. The server runs on a cloud platform and functions as infrastructure for data communication, storage, and analysis. The server implements a program that receives biometric information and analyzes it in real time. This analysis is performed using data analysis libraries such as Pandas and Scikit-learn, using Python or R. As a result, the server detects anomalies and generates a care plan based on those anomalies.

[0660] Next, wearable devices are used as terminals. These terminals are equipped with sensors to monitor biometric information such as heart rate and body temperature. These sensors use a small computer such as a Raspberry Pi to acquire biometric information, and then transmit the data to a server via Bluetooth or Wi-Fi.

[0661] Furthermore, users receive notifications and care plans sent from the server via a dedicated smartphone application. This application is developed using React Native and Flutter, providing an intuitive user interface. When an anomaly is detected, users can receive notifications immediately.

[0662] For example, if an elderly person's heart rate exceeds a predetermined threshold, the server generates a notification such as, "Your heart rate has reached an abnormal level. We recommend prompt attention," and sends it to the user's smartphone. This allows the user to take appropriate action.

[0663] Furthermore, by utilizing a generative AI model, prompt messages can be easily customized. For example, by entering a prompt message such as, "Create a plan to monitor the health information of elderly individuals and notify them in case of abnormalities," the server will perform appropriate analysis and generate a corresponding care plan.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The device collects biometric information from sensors. Inputs include heart rate and body temperature data. This data is processed on a small computer and converted into a digital format. This data is then transmitted to a server via Bluetooth or Wi-Fi.

[0667] Step 2:

[0668] The server temporarily stores biometric information received from the terminal in storage. The input is biometric information from the terminal, and the output is the stored dataset. Data cleaning is performed to prepare this dataset for analysis.

[0669] Step 3:

[0670] The server performs analysis using a machine learning model based on the formatted data. The input is a cleaned dataset, and the output is a health status assessment result. For data processing, an anomaly detection algorithm is executed using the Python Scikit-learn library.

[0671] Step 4:

[0672] The server generates a care plan if it detects an anomaly from the analysis results. The input is the health status assessment result, and the output is a specific care plan. Based on the generated AI model, the care content is designed based on the prompt messages.

[0673] Step 5:

[0674] The server notifies the user of the generated care plan. The input is the care plan, and the output is a push notification to the smartphone. The user receives the notification from the server in real time and is prompted to take the necessary action.

[0675] Step 6:

[0676] Users receive notifications and view detailed information through a smartphone application. Input is the notification from the server, and output is the user's action. Users can review care plans and provide additional instructions within the application.

[0677] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0678] This invention provides an advanced pet health management system that, in addition to conventional systems for collecting and analyzing animal health and behavioral data, also takes into account the user's emotions. Specifically, it is designed to improve the overall user experience by incorporating an emotion engine that recognizes the user's emotions and enables appropriate interaction based on those emotions into conventional technology that monitors the animal's health status and provides a corresponding care plan.

[0679] First, the device collects health data from the animals. This involves using sensors attached to the animals to obtain physiological data such as heart rate, body temperature, and activity level. This data is sent to and received by the server at regular intervals.

[0680] Next, the server analyzes the received data to detect abnormalities in the animal's health. A machine learning model is used, and deviations from the normal state are flagged as abnormal. Based on these results, a customized care plan for the animal is automatically generated.

[0681] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes the user's emotions. When a user accesses data through the system, emotion data is collected and analyzed through the camera of a smartphone or PC, voice input, etc. The emotion engine recognizes the user's emotional state, such as stress, relief, excitement, etc., and adjusts the content and method of notifications according to these emotions.

[0682] For example, if the server detects that a user is experiencing stress, it may deliver system notifications in a more calming tone or include additional reassuring information. Conversely, for users who are feeling reassured, it can provide normal notifications and recommend actions.

[0683] In this way, the present invention realizes personalized interactions for individual users through a system that combines animal health management information with the user's emotional state. This not only makes animal care more efficient and effective, but also provides a mechanism that is more familiar and supportive to the user.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The device uses sensors attached to the animal to collect health data such as heart rate, body temperature, and activity level in real time. The collected data is compiled into batches at regular intervals, preparing it for the next step.

[0687] Step 2:

[0688] The terminal transmits the prepared data wirelessly over the internet via a security protocol to the server. It is crucial that the data transmission is secure and efficient.

[0689] Step 3:

[0690] The server stores the received data in a database in the central processing system. In the database, the data is organized into animal profiles, making it easily accessible for subsequent analysis.

[0691] Step 4:

[0692] The server executes machine learning algorithms based on accumulated data to analyze anomalies and patterns related to the animals' health. If an anomaly is detected, an anomaly flag is set, and it is treated as an early warning.

