System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining biometric data of an animal; means for transmitting the obtained biometric data to a server; means for analyzing the biometric data to determine a current health condition of the animal; means for transmitting data from the audio input means to the server; means for analyzing the audio data and generating a response suitable for the condition of the animal; and means for converting the generated response to audio data and communicating the response to the animal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a problem that communication between humans and animals (specifically cats) is difficult. There is also the issue that it is difficult for information about the health management of cats to be conveyed to owners. This makes it difficult for owners to grasp the health status of their cats in real time and respond appropriately. Furthermore, there is a lack of means to detect abnormalities in an animal's health at an early stage and take appropriate action. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. A means for acquiring biometric data of an animal is provided, and this data is sent to a server, which then analyzes the biometric data and determines the animal's current health condition. A voice input means is also provided, and voice data when the user speaks to the animal is sent to the server, and the server analyzes the voice data and generates a response appropriate to the animal's condition. The generated response is converted into voice data and transmitted to the animal. The system also has a function for promptly notifying the user if an abnormal value in the biometric data is detected. Furthermore, a means is provided for storing the biometric data analyzed by the server in a database, making it a system that allows data to be accumulated and analyzed over a long period of time.

[0006] "Animal biometric data" refers to quantitative data that indicates the animal's health status, such as heart rate, body temperature, and number of steps taken.

[0007] "Means for acquiring" refers to methods and equipment for collecting biological data of animals using sensors, monitoring devices, etc.

[0008] A "server" is a computer system that provides computing resources for storing, analyzing, and processing data over a network.

[0009] "Analyzing" is the act of analyzing acquired data and extracting specific information or patterns.

[0010] "Assessing health status" refers to the process of evaluating an animal's health status and the presence or absence of abnormalities based on analyzed biological data.

[0011] "Voice input means" refers to equipment for collecting the user's voice using a microphone, voice recognition software, or the like.

[0012] "Generating a response" means using natural language processing or generative AI to create an appropriate reply to a user's voice input.

[0013] "Converting to voice data" refers to the process of converting the generated text response into voice data and conveying it to the user.

[0014] "Means of notification" refers to a method for sending an alert to a user's smartphone or other device when an abnormal value or specific event is detected.

[0015] "Storing in a database" refers to the act of recording analyzed biometric data in a database in order to centrally manage and store it for a long period of time. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] System Overview

[0039] This system acquires biological data from animals (specifically cats) and uses that data to support communication between the user and the animal. It is also possible to manage the cat's health. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[0040] System program and processing flow

[0041] Data collection and transmission

[0042] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in a buffer in real time and sent to a server at regular intervals.

[0043] Data analysis and health status assessment

[0044] The server analyzes the received vital data and evaluates the cat's current health status. Various analysis modules (e.g., heart rate monitoring, temperature evaluation, etc.) are used for the analysis. The analysis results are stored in a database and can be referenced later.

[0045] User interaction interface

[0046] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[0047] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[0048] Health Management and Notifications

[0049] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[0050] Specific examples

[0051] Example 1: Conversation with a cat

[0052] The user speaks to the smartphone app, asking, "How are you feeling today?"

[0053] The device collects this voice data and transmits it to the server in real time.

[0054] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "How are you feeling today?" and compares it with the cat's heart rate, body temperature, and step count data.

[0055] It verifies that the animal is in good condition and generates the response "I'm feeling good today. I've had lots of fun."

[0056] The generation AI converts the response into voice data and sends it to the device.

[0057] The device plays the audio, allowing the user to hear the cat's "reply."

[0058] Example 2: Health alert notification

[0059] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[0060] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0061] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0062] If necessary, the user can immediately consult a veterinarian.

[0063] This system allows users to deepen their relationship with their cat while efficiently managing their health.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[0067] Step 2:

[0068] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[0069] Step 3:

[0070] The server receives the biometric data sent from the device and immediately stores it in a database.

[0071] Step 4:

[0072] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[0073] Step 5:

[0074] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[0075] Step 6:

[0076] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[0077] Step 7:

[0078] The terminal transmits the recorded voice data to the server in real time.

[0079] Step 8:

[0080] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[0081] Step 9:

[0082] The server analyzes the converted text data and understands the user's question, for example, "How are you today?"

[0083] Step 10:

[0084] The server compares the analysis results with previously collected and analyzed biometric data and generates an appropriate response based on the animal's condition.

[0085] Step 11:

[0086] The server converts the generated response into audio data.

[0087] Step 12:

[0088] The server transmits the converted voice data to the terminal.

[0089] Step 13:

[0090] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[0091] Step 14:

[0092] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[0093] Step 15:

[0094] The server generates an alert message and sends it to the user's smartphone app.

[0095] Step 16:

[0096] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[0097] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Conventional animal health management systems not only collect biometric data, but also make it difficult for users to check the health status of animals while interacting with them. Furthermore, they lack the functionality to notify users in real time when an abnormality occurs, making it difficult to take early action. This has created a need for a means to properly monitor the health status of animals and take prompt action.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for using the generative AI model, a means for managing an API endpoint for receiving data in real time, and a means for storing and referencing the health status evaluation results in a database, allowing users to efficiently monitor the health status of their animals while enjoying interactions with them and receive prompt notification when abnormalities are detected.

[0103] "Biometric data" refers to numerical information related to an animal's body, such as its heart rate, body temperature, and number of steps taken.

[0104] A "server" is a computer system that analyzes and stores acquired data and manages communications with users and terminals.

[0105] A "generative AI model" is an algorithm that generates appropriate responses based on collected data and voice information.

[0106] A "terminal" is a device that is attached to an animal and has the function of acquiring biometric data and transmitting it to a server.

[0107] "Audio input means" is a device or function that collects audio that the user uses to talk to the animal.

[0108] An "API endpoint" is a connection point through which other systems or devices can exchange data with a server.

[0109] A "database" is a system that systematically stores information such as analysis results and biological data, and makes it available for reference as needed.

[0110] "Real-time" refers to an environment or process that operates instantly with almost no delay.

[0111] An "abnormal value" is a data value that deviates from the normal range, and is particularly a value that may affect health status.

[0112] "Notification" is a message that warns or informs the user when an abnormality is detected.

[0113] This invention is a system that acquires biometric data from animals (specifically cats) and supports communication between users and animals based on that data. It is also possible to manage the cat's health. The system consists of a device attached to the animal, a server, and a smartphone app used by the user.

[0114] Data collection

[0115] The terminal is a device attached to the cat's collar and uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in an internal buffer in real time and periodically transmitted to a server using a wireless communication module.

[0116] Data reception and storage

[0117] The server manages an API endpoint for receiving biometric data sent from the device in real time. The server checks the received data to ensure there is no invalid data. The verified data is stored in a database. Using a database management system (DBMS), this data can be efficiently stored and referenced.

[0118] Data analysis and health status assessment

[0119] The server uses a heart rate monitoring module and a temperature evaluation module to analyze the stored vital data, thereby assessing the cat's current health status. The analysis results are stored in a database for users to refer to later.

[0120] User interaction interface

[0121] Users can talk to the cat using a smartphone app. To do this, voice data is collected using a voice input means and sent to a server. The server then converts the received voice data into text using a natural language processing module. The converted text is analyzed by a generative AI model to generate a response appropriate to the situation. The generated response is then converted back into voice data and sent to the device. The device then plays this voice data back to the cat, allowing the user to hear the cat's "reply."

[0122] Health Management and Notifications

[0123] The server constantly monitors the vital signs and immediately notifies the user if any abnormal values ​​are detected. The notification is sent via a smartphone app, and the type of abnormal value and detailed information are provided, allowing the user to take prompt action.

[0124] Specific examples

[0125] Conversation with a cat

[0126] 1. The user speaks to the smartphone app, "How are you feeling today?"

[0127] 2. The device collects voice data and transmits it to the server in real time.

[0128] 3. The server converts the voice data into text using a natural language processing module.

[0129] 4. The generative AI model compares the cat's heart rate, body temperature, and step count data and generates a response such as, "I'm feeling good today. I've played a lot."

[0130] 5. The server converts the response into audio data and sends it to the device.

[0131] 6. The device plays the sound to the cat, and the user can hear it.

[0132] Health Alert Notifications

[0133] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[0134] 2. The server immediately identifies abnormal values ​​and creates an alert.

[0135] 3. The server sends the alert to the user's smartphone app.

[0136] 4. The user receives a notification and opens the app to see more information, such as a sudden rise in temperature from 38.5°C to 40°C.

[0137] 5. Users may consult a veterinarian if necessary.

[0138] This allows users to efficiently manage their health while enjoying interaction with their cat.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] System program processing flow

[0141] Step 1: Data collection

[0142] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps. The input is raw data from the sensor, and the output is formatted biological data stored in an internal buffer. Specifically, the sensor measures the cat's heart rate every second and stores the data as a byte sequence.

[0143] Step 2: Send data

[0144] The device periodically transmits data accumulated in its internal buffer to the server in batches. The input is the formatted biometric data in the buffer, and the output is the data packets transmitted to the server. Transmission is triggered when the number of accumulated data reaches 100.

[0145] Step 3: Receiving and storing data

[0146] The server receives data sent from the device in real time. The input is the data packet sent from the device, and the output is the formatted data to be stored in the database. The server's API endpoint receives the data, and the data is stored by the database management system (DBMS).

[0147] Step 4: Data analysis

[0148] The server passes the stored data to the analysis module to evaluate the health status. The input is the biometric data stored in the database, and the output is the analysis result (e.g., good health, outside normal range, etc.). Specifically, the heart rate monitoring module extracts data from the past hour and calculates the average heart rate.

[0149] Step 5: User interaction interface

[0150] A user talks to a cat using a smartphone app. The input is the user's voice data, and the output is the voice data sent to the server. The voice data is collected and sent to the server in real time.

[0151] Step 6: Analyze the audio data

[0152] The server analyzes the voice data through a natural language processing module. The input is the user's voice data, and the output is the analysis result in text format. For example, the voice data "How are you today?" is converted into text.

[0153] Step 7: Response Generation

[0154] The generative AI model generates a response based on the analyzed text. The input is the analysis result of the natural language processing module, and the output is the generated response text. The generative AI model compares the cat's heart rate, body temperature, and step count data to generate an appropriate response.

[0155] Step 8: Sending a Response

[0156] The server converts the generated response into audio data and sends it to the device. The input is the text response output by the generative AI model, and the output is audio data. A text-to-speech engine is used to convert the text into audio.

[0157] Step 9: Playing Audio

[0158] The device plays the audio data sent from the server and communicates the response to the cat. The input is the audio data sent from the server, and the output is the audio to be played. The audio is played using the device's speaker.

[0159] Step 10: Anomaly detection and notification

[0160] The server constantly monitors the received biometric data and immediately sends a notification to the user if an abnormal value is detected. The input is the biometric data stored on the server, and the output is a notification sent to a smartphone app. For example, a notification is triggered when the body temperature exceeds 40 degrees.

[0161] This allows the user to efficiently monitor the cat's health throughout all steps and respond quickly if any abnormalities occur.

[0162] (Application example 1)

[0163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0164] Conventional pet monitoring systems have had the problem of making it difficult to monitor the health status of pets while their owners are out, or to detect abnormalities in the home at an early stage. The present invention aims to solve these problems and provide a system that enables real-time monitoring of pet health status and prompt notification of abnormalities.

[0165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0166] In this invention, the server includes means for acquiring biometric data of the animal, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the current health condition of the animal, voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the state of the animal, means for converting the generated response into voice data and transmitting the response to the animal, and means for detecting abnormalities using the acquired biometric data and sending an alert to the user if an abnormality is found. This allows owners to keep track of their pet's health condition and the situation at home in real time even when they are out, and to respond quickly when an abnormality occurs.

[0167] "Animal biometric data" refers to data that indicates the animal's physical condition, such as heart rate, body temperature, and number of steps taken.

[0168] "Means of acquisition" refers to the method of collecting biological data of animals using sensors or devices.

[0169] The "means for transmitting to the server" refers to a method for transferring the collected biometric data to the server via wireless or wired communication.

[0170] A "server" is a computer system that stores and analyzes collected biometric data.

[0171] "Means of analysis" refers to methods of assessing the health status of animals using algorithms and software that process and analyze biological data.

[0172] The "voice input means for talking to animals" refers to a microphone or voice recognition device that allows the user to input a voice message to the animals.

[0173] The "means for transmitting data from the voice input means to the server" is a communication means for transferring input voice data to the server.

[0174] "Means for analyzing voice data" refers to natural language processing technology that converts input voice data into text data and understands the content.

[0175] The "means for generating a response appropriate to the animal's condition" is an AI model that generates an appropriate response according to the animal's condition based on analyzed voice data.

[0176] The "means for converting the generated response into voice data" is a voice synthesis technology that converts the generated text-format response into voice.

[0177] The "means for transmitting a response" refers to a terminal or speaker that allows the animal to hear the generated audio data.

[0178] The "means for detecting anomalies" is an algorithm for identifying abnormal data that exceeds a certain threshold based on the collected biometric data.

[0179] "Means for sending an alert to a user" refers to a system or technology for sending a notification to a user when an abnormality is detected.

[0180] System Overview

[0181] The system of the present invention acquires the animal's biological data, supports communication between the user and the animal based on that data, monitors the animal's health, and sends an alert to the user if an abnormality occurs. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[0182] Data collection and transmission

[0183] The device is equipped with sensors that measure biometric data such as heart rate, body temperature, and number of steps taken. The device collects this data in real time and transmits it to a server at regular intervals. Wireless communication technology (such as Bluetooth or Wi-Fi) is used for transmission.

[0184] Data analysis and health status assessment

[0185] The server analyzes the acquired vital data and evaluates the animal's current health condition. Analysis tools such as a heart rate monitoring module and a body temperature evaluation module are used for the analysis. The server stores the vital data in a database, and if abnormal data is detected, it sends an alert to the user. The alert is sent via push notification or email.

[0186] User interaction interface

[0187] Users can talk to animals using a smartphone app. The user's voice data is sent to the server via a voice input method (such as a smartphone microphone). The server then converts the voice data into text through a natural language processing module and analyzes what the user said. A generative AI model generates a response appropriate to the animal's condition, which is then converted into voice data and transmitted to the animal. This allows users to enjoy two-way communication with the animals.

[0188] Health Management and Notifications

[0189] The server constantly monitors the received vital signs and immediately sends an alert to the user if an abnormal value is detected. The notification is sent via a smartphone app, and the type of abnormality and detailed information are provided. This allows the user to quickly understand the animal's health condition and, if necessary, consult a veterinarian immediately.

[0190] Example

[0191] (Example 1: Home pet monitoring system)

[0192] This system allows users to detect abnormalities in their cat's heart rate or body temperature while they are out. For example, if a cat's heart rate suddenly increases while they are out, the server immediately detects the abnormality and sends an alert to the user's smartphone. This allows the user to quickly check the situation and take any necessary measures.

[0193] (Example of a prompt for a generative AI model)

[0194] I would like to build a system that detects abnormalities in the home using biological data from cats and sends alerts to users who are out. Specifically, how can I issue an alert based on abnormal behavior such as a sudden rise in heart rate or body temperature?

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] Collection of animal biometric data

[0198] The device uses sensors to collect biometric data such as heart rate, body temperature, and number of steps in real time.

[0199] Input: Biological data collected by sensors.

[0200] Output: The acquired biometric data is stored in a buffer as data packets.

[0201] Step 2:

[0202] Sending biometric data to the server

[0203] The device transmits the collected biometric data to a server at regular intervals (for example, every minute). Wireless technologies such as Bluetooth and Wi-Fi are used for communication.

[0204] Input: Buffered biometric data.

[0205] Data processing: Assembling multiple data points in a buffer into a data packet.

[0206] Output: Data packets sent to the server.

[0207] Step 3:

[0208] Biological data analysis

[0209] The server analyzes the received biometric data, using modules such as a heart rate monitoring module and a body temperature evaluation module.

[0210] Input: The data packet sent to the server.

[0211] Data calculations: Each data point is evaluated to see if it is within normal limits. Any outliers are flagged.

[0212] Output: Analysis results showing health status and abnormality flags.

[0213] Step 4:

[0214] Detecting outliers and sending alerts

[0215] Based on the analysis results, the server sends a notification to the user if any abnormal values ​​are detected.

[0216] Input: Analysis results and anomaly flags.

[0217] Data processing: If an abnormality flag is raised, an alarm message is generated.

[0218] Output: An alert notification sent to the user's smartphone.

[0219] Step 5:

[0220] User interaction

[0221] The user talks to the animals through a smartphone app, and the user's voice is picked up by a voice input means and sent to the server.

[0222] Input: User's voice data.

[0223] Output: The audio data sent to the server.

[0224] Step 6:

[0225] Voice data analysis and response generation

[0226] The server runs the user's voice data through a natural language processing module, converts it into text, and then uses a generative AI model to generate a response appropriate to the animal's condition.

[0227] Input: The audio data sent to the server.

[0228] Data Calculation: Converts voice data into text, analyzes the user's intent, and generates an appropriate response based on the analysis results.

[0229] Output: The response in text format.

[0230] Step 7:

[0231] Response communication

[0232] The server converts the generated response into audio data and sends it to the terminal, which plays the audio data to the animal.

[0233] Input: A text response.

[0234] Data processing: Converting text data into audio data.

