System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting location information of a user; means for transmitting the collected location information to a server; means having a generative AI for analyzing the collected location information and learning a behavior pattern of the user; means for detecting a behavior deviating from the analyzed behavior pattern; and means for notifying a registered contact when the deviating behavior is detected.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] In recent years, the adoption of portable GPS devices that can pinpoint the locations of children and elderly people with dementia has been increasing for crime prevention and family safety. However, these devices simply provide location information and are unable to detect abnormal behavior in real time and provide prompt notification. As a result, responses can be delayed or inappropriate. Furthermore, they lack the ability to immediately issue a warning when a user exhibits abnormal behavior. To address these issues, this invention aims to provide a system that can detect abnormal behavior and provide prompt notification. [Means for solving the problem]

[0005] The present invention provides a system comprising the following means: means for collecting user location information, means for transmitting the collected location information to a server, means for analyzing the collected location information and including a generation AI that learns the user's behavioral patterns, means for detecting behavior that deviates from the analyzed behavioral patterns, and means for notifying registered contacts when such behavior is detected. This system can quickly detect when a user deviates from their usual behavioral patterns, for example, when they move outside a designated area or intentionally turn off their device, and notify registered contacts. This strengthens crime prevention and safety measures and enables prompt and appropriate responses.

[0006] "User" means an individual who uses the System and provides Location Information.

[0007] "Location information" refers to coordinate information of a specific location obtained via GPS.

[0008] "Means of collection" refers to the technology and devices used to obtain user location information and collect it as data.

[0009] "Means for transmitting to the server" refers to the methods and technologies for sending the collected location information to the server via the Internet or a communication network.

[0010] "Generative AI" refers to programs or algorithms that use artificial intelligence technology to analyze data and learn user behavior patterns.

[0011] A "behavioral pattern" refers to a series of behavioral characteristics including a user's usual travel route and places of stay.

[0012] "Means for detection" means techniques or methods for comparing analyzed behavioral patterns with current location information and identifying anomalous behavior.

[0013] "Deviation" refers to a user's deviation from their normal range or pattern of behavior.

[0014] "Means of notification" refers to methods and technologies for notifying registered contacts of detected abnormal behavior in real time.

[0015] "Contact information" refers to a phone number or email address set up to receive notifications when abnormal user behavior is detected. [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 present invention is a system for detecting abnormal behavior based on location information of a user and immediately notifying registered contacts of the abnormal behavior. An embodiment of the present invention will be described below.

[0038] Overall system overview

[0039] This system is built around the user's device, a server, and a generation AI, and learns the user's behavioral patterns to detect abnormal behavior. Specifically, the system acquires the user's location information and sends it to the server, where the generation AI analyzes and learns from it to set criteria for detecting abnormal behavior. If abnormal behavior is detected, a notification is sent to registered contacts.

[0040] Server Operation

[0041] 1. Collection of basic information and location information

[0042] The server receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device. This data is stored in a database.

[0043] 2. Learning behavioral patterns

[0044] The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. The algorithm analyzes the user's route, location, time of day, etc. to identify typical behavioral patterns.

[0045] 3. Detecting Abnormal Behavior

[0046] The generative AI monitors location information transmitted in real time, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., movement outside of normal range, intentional power-off, etc.) is detected.

[0047] 4. Sending notifications

[0048] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[0049] User terminal operation

[0050] 1. Collection of location information

[0051] The user's device periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 minutes.

[0052] 2. Real-time data transmission

[0053] It also has the function of sending current location information to the server in real time. When the user is moving or deviates from a specific area, that information is sent to the server immediately.

[0054] 3. Audio warnings

[0055] If abnormal behavior is detected, the user device will issue an audio warning, such as a message telling the user, "You are outside your normal area, please be careful."

[0056] Specific examples

[0057] Scenario: Your child takes an unusual route home

[0058] User terminal operation

[0059] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0060] 2. Your child takes a different route home than usual.

