System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting animal behavior data, a means for analyzing the collected animal behavior data by using a generative AI, a means for predicting the possibility of an earthquake occurrence on the basis of the analysis result, and verbalizing the data, and a means for notifying a terminal and a user in an area of the verbalized prediction information.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] Conventional earthquake prediction technology relies mainly on physical sensors such as seismometers, and has limited prediction accuracy. Furthermore, while it is known that abnormal animal behavior can be a precursor to earthquakes, there was no technology to effectively collect and analyze this data and utilize it for earthquake prediction. Therefore, our goal is to provide a highly accurate earthquake prediction system that utilizes animal behavior data and minimizes earthquake damage. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting animal behavioral data, a means for analyzing the collected animal behavioral data using a generative AI, a means for predicting the possibility of an earthquake based on the analysis results and verbalizing the data, and a means for notifying a local terminal and a user of the verbalized prediction information. The system includes a sensor device for collecting the animal behavioral data, a means for transmitting data from the sensor device to a server, a means for preprocessing the received data at the server, a means for inputting the preprocessed data into a generative AI model and analyzing abnormal behavior, a means for generating earthquake prediction information from the analysis results, and a means for notifying a local terminal and a user of the generated prediction information. The system also includes an encryption communication means for encrypting and transmitting the animal behavioral data and decrypting it on the receiving side, a generative AI model for making earthquake predictions based on the transmitted data, and a warning generation means for notifying a user of the prediction information. This enables highly accurate and rapid earthquake predictions, effectively reducing earthquake damage.

[0006] "Animal behavior data" is data related to the behavior of animals (such as dogs and cats), such as movements, sounds, and vibrations.

[0007] A "sensor device" is a device for collecting animal behavior data, and includes a camera, microphone, vibration detector, etc.

[0008] "Generative AI" is an artificial intelligence model that analyzes collected animal behavior data and predicts the likelihood of earthquakes occurring.

[0009] "Analysis" is the process of analyzing collected data using generative AI to detect abnormal behavior and signs of earthquakes.

[0010] "Verbalization" means expressing the analysis results of generative AI in natural language.

[0011] "Notification" is a means of notifying the analysis results to local terminals and users.

[0012] The "server" is a central control system that receives data sent from the sensor devices and analyzes it using generative AI.

[0013] "Preprocessing" is the process of removing noise and shaping collected data to make it suitable for analysis.

[0014] "Encrypted communication means" refers to a means for encrypting data before sending it and decrypting it on the receiving side in order to ensure data security.

[0015] The "warning generation means" is a means for generating earthquake prediction information based on the analysis results of the generation AI and notifying the user. [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] The following describes an embodiment of the present invention. This system consists of a series of processes that collects animal behavior data, analyzes the data using a generative AI, and predicts the possibility of an earthquake occurring.

[0038] 1. Start data collection:

[0039] Terminal: Equipped with a sensor device for monitoring the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, vibration detector, etc., and collects animal behavior data in real time 24 hours a day.

[0040] 2. Behavioral Data Collection:

[0041] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0042] 3. Data preprocessing:

[0043] Terminal: The collected data is filtered to remove unnecessary noise, eliminating outliers and incomplete data.

[0044] 4. Data Encryption and Transmission:

[0045] Terminal: The pre-processed data is encrypted, ensuring data security. The encrypted data is sent to the server at regular intervals (for example, every minute or when a certain threshold is reached). A secure communication protocol (for example, HTTPS or MQTT) is used for transmission.

[0046] 5. Data Receipt and Analysis:

[0047] Server: Receives data sent from the device, decrypts it, and stores it in a database. The received data is formatted and cleansed to make it suitable for analysis. The formatted data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[0048] 6. Earthquake prediction using generative AI:

[0049] Server: The generation AI uses the analysis results to predict the possibility of an earthquake occurring and expresses the data in natural language. The results are summarized in a report format, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0050] 7. Alert Generation and Notification:

[0051] Server: Generates warning messages based on the generated forecast information. The warning messages are distributed to the appropriate areas based on the contract information for each region.

[0052] Device: Receives the warning message and notifies the user via audio alert, push notification on the smartphone app, or email.

[0053] 8. User Action:

[0054] User: After receiving the warning, check the notification content. For example, read the warning message displayed on the smartphone app. As soon as the warning is received, take action to evacuate to a safe place.

[0055] Through the above process, this system utilizes the natural earthquake prediction ability of animals, enabling highly accurate and rapid earthquake prediction and minimizing earthquake damage.The unique feature of this system is that it uses animal behavioral data, providing a flexible and new approach that does not rely on conventional physical sensors.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] Terminal: To monitor animal behavior, a sensor device equipped with cameras, microphones, and vibration detectors is used to collect data in real time 24 hours a day.

[0059] Step 2:

[0060] Device: Stores information on animal movements, sounds, and vibrations as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0061] Step 3:

[0062] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0063] Step 4:

[0064] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[0065] Step 5:

[0066] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[0067] Step 6:

[0068] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[0069] Step 7:

[0070] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0071] Step 8:

[0072] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0073] Step 9:

[0074] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0075] Step 10:

[0076] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[0077] Step 11:

[0078] Device: Receives warning messages and notifies the user via voice alerts, push notifications on smartphone apps, and emails.

[0079] Step 12:

[0080] User: After receiving the warning, the user checks the notification content. For example, the user reads the warning message displayed on the smartphone app. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[0081] In this way, the entire system processes everything from collecting and analyzing animal behavior data to issuing warnings to users, providing highly accurate earthquake predictions in real time.

[0082] Example 1

[0083] 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."

[0084] Conventional earthquake prediction systems rely on physical sensors and observation equipment, resulting in high installation costs and limited real-time prediction accuracy. Furthermore, these systems are unable to directly observe natural abnormal phenomena, necessitating the development of new approaches to improve prediction accuracy. This invention aims to provide a new system for earthquake prediction using animal behavior, thereby minimizing earthquake damage. In particular, the challenge is to create a system that can accurately predict the possibility of an earthquake occurring and promptly notify users by collecting animal behavior data and analyzing it using a generative AI model.

[0085] 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.

[0086] In this invention, the server includes: a sensor means for collecting information on animal movements, sounds, and vibrations; a preprocessing means for filtering the collected data and removing noise; a data transmission means for encrypting the preprocessed data and transmitting it using a secure communication protocol; a data receiving means for receiving, decrypting, and formatting the transmitted data; a means for inputting the formatted data into a generative AI model and analyzing abnormal behavior; an earthquake prediction means for predicting the possibility of an earthquake occurring from the analysis results and generating a report in natural language; a means for notifying the generated prediction information to a local terminal and a user as a warning message; and a support means for users who receive the prediction information to initiate evacuation behavior. This enables earthquake prediction using animal behavior in a flexible approach that differs from conventional physical sensors, making it possible to provide highly accurate and prompt prediction information.

[0087] "Animal movement, sound, and vibration information" refers to information on the animal's body movements, the sounds it makes, and the vibrations that accompany its movements.

[0088] "Sensor means" refers to devices or equipment used to collect animal movement, sound, and vibration information.

[0089] The "pre-processing means" refers to a series of processes for cleaning the collected data, such as filtering the data and removing noise.

[0090] "Data transmission means" refers to the devices and techniques that encrypt the pre-processed data and transmit it using a secure communications protocol.

[0091] "Data receiving means" refers to the devices and techniques used to receive, decode, and format transmitted data.

[0092] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and past earthquake data to predict the possibility of an earthquake occurring.

[0093] "Abnormal behavior" refers to behavior that deviates from normal animal behavior patterns and is predicted to be related to the occurrence of an earthquake.

[0094] "Earthquake prediction method" refers to a series of processes that use a generative AI model to predict the possibility of an earthquake occurring from the analysis results and generate a report in natural language.

[0095] The "warning message" refers to a message generated based on the possibility of an earthquake occurring, to alert the user.

[0096] "Support means" refers to devices and technologies that assist users who receive forecast information to begin appropriate evacuation actions.

[0097] The following describes an embodiment of the present invention. This system is designed to collect and analyze animal behavior data and predict the possibility of earthquakes. It primarily utilizes the natural predictive abilities of animals to provide earthquake prediction information to users. A detailed description of this system is provided below.

[0098] 1. Data Collection

[0099] The device operates a sensor device to monitor the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, and vibration detector, and collects animal behavior data in real time 24 hours a day. For example, the camera attached to the device records video at 30 frames per second or more, and the microphone captures audio data at a sampling rate of 44.1 kHz per second. The vibration detector detects vibrations caused by sudden animal movements.

[0100] 2. Data Preprocessing

[0101] The device filters the collected data to remove unwanted noise, including outliers and incomplete data. Specifically, it removes background noise from recorded audio data and trims unnecessary parts of video data (for example, periods when no animals are in the frame). It also removes outliers from vibration data and calculates average data over a certain period.

[0102] 3. Data Encryption and Transmission

[0103] The terminal encrypts the preprocessed data using encryption technology such as AES, and then transmits the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT. For example, the terminal encrypts the data accumulated every minute using AES-256 and sends a POST request to the server using HTTPS communication.

[0104] 4. Data Receipt and Analysis

[0105] The server receives the encrypted data sent from the terminal and immediately decrypts it using AES-256. The decrypted data is then stored in a database, where it is reformatted and cleansed into a format suitable for analysis. Specifically, the server receives an HTTP request, analyzes the encrypted payload, and decrypts it. The decrypted data is reformatted and stored in a database, where it is used to fill in missing data and recheck for outliers.

[0106] 5. Earthquake Prediction Using Generative AI

[0107] The server inputs the formatted data into a generative AI model, which compares and analyzes animal behavior patterns with past earthquake data. The generative AI model receives a prompt: "Compare the frequency of dog barking over the past 24 hours with past earthquake data, and predict the probability of an earthquake occurring within the next two hours." The generative AI then generates a prediction: "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[0108] 6. Alert Generation and Notification

[0109] The server generates a warning message based on the generated forecast information. This warning message is distributed to the appropriate area based on the contract information for each region. Specifically, the server generates a message such as "Caution: An earthquake may occur within the next two hours" based on the forecast information and sends a push notification to users in the contract area.

[0110] 7. User Response

[0111] After receiving the warning from the device, the user checks the notification and takes action to evacuate to a safe place. For example, the user checks the warning message on a smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[0112] Through the above process, this system utilizes the natural earthquake prediction ability of animals to enable accurate and rapid earthquake prediction, and also urges users to take early evacuation action via warning messages, thereby minimizing earthquake damage.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1: Start collecting data

[0115] The terminal operates sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats).

[0116] How it works: The camera records video at over 30 frames per second, the microphone captures audio data at a sampling rate of 44.1 kHz per second, and the vibration detector detects vibrations caused by sudden animal movements.

[0117] Input: Animal movement, sound, and vibration information.

[0118] Output: Raw data (video, audio, vibration) is saved to storage in real time.

[0119] Step 2: Preprocessing the data

[0120] The device filters the collected data to remove unwanted noise, which includes eliminating outliers and imperfections in the data.

[0121] Specific operations: Remove background noise from recorded audio data, trim unnecessary parts of video data (for example, times when no animals are in the frame), remove outliers from vibration data, and calculate average data over a certain period.

[0122] Input: Collected raw data (video, audio, vibration).

[0123] Output: Filtered and clean data.

[0124] Step 3: Encrypt the data

[0125] The terminal encrypts the preprocessed data using encryption technology such as AES.

[0126] Specific operation: The terminal sequentially encrypts the preprocessed data using the AES-256 method.

[0127] Input: Preprocessed clean data.

[0128] Output: The encrypted data.

[0129] Step 4: Sending data

[0130] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT.

[0131] Specific operation: Every minute, the device sends a POST request with encrypted data to the server using HTTPS communication.

[0132] Input: Encrypted data.

[0133] Output: Notification of completion of transmission to the server.

[0134] Step 5: Receiving the data

[0135] The server receives the encrypted data sent from the terminal.

[0136] Specific operation: The server receives the HTTP request, analyzes the encrypted payload, and saves it to storage.

[0137] Input: Encrypted data.

[0138] Output: The stored encrypted data.

[0139] Step 6: Decrypt the data

[0140] The server decrypts the received encrypted data using the AES-256 method.

[0141] Specific operation: The stored encrypted data is decrypted using AES-256 as appropriate and the raw data is extracted.

[0142] Input: Encrypted data.

[0143] Output: The decrypted data.

[0144] Step 7: Data Shaping and Cleansing

[0145] The server then formats and cleanses the decrypted data into a format suitable for analysis.

[0146] Specific operations: Fill in missing values ​​in the decoded data, recheck for outliers, and standardize the data format.

[0147] Input: Decrypted data.

[0148] Output: Formatted and cleansed data.

[0149] Step 8: Analyze abnormal behavior

[0150] The server inputs the formatted data into a generative AI model to analyze abnormal animal behavior.

[0151] Specific operation: The generative AI model is given a prompt sentence: "Please analyze what would happen if a dog suddenly started barking," and the model compares the animal's behavioral patterns with past earthquake data.

[0152] Input: Formatted and cleansed data.

[0153] Output: Analysis of abnormal animal behavior.

[0154] Step 9: Conducting earthquake predictions

[0155] The server predicts the possibility of an earthquake occurring based on the results of analysis by the generative AI model.

[0156] Specific operation: The generative AI model is given a prompt sentence: "Please predict the probability of an earthquake occurring within the next two hours," and a prediction result is generated.

[0157] Input: Analysis results of abnormal behavior.

[0158] Output: Earthquake occurrence forecast information.

[0159] Step 10: Generate warnings

[0160] The server generates a warning message based on the generated prediction information.

[0161] What it does: The server generates a message saying "An earthquake may occur within the next two hours" and formats it to notify the user.

[0162] Input: Earthquake occurrence prediction information.

[0163] Output: A warning message.

[0164] Step 11: User Notification

[0165] The terminal receives the generated warning message and notifies the user.

[0166] Specific operation: The device will send a warning message to the user via voice alert, push notification on the smartphone app, email, etc.

[0167] Input: Warning message.

[0168] Output: Notification to the user.

[0169] Step 12: User Action

[0170] After receiving the warning, the user checks the notification content and begins taking action to evacuate to a safe place.

[0171] Specific actions: The user checks the warning message on the smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[0172] Input: Warning message.

[0173] Output: Evacuation action initiated.

[0174] (Application example 1)

[0175] 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."

[0176] To improve the accuracy of earthquake prediction and provide real-time warnings, a system that can quickly and accurately collect and analyze animal behavioral data is needed. However, existing earthquake prediction systems mainly rely on physical sensors, and no means have been established to utilize animals' natural ability to predict earthquakes. Therefore, there is a need for accurate real-time earthquake predictions and to provide users with prompt warnings.

[0177] 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.

[0178] In this invention, the server includes a means for preprocessing the animal behavior data, a means for encrypting and transmitting the preprocessed data, and a means for generating a warning message based on the analysis results and notifying the user via a smartphone app, thereby enabling highly accurate and rapid earthquake prediction using animal behavior data.

[0179] "Animal behavior data" refers to information such as movements, sounds, and vibrations generated when animals behave.

[0180] "Generative AI" refers to artificial intelligence techniques used to analyze collected data, particularly those that use unsupervised learning and deep learning models.

[0181] "Earthquake probability" refers to predicting the probability of an earthquake occurring in a particular area based on the results of the analysis.

[0182] "Means of verbalization" refers to the technology of converting analysis results and predictive information into natural language and generating reports and warning messages.

[0183] "Means for notifying local terminals and users" refers to communication technologies for delivering forecast information and warning messages to specific regions and users.

[0184] "Preprocessing means" refers to techniques for removing noise from collected data, eliminating outliers, and formatting the data into a form suitable for analysis.

[0185] "Encryption means" refers to technology that encrypts data to prevent access by third parties when transmitting the data.

[0186] "Means for decryption" refers to the technology that returns encrypted data to its original form at the receiving end.

[0187] "Means for generating a warning message" refers to a technology for generating a message to notify a user of the possibility of an earthquake based on the analysis results.

[0188] "Smartphone app" refers to software that runs on a smartphone and notifies users of warning messages and predictive information in real time.

[0189] A "secure communication protocol" is a communication technology that ensures security when sending and receiving data, and examples include HTTPS and MQTT.

[0190] The detailed description of the embodiment of this invention will be given below. This system consists of a series of steps to collect animal behavior data, analyze the data using generative AI, predict the possibility of an earthquake occurring, and notify the user of a warning.

[0191] System configuration

[0192] The system consists of the following main components:

[0193] 1. Animal behavior data collection device:

[0194] The (terminal) is equipped with sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats). The terminal collects animal behavior data in real time 24 hours a day.

[0195] 2. Data preprocessing:

[0196] (Device) filters the collected data to remove noise and eliminate outliers and incomplete data.

[0197] 3. Data Encryption and Transmission:

[0198] The terminal encrypts the preprocessed data using AES encryption, and the encrypted data is sent to the cloud server via a secure communication protocol (e.g., HTTPS).

[0199] 4. Data Receipt and Analysis:

[0200] The server receives the data sent from the device, decrypts it, and stores it in a database. The received data is then formatted and cleansed to make it suitable for analysis. This data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[0201] 5. Earthquake prediction and warning generation:

[0202] The server uses generative AI to predict the possibility of an earthquake. The results are expressed in natural language and generated as a warning message. For example, it might say, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0203] 6. Warning Notice:

[0204] The server delivers the generated warning message to the user via a smartphone app. The user can receive and check the warning message on their smartphone and take safety measures.

[0205] Hardware and software used

[0206] Hardware: Sensor devices for collecting animal behavior data include cameras (e.g., Raspberry Pi Camera Module), microphones, vibration detectors, etc. Data processing is performed using a Raspberry Pi or a smartphone.

[0207] software:

[0208] Data preprocessing: OpenCV (camera data processing), Librosa (audio data processing)

[0209] Data encryption: PyCryptodome (encryption)

[0210] Communication protocol: Requests (HTTPS communication)

[0211] Generative AI model: TENSORFLOW (registered trademark), PyTorch

[0212] Specific examples

[0213] For example, behavioral data is collected, such as when a dog in the home suddenly starts barking or exhibits abnormal behavior during times when it is usually quiet. This data is preprocessed on the device to remove unnecessary noise. After preprocessing, the data is AES encrypted and securely sent to a cloud server. The cloud server uses a generative AI model to analyze the possibility of an earthquake and reports the results in natural language. For example, a generated prompt might be, "Analyze data when a dog exhibits unusual behavioral patterns and build a generative AI model to predict the possibility of an earthquake." Based on the analysis results, the smartphone app notifies the user with a warning message such as, "There is a high possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[0214] In this way, by utilizing animal behavioral data, this invention provides a flexible and new approach to earthquake prediction that does not rely on conventional physical sensors, and can provide users with highly accurate and prompt warnings.

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1:

[0217] The device collects animal behavior data. Specifically, it uses sensor devices (cameras, microphones, and vibration detectors) installed in the home to record animal movements, sounds, and vibration information as digital data in real time. The input is raw data captured by the sensors, and the output is time-series behavior data.

[0218] Step 2:

[0219] The terminal preprocesses the collected animal behavior data. In this step, specific operations are performed to remove noise from the collected data and eliminate outliers and incomplete data. The input is the raw data collected in step 1, and the output is clean behavior data that has been filtered and cleansed. OpenCV (camera data) and Librosa (audio data) are used for noise removal.

[0220] Step 3:

[0221] The terminal encrypts the preprocessed data. Specifically, it uses the AES encryption algorithm to keep the data secure. The input is the preprocessed clean behavioral data, and the output is the encrypted data. It uses PyCryptodome for encryption.

[0222] Step 4:

[0223] The device sends encrypted data to the cloud server. The data is sent securely using a secure communication protocol (e.g., HTTPS). The input is the encrypted data, and the output is a notification that the data has been sent. The Requests library is used for communication.

[0224] Step 5:

[0225] The server receives and decrypts the data sent from the terminal. The received encrypted data is decrypted using the AES encryption method. The input is the encrypted data, and the output is the decrypted clean behavioral data. PyCryptodome is used for decryption.

[0226] Step 6:

[0227] The server formats and cleanses the decrypted data, processing it to convert it into a format suitable for analysis. The input is the clean decrypted behavioral data, and the output is the formatted data.

[0228] Step 7:

[0229] The server uses a generative AI model to analyze the formatted data and compare animal behavior patterns with past earthquake data. It identifies abnormal behavior and predicts the likelihood of an earthquake. TensorFlow and PyTorch are used for data calculations in this step. The input is the formatted data, and the output is the analysis results indicating the likelihood of an earthquake.

[0230] Step 8:

[0231] The server generates a warning message in natural language based on the analysis results. For example, it may generate a message such as, "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours." The input is the analysis results of the generative AI model, and the output is a warning message in natural language.