[0235] Output: The audio data sent to the device.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] MODE FOR CARRYING OUT THE INVENTION

[0238] System Overview

[0239] This system acquires biometric data from animals (specifically cats) and uses that data to support communication between the user and the animal. It also manages the cat's health. Furthermore, it uses an emotion engine to recognize the user's emotions based on the user's voice input data and generates responses based on those emotions, enabling more natural and emotional communication. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[0240] System program and processing flow

[0241] Data collection and transmission

[0242] The device uses sensors to capture the cat's heart rate, body temperature, and number of steps taken. This biological data is stored in a buffer in real time and then sent to a server at regular intervals.

[0243] Data analysis and health status assessment

[0244] The server analyzes the received biometric data and evaluates the cat's current health status. The analysis utilizes modules such as heart rate monitoring and temperature evaluation. The analysis results are stored in a database and can be referenced later.

[0245] User interaction interface

[0246] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing (NLP) module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[0247] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[0248] Recognizing emotions and regulating responses

[0249] As the server analyzes the user's voice data, it uses an emotion engine to recognize the user's emotions (happiness, sadness, anger, etc.). The recognized emotion data is reflected in the generated response. For example, if the user is speaking happily, the response will be generated in a bright tone. This enables more natural and emotional communication.

[0250] Health Management and Notifications

[0251] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[0252] Specific examples

[0253] Example 1: Emotional conversation with a cat

[0254] The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[0255] The device collects this voice data and transmits it to the server in real time.

[0256] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "Did you have fun today?" and compares it with the cat's heart rate, body temperature, and step count data.

[0257] The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[0258] The generation AI converts the response into voice data and sends it to the device.

[0259] The device plays the audio, allowing the user to hear the cat's "reply."

[0260] Example 2: Health alert notification

[0261] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[0262] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0263] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0264] If necessary, the user can immediately consult a veterinarian.

[0265] This system allows users to deepen their relationship with their cat, while enjoying emotional conversations and efficiently managing their health.

[0266] The processing flow will be explained below.

[0267] Step 1:

[0268] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[0269] Step 2:

[0270] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[0271] Step 3:

[0272] The server receives the biometric data sent from the device and immediately stores it in a database.

[0273] Step 4:

[0274] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[0275] Step 5:

[0276] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[0277] Step 6:

[0278] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[0279] Step 7:

[0280] The terminal transmits the recorded voice data to the server in real time.

[0281] Step 8:

[0282] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[0283] Step 9:

[0284] The server analyzes the text data and understands the user's question, for example, "How are you feeling today?"

[0285] Step 10:

[0286] The server runs the analyzed text data through an emotion engine to recognize the user's emotions (joy, sadness, anger, etc.).

[0287] Step 11:

[0288] The server compares the user's emotional data with the evaluation results of the biometric data and generates an appropriate response based on the animal's state. For example, if the user is talking to the animal in a happy manner, the response will also be generated in a bright tone.

[0289] Step 12:

[0290] The server converts the generated response into audio data.

[0291] Step 13:

[0292] The server transmits the converted voice data to the terminal.

[0293] Step 14:

[0294] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[0295] Step 15:

[0296] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[0297] Step 16:

[0298] The server generates an alert message and sends it to the user's smartphone app.

[0299] Step 17:

[0300] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[0301] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[0302] Example 2

[0303] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] Conventional animal health management systems lack the ability to collect and analyze real-time health data, making it difficult for users to immediately understand the health status of their animals. Furthermore, communication between users and animals is rarely emotional or natural, making it difficult to achieve emotionally rich dialogue. Furthermore, the timing of abnormal value detection and notification can be delayed, potentially delaying a prompt response to sudden health abnormalities.

[0305] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for converting voice data into text and recognizing the user's emotion, a means for generating a response appropriate to the animal's condition using a generative AI model based on the emotion, and a means for converting the generated response into voice data and transmitting the response to the animal. This allows the user to understand the animal's health condition in real time and communicate with the animal through emotional and natural dialogue. In addition, detection of abnormal values ​​and immediate notification enable rapid response.

[0306] "Animal biometric data" refers to data relating to an animal's physical activity and physiological state, such as heart rate, body temperature, and number of steps taken.

[0307] "Means of acquisition" refers to methods and devices for collecting biological data of animals using devices such as sensors.

[0308] "Server" refers to a computer system that analyzes collected biometric and voice data, determines health status, and generates dialogue responses.

[0309] "Voice input means" refers to a device or function for collecting voices uttered by a user, such as a microphone on a smartphone.

[0310] "Means for converting to text" refers to a method or device that converts voice data into text information using natural language processing technology.

[0311] "Means for recognizing user emotions" refers to a method or device for analyzing user emotions (happiness, sadness, anger, etc.) from text or voice data.

[0312] A "generative AI model" refers to a model that uses artificial intelligence to generate responses based on input data, such as a model that performs natural language processing.

[0313] "Means for generating a response" refers to a method or device that uses a generative AI model to generate a response that is adapted to the user's input and the animal's state.

[0314] "Means for converting into voice data" refers to a method or device that converts the generated text response into voice data using voice synthesis technology.

[0315] This invention is a system that acquires biometric data from animals (specifically cats) and allows natural interaction with users and health management. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[0316] Hardware and Software

[0317] The device is equipped with built-in sensors to measure the cat's heart rate, body temperature, and steps taken. The sensors include an optical heart rate sensor, a thermistor, and a three-axis accelerometer. The device buffers the data in real time and transmits it to a server via Bluetooth.

[0318] The server is a high-performance computing system that uses Python to acquire, analyze, and store biometric data. Specifically, it uses Python's Pandas library and Scikit-learn models to perform data analysis and anomaly detection. The analysis results are stored in a MongoDB database. The server also converts voice data into text using Google Cloud's Speech-to-Text API and has the ability to recognize user emotions using Microsoft Azure's Text Analytics API.

[0319] The smartphone app was developed using Flutter and provides a voice input method for users to talk to the cat. The collected voice data is converted into FLAC format and sent to a server in real time.

[0320] Specific examples of processing

[0321] Example 1: Emotional conversation with a cat

[0322] 1. The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[0323] 2. The device collects this voice data and sends it to the server in real time.

[0324] 3. The server converts the audio data into text using Google Cloud's Speech-to-Text API. The server analyzes the question, "Did you have fun today?" and compares it with the cat's biometric data.

[0325] 4. The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[0326] 5. The generative AI model (e.g., GPT-4) generates a response and converts this response into audio data (e.g., Amazon Polly).

[0327] 6. The device will play the audio and the user will be able to hear the cat's "reply."

[0328] Example prompt sentence:

[0329] "Based on the cat's biometric data, generate a response to the question, 'Did you have fun today?' The user's emotion is joy."

[0330] Example 2: Health alert notification

[0331] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[0332] 2. The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0333] 3. When the user receives the notification, they can open the app and check detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0334] 4. If necessary, the user may seek immediate veterinary advice.

[0335] The system allows users to monitor their animals' health status in real time, communicate with them through emotional dialogue, and instantly notify them of any abnormalities, allowing for rapid response.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1: Data collection

[0338] The device acquires the cat's heart rate, body temperature, and step count using built-in sensors (optical heart rate sensor, thermistor, and three-axis acceleration sensor). The acquired data is stored in buffer memory in real time. The input data includes biometric data such as heart rate, body temperature, and step count. The output is the biometric data stored in the buffer memory. The sensors are set to measure the heart rate every second and the body temperature every minute.

[0339] Step 2: Send data

[0340] The terminal transmits the biometric data stored in the buffer memory to the server via Bluetooth at regular intervals (for example, every 30 seconds). The input data is the biometric data in the buffer memory, and the output is the biometric data transmitted to the server. The Bluetooth transceiver divides the data into packets and transmits them.

[0341] Step 3: Data analysis

[0342] The server analyzes the received biometric data using Python's Pandas library. The analysis involves identifying trends in heart rate, body temperature, and step count, and detecting outliers. The input data is the transmitted biometric data, and the output is the analysis results. Scikit-learn models are used to calculate Z-scores and detect anomalies, for example.

[0343] Step 4: Save Data

[0344] The server stores the analyzed data in a MongoDB database. The input data is the analyzed biometric data, and the output is the records stored in the database. The database records fluctuations in heart rate, body temperature, and number of steps as time series data.

[0345] Step 5: Voice Input

[0346] The user talks to the cat using a smartphone app. Audio data is collected through the smartphone's microphone and converted to FLAC format. The input is the user's voice, and the output is audio data in FLAC format. The smartphone app's recording function collects up to 60 seconds of audio.

[0347] Step 6: Sending audio data

[0348] The smartphone app transmits the collected audio data to the server in real time. The input data is audio data in FLAC format, and the output is the audio data transmitted to the server. The audio data is transmitted using Wi-Fi or mobile networks.

[0349] Step 7: Text conversion

[0350] The server converts the audio data into text using Google Cloud's Speech-to-Text API. The input data is audio data in FLAC format, and the output is text information. The converted text is then stored on the server for further analysis.

[0351] Step 8: Emotion Recognition

[0352] The server uses Microsoft Azure's Text Analytics API to recognize the user's emotions. The input data is character information (text), and the output is an emotion score (happiness, sadness, anger, etc.). The emotion score is stored in an internal emotion database.

[0353] Step 9: Response Generation

[0354] The server generates an appropriate response using a generative AI model (e.g., GPT-4) based on the emotion score and text information. The input data are the emotion score and text information, and the output is the generated text response. The generation process includes adjusting the response based on the emotion.

[0355] Step 10: Audio conversion

[0356] The server uses speech synthesis technology (e.g., Amazon Polly) to convert the generated text response into audio data. The input data is the text response, and the output is audio data, which is then sent back to the device.

[0357] Step 11: Play audio

[0358] The device plays the generated voice data to the cat. The input data is the voice data, and the output is the voice that is played as voice output. The voice output is done using the built-in speaker, so that the user can hear the cat's response.

[0359] (Application example 2)

[0360] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0361] In conventional food delivery systems, health management of delivery personnel is inadequate, resulting in excessive workload and health risks. Furthermore, smooth communication with customers is often lacking, creating a need for improved customer satisfaction. Furthermore, there is a lack of mechanisms for understanding customer emotions and responding appropriately, so these issues must be addressed simultaneously.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0363] In this invention, the server includes means for acquiring the animal's biometric data, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the animal's current health condition, a voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the animal's condition, means for converting the generated response into voice data and transmitting the response to the animal, means for acquiring the delivery person's heart rate, body temperature, and activity level, means for detecting abnormalities in the delivery person's biometric data and notifying them if detected, means for analyzing the transmitted user voice data and generating an appropriate response using a generative AI model, and means for converting the generated response into voice data and transmitting the response to the user. This enables real-time management of the delivery person's health and early detection and notification of abnormalities. Furthermore, understanding customer emotions and generating appropriate responses can facilitate communication with customers and improve customer satisfaction.

[0364] "Biological data of an animal" refers to data that indicates the physiological state of an animal, such as the animal's heart rate, body temperature, and number of steps.

[0365] A "server" is a computer system for collecting, analyzing, storing, and generating responses to data.

[0366] The "voice input means" is a device such as a microphone that allows the user to input information by voice.

[0367] "Analysis" is the act of statistically or computationally processing acquired data to extract meaningful information.

[0368] A "response generator" is an algorithm or software that constructs an appropriate response based on input data.

[0369] "Biometric data of the delivery person" refers to data indicating the physiological condition of the delivery person, such as the delivery person's heart rate, body temperature, and activity level.

[0370] "Notification" refers to the act of notifying a user or administrator when an abnormality is detected.

[0371] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to generate appropriate output from input data.

[0372] "Real time" refers to the acquisition and processing of information almost immediately.

[0373] "Customer emotions" are psychological states such as joy, sadness, and anger expressed by customers.

[0374] "Communication" is the act of exchanging information and emotions.

[0375] "Customer satisfaction" refers to the degree of satisfaction a customer feels with a service.

[0376] An "analysis module" is a software component designed to analyze specific data.

[0377] The present invention relates to a system that acquires biometric data of animals and delivery personnel, and uses that data to communicate with them and manage their health. This system is composed of the following components:

[0378] System Overview

[0379] Hardware and Software Configuration

[0380] 1. Means of obtaining biometric data of animals and delivery personnel:

[0381] The heart rate, body temperature, and number of steps of the animal, as well as the heart rate, body temperature, and activity of the delivery person, are acquired using sensors (e.g., Polar H10 heart rate sensor, thermistor body temperature module).

[0382] 2. Means for transmitting biometric data to the server:

[0383] Wireless communication modules (e.g., Bluetooth, Wi-Fi) are used to transmit the acquired biometric data in real time to the server, which receives the data using the Django framework.

[0384] 3. Data analysis and judgment:

[0385] The server uses specialized analysis modules (e.g., TensorFlow, Pandas) to analyze the received data, thereby determining the health status of the animals and delivery personnel.

[0386] The analyzed biometric data is stored in an SQLite database.

[0387] 4. Voice input method:

[0388] The smartphone app uses a built-in microphone for voice input, and sends the data entered by the user via voice to the server.

[0389] 5. Speech data analysis and response generation:

[0390] The server analyzes the received voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text format.

[0391] An appropriate response is generated using a generative AI model (e.g., GPT-3), taking into account the state of the animal and delivery person.

[0392] 6. Response voice data conversion and transmission method:

[0393] Use a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated response into audio data and play it back to the animal or user.

[0394] 7. Outlier detection and notification:

[0395] The server constantly monitors the biometric data, and if it detects any abnormal values, it immediately sends a push notification (e.g., Firebase Cloud Messaging) to the user.

[0396] Specific examples

[0397] Example 1: Health management of animals and delivery personnel

[0398] The delivery person's heart rate is monitored, and if a sudden increase is detected, the server immediately sends a notification to the delivery person's smartphone saying, "Your heart rate is rising rapidly. Please take a break."

[0399] Example 2: Emotional communication with customers

[0400] If a customer requests a quick delivery by voice input on a smartphone app, saying, "I'm busy today, so please deliver quickly," the server analyzes the request using an emotion engine and uses a generative AI model to generate an appropriate response, such as, "We will deliver it right away. Please wait."

[0401] Prompt Sentence Examples

[0402] If the delivery person's heart rate exceeds 180, the notification will read:

[0403] "Your heart rate is increasing rapidly. Please take a break."

[0404] If the customer is analyzed as being impatient, the response would be:

[0405] "We will deliver it to you shortly. Please wait."

[0406] The above configuration and processing allows for real-time health management of animals and delivery personnel, and immediate notification of any abnormalities. It also enables natural, emotional responses when communicating with customers, which is expected to improve customer satisfaction.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] Input: Animal and delivery personnel biometric data (heart rate, body temperature, steps, activity level)

[0410] Processing: The device uses sensors (e.g., Polar H10, thermistor) to measure heart rate, body temperature, steps, and activity in real time. This data is temporarily stored in a buffer on the device.

[0411] Output: Raw biometric data obtained from sensors (heart rate, body temperature, steps, activity level)

[0412] Step 2:

[0413] Input: Raw biometric data (heart rate, body temperature, steps, activity level)

[0414] Processing: The raw data acquired by the device is sent to the server at regular intervals. The communication method is a wireless communication module (e.g., Bluetooth, Wi-Fi).

[0415] Output: Biometric data sent to the server

[0416] Step 3:

[0417] Input: Biometric data sent to the server

[0418] Processing: The server receives the biometric data and analyzes it using an analysis module (e.g., TensorFlow, Pandas). Specifically, it calculates the average heart rate and temperature fluctuation patterns to evaluate the health status.

[0419] Output: Analysis results (health status evaluation)

[0420] Step 4:

[0421] Input: Analysis results (health status evaluation)

[0422] Processing: The server saves the analysis results in an SQLite database, which allows for analysis by comparing them with past data and makes it easier to detect outliers.

[0423] Output: Saved analysis result data

[0424] Step 5:

[0425] Input: User's voice input data

[0426] Processing: The user inputs voice through the smartphone app. The smartphone's microphone collects the voice data and sends it to the server.

[0427] Output: Audio data sent to the server

[0428] Step 6:

[0429] Input: Audio data sent to the server

[0430] Processing: The server analyzes the voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text. The converted text data is then used by a generative AI model (e.g., GPT-3) to generate an appropriate response.

[0431] Output: The generated response text

[0432] Step 7:

[0433] Input: Generated response text

[0434] Processing: The server uses a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated text into audio data, which is then sent to the user's smartphone app.

[0435] Output: Audio data sent to the user's smartphone

[0436] Step 8:

[0437] Input: Analysis results (health status evaluation)

[0438] Processing: The server constantly monitors the analysis results, and if an abnormal value is detected, it sends a push notification to the user using Firebase Cloud Messaging or similar.

[0439] Output: Notification of anomaly detection sent to the user

[0440] Example prompt:

[0441] If the delivery person's heart rate exceeds 180, the notification will read:

[0442] "Your heart rate is increasing rapidly. Please take a break."

[0443] If the customer is analyzed as being impatient, the response would be:

[0444] "We will deliver it to you shortly. Please wait."

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0446] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0448] [Second embodiment]

[0449] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0450] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0451] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0453] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0456] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0457] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0458] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0460] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0461] MODE FOR CARRYING OUT THE INVENTION

[0462] System Overview

[0463] This system acquires biological data from animals (specifically cats) and uses that data to support communication between the user and the animal. It is also possible to manage the cat's health. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[0464] System program and processing flow

[0465] Data collection and transmission

[0466] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in a buffer in real time and sent to a server at regular intervals.