[0693] Step 5:

[0694] The server generates an appropriate care plan for the animal based on the analysis results. The generated care plan includes specific instructions to optimize the animal's health, such as exercise restrictions and special diet plans tailored to the animal's condition on that day.

[0695] Step 6:

[0696] The server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes facial expression data and voice data obtained from smartphone or PC cameras to identify the user's mental state.

[0697] Step 7:

[0698] The server adjusts notification content and communication style based on the user's emotional state. For example, it might provide details in a calm tone or include additional information to reassure users who are feeling stressed.

[0699] Step 8:

[0700] Users receive tailored care plans and notifications, and review the content through a smartphone app. They can submit feedback and request additional support within the app as needed. This feedback is used by the system to improve future care plans.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] Conventional animal health management systems can monitor an animal's health and propose care plans, but they lack personalized responses that take into account the user's emotional state. Such systems have problems in reducing the user's mental burden and responding appropriately to emotional changes such as stress.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for analyzing animal physiological data to detect abnormalities in health, means for acquiring user emotional information, and means for adjusting notification content based on the acquired emotional information. This enables not only the provision of animal health management information but also personalized interaction that takes into account the user's emotional state.

[0706] "Animal physiological data" refers to data that indicates the internal state of an animal's body, such as heart rate, body temperature, and activity level.

[0707] An "information processing device" is a central computer device that analyzes received data and performs anomaly detection and generates care plans.

[0708] "Means for detecting abnormalities in health status" refer to devices or methods that have the function of identifying deviations from the normal state by analyzing physiological data.

[0709] A "care plan" is a plan that outlines specific care and treatment options based on the animal's health condition.

[0710] "User emotional information" refers to data that indicates the user's emotional state, such as stress levels and feelings of security, which is analyzed from information such as audio and images.

[0711] "Means for adjusting notification content" refers to devices or methods that have the function of changing the expression and tone of messages and alerts from the system according to the user's emotional state.

[0712] A "tailored notification" is a message whose content has been processed based on the user's emotional state in order to optimize the interaction with the user.

[0713] This system comprehensively monitors the animal's health and the user's emotional state, providing individually personalized interactions. This section describes the specific implementation method of the system.

[0714] The device collects physiological data from animals using sensors attached to them (e.g., heart rate monitor, temperature sensor, accelerometer). This physiological data includes heart rate, body temperature, and activity level, and is collected at regular intervals. The data is then encrypted and transmitted to the server via Bluetooth or Wi-Fi. The SSL / TLS protocol is used for communication to ensure secure data transmission.

[0715] The server analyzes the received physiological data. A machine learning model is employed to perform anomaly detection, identifying deviations from normal conditions. If an anomaly is detected, a personalized care plan is automatically generated for the animal based on its specific characteristics. The generative AI model suggests appropriate care methods for the animal's health condition. This process enables animal caregivers to take quick and effective action.

[0716] Next, data necessary to understand the user's emotional state is collected. When a user accesses the system, emotional data is collected using the camera on their smartphone or PC, as well as the voice input function. This information is analyzed through facial recognition technology and voice analysis algorithms to identify emotional states such as stress, reassurance, and excitement.

[0717] The server adjusts notification content based on analyzed sentiment data. For example, when a user is stressed, the notification can be made gentler and include information that enhances their sense of security. For users who feel secure, information can be provided in the usual notification format, and the next action can be recommended.

[0718] As a concrete example, consider the prompt message: "If the dog's heart rate is above normal, generate a notification message indicating that the user is stressed." This allows the system to optimize the animal's healthcare information according to the user's emotions, providing personalized care.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The device collects physiological data from sensors attached to the animal. The inputs here are the animal's heart rate, body temperature, and activity level, as captured by the sensors. This data is recorded at regular intervals and temporarily stored as hourly data to obtain output. This data, measured in real-time by the sensors, serves as fundamental data for understanding the animal's health status.

[0722] Step 2:

[0723] The terminal encrypts the collected physiological data and sends it to the server. The input is the animal's physiological data recorded in the previous step. This data is encrypted using the SSL / TLS protocol and transmitted via a secure and high-speed communication method (Bluetooth or Wi-Fi) to achieve output. This step aims to ensure the reliability and security of the data.

[0724] Step 3:

[0725] The server analyzes the received physiological data. The input is encrypted physiological data received from the terminal. Using a machine learning model, the server analyzes the data and determines abnormalities by detecting patterns that are different from the norm. The output generates whether or not there is an abnormality and details thereof, providing basic information for monitoring the animal's health status.