[0232] Step 9:

[0233] The server notifies the user of the generated warning message in real time via a smartphone app using a push notification service such as Firebase Cloud Messaging (FCM). The input is a natural language warning message, and the output is a warning notification displayed on the smartphone.

[0234] Step 10:

[0235] The user checks the warning message received on the smartphone app and begins taking action to prepare for the possibility of an earthquake, such as evacuating to a safe place. The input is the warning message displayed on the smartphone, and the output is the user's evacuation behavior.

[0236] In this way, the system can use animal behavior data to accurately predict the possibility of earthquakes and provide users with prompt warnings. For example, the prompt text might include, "Analyze data on when dogs exhibit unusual behavioral patterns and build a generative AI model that predicts the likelihood of earthquakes."

[0237] 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.

[0238] This invention is a system that collects animal behavior data, analyzes the data using generative AI, and predicts the possibility of earthquakes. In addition to this system, it also includes an emotion engine that recognizes the user's emotions. The processing of this system covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[0239] Animal behavior data collection

[0240] 1. Start data collection:

[0241] Device: Sensor devices are used to monitor the behavior of animals (e.g., dogs and cats) and collect behavioral data in real time 24 hours a day. The sensor devices include cameras, microphones, and vibration detectors.

[0242] 2. Behavioral Data Collection:

[0243] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0244] 3. Data preprocessing:

[0245] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0246] Data encryption and transmission

[0247] 4. Data Encryption:

[0248] Terminal: Encrypts the pre-processed data. Encryption ensures the security of the data.

[0249] 5. Sending data to the server:

[0250] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[0251] Data reception and analysis

[0252] 6. Receiving and Decrypting Data:

[0253] Server: Receives encrypted data sent from the device, decrypts it, and stores it in a database.

[0254] 7. Data cleansing and shaping:

[0255] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0256] 8. Earthquake prediction analysis using generative AI:

[0257] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0258] 9. Verbalizing earthquake prediction:

[0259] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0260] Alert Generation and Notification

[0261] 10. Generate warning messages:

[0262] Server: Generates a warning message based on the generated earthquake prediction information.

[0263] 11. WARNING DELIVERY:

[0264] Server: Sends warning messages to terminals in the appropriate area based on regional contract information.

[0265] User emotion recognition and customized notifications

[0266] 12. Leveraging the Emotion Engine:

[0267] Terminal: When receiving a warning message, an emotion engine is used to monitor the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input.

[0268] 13. Customizing Notifications:

[0269] On the device: Customize the content and format of notifications based on the emotion engine's recognition results. For example, add advice on staying calm to a nervous user.

[0270] 14. User Action:

[0271] User: After receiving the warning, the user checks the notification content and reads the warning message displayed on the smartphone app, for example. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[0272] Follow up and provide additional information

[0273] 15. Emotional state monitoring:

[0274] Device: Continues to monitor the user's emotional state even after the warning notification.

[0275] 16. Providing Additional Information:

[0276] Devices: If needed, provide additional information or instructions to reduce anxiety and panic, such as the location of nearby evacuation shelters or emergency contact information.

[0277] This system utilizes the natural earthquake prediction abilities of animals, and also recognizes the user's emotional state in real time and notifies them in the most appropriate way, thereby realizing a highly accurate and user-friendly earthquake prediction and warning system.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] Terminal: To monitor the behavior of animals (e.g., dogs and cats), a sensor device equipped with a camera, microphone, and vibration detector is used to collect data in real time 24 hours a day.

[0281] Step 2:

[0282] Device: Collected animal movement, sound, and vibration information is recorded as digital data. For example, if a dog is usually quiet but suddenly starts barking, its sound data and movement patterns are recorded.

[0283] Step 3:

[0284] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0285] Step 4:

[0286] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[0287] Step 5:

[0288] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) and periodically sends data to the server.

[0289] Step 6:

[0290] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[0291] Step 7:

[0292] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0293] Step 8:

[0294] Server: The formatted data is input into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0295] Step 9:

[0296] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0297] Step 10:

[0298] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[0299] Step 11:

[0300] Terminal: Receives the warning message. At the same time, an emotion engine monitors the user's emotional state and recognizes emotions from the user's voice, facial expressions, and text input.

[0301] Step 12:

[0302] On the device: The emotion engine can customize the content and format of notifications for users whose emotional state is recognized by the engine. For example, if a user is nervous, the engine can provide additional advice on how to stay calm.

[0303] Step 13:

[0304] User: Receives customized alert messages and checks the notification content, for example, by reading the alert message displayed on a smartphone app.

[0305] Step 14:

[0306] User: Upon receiving the warning, take action to evacuate to a safe location, for example, to a safe area in your home or a nearby evacuation center.

[0307] Step 15:

[0308] Device: Continue to monitor the user's emotional state after the warning notification and provide additional information or instructions to reduce anxiety or panic, such as evacuation shelter locations, emergency contact information, and first aid instructions.

[0309] In this way, by analyzing animal behavior data using generative AI and then using an emotion engine to recognize the user's emotional state in real time and provide notifications in the most optimal way, we have created a highly accurate and user-friendly earthquake prediction and warning system.

[0310] Example 2

[0311] 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."

[0312] Current earthquake prediction systems rely on seismometers and GPS data, and issues include prediction accuracy and real-time response. Furthermore, because they issue uniform warnings without considering the emotional state of the user receiving the forecast information, users may not be able to respond appropriately. Furthermore, the security of the collected data is also a concern. To solve these problems, a highly accurate earthquake prediction system that utilizes animal behavior data and takes the user's emotional state into account is needed.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0314] In this invention, the server includes a means for collecting animal behavior data, a means for preprocessing the collected animal behavior data to remove noise, and a means for encrypting the preprocessed data. This enables the analysis of abnormal animal behavior patterns and highly accurate earthquake prediction. It also monitors the user's emotional state and customizes notifications to help the user respond appropriately. Furthermore, data encryption ensures security and maintains data confidentiality.

[0315] "Animal behavior data" refers to information recorded in digital format about animal movements, sounds, vibrations, etc.

[0316] "Collecting means" refers to devices and methods that continuously acquire animal behavioral data using sensor devices, cameras, microphones, vibration detectors, etc.

[0317] "Preprocessing" refers to the process of removing noise and unnecessary information from collected data and converting it into a format suitable for analysis.

[0318] "Encryption means" means a method of transforming data using a cryptographic algorithm (e.g., AES) to store or transmit it in a secure format.

[0319] "Server" refers to a computer system for receiving and storing animal behavioral data and the software running on it.

[0320] "Means for decryption" refers to a method for processing encrypted data back into its original format.

[0321] A "database" is a storage system for storing collected data in a structured format.

[0322] "Cleansing" is the process of filling in missing values ​​from data and organizing it in timestamp order.

[0323] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and detects signs of an earthquake.

[0324] "Means of analysis" refers to the use of generative AI models to analyze data and identify abnormal behavior and earthquake precursors.

[0325] "Means of verbalization" refers to a method for converting the analysis results into natural language and generating reports and warning messages.

[0326] The "notification means" is a method for distributing the generated forecast information and warning messages to terminals and users in the area.

[0327] The "means for monitoring emotional state" is a method for analyzing the user's voice, facial expressions, and text to recognize the user's emotional state in real time.

[0328] A "means for customizing notifications" is a method for changing the content and format of alert messages based on the user's emotional state.

[0329] This invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of earthquakes. This system also includes an emotion engine that recognizes the user's emotions, and covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[0330] Animal behavior data collection

[0331] Device: To monitor the behavior of animals (e.g., dogs and cats), sensor devices such as cameras, microphones, and vibration detectors are used. The sensor devices collect animal behavior data in real time 24 hours a day and store it in digital format. For example, if a dog that is usually quiet suddenly starts barking, its audio data and movement patterns will be recorded.

[0332] Data Preprocessing and Encryption

[0333] Terminal: The collected data is preprocessed and noise is removed. For example, environmental and background sounds are removed from the audio data, and only important behavioral data is extracted. The preprocessed data is then encrypted using the AES encryption algorithm, ensuring data security.

[0334] Sending and receiving data to the server

[0335] Terminal: Encrypted data is sent to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server has a receiving module that decrypts and interprets the received data packets. The data is then stored appropriately in a database.

[0336] Data cleansing and analysis with generative AI models

[0337] Server: The stored data is cleansed, missing values ​​are filled, and the data is sorted by timestamp. The cleansed data is then input into a generative AI model. The generative AI model compares animal behavior data with past earthquake data and performs analysis to identify abnormal behavior. For example, it compares the pattern of a dog suddenly barking with past data and identifies it as a sign of an upcoming earthquake.

[0338] Verbalization of earthquake predictions, generation and distribution of warnings

[0339] Server: The prediction results obtained from the generation AI are converted into natural language and a report is created, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." The generated report is sent to devices in the appropriate area based on the contract information for each region. The HTTPS protocol is again used to deliver the warning message.

[0340] User emotion recognition and customized notifications

[0341] Device: When receiving a warning message, the emotion engine analyzes the user's voice, facial expressions, and text to recognize their emotional state in real time. If the user is nervous, the engine will detect this and customize the content and format of the notification, such as adding a message like "Please stay calm. The nearest evacuation shelter is the nearest park."

[0342] Monitoring emotional state and providing additional information

[0343] Device: Continue to monitor the user's emotional state even after the warning notification. If the user is experiencing anxiety or panic, provide additional information or instructions. For example, send specific instructions such as "Evacuate now. The evacuation shelter is 300 meters away."

[0344] Examples of concrete examples and prompts

[0345] Example: If a device in a certain area detects the sudden barking of a dog, the data is immediately encrypted and sent to a server. The server decrypts the data and analyzes it using a generative AI model. If the result suggests that the dog's abnormal behavior is a sign of an upcoming earthquake, a warning message such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours" is sent to the devices of local residents. Users who receive the notification can take action, such as evacuating to a safe location.

[0346] Example prompt sentence:

[0347] "Analyze the animal behavior data below to see if there are any signs of an earthquake.

[0348] Date and Time: 2023-10-01 10:00

[0349] Behavioral data: Dog suddenly started barking and jumping more

[0350] Area: Shibuya Ward, Tokyo

[0351] This invention combines the natural earthquake prediction abilities of animals with generative AI to realize a highly accurate and user-friendly earthquake prediction and warning system. Furthermore, it uses an emotion engine to provide notifications according to the user's emotional state, helping the user respond calmly and quickly.

[0352] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0353] Step 1: Start data collection and collect behavioral data

[0354] Input: Real-time animal behavior information

[0355] Movement: The device monitors the animal's movements, sounds, and vibration data 24 hours a day through cameras, microphones, and vibration detectors, and records them digitally. Specifically, the device collects audio data when the dog barks, motion data when it moves, and vibration data when it jumps. For example, if a dog suddenly starts barking, the device captures the audio data and changes in its movements.

[0356] Output: Collected animal behavior data

[0357] Step 2: Data preprocessing and denoising

[0358] Input: Collected animal behavior data

[0359] Operation: The device preprocesses the collected data and removes noise. For example, it removes environmental and background sounds from audio data and eliminates outliers from behavioral data to generate clean data for analysis. It also removes outliers from video data and extracts only the frames containing important actions.

[0360] Output: Preprocessed and clean behavioral data

[0361] Step 3: Encrypt the data

[0362] Input: Preprocessed and clean behavioral data

[0363] How it works: The device encrypts the preprocessed data using the AES encryption algorithm, ensuring data security and making the encrypted data suitable for transfer and storage.

[0364] Output: Encrypted animal behavior data

[0365] Step 4: Send the encrypted data to the server

[0366] Input: Encrypted animal behavior data

[0367] Operation: The device sends encrypted data to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server can receive the data via a secure communication channel.

[0368] Output: Encrypted data sent to the server

[0369] Step 5: Decrypt the data and save it to the database

[0370] Input: Encrypted data sent to the server

[0371] Operation: The server receives the encrypted data through the receiving module, decrypts it using the AES encryption algorithm, and then stores the decrypted data in the database.

[0372] Output: Clean behavioral data stored in a database

[0373] Step 6: Cleanse the data

[0374] Input: Clean behavioral data stored in a database

[0375] How it works: The server rechecks the stored data, imputes missing values, sorts it by timestamp, and converts the cleansed data into a format that can be fed into a generative AI model.

[0376] Output: Formatted data suitable for generative AI models

[0377] Step 7: Earthquake prediction analysis using generative AI

[0378] Input: Formatted data suitable for generative AI models

[0379] How it works: The server inputs the cleansed data into a generative AI model, which then compares past earthquake data with animal behavior patterns and performs analysis to identify abnormal behavior. For example, it can identify whether an abnormal dog behavior pattern is a sign of an earthquake.

[0380] Output: Analysis results showing the possibility of an earthquake occurring

[0381] Step 8: Verbalizing earthquake predictions and generating warnings

[0382] Input: Analysis results showing the possibility of an earthquake occurring

[0383] How it works: The server converts the analysis results into natural language and generates a forecast report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." Based on the generated forecast report, it creates an appropriate warning message.

[0384] Output: Earthquake forecast report and warning message in natural language

[0385] Step 9: Delivering a warning message

[0386] Input: Earthquake forecast report and warning message in natural language

[0387] Operation: The server sends the generated warning message to the terminal in the appropriate area using the HTTPS protocol based on the contract information for each region.

[0388] Output: Warning message sent to the region terminal

[0389] Step 10: User Emotion Recognition and Notification Customization

[0390] Input: The warning message sent to the terminal

[0391] How it works: When receiving a warning message, the device uses its emotion engine to analyze the user's voice, facial expressions, and text input to recognize their emotional state. Depending on the user's emotion, the device can create additional messages, such as "Please stay calm. The nearest evacuation shelter is the nearest park," and customize the notification content.

[0392] Output: Displaying a customized warning message

[0393] Step 11: Monitor emotional state and provide additional information

[0394] Input: Customized warning message

[0395] How it works: Even after receiving the warning, the device continues to monitor the user's emotional state using its emotion engine. If the user is feeling anxious or panicked, the device will provide additional specific instructions and advice, such as "Evacuate now. An evacuation shelter is 300 meters away."

[0396] Output: Providing additional information depending on the user's emotional state

[0397] (Application example 2)

[0398] 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."

[0399] Although earthquake prediction technology has advanced in recent years, accurate prediction is still said to be difficult. Furthermore, it is difficult to take prompt and appropriate action when an earthquake is predicted, which could put many lives and property at risk. With the spread of autonomous vehicles in urban areas, there is a need to ensure the safety of vehicles and promptly notify drivers when an earthquake occurs. The challenge is to solve these problems and provide more accurate and user-friendly earthquake prediction and safety assurance methods.

[0400] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting animal behavioral data, means for analyzing the collected animal behavioral data using a generation AI, means for predicting the possibility of an earthquake based on the analysis results and verbalizing the data, means for notifying the verbalized prediction information to local terminals and users, means for transmitting the prediction information to the vehicle control system via an interface and instructing the vehicle's emergency action, and means for recognizing the user's emotional state and adjusting the notification content. This enables early prediction of the risk of an earthquake and appropriate response. At the same time, by appropriately adjusting the notification content according to the user's emotional state, the user can be provided with necessary information and encouraged to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[0401] "Animal behavior data" is digital information such as the movements, sounds, and vibrations of dogs, cats, and other animals collected using sensor devices.

[0402] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[0403] "Analysis results" refers to information obtained as a result of data analysis by the generating AI, and includes information such as the possibility of an earthquake occurring.

[0404] "Predicting the possibility of earthquakes" means predicting whether an earthquake will occur in the future based on animal behavior data.

[0405] "Verbalization" means expressing data and information such as analysis results in natural language.

[0406] A "local terminal" is an electronic device such as a computer, smartphone, or tablet that is located in a specific area.

[0407] "User" means an individual or member of an organization who uses the system.

[0408] An "interface" is a point of contact or protocol that allows different systems or devices to communicate with each other.

[0409] A "vehicle control system" is a system for managing and operating autonomous vehicles.

[0410] "Emergency action instructions" are instructions on immediate countermeasures to be taken in response to anticipated danger.

[0411] "User's emotional state" refers to the emotions and mental state that the user is currently experiencing.

[0412] "Adjusting notification content" means appropriately changing the information to be communicated and the way it is expressed depending on the user's emotional state.

[0413] The present invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of an earthquake. This system includes a function that recognizes the user's emotional state and issues appropriate warning notifications. Specific embodiments are described below.

[0414] Animal behavior data collection

[0415] The device uses sensors to monitor the behavior of animals (such as dogs and cats) and collects behavioral data in real time 24 hours a day. The sensors include cameras, microphones, and vibration detectors. For example, if a dog that is usually quiet suddenly starts barking, the device will record its audio data and movement patterns.

[0416] Data preprocessing and transmission

[0417] The device preprocesses the collected data to remove noise, eliminating outliers and incomplete data, and formats it for analysis. The preprocessed data is then encrypted and sent to the server using a secure communication protocol (e.g., HTTPS or MQTT).

[0418] Data reception and analysis

[0419] The server receives the encrypted data sent from the device, decrypts it, and stores it in a database. The stored data is cleansed and formatted to be input into the generative AI model. The server then inputs the formatted data into the generative AI model, where it performs an analysis to identify abnormal behavior by comparing animal behavior patterns with past earthquake data.

[0420] Earthquake prediction and warning notifications

[0421] The server determines the possibility of an earthquake occurring based on the results of the earthquake prediction analysis by the generation AI and generates forecast information in natural language. The generated forecast information is sent to local terminals and users' smartphones and computers. At this time, the forecast information is transmitted to the vehicle control system via an interface, and emergency action is instructed for the autonomous vehicle.

[0422] User emotion recognition and notification customization

[0423] When receiving a warning message, the device monitors the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. The content and format of the notification are customized based on the recognition results. For example, if a user is nervous, advice on how to stay calm will be added.

[0424] Specific examples

[0425] A concrete example of this system is a scenario in which an autonomous vehicle moves to a safe location based on an earthquake prediction. Generative AI analyzes animal behavior data, and if there is a high possibility of an earthquake, the vehicle control system is instructed to "move to a safe location." At the same time, a notification based on the user's emotional state is displayed, such as "Please remain calm, the vehicle is heading to a safe location."

[0426] Prompt Sentence Examples

[0427] An example of a prompt to input to the generative AI model is:

[0428] "Based on animal behavior data, an earthquake may occur within the next two hours. The specific behavioral patterns are sudden barking, fast moving around, and abnormal vibrations."

[0429] This system is user-friendly and has highly accurate earthquake prediction and warning functions, enabling safe and rapid response in the event of an earthquake.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] The device collects animal behavior data. It uses sensors (cameras, microphones, vibration detectors, etc.) to digitally record animal movements, sounds, and vibration information in real time 24 hours a day. For example, if a dog suddenly starts barking, its sound data and movement patterns will be recorded.

[0433] Input: Real-time animal behavior

[0434] Output: Digital animal behavior data

[0435] Step 2:

[0436] The device preprocesses the collected data by removing noise, eliminating outliers and incomplete data, and formatting it for analysis, such as normalizing the data and imputing missing values.

[0437] Input: Collected animal behavior data

[0438] Output: Preprocessed clean data

[0439] Step 3:

[0440] The terminal encrypts the pre-processed data. This encryption ensures data security. For example, an encryption algorithm such as AES (Advanced Encryption Standard) is used.

[0441] Input: Preprocessed clean data

[0442] Output: Encrypted data

[0443] Step 4:

[0444] The device sends encrypted data to the server via a secure communication protocol (such as HTTPS or MQTT) at regular intervals.

[0445] Input: Encrypted data

[0446] Output: Send to server (via HTTPS or MQTT)

[0447] Step 5:

[0448] The server receives the encrypted data, decrypts it, and stores it in the database. After the data is decrypted, it performs a cleansing process before storing it in the database.

[0449] Input: Encrypted data

[0450] Output: Cleansed data stored in a database

[0451] Step 6:

[0452] The server inputs the stored data into a generative AI model to analyze abnormal animal behavior. The generative AI model compares animal behavior patterns with past earthquake data and determines the likelihood of an earthquake occurring.

[0453] Input: Cleansed data

[0454] Output: Generative AI predicts the likelihood of an earthquake occurring

[0455] Step 7:

[0456] The server generates notification messages based on the earthquake prediction results determined by the generation AI, and in particular, uses natural language processing to convert the prediction information into a format that humans can understand.

[0457] Input: Earthquake prediction data

[0458] Output: Predictive notification message in natural language

[0459] Step 8:

[0460] The server then sends the generated prediction messages to local terminals and users, and simultaneously transmits the prediction information to the vehicle control system through an interface to instruct the autonomous vehicle to take emergency action.

[0461] Input: Natural language forecast notification message, earthquake forecast data

[0462] Output: Warning notification to user terminal, instructions to vehicle control system

[0463] Step 9:

[0464] When receiving a warning message, the device monitors the user's emotional state. It recognizes emotions from voice, facial expressions, and text input, and customizes the notification content based on the results. For example, if a user is nervous, it will deliver "advice on how to stay calm."