[0467] Data analysis and health status assessment

[0468] The server analyzes the received vital data and evaluates the cat's current health status. Various analysis modules (e.g., heart rate monitoring, temperature evaluation, etc.) are used for the analysis. The analysis results are stored in a database and can be referenced later.

[0469] User interaction interface

[0470] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[0471] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[0472] Health Management and Notifications

[0473] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[0474] Specific examples

[0475] Example 1: Conversation with a cat

[0476] The user speaks to the smartphone app, asking, "How are you feeling today?"

[0477] The device collects this voice data and transmits it to the server in real time.

[0478] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "How are you feeling today?" and compares it with the cat's heart rate, body temperature, and step count data.

[0479] It verifies that the animal is in good condition and generates the response "I'm feeling good today. I've had lots of fun."

[0480] The generation AI converts the response into voice data and sends it to the device.

[0481] The device plays the audio, allowing the user to hear the cat's "reply."

[0482] Example 2: Health alert notification

[0483] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[0484] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0485] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0486] If necessary, the user can immediately consult a veterinarian.

[0487] This system allows users to deepen their relationship with their cat while efficiently managing their health.

[0488] The processing flow will be explained below.

[0489] Step 1:

[0490] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[0491] Step 2:

[0492] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[0493] Step 3:

[0494] The server receives the biometric data sent from the device and immediately stores it in a database.

[0495] Step 4:

[0496] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[0497] Step 5:

[0498] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[0499] Step 6:

[0500] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[0501] Step 7:

[0502] The terminal transmits the recorded voice data to the server in real time.

[0503] Step 8:

[0504] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[0505] Step 9:

[0506] The server analyzes the converted text data and understands the user's question, for example, "How are you today?"

[0507] Step 10:

[0508] The server compares the analysis results with previously collected and analyzed biometric data and generates an appropriate response based on the animal's condition.

[0509] Step 11:

[0510] The server converts the generated response into audio data.

[0511] Step 12:

[0512] The server transmits the converted voice data to the terminal.

[0513] Step 13:

[0514] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[0515] Step 14:

[0516] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[0517] Step 15:

[0518] The server generates an alert message and sends it to the user's smartphone app.

[0519] Step 16:

[0520] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[0521] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[0522] Example 1

[0523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] Conventional animal health management systems not only collect biometric data, but also make it difficult for users to check the health status of animals while interacting with them. Furthermore, they lack the functionality to notify users in real time when an abnormality occurs, making it difficult to take early action. This has created a need for a means to properly monitor the health status of animals and take prompt action.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0526] In this invention, the server includes a means for using the generative AI model, a means for managing an API endpoint for receiving data in real time, and a means for storing and referencing the health status evaluation results in a database, allowing users to efficiently monitor the health status of their animals while enjoying interactions with them and receive prompt notification when abnormalities are detected.

[0527] "Biometric data" refers to numerical information related to an animal's body, such as its heart rate, body temperature, and number of steps taken.

[0528] A "server" is a computer system that analyzes and stores acquired data and manages communications with users and terminals.

[0529] A "generative AI model" is an algorithm that generates appropriate responses based on collected data and voice information.

[0530] A "terminal" is a device that is attached to an animal and has the function of acquiring biometric data and transmitting it to a server.

[0531] "Audio input means" is a device or function that collects audio that the user uses to talk to the animal.

[0532] An "API endpoint" is a connection point through which other systems or devices can exchange data with a server.

[0533] A "database" is a system that systematically stores information such as analysis results and biological data, and makes it available for reference as needed.

[0534] "Real-time" refers to an environment or process that operates instantly with almost no delay.

[0535] An "abnormal value" is a data value that deviates from the normal range, and is particularly a value that may affect health status.

[0536] "Notification" is a message that warns or informs the user when an abnormality is detected.

[0537] This invention is a system that acquires biometric data from animals (specifically cats) and supports communication between users and animals based on that data. It is also possible to manage the cat's health. The system consists of a device attached to the animal, a server, and a smartphone app used by the user.

[0538] Data collection

[0539] The terminal is a device attached to the cat's collar and uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in an internal buffer in real time and periodically transmitted to a server using a wireless communication module.

[0540] Data reception and storage

[0541] The server manages an API endpoint for receiving biometric data sent from the device in real time. The server checks the received data to ensure there is no invalid data. The verified data is stored in a database. Using a database management system (DBMS), this data can be efficiently stored and referenced.

[0542] Data analysis and health status assessment

[0543] The server uses a heart rate monitoring module and a temperature evaluation module to analyze the stored vital data, thereby assessing the cat's current health status. The analysis results are stored in a database for users to refer to later.

[0544] User interaction interface

[0545] Users can talk to the cat using a smartphone app. To do this, voice data is collected using a voice input means and sent to a server. The server then converts the received voice data into text using a natural language processing module. The converted text is analyzed by a generative AI model to generate a response appropriate to the situation. The generated response is then converted back into voice data and sent to the device. The device then plays this voice data back to the cat, allowing the user to hear the cat's "reply."

[0546] Health Management and Notifications

[0547] The server constantly monitors the vital signs and immediately notifies the user if any abnormal values ​​are detected. The notification is sent via a smartphone app, and the type of abnormal value and detailed information are provided, allowing the user to take prompt action.

[0548] Specific examples

[0549] Conversation with a cat

[0550] 1. The user speaks to the smartphone app, "How are you feeling today?"

[0551] 2. The device collects voice data and transmits it to the server in real time.

[0552] 3. The server converts the voice data into text using a natural language processing module.

[0553] 4. The generative AI model compares the cat's heart rate, body temperature, and step count data and generates a response such as, "I'm feeling good today. I've played a lot."

[0554] 5. The server converts the response into audio data and sends it to the device.

[0555] 6. The device plays the sound to the cat, and the user can hear it.

[0556] Health Alert Notifications

[0557] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[0558] 2. The server immediately identifies abnormal values ​​and creates an alert.

[0559] 3. The server sends the alert to the user's smartphone app.

[0560] 4. The user receives a notification and opens the app to see more information, such as a sudden rise in temperature from 38.5°C to 40°C.

[0561] 5. Users may consult a veterinarian if necessary.

[0562] This allows users to efficiently manage their health while enjoying interaction with their cat.

[0563] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0564] System program processing flow

[0565] Step 1: Data collection

[0566] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps. The input is raw data from the sensor, and the output is formatted biological data stored in an internal buffer. Specifically, the sensor measures the cat's heart rate every second and stores the data as a byte sequence.

[0567] Step 2: Send data

[0568] The device periodically transmits data accumulated in its internal buffer to the server in batches. The input is the formatted biometric data in the buffer, and the output is the data packets transmitted to the server. Transmission is triggered when the number of accumulated data reaches 100.

[0569] Step 3: Receiving and storing data

[0570] The server receives data sent from the device in real time. The input is the data packet sent from the device, and the output is the formatted data to be stored in the database. The server's API endpoint receives the data, and the data is stored by the database management system (DBMS).

[0571] Step 4: Data analysis

[0572] The server passes the stored data to the analysis module to evaluate the health status. The input is the biometric data stored in the database, and the output is the analysis result (e.g., good health, outside normal range, etc.). Specifically, the heart rate monitoring module extracts data from the past hour and calculates the average heart rate.

[0573] Step 5: User interaction interface

[0574] A user talks to a cat using a smartphone app. The input is the user's voice data, and the output is the voice data sent to the server. The voice data is collected and sent to the server in real time.

[0575] Step 6: Analyze the audio data

[0576] The server analyzes the voice data through a natural language processing module. The input is the user's voice data, and the output is the analysis result in text format. For example, the voice data "How are you today?" is converted into text.

[0577] Step 7: Response Generation

[0578] The generative AI model generates a response based on the analyzed text. The input is the analysis result of the natural language processing module, and the output is the generated response text. The generative AI model compares the cat's heart rate, body temperature, and step count data to generate an appropriate response.

[0579] Step 8: Sending a Response

[0580] The server converts the generated response into audio data and sends it to the device. The input is the text response output by the generative AI model, and the output is audio data. A text-to-speech engine is used to convert the text into audio.

[0581] Step 9: Playing Audio

[0582] The device plays the audio data sent from the server and communicates the response to the cat. The input is the audio data sent from the server, and the output is the audio to be played. The audio is played using the device's speaker.

[0583] Step 10: Anomaly detection and notification

[0584] The server constantly monitors the received biometric data and immediately sends a notification to the user if an abnormal value is detected. The input is the biometric data stored on the server, and the output is a notification sent to a smartphone app. For example, a notification is triggered when the body temperature exceeds 40 degrees.

[0585] This allows the user to efficiently monitor the cat's health throughout all steps and respond quickly if any abnormalities occur.

[0586] (Application example 1)

[0587] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] Conventional pet monitoring systems have had the problem of making it difficult to monitor the health status of pets while their owners are out, or to detect abnormalities in the home at an early stage. The present invention aims to solve these problems and provide a system that enables real-time monitoring of pet health status and prompt notification of abnormalities.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0590] In this invention, the server includes means for acquiring biometric data of the animal, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the current health condition of the animal, voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the state of the animal, means for converting the generated response into voice data and transmitting the response to the animal, and means for detecting abnormalities using the acquired biometric data and sending an alert to the user if an abnormality is found. This allows owners to keep track of their pet's health condition and the situation at home in real time even when they are out, and to respond quickly when an abnormality occurs.

[0591] "Animal biometric data" refers to data that indicates the animal's physical condition, such as heart rate, body temperature, and number of steps taken.

[0592] "Means of acquisition" refers to the method of collecting biological data of animals using sensors or devices.

[0593] The "means for transmitting to the server" refers to a method for transferring the collected biometric data to the server via wireless or wired communication.

[0594] A "server" is a computer system that stores and analyzes collected biometric data.

[0595] "Means of analysis" refers to methods of assessing the health status of animals using algorithms and software that process and analyze biological data.

[0596] The "voice input means for talking to animals" refers to a microphone or voice recognition device that allows the user to input a voice message to the animals.

[0597] The "means for transmitting data from the voice input means to the server" is a communication means for transferring input voice data to the server.

[0598] "Means for analyzing voice data" refers to natural language processing technology that converts input voice data into text data and understands the content.

[0599] The "means for generating a response appropriate to the animal's condition" is an AI model that generates an appropriate response according to the animal's condition based on analyzed voice data.

[0600] The "means for converting the generated response into voice data" is a voice synthesis technology that converts the generated text-format response into voice.

[0601] The "means for transmitting a response" refers to a terminal or speaker that allows the animal to hear the generated audio data.

[0602] The "means for detecting anomalies" is an algorithm for identifying abnormal data that exceeds a certain threshold based on the collected biometric data.

[0603] "Means for sending an alert to a user" refers to a system or technology for sending a notification to a user when an abnormality is detected.

[0604] System Overview

[0605] The system of the present invention acquires the animal's biological data, supports communication between the user and the animal based on that data, monitors the animal's health, and sends an alert to the user if an abnormality occurs. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[0606] Data collection and transmission

[0607] The device is equipped with sensors that measure biometric data such as heart rate, body temperature, and number of steps taken. The device collects this data in real time and transmits it to a server at regular intervals. Wireless communication technology (such as Bluetooth or Wi-Fi) is used for transmission.

[0608] Data analysis and health status assessment

[0609] The server analyzes the acquired vital data and evaluates the animal's current health condition. Analysis tools such as a heart rate monitoring module and a body temperature evaluation module are used for the analysis. The server stores the vital data in a database, and if abnormal data is detected, it sends an alert to the user. The alert is sent via push notification or email.

[0610] User interaction interface

[0611] Users can talk to animals using a smartphone app. The user's voice data is sent to the server via a voice input method (such as a smartphone microphone). The server then converts the voice data into text through a natural language processing module and analyzes what the user said. A generative AI model generates a response appropriate to the animal's condition, which is then converted into voice data and transmitted to the animal. This allows users to enjoy two-way communication with the animals.

[0612] Health Management and Notifications

[0613] The server constantly monitors the received vital signs and immediately sends an alert to the user if an abnormal value is detected. The notification is sent via a smartphone app, and the type of abnormality and detailed information are provided. This allows the user to quickly understand the animal's health condition and, if necessary, consult a veterinarian immediately.

[0614] Example

[0615] (Example 1: Home pet monitoring system)

[0616] This system allows users to detect abnormalities in their cat's heart rate or body temperature while they are out. For example, if a cat's heart rate suddenly increases while they are out, the server immediately detects the abnormality and sends an alert to the user's smartphone. This allows the user to quickly check the situation and take any necessary measures.

[0617] (Example of a prompt for a generative AI model)

[0618] I would like to build a system that detects abnormalities in the home using biological data from cats and sends alerts to users who are out. Specifically, how can I issue an alert based on abnormal behavior such as a sudden rise in heart rate or body temperature?

[0619] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0620] Step 1:

[0621] Collection of animal biometric data

[0622] The device uses sensors to collect biometric data such as heart rate, body temperature, and number of steps in real time.

[0623] Input: Biological data collected by sensors.

[0624] Output: The acquired biometric data is stored in a buffer as data packets.

[0625] Step 2:

[0626] Sending biometric data to the server

[0627] The device transmits the collected biometric data to a server at regular intervals (for example, every minute). Wireless technologies such as Bluetooth and Wi-Fi are used for communication.

[0628] Input: Buffered biometric data.

[0629] Data processing: Assembling multiple data points in a buffer into a data packet.

[0630] Output: Data packets sent to the server.

[0631] Step 3:

[0632] Biological data analysis

[0633] The server analyzes the received biometric data, using modules such as a heart rate monitoring module and a body temperature evaluation module.

[0634] Input: The data packet sent to the server.

[0635] Data calculations: Each data point is evaluated to see if it is within normal limits. Any outliers are flagged.

[0636] Output: Analysis results showing health status and abnormality flags.

[0637] Step 4:

[0638] Detecting outliers and sending alerts

[0639] Based on the analysis results, the server sends a notification to the user if any abnormal values ​​are detected.

[0640] Input: Analysis results and anomaly flags.

[0641] Data processing: If an abnormality flag is raised, an alarm message is generated.

[0642] Output: An alert notification sent to the user's smartphone.

[0643] Step 5:

[0644] User interaction

[0645] The user talks to the animals through a smartphone app, and the user's voice is picked up by a voice input means and sent to the server.

[0646] Input: User's voice data.

[0647] Output: The audio data sent to the server.

[0648] Step 6:

[0649] Voice data analysis and response generation

[0650] The server runs the user's voice data through a natural language processing module, converts it into text, and then uses a generative AI model to generate a response appropriate to the animal's condition.

[0651] Input: The audio data sent to the server.

[0652] Data Calculation: Converts voice data into text, analyzes the user's intent, and generates an appropriate response based on the analysis results.

[0653] Output: The response in text format.

[0654] Step 7:

[0655] Response communication

[0656] The server converts the generated response into audio data and sends it to the terminal, which plays the audio data to the animal.

[0657] Input: A text response.

[0658] Data processing: Converting text data into audio data.

[0659] Output: The audio data sent to the device.

[0660] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0661] MODE FOR CARRYING OUT THE INVENTION

[0662] System Overview

[0663] This system acquires biometric data from animals (specifically cats) and uses that data to support communication between the user and the animal. It also manages the cat's health. Furthermore, it uses an emotion engine to recognize the user's emotions based on the user's voice input data and generates responses based on those emotions, enabling more natural and emotional communication. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[0664] System program and processing flow

[0665] Data collection and transmission

[0666] The device uses sensors to capture the cat's heart rate, body temperature, and number of steps taken. This biological data is stored in a buffer in real time and then sent to a server at regular intervals.

[0667] Data analysis and health status assessment

[0668] The server analyzes the received biometric data and evaluates the cat's current health status. The analysis utilizes modules such as heart rate monitoring and temperature evaluation. The analysis results are stored in a database and can be referenced later.

[0669] User interaction interface

[0670] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing (NLP) module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[0671] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[0672] Recognizing emotions and regulating responses

[0673] As the server analyzes the user's voice data, it uses an emotion engine to recognize the user's emotions (happiness, sadness, anger, etc.). The recognized emotion data is reflected in the generated response. For example, if the user is speaking happily, the response will be generated in a bright tone. This enables more natural and emotional communication.

[0674] Health Management and Notifications

[0675] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[0676] Specific examples

[0677] Example 1: Emotional conversation with a cat

[0678] The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[0679] The device collects this voice data and transmits it to the server in real time.

[0680] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "Did you have fun today?" and compares it with the cat's heart rate, body temperature, and step count data.

[0681] The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[0682] The generation AI converts the response into voice data and sends it to the device.

[0683] The device plays the audio, allowing the user to hear the cat's "reply."

[0684] Example 2: Health alert notification

[0685] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[0686] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0687] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0688] If necessary, the user can immediately consult a veterinarian.

[0689] This system allows users to deepen their relationship with their cat, while enjoying emotional conversations and efficiently managing their health.

[0690] The processing flow will be explained below.

[0691] Step 1:

[0692] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[0693] Step 2:

[0694] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[0695] Step 3:

[0696] The server receives the biometric data sent from the device and immediately stores it in a database.

[0697] Step 4:

[0698] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[0699] Step 5:

[0700] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[0701] Step 6:

[0702] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[0703] Step 7:

[0704] The terminal transmits the recorded voice data to the server in real time.

[0705] Step 8:

[0706] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[0707] Step 9:

[0708] The server analyzes the text data and understands the user's question, for example, "How are you feeling today?"