[0061] Server Operation

[0062] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0063] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0064] User terminal operation

[0065] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0066] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting it up to allow collection of GPS data.

[0070] Step 2:

[0071] The device sends the basic information entered by the user to the server. If permission to collect GPS data is granted, the collection setting is enabled.

[0072] Step 3:

[0073] The server records the basic information received from the device in a database, and also sets up a table for collecting GPS data in the database.

[0074] Step 4:

[0075] The device periodically (for example, every 10 minutes) acquires the user's current location and sends the GPS data to the server.

[0076] Step 5:

[0077] The server stores the received GPS data in a database in chronological order.

[0078] Step 6:

[0079] The server periodically collects GPS data and provides it to the AI ​​generator, which analyzes and learns the user's daily behavior patterns. The AI ​​generator identifies normal behavior patterns.

[0080] Step 7:

[0081] The device continues to send its current location information to the server in real time, even if the user intentionally turns it off.

[0082] Step 8:

[0083] The server analyzes the real-time location information received, and the AI ​​compares it with normal behavior patterns to detect abnormal behavior, such as moving outside the designated area or turning the power off.

[0084] Step 9:

[0085] If the server detects any abnormal behavior, it extracts details (such as the current location and the nature of the abnormality) and sends a notification to the registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[0086] Step 10:

[0087] If the device detects abnormal behavior, it will issue a voice warning to the user, for example, "You are outside the normal area, please be careful."

[0088] Through these steps, the system can detect abnormal user behavior in real time and respond quickly.

[0089] Example 1

[0090] 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."

[0091] There is a need for a method to quickly detect abnormal behavior based on the user's location information and immediately notify the user and their emergency contacts. In particular, a system that can respond effectively and quickly when a user deviates from their usual range of movement or intentionally turns off the power is required. There is also a need for a means to make users themselves aware of abnormal behavior and improve safety.

[0092] 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.

[0093] In this invention, the server includes means for collecting user location information, means for transmitting the collected location information to the server, means for analyzing the collected location information and having a machine learning algorithm for learning the user's behavioral patterns, means for detecting behavior that deviates from the analyzed behavioral patterns, means for sending a notification to registered contacts when deviating behavior is detected, and means for issuing a voice alert from the terminal when abnormal behavior is detected. This makes it possible to quickly detect abnormal user behavior, immediately notify emergency contacts, and also alert the user themselves.

[0094] "Collecting user location information" refers to the user's device acquiring GPS data and recording that location information periodically or in real time.

[0095] "Sending the collected location information to the server" refers to transferring the location information acquired on the user's device to the server using a communication protocol such as an HTTP request.

[0096] "Equipped with a machine learning algorithm that analyzes collected location information and learns user behavior patterns" refers to the operation of a machine learning model on the server that uses collected location information data to identify and learn daily behavior patterns.

[0097] "Detecting behavior that deviates from analyzed behavioral patterns" refers to comparing learned normal behavioral patterns with location information collected in real time and identifying behavior that deviates from them.

[0098] "Send a notification to registered contacts when deviant behavior is detected" means that when abnormal behavior is detected, the server will send a warning message to registered emergency contacts (e.g., parents or administrators) via email, SMS, app notification, etc.

[0099] "When abnormal behavior is detected, the device issues a voice message to warn the user" refers to playing a voice message to warn the user when the user's device detects abnormal behavior.

[0100] This invention is a system that detects abnormal behavior based on a user's location information and immediately notifies registered contacts. This system is built around the user's terminal, a server, and a generation AI. The following describes an embodiment of the system.

[0101] Server Operation

[0102] The server has several important functions. First, it receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device and stores them in a database. This database stores each user's past location information. The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. This generative AI analyzes information such as travel routes, locations, and time periods to identify normal behavioral patterns.

[0103] The real-time location information sent is monitored and compared with the normal behavioral patterns that the generation AI has learned. If abnormal behavior is detected, the server immediately sends a notification to the registered emergency contacts. The notification includes the current location information and details of the abnormal behavior. The notification is sent via SMS, email, or dedicated app notification.