[0465] Input: Predictive notification message, user emotion data

[0466] Output: Customized notification message

[0467] Step 10:

[0468] The user checks the warning message displayed on the device and takes appropriate action, such as evacuating to a safe place.

[0469] Input: Customized notification message

[0470] Output: User action (e.g., evacuation)

[0471] The above steps enable early prediction of the risk of an earthquake and appropriate responses. At the same time, by appropriately adjusting the content of notifications according to the user's emotional state, it is possible to provide the user with the necessary information and encourage them to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[0472] 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.

[0473] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0474] 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.

[0475] [Second embodiment]

[0476] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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).

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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."

[0488] The following describes an embodiment of the present invention. This system consists of a series of processes that collects animal behavior data, analyzes the data using a generative AI, and predicts the possibility of an earthquake occurring.

[0489] 1. Start data collection:

[0490] Terminal: Equipped with a sensor device for monitoring the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, vibration detector, etc., and collects animal behavior data in real time 24 hours a day.

[0491] 2. Behavioral Data Collection:

[0492] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0493] 3. Data preprocessing:

[0494] Terminal: The collected data is filtered to remove unnecessary noise, eliminating outliers and incomplete data.

[0495] 4. Data Encryption and Transmission:

[0496] Terminal: The pre-processed data is encrypted, ensuring data security. The encrypted data is sent to the server at regular intervals (for example, every minute or when a certain threshold is reached). A secure communication protocol (for example, HTTPS or MQTT) is used for transmission.

[0497] 5. Data Receipt and Analysis:

[0498] Server: Receives data sent from the device, decrypts it, and stores it in a database. The received data is formatted and cleansed to make it suitable for analysis. The formatted data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[0499] 6. Earthquake prediction using generative AI:

[0500] Server: The generation AI uses the analysis results to predict the possibility of an earthquake occurring and expresses the data in natural language. The results are summarized in a report format, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0501] 7. Alert Generation and Notification:

[0502] Server: Generates warning messages based on the generated forecast information. The warning messages are distributed to the appropriate areas based on the contract information for each region.

[0503] Device: Receives the warning message and notifies the user via audio alert, push notification on the smartphone app, or email.

[0504] 8. User Action:

[0505] User: After receiving the warning, check the notification content. For example, read the warning message displayed on the smartphone app. As soon as the warning is received, take action to evacuate to a safe place.

[0506] Through the above process, this system utilizes the natural earthquake prediction ability of animals, enabling highly accurate and rapid earthquake prediction and minimizing earthquake damage.The unique feature of this system is that it uses animal behavioral data, providing a flexible and new approach that does not rely on conventional physical sensors.

[0507] The processing flow will be explained below.

[0508] Step 1:

[0509] Terminal: To monitor animal behavior, a sensor device equipped with cameras, microphones, and vibration detectors is used to collect data in real time 24 hours a day.

[0510] Step 2:

[0511] Device: Stores information on animal movements, sounds, and vibrations as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0512] Step 3:

[0513] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0514] Step 4:

[0515] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[0516] Step 5:

[0517] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[0518] Step 6:

[0519] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[0520] Step 7:

[0521] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0522] Step 8:

[0523] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0524] Step 9:

[0525] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0526] Step 10:

[0527] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[0528] Step 11:

[0529] Device: Receives warning messages and notifies the user via voice alerts, push notifications on smartphone apps, and emails.

[0530] Step 12:

[0531] User: After receiving the warning, the user checks the notification content. For example, the user reads the warning message displayed on the smartphone app. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[0532] In this way, the entire system processes everything from collecting and analyzing animal behavior data to issuing warnings to users, providing highly accurate earthquake predictions in real time.

[0533] Example 1

[0534] 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."

[0535] Conventional earthquake prediction systems rely on physical sensors and observation equipment, resulting in high installation costs and limited real-time prediction accuracy. Furthermore, these systems are unable to directly observe natural abnormal phenomena, necessitating the development of new approaches to improve prediction accuracy. This invention aims to provide a new system for earthquake prediction using animal behavior, thereby minimizing earthquake damage. In particular, the challenge is to create a system that can accurately predict the possibility of an earthquake occurring and promptly notify users by collecting animal behavior data and analyzing it using a generative AI model.

[0536] 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.

[0537] In this invention, the server includes: a sensor means for collecting information on animal movements, sounds, and vibrations; a preprocessing means for filtering the collected data and removing noise; a data transmission means for encrypting the preprocessed data and transmitting it using a secure communication protocol; a data receiving means for receiving, decrypting, and formatting the transmitted data; a means for inputting the formatted data into a generative AI model and analyzing abnormal behavior; an earthquake prediction means for predicting the possibility of an earthquake occurring from the analysis results and generating a report in natural language; a means for notifying the generated prediction information to a local terminal and a user as a warning message; and a support means for users who receive the prediction information to initiate evacuation behavior. This enables earthquake prediction using animal behavior in a flexible approach that differs from conventional physical sensors, making it possible to provide highly accurate and prompt prediction information.

[0538] "Animal movement, sound, and vibration information" refers to information on the animal's body movements, the sounds it makes, and the vibrations that accompany its movements.

[0539] "Sensor means" refers to devices or equipment used to collect animal movement, sound, and vibration information.

[0540] The "pre-processing means" refers to a series of processes for cleaning the collected data, such as filtering the data and removing noise.

[0541] "Data transmission means" refers to the devices and techniques that encrypt the pre-processed data and transmit it using a secure communications protocol.

[0542] "Data receiving means" refers to the devices and techniques used to receive, decode, and format transmitted data.

[0543] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and past earthquake data to predict the possibility of an earthquake occurring.

[0544] "Abnormal behavior" refers to behavior that deviates from normal animal behavior patterns and is predicted to be related to the occurrence of an earthquake.

[0545] "Earthquake prediction method" refers to a series of processes that use a generative AI model to predict the possibility of an earthquake occurring from the analysis results and generate a report in natural language.

[0546] The "warning message" refers to a message generated based on the possibility of an earthquake occurring, to alert the user.

[0547] "Support means" refers to devices and technologies that assist users who receive forecast information to begin appropriate evacuation actions.

[0548] The following describes an embodiment of the present invention. This system is designed to collect and analyze animal behavior data and predict the possibility of earthquakes. It primarily utilizes the natural predictive abilities of animals to provide earthquake prediction information to users. A detailed description of this system is provided below.

[0549] 1. Data Collection

[0550] The device operates a sensor device to monitor the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, and vibration detector, and collects animal behavior data in real time 24 hours a day. For example, the camera attached to the device records video at 30 frames per second or more, and the microphone captures audio data at a sampling rate of 44.1 kHz per second. The vibration detector detects vibrations caused by sudden animal movements.

[0551] 2. Data Preprocessing

[0552] The device filters the collected data to remove unwanted noise, including outliers and incomplete data. Specifically, it removes background noise from recorded audio data and trims unnecessary parts of video data (for example, periods when no animals are in the frame). It also removes outliers from vibration data and calculates average data over a certain period.

[0553] 3. Data Encryption and Transmission

[0554] The terminal encrypts the preprocessed data using encryption technology such as AES, and then transmits the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT. For example, the terminal encrypts the data accumulated every minute using AES-256 and sends a POST request to the server using HTTPS communication.

[0555] 4. Data Receipt and Analysis

[0556] The server receives the encrypted data sent from the terminal and immediately decrypts it using AES-256. The decrypted data is then stored in a database, where it is reformatted and cleansed into a format suitable for analysis. Specifically, the server receives an HTTP request, analyzes the encrypted payload, and decrypts it. The decrypted data is reformatted and stored in a database, where it is used to fill in missing data and recheck for outliers.

[0557] 5. Earthquake Prediction Using Generative AI

[0558] The server inputs the formatted data into a generative AI model, which compares and analyzes animal behavior patterns with past earthquake data. The generative AI model receives a prompt: "Compare the frequency of dog barking over the past 24 hours with past earthquake data, and predict the probability of an earthquake occurring within the next two hours." The generative AI then generates a prediction: "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[0559] 6. Alert Generation and Notification

[0560] The server generates a warning message based on the generated forecast information. This warning message is distributed to the appropriate area based on the contract information for each region. Specifically, the server generates a message such as "Caution: An earthquake may occur within the next two hours" based on the forecast information and sends a push notification to users in the contract area.

[0561] 7. User Response

[0562] After receiving the warning from the device, the user checks the notification and takes action to evacuate to a safe place. For example, the user checks the warning message on a smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[0563] Through the above process, this system utilizes the natural earthquake prediction ability of animals to enable accurate and rapid earthquake prediction, and also urges users to take early evacuation action via warning messages, thereby minimizing earthquake damage.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1: Start collecting data

[0566] The terminal operates sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats).

[0567] How it works: The camera records video at over 30 frames per second, the microphone captures audio data at a sampling rate of 44.1 kHz per second, and the vibration detector detects vibrations caused by sudden animal movements.

[0568] Input: Animal movement, sound, and vibration information.

[0569] Output: Raw data (video, audio, vibration) is saved to storage in real time.

[0570] Step 2: Preprocessing the data

[0571] The device filters the collected data to remove unwanted noise, which includes eliminating outliers and imperfections in the data.

[0572] Specific operations: Remove background noise from recorded audio data, trim unnecessary parts of video data (for example, times when no animals are in the frame), remove outliers from vibration data, and calculate average data over a certain period.

[0573] Input: Collected raw data (video, audio, vibration).

[0574] Output: Filtered and clean data.

[0575] Step 3: Encrypt the data

[0576] The terminal encrypts the preprocessed data using encryption technology such as AES.

[0577] Specific operation: The terminal sequentially encrypts the preprocessed data using the AES-256 method.

[0578] Input: Preprocessed clean data.

[0579] Output: The encrypted data.

[0580] Step 4: Sending data

[0581] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT.

[0582] Specific operation: Every minute, the device sends a POST request with encrypted data to the server using HTTPS communication.

[0583] Input: Encrypted data.

[0584] Output: Notification of completion of transmission to the server.

[0585] Step 5: Receiving the data

[0586] The server receives the encrypted data sent from the terminal.

[0587] Specific operation: The server receives the HTTP request, analyzes the encrypted payload, and saves it to storage.

[0588] Input: Encrypted data.

[0589] Output: The stored encrypted data.

[0590] Step 6: Decrypt the data

[0591] The server decrypts the received encrypted data using the AES-256 method.

[0592] Specific operation: The stored encrypted data is decrypted using AES-256 as appropriate and the raw data is extracted.

[0593] Input: Encrypted data.

[0594] Output: The decrypted data.

[0595] Step 7: Data Shaping and Cleansing

[0596] The server then formats and cleanses the decrypted data into a format suitable for analysis.

[0597] Specific operations: Fill in missing values ​​in the decoded data, recheck for outliers, and standardize the data format.

[0598] Input: Decrypted data.

[0599] Output: Formatted and cleansed data.

[0600] Step 8: Analyze abnormal behavior

[0601] The server inputs the formatted data into a generative AI model to analyze abnormal animal behavior.

[0602] Specific operation: The generative AI model is given a prompt sentence: "Please analyze what would happen if a dog suddenly started barking," and the model compares the animal's behavioral patterns with past earthquake data.

[0603] Input: Formatted and cleansed data.

[0604] Output: Analysis of abnormal animal behavior.

[0605] Step 9: Conducting earthquake predictions

[0606] The server predicts the possibility of an earthquake occurring based on the results of analysis by the generative AI model.

[0607] Specific operation: The generative AI model is given a prompt sentence: "Please predict the probability of an earthquake occurring within the next two hours," and a prediction result is generated.

[0608] Input: Analysis results of abnormal behavior.

[0609] Output: Earthquake occurrence forecast information.

[0610] Step 10: Generate warnings

[0611] The server generates a warning message based on the generated prediction information.

[0612] What it does: The server generates a message saying "An earthquake may occur within the next two hours" and formats it to notify the user.

[0613] Input: Earthquake occurrence prediction information.

[0614] Output: A warning message.

[0615] Step 11: User Notification

[0616] The terminal receives the generated warning message and notifies the user.

[0617] Specific operation: The device will send a warning message to the user via voice alert, push notification on the smartphone app, email, etc.

[0618] Input: Warning message.

[0619] Output: Notification to the user.

[0620] Step 12: User Action

[0621] After receiving the warning, the user checks the notification content and begins taking action to evacuate to a safe place.

[0622] Specific actions: The user checks the warning message on the smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[0623] Input: Warning message.

[0624] Output: Evacuation action initiated.

[0625] (Application example 1)

[0626] 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."

[0627] To improve the accuracy of earthquake prediction and provide real-time warnings, a system that can quickly and accurately collect and analyze animal behavioral data is needed. However, existing earthquake prediction systems mainly rely on physical sensors, and no means have been established to utilize animals' natural ability to predict earthquakes. Therefore, there is a need for accurate real-time earthquake predictions and to provide users with prompt warnings.

[0628] 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.

[0629] In this invention, the server includes a means for preprocessing the animal behavior data, a means for encrypting and transmitting the preprocessed data, and a means for generating a warning message based on the analysis results and notifying the user via a smartphone app, thereby enabling highly accurate and rapid earthquake prediction using animal behavior data.

[0630] "Animal behavior data" refers to information such as movements, sounds, and vibrations generated when animals behave.

[0631] "Generative AI" refers to artificial intelligence techniques used to analyze collected data, particularly those that use unsupervised learning and deep learning models.

[0632] "Earthquake probability" refers to predicting the probability of an earthquake occurring in a particular area based on the results of the analysis.

[0633] "Means of verbalization" refers to the technology of converting analysis results and predictive information into natural language and generating reports and warning messages.

[0634] "Means for notifying local terminals and users" refers to communication technologies for delivering forecast information and warning messages to specific regions and users.

[0635] "Preprocessing means" refers to techniques for removing noise from collected data, eliminating outliers, and formatting the data into a form suitable for analysis.

[0636] "Encryption means" refers to technology that encrypts data to prevent access by third parties when transmitting the data.

[0637] "Means for decryption" refers to the technology that returns encrypted data to its original form at the receiving end.

[0638] "Means for generating a warning message" refers to a technology for generating a message to notify a user of the possibility of an earthquake based on the analysis results.

[0639] "Smartphone app" refers to software that runs on a smartphone and notifies users of warning messages and predictive information in real time.

[0640] A "secure communication protocol" is a communication technology that ensures security when sending and receiving data, and examples include HTTPS and MQTT.

[0641] The detailed description of the embodiment of this invention will be given below. This system consists of a series of steps to collect animal behavior data, analyze the data using generative AI, predict the possibility of an earthquake occurring, and notify the user of a warning.

[0642] System configuration

[0643] The system consists of the following main components:

[0644] 1. Animal behavior data collection device:

[0645] The (terminal) is equipped with sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats). The terminal collects animal behavior data in real time 24 hours a day.

[0646] 2. Data preprocessing:

[0647] (Device) filters the collected data to remove noise and eliminate outliers and incomplete data.

[0648] 3. Data Encryption and Transmission:

[0649] The terminal encrypts the preprocessed data using AES encryption, and the encrypted data is sent to the cloud server via a secure communication protocol (e.g., HTTPS).

[0650] 4. Data Receipt and Analysis:

[0651] The server receives the data sent from the device, decrypts it, and stores it in a database. The received data is then formatted and cleansed to make it suitable for analysis. This data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[0652] 5. Earthquake prediction and warning generation:

[0653] The server uses generative AI to predict the possibility of an earthquake. The results are expressed in natural language and generated as a warning message. For example, it might say, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0654] 6. Warning Notice:

[0655] The server delivers the generated warning message to the user via a smartphone app. The user can receive and check the warning message on their smartphone and take safety measures.

[0656] Hardware and software used

[0657] Hardware: Sensor devices for collecting animal behavior data include cameras (e.g., Raspberry Pi Camera Module), microphones, vibration detectors, etc. Data processing is performed using a Raspberry Pi or a smartphone.

[0658] software:

[0659] Data preprocessing: OpenCV (camera data processing), Librosa (audio data processing)

[0660] Data encryption: PyCryptodome (encryption)

[0661] Communication protocol: Requests (HTTPS communication)

[0662] Generative AI models: TensorFlow, PyTorch

[0663] Specific examples

[0664] For example, behavioral data is collected, such as when a dog in the home suddenly starts barking or exhibits abnormal behavior during times when it is usually quiet. This data is preprocessed on the device to remove unnecessary noise. After preprocessing, the data is AES encrypted and securely sent to a cloud server. The cloud server uses a generative AI model to analyze the possibility of an earthquake and reports the results in natural language. For example, a generated prompt might be, "Analyze data when a dog exhibits unusual behavioral patterns and build a generative AI model to predict the possibility of an earthquake." Based on the analysis results, the smartphone app notifies the user with a warning message such as, "There is a high possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[0665] In this way, by utilizing animal behavioral data, this invention provides a flexible and new approach to earthquake prediction that does not rely on conventional physical sensors, and can provide users with highly accurate and prompt warnings.

[0666] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0667] Step 1:

[0668] The device collects animal behavior data. Specifically, it uses sensor devices (cameras, microphones, and vibration detectors) installed in the home to record animal movements, sounds, and vibration information as digital data in real time. The input is raw data captured by the sensors, and the output is time-series behavior data.

[0669] Step 2:

[0670] The terminal preprocesses the collected animal behavior data. In this step, specific operations are performed to remove noise from the collected data and eliminate outliers and incomplete data. The input is the raw data collected in step 1, and the output is clean behavior data that has been filtered and cleansed. OpenCV (camera data) and Librosa (audio data) are used for noise removal.

[0671] Step 3:

[0672] The terminal encrypts the preprocessed data. Specifically, it uses the AES encryption algorithm to keep the data secure. The input is the preprocessed clean behavioral data, and the output is the encrypted data. It uses PyCryptodome for encryption.

[0673] Step 4:

[0674] The device sends encrypted data to the cloud server. The data is sent securely using a secure communication protocol (e.g., HTTPS). The input is the encrypted data, and the output is a notification that the data has been sent. The Requests library is used for communication.

[0675] Step 5:

[0676] The server receives and decrypts the data sent from the terminal. The received encrypted data is decrypted using the AES encryption method. The input is the encrypted data, and the output is the decrypted clean behavioral data. PyCryptodome is used for decryption.

[0677] Step 6:

[0678] The server formats and cleanses the decrypted data, processing it to convert it into a format suitable for analysis. The input is the clean decrypted behavioral data, and the output is the formatted data.

[0679] Step 7:

[0680] The server uses a generative AI model to analyze the formatted data and compare animal behavior patterns with past earthquake data. It identifies abnormal behavior and predicts the likelihood of an earthquake. TensorFlow and PyTorch are used for data calculations in this step. The input is the formatted data, and the output is the analysis results indicating the likelihood of an earthquake.

[0681] Step 8:

[0682] The server generates a warning message in natural language based on the analysis results. For example, it may generate a message such as, "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours." The input is the analysis results of the generative AI model, and the output is a warning message in natural language.

[0683] Step 9:

[0684] The server notifies the user of the generated warning message in real time via a smartphone app using a push notification service such as Firebase Cloud Messaging (FCM). The input is a natural language warning message, and the output is a warning notification displayed on the smartphone.

[0685] Step 10:

[0686] The user checks the warning message received on the smartphone app and begins taking action to prepare for the possibility of an earthquake, such as evacuating to a safe place. The input is the warning message displayed on the smartphone, and the output is the user's evacuation behavior.

[0687] In this way, the system can use animal behavior data to accurately predict the possibility of earthquakes and provide users with prompt warnings. For example, the prompt text might include, "Analyze data on when dogs exhibit unusual behavioral patterns and build a generative AI model that predicts the likelihood of earthquakes."

[0688] 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.

[0689] This invention is a system that collects animal behavior data, analyzes the data using generative AI, and predicts the possibility of earthquakes. In addition to this system, it also includes an emotion engine that recognizes the user's emotions. The processing of this system covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[0690] Animal behavior data collection

[0691] 1. Start data collection:

[0692] Device: Sensor devices are used to monitor the behavior of animals (e.g., dogs and cats) and collect behavioral data in real time 24 hours a day. The sensor devices include cameras, microphones, and vibration detectors.

[0693] 2. Behavioral Data Collection:

[0694] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0695] 3. Data preprocessing:

[0696] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0697] Data encryption and transmission

[0698] 4. Data Encryption:

[0699] Terminal: Encrypts the pre-processed data. Encryption ensures the security of the data.

[0700] 5. Sending data to the server:

[0701] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[0702] Data reception and analysis

[0703] 6. Receiving and Decrypting Data:

[0704] Server: Receives encrypted data sent from the device, decrypts it, and stores it in a database.