[0709] Step 10:

[0710] The server runs the analyzed text data through an emotion engine to recognize the user's emotions (joy, sadness, anger, etc.).

[0711] Step 11:

[0712] The server compares the user's emotional data with the evaluation results of the biometric data and generates an appropriate response based on the animal's state. For example, if the user is talking to the animal in a happy manner, the response will also be generated in a bright tone.

[0713] Step 12:

[0714] The server converts the generated response into audio data.

[0715] Step 13:

[0716] The server transmits the converted voice data to the terminal.

[0717] Step 14:

[0718] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[0719] Step 15:

[0720] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[0721] Step 16:

[0722] The server generates an alert message and sends it to the user's smartphone app.

[0723] Step 17:

[0724] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[0725] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[0726] Example 2

[0727] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0728] Conventional animal health management systems lack the ability to collect and analyze real-time health data, making it difficult for users to immediately understand the health status of their animals. Furthermore, communication between users and animals is rarely emotional or natural, making it difficult to achieve emotionally rich dialogue. Furthermore, the timing of abnormal value detection and notification can be delayed, potentially delaying a prompt response to sudden health abnormalities.

[0729] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for converting voice data into text and recognizing the user's emotion, a means for generating a response appropriate to the animal's condition using a generative AI model based on the emotion, and a means for converting the generated response into voice data and transmitting the response to the animal. This allows the user to understand the animal's health condition in real time and communicate with the animal through emotional and natural dialogue. In addition, detection of abnormal values ​​and immediate notification enable rapid response.

[0730] "Animal biometric data" refers to data relating to an animal's physical activity and physiological state, such as heart rate, body temperature, and number of steps taken.

[0731] "Means of acquisition" refers to methods and devices for collecting biological data of animals using devices such as sensors.

[0732] "Server" refers to a computer system that analyzes collected biometric and voice data, determines health status, and generates dialogue responses.

[0733] "Voice input means" refers to a device or function for collecting voices uttered by a user, such as a microphone on a smartphone.

[0734] "Means for converting to text" refers to a method or device that converts voice data into text information using natural language processing technology.

[0735] "Means for recognizing user emotions" refers to a method or device for analyzing user emotions (happiness, sadness, anger, etc.) from text or voice data.

[0736] A "generative AI model" refers to a model that uses artificial intelligence to generate responses based on input data, such as a model that performs natural language processing.

[0737] "Means for generating a response" refers to a method or device that uses a generative AI model to generate a response that is adapted to the user's input and the animal's state.

[0738] "Means for converting into voice data" refers to a method or device that converts the generated text response into voice data using voice synthesis technology.

[0739] This invention is a system that acquires biometric data from animals (specifically cats) and allows natural interaction with users and health management. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[0740] Hardware and Software

[0741] The device is equipped with built-in sensors to measure the cat's heart rate, body temperature, and steps taken. The sensors include an optical heart rate sensor, a thermistor, and a three-axis accelerometer. The device buffers the data in real time and transmits it to a server via Bluetooth.

[0742] The server is a high-performance computing system that uses Python to acquire, analyze, and store biometric data. Specifically, it uses Python's Pandas library and Scikit-learn models to perform data analysis and anomaly detection. The analysis results are stored in a MongoDB database. The server also converts voice data into text using Google Cloud's Speech-to-Text API and has the ability to recognize user emotions using Microsoft Azure's Text Analytics API.

[0743] The smartphone app was developed using Flutter and provides a voice input method for users to talk to the cat. The collected voice data is converted into FLAC format and sent to a server in real time.

[0744] Specific examples of processing

[0745] Example 1: Emotional conversation with a cat

[0746] 1. The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[0747] 2. The device collects this voice data and sends it to the server in real time.

[0748] 3. The server converts the audio data into text using Google Cloud's Speech-to-Text API. The server analyzes the question, "Did you have fun today?" and compares it with the cat's biometric data.

[0749] 4. The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[0750] 5. The generative AI model (e.g., GPT-4) generates a response and converts this response into audio data (e.g., Amazon Polly).

[0751] 6. The device will play the audio and the user will be able to hear the cat's "reply."

[0752] Example prompt sentence:

[0753] "Based on the cat's biometric data, generate a response to the question, 'Did you have fun today?' The user's emotion is joy."

[0754] Example 2: Health alert notification

[0755] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[0756] 2. The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0757] 3. When the user receives the notification, they can open the app and check detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0758] 4. If necessary, the user may seek immediate veterinary advice.

[0759] The system allows users to monitor their animals' health status in real time, communicate with them through emotional dialogue, and instantly notify them of any abnormalities, allowing for rapid response.

[0760] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0761] Step 1: Data collection

[0762] The device acquires the cat's heart rate, body temperature, and step count using built-in sensors (optical heart rate sensor, thermistor, and three-axis acceleration sensor). The acquired data is stored in buffer memory in real time. The input data includes biometric data such as heart rate, body temperature, and step count. The output is the biometric data stored in the buffer memory. The sensors are set to measure the heart rate every second and the body temperature every minute.

[0763] Step 2: Send data

[0764] The terminal transmits the biometric data stored in the buffer memory to the server via Bluetooth at regular intervals (for example, every 30 seconds). The input data is the biometric data in the buffer memory, and the output is the biometric data transmitted to the server. The Bluetooth transceiver divides the data into packets and transmits them.

[0765] Step 3: Data analysis

[0766] The server analyzes the received biometric data using Python's Pandas library. The analysis involves identifying trends in heart rate, body temperature, and step count, and detecting outliers. The input data is the transmitted biometric data, and the output is the analysis results. Scikit-learn models are used to calculate Z-scores and detect anomalies, for example.

[0767] Step 4: Save Data

[0768] The server stores the analyzed data in a MongoDB database. The input data is the analyzed biometric data, and the output is the records stored in the database. The database records fluctuations in heart rate, body temperature, and number of steps as time series data.

[0769] Step 5: Voice Input

[0770] The user talks to the cat using a smartphone app. Audio data is collected through the smartphone's microphone and converted to FLAC format. The input is the user's voice, and the output is audio data in FLAC format. The smartphone app's recording function collects up to 60 seconds of audio.

[0771] Step 6: Sending audio data

[0772] The smartphone app transmits the collected audio data to the server in real time. The input data is audio data in FLAC format, and the output is the audio data transmitted to the server. The audio data is transmitted using Wi-Fi or mobile networks.

[0773] Step 7: Text conversion

[0774] The server converts the audio data into text using Google Cloud's Speech-to-Text API. The input data is audio data in FLAC format, and the output is text information. The converted text is then stored on the server for further analysis.

[0775] Step 8: Emotion Recognition

[0776] The server uses Microsoft Azure's Text Analytics API to recognize the user's emotions. The input data is character information (text), and the output is an emotion score (happiness, sadness, anger, etc.). The emotion score is stored in an internal emotion database.

[0777] Step 9: Response Generation

[0778] The server generates an appropriate response using a generative AI model (e.g., GPT-4) based on the emotion score and text information. The input data are the emotion score and text information, and the output is the generated text response. The generation process includes adjusting the response based on the emotion.

[0779] Step 10: Audio conversion

[0780] The server uses speech synthesis technology (e.g., Amazon Polly) to convert the generated text response into audio data. The input data is the text response, and the output is audio data, which is then sent back to the device.

[0781] Step 11: Play audio

[0782] The device plays the generated voice data to the cat. The input data is the voice data, and the output is the voice that is played as voice output. The voice output is done using the built-in speaker, so that the user can hear the cat's response.

[0783] (Application example 2)

[0784] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0785] In conventional food delivery systems, health management of delivery personnel is inadequate, resulting in excessive workload and health risks. Furthermore, smooth communication with customers is often lacking, creating a need for improved customer satisfaction. Furthermore, there is a lack of mechanisms for understanding customer emotions and responding appropriately, so these issues must be addressed simultaneously.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0787] In this invention, the server includes means for acquiring the animal's biometric data, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the animal's current health condition, a voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the animal's condition, means for converting the generated response into voice data and transmitting the response to the animal, means for acquiring the delivery person's heart rate, body temperature, and activity level, means for detecting abnormalities in the delivery person's biometric data and notifying them if detected, means for analyzing the transmitted user voice data and generating an appropriate response using a generative AI model, and means for converting the generated response into voice data and transmitting the response to the user. This enables real-time management of the delivery person's health and early detection and notification of abnormalities. Furthermore, understanding customer emotions and generating appropriate responses can facilitate communication with customers and improve customer satisfaction.

[0788] "Biological data of an animal" refers to data that indicates the physiological state of an animal, such as the animal's heart rate, body temperature, and number of steps.

[0789] A "server" is a computer system for collecting, analyzing, storing, and generating responses to data.

[0790] The "voice input means" is a device such as a microphone that allows the user to input information by voice.

[0791] "Analysis" is the act of statistically or computationally processing acquired data to extract meaningful information.

[0792] A "response generator" is an algorithm or software that constructs an appropriate response based on input data.

[0793] "Biometric data of the delivery person" refers to data indicating the physiological condition of the delivery person, such as the delivery person's heart rate, body temperature, and activity level.

[0794] "Notification" refers to the act of notifying a user or administrator when an abnormality is detected.

[0795] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to generate appropriate output from input data.

[0796] "Real time" refers to the acquisition and processing of information almost immediately.

[0797] "Customer emotions" are psychological states such as joy, sadness, and anger expressed by customers.

[0798] "Communication" is the act of exchanging information and emotions.

[0799] "Customer satisfaction" refers to the degree of satisfaction a customer feels with a service.

[0800] An "analysis module" is a software component designed to analyze specific data.

[0801] The present invention relates to a system that acquires biometric data of animals and delivery personnel, and uses that data to communicate with them and manage their health. This system is composed of the following components:

[0802] System Overview

[0803] Hardware and Software Configuration

[0804] 1. Means of obtaining biometric data of animals and delivery personnel:

[0805] The heart rate, body temperature, and number of steps of the animal, as well as the heart rate, body temperature, and activity of the delivery person, are acquired using sensors (e.g., Polar H10 heart rate sensor, thermistor body temperature module).

[0806] 2. Means for transmitting biometric data to the server:

[0807] Wireless communication modules (e.g., Bluetooth, Wi-Fi) are used to transmit the acquired biometric data in real time to the server, which receives the data using the Django framework.

[0808] 3. Data analysis and judgment:

[0809] The server uses specialized analysis modules (e.g., TensorFlow, Pandas) to analyze the received data, thereby determining the health status of the animals and delivery personnel.

[0810] The analyzed biometric data is stored in an SQLite database.

[0811] 4. Voice input method:

[0812] The smartphone app uses a built-in microphone for voice input, and sends the data entered by the user via voice to the server.

[0813] 5. Speech data analysis and response generation:

[0814] The server analyzes the received voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text format.

[0815] An appropriate response is generated using a generative AI model (e.g., GPT-3), taking into account the state of the animal and delivery person.

[0816] 6. Response voice data conversion and transmission method:

[0817] Use a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated response into audio data and play it back to the animal or user.

[0818] 7. Outlier detection and notification:

[0819] The server constantly monitors the biometric data, and if it detects any abnormal values, it immediately sends a push notification (e.g., Firebase Cloud Messaging) to the user.

[0820] Specific examples

[0821] Example 1: Health management of animals and delivery personnel

[0822] The delivery person's heart rate is monitored, and if a sudden increase is detected, the server immediately sends a notification to the delivery person's smartphone saying, "Your heart rate is rising rapidly. Please take a break."

[0823] Example 2: Emotional communication with customers

[0824] If a customer requests a quick delivery by voice input on a smartphone app, saying, "I'm busy today, so please deliver quickly," the server analyzes the request using an emotion engine and uses a generative AI model to generate an appropriate response, such as, "We will deliver it right away. Please wait."

[0825] Prompt Sentence Examples

[0826] If the delivery person's heart rate exceeds 180, the notification will read:

[0827] "Your heart rate is increasing rapidly. Please take a break."

[0828] If the customer is analyzed as being impatient, the response would be:

[0829] "We will deliver it to you shortly. Please wait."

[0830] The above configuration and processing allows for real-time health management of animals and delivery personnel, and immediate notification of any abnormalities. It also enables natural, emotional responses when communicating with customers, which is expected to improve customer satisfaction.

[0831] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] Input: Animal and delivery personnel biometric data (heart rate, body temperature, steps, activity level)

[0834] Processing: The device uses sensors (e.g., Polar H10, thermistor) to measure heart rate, body temperature, steps, and activity in real time. This data is temporarily stored in a buffer on the device.

[0835] Output: Raw biometric data obtained from sensors (heart rate, body temperature, steps, activity level)

[0836] Step 2:

[0837] Input: Raw biometric data (heart rate, body temperature, steps, activity level)

[0838] Processing: The raw data acquired by the device is sent to the server at regular intervals. The communication method is a wireless communication module (e.g., Bluetooth, Wi-Fi).

[0839] Output: Biometric data sent to the server

[0840] Step 3:

[0841] Input: Biometric data sent to the server

[0842] Processing: The server receives the biometric data and analyzes it using an analysis module (e.g., TensorFlow, Pandas). Specifically, it calculates the average heart rate and temperature fluctuation patterns to evaluate the health status.

[0843] Output: Analysis results (health status evaluation)

[0844] Step 4:

[0845] Input: Analysis results (health status evaluation)

[0846] Processing: The server saves the analysis results in an SQLite database, which allows for analysis by comparing them with past data and makes it easier to detect outliers.

[0847] Output: Saved analysis result data

[0848] Step 5:

[0849] Input: User's voice input data

[0850] Processing: The user inputs voice through the smartphone app. The smartphone's microphone collects the voice data and sends it to the server.

[0851] Output: Audio data sent to the server

[0852] Step 6:

[0853] Input: Audio data sent to the server

[0854] Processing: The server analyzes the voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text. The converted text data is then used by a generative AI model (e.g., GPT-3) to generate an appropriate response.

[0855] Output: The generated response text

[0856] Step 7:

[0857] Input: Generated response text

[0858] Processing: The server uses a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated text into audio data, which is then sent to the user's smartphone app.

[0859] Output: Audio data sent to the user's smartphone

[0860] Step 8:

[0861] Input: Analysis results (health status evaluation)

[0862] Processing: The server constantly monitors the analysis results, and if an abnormal value is detected, it sends a push notification to the user using Firebase Cloud Messaging or similar.

[0863] Output: Notification of anomaly detection sent to the user

[0864] Example prompt:

[0865] If the delivery person's heart rate exceeds 180, the notification will read:

[0866] "Your heart rate is increasing rapidly. Please take a break."

[0867] If the customer is analyzed as being impatient, the response would be:

[0868] "We will deliver it to you shortly. Please wait."

[0869] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0870] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0871] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0872] [Third embodiment]

[0873] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0874] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0875] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0876] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0877] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0878] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0879] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0880] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0881] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0882] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0883] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0884] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0885] MODE FOR CARRYING OUT THE INVENTION

[0886] System Overview

[0887] This system acquires biological data from animals (specifically cats) and uses that data to support communication between the user and the animal. It is also possible to manage the cat's health. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[0888] System program and processing flow

[0889] Data collection and transmission

[0890] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in a buffer in real time and sent to a server at regular intervals.

[0891] Data analysis and health status assessment

[0892] The server analyzes the received vital data and evaluates the cat's current health status. Various analysis modules (e.g., heart rate monitoring, temperature evaluation, etc.) are used for the analysis. The analysis results are stored in a database and can be referenced later.

[0893] User interaction interface

[0894] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[0895] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[0896] Health Management and Notifications

[0897] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[0898] Specific examples

[0899] Example 1: Conversation with a cat

[0900] The user speaks to the smartphone app, asking, "How are you feeling today?"

[0901] The device collects this voice data and transmits it to the server in real time.

[0902] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "How are you feeling today?" and compares it with the cat's heart rate, body temperature, and step count data.

[0903] It verifies that the animal is in good condition and generates the response "I'm feeling good today. I've had lots of fun."

[0904] The generation AI converts the response into voice data and sends it to the device.

[0905] The device plays the audio, allowing the user to hear the cat's "reply."

[0906] Example 2: Health alert notification

[0907] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[0908] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[0909] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[0910] If necessary, the user can immediately consult a veterinarian.

[0911] This system allows users to deepen their relationship with their cat while efficiently managing their health.

[0912] The processing flow will be explained below.

[0913] Step 1:

[0914] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[0915] Step 2:

[0916] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[0917] Step 3:

[0918] The server receives the biometric data sent from the device and immediately stores it in a database.

[0919] Step 4:

[0920] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[0921] Step 5:

[0922] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[0923] Step 6:

[0924] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[0925] Step 7:

[0926] The terminal transmits the recorded voice data to the server in real time.

[0927] Step 8:

[0928] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[0929] Step 9:

[0930] The server analyzes the converted text data and understands the user's question, for example, "How are you today?"

[0931] Step 10:

[0932] The server compares the analysis results with previously collected and analyzed biometric data and generates an appropriate response based on the animal's condition.

[0933] Step 11:

[0934] The server converts the generated response into audio data.

[0935] Step 12:

[0936] The server transmits the converted voice data to the terminal.

[0937] Step 13:

[0938] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[0939] Step 14:

[0940] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[0941] Step 15:

[0942] The server generates an alert message and sends it to the user's smartphone app.