[0104] User terminal operation

[0105] The user's device periodically acquires GPS data and sends that location information to the server. This transmission frequency is set at regular intervals, such as every 10 minutes. It also has a function that sends current location information to the server in real time when the user is moving or deviates from a specific area. If abnormal behavior is detected, the user's device will issue an audio warning. Specific messages include, "You are outside the normal area, please be careful."

[0106] Specific examples

[0107] Scenario: Your child takes an unusual route home

[0108] User terminal behavior:

[0109] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0110] 2. Along the way, your child takes a different route home than usual.

[0111] Server behavior:

[0112] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0113] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0114] User terminal behavior:

[0115] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0116] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0117] Prompt Sentence Examples

[0118] "Design a program to notify users when they deviate from their usual range of activity."

[0119] "Write a program that implements an algorithm that analyzes a user's GPS data and detects anomalous behavior."

[0120] The present invention can be specifically implemented using the above-described configuration and procedures.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1:

[0123] The user terminal acquires location information.

[0124] Specifically, the device's GPS sensor measures the current location and obtains data in the format "longitude: 35.6895, latitude: 139.6917". This data is obtained every 10 minutes.

[0125] Input: "GPS sensor"

[0126] Output: "Current location information (longitude and latitude)"

[0127] Step 2:

[0128] The user terminal transmits the collected location information to the server.

[0129] The device periodically sends the data "longitude: 35.6895, latitude: 139.6917" to the server using an HTTP POST request.

[0130] Input: "Current location information (longitude and latitude)"

[0131] Output: "Send location data to server"

[0132] Specific operation: The user device sends data to the server in the format "POST / location HTTP / 1.1 Host: server.com Content-Type: application / json {"longitude": 35.6895, "latitude": 139.6917}".

[0133] Step 3:

[0134] The server receives the location information and stores it in a database.

[0135] The server receives the location information sent from the terminal and stores it in a database.

[0136] Input: "Location information sent from the device"

[0137] Output: "Saved to database"

[0138] Specific operation: The location information received by the server is saved in the database in the format "INSERT INTO location_table (longitude, latitude, timestamp) VALUES (35.6895, 139.6917, CURRENT_TIMESTAMP)".

[0139] Step 4:

[0140] The server's generated AI learns behavioral patterns.

[0141] The server analyzes the location information stored in the database and learns daily behavior patterns.

[0142] Input: "Past location information in database"

[0143] Output: "Learned behavioral patterns"

[0144] Specific operation: The generation AI uses location data from the past three months to learn travel routes and time periods for each day of the week, and extracts a "pattern for traveling from station A to station B at 7 a.m. on a weekday."

[0145] Step 5:

[0146] The server-generated AI detects abnormal behavior in real time.

[0147] The generative AI monitors the location information transmitted in real time, compares it with learned behavioral patterns, and flags any abnormal behavior it detects.

[0148] Input: "Location information sent in real time" "Learned behavioral patterns"

[0149] Output: "Flag of abnormal behavior"

[0150] Specific behavior: If a user is at an unusual station C at 8:00 AM, the generation AI will flag this as "abnormal behavior."

[0151] Step 6:

[0152] Sends notifications when the server detects abnormal behavior.

[0153] When abnormal behavior is detected, the server sends a warning message to registered emergency contacts via email, SMS, or dedicated app notification.

[0154] Input: "Flag for abnormal behavior"

[0155] Output: "Notification sent to emergency contacts"

[0156] Specific behavior: The server sends an SMS to the parent with the message "Your child is taking an unusual route home."

[0157] Step 7:

[0158] The user terminal issues an audio warning.

[0159] If abnormal behavior is detected, the user's device will issue an audio warning.

[0160] Input: "Flag for abnormal behavior"

[0161] Output: "Audio warning message"

[0162] Action: A recorded message will be played saying, "You are off your normal route home, be careful."

[0163] (Application example 1)

[0164] 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."