[0705] 7. Data cleansing and shaping:

[0706] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0707] 8. Earthquake prediction analysis using generative AI:

[0708] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0709] 9. Verbalizing earthquake prediction:

[0710] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0711] Alert Generation and Notification

[0712] 10. Generate warning messages:

[0713] Server: Generates a warning message based on the generated earthquake prediction information.

[0714] 11. WARNING DELIVERY:

[0715] Server: Sends warning messages to terminals in the appropriate area based on regional contract information.

[0716] User emotion recognition and customized notifications

[0717] 12. Leveraging the Emotion Engine:

[0718] Terminal: When receiving a warning message, an emotion engine is used to monitor the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input.

[0719] 13. Customizing Notifications:

[0720] On the device: Customize the content and format of notifications based on the emotion engine's recognition results. For example, add advice on staying calm to a nervous user.

[0721] 14. User Action:

[0722] User: After receiving the warning, the user checks the notification content and reads the warning message displayed on the smartphone app, for example. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[0723] Follow up and provide additional information

[0724] 15. Emotional state monitoring:

[0725] Device: Continues to monitor the user's emotional state even after the warning notification.

[0726] 16. Providing Additional Information:

[0727] Devices: If needed, provide additional information or instructions to reduce anxiety and panic, such as the location of nearby evacuation shelters or emergency contact information.

[0728] This system utilizes the natural earthquake prediction abilities of animals, and also recognizes the user's emotional state in real time and notifies them in the most appropriate way, thereby realizing a highly accurate and user-friendly earthquake prediction and warning system.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] Terminal: To monitor the behavior of animals (e.g., dogs and cats), a sensor device equipped with a camera, microphone, and vibration detector is used to collect data in real time 24 hours a day.

[0732] Step 2:

[0733] Device: Collected animal movement, sound, and vibration information is recorded as digital data. For example, if a dog is usually quiet but suddenly starts barking, its sound data and movement patterns are recorded.

[0734] Step 3:

[0735] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0736] Step 4:

[0737] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[0738] Step 5:

[0739] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) and periodically sends data to the server.

[0740] Step 6:

[0741] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[0742] Step 7:

[0743] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0744] Step 8:

[0745] Server: The formatted data is input into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0746] Step 9:

[0747] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0748] Step 10:

[0749] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[0750] Step 11:

[0751] Terminal: Receives the warning message. At the same time, an emotion engine monitors the user's emotional state and recognizes emotions from the user's voice, facial expressions, and text input.

[0752] Step 12:

[0753] On the device: The emotion engine can customize the content and format of notifications for users whose emotional state is recognized by the engine. For example, if a user is nervous, the engine can provide additional advice on how to stay calm.

[0754] Step 13:

[0755] User: Receives customized alert messages and checks the notification content, for example, by reading the alert message displayed on a smartphone app.

[0756] Step 14:

[0757] User: Upon receiving the warning, take action to evacuate to a safe location, for example, to a safe area in your home or a nearby evacuation center.

[0758] Step 15:

[0759] Device: Continue to monitor the user's emotional state after the warning notification and provide additional information or instructions to reduce anxiety or panic, such as evacuation shelter locations, emergency contact information, and first aid instructions.

[0760] In this way, by analyzing animal behavior data using generative AI and then using an emotion engine to recognize the user's emotional state in real time and provide notifications in the most optimal way, we have created a highly accurate and user-friendly earthquake prediction and warning system.

[0761] Example 2

[0762] 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."

[0763] Current earthquake prediction systems rely on seismometers and GPS data, and issues include prediction accuracy and real-time response. Furthermore, because they issue uniform warnings without considering the emotional state of the user receiving the forecast information, users may not be able to respond appropriately. Furthermore, the security of the collected data is also a concern. To solve these problems, a highly accurate earthquake prediction system that utilizes animal behavior data and takes the user's emotional state into account is needed.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0765] In this invention, the server includes a means for collecting animal behavior data, a means for preprocessing the collected animal behavior data to remove noise, and a means for encrypting the preprocessed data. This enables the analysis of abnormal animal behavior patterns and highly accurate earthquake prediction. It also monitors the user's emotional state and customizes notifications to help the user respond appropriately. Furthermore, data encryption ensures security and maintains data confidentiality.

[0766] "Animal behavior data" refers to information recorded in digital format about animal movements, sounds, vibrations, etc.

[0767] "Collecting means" refers to devices and methods that continuously acquire animal behavioral data using sensor devices, cameras, microphones, vibration detectors, etc.

[0768] "Preprocessing" refers to the process of removing noise and unnecessary information from collected data and converting it into a format suitable for analysis.

[0769] "Encryption means" means a method of transforming data using a cryptographic algorithm (e.g., AES) to store or transmit it in a secure format.

[0770] "Server" refers to a computer system for receiving and storing animal behavioral data and the software running on it.

[0771] "Means for decryption" refers to a method for processing encrypted data back into its original format.

[0772] A "database" is a storage system for storing collected data in a structured format.

[0773] "Cleansing" is the process of filling in missing values ​​from data and organizing it in timestamp order.

[0774] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and detects signs of an earthquake.

[0775] "Means of analysis" refers to the use of generative AI models to analyze data and identify abnormal behavior and earthquake precursors.

[0776] "Means of verbalization" refers to a method for converting the analysis results into natural language and generating reports and warning messages.

[0777] The "notification means" is a method for distributing the generated forecast information and warning messages to terminals and users in the area.

[0778] The "means for monitoring emotional state" is a method for analyzing the user's voice, facial expressions, and text to recognize the user's emotional state in real time.

[0779] A "means for customizing notifications" is a method for changing the content and format of alert messages based on the user's emotional state.

[0780] This invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of earthquakes. This system also includes an emotion engine that recognizes the user's emotions, and covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[0781] Animal behavior data collection

[0782] Device: To monitor the behavior of animals (e.g., dogs and cats), sensor devices such as cameras, microphones, and vibration detectors are used. The sensor devices collect animal behavior data in real time 24 hours a day and store it in digital format. For example, if a dog that is usually quiet suddenly starts barking, its audio data and movement patterns will be recorded.

[0783] Data Preprocessing and Encryption

[0784] Terminal: The collected data is preprocessed and noise is removed. For example, environmental and background sounds are removed from the audio data, and only important behavioral data is extracted. The preprocessed data is then encrypted using the AES encryption algorithm, ensuring data security.

[0785] Sending and receiving data to the server

[0786] Terminal: Encrypted data is sent to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server has a receiving module that decrypts and interprets the received data packets. The data is then stored appropriately in a database.

[0787] Data cleansing and analysis with generative AI models

[0788] Server: The stored data is cleansed, missing values ​​are filled, and the data is sorted by timestamp. The cleansed data is then input into a generative AI model. The generative AI model compares animal behavior data with past earthquake data and performs analysis to identify abnormal behavior. For example, it compares the pattern of a dog suddenly barking with past data and identifies it as a sign of an upcoming earthquake.

[0789] Verbalization of earthquake predictions, generation and distribution of warnings

[0790] Server: The prediction results obtained from the generation AI are converted into natural language and a report is created, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." The generated report is sent to devices in the appropriate area based on the contract information for each region. The HTTPS protocol is again used to deliver the warning message.

[0791] User emotion recognition and customized notifications

[0792] Device: When receiving a warning message, the emotion engine analyzes the user's voice, facial expressions, and text to recognize their emotional state in real time. If the user is nervous, the engine will detect this and customize the content and format of the notification, such as adding a message like "Please stay calm. The nearest evacuation shelter is the nearest park."

[0793] Monitoring emotional state and providing additional information

[0794] Device: Continue to monitor the user's emotional state even after the warning notification. If the user is experiencing anxiety or panic, provide additional information or instructions. For example, send specific instructions such as "Evacuate now. The evacuation shelter is 300 meters away."

[0795] Examples of concrete examples and prompts

[0796] Example: If a device in a certain area detects the sudden barking of a dog, the data is immediately encrypted and sent to a server. The server decrypts the data and analyzes it using a generative AI model. If the result suggests that the dog's abnormal behavior is a sign of an upcoming earthquake, a warning message such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours" is sent to the devices of local residents. Users who receive the notification can take action, such as evacuating to a safe location.

[0797] Example prompt sentence:

[0798] "Analyze the animal behavior data below to see if there are any signs of an earthquake.

[0799] Date and Time: 2023-10-01 10:00

[0800] Behavioral data: Dog suddenly started barking and jumping more

[0801] Area: Shibuya Ward, Tokyo

[0802] This invention combines the natural earthquake prediction abilities of animals with generative AI to realize a highly accurate and user-friendly earthquake prediction and warning system. Furthermore, it uses an emotion engine to provide notifications according to the user's emotional state, helping the user respond calmly and quickly.

[0803] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0804] Step 1: Start data collection and collect behavioral data

[0805] Input: Real-time animal behavior information

[0806] Movement: The device monitors the animal's movements, sounds, and vibration data 24 hours a day through cameras, microphones, and vibration detectors, and records them digitally. Specifically, the device collects audio data when the dog barks, motion data when it moves, and vibration data when it jumps. For example, if a dog suddenly starts barking, the device captures the audio data and changes in its movements.

[0807] Output: Collected animal behavior data

[0808] Step 2: Data preprocessing and denoising

[0809] Input: Collected animal behavior data

[0810] Operation: The device preprocesses the collected data and removes noise. For example, it removes environmental and background sounds from audio data and eliminates outliers from behavioral data to generate clean data for analysis. It also removes outliers from video data and extracts only the frames containing important actions.

[0811] Output: Preprocessed and clean behavioral data

[0812] Step 3: Encrypt the data

[0813] Input: Preprocessed and clean behavioral data

[0814] How it works: The device encrypts the preprocessed data using the AES encryption algorithm, ensuring data security and making the encrypted data suitable for transfer and storage.

[0815] Output: Encrypted animal behavior data

[0816] Step 4: Send the encrypted data to the server

[0817] Input: Encrypted animal behavior data

[0818] Operation: The device sends encrypted data to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server can receive the data via a secure communication channel.

[0819] Output: Encrypted data sent to the server

[0820] Step 5: Decrypt the data and save it to the database

[0821] Input: Encrypted data sent to the server

[0822] Operation: The server receives the encrypted data through the receiving module, decrypts it using the AES encryption algorithm, and then stores the decrypted data in the database.

[0823] Output: Clean behavioral data stored in a database

[0824] Step 6: Cleanse the data

[0825] Input: Clean behavioral data stored in a database

[0826] How it works: The server rechecks the stored data, imputes missing values, sorts it by timestamp, and converts the cleansed data into a format that can be fed into a generative AI model.

[0827] Output: Formatted data suitable for generative AI models

[0828] Step 7: Earthquake prediction analysis using generative AI

[0829] Input: Formatted data suitable for generative AI models

[0830] How it works: The server inputs the cleansed data into a generative AI model, which then compares past earthquake data with animal behavior patterns and performs analysis to identify abnormal behavior. For example, it can identify whether an abnormal dog behavior pattern is a sign of an earthquake.

[0831] Output: Analysis results showing the possibility of an earthquake occurring

[0832] Step 8: Verbalizing earthquake predictions and generating warnings

[0833] Input: Analysis results showing the possibility of an earthquake occurring

[0834] How it works: The server converts the analysis results into natural language and generates a forecast report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." Based on the generated forecast report, it creates an appropriate warning message.

[0835] Output: Earthquake forecast report and warning message in natural language

[0836] Step 9: Delivering a warning message

[0837] Input: Earthquake forecast report and warning message in natural language

[0838] Operation: The server sends the generated warning message to the terminal in the appropriate area using the HTTPS protocol based on the contract information for each region.

[0839] Output: Warning message sent to the region terminal

[0840] Step 10: User Emotion Recognition and Notification Customization

[0841] Input: The warning message sent to the terminal

[0842] How it works: When receiving a warning message, the device uses its emotion engine to analyze the user's voice, facial expressions, and text input to recognize their emotional state. Depending on the user's emotion, the device can create additional messages, such as "Please stay calm. The nearest evacuation shelter is the nearest park," and customize the notification content.

[0843] Output: Displaying a customized warning message

[0844] Step 11: Monitor emotional state and provide additional information

[0845] Input: Customized warning message

[0846] How it works: Even after receiving the warning, the device continues to monitor the user's emotional state using its emotion engine. If the user is feeling anxious or panicked, the device will provide additional specific instructions and advice, such as "Evacuate now. An evacuation shelter is 300 meters away."

[0847] Output: Providing additional information depending on the user's emotional state

[0848] (Application example 2)

[0849] 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."

[0850] Although earthquake prediction technology has advanced in recent years, accurate prediction is still said to be difficult. Furthermore, it is difficult to take prompt and appropriate action when an earthquake is predicted, which could put many lives and property at risk. With the spread of autonomous vehicles in urban areas, there is a need to ensure the safety of vehicles and promptly notify drivers when an earthquake occurs. The challenge is to solve these problems and provide more accurate and user-friendly earthquake prediction and safety assurance methods.

[0851] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting animal behavioral data, means for analyzing the collected animal behavioral data using a generation AI, means for predicting the possibility of an earthquake based on the analysis results and verbalizing the data, means for notifying the verbalized prediction information to local terminals and users, means for transmitting the prediction information to the vehicle control system via an interface and instructing the vehicle's emergency action, and means for recognizing the user's emotional state and adjusting the notification content. This enables early prediction of the risk of an earthquake and appropriate response. At the same time, by appropriately adjusting the notification content according to the user's emotional state, the user can be provided with necessary information and encouraged to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[0852] "Animal behavior data" is digital information such as the movements, sounds, and vibrations of dogs, cats, and other animals collected using sensor devices.

[0853] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[0854] "Analysis results" refers to information obtained as a result of data analysis by the generating AI, and includes information such as the possibility of an earthquake occurring.

[0855] "Predicting the possibility of earthquakes" means predicting whether an earthquake will occur in the future based on animal behavior data.

[0856] "Verbalization" means expressing data and information such as analysis results in natural language.

[0857] A "local terminal" is an electronic device such as a computer, smartphone, or tablet that is located in a specific area.

[0858] "User" means an individual or member of an organization who uses the system.

[0859] An "interface" is a point of contact or protocol that allows different systems or devices to communicate with each other.

[0860] A "vehicle control system" is a system for managing and operating autonomous vehicles.

[0861] "Emergency action instructions" are instructions on immediate countermeasures to be taken in response to anticipated danger.

[0862] "User's emotional state" refers to the emotions and mental state that the user is currently experiencing.

[0863] "Adjusting notification content" means appropriately changing the information to be communicated and the way it is expressed depending on the user's emotional state.

[0864] The present invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of an earthquake. This system includes a function that recognizes the user's emotional state and issues appropriate warning notifications. Specific embodiments are described below.

[0865] Animal behavior data collection

[0866] The device uses sensors to monitor the behavior of animals (such as dogs and cats) and collects behavioral data in real time 24 hours a day. The sensors include cameras, microphones, and vibration detectors. For example, if a dog that is usually quiet suddenly starts barking, the device will record its audio data and movement patterns.

[0867] Data preprocessing and transmission

[0868] The device preprocesses the collected data to remove noise, eliminating outliers and incomplete data, and formats it for analysis. The preprocessed data is then encrypted and sent to the server using a secure communication protocol (e.g., HTTPS or MQTT).

[0869] Data reception and analysis

[0870] The server receives the encrypted data sent from the device, decrypts it, and stores it in a database. The stored data is cleansed and formatted to be input into the generative AI model. The server then inputs the formatted data into the generative AI model, where it performs an analysis to identify abnormal behavior by comparing animal behavior patterns with past earthquake data.

[0871] Earthquake prediction and warning notifications

[0872] The server determines the possibility of an earthquake occurring based on the results of the earthquake prediction analysis by the generation AI and generates forecast information in natural language. The generated forecast information is sent to local terminals and users' smartphones and computers. At this time, the forecast information is transmitted to the vehicle control system via an interface, and emergency action is instructed for the autonomous vehicle.

[0873] User emotion recognition and notification customization

[0874] When receiving a warning message, the device monitors the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. The content and format of the notification are customized based on the recognition results. For example, if a user is nervous, advice on how to stay calm will be added.

[0875] Specific examples

[0876] A concrete example of this system is a scenario in which an autonomous vehicle moves to a safe location based on an earthquake prediction. Generative AI analyzes animal behavior data, and if there is a high possibility of an earthquake, the vehicle control system is instructed to "move to a safe location." At the same time, a notification based on the user's emotional state is displayed, such as "Please remain calm, the vehicle is heading to a safe location."

[0877] Prompt Sentence Examples

[0878] An example of a prompt to input to the generative AI model is:

[0879] "Based on animal behavior data, an earthquake may occur within the next two hours. The specific behavioral patterns are sudden barking, fast moving around, and abnormal vibrations."

[0880] This system is user-friendly and has highly accurate earthquake prediction and warning functions, enabling safe and rapid response in the event of an earthquake.

[0881] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0882] Step 1:

[0883] The device collects animal behavior data. It uses sensors (cameras, microphones, vibration detectors, etc.) to digitally record animal movements, sounds, and vibration information in real time 24 hours a day. For example, if a dog suddenly starts barking, its sound data and movement patterns will be recorded.

[0884] Input: Real-time animal behavior

[0885] Output: Digital animal behavior data

[0886] Step 2:

[0887] The device preprocesses the collected data by removing noise, eliminating outliers and incomplete data, and formatting it for analysis, such as normalizing the data and imputing missing values.

[0888] Input: Collected animal behavior data

[0889] Output: Preprocessed clean data

[0890] Step 3:

[0891] The terminal encrypts the pre-processed data. This encryption ensures data security. For example, an encryption algorithm such as AES (Advanced Encryption Standard) is used.

[0892] Input: Preprocessed clean data

[0893] Output: Encrypted data

[0894] Step 4:

[0895] The device sends encrypted data to the server via a secure communication protocol (such as HTTPS or MQTT) at regular intervals.

[0896] Input: Encrypted data

[0897] Output: Send to server (via HTTPS or MQTT)

[0898] Step 5:

[0899] The server receives the encrypted data, decrypts it, and stores it in the database. After the data is decrypted, it performs a cleansing process before storing it in the database.

[0900] Input: Encrypted data

[0901] Output: Cleansed data stored in a database

[0902] Step 6:

[0903] The server inputs the stored data into a generative AI model to analyze abnormal animal behavior. The generative AI model compares animal behavior patterns with past earthquake data and determines the likelihood of an earthquake occurring.

[0904] Input: Cleansed data

[0905] Output: Generative AI predicts the likelihood of an earthquake occurring

[0906] Step 7:

[0907] The server generates notification messages based on the earthquake prediction results determined by the generation AI, and in particular, uses natural language processing to convert the prediction information into a format that humans can understand.

[0908] Input: Earthquake prediction data

[0909] Output: Predictive notification message in natural language

[0910] Step 8:

[0911] The server then sends the generated prediction messages to local terminals and users, and simultaneously transmits the prediction information to the vehicle control system through an interface to instruct the autonomous vehicle to take emergency action.

[0912] Input: Natural language forecast notification message, earthquake forecast data

[0913] Output: Warning notification to user terminal, instructions to vehicle control system

[0914] Step 9:

[0915] When receiving a warning message, the device monitors the user's emotional state. It recognizes emotions from voice, facial expressions, and text input, and customizes the notification content based on the results. For example, if a user is nervous, it will deliver "advice on how to stay calm."

[0916] Input: Predictive notification message, user emotion data

[0917] Output: Customized notification message

[0918] Step 10:

[0919] The user checks the warning message displayed on the device and takes appropriate action, such as evacuating to a safe place.

[0920] Input: Customized notification message

[0921] Output: User action (e.g., evacuation)

[0922] The above steps enable early prediction of the risk of an earthquake and appropriate responses. At the same time, by appropriately adjusting the content of notifications according to the user's emotional state, it is possible to provide the user with the necessary information and encourage them to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] [Third embodiment]

[0927] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0928] 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.

[0929] 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).

[0930] 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.

[0931] 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.

[0932] 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).

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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."

[0939] The following describes an embodiment of the present invention. This system consists of a series of processes that collects animal behavior data, analyzes the data using a generative AI, and predicts the possibility of an earthquake occurring.

[0940] 1. Start data collection:

[0941] Terminal: Equipped with a sensor device for monitoring the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, vibration detector, etc., and collects animal behavior data in real time 24 hours a day.

[0942] 2. Behavioral Data Collection:

[0943] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0944] 3. Data preprocessing:

[0945] Terminal: The collected data is filtered to remove unnecessary noise, eliminating outliers and incomplete data.

[0946] 4. Data Encryption and Transmission:

[0947] Terminal: The pre-processed data is encrypted, ensuring data security. The encrypted data is sent to the server at regular intervals (for example, every minute or when a certain threshold is reached). A secure communication protocol (for example, HTTPS or MQTT) is used for transmission.