[0943] Step 16:

[0944] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[0945] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[0946] Example 1

[0947] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0948] Conventional animal health management systems not only collect biometric data, but also make it difficult for users to check the health status of animals while interacting with them. Furthermore, they lack the functionality to notify users in real time when an abnormality occurs, making it difficult to take early action. This has created a need for a means to properly monitor the health status of animals and take prompt action.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0950] In this invention, the server includes a means for using the generative AI model, a means for managing an API endpoint for receiving data in real time, and a means for storing and referencing the health status evaluation results in a database, allowing users to efficiently monitor the health status of their animals while enjoying interactions with them and receive prompt notification when abnormalities are detected.

[0951] "Biometric data" refers to numerical information related to an animal's body, such as its heart rate, body temperature, and number of steps taken.

[0952] A "server" is a computer system that analyzes and stores acquired data and manages communications with users and terminals.

[0953] A "generative AI model" is an algorithm that generates appropriate responses based on collected data and voice information.

[0954] A "terminal" is a device that is attached to an animal and has the function of acquiring biometric data and transmitting it to a server.

[0955] "Audio input means" is a device or function that collects audio that the user uses to talk to the animal.

[0956] An "API endpoint" is a connection point through which other systems or devices can exchange data with a server.

[0957] A "database" is a system that systematically stores information such as analysis results and biological data, and makes it available for reference as needed.

[0958] "Real-time" refers to an environment or process that operates instantly with almost no delay.

[0959] An "abnormal value" is a data value that deviates from the normal range, and is particularly a value that may affect health status.

[0960] "Notification" is a message that warns or informs the user when an abnormality is detected.

[0961] This invention is a system that acquires biometric data from animals (specifically cats) and supports communication between users and animals based on that data. It is also possible to manage the cat's health. The system consists of a device attached to the animal, a server, and a smartphone app used by the user.

[0962] Data collection

[0963] The terminal is a device attached to the cat's collar and uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in an internal buffer in real time and periodically transmitted to a server using a wireless communication module.

[0964] Data reception and storage

[0965] The server manages an API endpoint for receiving biometric data sent from the device in real time. The server checks the received data to ensure there is no invalid data. The verified data is stored in a database. Using a database management system (DBMS), this data can be efficiently stored and referenced.

[0966] Data analysis and health status assessment

[0967] The server uses a heart rate monitoring module and a temperature evaluation module to analyze the stored vital data, thereby assessing the cat's current health status. The analysis results are stored in a database for users to refer to later.

[0968] User interaction interface

[0969] Users can talk to the cat using a smartphone app. To do this, voice data is collected using a voice input means and sent to a server. The server then converts the received voice data into text using a natural language processing module. The converted text is analyzed by a generative AI model to generate a response appropriate to the situation. The generated response is then converted back into voice data and sent to the device. The device then plays this voice data back to the cat, allowing the user to hear the cat's "reply."

[0970] Health Management and Notifications

[0971] The server constantly monitors the vital signs and immediately notifies the user if any abnormal values ​​are detected. The notification is sent via a smartphone app, and the type of abnormal value and detailed information are provided, allowing the user to take prompt action.

[0972] Specific examples

[0973] Conversation with a cat

[0974] 1. The user speaks to the smartphone app, "How are you feeling today?"

[0975] 2. The device collects voice data and transmits it to the server in real time.

[0976] 3. The server converts the voice data into text using a natural language processing module.

[0977] 4. The generative AI model compares the cat's heart rate, body temperature, and step count data and generates a response such as, "I'm feeling good today. I've played a lot."

[0978] 5. The server converts the response into audio data and sends it to the device.

[0979] 6. The device plays the sound to the cat, and the user can hear it.

[0980] Health Alert Notifications

[0981] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[0982] 2. The server immediately identifies abnormal values ​​and creates an alert.

[0983] 3. The server sends the alert to the user's smartphone app.

[0984] 4. The user receives a notification and opens the app to see more information, such as a sudden rise in temperature from 38.5°C to 40°C.

[0985] 5. Users may consult a veterinarian if necessary.

[0986] This allows users to efficiently manage their health while enjoying interaction with their cat.

[0987] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0988] System program processing flow

[0989] Step 1: Data collection

[0990] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps. The input is raw data from the sensor, and the output is formatted biological data stored in an internal buffer. Specifically, the sensor measures the cat's heart rate every second and stores the data as a byte sequence.

[0991] Step 2: Send data

[0992] The device periodically transmits data accumulated in its internal buffer to the server in batches. The input is the formatted biometric data in the buffer, and the output is the data packets transmitted to the server. Transmission is triggered when the number of accumulated data reaches 100.

[0993] Step 3: Receiving and storing data

[0994] The server receives data sent from the device in real time. The input is the data packet sent from the device, and the output is the formatted data to be stored in the database. The server's API endpoint receives the data, and the data is stored by the database management system (DBMS).

[0995] Step 4: Data analysis

[0996] The server passes the stored data to the analysis module to evaluate the health status. The input is the biometric data stored in the database, and the output is the analysis result (e.g., good health, outside normal range, etc.). Specifically, the heart rate monitoring module extracts data from the past hour and calculates the average heart rate.

[0997] Step 5: User interaction interface

[0998] A user talks to a cat using a smartphone app. The input is the user's voice data, and the output is the voice data sent to the server. The voice data is collected and sent to the server in real time.

[0999] Step 6: Analyze the audio data

[1000] The server analyzes the voice data through a natural language processing module. The input is the user's voice data, and the output is the analysis result in text format. For example, the voice data "How are you today?" is converted into text.

[1001] Step 7: Response Generation

[1002] The generative AI model generates a response based on the analyzed text. The input is the analysis result of the natural language processing module, and the output is the generated response text. The generative AI model compares the cat's heart rate, body temperature, and step count data to generate an appropriate response.

[1003] Step 8: Sending a Response

[1004] The server converts the generated response into audio data and sends it to the device. The input is the text response output by the generative AI model, and the output is audio data. A text-to-speech engine is used to convert the text into audio.

[1005] Step 9: Playing Audio

[1006] The device plays the audio data sent from the server and communicates the response to the cat. The input is the audio data sent from the server, and the output is the audio to be played. The audio is played using the device's speaker.

[1007] Step 10: Anomaly detection and notification

[1008] The server constantly monitors the received biometric data and immediately sends a notification to the user if an abnormal value is detected. The input is the biometric data stored on the server, and the output is a notification sent to a smartphone app. For example, a notification is triggered when the body temperature exceeds 40 degrees.

[1009] This allows the user to efficiently monitor the cat's health throughout all steps and respond quickly if any abnormalities occur.

[1010] (Application example 1)

[1011] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1012] Conventional pet monitoring systems have had the problem of making it difficult to monitor the health status of pets while their owners are out, or to detect abnormalities in the home at an early stage. The present invention aims to solve these problems and provide a system that enables real-time monitoring of pet health status and prompt notification of abnormalities.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1014] In this invention, the server includes means for acquiring biometric data of the animal, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the current health condition of the animal, voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the state of the animal, means for converting the generated response into voice data and transmitting the response to the animal, and means for detecting abnormalities using the acquired biometric data and sending an alert to the user if an abnormality is found. This allows owners to keep track of their pet's health condition and the situation at home in real time even when they are out, and to respond quickly when an abnormality occurs.

[1015] "Animal biometric data" refers to data that indicates the animal's physical condition, such as heart rate, body temperature, and number of steps taken.

[1016] "Means of acquisition" refers to the method of collecting biological data of animals using sensors or devices.

[1017] The "means for transmitting to the server" refers to a method for transferring the collected biometric data to the server via wireless or wired communication.

[1018] A "server" is a computer system that stores and analyzes collected biometric data.

[1019] "Means of analysis" refers to methods of assessing the health status of animals using algorithms and software that process and analyze biological data.

[1020] The "voice input means for talking to animals" refers to a microphone or voice recognition device that allows the user to input a voice message to the animals.

[1021] The "means for transmitting data from the voice input means to the server" is a communication means for transferring input voice data to the server.

[1022] "Means for analyzing voice data" refers to natural language processing technology that converts input voice data into text data and understands the content.

[1023] The "means for generating a response appropriate to the animal's condition" is an AI model that generates an appropriate response according to the animal's condition based on analyzed voice data.

[1024] The "means for converting the generated response into voice data" is a voice synthesis technology that converts the generated text-format response into voice.

[1025] The "means for transmitting a response" refers to a terminal or speaker that allows the animal to hear the generated audio data.

[1026] The "means for detecting anomalies" is an algorithm for identifying abnormal data that exceeds a certain threshold based on the collected biometric data.

[1027] "Means for sending an alert to a user" refers to a system or technology for sending a notification to a user when an abnormality is detected.

[1028] System Overview

[1029] The system of the present invention acquires the animal's biological data, supports communication between the user and the animal based on that data, monitors the animal's health, and sends an alert to the user if an abnormality occurs. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[1030] Data collection and transmission

[1031] The device is equipped with sensors that measure biometric data such as heart rate, body temperature, and number of steps taken. The device collects this data in real time and transmits it to a server at regular intervals. Wireless communication technology (such as Bluetooth or Wi-Fi) is used for transmission.

[1032] Data analysis and health status assessment

[1033] The server analyzes the acquired vital data and evaluates the animal's current health condition. Analysis tools such as a heart rate monitoring module and a body temperature evaluation module are used for the analysis. The server stores the vital data in a database, and if abnormal data is detected, it sends an alert to the user. The alert is sent via push notification or email.

[1034] User interaction interface

[1035] Users can talk to animals using a smartphone app. The user's voice data is sent to the server via a voice input method (such as a smartphone microphone). The server then converts the voice data into text through a natural language processing module and analyzes what the user said. A generative AI model generates a response appropriate to the animal's condition, which is then converted into voice data and transmitted to the animal. This allows users to enjoy two-way communication with the animals.

[1036] Health Management and Notifications

[1037] The server constantly monitors the received vital signs and immediately sends an alert to the user if an abnormal value is detected. The notification is sent via a smartphone app, and the type of abnormality and detailed information are provided. This allows the user to quickly understand the animal's health condition and, if necessary, consult a veterinarian immediately.

[1038] Example

[1039] (Example 1: Home pet monitoring system)

[1040] This system allows users to detect abnormalities in their cat's heart rate or body temperature while they are out. For example, if a cat's heart rate suddenly increases while they are out, the server immediately detects the abnormality and sends an alert to the user's smartphone. This allows the user to quickly check the situation and take any necessary measures.

[1041] (Example of a prompt for a generative AI model)

[1042] I would like to build a system that detects abnormalities in the home using biological data from cats and sends alerts to users who are out. Specifically, how can I issue an alert based on abnormal behavior such as a sudden rise in heart rate or body temperature?

[1043] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1044] Step 1:

[1045] Collection of animal biometric data

[1046] The device uses sensors to collect biometric data such as heart rate, body temperature, and number of steps in real time.

[1047] Input: Biological data collected by sensors.

[1048] Output: The acquired biometric data is stored in a buffer as data packets.

[1049] Step 2:

[1050] Sending biometric data to the server

[1051] The device transmits the collected biometric data to a server at regular intervals (for example, every minute). Wireless technologies such as Bluetooth and Wi-Fi are used for communication.

[1052] Input: Buffered biometric data.

[1053] Data processing: Assembling multiple data points in a buffer into a data packet.

[1054] Output: Data packets sent to the server.

[1055] Step 3:

[1056] Biological data analysis

[1057] The server analyzes the received biometric data, using modules such as a heart rate monitoring module and a body temperature evaluation module.

[1058] Input: The data packet sent to the server.

[1059] Data calculations: Each data point is evaluated to see if it is within normal limits. Any outliers are flagged.

[1060] Output: Analysis results showing health status and abnormality flags.

[1061] Step 4:

[1062] Detecting outliers and sending alerts

[1063] Based on the analysis results, the server sends a notification to the user if any abnormal values ​​are detected.

[1064] Input: Analysis results and anomaly flags.

[1065] Data processing: If an abnormality flag is raised, an alarm message is generated.

[1066] Output: An alert notification sent to the user's smartphone.

[1067] Step 5:

[1068] User interaction

[1069] The user talks to the animals through a smartphone app, and the user's voice is picked up by a voice input means and sent to the server.

[1070] Input: User's voice data.

[1071] Output: The audio data sent to the server.

[1072] Step 6:

[1073] Voice data analysis and response generation

[1074] The server runs the user's voice data through a natural language processing module, converts it into text, and then uses a generative AI model to generate a response appropriate to the animal's condition.

[1075] Input: The audio data sent to the server.

[1076] Data Calculation: Converts voice data into text, analyzes the user's intent, and generates an appropriate response based on the analysis results.

[1077] Output: The response in text format.

[1078] Step 7:

[1079] Response communication

[1080] The server converts the generated response into audio data and sends it to the terminal, which plays the audio data to the animal.

[1081] Input: A text response.

[1082] Data processing: Converting text data into audio data.

[1083] Output: The audio data sent to the device.

[1084] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1085] MODE FOR CARRYING OUT THE INVENTION

[1086] System Overview

[1087] This system acquires biometric data from animals (specifically cats) and uses that data to support communication between the user and the animal. It also manages the cat's health. Furthermore, it uses an emotion engine to recognize the user's emotions based on the user's voice input data and generates responses based on those emotions, enabling more natural and emotional communication. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[1088] System program and processing flow

[1089] Data collection and transmission

[1090] The device uses sensors to capture the cat's heart rate, body temperature, and number of steps taken. This biological data is stored in a buffer in real time and then sent to a server at regular intervals.

[1091] Data analysis and health status assessment

[1092] The server analyzes the received biometric data and evaluates the cat's current health status. The analysis utilizes modules such as heart rate monitoring and temperature evaluation. The analysis results are stored in a database and can be referenced later.

[1093] User interaction interface

[1094] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing (NLP) module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[1095] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[1096] Recognizing emotions and regulating responses

[1097] As the server analyzes the user's voice data, it uses an emotion engine to recognize the user's emotions (happiness, sadness, anger, etc.). The recognized emotion data is reflected in the generated response. For example, if the user is speaking happily, the response will be generated in a bright tone. This enables more natural and emotional communication.

[1098] Health Management and Notifications

[1099] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[1100] Specific examples

[1101] Example 1: Emotional conversation with a cat

[1102] The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[1103] The device collects this voice data and transmits it to the server in real time.

[1104] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "Did you have fun today?" and compares it with the cat's heart rate, body temperature, and step count data.

[1105] The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[1106] The generation AI converts the response into voice data and sends it to the device.

[1107] The device plays the audio, allowing the user to hear the cat's "reply."

[1108] Example 2: Health alert notification

[1109] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[1110] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[1111] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[1112] If necessary, the user can immediately consult a veterinarian.

[1113] This system allows users to deepen their relationship with their cat, while enjoying emotional conversations and efficiently managing their health.

[1114] The processing flow will be explained below.

[1115] Step 1:

[1116] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[1117] Step 2:

[1118] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[1119] Step 3:

[1120] The server receives the biometric data sent from the device and immediately stores it in a database.

[1121] Step 4:

[1122] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[1123] Step 5:

[1124] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[1125] Step 6:

[1126] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[1127] Step 7:

[1128] The terminal transmits the recorded voice data to the server in real time.

[1129] Step 8:

[1130] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[1131] Step 9:

[1132] The server analyzes the text data and understands the user's question, for example, "How are you feeling today?"

[1133] Step 10:

[1134] The server runs the analyzed text data through an emotion engine to recognize the user's emotions (joy, sadness, anger, etc.).

[1135] Step 11:

[1136] The server compares the user's emotional data with the evaluation results of the biometric data and generates an appropriate response based on the animal's state. For example, if the user is talking to the animal in a happy manner, the response will also be generated in a bright tone.

[1137] Step 12:

[1138] The server converts the generated response into audio data.

[1139] Step 13:

[1140] The server transmits the converted voice data to the terminal.

[1141] Step 14:

[1142] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[1143] Step 15:

[1144] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[1145] Step 16:

[1146] The server generates an alert message and sends it to the user's smartphone app.

[1147] Step 17:

[1148] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[1149] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[1150] Example 2

[1151] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1152] Conventional animal health management systems lack the ability to collect and analyze real-time health data, making it difficult for users to immediately understand the health status of their animals. Furthermore, communication between users and animals is rarely emotional or natural, making it difficult to achieve emotionally rich dialogue. Furthermore, the timing of abnormal value detection and notification can be delayed, potentially delaying a prompt response to sudden health abnormalities.

[1153] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for converting voice data into text and recognizing the user's emotion, a means for generating a response appropriate to the animal's condition using a generative AI model based on the emotion, and a means for converting the generated response into voice data and transmitting the response to the animal. This allows the user to understand the animal's health condition in real time and communicate with the animal through emotional and natural dialogue. In addition, detection of abnormal values ​​and immediate notification enable rapid response.

[1154] "Animal biometric data" refers to data relating to an animal's physical activity and physiological state, such as heart rate, body temperature, and number of steps taken.

[1155] "Means of acquisition" refers to methods and devices for collecting biological data of animals using devices such as sensors.

[1156] "Server" refers to a computer system that analyzes collected biometric and voice data, determines health status, and generates dialogue responses.

[1157] "Voice input means" refers to a device or function for collecting voices uttered by a user, such as a microphone on a smartphone.

[1158] "Means for converting to text" refers to a method or device that converts voice data into text information using natural language processing technology.

[1159] "Means for recognizing user emotions" refers to a method or device for analyzing user emotions (happiness, sadness, anger, etc.) from text or voice data.

[1160] A "generative AI model" refers to a model that uses artificial intelligence to generate responses based on input data, such as a model that performs natural language processing.