[0165] In autonomous vehicles, the lack of driver intervention makes it difficult to immediately detect and respond to abnormal driving behavior and associated emergency situations. Furthermore, it is necessary to quickly notify abnormalities using multiple communication methods. Under these circumstances, there is a demand for a system that can detect abnormal driving behavior in real time and take appropriate action.

[0166] 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.

[0167] In this invention, the server includes a means for collecting user location information, a means for transmitting the collected location information to the server, and a means for analyzing the collected location information and having a generation AI that learns the user's behavioral patterns, thereby enabling a means for detecting behavior that deviates from the analyzed behavioral patterns, a means for notifying registered contacts when a deviating behavior is detected, a means for detecting abnormal vehicle driving behavior, and a means for issuing an audio warning to vehicle passengers based on the detected abnormal driving behavior.

[0168] "Means for collecting user location information" refers to devices or systems that obtain the user's current location using GPS or other location information technology.

[0169] The "means for transmitting collected location information to a server" refers to a communication means for transferring location information from a user terminal to a server in real time or at regular intervals.

[0170] "Means equipped with a generative AI that analyzes collected location information and learns user behavior patterns" refers to a system equipped with artificial intelligence that can learn daily behavior patterns using location data collected from users and detect abnormalities.

[0171] The "means for detecting behavior that deviates from the analyzed behavior pattern" is a technology that identifies abnormal behavior by comparing it with learned normal behavior patterns.

[0172] "Means for notifying registered contacts when deviant behavior is detected" is a function that sends a notification to pre-registered emergency contacts when abnormal behavior is detected.

[0173] "Means for detecting abnormal vehicle driving behavior" refers to a system that monitors vehicle location and behavior data and identifies behavior that deviates from normal driving patterns.

[0174] "Means for issuing an audio warning to vehicle passengers based on detected abnormal driving behavior" refers to a function that uses speakers in the vehicle to audibly warn passengers when abnormal driving behavior is detected.

[0175] Overall system overview

[0176] This invention is a system that can detect abnormal driving behavior in autonomous vehicles and respond immediately. The system consists of vehicle sensors, a terminal that transmits location information, a server that processes the data, and a generative AI model. It collects user location information and driving data, detects abnormal behavior based on that, and issues warnings as necessary.

[0177] Server Operation

[0178] 1. Collection of basic information and location information

[0179] The server receives basic information (vehicle ID, driver ID, emergency contact information, etc.) and location information (GPS data) sent from the vehicle's sensors. This data is stored in a database.

[0180] 2. Learning behavioral patterns

[0181] The server is equipped with a generation AI that learns the vehicle's daily driving patterns based on the collected location information. The generation AI analyzes the vehicle's driving route, stopping locations, driving time zones, etc. to identify normal driving patterns.

[0182] 3. Detecting Abnormal Behavior

[0183] The generative AI monitors real-time location and driving data, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., sudden braking or erratic steering) is detected.

[0184] 4. Sending notifications

[0185] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[0186] 5. Audio warnings

[0187] If abnormal behavior is detected, an audio warning will be issued using the vehicle's speakers, with a message such as "Abnormal driving behavior detected, please be careful" being conveyed to passengers.

[0188] User terminal operation

[0189] 1. Collection of location information

[0190] The user's device (a communication device in the vehicle) periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 seconds.

[0191] 2. Real-time data transmission

[0192] It also has the function of transmitting current location information and driving data to a server in real time. When the vehicle is moving or deviates from a specific area, the information is sent to the server immediately.

[0193] Hardware and software used

[0194] GPS module: Uses the GPS module installed in the vehicle to obtain location information.

[0195] Base server: Uses cloud services such as Amazon Web Services (AWS).

[0196] Generative AI models: Use deep learning platforms such as TensorFlow to learn behavioral patterns and detect anomalous behavior.

[0197] Notification system: Uses communication methods such as Twilio (SMS and email notifications) and Firebase (app notifications).