[0948] 5. Data Receipt and Analysis:

[0949] Server: Receives data sent from the device, decrypts it, and stores it in a database. The received data is formatted and cleansed to make it suitable for analysis. The formatted data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[0950] 6. Earthquake prediction using generative AI:

[0951] Server: The generation AI uses the analysis results to predict the possibility of an earthquake occurring and expresses the data in natural language. The results are summarized in a report format, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0952] 7. Alert Generation and Notification:

[0953] Server: Generates warning messages based on the generated forecast information. The warning messages are distributed to the appropriate areas based on the contract information for each region.

[0954] Device: Receives the warning message and notifies the user via audio alert, push notification on the smartphone app, or email.

[0955] 8. User Action:

[0956] User: After receiving the warning, check the notification content. For example, read the warning message displayed on the smartphone app. As soon as the warning is received, take action to evacuate to a safe place.

[0957] Through the above process, this system utilizes the natural earthquake prediction ability of animals, enabling highly accurate and rapid earthquake prediction and minimizing earthquake damage.The unique feature of this system is that it uses animal behavioral data, providing a flexible and new approach that does not rely on conventional physical sensors.

[0958] The processing flow will be explained below.

[0959] Step 1:

[0960] Terminal: To monitor animal behavior, a sensor device equipped with cameras, microphones, and vibration detectors is used to collect data in real time 24 hours a day.

[0961] Step 2:

[0962] Device: Stores information on animal movements, sounds, and vibrations as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[0963] Step 3:

[0964] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[0965] Step 4:

[0966] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[0967] Step 5:

[0968] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[0969] Step 6:

[0970] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[0971] Step 7:

[0972] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[0973] Step 8:

[0974] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[0975] Step 9:

[0976] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[0977] Step 10:

[0978] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[0979] Step 11:

[0980] Device: Receives warning messages and notifies the user via voice alerts, push notifications on smartphone apps, and emails.

[0981] Step 12:

[0982] User: After receiving the warning, the user checks the notification content. For example, the user reads the warning message displayed on the smartphone app. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[0983] In this way, the entire system processes everything from collecting and analyzing animal behavior data to issuing warnings to users, providing highly accurate earthquake predictions in real time.

[0984] Example 1

[0985] 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."

[0986] Conventional earthquake prediction systems rely on physical sensors and observation equipment, resulting in high installation costs and limited real-time prediction accuracy. Furthermore, these systems are unable to directly observe natural abnormal phenomena, necessitating the development of new approaches to improve prediction accuracy. This invention aims to provide a new system for earthquake prediction using animal behavior, thereby minimizing earthquake damage. In particular, the challenge is to create a system that can accurately predict the possibility of an earthquake occurring and promptly notify users by collecting animal behavior data and analyzing it using a generative AI model.

[0987] 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.

[0988] In this invention, the server includes: a sensor means for collecting information on animal movements, sounds, and vibrations; a preprocessing means for filtering the collected data and removing noise; a data transmission means for encrypting the preprocessed data and transmitting it using a secure communication protocol; a data receiving means for receiving, decrypting, and formatting the transmitted data; a means for inputting the formatted data into a generative AI model and analyzing abnormal behavior; an earthquake prediction means for predicting the possibility of an earthquake occurring from the analysis results and generating a report in natural language; a means for notifying the generated prediction information to a local terminal and a user as a warning message; and a support means for users who receive the prediction information to initiate evacuation behavior. This enables earthquake prediction using animal behavior in a flexible approach that differs from conventional physical sensors, making it possible to provide highly accurate and prompt prediction information.

[0989] "Animal movement, sound, and vibration information" refers to information on the animal's body movements, the sounds it makes, and the vibrations that accompany its movements.

[0990] "Sensor means" refers to devices or equipment used to collect animal movement, sound, and vibration information.

[0991] The "pre-processing means" refers to a series of processes for cleaning the collected data, such as filtering the data and removing noise.

[0992] "Data transmission means" refers to the devices and techniques that encrypt the pre-processed data and transmit it using a secure communications protocol.

[0993] "Data receiving means" refers to the devices and techniques used to receive, decode, and format transmitted data.

[0994] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and past earthquake data to predict the possibility of an earthquake occurring.

[0995] "Abnormal behavior" refers to behavior that deviates from normal animal behavior patterns and is predicted to be related to the occurrence of an earthquake.

[0996] "Earthquake prediction method" refers to a series of processes that use a generative AI model to predict the possibility of an earthquake occurring from the analysis results and generate a report in natural language.

[0997] The "warning message" refers to a message generated based on the possibility of an earthquake occurring, to alert the user.

[0998] "Support means" refers to devices and technologies that assist users who receive forecast information to begin appropriate evacuation actions.

[0999] The following describes an embodiment of the present invention. This system is designed to collect and analyze animal behavior data and predict the possibility of earthquakes. It primarily utilizes the natural predictive abilities of animals to provide earthquake prediction information to users. A detailed description of this system is provided below.

[1000] 1. Data Collection

[1001] The device operates a sensor device to monitor the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, and vibration detector, and collects animal behavior data in real time 24 hours a day. For example, the camera attached to the device records video at 30 frames per second or more, and the microphone captures audio data at a sampling rate of 44.1 kHz per second. The vibration detector detects vibrations caused by sudden animal movements.

[1002] 2. Data Preprocessing

[1003] The device filters the collected data to remove unwanted noise, including outliers and incomplete data. Specifically, it removes background noise from recorded audio data and trims unnecessary parts of video data (for example, periods when no animals are in the frame). It also removes outliers from vibration data and calculates average data over a certain period.

[1004] 3. Data Encryption and Transmission

[1005] The terminal encrypts the preprocessed data using encryption technology such as AES, and then transmits the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT. For example, the terminal encrypts the data accumulated every minute using AES-256 and sends a POST request to the server using HTTPS communication.

[1006] 4. Data Receipt and Analysis

[1007] The server receives the encrypted data sent from the terminal and immediately decrypts it using AES-256. The decrypted data is then stored in a database, where it is reformatted and cleansed into a format suitable for analysis. Specifically, the server receives an HTTP request, analyzes the encrypted payload, and decrypts it. The decrypted data is reformatted and stored in a database, where it is used to fill in missing data and recheck for outliers.

[1008] 5. Earthquake Prediction Using Generative AI

[1009] The server inputs the formatted data into a generative AI model, which compares and analyzes animal behavior patterns with past earthquake data. The generative AI model receives a prompt: "Compare the frequency of dog barking over the past 24 hours with past earthquake data, and predict the probability of an earthquake occurring within the next two hours." The generative AI then generates a prediction: "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[1010] 6. Alert Generation and Notification

[1011] The server generates a warning message based on the generated forecast information. This warning message is distributed to the appropriate area based on the contract information for each region. Specifically, the server generates a message such as "Caution: An earthquake may occur within the next two hours" based on the forecast information and sends a push notification to users in the contract area.

[1012] 7. User Response

[1013] After receiving the warning from the device, the user checks the notification and takes action to evacuate to a safe place. For example, the user checks the warning message on a smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[1014] Through the above process, this system utilizes the natural earthquake prediction ability of animals to enable accurate and rapid earthquake prediction, and also urges users to take early evacuation action via warning messages, thereby minimizing earthquake damage.

[1015] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1016] Step 1: Start collecting data

[1017] The terminal operates sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats).

[1018] How it works: The camera records video at over 30 frames per second, the microphone captures audio data at a sampling rate of 44.1 kHz per second, and the vibration detector detects vibrations caused by sudden animal movements.

[1019] Input: Animal movement, sound, and vibration information.

[1020] Output: Raw data (video, audio, vibration) is saved to storage in real time.

[1021] Step 2: Preprocessing the data

[1022] The device filters the collected data to remove unwanted noise, which includes eliminating outliers and imperfections in the data.

[1023] Specific operations: Remove background noise from recorded audio data, trim unnecessary parts of video data (for example, times when no animals are in the frame), remove outliers from vibration data, and calculate average data over a certain period.

[1024] Input: Collected raw data (video, audio, vibration).

[1025] Output: Filtered and clean data.

[1026] Step 3: Encrypt the data

[1027] The terminal encrypts the preprocessed data using encryption technology such as AES.

[1028] Specific operation: The terminal sequentially encrypts the preprocessed data using the AES-256 method.

[1029] Input: Preprocessed clean data.

[1030] Output: The encrypted data.

[1031] Step 4: Sending data

[1032] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT.

[1033] Specific operation: Every minute, the device sends a POST request with encrypted data to the server using HTTPS communication.

[1034] Input: Encrypted data.

[1035] Output: Notification of completion of transmission to the server.

[1036] Step 5: Receiving the data

[1037] The server receives the encrypted data sent from the terminal.

[1038] Specific operation: The server receives the HTTP request, analyzes the encrypted payload, and saves it to storage.

[1039] Input: Encrypted data.

[1040] Output: The stored encrypted data.

[1041] Step 6: Decrypt the data

[1042] The server decrypts the received encrypted data using the AES-256 method.

[1043] Specific operation: The stored encrypted data is decrypted using AES-256 as appropriate and the raw data is extracted.

[1044] Input: Encrypted data.

[1045] Output: The decrypted data.

[1046] Step 7: Data Shaping and Cleansing

[1047] The server then formats and cleanses the decrypted data into a format suitable for analysis.

[1048] Specific operations: Fill in missing values ​​in the decoded data, recheck for outliers, and standardize the data format.

[1049] Input: Decrypted data.

[1050] Output: Formatted and cleansed data.

[1051] Step 8: Analyze abnormal behavior

[1052] The server inputs the formatted data into a generative AI model to analyze abnormal animal behavior.

[1053] Specific operation: The generative AI model is given a prompt sentence: "Please analyze what would happen if a dog suddenly started barking," and the model compares the animal's behavioral patterns with past earthquake data.

[1054] Input: Formatted and cleansed data.

[1055] Output: Analysis of abnormal animal behavior.

[1056] Step 9: Conducting earthquake predictions

[1057] The server predicts the possibility of an earthquake occurring based on the results of analysis by the generative AI model.

[1058] Specific operation: The generative AI model is given a prompt sentence: "Please predict the probability of an earthquake occurring within the next two hours," and a prediction result is generated.

[1059] Input: Analysis results of abnormal behavior.

[1060] Output: Earthquake occurrence forecast information.

[1061] Step 10: Generate warnings

[1062] The server generates a warning message based on the generated prediction information.

[1063] What it does: The server generates a message saying "An earthquake may occur within the next two hours" and formats it to notify the user.

[1064] Input: Earthquake occurrence prediction information.

[1065] Output: A warning message.

[1066] Step 11: User Notification

[1067] The terminal receives the generated warning message and notifies the user.

[1068] Specific operation: The device will send a warning message to the user via voice alert, push notification on the smartphone app, email, etc.

[1069] Input: Warning message.

[1070] Output: Notification to the user.

[1071] Step 12: User Action

[1072] After receiving the warning, the user checks the notification content and begins taking action to evacuate to a safe place.

[1073] Specific actions: The user checks the warning message on the smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[1074] Input: Warning message.

[1075] Output: Evacuation action initiated.

[1076] (Application example 1)

[1077] 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."

[1078] To improve the accuracy of earthquake prediction and provide real-time warnings, a system that can quickly and accurately collect and analyze animal behavioral data is needed. However, existing earthquake prediction systems mainly rely on physical sensors, and no means have been established to utilize animals' natural ability to predict earthquakes. Therefore, there is a need for accurate real-time earthquake predictions and to provide users with prompt warnings.

[1079] 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.

[1080] In this invention, the server includes a means for preprocessing the animal behavior data, a means for encrypting and transmitting the preprocessed data, and a means for generating a warning message based on the analysis results and notifying the user via a smartphone app, thereby enabling highly accurate and rapid earthquake prediction using animal behavior data.

[1081] "Animal behavior data" refers to information such as movements, sounds, and vibrations generated when animals behave.

[1082] "Generative AI" refers to artificial intelligence techniques used to analyze collected data, particularly those that use unsupervised learning and deep learning models.

[1083] "Earthquake probability" refers to predicting the probability of an earthquake occurring in a particular area based on the results of the analysis.

[1084] "Means of verbalization" refers to the technology of converting analysis results and predictive information into natural language and generating reports and warning messages.

[1085] "Means for notifying local terminals and users" refers to communication technologies for delivering forecast information and warning messages to specific regions and users.

[1086] "Preprocessing means" refers to techniques for removing noise from collected data, eliminating outliers, and formatting the data into a form suitable for analysis.

[1087] "Encryption means" refers to technology that encrypts data to prevent access by third parties when transmitting the data.

[1088] "Means for decryption" refers to the technology that returns encrypted data to its original form at the receiving end.

[1089] "Means for generating a warning message" refers to a technology for generating a message to notify a user of the possibility of an earthquake based on the analysis results.

[1090] "Smartphone app" refers to software that runs on a smartphone and notifies users of warning messages and predictive information in real time.

[1091] A "secure communication protocol" is a communication technology that ensures security when sending and receiving data, and examples include HTTPS and MQTT.

[1092] The detailed description of the embodiment of this invention will be given below. This system consists of a series of steps to collect animal behavior data, analyze the data using generative AI, predict the possibility of an earthquake occurring, and notify the user of a warning.

[1093] System configuration

[1094] The system consists of the following main components:

[1095] 1. Animal behavior data collection device:

[1096] The (terminal) is equipped with sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats). The terminal collects animal behavior data in real time 24 hours a day.

[1097] 2. Data preprocessing:

[1098] (Device) filters the collected data to remove noise and eliminate outliers and incomplete data.

[1099] 3. Data Encryption and Transmission:

[1100] The terminal encrypts the preprocessed data using AES encryption, and the encrypted data is sent to the cloud server via a secure communication protocol (e.g., HTTPS).

[1101] 4. Data Receipt and Analysis:

[1102] The server receives the data sent from the device, decrypts it, and stores it in a database. The received data is then formatted and cleansed to make it suitable for analysis. This data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[1103] 5. Earthquake prediction and warning generation:

[1104] The server uses generative AI to predict the possibility of an earthquake. The results are expressed in natural language and generated as a warning message. For example, it might say, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1105] 6. Warning Notice:

[1106] The server delivers the generated warning message to the user via a smartphone app. The user can receive and check the warning message on their smartphone and take safety measures.

[1107] Hardware and software used

[1108] Hardware: Sensor devices for collecting animal behavior data include cameras (e.g., Raspberry Pi Camera Module), microphones, vibration detectors, etc. Data processing is performed using a Raspberry Pi or a smartphone.

[1109] software:

[1110] Data preprocessing: OpenCV (camera data processing), Librosa (audio data processing)

[1111] Data encryption: PyCryptodome (encryption)

[1112] Communication protocol: Requests (HTTPS communication)

[1113] Generative AI models: TensorFlow, PyTorch

[1114] Specific examples

[1115] For example, behavioral data is collected, such as when a dog in the home suddenly starts barking or exhibits abnormal behavior during times when it is usually quiet. This data is preprocessed on the device to remove unnecessary noise. After preprocessing, the data is AES encrypted and securely sent to a cloud server. The cloud server uses a generative AI model to analyze the possibility of an earthquake and reports the results in natural language. For example, a generated prompt might be, "Analyze data when a dog exhibits unusual behavioral patterns and build a generative AI model to predict the possibility of an earthquake." Based on the analysis results, the smartphone app notifies the user with a warning message such as, "There is a high possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[1116] In this way, by utilizing animal behavioral data, this invention provides a flexible and new approach to earthquake prediction that does not rely on conventional physical sensors, and can provide users with highly accurate and prompt warnings.

[1117] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1118] Step 1:

[1119] The device collects animal behavior data. Specifically, it uses sensor devices (cameras, microphones, and vibration detectors) installed in the home to record animal movements, sounds, and vibration information as digital data in real time. The input is raw data captured by the sensors, and the output is time-series behavior data.

[1120] Step 2:

[1121] The terminal preprocesses the collected animal behavior data. In this step, specific operations are performed to remove noise from the collected data and eliminate outliers and incomplete data. The input is the raw data collected in step 1, and the output is clean behavior data that has been filtered and cleansed. OpenCV (camera data) and Librosa (audio data) are used for noise removal.

[1122] Step 3:

[1123] The terminal encrypts the preprocessed data. Specifically, it uses the AES encryption algorithm to keep the data secure. The input is the preprocessed clean behavioral data, and the output is the encrypted data. It uses PyCryptodome for encryption.

[1124] Step 4:

[1125] The device sends encrypted data to the cloud server. The data is sent securely using a secure communication protocol (e.g., HTTPS). The input is the encrypted data, and the output is a notification that the data has been sent. The Requests library is used for communication.

[1126] Step 5:

[1127] The server receives and decrypts the data sent from the terminal. The received encrypted data is decrypted using the AES encryption method. The input is the encrypted data, and the output is the decrypted clean behavioral data. PyCryptodome is used for decryption.

[1128] Step 6:

[1129] The server formats and cleanses the decrypted data, processing it to convert it into a format suitable for analysis. The input is the clean decrypted behavioral data, and the output is the formatted data.

[1130] Step 7:

[1131] The server uses a generative AI model to analyze the formatted data and compare animal behavior patterns with past earthquake data. It identifies abnormal behavior and predicts the likelihood of an earthquake. TensorFlow and PyTorch are used for data calculations in this step. The input is the formatted data, and the output is the analysis results indicating the likelihood of an earthquake.

[1132] Step 8:

[1133] The server generates a warning message in natural language based on the analysis results. For example, it may generate a message such as, "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours." The input is the analysis results of the generative AI model, and the output is a warning message in natural language.

[1134] Step 9:

[1135] The server notifies the user of the generated warning message in real time via a smartphone app using a push notification service such as Firebase Cloud Messaging (FCM). The input is a natural language warning message, and the output is a warning notification displayed on the smartphone.

[1136] Step 10:

[1137] The user checks the warning message received on the smartphone app and begins taking action to prepare for the possibility of an earthquake, such as evacuating to a safe place. The input is the warning message displayed on the smartphone, and the output is the user's evacuation behavior.

[1138] In this way, the system can use animal behavior data to accurately predict the possibility of earthquakes and provide users with prompt warnings. For example, the prompt text might include, "Analyze data on when dogs exhibit unusual behavioral patterns and build a generative AI model that predicts the likelihood of earthquakes."

[1139] 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.

[1140] This invention is a system that collects animal behavior data, analyzes the data using generative AI, and predicts the possibility of earthquakes. In addition to this system, it also includes an emotion engine that recognizes the user's emotions. The processing of this system covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[1141] Animal behavior data collection

[1142] 1. Start data collection:

[1143] Device: Sensor devices are used to monitor the behavior of animals (e.g., dogs and cats) and collect behavioral data in real time 24 hours a day. The sensor devices include cameras, microphones, and vibration detectors.

[1144] 2. Behavioral Data Collection:

[1145] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[1146] 3. Data preprocessing:

[1147] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[1148] Data encryption and transmission

[1149] 4. Data Encryption:

[1150] Terminal: Encrypts the pre-processed data. Encryption ensures the security of the data.

[1151] 5. Sending data to the server:

[1152] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[1153] Data reception and analysis

[1154] 6. Receiving and Decrypting Data:

[1155] Server: Receives encrypted data sent from the device, decrypts it, and stores it in a database.

[1156] 7. Data cleansing and shaping:

[1157] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[1158] 8. Earthquake prediction analysis using generative AI:

[1159] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[1160] 9. Verbalizing earthquake prediction:

[1161] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1162] Alert Generation and Notification

[1163] 10. Generate warning messages:

[1164] Server: Generates a warning message based on the generated earthquake prediction information.

[1165] 11. WARNING DELIVERY:

[1166] Server: Sends warning messages to terminals in the appropriate area based on regional contract information.

[1167] User emotion recognition and customized notifications

[1168] 12. Leveraging the Emotion Engine:

[1169] Terminal: When receiving a warning message, an emotion engine is used to monitor the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input.

[1170] 13. Customizing Notifications:

[1171] On the device: Customize the content and format of notifications based on the emotion engine's recognition results. For example, add advice on staying calm to a nervous user.

[1172] 14. User Action:

[1173] User: After receiving the warning, the user checks the notification content and reads the warning message displayed on the smartphone app, for example. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[1174] Follow up and provide additional information

[1175] 15. Emotional state monitoring:

[1176] Device: Continues to monitor the user's emotional state even after the warning notification.

[1177] 16. Providing Additional Information:

[1178] Devices: If needed, provide additional information or instructions to reduce anxiety and panic, such as the location of nearby evacuation shelters or emergency contact information.

[1179] This system utilizes the natural earthquake prediction abilities of animals, and also recognizes the user's emotional state in real time and notifies them in the most appropriate way, thereby realizing a highly accurate and user-friendly earthquake prediction and warning system.

[1180] The processing flow will be explained below.

[1181] Step 1:

[1182] Terminal: To monitor the behavior of animals (e.g., dogs and cats), a sensor device equipped with a camera, microphone, and vibration detector is used to collect data in real time 24 hours a day.