[0726] Step 4:

[0727] The server generates a customized care plan based on the detected anomalies. Here, the anomaly information obtained in step 3 is used as input. Using the generated AI model, the optimal care methods (such as diet, exercise, and diagnostic suggestions) corresponding to the animal's health condition are formulated. This results in output that instructs the user on specific intervention methods.

[0728] Step 5:

[0729] Users access care plans and animal health data via their smartphones or PCs. During this process, the user's device collects emotional information through its camera and microphone. The user's facial expressions and voice are used as input, and based on this, emotional analysis is performed to output the user's emotional state (stress level, sense of security, etc.).

[0730] Step 6:

[0731] The server analyzes emotional data and adjusts the notification content according to the user's emotional state. In this step, the emotional state obtained in step 5 is used as input. The emotion engine analyzes this and generates an adjusted output, such as changing the notification to a calmer one if the user is stressed.

[0732] Step 7:

[0733] The server sends a tailored notification to the user. Here, the notification content generated in step 6 is entered and sent to the user's device via push notification or email. This allows the user to understand the animal's care situation and receive emotionally appropriate support.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In modern society, many households keep pets, and properly managing their health is crucial. However, conventional health management systems rely solely on the animal's physiological data and do not consider the user's psychological state or emotions. As a result, it is difficult to provide flexible interactions that respond to the user's emotions or to present appropriate care plans, leading to challenges in pet health management.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0738] In this invention, the server includes means for collecting physiological data of animals, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device to detect abnormalities in the animal's health, means for generating an animal care plan based on the abnormalities, means for notifying the user of the generated care plan, and means for recognizing the user's emotions and adjusting the content and method of notification based on those emotions. This enables interaction that responds to the user's emotional state and provides effective animal health management.

[0739] "Animal physiological data" refers to physiological information that indicates the health status of an animal, such as its heart rate, body temperature, and activity level.

[0740] An "information processing device" is a computer system that has the function of analyzing collected data and detecting anomalies.

[0741] "Abnormal health status" refers to physiological indicators or behaviors of an animal that deviate from a normal state of health.

[0742] A "care plan" is a plan for the care and management of an animal, formulated based on abnormalities in the animal's health condition.

[0743] "Users" refers to people who care for and manage the health of animals.

[0744] "Means of recognizing emotions" refers to technologies that use cameras and voice input to analyze a user's psychological state and understand their emotions.

[0745] "Means for adjusting the content and method of notifications" refers to system functions that change the tone and delivery method of notification messages according to the user's emotional state.

[0746] The pet health management system of the present invention aims to understand the health status of animals and generate appropriate care plans by collecting and analyzing their physiological data. Furthermore, it provides personalized interaction by recognizing the user's emotional state and dynamically adjusting the content and method of notifications. Specific embodiments are described below.

[0747] First, the device collects physiological data such as heart rate, body temperature, and activity level through sensors attached to the animal. This device can utilize platforms such as Raspberry Pi or NVIDIA Jetson. The collected data is transmitted wirelessly to a central information processing unit.

[0748] The server (information processing device) runs a program developed using Python to analyze the collected physiological data. The analysis utilizes machine learning libraries such as TensorFlow and PyTorch to detect data points that deviate from the normal range as health abnormalities. Based on these abnormalities, the system automatically generates an animal care plan and notifies the user.

[0749] On the other hand, emotional data is collected from users through their cameras and microphones and analyzed using Google Cloud's Natural Language API and Microsoft Azure's Emotion Recognition API. This allows the server to understand the user's emotional state and adjust the content and presentation of notifications in the generated care plan. For example, if the system determines that the user is stressed, it will use a calmer tone for notifications and add advice to help them relax.

[0750] As a concrete example, consider a scenario where the owner is at home on a holiday, and the server detects that the pet's resting heart rate is abnormal. If the server perceives that the user is experiencing some stress, it will send a notification saying, "Thank you for your hard work. It seems your pet needs a little rest. Try giving them a gentle hug. I'm sure your pet will relax."

[0751] An example of a prompt statement is as follows:

[0752] "Check on the pet's condition and generate a comforting message for users who are experiencing stress."

[0753] "Adjust care plan notifications flexibly to match the emotions the user is feeling."

[0754] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0755] Step 1:

[0756] The device collects physiological data from sensors attached to the animal. At this stage, it acquires data such as heart rate, body temperature, and activity level from the sensors. This input data represents the animal's real-time state, and this data is transmitted to the server wirelessly as output.

[0757] Step 2:

[0758] The server analyzes the received physiological data. A Python-based program applies a TensorFlow or PyTorch machine learning model to the input physiological data to detect deviations from the normal range. The output here is the result of determining whether or not an abnormality is present.

[0759] Step 3:

[0760] The server automatically generates an animal care plan based on detected anomaly data. Using the anomaly detection results as input, it creates an individual plan by fitting them to a predefined care plan template. The output is a specific action list representing the care plan.