[0198] Voice output: When abnormal behavior is detected, a warning voice is generated using a speech synthesis service such as AWS Polly and broadcast through the vehicle's speakers.

[0199] Specific examples

[0200] Scenario: An autonomous vehicle makes a sharp turn

[0201] User terminal behavior:

[0202] 1. A communication device in the vehicle acquires GPS data and driving data in real time and transmits it to a server.

[0203] 2. If the vehicle makes a sudden turn, the data is sent to the server immediately.

[0204] Server behavior:

[0205] 1. The server analyzes the location information and sudden change of direction data received, and the generating AI detects abnormal driving behavior.

[0206] 2. The server recognizes the abnormal behavior and sends an SMS notification to the registered emergency contacts stating, "The vehicle has made a sudden turn. Please check."

[0207] 3. At the same time, the server uses AWS Polly to generate an audio warning that plays over the vehicle's speaker: "Abnormal driving behavior detected, please be careful."

[0208] Examples of prompt statements

[0209] An example of a prompt to be input to the generative AI model when the vehicle detects abnormal driving behavior is as follows:

[0210] "A sudden turn has been detected. We will analyze the abnormal behavior based on detailed location and driving data and send you a notification."

[0211] This will significantly improve the safety of self-driving vehicles and enable rapid response in the event of an abnormality.

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] Input: Basic information of the user (driver and vehicle) and location information (GPS data)

[0215] Processing: The device collects location and basic information from sensors and GPS modules in the vehicle.

[0216] Output: Acquired location information and basic information data

[0217] The terminal obtains the vehicle's current location from the GPS module and collects basic information such as the vehicle ID and driver ID, and prepares to send this information to the server.

[0218] Step 2:

[0219] Input: Location and basic information data collected on your device

[0220] Processing: The device sends this information to the server at regular intervals (e.g., every 10 seconds).

[0221] Output: Location and basic information data sent to the server

[0222] The device periodically sends the collected location information and basic information to a server, and transfers the data to a cloud server via the Internet.

[0223] Step 3:

[0224] Input: Location and basic information data sent to the server

[0225] Processing: The server stores the received location information and basic information in a database.

[0226] Output: Location and basic information stored in a database

[0227] The server stores the received location information and basic information in a cloud service database (e.g., AWS RDS), making this data easily accessible and analyzable.

[0228] Step 4:

[0229] Input: Location data stored in a database

[0230] Processing: The server uses a generative AI model to learn the vehicle's daily driving patterns from the location information.

[0231] Output: Learned normal driving pattern model

[0232] The server uses deep learning platforms such as TensorFlow to learn typical driving patterns based on historical location data, including driving routes, stopping locations, and driving times.

[0233] Step 5:

[0234] Input: Real-time location and driving data

[0235] Processing: The server compares the data received in real time with learned driving patterns to detect abnormal behavior.

[0236] Output: Detected abnormal behavior information

[0237] The server compares real-time data with previously learned patterns to determine if there are any irregular driving behaviors (e.g., hard braking, sudden turns). If an anomaly is detected, the information is logged.

[0238] Step 6:

[0239] Input: Detected abnormal behavior information

[0240] Action: If abnormal activity is detected, the server will send a notification to the registered emergency contacts, including the current location and details of the abnormal activity.

[0241] Output: Notification to emergency contacts (SMS, email, app notification)

[0242] The server uses the Twilio API or Firebase to send a notification of abnormal behavior to emergency contacts, such as a message saying, "Vehicle has made a sudden turn. Please check."

[0243] Step 7:

[0244] Input: Detected abnormal behavior information

[0245] Processing: If abnormal behavior is detected, the server generates an audio alert and plays a warning message through the vehicle's speaker.

[0246] Output: Audio warning played in the vehicle

[0247] The server uses AWS Polly to generate a voice message warning the driver, saying, "Abnormal driving behavior detected. Please be careful." This voice message is then played through the vehicle's speakers.

[0248] This makes it possible to detect abnormal operating behavior in real time and respond quickly.