[1183] Step 2:

[1184] Device: Collected animal movement, sound, and vibration information is recorded as digital data. For example, if a dog is usually quiet but suddenly starts barking, its sound data and movement patterns are recorded.

[1185] Step 3:

[1186] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[1187] Step 4:

[1188] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[1189] Step 5:

[1190] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) and periodically sends data to the server.

[1191] Step 6:

[1192] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[1193] Step 7:

[1194] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[1195] Step 8:

[1196] Server: The formatted data is input into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[1197] Step 9:

[1198] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1199] Step 10:

[1200] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[1201] Step 11:

[1202] Terminal: Receives the warning message. At the same time, an emotion engine monitors the user's emotional state and recognizes emotions from the user's voice, facial expressions, and text input.

[1203] Step 12:

[1204] On the device: The emotion engine can customize the content and format of notifications for users whose emotional state is recognized by the engine. For example, if a user is nervous, the engine can provide additional advice on how to stay calm.

[1205] Step 13:

[1206] User: Receives customized alert messages and checks the notification content, for example, by reading the alert message displayed on a smartphone app.

[1207] Step 14:

[1208] User: Upon receiving the warning, take action to evacuate to a safe location, for example, to a safe area in your home or a nearby evacuation center.

[1209] Step 15:

[1210] Device: Continue to monitor the user's emotional state after the warning notification and provide additional information or instructions to reduce anxiety or panic, such as evacuation shelter locations, emergency contact information, and first aid instructions.

[1211] In this way, by analyzing animal behavior data using generative AI and then using an emotion engine to recognize the user's emotional state in real time and provide notifications in the most optimal way, we have created a highly accurate and user-friendly earthquake prediction and warning system.

[1212] Example 2

[1213] 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."

[1214] Current earthquake prediction systems rely on seismometers and GPS data, and issues include prediction accuracy and real-time response. Furthermore, because they issue uniform warnings without considering the emotional state of the user receiving the forecast information, users may not be able to respond appropriately. Furthermore, the security of the collected data is also a concern. To solve these problems, a highly accurate earthquake prediction system that utilizes animal behavior data and takes the user's emotional state into account is needed.

[1215] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1216] In this invention, the server includes a means for collecting animal behavior data, a means for preprocessing the collected animal behavior data to remove noise, and a means for encrypting the preprocessed data. This enables the analysis of abnormal animal behavior patterns and highly accurate earthquake prediction. It also monitors the user's emotional state and customizes notifications to help the user respond appropriately. Furthermore, data encryption ensures security and maintains data confidentiality.

[1217] "Animal behavior data" refers to information recorded in digital format about animal movements, sounds, vibrations, etc.

[1218] "Collecting means" refers to devices and methods that continuously acquire animal behavioral data using sensor devices, cameras, microphones, vibration detectors, etc.

[1219] "Preprocessing" refers to the process of removing noise and unnecessary information from collected data and converting it into a format suitable for analysis.

[1220] "Encryption means" means a method of transforming data using a cryptographic algorithm (e.g., AES) to store or transmit it in a secure format.

[1221] "Server" refers to a computer system for receiving and storing animal behavioral data and the software running on it.

[1222] "Means for decryption" refers to a method for processing encrypted data back into its original format.

[1223] A "database" is a storage system for storing collected data in a structured format.

[1224] "Cleansing" is the process of filling in missing values ​​from data and organizing it in timestamp order.

[1225] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and detects signs of an earthquake.

[1226] "Means of analysis" refers to the use of generative AI models to analyze data and identify abnormal behavior and earthquake precursors.

[1227] "Means of verbalization" refers to a method for converting the analysis results into natural language and generating reports and warning messages.

[1228] The "notification means" is a method for distributing the generated forecast information and warning messages to terminals and users in the area.

[1229] The "means for monitoring emotional state" is a method for analyzing the user's voice, facial expressions, and text to recognize the user's emotional state in real time.

[1230] A "means for customizing notifications" is a method for changing the content and format of alert messages based on the user's emotional state.

[1231] This invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of earthquakes. This system also includes an emotion engine that recognizes the user's emotions, and covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[1232] Animal behavior data collection

[1233] Device: To monitor the behavior of animals (e.g., dogs and cats), sensor devices such as cameras, microphones, and vibration detectors are used. The sensor devices collect animal behavior data in real time 24 hours a day and store it in digital format. For example, if a dog that is usually quiet suddenly starts barking, its audio data and movement patterns will be recorded.

[1234] Data Preprocessing and Encryption

[1235] Terminal: The collected data is preprocessed and noise is removed. For example, environmental and background sounds are removed from the audio data, and only important behavioral data is extracted. The preprocessed data is then encrypted using the AES encryption algorithm, ensuring data security.

[1236] Sending and receiving data to the server

[1237] Terminal: Encrypted data is sent to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server has a receiving module that decrypts and interprets the received data packets. The data is then stored appropriately in a database.

[1238] Data cleansing and analysis with generative AI models

[1239] Server: The stored data is cleansed, missing values ​​are filled, and the data is sorted by timestamp. The cleansed data is then input into a generative AI model. The generative AI model compares animal behavior data with past earthquake data and performs analysis to identify abnormal behavior. For example, it compares the pattern of a dog suddenly barking with past data and identifies it as a sign of an upcoming earthquake.

[1240] Verbalization of earthquake predictions, generation and distribution of warnings

[1241] Server: The prediction results obtained from the generation AI are converted into natural language and a report is created, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." The generated report is sent to devices in the appropriate area based on the contract information for each region. The HTTPS protocol is again used to deliver the warning message.

[1242] User emotion recognition and customized notifications

[1243] Device: When receiving a warning message, the emotion engine analyzes the user's voice, facial expressions, and text to recognize their emotional state in real time. If the user is nervous, the engine will detect this and customize the content and format of the notification, such as adding a message like "Please stay calm. The nearest evacuation shelter is the nearest park."

[1244] Monitoring emotional state and providing additional information

[1245] Device: Continue to monitor the user's emotional state even after the warning notification. If the user is experiencing anxiety or panic, provide additional information or instructions. For example, send specific instructions such as "Evacuate now. The evacuation shelter is 300 meters away."

[1246] Examples of concrete examples and prompts

[1247] Example: If a device in a certain area detects the sudden barking of a dog, the data is immediately encrypted and sent to a server. The server decrypts the data and analyzes it using a generative AI model. If the result suggests that the dog's abnormal behavior is a sign of an upcoming earthquake, a warning message such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours" is sent to the devices of local residents. Users who receive the notification can take action, such as evacuating to a safe location.

[1248] Example prompt sentence:

[1249] "Analyze the animal behavior data below to see if there are any signs of an earthquake.

[1250] Date and Time: 2023-10-01 10:00

[1251] Behavioral data: Dog suddenly started barking and jumping more

[1252] Area: Shibuya Ward, Tokyo

[1253] This invention combines the natural earthquake prediction abilities of animals with generative AI to realize a highly accurate and user-friendly earthquake prediction and warning system. Furthermore, it uses an emotion engine to provide notifications according to the user's emotional state, helping the user respond calmly and quickly.

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1: Start data collection and collect behavioral data

[1256] Input: Real-time animal behavior information

[1257] Movement: The device monitors the animal's movements, sounds, and vibration data 24 hours a day through cameras, microphones, and vibration detectors, and records them digitally. Specifically, the device collects audio data when the dog barks, motion data when it moves, and vibration data when it jumps. For example, if a dog suddenly starts barking, the device captures the audio data and changes in its movements.

[1258] Output: Collected animal behavior data

[1259] Step 2: Data preprocessing and denoising

[1260] Input: Collected animal behavior data

[1261] Operation: The device preprocesses the collected data and removes noise. For example, it removes environmental and background sounds from audio data and eliminates outliers from behavioral data to generate clean data for analysis. It also removes outliers from video data and extracts only the frames containing important actions.

[1262] Output: Preprocessed and clean behavioral data

[1263] Step 3: Encrypt the data

[1264] Input: Preprocessed and clean behavioral data

[1265] How it works: The device encrypts the preprocessed data using the AES encryption algorithm, ensuring data security and making the encrypted data suitable for transfer and storage.

[1266] Output: Encrypted animal behavior data

[1267] Step 4: Send the encrypted data to the server

[1268] Input: Encrypted animal behavior data

[1269] Operation: The device sends encrypted data to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server can receive the data via a secure communication channel.

[1270] Output: Encrypted data sent to the server

[1271] Step 5: Decrypt the data and save it to the database

[1272] Input: Encrypted data sent to the server

[1273] Operation: The server receives the encrypted data through the receiving module, decrypts it using the AES encryption algorithm, and then stores the decrypted data in the database.

[1274] Output: Clean behavioral data stored in a database

[1275] Step 6: Cleanse the data

[1276] Input: Clean behavioral data stored in a database

[1277] How it works: The server rechecks the stored data, imputes missing values, sorts it by timestamp, and converts the cleansed data into a format that can be fed into a generative AI model.

[1278] Output: Formatted data suitable for generative AI models

[1279] Step 7: Earthquake prediction analysis using generative AI

[1280] Input: Formatted data suitable for generative AI models

[1281] How it works: The server inputs the cleansed data into a generative AI model, which then compares past earthquake data with animal behavior patterns and performs analysis to identify abnormal behavior. For example, it can identify whether an abnormal dog behavior pattern is a sign of an earthquake.

[1282] Output: Analysis results showing the possibility of an earthquake occurring

[1283] Step 8: Verbalizing earthquake predictions and generating warnings

[1284] Input: Analysis results showing the possibility of an earthquake occurring

[1285] How it works: The server converts the analysis results into natural language and generates a forecast report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." Based on the generated forecast report, it creates an appropriate warning message.

[1286] Output: Earthquake forecast report and warning message in natural language

[1287] Step 9: Delivering a warning message

[1288] Input: Earthquake forecast report and warning message in natural language

[1289] Operation: The server sends the generated warning message to the terminal in the appropriate area using the HTTPS protocol based on the contract information for each region.

[1290] Output: Warning message sent to the region terminal

[1291] Step 10: User Emotion Recognition and Notification Customization

[1292] Input: The warning message sent to the terminal

[1293] How it works: When receiving a warning message, the device uses its emotion engine to analyze the user's voice, facial expressions, and text input to recognize their emotional state. Depending on the user's emotion, the device can create additional messages, such as "Please stay calm. The nearest evacuation shelter is the nearest park," and customize the notification content.

[1294] Output: Displaying a customized warning message

[1295] Step 11: Monitor emotional state and provide additional information

[1296] Input: Customized warning message

[1297] How it works: Even after receiving the warning, the device continues to monitor the user's emotional state using its emotion engine. If the user is feeling anxious or panicked, the device will provide additional specific instructions and advice, such as "Evacuate now. An evacuation shelter is 300 meters away."

[1298] Output: Providing additional information depending on the user's emotional state

[1299] (Application example 2)

[1300] 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."

[1301] Although earthquake prediction technology has advanced in recent years, accurate prediction is still said to be difficult. Furthermore, it is difficult to take prompt and appropriate action when an earthquake is predicted, which could put many lives and property at risk. With the spread of autonomous vehicles in urban areas, there is a need to ensure the safety of vehicles and promptly notify drivers when an earthquake occurs. The challenge is to solve these problems and provide more accurate and user-friendly earthquake prediction and safety assurance methods.

[1302] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting animal behavioral data, means for analyzing the collected animal behavioral data using a generation AI, means for predicting the possibility of an earthquake based on the analysis results and verbalizing the data, means for notifying the verbalized prediction information to local terminals and users, means for transmitting the prediction information to the vehicle control system via an interface and instructing the vehicle's emergency action, and means for recognizing the user's emotional state and adjusting the notification content. This enables early prediction of the risk of an earthquake and appropriate response. At the same time, by appropriately adjusting the notification content according to the user's emotional state, the user can be provided with necessary information and encouraged to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[1303] "Animal behavior data" is digital information such as the movements, sounds, and vibrations of dogs, cats, and other animals collected using sensor devices.

[1304] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[1305] "Analysis results" refers to information obtained as a result of data analysis by the generating AI, and includes information such as the possibility of an earthquake occurring.

[1306] "Predicting the possibility of earthquakes" means predicting whether an earthquake will occur in the future based on animal behavior data.

[1307] "Verbalization" means expressing data and information such as analysis results in natural language.

[1308] A "local terminal" is an electronic device such as a computer, smartphone, or tablet that is located in a specific area.

[1309] "User" means an individual or member of an organization who uses the system.

[1310] An "interface" is a point of contact or protocol that allows different systems or devices to communicate with each other.

[1311] A "vehicle control system" is a system for managing and operating autonomous vehicles.

[1312] "Emergency action instructions" are instructions on immediate countermeasures to be taken in response to anticipated danger.

[1313] "User's emotional state" refers to the emotions and mental state that the user is currently experiencing.

[1314] "Adjusting notification content" means appropriately changing the information to be communicated and the way it is expressed depending on the user's emotional state.

[1315] The present invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of an earthquake. This system includes a function that recognizes the user's emotional state and issues appropriate warning notifications. Specific embodiments are described below.

[1316] Animal behavior data collection

[1317] The device uses sensors to monitor the behavior of animals (such as dogs and cats) and collects behavioral data in real time 24 hours a day. The sensors include cameras, microphones, and vibration detectors. For example, if a dog that is usually quiet suddenly starts barking, the device will record its audio data and movement patterns.

[1318] Data preprocessing and transmission

[1319] The device preprocesses the collected data to remove noise, eliminating outliers and incomplete data, and formats it for analysis. The preprocessed data is then encrypted and sent to the server using a secure communication protocol (e.g., HTTPS or MQTT).

[1320] Data reception and analysis

[1321] The server receives the encrypted data sent from the device, decrypts it, and stores it in a database. The stored data is cleansed and formatted to be input into the generative AI model. The server then inputs the formatted data into the generative AI model, where it performs an analysis to identify abnormal behavior by comparing animal behavior patterns with past earthquake data.

[1322] Earthquake prediction and warning notifications

[1323] The server determines the possibility of an earthquake occurring based on the results of the earthquake prediction analysis by the generation AI and generates forecast information in natural language. The generated forecast information is sent to local terminals and users' smartphones and computers. At this time, the forecast information is transmitted to the vehicle control system via an interface, and emergency action is instructed for the autonomous vehicle.

[1324] User emotion recognition and notification customization

[1325] When receiving a warning message, the device monitors the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. The content and format of the notification are customized based on the recognition results. For example, if a user is nervous, advice on how to stay calm will be added.

[1326] Specific examples

[1327] A concrete example of this system is a scenario in which an autonomous vehicle moves to a safe location based on an earthquake prediction. Generative AI analyzes animal behavior data, and if there is a high possibility of an earthquake, the vehicle control system is instructed to "move to a safe location." At the same time, a notification based on the user's emotional state is displayed, such as "Please remain calm, the vehicle is heading to a safe location."

[1328] Prompt Sentence Examples

[1329] An example of a prompt to input to the generative AI model is:

[1330] "Based on animal behavior data, an earthquake may occur within the next two hours. The specific behavioral patterns are sudden barking, fast moving around, and abnormal vibrations."

[1331] This system is user-friendly and has highly accurate earthquake prediction and warning functions, enabling safe and rapid response in the event of an earthquake.

[1332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] The device collects animal behavior data. It uses sensors (cameras, microphones, vibration detectors, etc.) to digitally record animal movements, sounds, and vibration information in real time 24 hours a day. For example, if a dog suddenly starts barking, its sound data and movement patterns will be recorded.

[1335] Input: Real-time animal behavior

[1336] Output: Digital animal behavior data

[1337] Step 2:

[1338] The device preprocesses the collected data by removing noise, eliminating outliers and incomplete data, and formatting it for analysis, such as normalizing the data and imputing missing values.

[1339] Input: Collected animal behavior data

[1340] Output: Preprocessed clean data

[1341] Step 3:

[1342] The terminal encrypts the pre-processed data. This encryption ensures data security. For example, an encryption algorithm such as AES (Advanced Encryption Standard) is used.

[1343] Input: Preprocessed clean data

[1344] Output: Encrypted data

[1345] Step 4:

[1346] The device sends encrypted data to the server via a secure communication protocol (such as HTTPS or MQTT) at regular intervals.

[1347] Input: Encrypted data

[1348] Output: Send to server (via HTTPS or MQTT)

[1349] Step 5:

[1350] The server receives the encrypted data, decrypts it, and stores it in the database. After the data is decrypted, it performs a cleansing process before storing it in the database.

[1351] Input: Encrypted data

[1352] Output: Cleansed data stored in a database

[1353] Step 6:

[1354] The server inputs the stored data into a generative AI model to analyze abnormal animal behavior. The generative AI model compares animal behavior patterns with past earthquake data and determines the likelihood of an earthquake occurring.

[1355] Input: Cleansed data

[1356] Output: Generative AI predicts the likelihood of an earthquake occurring

[1357] Step 7:

[1358] The server generates notification messages based on the earthquake prediction results determined by the generation AI, and in particular, uses natural language processing to convert the prediction information into a format that humans can understand.

[1359] Input: Earthquake prediction data

[1360] Output: Predictive notification message in natural language

[1361] Step 8:

[1362] The server then sends the generated prediction messages to local terminals and users, and simultaneously transmits the prediction information to the vehicle control system through an interface to instruct the autonomous vehicle to take emergency action.

[1363] Input: Natural language forecast notification message, earthquake forecast data

[1364] Output: Warning notification to user terminal, instructions to vehicle control system

[1365] Step 9:

[1366] When receiving a warning message, the device monitors the user's emotional state. It recognizes emotions from voice, facial expressions, and text input, and customizes the notification content based on the results. For example, if a user is nervous, it will deliver "advice on how to stay calm."

[1367] Input: Predictive notification message, user emotion data

[1368] Output: Customized notification message

[1369] Step 10:

[1370] The user checks the warning message displayed on the device and takes appropriate action, such as evacuating to a safe place.

[1371] Input: Customized notification message

[1372] Output: User action (e.g., evacuation)

[1373] The above steps enable early prediction of the risk of an earthquake and appropriate responses. At the same time, by appropriately adjusting the content of notifications according to the user's emotional state, it is possible to provide the user with the necessary information and encourage them to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] [Fourth embodiment]

[1378] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1379] 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.

[1380] 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).

[1381] 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.

[1382] 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.

[1383] 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).

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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."

[1391] The following describes an embodiment of the present invention. This system consists of a series of processes that collects animal behavior data, analyzes the data using a generative AI, and predicts the possibility of an earthquake occurring.

[1392] 1. Start data collection:

[1393] Terminal: Equipped with a sensor device for monitoring the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, vibration detector, etc., and collects animal behavior data in real time 24 hours a day.

[1394] 2. Behavioral Data Collection:

[1395] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[1396] 3. Data preprocessing:

[1397] Terminal: The collected data is filtered to remove unnecessary noise, eliminating outliers and incomplete data.

[1398] 4. Data Encryption and Transmission:

[1399] Terminal: The pre-processed data is encrypted, ensuring data security. The encrypted data is sent to the server at regular intervals (for example, every minute or when a certain threshold is reached). A secure communication protocol (for example, HTTPS or MQTT) is used for transmission.

[1400] 5. Data Receipt and Analysis:

[1401] Server: Receives data sent from the device, decrypts it, and stores it in a database. The received data is formatted and cleansed to make it suitable for analysis. The formatted data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[1402] 6. Earthquake prediction using generative AI:

[1403] Server: The generation AI uses the analysis results to predict the possibility of an earthquake occurring and expresses the data in natural language. The results are summarized in a report format, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1404] 7. Alert Generation and Notification:

[1405] Server: Generates warning messages based on the generated forecast information. The warning messages are distributed to the appropriate areas based on the contract information for each region.

[1406] Device: Receives the warning message and notifies the user via audio alert, push notification on the smartphone app, or email.

[1407] 8. User Action:

[1408] User: After receiving the warning, check the notification content. For example, read the warning message displayed on the smartphone app. As soon as the warning is received, take action to evacuate to a safe place.

[1409] Through the above process, this system utilizes the natural earthquake prediction ability of animals, enabling highly accurate and rapid earthquake prediction and minimizing earthquake damage.The unique feature of this system is that it uses animal behavioral data, providing a flexible and new approach that does not rely on conventional physical sensors.

[1410] The processing flow will be explained below.

[1411] Step 1:

[1412] Terminal: To monitor animal behavior, a sensor device equipped with cameras, microphones, and vibration detectors is used to collect data in real time 24 hours a day.

[1413] Step 2:

[1414] Device: Stores information on animal movements, sounds, and vibrations as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[1415] Step 3:

[1416] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[1417] Step 4:

[1418] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[1419] Step 5:

[1420] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[1421] Step 6:

[1422] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[1423] Step 7:

[1424] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[1425] Step 8:

[1426] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[1427] Step 9:

[1428] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1429] Step 10:

[1430] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[1431] Step 11:

[1432] Device: Receives warning messages and notifies the user via voice alerts, push notifications on smartphone apps, and emails.