[0761] Step 4:

[0762] Users provide emotional data through the camera and microphone of their smartphone or computer. This input is sent to the server as image and audio data.

[0763] Step 5:

[0764] The server analyzes the user's emotional data using an emotion recognition API. Specifically, it processes input data using services from Google Cloud and Microsoft Azure to determine emotional states such as stress, reassurance, and excitement. The output is the user's current emotional state.

[0765] Step 6:

[0766] The server takes the user's emotional state into consideration and adjusts the content and method of notifications accordingly. Using the emotional assessment results as input, it customizes the tone and message content of the care plan and notifies the user in an appropriate format. The output is a notification message adjusted according to the emotional state.

[0767] Step 7:

[0768] The user performs animal care based on the notification message received. Here, specific actions are taken according to the care plan notified by the server. The input is the pre-arranged notification message, and the output is the actual care action.

[0769] 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.

[0770] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0771] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0772] 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.

[0773] Figure 9 shows an 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.

[0774] 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.

[0775] 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.

[0776] 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, motorcycles, etc., 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, for example, based 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.

[0777] 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."

[0778] 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.

[0779] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0780] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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 the like 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.

[0789] 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.

[0790] The following is further disclosed regarding the embodiments described above.

[0791] (Claim 1)

[0792] Means for collecting animal health data,

[0793] A means for transmitting the collected health data to a central processing unit,

[0794] In the central processing unit, means for analyzing the health data to detect abnormalities in the health status,

[0795] A means for generating an animal care plan based on detected abnormalities,

[0796] A system that includes a means of notifying the user of the generated care plan.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising means for identifying patterns of abnormal behavior based on animal behavioral data.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising means for responding to additional questions or requests from the user.

[0801] "Example 1"

[0802] (Claim 1)

[0803] Means for collecting animal physiological data,

[0804] A means for transmitting collected physiological data to an information processing device via wireless communication,

[0805] In an information processing device, a means for accumulating physiological data and analyzing it using a generated AI model,

[0806] A means for generating a care plan when an abnormality in health status is detected based on analyzed physiological data,

[0807] A system that includes means for notifying users of the generated care plan via a communication terminal.

[0808] (Claim 2)

[0809] The system according to claim 1, further comprising means for identifying abnormal patterns based on animal activity data.

[0810] (Claim 3)

[0811] The system according to claim 1, further comprising a function to respond to additional inquiries or requests from users.

[0812] "Application Example 1"

[0813] (Claim 1)

[0814] Means for collecting biometric information,

[0815] A means for transmitting collected biological information to an information processing device,

[0816] An information processing device includes means for analyzing the biological information to detect abnormalities in health status,

[0817] Means for generating individual care plans based on detected abnormalities,

[0818] A means of notifying the user of the generated care plan,

[0819] A means of monitoring changes in biological information in real time,

[0820] A system that includes means for quickly sending notifications when an anomaly is detected.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means based on behavioral information for identifying patterns of abnormal behavior.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising interactive means for responding to additional questions or requests from the user.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] Means for collecting animal physiological data,

[0828] A means for transmitting the collected physiological data to an information processing device,

[0829] An information processing device includes means for analyzing the physiological data to detect abnormalities in health status,

[0830] Means for generating an animal care plan based on detected abnormalities,

[0831] Means for obtaining user emotional information,

[0832] A means of adjusting notification content based on acquired emotional information,

[0833] A system that includes means for providing users with tailored notifications.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for identifying patterns of abnormal behavior based on animal behavior data.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising means for responding to additional questions or requests from the user.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] Means for collecting physiological data from animals,

[0841] A means for transmitting the collected data to an information processing device,

[0842] An information processing device includes means for analyzing the data to detect abnormalities in health status,

[0843] Means for generating an animal care plan based on abnormalities,

[0844] A means of notifying the user of the generated care plan,

[0845] A system that recognizes the user's emotions and includes means to adjust the content and method of notifications based on those emotions.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for identifying patterns of abnormal behavior based on animal behavioral information.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for responding to additional requests or instructions from the user. [Explanation of Symbols]

[0850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting biometric information, A means for transmitting collected biological information to an information processing device, An information processing device includes means for analyzing the biological information to detect abnormalities in health status, Means for generating individual care plans based on detected abnormalities, A means of notifying the user of the generated care plan, A means of monitoring changes in biological information in real time, A system that includes means for quickly sending notifications when an anomaly is detected.

2. The system according to claim 1, further comprising means based on behavioral information for identifying patterns of abnormal behavior.

3. The system according to claim 1, further comprising interactive means for responding to additional questions or requests from the user.

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