[1161] "Means for generating a response" refers to a method or device that uses a generative AI model to generate a response that is adapted to the user's input and the animal's state.

[1162] "Means for converting into voice data" refers to a method or device that converts the generated text response into voice data using voice synthesis technology.

[1163] This invention is a system that acquires biometric data from animals (specifically cats) and allows natural interaction with users and health management. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[1164] Hardware and Software

[1165] The device is equipped with built-in sensors to measure the cat's heart rate, body temperature, and steps taken. The sensors include an optical heart rate sensor, a thermistor, and a three-axis accelerometer. The device buffers the data in real time and transmits it to a server via Bluetooth.

[1166] The server is a high-performance computing system that uses Python to acquire, analyze, and store biometric data. Specifically, it uses Python's Pandas library and Scikit-learn models to perform data analysis and anomaly detection. The analysis results are stored in a MongoDB database. The server also converts voice data into text using Google Cloud's Speech-to-Text API and has the ability to recognize user emotions using Microsoft Azure's Text Analytics API.

[1167] The smartphone app was developed using Flutter and provides a voice input method for users to talk to the cat. The collected voice data is converted into FLAC format and sent to a server in real time.

[1168] Specific examples of processing

[1169] Example 1: Emotional conversation with a cat

[1170] 1. The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[1171] 2. The device collects this voice data and sends it to the server in real time.

[1172] 3. The server converts the audio data into text using Google Cloud's Speech-to-Text API. The server analyzes the question, "Did you have fun today?" and compares it with the cat's biometric data.

[1173] 4. The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[1174] 5. The generative AI model (e.g., GPT-4) generates a response and converts this response into audio data (e.g., Amazon Polly).

[1175] 6. The device will play the audio and the user will be able to hear the cat's "reply."

[1176] Example prompt sentence:

[1177] "Based on the cat's biometric data, generate a response to the question, 'Did you have fun today?' The user's emotion is joy."

[1178] Example 2: Health alert notification

[1179] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[1180] 2. The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[1181] 3. When the user receives the notification, they can open the app and check detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[1182] 4. If necessary, the user may seek immediate veterinary advice.

[1183] The system allows users to monitor their animals' health status in real time, communicate with them through emotional dialogue, and instantly notify them of any abnormalities, allowing for rapid response.

[1184] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1185] Step 1: Data collection

[1186] The device acquires the cat's heart rate, body temperature, and step count using built-in sensors (optical heart rate sensor, thermistor, and three-axis acceleration sensor). The acquired data is stored in buffer memory in real time. The input data includes biometric data such as heart rate, body temperature, and step count. The output is the biometric data stored in the buffer memory. The sensors are set to measure the heart rate every second and the body temperature every minute.

[1187] Step 2: Send data

[1188] The terminal transmits the biometric data stored in the buffer memory to the server via Bluetooth at regular intervals (for example, every 30 seconds). The input data is the biometric data in the buffer memory, and the output is the biometric data transmitted to the server. The Bluetooth transceiver divides the data into packets and transmits them.

[1189] Step 3: Data analysis

[1190] The server analyzes the received biometric data using Python's Pandas library. The analysis involves identifying trends in heart rate, body temperature, and step count, and detecting outliers. The input data is the transmitted biometric data, and the output is the analysis results. Scikit-learn models are used to calculate Z-scores and detect anomalies, for example.

[1191] Step 4: Save Data

[1192] The server stores the analyzed data in a MongoDB database. The input data is the analyzed biometric data, and the output is the records stored in the database. The database records fluctuations in heart rate, body temperature, and number of steps as time series data.

[1193] Step 5: Voice Input

[1194] The user talks to the cat using a smartphone app. Audio data is collected through the smartphone's microphone and converted to FLAC format. The input is the user's voice, and the output is audio data in FLAC format. The smartphone app's recording function collects up to 60 seconds of audio.

[1195] Step 6: Sending audio data

[1196] The smartphone app transmits the collected audio data to the server in real time. The input data is audio data in FLAC format, and the output is the audio data transmitted to the server. The audio data is transmitted using Wi-Fi or mobile networks.

[1197] Step 7: Text conversion

[1198] The server converts the audio data into text using Google Cloud's Speech-to-Text API. The input data is audio data in FLAC format, and the output is text information. The converted text is then stored on the server for further analysis.

[1199] Step 8: Emotion Recognition

[1200] The server uses Microsoft Azure's Text Analytics API to recognize the user's emotions. The input data is character information (text), and the output is an emotion score (happiness, sadness, anger, etc.). The emotion score is stored in an internal emotion database.

[1201] Step 9: Response Generation

[1202] The server generates an appropriate response using a generative AI model (e.g., GPT-4) based on the emotion score and text information. The input data are the emotion score and text information, and the output is the generated text response. The generation process includes adjusting the response based on the emotion.

[1203] Step 10: Audio conversion

[1204] The server uses speech synthesis technology (e.g., Amazon Polly) to convert the generated text response into audio data. The input data is the text response, and the output is audio data, which is then sent back to the device.

[1205] Step 11: Play audio

[1206] The device plays the generated voice data to the cat. The input data is the voice data, and the output is the voice that is played as voice output. The voice output is done using the built-in speaker, so that the user can hear the cat's response.

[1207] (Application example 2)

[1208] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1209] In conventional food delivery systems, health management of delivery personnel is inadequate, resulting in excessive workload and health risks. Furthermore, smooth communication with customers is often lacking, creating a need for improved customer satisfaction. Furthermore, there is a lack of mechanisms for understanding customer emotions and responding appropriately, so these issues must be addressed simultaneously.

[1210] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1211] In this invention, the server includes means for acquiring the animal's biometric data, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the animal's current health condition, a voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the animal's condition, means for converting the generated response into voice data and transmitting the response to the animal, means for acquiring the delivery person's heart rate, body temperature, and activity level, means for detecting abnormalities in the delivery person's biometric data and notifying them if detected, means for analyzing the transmitted user voice data and generating an appropriate response using a generative AI model, and means for converting the generated response into voice data and transmitting the response to the user. This enables real-time management of the delivery person's health and early detection and notification of abnormalities. Furthermore, understanding customer emotions and generating appropriate responses can facilitate communication with customers and improve customer satisfaction.

[1212] "Biological data of an animal" refers to data that indicates the physiological state of an animal, such as the animal's heart rate, body temperature, and number of steps.

[1213] A "server" is a computer system for collecting, analyzing, storing, and generating responses to data.

[1214] The "voice input means" is a device such as a microphone that allows the user to input information by voice.

[1215] "Analysis" is the act of statistically or computationally processing acquired data to extract meaningful information.

[1216] A "response generator" is an algorithm or software that constructs an appropriate response based on input data.

[1217] "Biometric data of the delivery person" refers to data indicating the physiological condition of the delivery person, such as the delivery person's heart rate, body temperature, and activity level.

[1218] "Notification" refers to the act of notifying a user or administrator when an abnormality is detected.

[1219] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to generate appropriate output from input data.

[1220] "Real time" refers to the acquisition and processing of information almost immediately.

[1221] "Customer emotions" are psychological states such as joy, sadness, and anger expressed by customers.

[1222] "Communication" is the act of exchanging information and emotions.

[1223] "Customer satisfaction" refers to the degree of satisfaction a customer feels with a service.

[1224] An "analysis module" is a software component designed to analyze specific data.

[1225] The present invention relates to a system that acquires biometric data of animals and delivery personnel, and uses that data to communicate with them and manage their health. This system is composed of the following components:

[1226] System Overview

[1227] Hardware and Software Configuration

[1228] 1. Means of obtaining biometric data of animals and delivery personnel:

[1229] The heart rate, body temperature, and number of steps of the animal, as well as the heart rate, body temperature, and activity of the delivery person, are acquired using sensors (e.g., Polar H10 heart rate sensor, thermistor body temperature module).

[1230] 2. Means for transmitting biometric data to the server:

[1231] Wireless communication modules (e.g., Bluetooth, Wi-Fi) are used to transmit the acquired biometric data in real time to the server, which receives the data using the Django framework.

[1232] 3. Data analysis and judgment:

[1233] The server uses specialized analysis modules (e.g., TensorFlow, Pandas) to analyze the received data, thereby determining the health status of the animals and delivery personnel.

[1234] The analyzed biometric data is stored in an SQLite database.

[1235] 4. Voice input method:

[1236] The smartphone app uses a built-in microphone for voice input, and sends the data entered by the user via voice to the server.

[1237] 5. Speech data analysis and response generation:

[1238] The server analyzes the received voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text format.

[1239] An appropriate response is generated using a generative AI model (e.g., GPT-3), taking into account the state of the animal and delivery person.

[1240] 6. Response voice data conversion and transmission method:

[1241] Use a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated response into audio data and play it back to the animal or user.

[1242] 7. Outlier detection and notification:

[1243] The server constantly monitors the biometric data, and if it detects any abnormal values, it immediately sends a push notification (e.g., Firebase Cloud Messaging) to the user.

[1244] Specific examples

[1245] Example 1: Health management of animals and delivery personnel

[1246] The delivery person's heart rate is monitored, and if a sudden increase is detected, the server immediately sends a notification to the delivery person's smartphone saying, "Your heart rate is rising rapidly. Please take a break."

[1247] Example 2: Emotional communication with customers

[1248] If a customer requests a quick delivery by voice input on a smartphone app, saying, "I'm busy today, so please deliver quickly," the server analyzes the request using an emotion engine and uses a generative AI model to generate an appropriate response, such as, "We will deliver it right away. Please wait."

[1249] Prompt Sentence Examples

[1250] If the delivery person's heart rate exceeds 180, the notification will read:

[1251] "Your heart rate is increasing rapidly. Please take a break."

[1252] If the customer is analyzed as being impatient, the response would be:

[1253] "We will deliver it to you shortly. Please wait."

[1254] The above configuration and processing allows for real-time health management of animals and delivery personnel, and immediate notification of any abnormalities. It also enables natural, emotional responses when communicating with customers, which is expected to improve customer satisfaction.

[1255] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1256] Step 1:

[1257] Input: Animal and delivery personnel biometric data (heart rate, body temperature, steps, activity level)

[1258] Processing: The device uses sensors (e.g., Polar H10, thermistor) to measure heart rate, body temperature, steps, and activity in real time. This data is temporarily stored in a buffer on the device.

[1259] Output: Raw biometric data obtained from sensors (heart rate, body temperature, steps, activity level)

[1260] Step 2:

[1261] Input: Raw biometric data (heart rate, body temperature, steps, activity level)

[1262] Processing: The raw data acquired by the device is sent to the server at regular intervals. The communication method is a wireless communication module (e.g., Bluetooth, Wi-Fi).

[1263] Output: Biometric data sent to the server

[1264] Step 3:

[1265] Input: Biometric data sent to the server

[1266] Processing: The server receives the biometric data and analyzes it using an analysis module (e.g., TensorFlow, Pandas). Specifically, it calculates the average heart rate and temperature fluctuation patterns to evaluate the health status.

[1267] Output: Analysis results (health status evaluation)

[1268] Step 4:

[1269] Input: Analysis results (health status evaluation)

[1270] Processing: The server saves the analysis results in an SQLite database, which allows for analysis by comparing them with past data and makes it easier to detect outliers.

[1271] Output: Saved analysis result data

[1272] Step 5:

[1273] Input: User's voice input data

[1274] Processing: The user inputs voice through the smartphone app. The smartphone's microphone collects the voice data and sends it to the server.

[1275] Output: Audio data sent to the server

[1276] Step 6:

[1277] Input: Audio data sent to the server

[1278] Processing: The server analyzes the voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text. The converted text data is then used by a generative AI model (e.g., GPT-3) to generate an appropriate response.

[1279] Output: The generated response text

[1280] Step 7:

[1281] Input: Generated response text

[1282] Processing: The server uses a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated text into audio data, which is then sent to the user's smartphone app.

[1283] Output: Audio data sent to the user's smartphone

[1284] Step 8:

[1285] Input: Analysis results (health status evaluation)

[1286] Processing: The server constantly monitors the analysis results, and if an abnormal value is detected, it sends a push notification to the user using Firebase Cloud Messaging or similar.

[1287] Output: Notification of anomaly detection sent to the user

[1288] Example prompt:

[1289] If the delivery person's heart rate exceeds 180, the notification will read:

[1290] "Your heart rate is increasing rapidly. Please take a break."

[1291] If the customer is analyzed as being impatient, the response would be:

[1292] "We will deliver it to you shortly. Please wait."

[1293] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1294] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1295] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1296] [Fourth embodiment]

[1297] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1298] 7, a 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.

[1299] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1300] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1301] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1302] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1303] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1304] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1305] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1306] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1307] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1308] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1309] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] MODE FOR CARRYING OUT THE INVENTION

[1311] System Overview

[1312] This system acquires biological data from animals (specifically cats) and uses that data to support communication between the user and the animal. It is also possible to manage the cat's health. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[1313] System program and processing flow

[1314] Data collection and transmission

[1315] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in a buffer in real time and sent to a server at regular intervals.

[1316] Data analysis and health status assessment

[1317] The server analyzes the received vital data and evaluates the cat's current health status. Various analysis modules (e.g., heart rate monitoring, temperature evaluation, etc.) are used for the analysis. The analysis results are stored in a database and can be referenced later.

[1318] User interaction interface

[1319] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[1320] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[1321] Health Management and Notifications

[1322] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[1323] Specific examples

[1324] Example 1: Conversation with a cat

[1325] The user speaks to the smartphone app, asking, "How are you feeling today?"

[1326] The device collects this voice data and transmits it to the server in real time.

[1327] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "How are you feeling today?" and compares it with the cat's heart rate, body temperature, and step count data.

[1328] It verifies that the animal is in good condition and generates the response "I'm feeling good today. I've had lots of fun."

[1329] The generation AI converts the response into voice data and sends it to the device.

[1330] The device plays the audio, allowing the user to hear the cat's "reply."

[1331] Example 2: Health alert notification

[1332] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[1333] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[1334] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[1335] If necessary, the user can immediately consult a veterinarian.

[1336] This system allows users to deepen their relationship with their cat while efficiently managing their health.

[1337] The processing flow will be explained below.

[1338] Step 1:

[1339] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[1340] Step 2:

[1341] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[1342] Step 3:

[1343] The server receives the biometric data sent from the device and immediately stores it in a database.

[1344] Step 4:

[1345] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[1346] Step 5:

[1347] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[1348] Step 6:

[1349] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[1350] Step 7:

[1351] The terminal transmits the recorded voice data to the server in real time.

[1352] Step 8:

[1353] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[1354] Step 9:

[1355] The server analyzes the converted text data and understands the user's question, for example, "How are you today?"

[1356] Step 10:

[1357] The server compares the analysis results with previously collected and analyzed biometric data and generates an appropriate response based on the animal's condition.

[1358] Step 11:

[1359] The server converts the generated response into audio data.

[1360] Step 12:

[1361] The server transmits the converted voice data to the terminal.

[1362] Step 13:

[1363] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[1364] Step 14:

[1365] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[1366] Step 15:

[1367] The server generates an alert message and sends it to the user's smartphone app.

[1368] Step 16:

[1369] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[1370] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[1371] Example 1

[1372] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] Conventional animal health management systems not only collect biometric data, but also make it difficult for users to check the health status of animals while interacting with them. Furthermore, they lack the functionality to notify users in real time when an abnormality occurs, making it difficult to take early action. This has created a need for a means to properly monitor the health status of animals and take prompt action.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1375] In this invention, the server includes a means for using the generative AI model, a means for managing an API endpoint for receiving data in real time, and a means for storing and referencing the health status evaluation results in a database, allowing users to efficiently monitor the health status of their animals while enjoying interactions with them and receive prompt notification when abnormalities are detected.

[1376] "Biometric data" refers to numerical information related to an animal's body, such as its heart rate, body temperature, and number of steps taken.

[1377] A "server" is a computer system that analyzes and stores acquired data and manages communications with users and terminals.

[1378] A "generative AI model" is an algorithm that generates appropriate responses based on collected data and voice information.

[1379] A "terminal" is a device that is attached to an animal and has the function of acquiring biometric data and transmitting it to a server.

[1380] "Audio input means" is a device or function that collects audio that the user uses to talk to the animal.

[1381] An "API endpoint" is a connection point through which other systems or devices can exchange data with a server.

[1382] A "database" is a system that systematically stores information such as analysis results and biological data, and makes it available for reference as needed.

[1383] "Real-time" refers to an environment or process that operates instantly with almost no delay.

[1384] An "abnormal value" is a data value that deviates from the normal range, and is particularly a value that may affect health status.

[1385] "Notification" is a message that warns or informs the user when an abnormality is detected.

[1386] This invention is a system that acquires biometric data from animals (specifically cats) and supports communication between users and animals based on that data. It is also possible to manage the cat's health. The system consists of a device attached to the animal, a server, and a smartphone app used by the user.

[1387] Data collection

[1388] The terminal is a device attached to the cat's collar and uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps taken. The collected data is stored in an internal buffer in real time and periodically transmitted to a server using a wireless communication module.

[1389] Data reception and storage

[1390] The server manages an API endpoint for receiving biometric data sent from the device in real time. The server checks the received data to ensure there is no invalid data. The verified data is stored in a database. Using a database management system (DBMS), this data can be efficiently stored and referenced.

[1391] Data analysis and health status assessment

[1392] The server uses a heart rate monitoring module and a temperature evaluation module to analyze the stored vital data, thereby assessing the cat's current health status. The analysis results are stored in a database for users to refer to later.