[1433] Step 12:

[1434] User: After receiving the warning, the user checks the notification content. For example, the user reads the warning message displayed on the smartphone app. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[1435] In this way, the entire system processes everything from collecting and analyzing animal behavior data to issuing warnings to users, providing highly accurate earthquake predictions in real time.

[1436] Example 1

[1437] 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."

[1438] Conventional earthquake prediction systems rely on physical sensors and observation equipment, resulting in high installation costs and limited real-time prediction accuracy. Furthermore, these systems are unable to directly observe natural abnormal phenomena, necessitating the development of new approaches to improve prediction accuracy. This invention aims to provide a new system for earthquake prediction using animal behavior, thereby minimizing earthquake damage. In particular, the challenge is to create a system that can accurately predict the possibility of an earthquake occurring and promptly notify users by collecting animal behavior data and analyzing it using a generative AI model.

[1439] 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.

[1440] In this invention, the server includes: a sensor means for collecting information on animal movements, sounds, and vibrations; a preprocessing means for filtering the collected data and removing noise; a data transmission means for encrypting the preprocessed data and transmitting it using a secure communication protocol; a data receiving means for receiving, decrypting, and formatting the transmitted data; a means for inputting the formatted data into a generative AI model and analyzing abnormal behavior; an earthquake prediction means for predicting the possibility of an earthquake occurring from the analysis results and generating a report in natural language; a means for notifying the generated prediction information to a local terminal and a user as a warning message; and a support means for users who receive the prediction information to initiate evacuation behavior. This enables earthquake prediction using animal behavior in a flexible approach that differs from conventional physical sensors, making it possible to provide highly accurate and prompt prediction information.

[1441] "Animal movement, sound, and vibration information" refers to information on the animal's body movements, the sounds it makes, and the vibrations that accompany its movements.

[1442] "Sensor means" refers to devices or equipment used to collect animal movement, sound, and vibration information.

[1443] The "pre-processing means" refers to a series of processes for cleaning the collected data, such as filtering the data and removing noise.

[1444] "Data transmission means" refers to the devices and techniques that encrypt the pre-processed data and transmit it using a secure communications protocol.

[1445] "Data receiving means" refers to the devices and techniques used to receive, decode, and format transmitted data.

[1446] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and past earthquake data to predict the possibility of an earthquake occurring.

[1447] "Abnormal behavior" refers to behavior that deviates from normal animal behavior patterns and is predicted to be related to the occurrence of an earthquake.

[1448] "Earthquake prediction method" refers to a series of processes that use a generative AI model to predict the possibility of an earthquake occurring from the analysis results and generate a report in natural language.

[1449] The "warning message" refers to a message generated based on the possibility of an earthquake occurring, to alert the user.

[1450] "Support means" refers to devices and technologies that assist users who receive forecast information to begin appropriate evacuation actions.

[1451] The following describes an embodiment of the present invention. This system is designed to collect and analyze animal behavior data and predict the possibility of earthquakes. It primarily utilizes the natural predictive abilities of animals to provide earthquake prediction information to users. A detailed description of this system is provided below.

[1452] 1. Data Collection

[1453] The device operates a sensor device to monitor the behavior of animals (e.g., dogs and cats). The sensor device includes a camera, microphone, and vibration detector, and collects animal behavior data in real time 24 hours a day. For example, the camera attached to the device records video at 30 frames per second or more, and the microphone captures audio data at a sampling rate of 44.1 kHz per second. The vibration detector detects vibrations caused by sudden animal movements.

[1454] 2. Data Preprocessing

[1455] The device filters the collected data to remove unwanted noise, including outliers and incomplete data. Specifically, it removes background noise from recorded audio data and trims unnecessary parts of video data (for example, periods when no animals are in the frame). It also removes outliers from vibration data and calculates average data over a certain period.

[1456] 3. Data Encryption and Transmission

[1457] The terminal encrypts the preprocessed data using encryption technology such as AES, and then transmits the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT. For example, the terminal encrypts the data accumulated every minute using AES-256 and sends a POST request to the server using HTTPS communication.

[1458] 4. Data Receipt and Analysis

[1459] The server receives the encrypted data sent from the terminal and immediately decrypts it using AES-256. The decrypted data is then stored in a database, where it is reformatted and cleansed into a format suitable for analysis. Specifically, the server receives an HTTP request, analyzes the encrypted payload, and decrypts it. The decrypted data is reformatted and stored in a database, where it is used to fill in missing data and recheck for outliers.

[1460] 5. Earthquake Prediction Using Generative AI

[1461] The server inputs the formatted data into a generative AI model, which compares and analyzes animal behavior patterns with past earthquake data. The generative AI model receives a prompt: "Compare the frequency of dog barking over the past 24 hours with past earthquake data, and predict the probability of an earthquake occurring within the next two hours." The generative AI then generates a prediction: "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[1462] 6. Alert Generation and Notification

[1463] The server generates a warning message based on the generated forecast information. This warning message is distributed to the appropriate area based on the contract information for each region. Specifically, the server generates a message such as "Caution: An earthquake may occur within the next two hours" based on the forecast information and sends a push notification to users in the contract area.

[1464] 7. User Response

[1465] After receiving the warning from the device, the user checks the notification and takes action to evacuate to a safe place. For example, the user checks the warning message on a smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[1466] Through the above process, this system utilizes the natural earthquake prediction ability of animals to enable accurate and rapid earthquake prediction, and also urges users to take early evacuation action via warning messages, thereby minimizing earthquake damage.

[1467] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1468] Step 1: Start collecting data

[1469] The terminal operates sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats).

[1470] How it works: The camera records video at over 30 frames per second, the microphone captures audio data at a sampling rate of 44.1 kHz per second, and the vibration detector detects vibrations caused by sudden animal movements.

[1471] Input: Animal movement, sound, and vibration information.

[1472] Output: Raw data (video, audio, vibration) is saved to storage in real time.

[1473] Step 2: Preprocessing the data

[1474] The device filters the collected data to remove unwanted noise, which includes eliminating outliers and imperfections in the data.

[1475] Specific operations: Remove background noise from recorded audio data, trim unnecessary parts of video data (for example, times when no animals are in the frame), remove outliers from vibration data, and calculate average data over a certain period.

[1476] Input: Collected raw data (video, audio, vibration).

[1477] Output: Filtered and clean data.

[1478] Step 3: Encrypt the data

[1479] The terminal encrypts the preprocessed data using encryption technology such as AES.

[1480] Specific operation: The terminal sequentially encrypts the preprocessed data using the AES-256 method.

[1481] Input: Preprocessed clean data.

[1482] Output: The encrypted data.

[1483] Step 4: Sending data

[1484] The device sends the encrypted data to the server using a secure communication protocol such as HTTPS or MQTT.

[1485] Specific operation: Every minute, the device sends a POST request with encrypted data to the server using HTTPS communication.

[1486] Input: Encrypted data.

[1487] Output: Notification of completion of transmission to the server.

[1488] Step 5: Receiving the data

[1489] The server receives the encrypted data sent from the terminal.

[1490] Specific operation: The server receives the HTTP request, analyzes the encrypted payload, and saves it to storage.

[1491] Input: Encrypted data.

[1492] Output: The stored encrypted data.

[1493] Step 6: Decrypt the data

[1494] The server decrypts the received encrypted data using the AES-256 method.

[1495] Specific operation: The stored encrypted data is decrypted using AES-256 as appropriate and the raw data is extracted.

[1496] Input: Encrypted data.

[1497] Output: The decrypted data.

[1498] Step 7: Data Shaping and Cleansing

[1499] The server then formats and cleanses the decrypted data into a format suitable for analysis.

[1500] Specific operations: Fill in missing values ​​in the decoded data, recheck for outliers, and standardize the data format.

[1501] Input: Decrypted data.

[1502] Output: Formatted and cleansed data.

[1503] Step 8: Analyze abnormal behavior

[1504] The server inputs the formatted data into a generative AI model to analyze abnormal animal behavior.

[1505] Specific operation: The generative AI model is given a prompt sentence: "Please analyze what would happen if a dog suddenly started barking," and the model compares the animal's behavioral patterns with past earthquake data.

[1506] Input: Formatted and cleansed data.

[1507] Output: Analysis of abnormal animal behavior.

[1508] Step 9: Conducting earthquake predictions

[1509] The server predicts the possibility of an earthquake occurring based on the results of analysis by the generative AI model.

[1510] Specific operation: The generative AI model is given a prompt sentence: "Please predict the probability of an earthquake occurring within the next two hours," and a prediction result is generated.

[1511] Input: Analysis results of abnormal behavior.

[1512] Output: Earthquake occurrence forecast information.

[1513] Step 10: Generate warnings

[1514] The server generates a warning message based on the generated prediction information.

[1515] What it does: The server generates a message saying "An earthquake may occur within the next two hours" and formats it to notify the user.

[1516] Input: Earthquake occurrence prediction information.

[1517] Output: A warning message.

[1518] Step 11: User Notification

[1519] The terminal receives the generated warning message and notifies the user.

[1520] Specific operation: The device will send a warning message to the user via voice alert, push notification on the smartphone app, email, etc.

[1521] Input: Warning message.

[1522] Output: Notification to the user.

[1523] Step 12: User Action

[1524] After receiving the warning, the user checks the notification content and begins taking action to evacuate to a safe place.

[1525] Specific actions: The user checks the warning message on the smartphone app and tells their family, "An earthquake may be coming, so let's evacuate."

[1526] Input: Warning message.

[1527] Output: Evacuation action initiated.

[1528] (Application example 1)

[1529] 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."

[1530] To improve the accuracy of earthquake prediction and provide real-time warnings, a system that can quickly and accurately collect and analyze animal behavioral data is needed. However, existing earthquake prediction systems mainly rely on physical sensors, and no means have been established to utilize animals' natural ability to predict earthquakes. Therefore, there is a need for accurate real-time earthquake predictions and to provide users with prompt warnings.

[1531] 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.

[1532] In this invention, the server includes a means for preprocessing the animal behavior data, a means for encrypting and transmitting the preprocessed data, and a means for generating a warning message based on the analysis results and notifying the user via a smartphone app, thereby enabling highly accurate and rapid earthquake prediction using animal behavior data.

[1533] "Animal behavior data" refers to information such as movements, sounds, and vibrations generated when animals behave.

[1534] "Generative AI" refers to artificial intelligence techniques used to analyze collected data, particularly those that use unsupervised learning and deep learning models.

[1535] "Earthquake probability" refers to predicting the probability of an earthquake occurring in a particular area based on the results of the analysis.

[1536] "Means of verbalization" refers to the technology of converting analysis results and predictive information into natural language and generating reports and warning messages.

[1537] "Means for notifying local terminals and users" refers to communication technologies for delivering forecast information and warning messages to specific regions and users.

[1538] "Preprocessing means" refers to techniques for removing noise from collected data, eliminating outliers, and formatting the data into a form suitable for analysis.

[1539] "Encryption means" refers to technology that encrypts data to prevent access by third parties when transmitting the data.

[1540] "Means for decryption" refers to the technology that returns encrypted data to its original form at the receiving end.

[1541] "Means for generating a warning message" refers to a technology for generating a message to notify a user of the possibility of an earthquake based on the analysis results.

[1542] "Smartphone app" refers to software that runs on a smartphone and notifies users of warning messages and predictive information in real time.

[1543] A "secure communication protocol" is a communication technology that ensures security when sending and receiving data, and examples include HTTPS and MQTT.

[1544] The detailed description of the embodiment of this invention will be given below. This system consists of a series of steps to collect animal behavior data, analyze the data using generative AI, predict the possibility of an earthquake occurring, and notify the user of a warning.

[1545] System configuration

[1546] The system consists of the following main components:

[1547] 1. Animal behavior data collection device:

[1548] The (terminal) is equipped with sensor devices (cameras, microphones, vibration detectors, etc.) to monitor the behavior of animals (e.g., dogs and cats). The terminal collects animal behavior data in real time 24 hours a day.

[1549] 2. Data preprocessing:

[1550] (Device) filters the collected data to remove noise and eliminate outliers and incomplete data.

[1551] 3. Data Encryption and Transmission:

[1552] The terminal encrypts the preprocessed data using AES encryption, and the encrypted data is sent to the cloud server via a secure communication protocol (e.g., HTTPS).

[1553] 4. Data Receipt and Analysis:

[1554] The server receives the data sent from the device, decrypts it, and stores it in a database. The received data is then formatted and cleansed to make it suitable for analysis. This data is then fed into a generative AI model, which compares animal behavior patterns with past earthquake data to identify and analyze abnormal behavior.

[1555] 5. Earthquake prediction and warning generation:

[1556] The server uses generative AI to predict the possibility of an earthquake. The results are expressed in natural language and generated as a warning message. For example, it might say, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1557] 6. Warning Notice:

[1558] The server delivers the generated warning message to the user via a smartphone app. The user can receive and check the warning message on their smartphone and take safety measures.

[1559] Hardware and software used

[1560] Hardware: Sensor devices for collecting animal behavior data include cameras (e.g., Raspberry Pi Camera Module), microphones, vibration detectors, etc. Data processing is performed using a Raspberry Pi or a smartphone.

[1561] software:

[1562] Data preprocessing: OpenCV (camera data processing), Librosa (audio data processing)

[1563] Data encryption: PyCryptodome (encryption)

[1564] Communication protocol: Requests (HTTPS communication)

[1565] Generative AI models: TensorFlow, PyTorch

[1566] Specific examples

[1567] For example, behavioral data is collected, such as when a dog in the home suddenly starts barking or exhibits abnormal behavior during times when it is usually quiet. This data is preprocessed on the device to remove unnecessary noise. After preprocessing, the data is AES encrypted and securely sent to a cloud server. The cloud server uses a generative AI model to analyze the possibility of an earthquake and reports the results in natural language. For example, a generated prompt might be, "Analyze data when a dog exhibits unusual behavioral patterns and build a generative AI model to predict the possibility of an earthquake." Based on the analysis results, the smartphone app notifies the user with a warning message such as, "There is a high possibility of a magnitude 6.0 earthquake occurring within the next two hours."

[1568] In this way, by utilizing animal behavioral data, this invention provides a flexible and new approach to earthquake prediction that does not rely on conventional physical sensors, and can provide users with highly accurate and prompt warnings.

[1569] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1570] Step 1:

[1571] The device collects animal behavior data. Specifically, it uses sensor devices (cameras, microphones, and vibration detectors) installed in the home to record animal movements, sounds, and vibration information as digital data in real time. The input is raw data captured by the sensors, and the output is time-series behavior data.

[1572] Step 2:

[1573] The terminal preprocesses the collected animal behavior data. In this step, specific operations are performed to remove noise from the collected data and eliminate outliers and incomplete data. The input is the raw data collected in step 1, and the output is clean behavior data that has been filtered and cleansed. OpenCV (camera data) and Librosa (audio data) are used for noise removal.

[1574] Step 3:

[1575] The terminal encrypts the preprocessed data. Specifically, it uses the AES encryption algorithm to keep the data secure. The input is the preprocessed clean behavioral data, and the output is the encrypted data. It uses PyCryptodome for encryption.

[1576] Step 4:

[1577] The device sends encrypted data to the cloud server. The data is sent securely using a secure communication protocol (e.g., HTTPS). The input is the encrypted data, and the output is a notification that the data has been sent. The Requests library is used for communication.

[1578] Step 5:

[1579] The server receives and decrypts the data sent from the terminal. The received encrypted data is decrypted using the AES encryption method. The input is the encrypted data, and the output is the decrypted clean behavioral data. PyCryptodome is used for decryption.

[1580] Step 6:

[1581] The server formats and cleanses the decrypted data, processing it to convert it into a format suitable for analysis. The input is the clean decrypted behavioral data, and the output is the formatted data.

[1582] Step 7:

[1583] The server uses a generative AI model to analyze the formatted data and compare animal behavior patterns with past earthquake data. It identifies abnormal behavior and predicts the likelihood of an earthquake. TensorFlow and PyTorch are used for data calculations in this step. The input is the formatted data, and the output is the analysis results indicating the likelihood of an earthquake.

[1584] Step 8:

[1585] The server generates a warning message in natural language based on the analysis results. For example, it may generate a message such as, "There is a possibility of a magnitude 6.0 earthquake occurring within the next two hours." The input is the analysis results of the generative AI model, and the output is a warning message in natural language.

[1586] Step 9:

[1587] The server notifies the user of the generated warning message in real time via a smartphone app using a push notification service such as Firebase Cloud Messaging (FCM). The input is a natural language warning message, and the output is a warning notification displayed on the smartphone.

[1588] Step 10:

[1589] The user checks the warning message received on the smartphone app and begins taking action to prepare for the possibility of an earthquake, such as evacuating to a safe place. The input is the warning message displayed on the smartphone, and the output is the user's evacuation behavior.

[1590] In this way, the system can use animal behavior data to accurately predict the possibility of earthquakes and provide users with prompt warnings. For example, the prompt text might include, "Analyze data on when dogs exhibit unusual behavioral patterns and build a generative AI model that predicts the likelihood of earthquakes."

[1591] 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.

[1592] This invention is a system that collects animal behavior data, analyzes the data using generative AI, and predicts the possibility of earthquakes. In addition to this system, it also includes an emotion engine that recognizes the user's emotions. The processing of this system covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[1593] Animal behavior data collection

[1594] 1. Start data collection:

[1595] Device: Sensor devices are used to monitor the behavior of animals (e.g., dogs and cats) and collect behavioral data in real time 24 hours a day. The sensor devices include cameras, microphones, and vibration detectors.

[1596] 2. Behavioral Data Collection:

[1597] Device: Records animal movements, sounds, and vibration information as digital data. For example, if a dog is usually quiet but suddenly starts barking, the device will record its sound data and movement patterns.

[1598] 3. Data preprocessing:

[1599] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[1600] Data encryption and transmission

[1601] 4. Data Encryption:

[1602] Terminal: Encrypts the pre-processed data. Encryption ensures the security of the data.

[1603] 5. Sending data to the server:

[1604] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) at regular intervals.

[1605] Data reception and analysis

[1606] 6. Receiving and Decrypting Data:

[1607] Server: Receives encrypted data sent from the device, decrypts it, and stores it in a database.

[1608] 7. Data cleansing and shaping:

[1609] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[1610] 8. Earthquake prediction analysis using generative AI:

[1611] Server: The formatted data is fed into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[1612] 9. Verbalizing earthquake prediction:

[1613] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1614] Alert Generation and Notification

[1615] 10. Generate warning messages:

[1616] Server: Generates a warning message based on the generated earthquake prediction information.

[1617] 11. WARNING DELIVERY:

[1618] Server: Sends warning messages to terminals in the appropriate area based on regional contract information.

[1619] User emotion recognition and customized notifications

[1620] 12. Leveraging the Emotion Engine:

[1621] Terminal: When receiving a warning message, an emotion engine is used to monitor the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input.

[1622] 13. Customizing Notifications:

[1623] On the device: Customize the content and format of notifications based on the emotion engine's recognition results. For example, add advice on staying calm to a nervous user.

[1624] 14. User Action:

[1625] User: After receiving the warning, the user checks the notification content and reads the warning message displayed on the smartphone app, for example. The user who receives the warning immediately evacuates to a safe place and takes the necessary measures.

[1626] Follow up and provide additional information

[1627] 15. Emotional state monitoring:

[1628] Device: Continues to monitor the user's emotional state even after the warning notification.

[1629] 16. Providing Additional Information:

[1630] Devices: If needed, provide additional information or instructions to reduce anxiety and panic, such as the location of nearby evacuation shelters or emergency contact information.

[1631] This system utilizes the natural earthquake prediction abilities of animals, and also recognizes the user's emotional state in real time and notifies them in the most appropriate way, thereby realizing a highly accurate and user-friendly earthquake prediction and warning system.

[1632] The processing flow will be explained below.

[1633] Step 1:

[1634] Terminal: To monitor the behavior of animals (e.g., dogs and cats), a sensor device equipped with a camera, microphone, and vibration detector is used to collect data in real time 24 hours a day.

[1635] Step 2:

[1636] Device: Collected animal movement, sound, and vibration information is recorded as digital data. For example, if a dog is usually quiet but suddenly starts barking, its sound data and movement patterns are recorded.

[1637] Step 3:

[1638] Terminal: Preprocessing the collected data and removing noise. Preprocessing involves removing outliers and incomplete data and formatting it in a way that is suitable for analysis.

[1639] Step 4:

[1640] Terminal: Encrypts the pre-processed data. Encryption ensures data privacy and security.

[1641] Step 5:

[1642] Terminal: Sends encrypted data to the server using a secure communication protocol (HTTPS or MQTT) and periodically sends data to the server.

[1643] Step 6:

[1644] Server: Receives the encrypted data sent from the device. The received data is decrypted and stored in a database.

[1645] Step 7:

[1646] Server: The server formats the data stored in the database and cleanses it into a format that can be input into the generative AI model. Here, it fills in missing values ​​and organizes the data along the time axis.

[1647] Step 8:

[1648] Server: The formatted data is input into a generative AI model, which compares animal behavior patterns with past earthquake data and performs an analysis to identify abnormal behavior.

[1649] Step 9:

[1650] Server: The generation AI determines the possibility of an earthquake occurring and expresses the analysis results in natural language, generating a report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours."

[1651] Step 10:

[1652] Server: Generates a warning message based on the generated earthquake prediction information. The warning message is sent to terminals in the appropriate area based on the contract information for each region.

[1653] Step 11:

[1654] Terminal: Receives the warning message. At the same time, an emotion engine monitors the user's emotional state and recognizes emotions from the user's voice, facial expressions, and text input.

[1655] Step 12:

[1656] On the device: The emotion engine can customize the content and format of notifications for users whose emotional state is recognized by the engine. For example, if a user is nervous, the engine can provide additional advice on how to stay calm.

[1657] Step 13:

[1658] User: Receives customized alert messages and checks the notification content, for example, by reading the alert message displayed on a smartphone app.

[1659] Step 14:

[1660] User: Upon receiving the warning, take action to evacuate to a safe location, for example, to a safe area in your home or a nearby evacuation center.

[1661] Step 15:

[1662] Device: Continue to monitor the user's emotional state after the warning notification and provide additional information or instructions to reduce anxiety or panic, such as evacuation shelter locations, emergency contact information, and first aid instructions.

[1663] In this way, by analyzing animal behavior data using generative AI and then using an emotion engine to recognize the user's emotional state in real time and provide notifications in the most optimal way, we have created a highly accurate and user-friendly earthquake prediction and warning system.

[1664] Example 2

[1665] 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."

[1666] Current earthquake prediction systems rely on seismometers and GPS data, and issues include prediction accuracy and real-time response. Furthermore, because they issue uniform warnings without considering the emotional state of the user receiving the forecast information, users may not be able to respond appropriately. Furthermore, the security of the collected data is also a concern. To solve these problems, a highly accurate earthquake prediction system that utilizes animal behavior data and takes the user's emotional state into account is needed.

[1667] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1668] In this invention, the server includes a means for collecting animal behavior data, a means for preprocessing the collected animal behavior data to remove noise, and a means for encrypting the preprocessed data. This enables the analysis of abnormal animal behavior patterns and highly accurate earthquake prediction. It also monitors the user's emotional state and customizes notifications to help the user respond appropriately. Furthermore, data encryption ensures security and maintains data confidentiality.

[1669] "Animal behavior data" refers to information recorded in digital format about animal movements, sounds, vibrations, etc.

[1670] "Collecting means" refers to devices and methods that continuously acquire animal behavioral data using sensor devices, cameras, microphones, vibration detectors, etc.

[1671] "Preprocessing" refers to the process of removing noise and unnecessary information from collected data and converting it into a format suitable for analysis.

[1672] "Encryption means" means a method of transforming data using a cryptographic algorithm (e.g., AES) to store or transmit it in a secure format.

[1673] "Server" refers to a computer system for receiving and storing animal behavioral data and the software running on it.

[1674] "Means for decryption" refers to a method for processing encrypted data back into its original format.

[1675] A "database" is a storage system for storing collected data in a structured format.

[1676] "Cleansing" is the process of filling in missing values ​​from data and organizing it in timestamp order.

[1677] A "generative AI model" refers to an artificial intelligence model that analyzes animal behavior data and detects signs of an earthquake.

[1678] "Means of analysis" refers to the use of generative AI models to analyze data and identify abnormal behavior and earthquake precursors.

[1679] "Means of verbalization" refers to a method for converting the analysis results into natural language and generating reports and warning messages.

[1680] The "notification means" is a method for distributing the generated forecast information and warning messages to terminals and users in the area.

[1681] The "means for monitoring emotional state" is a method for analyzing the user's voice, facial expressions, and text to recognize the user's emotional state in real time.

[1682] A "means for customizing notifications" is a method for changing the content and format of alert messages based on the user's emotional state.

[1683] This invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of earthquakes. This system also includes an emotion engine that recognizes the user's emotions, and covers everything from collecting and analyzing animal behavior data to earthquake prediction and emotion-based warning notifications.

[1684] Animal behavior data collection

[1685] Device: To monitor the behavior of animals (e.g., dogs and cats), sensor devices such as cameras, microphones, and vibration detectors are used. The sensor devices collect animal behavior data in real time 24 hours a day and store it in digital format. For example, if a dog that is usually quiet suddenly starts barking, its audio data and movement patterns will be recorded.

[1686] Data Preprocessing and Encryption

[1687] Terminal: The collected data is preprocessed and noise is removed. For example, environmental and background sounds are removed from the audio data, and only important behavioral data is extracted. The preprocessed data is then encrypted using the AES encryption algorithm, ensuring data security.

[1688] Sending and receiving data to the server

[1689] Terminal: Encrypted data is sent to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server has a receiving module that decrypts and interprets the received data packets. The data is then stored appropriately in a database.

[1690] Data cleansing and analysis with generative AI models

[1691] Server: The stored data is cleansed, missing values ​​are filled, and the data is sorted by timestamp. The cleansed data is then input into a generative AI model. The generative AI model compares animal behavior data with past earthquake data and performs analysis to identify abnormal behavior. For example, it compares the pattern of a dog suddenly barking with past data and identifies it as a sign of an upcoming earthquake.

[1692] Verbalization of earthquake predictions, generation and distribution of warnings

[1693] Server: The prediction results obtained from the generation AI are converted into natural language and a report is created, such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." The generated report is sent to devices in the appropriate area based on the contract information for each region. The HTTPS protocol is again used to deliver the warning message.

[1694] User emotion recognition and customized notifications

[1695] Device: When receiving a warning message, the emotion engine analyzes the user's voice, facial expressions, and text to recognize their emotional state in real time. If the user is nervous, the engine will detect this and customize the content and format of the notification, such as adding a message like "Please stay calm. The nearest evacuation shelter is the nearest park."

[1696] Monitoring emotional state and providing additional information

[1697] Device: Continue to monitor the user's emotional state even after the warning notification. If the user is experiencing anxiety or panic, provide additional information or instructions. For example, send specific instructions such as "Evacuate now. The evacuation shelter is 300 meters away."

[1698] Examples of concrete examples and prompts

[1699] Example: If a device in a certain area detects the sudden barking of a dog, the data is immediately encrypted and sent to a server. The server decrypts the data and analyzes it using a generative AI model. If the result suggests that the dog's abnormal behavior is a sign of an upcoming earthquake, a warning message such as "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours" is sent to the devices of local residents. Users who receive the notification can take action, such as evacuating to a safe location.

[1700] Example prompt sentence:

[1701] "Analyze the animal behavior data below to see if there are any signs of an earthquake.

[1702] Date and Time: 2023-10-01 10:00

[1703] Behavioral data: Dog suddenly started barking and jumping more

[1704] Area: Shibuya Ward, Tokyo

[1705] This invention combines the natural earthquake prediction abilities of animals with generative AI to realize a highly accurate and user-friendly earthquake prediction and warning system. Furthermore, it uses an emotion engine to provide notifications according to the user's emotional state, helping the user respond calmly and quickly.

[1706] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1707] Step 1: Start data collection and collect behavioral data

[1708] Input: Real-time animal behavior information

[1709] Movement: The device monitors the animal's movements, sounds, and vibration data 24 hours a day through cameras, microphones, and vibration detectors, and records them digitally. Specifically, the device collects audio data when the dog barks, motion data when it moves, and vibration data when it jumps. For example, if a dog suddenly starts barking, the device captures the audio data and changes in its movements.

[1710] Output: Collected animal behavior data

[1711] Step 2: Data preprocessing and denoising

[1712] Input: Collected animal behavior data

[1713] Operation: The device preprocesses the collected data and removes noise. For example, it removes environmental and background sounds from audio data and eliminates outliers from behavioral data to generate clean data for analysis. It also removes outliers from video data and extracts only the frames containing important actions.

[1714] Output: Preprocessed and clean behavioral data

[1715] Step 3: Encrypt the data

[1716] Input: Preprocessed and clean behavioral data

[1717] How it works: The device encrypts the preprocessed data using the AES encryption algorithm, ensuring data security and making the encrypted data suitable for transfer and storage.

[1718] Output: Encrypted animal behavior data

[1719] Step 4: Send the encrypted data to the server

[1720] Input: Encrypted animal behavior data

[1721] Operation: The device sends encrypted data to the server at regular intervals (for example, every 10 minutes) using the HTTPS protocol. The server can receive the data via a secure communication channel.

[1722] Output: Encrypted data sent to the server

[1723] Step 5: Decrypt the data and save it to the database

[1724] Input: Encrypted data sent to the server

[1725] Operation: The server receives the encrypted data through the receiving module, decrypts it using the AES encryption algorithm, and then stores the decrypted data in the database.

[1726] Output: Clean behavioral data stored in a database

[1727] Step 6: Cleanse the data

[1728] Input: Clean behavioral data stored in a database

[1729] How it works: The server rechecks the stored data, imputes missing values, sorts it by timestamp, and converts the cleansed data into a format that can be fed into a generative AI model.

[1730] Output: Formatted data suitable for generative AI models

[1731] Step 7: Earthquake prediction analysis using generative AI

[1732] Input: Formatted data suitable for generative AI models

[1733] How it works: The server inputs the cleansed data into a generative AI model, which then compares past earthquake data with animal behavior patterns and performs analysis to identify abnormal behavior. For example, it can identify whether an abnormal dog behavior pattern is a sign of an earthquake.

[1734] Output: Analysis results showing the possibility of an earthquake occurring

[1735] Step 8: Verbalizing earthquake predictions and generating warnings

[1736] Input: Analysis results showing the possibility of an earthquake occurring

[1737] How it works: The server converts the analysis results into natural language and generates a forecast report such as, "There is a possibility of a magnitude 6.0 earthquake occurring in this area within the next two hours." Based on the generated forecast report, it creates an appropriate warning message.

[1738] Output: Earthquake forecast report and warning message in natural language

[1739] Step 9: Delivering a warning message

[1740] Input: Earthquake forecast report and warning message in natural language

[1741] Operation: The server sends the generated warning message to the terminal in the appropriate area using the HTTPS protocol based on the contract information for each region.

[1742] Output: Warning message sent to the region terminal

[1743] Step 10: User Emotion Recognition and Notification Customization

[1744] Input: The warning message sent to the terminal

[1745] How it works: When receiving a warning message, the device uses its emotion engine to analyze the user's voice, facial expressions, and text input to recognize their emotional state. Depending on the user's emotion, the device can create additional messages, such as "Please stay calm. The nearest evacuation shelter is the nearest park," and customize the notification content.

[1746] Output: Displaying a customized warning message

[1747] Step 11: Monitor emotional state and provide additional information

[1748] Input: Customized warning message

[1749] How it works: Even after receiving the warning, the device continues to monitor the user's emotional state using its emotion engine. If the user is feeling anxious or panicked, the device will provide additional specific instructions and advice, such as "Evacuate now. An evacuation shelter is 300 meters away."

[1750] Output: Providing additional information depending on the user's emotional state

[1751] (Application example 2)

[1752] 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."

[1753] Although earthquake prediction technology has advanced in recent years, accurate prediction is still said to be difficult. Furthermore, it is difficult to take prompt and appropriate action when an earthquake is predicted, which could put many lives and property at risk. With the spread of autonomous vehicles in urban areas, there is a need to ensure the safety of vehicles and promptly notify drivers when an earthquake occurs. The challenge is to solve these problems and provide more accurate and user-friendly earthquake prediction and safety assurance methods.

[1754] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting animal behavioral data, means for analyzing the collected animal behavioral data using a generation AI, means for predicting the possibility of an earthquake based on the analysis results and verbalizing the data, means for notifying the verbalized prediction information to local terminals and users, means for transmitting the prediction information to the vehicle control system via an interface and instructing the vehicle's emergency action, and means for recognizing the user's emotional state and adjusting the notification content. This enables early prediction of the risk of an earthquake and appropriate response. At the same time, by appropriately adjusting the notification content according to the user's emotional state, the user can be provided with necessary information and encouraged to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[1755] "Animal behavior data" is digital information such as the movements, sounds, and vibrations of dogs, cats, and other animals collected using sensor devices.

[1756] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[1757] "Analysis results" refers to information obtained as a result of data analysis by the generating AI, and includes information such as the possibility of an earthquake occurring.

[1758] "Predicting the possibility of earthquakes" means predicting whether an earthquake will occur in the future based on animal behavior data.

[1759] "Verbalization" means expressing data and information such as analysis results in natural language.

[1760] A "local terminal" is an electronic device such as a computer, smartphone, or tablet that is located in a specific area.

[1761] "User" means an individual or member of an organization who uses the system.

[1762] An "interface" is a point of contact or protocol that allows different systems or devices to communicate with each other.

[1763] A "vehicle control system" is a system for managing and operating autonomous vehicles.

[1764] "Emergency action instructions" are instructions on immediate countermeasures to be taken in response to anticipated danger.

[1765] "User's emotional state" refers to the emotions and mental state that the user is currently experiencing.

[1766] "Adjusting notification content" means appropriately changing the information to be communicated and the way it is expressed depending on the user's emotional state.

[1767] The present invention is a system that collects animal behavior data, analyzes it using generative AI, and predicts the possibility of an earthquake. This system includes a function that recognizes the user's emotional state and issues appropriate warning notifications. Specific embodiments are described below.

[1768] Animal behavior data collection

[1769] The device uses sensors to monitor the behavior of animals (such as dogs and cats) and collects behavioral data in real time 24 hours a day. The sensors include cameras, microphones, and vibration detectors. For example, if a dog that is usually quiet suddenly starts barking, the device will record its audio data and movement patterns.

[1770] Data preprocessing and transmission

[1771] The device preprocesses the collected data to remove noise, eliminating outliers and incomplete data, and formats it for analysis. The preprocessed data is then encrypted and sent to the server using a secure communication protocol (e.g., HTTPS or MQTT).

[1772] Data reception and analysis

[1773] The server receives the encrypted data sent from the device, decrypts it, and stores it in a database. The stored data is cleansed and formatted to be input into the generative AI model. The server then inputs the formatted data into the generative AI model, where it performs an analysis to identify abnormal behavior by comparing animal behavior patterns with past earthquake data.

[1774] Earthquake prediction and warning notifications

[1775] The server determines the possibility of an earthquake occurring based on the results of the earthquake prediction analysis by the generation AI and generates forecast information in natural language. The generated forecast information is sent to local terminals and users' smartphones and computers. At this time, the forecast information is transmitted to the vehicle control system via an interface, and emergency action is instructed for the autonomous vehicle.

[1776] User emotion recognition and notification customization

[1777] When receiving a warning message, the device monitors the user's emotional state. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. The content and format of the notification are customized based on the recognition results. For example, if a user is nervous, advice on how to stay calm will be added.

[1778] Specific examples

[1779] A concrete example of this system is a scenario in which an autonomous vehicle moves to a safe location based on an earthquake prediction. Generative AI analyzes animal behavior data, and if there is a high possibility of an earthquake, the vehicle control system is instructed to "move to a safe location." At the same time, a notification based on the user's emotional state is displayed, such as "Please remain calm, the vehicle is heading to a safe location."

[1780] Prompt Sentence Examples

[1781] An example of a prompt to input to the generative AI model is:

[1782] "Based on animal behavior data, an earthquake may occur within the next two hours. The specific behavioral patterns are sudden barking, fast moving around, and abnormal vibrations."

[1783] This system is user-friendly and has highly accurate earthquake prediction and warning functions, enabling safe and rapid response in the event of an earthquake.

[1784] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1785] Step 1:

[1786] The device collects animal behavior data. It uses sensors (cameras, microphones, vibration detectors, etc.) to digitally record animal movements, sounds, and vibration information in real time 24 hours a day. For example, if a dog suddenly starts barking, its sound data and movement patterns will be recorded.

[1787] Input: Real-time animal behavior

[1788] Output: Digital animal behavior data

[1789] Step 2:

[1790] The device preprocesses the collected data by removing noise, eliminating outliers and incomplete data, and formatting it for analysis, such as normalizing the data and imputing missing values.

[1791] Input: Collected animal behavior data

[1792] Output: Preprocessed clean data

[1793] Step 3:

[1794] The terminal encrypts the pre-processed data. This encryption ensures data security. For example, an encryption algorithm such as AES (Advanced Encryption Standard) is used.

[1795] Input: Preprocessed clean data

[1796] Output: Encrypted data

[1797] Step 4:

[1798] The device sends encrypted data to the server via a secure communication protocol (such as HTTPS or MQTT) at regular intervals.

[1799] Input: Encrypted data

[1800] Output: Send to server (via HTTPS or MQTT)

[1801] Step 5:

[1802] The server receives the encrypted data, decrypts it, and stores it in the database. After the data is decrypted, it performs a cleansing process before storing it in the database.

[1803] Input: Encrypted data

[1804] Output: Cleansed data stored in a database

[1805] Step 6:

[1806] The server inputs the stored data into a generative AI model to analyze abnormal animal behavior. The generative AI model compares animal behavior patterns with past earthquake data and determines the likelihood of an earthquake occurring.

[1807] Input: Cleansed data

[1808] Output: Generative AI predicts the likelihood of an earthquake occurring

[1809] Step 7:

[1810] The server generates notification messages based on the earthquake prediction results determined by the generation AI, and in particular, uses natural language processing to convert the prediction information into a format that humans can understand.

[1811] Input: Earthquake prediction data

[1812] Output: Predictive notification message in natural language

[1813] Step 8:

[1814] The server then sends the generated prediction messages to local terminals and users, and simultaneously transmits the prediction information to the vehicle control system through an interface to instruct the autonomous vehicle to take emergency action.

[1815] Input: Natural language forecast notification message, earthquake forecast data

[1816] Output: Warning notification to user terminal, instructions to vehicle control system

[1817] Step 9:

[1818] When receiving a warning message, the device monitors the user's emotional state. It recognizes emotions from voice, facial expressions, and text input, and customizes the notification content based on the results. For example, if a user is nervous, it will deliver "advice on how to stay calm."

[1819] Input: Predictive notification message, user emotion data

[1820] Output: Customized notification message

[1821] Step 10:

[1822] The user checks the warning message displayed on the device and takes appropriate action, such as evacuating to a safe place.

[1823] Input: Customized notification message

[1824] Output: User action (e.g., evacuation)

[1825] The above steps enable early prediction of the risk of an earthquake and appropriate responses. At the same time, by appropriately adjusting the content of notifications according to the user's emotional state, it is possible to provide the user with the necessary information and encourage them to respond calmly. Furthermore, to ensure the safety of autonomous vehicles, it is possible to move the vehicle to an appropriate location in an emergency.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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).

[1833] 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.

[1834] 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."

[1835] 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.

[1836] 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).

[1837] 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.

[1838] 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.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] The following is further disclosed regarding the above embodiment.

[1848] (Claim 1)

[1849] a means for collecting animal behavioral data;

[1850] A means of analyzing collected animal behavior data using generative AI;

[1851] A method to predict the possibility of an earthquake occurring based on the analysis results and verbalize the data,

[1852] A system including a means for notifying local terminals and users of verbalized forecast information.

[1853] (Claim 2)

[1854] a sensor device for collecting animal behavior data;

[1855] means for transmitting data from the sensor device to a server;

[1856] means for pre-processing the received data at the server;

[1857] A means of inputting the preprocessed data into a generative AI model to analyze abnormal behavior;

[1858] A means for generating earthquake prediction information from the analysis results;

[1859] 2. The system according to claim 1, further comprising means for notifying the generated forecast information to the local terminal and the user.

[1860] (Claim 3)

[1861] an encryption communication means for ...

Claims

1. a means for collecting animal behavioral data; A means of analyzing collected animal behavior data using generative AI; A method to predict the possibility of an earthquake occurring based on the analysis results and verbalize the data, A system including a means for notifying local terminals and users of verbalized forecast information.

2. a sensor device for collecting animal behavior data; means for transmitting data from the sensor device to a server; means for pre-processing the received data at the server; A means of inputting the preprocessed data into a generative AI model to analyze abnormal behavior; A means for generating earthquake prediction information from the analysis results; 2. The system according to claim 1, further comprising means for notifying the generated forecast information to the local terminal and the user.

3. an encryption communication means for encrypting and transmitting the animal behavior data and decrypting it on the receiving side; A generative AI model for earthquake prediction based on the transmitted data, 2. The system of claim 1, further comprising an alert generating means for notifying a user of the forecast information.

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