[1393] User interaction interface

[1394] Users can talk to the cat using a smartphone app. To do this, voice data is collected using a voice input means and sent to a server. The server then converts the received voice data into text using a natural language processing module. The converted text is analyzed by a generative AI model to generate a response appropriate to the situation. The generated response is then converted back into voice data and sent to the device. The device then plays this voice data back to the cat, allowing the user to hear the cat's "reply."

[1395] Health Management and Notifications

[1396] The server constantly monitors the vital signs and immediately notifies the user if any abnormal values ​​are detected. The notification is sent via a smartphone app, and the type of abnormal value and detailed information are provided, allowing the user to take prompt action.

[1397] Specific examples

[1398] Conversation with a cat

[1399] 1. The user speaks to the smartphone app, "How are you feeling today?"

[1400] 2. The device collects voice data and transmits it to the server in real time.

[1401] 3. The server converts the voice data into text using a natural language processing module.

[1402] 4. The generative AI model compares the cat's heart rate, body temperature, and step count data and generates a response such as, "I'm feeling good today. I've played a lot."

[1403] 5. The server converts the response into audio data and sends it to the device.

[1404] 6. The device plays the sound to the cat, and the user can hear it.

[1405] Health Alert Notifications

[1406] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[1407] 2. The server immediately identifies abnormal values ​​and creates an alert.

[1408] 3. The server sends the alert to the user's smartphone app.

[1409] 4. The user receives a notification and opens the app to see more information, such as a sudden rise in temperature from 38.5°C to 40°C.

[1410] 5. Users may consult a veterinarian if necessary.

[1411] This allows users to efficiently manage their health while enjoying interaction with their cat.

[1412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1413] System program processing flow

[1414] Step 1: Data collection

[1415] The device uses sensors to collect biological data such as the cat's heart rate, body temperature, and number of steps. The input is raw data from the sensor, and the output is formatted biological data stored in an internal buffer. Specifically, the sensor measures the cat's heart rate every second and stores the data as a byte sequence.

[1416] Step 2: Send data

[1417] The device periodically transmits data accumulated in its internal buffer to the server in batches. The input is the formatted biometric data in the buffer, and the output is the data packets transmitted to the server. Transmission is triggered when the number of accumulated data reaches 100.

[1418] Step 3: Receiving and storing data

[1419] The server receives data sent from the device in real time. The input is the data packet sent from the device, and the output is the formatted data to be stored in the database. The server's API endpoint receives the data, and the data is stored by the database management system (DBMS).

[1420] Step 4: Data analysis

[1421] The server passes the stored data to the analysis module to evaluate the health status. The input is the biometric data stored in the database, and the output is the analysis result (e.g., good health, outside normal range, etc.). Specifically, the heart rate monitoring module extracts data from the past hour and calculates the average heart rate.

[1422] Step 5: User interaction interface

[1423] A user talks to a cat using a smartphone app. The input is the user's voice data, and the output is the voice data sent to the server. The voice data is collected and sent to the server in real time.

[1424] Step 6: Analyze the audio data

[1425] The server analyzes the voice data through a natural language processing module. The input is the user's voice data, and the output is the analysis result in text format. For example, the voice data "How are you today?" is converted into text.

[1426] Step 7: Response Generation

[1427] The generative AI model generates a response based on the analyzed text. The input is the analysis result of the natural language processing module, and the output is the generated response text. The generative AI model compares the cat's heart rate, body temperature, and step count data to generate an appropriate response.

[1428] Step 8: Sending a Response

[1429] The server converts the generated response into audio data and sends it to the device. The input is the text response output by the generative AI model, and the output is audio data. A text-to-speech engine is used to convert the text into audio.

[1430] Step 9: Playing Audio

[1431] The device plays the audio data sent from the server and communicates the response to the cat. The input is the audio data sent from the server, and the output is the audio to be played. The audio is played using the device's speaker.

[1432] Step 10: Anomaly detection and notification

[1433] The server constantly monitors the received biometric data and immediately sends a notification to the user if an abnormal value is detected. The input is the biometric data stored on the server, and the output is a notification sent to a smartphone app. For example, a notification is triggered when the body temperature exceeds 40 degrees.

[1434] This allows the user to efficiently monitor the cat's health throughout all steps and respond quickly if any abnormalities occur.

[1435] (Application example 1)

[1436] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1437] Conventional pet monitoring systems have had the problem of making it difficult to monitor the health status of pets while their owners are out, or to detect abnormalities in the home at an early stage. The present invention aims to solve these problems and provide a system that enables real-time monitoring of pet health status and prompt notification of abnormalities.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1439] In this invention, the server includes means for acquiring biometric data of the animal, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the current health condition of the animal, voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the state of the animal, means for converting the generated response into voice data and transmitting the response to the animal, and means for detecting abnormalities using the acquired biometric data and sending an alert to the user if an abnormality is found. This allows owners to keep track of their pet's health condition and the situation at home in real time even when they are out, and to respond quickly when an abnormality occurs.

[1440] "Animal biometric data" refers to data that indicates the animal's physical condition, such as heart rate, body temperature, and number of steps taken.

[1441] "Means of acquisition" refers to the method of collecting biological data of animals using sensors or devices.

[1442] The "means for transmitting to the server" refers to a method for transferring the collected biometric data to the server via wireless or wired communication.

[1443] A "server" is a computer system that stores and analyzes collected biometric data.

[1444] "Means of analysis" refers to methods of assessing the health status of animals using algorithms and software that process and analyze biological data.

[1445] The "voice input means for talking to animals" refers to a microphone or voice recognition device that allows the user to input a voice message to the animals.

[1446] The "means for transmitting data from the voice input means to the server" is a communication means for transferring input voice data to the server.

[1447] "Means for analyzing voice data" refers to natural language processing technology that converts input voice data into text data and understands the content.

[1448] The "means for generating a response appropriate to the animal's condition" is an AI model that generates an appropriate response according to the animal's condition based on analyzed voice data.

[1449] The "means for converting the generated response into voice data" is a voice synthesis technology that converts the generated text-format response into voice.

[1450] The "means for transmitting a response" refers to a terminal or speaker that allows the animal to hear the generated audio data.

[1451] The "means for detecting anomalies" is an algorithm for identifying abnormal data that exceeds a certain threshold based on the collected biometric data.

[1452] "Means for sending an alert to a user" refers to a system or technology for sending a notification to a user when an abnormality is detected.

[1453] System Overview

[1454] The system of the present invention acquires the animal's biological data, supports communication between the user and the animal based on that data, monitors the animal's health, and sends an alert to the user if an abnormality occurs. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[1455] Data collection and transmission

[1456] The device is equipped with sensors that measure biometric data such as heart rate, body temperature, and number of steps taken. The device collects this data in real time and transmits it to a server at regular intervals. Wireless communication technology (such as Bluetooth or Wi-Fi) is used for transmission.

[1457] Data analysis and health status assessment

[1458] The server analyzes the acquired vital data and evaluates the animal's current health condition. Analysis tools such as a heart rate monitoring module and a body temperature evaluation module are used for the analysis. The server stores the vital data in a database, and if abnormal data is detected, it sends an alert to the user. The alert is sent via push notification or email.

[1459] User interaction interface

[1460] Users can talk to animals using a smartphone app. The user's voice data is sent to the server via a voice input method (such as a smartphone microphone). The server then converts the voice data into text through a natural language processing module and analyzes what the user said. A generative AI model generates a response appropriate to the animal's condition, which is then converted into voice data and transmitted to the animal. This allows users to enjoy two-way communication with the animals.

[1461] Health Management and Notifications

[1462] The server constantly monitors the received vital signs and immediately sends an alert to the user if an abnormal value is detected. The notification is sent via a smartphone app, and the type of abnormality and detailed information are provided. This allows the user to quickly understand the animal's health condition and, if necessary, consult a veterinarian immediately.

[1463] Example

[1464] (Example 1: Home pet monitoring system)

[1465] This system allows users to detect abnormalities in their cat's heart rate or body temperature while they are out. For example, if a cat's heart rate suddenly increases while they are out, the server immediately detects the abnormality and sends an alert to the user's smartphone. This allows the user to quickly check the situation and take any necessary measures.

[1466] (Example of a prompt for a generative AI model)

[1467] I would like to build a system that detects abnormalities in the home using biological data from cats and sends alerts to users who are out. Specifically, how can I issue an alert based on abnormal behavior such as a sudden rise in heart rate or body temperature?

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] Collection of animal biometric data

[1471] The device uses sensors to collect biometric data such as heart rate, body temperature, and number of steps in real time.

[1472] Input: Biological data collected by sensors.

[1473] Output: The acquired biometric data is stored in a buffer as data packets.

[1474] Step 2:

[1475] Sending biometric data to the server

[1476] The device transmits the collected biometric data to a server at regular intervals (for example, every minute). Wireless technologies such as Bluetooth and Wi-Fi are used for communication.

[1477] Input: Buffered biometric data.

[1478] Data processing: Assembling multiple data points in a buffer into a data packet.

[1479] Output: Data packets sent to the server.

[1480] Step 3:

[1481] Biological data analysis

[1482] The server analyzes the received biometric data, using modules such as a heart rate monitoring module and a body temperature evaluation module.

[1483] Input: The data packet sent to the server.

[1484] Data calculations: Each data point is evaluated to see if it is within normal limits. Any outliers are flagged.

[1485] Output: Analysis results showing health status and abnormality flags.

[1486] Step 4:

[1487] Detecting outliers and sending alerts

[1488] Based on the analysis results, the server sends a notification to the user if any abnormal values ​​are detected.

[1489] Input: Analysis results and anomaly flags.

[1490] Data processing: If an abnormality flag is raised, an alarm message is generated.

[1491] Output: An alert notification sent to the user's smartphone.

[1492] Step 5:

[1493] User interaction

[1494] The user talks to the animals through a smartphone app, and the user's voice is picked up by a voice input means and sent to the server.

[1495] Input: User's voice data.

[1496] Output: The audio data sent to the server.

[1497] Step 6:

[1498] Voice data analysis and response generation

[1499] The server runs the user's voice data through a natural language processing module, converts it into text, and then uses a generative AI model to generate a response appropriate to the animal's condition.

[1500] Input: The audio data sent to the server.

[1501] Data Calculation: Converts voice data into text, analyzes the user's intent, and generates an appropriate response based on the analysis results.

[1502] Output: The response in text format.

[1503] Step 7:

[1504] Response communication

[1505] The server converts the generated response into audio data and sends it to the terminal, which plays the audio data to the animal.

[1506] Input: A text response.

[1507] Data processing: Converting text data into audio data.

[1508] Output: The audio data sent to the device.

[1509] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1510] MODE FOR CARRYING OUT THE INVENTION

[1511] System Overview

[1512] This system acquires biometric data from animals (specifically cats) and uses that data to support communication between the user and the animal. It also manages the cat's health. Furthermore, it uses an emotion engine to recognize the user's emotions based on the user's voice input data and generates responses based on those emotions, enabling more natural and emotional communication. The system consists of a device attached to the animal's collar, a server, and a smartphone app used by the user.

[1513] System program and processing flow

[1514] Data collection and transmission

[1515] The device uses sensors to capture the cat's heart rate, body temperature, and number of steps taken. This biological data is stored in a buffer in real time and then sent to a server at regular intervals.

[1516] Data analysis and health status assessment

[1517] The server analyzes the received biometric data and evaluates the cat's current health status. The analysis utilizes modules such as heart rate monitoring and temperature evaluation. The analysis results are stored in a database and can be referenced later.

[1518] User interaction interface

[1519] The user speaks to the cat through a smartphone app. The voice data is collected using a voice input method (such as a smartphone microphone) and sent to a server. The server then processes the voice data through a natural language processing (NLP) module and converts it into text. Based on the analyzed text, a generative AI model generates a response appropriate to the animal's condition.

[1520] The generated response is converted into voice data and sent to the device, which then plays the voice data back to the cat, allowing the user to enjoy the interaction by listening to the cat's "reply."

[1521] Recognizing emotions and regulating responses

[1522] As the server analyzes the user's voice data, it uses an emotion engine to recognize the user's emotions (happiness, sadness, anger, etc.). The recognized emotion data is reflected in the generated response. For example, if the user is speaking happily, the response will be generated in a bright tone. This enables more natural and emotional communication.

[1523] Health Management and Notifications

[1524] The server constantly monitors the received biometric data, and if an abnormal value is detected, it immediately notifies the user via a smartphone app, providing the type of abnormality and detailed information.

[1525] Specific examples

[1526] Example 1: Emotional conversation with a cat

[1527] The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[1528] The device collects this voice data and transmits it to the server in real time.

[1529] The server processes the voice data using natural language processing to convert it into text. It analyzes the question, "Did you have fun today?" and compares it with the cat's heart rate, body temperature, and step count data.

[1530] The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[1531] The generation AI converts the response into voice data and sends it to the device.

[1532] The device plays the audio, allowing the user to hear the cat's "reply."

[1533] Example 2: Health alert notification

[1534] When the server monitors the cat's temperature, it detects a sudden rise in temperature.

[1535] The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[1536] When users receive a notification, they can open the app and view detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[1537] If necessary, the user can immediately consult a veterinarian.

[1538] This system allows users to deepen their relationship with their cat, while enjoying emotional conversations and efficiently managing their health.

[1539] The processing flow will be explained below.

[1540] Step 1:

[1541] The device uses sensors to capture the cat's heart rate, body temperature, and steps, and these biological data are stored in a buffer in real time.

[1542] Step 2:

[1543] The terminal organizes the biometric data stored in the buffer at regular intervals and transmits it to the server using wireless communication means.

[1544] Step 3:

[1545] The server receives the biometric data sent from the device and immediately stores it in a database.

[1546] Step 4:

[1547] The server runs the received biometric data through multiple analysis modules, including a heart rate monitoring module, a body temperature assessment module, and others.

[1548] Step 5:

[1549] The server determines the animal's current health status based on the analysis results, and if any abnormal values ​​are detected, an alert flag is set.

[1550] Step 6:

[1551] Users open the smartphone app and use the microphone to talk to the cat, and the voice input is recorded within the app.

[1552] Step 7:

[1553] The terminal transmits the recorded voice data to the server in real time.

[1554] Step 8:

[1555] The server inputs the received voice data into a natural language processing (NLP) module and converts it into text format.

[1556] Step 9:

[1557] The server analyzes the text data and understands the user's question, for example, "How are you feeling today?"

[1558] Step 10:

[1559] The server runs the analyzed text data through an emotion engine to recognize the user's emotions (joy, sadness, anger, etc.).

[1560] Step 11:

[1561] The server compares the user's emotional data with the evaluation results of the biometric data and generates an appropriate response based on the animal's state. For example, if the user is talking to the animal in a happy manner, the response will also be generated in a bright tone.

[1562] Step 12:

[1563] The server converts the generated response into audio data.

[1564] Step 13:

[1565] The server transmits the converted voice data to the terminal.

[1566] Step 14:

[1567] The device plays the received audio data through a speaker, providing the user with the cat's "reply."

[1568] Step 15:

[1569] If the server detects abnormal data during analysis, it immediately prepares to send an alert to the user's smartphone app.

[1570] Step 16:

[1571] The server generates an alert message and sends it to the user's smartphone app.

[1572] Step 17:

[1573] Users will receive notifications on their smartphone app that will allow them to view detailed health data, such as temperature spikes or abnormal heart rates.

[1574] In this way, the system can manage the health of animals in real time, quickly detect abnormalities, and at the same time support communication between users and animals.

[1575] Example 2

[1576] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1577] Conventional animal health management systems lack the ability to collect and analyze real-time health data, making it difficult for users to immediately understand the health status of their animals. Furthermore, communication between users and animals is rarely emotional or natural, making it difficult to achieve emotionally rich dialogue. Furthermore, the timing of abnormal value detection and notification can be delayed, potentially delaying a prompt response to sudden health abnormalities.

[1578] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for converting voice data into text and recognizing the user's emotion, a means for generating a response appropriate to the animal's condition using a generative AI model based on the emotion, and a means for converting the generated response into voice data and transmitting the response to the animal. This allows the user to understand the animal's health condition in real time and communicate with the animal through emotional and natural dialogue. In addition, detection of abnormal values ​​and immediate notification enable rapid response.

[1579] "Animal biometric data" refers to data relating to an animal's physical activity and physiological state, such as heart rate, body temperature, and number of steps taken.

[1580] "Means of acquisition" refers to methods and devices for collecting biological data of animals using devices such as sensors.

[1581] "Server" refers to a computer system that analyzes collected biometric and voice data, determines health status, and generates dialogue responses.

[1582] "Voice input means" refers to a device or function for collecting voices uttered by a user, such as a microphone on a smartphone.

[1583] "Means for converting to text" refers to a method or device that converts voice data into text information using natural language processing technology.

[1584] "Means for recognizing user emotions" refers to a method or device for analyzing user emotions (happiness, sadness, anger, etc.) from text or voice data.

[1585] A "generative AI model" refers to a model that uses artificial intelligence to generate responses based on input data, such as a model that performs natural language processing.

[1586] "Means for generating a response" refers to a method or device that uses a generative AI model to generate a response that is adapted to the user's input and the animal's state.

[1587] "Means for converting into voice data" refers to a method or device that converts the generated text response into voice data using voice synthesis technology.

[1588] This invention is a system that acquires biometric data from animals (specifically cats) and allows natural interaction with users and health management. The system consists of a terminal attached to the animal's collar, a server, and a smartphone app used by the user.

[1589] Hardware and Software

[1590] The device is equipped with built-in sensors to measure the cat's heart rate, body temperature, and steps taken. The sensors include an optical heart rate sensor, a thermistor, and a three-axis accelerometer. The device buffers the data in real time and transmits it to a server via Bluetooth.

[1591] The server is a high-performance computing system that uses Python to acquire, analyze, and store biometric data. Specifically, it uses Python's Pandas library and Scikit-learn models to perform data analysis and anomaly detection. The analysis results are stored in a MongoDB database. The server also converts voice data into text using Google Cloud's Speech-to-Text API and has the ability to recognize user emotions using Microsoft Azure's Text Analytics API.

[1592] The smartphone app was developed using Flutter and provides a voice input method for users to talk to the cat. The collected voice data is converted into FLAC format and sent to a server in real time.

[1593] Specific examples of processing

[1594] Example 1: Emotional conversation with a cat

[1595] 1. The user speaks to the smartphone app in a cheerful tone, asking, "Did you have fun today?"

[1596] 2. The device collects this voice data and sends it to the server in real time.

[1597] 3. The server converts the audio data into text using Google Cloud's Speech-to-Text API. The server analyzes the question, "Did you have fun today?" and compares it with the cat's biometric data.

[1598] 4. The emotion engine recognizes the user's emotion as "joy" and generates a response that reflects this: "I had so much fun today!"

[1599] 5. The generative AI model (e.g., GPT-4) generates a response and converts this response into audio data (e.g., Amazon Polly).

[1600] 6. The device will play the audio and the user will be able to hear the cat's "reply."

[1601] Example prompt sentence:

[1602] "Based on the cat's biometric data, generate a response to the question, 'Did you have fun today?' The user's emotion is joy."

[1603] Example 2: Health alert notification

[1604] 1. The server is monitoring the cat's temperature and detects a sudden rise in temperature.

[1605] 2. The server immediately determines that the value is abnormal and sends an alert to the user's smartphone app.

[1606] 3. When the user receives the notification, they can open the app and check detailed information, such as how much their body temperature has risen or whether their heart rate is normal.

[1607] 4. If necessary, the user may seek immediate veterinary advice.

[1608] The system allows users to monitor their animals' health status in real time, communicate with them through emotional dialogue, and instantly notify them of any abnormalities, allowing for rapid response.

[1609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1610] Step 1: Data collection

[1611] The device acquires the cat's heart rate, body temperature, and step count using built-in sensors (optical heart rate sensor, thermistor, and three-axis acceleration sensor). The acquired data is stored in buffer memory in real time. The input data includes biometric data such as heart rate, body temperature, and step count. The output is the biometric data stored in the buffer memory. The sensors are set to measure the heart rate every second and the body temperature every minute.

[1612] Step 2: Send data

[1613] The terminal transmits the biometric data stored in the buffer memory to the server via Bluetooth at regular intervals (for example, every 30 seconds). The input data is the biometric data in the buffer memory, and the output is the biometric data transmitted to the server. The Bluetooth transceiver divides the data into packets and transmits them.

[1614] Step 3: Data analysis

[1615] The server analyzes the received biometric data using Python's Pandas library. The analysis involves identifying trends in heart rate, body temperature, and step count, and detecting outliers. The input data is the transmitted biometric data, and the output is the analysis results. Scikit-learn models are used to calculate Z-scores and detect anomalies, for example.

[1616] Step 4: Save Data

[1617] The server stores the analyzed data in a MongoDB database. The input data is the analyzed biometric data, and the output is the records stored in the database. The database records fluctuations in heart rate, body temperature, and number of steps as time series data.

[1618] Step 5: Voice Input

[1619] The user talks to the cat using a smartphone app. Audio data is collected through the smartphone's microphone and converted to FLAC format. The input is the user's voice, and the output is audio data in FLAC format. The smartphone app's recording function collects up to 60 seconds of audio.

[1620] Step 6: Sending audio data

[1621] The smartphone app transmits the collected audio data to the server in real time. The input data is audio data in FLAC format, and the output is the audio data transmitted to the server. The audio data is transmitted using Wi-Fi or mobile networks.

[1622] Step 7: Text conversion

[1623] The server converts the audio data into text using Google Cloud's Speech-to-Text API. The input data is audio data in FLAC format, and the output is text information. The converted text is then stored on the server for further analysis.

[1624] Step 8: Emotion Recognition

[1625] The server uses Microsoft Azure's Text Analytics API to recognize the user's emotions. The input data is character information (text), and the output is an emotion score (happiness, sadness, anger, etc.). The emotion score is stored in an internal emotion database.

[1626] Step 9: Response Generation

[1627] The server generates an appropriate response using a generative AI model (e.g., GPT-4) based on the emotion score and text information. The input data are the emotion score and text information, and the output is the generated text response. The generation process includes adjusting the response based on the emotion.

[1628] Step 10: Audio conversion

[1629] The server uses speech synthesis technology (e.g., Amazon Polly) to convert the generated text response into audio data. The input data is the text response, and the output is audio data, which is then sent back to the device.

[1630] Step 11: Play audio

[1631] The device plays the generated voice data to the cat. The input data is the voice data, and the output is the voice that is played as voice output. The voice output is done using the built-in speaker, so that the user can hear the cat's response.

[1632] (Application example 2)

[1633] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1634] In conventional food delivery systems, health management of delivery personnel is inadequate, resulting in excessive workload and health risks. Furthermore, smooth communication with customers is often lacking, creating a need for improved customer satisfaction. Furthermore, there is a lack of mechanisms for understanding customer emotions and responding appropriately, so these issues must be addressed simultaneously.

[1635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1636] In this invention, the server includes means for acquiring the animal's biometric data, means for transmitting the acquired biometric data to the server, means for the server to analyze the biometric data and determine the animal's current health condition, a voice input means for speaking to the animal, means for transmitting data from the voice input means to the server, means for the server to analyze the voice data and generate a response appropriate to the animal's condition, means for converting the generated response into voice data and transmitting the response to the animal, means for acquiring the delivery person's heart rate, body temperature, and activity level, means for detecting abnormalities in the delivery person's biometric data and notifying them if detected, means for analyzing the transmitted user voice data and generating an appropriate response using a generative AI model, and means for converting the generated response into voice data and transmitting the response to the user. This enables real-time management of the delivery person's health and early detection and notification of abnormalities. Furthermore, understanding customer emotions and generating appropriate responses can facilitate communication with customers and improve customer satisfaction.

[1637] "Biological data of an animal" refers to data that indicates the physiological state of an animal, such as the animal's heart rate, body temperature, and number of steps.

[1638] A "server" is a computer system for collecting, analyzing, storing, and generating responses to data.

[1639] The "voice input means" is a device such as a microphone that allows the user to input information by voice.

[1640] "Analysis" is the act of statistically or computationally processing acquired data to extract meaningful information.

[1641] A "response generator" is an algorithm or software that constructs an appropriate response based on input data.

[1642] "Biometric data of the delivery person" refers to data indicating the physiological condition of the delivery person, such as the delivery person's heart rate, body temperature, and activity level.

[1643] "Notification" refers to the act of notifying a user or administrator when an abnormality is detected.

[1644] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to generate appropriate output from input data.

[1645] "Real time" refers to the acquisition and processing of information almost immediately.

[1646] "Customer emotions" are psychological states such as joy, sadness, and anger expressed by customers.

[1647] "Communication" is the act of exchanging information and emotions.

[1648] "Customer satisfaction" refers to the degree of satisfaction a customer feels with a service.

[1649] An "analysis module" is a software component designed to analyze specific data.

[1650] The present invention relates to a system that acquires biometric data of animals and delivery personnel, and uses that data to communicate with them and manage their health. This system is composed of the following components:

[1651] System Overview

[1652] Hardware and Software Configuration

[1653] 1. Means of obtaining biometric data of animals and delivery personnel:

[1654] The heart rate, body temperature, and number of steps of the animal, as well as the heart rate, body temperature, and activity of the delivery person, are acquired using sensors (e.g., Polar H10 heart rate sensor, thermistor body temperature module).

[1655] 2. Means for transmitting biometric data to the server:

[1656] Wireless communication modules (e.g., Bluetooth, Wi-Fi) are used to transmit the acquired biometric data in real time to the server, which receives the data using the Django framework.

[1657] 3. Data analysis and judgment:

[1658] The server uses specialized analysis modules (e.g., TensorFlow, Pandas) to analyze the received data, thereby determining the health status of the animals and delivery personnel.

[1659] The analyzed biometric data is stored in an SQLite database.

[1660] 4. Voice input method:

[1661] The smartphone app uses a built-in microphone for voice input, and sends the data entered by the user via voice to the server.

[1662] 5. Speech data analysis and response generation:

[1663] The server analyzes the received voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text format.

[1664] An appropriate response is generated using a generative AI model (e.g., GPT-3), taking into account the state of the animal and delivery person.

[1665] 6. Response voice data conversion and transmission method:

[1666] Use a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated response into audio data and play it back to the animal or user.

[1667] 7. Outlier detection and notification:

[1668] The server constantly monitors the biometric data, and if it detects any abnormal values, it immediately sends a push notification (e.g., Firebase Cloud Messaging) to the user.

[1669] Specific examples

[1670] Example 1: Health management of animals and delivery personnel

[1671] The delivery person's heart rate is monitored, and if a sudden increase is detected, the server immediately sends a notification to the delivery person's smartphone saying, "Your heart rate is rising rapidly. Please take a break."

[1672] Example 2: Emotional communication with customers

[1673] If a customer requests a quick delivery by voice input on a smartphone app, saying, "I'm busy today, so please deliver quickly," the server analyzes the request using an emotion engine and uses a generative AI model to generate an appropriate response, such as, "We will deliver it right away. Please wait."

[1674] Prompt Sentence Examples

[1675] If the delivery person's heart rate exceeds 180, the notification will read:

[1676] "Your heart rate is increasing rapidly. Please take a break."

[1677] If the customer is analyzed as being impatient, the response would be:

[1678] "We will deliver it to you shortly. Please wait."

[1679] The above configuration and processing allows for real-time health management of animals and delivery personnel, and immediate notification of any abnormalities. It also enables natural, emotional responses when communicating with customers, which is expected to improve customer satisfaction.

[1680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1681] Step 1:

[1682] Input: Animal and delivery personnel biometric data (heart rate, body temperature, steps, activity level)

[1683] Processing: The device uses sensors (e.g., Polar H10, thermistor) to measure heart rate, body temperature, steps, and activity in real time. This data is temporarily stored in a buffer on the device.

[1684] Output: Raw biometric data obtained from sensors (heart rate, body temperature, steps, activity level)

[1685] Step 2:

[1686] Input: Raw biometric data (heart rate, body temperature, steps, activity level)

[1687] Processing: The raw data acquired by the device is sent to the server at regular intervals. The communication method is a wireless communication module (e.g., Bluetooth, Wi-Fi).

[1688] Output: Biometric data sent to the server

[1689] Step 3:

[1690] Input: Biometric data sent to the server

[1691] Processing: The server receives the biometric data and analyzes it using an analysis module (e.g., TensorFlow, Pandas). Specifically, it calculates the average heart rate and temperature fluctuation patterns to evaluate the health status.

[1692] Output: Analysis results (health status evaluation)

[1693] Step 4:

[1694] Input: Analysis results (health status evaluation)

[1695] Processing: The server saves the analysis results in an SQLite database, which allows for analysis by comparing them with past data and makes it easier to detect outliers.

[1696] Output: Saved analysis result data

[1697] Step 5:

[1698] Input: User's voice input data

[1699] Processing: The user inputs voice through the smartphone app. The smartphone's microphone collects the voice data and sends it to the server.

[1700] Output: Audio data sent to the server

[1701] Step 6:

[1702] Input: Audio data sent to the server

[1703] Processing: The server analyzes the voice data using a natural language processing (NLP) module (e.g., NLTK, spaCy) and converts it into text. The converted text data is then used by a generative AI model (e.g., GPT-3) to generate an appropriate response.

[1704] Output: The generated response text

[1705] Step 7:

[1706] Input: Generated response text

[1707] Processing: The server uses a Text-to-Speech (TTS) engine (e.g., Google Text-to-Speech API) to convert the generated text into audio data, which is then sent to the user's smartphone app.

[1708] Output: Audio data sent to the user's smartphone

[1709] Step 8:

[1710] Input: Analysis results (health status evaluation)

[1711] Processing: The server constantly monitors the analysis results, and if an abnormal value is detected, it sends a push notification to the user using Firebase Cloud Messaging or similar.

[1712] Output: Notification of anomaly detection sent to the user

[1713] Example prompt:

[1714] If the delivery person's heart rate exceeds 180, the notification will read:

[1715] "Your heart rate is increasing rapidly. Please take a break."

[1716] If the customer is analyzed as being impatient, the response would be:

[1717] "We will deliver it to you shortly. Please wait."

[1718] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1719] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1720] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1721] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1722] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1723] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1724] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1725] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1726] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1727] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1728] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1729] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1730] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1731] 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.

[1732] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1733] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1734] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1735] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1736] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1737] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1738] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1739] The following is further disclosed regarding the above embodiment.

[1740] (Claim 1)

[1741] a means for acquiring biometric data of the animal;

[1742] means for transmitting the acquired biometric data to a server;

[1743] a means by which the server analyzes the biometric data to determine the animal's current health status;

[1744] a voice input means for talking to the animal;

[1745] means for transmitting data from the voice input means to a server;

[1746] A means for the server to analyze the voice data and generate a response appropriate to the state of the animal;

[1747] The system includes means for converting the generated response into audio data and communicating the response to the animal.

[1748] (Claim 2)

[1749] 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

[1750] (Claim 3)

[1751] 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database.

[1752] "Example 1"

[1753] (Claim 1)

[1754] a means for acquiring biometric data of the animal;

[1755] means for transmitting the acquired biometric data to a server;

[1756] a means by which the server analyzes the biometric data to determine the animal's current health status;

[1757] a voice input means for talking to the animal;

[1758] means for transmitting data from the voice input means to a server;

[1759] A server uses a generative AI model to analyze the voice data and generate a response appropriate to the animal's condition;

[1760] means for converting the generated response into audio data and communicating the response to the animal;

[1761] A means for the server to manage API endpoints for receiving data in real time;

[1762] A system including a means for storing and making available the results of health status assessment in a database.

[1763] (Claim 2)

[1764] 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

[1765] (Claim 3)

[1766] 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database.

[1767] "Application Example 1"

[1768] (Claim 1)

[1769] a means for acquiring biometric data of the animal;

[1770] means for transmitting the acquired biometric data to a server;

[1771] a means by which the server analyzes the biometric data to determine the animal's current health status;

[1772] a voice input means for talking to the animal;

[1773] means for transmitting data from the voice input means to a server;

[1774] A means for the server to analyze the voice data and generate a response appropriate to the state of the animal;

[1775] means for converting the generated response into audio data and communicating the response to the animal;

[1776] A system including means for detecting abnormalities using acquired biometric data and sending an alert to the user if an abnormality is found.

[1777] (Claim 2)

[1778] 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

[1779] (Claim 3)

[1780] 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database.

[1781] "Example 2: Combining Emotion Engines"

[1782] (Claim 1)

[1783] a means for acquiring biometric data of the animal;

[1784] means for transmitting the acquired biometric data to a server;

[1785] a means by which the server analyzes the biometric data to determine the animal's current health status;

[1786] a voice input means for talking to the animal;

[1787] means for transmitting data from the voice input means to a server;

[1788] A server converts the voice data into text and recognizes the user's emotions;

[1789] a means for generating a response appropriate to the animal's state using a generative AI model based on emotion;

[1790] The system includes means for converting the generated response into audio data and communicating the response to the animal.

[1791] (Claim 2)

[1792] 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

[1793] (Claim 3)

[1794] 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database.

[1795] "Application example 2 when combining emotion engines"

[1796] (Claim 1)

[1797] a means for acquiring biometric data of the animal;

[1798] means for transmitting the acquired biometric data to a server;

[1799] a means by which the server analyzes the biometric data to determine the animal's current health status;

[1800] a voice input means for talking to the animal;

[1801] means for transmitting data from the voice input means to a server;

[1802] A means for the server to analyze the voice data and generate a response appropriate to the state of the animal;

[1803] means for converting the generated response into audio data and communicating the response to the animal;

[1804] A means for acquiring the delivery person's heart rate, body temperature, and activity level;

[1805] A means for detecting abnormalities in the biometric data of the delivery person and notifying the delivery person when such abnormalities are detected;

[1806] means for analyzing the transmitted user voice data and generating an appropriate response using a generative AI model;

[1807] The system includes means for converting the generated response into audio data and communicating the response to the user.

[1808] (Claim 2)

[1809] 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

[1810] (Claim 3)

[1811] 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database. [Explanation of symbols]

[1812] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring biometric data of the animal; means for transmitting the acquired biometric data to a server; a means by which the server analyzes the biometric data to determine the animal's current health status; a voice input means for talking to the animal; means for transmitting data from the voice input means to a server; A means for the server to analyze the voice data and generate a response appropriate to the state of the animal; The system includes means for converting the generated response into audio data and communicating the response to the animal.

2. 2. The system according to claim 1, further comprising means for detecting abnormal values ​​in the biometric data and notifying the user when an abnormality is detected.

3. 2. The system according to claim 1, further comprising means for storing the analyzed biometric data in a database.

Images