[0249] 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.

[0250] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and quickly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generative AI and an emotion engine. The following describes an embodiment of the invention.

[0251] Overall system overview

[0252] This system is built around the user's device, server, generation AI, and emotion engine, and learns the user's behavioral patterns and emotions to detect abnormal behavior. Specifically, it acquires the user's location and emotional information and sends it to the server. The generation AI analyzes and learns from this information to set criteria for detecting abnormal behavior, and the emotion engine analyzes the emotional information to detect abnormal emotional changes. If abnormal behavior or abnormal emotional changes are detected, a notification is sent to registered contacts.

[0253] Server Operation

[0254] 1. Collecting basic information, location information, and emotional information

[0255] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[0256] 2. Learning behavioral and emotional patterns

[0257] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[0258] 3. Detection of abnormal behavior and abnormal emotional changes

[0259] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[0260] 4. Sending notifications

[0261] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[0262] User terminal operation

[0263] 1. Collecting location and emotional information

[0264] The user's device periodically acquires GPS data, uses an emotion engine to collect the user's emotional information, and sends this information to the server at regular intervals, such as every 10 minutes.

[0265] 2. Real-time data transmission

[0266] It also has the ability to send current location and emotional information to a server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is sent to the server immediately.

[0267] 3. Audio warnings

[0268] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0269] Specific examples

[0270] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0271] User terminal operation

[0272] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[0273] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[0274] Server Operation

[0275] 1. The location information received by the server is analyzed by the generation AI, and it is recognized that the person has deviated from their usual route home.

[0276] 2. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[0277] 3. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[0278] User terminal operation

[0279] 1. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[0280] This allows parents to immediately recognize abnormal behavior and emotional changes and respond quickly, and also alerts the child themselves, further improving safety.

[0281] The processing flow will be explained below.

[0282] Step 1:

[0283] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting up the device to allow collection of GPS data and emotional data.

[0284] Step 2:

[0285] The device sends the basic information entered by the user to the server. If permission to collect GPS data and emotion data is granted, the collection settings are enabled.

[0286] Step 3:

[0287] The server records the basic information received from the device in a database, and also sets up tables in the database for collecting GPS data and emotion data.

[0288] Step 4:

[0289] The device periodically (for example, every 10 minutes) acquires the user's current location and emotion data and transmits this information to the server.

[0290] Step 5:

[0291] The server stores the received GPS data and emotion data in a database in chronological order.

[0292] Step 6:

[0293] The server periodically collects GPS data and emotional data and provides it to the generative AI and emotion engine, which then analyzes and learns the user's daily behavioral and emotional patterns. The generative AI and emotion engine then identify normal behavioral and emotional patterns.

[0294] Step 7:

[0295] The device continuously transmits its current location and emotional information to the server in real time. If the user intentionally turns off the device, that information is also sent to the server.

[0296] Step 8:

[0297] The server analyzes the real-time location and emotional information received, and the AI ​​compares it with the user's usual behavior and emotional patterns to detect abnormal behavior or emotional changes. Criteria for abnormal behavior include moving outside the designated area, turning off the power, and abnormal emotional changes.

[0298] Step 9:

[0299] If the server detects abnormal behavior or emotional changes, it extracts details (current location, emotional state, details of the abnormality, etc.) and sends a notification to registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[0300] Step 10:

[0301] If the device detects abnormal behavior or emotional changes, it will issue a voice warning to the user, such as "You are outside your normal range, be careful" or "Your emotional state is abnormal, please take a deep breath."

[0302] Example 2

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

[0304] Conventional systems detect abnormal behavior based solely on the user's location information, which limits their ability to accurately identify abnormal behavior patterns or dangerous situations. Furthermore, when sending notifications to emergency contacts, information about the user's emotional state is not included, making it difficult to quickly recognize true abnormal situations and take appropriate action.

[0305] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user location information and emotional information, means for transmitting the collected location information and emotional information to the server, means for analyzing the collected location information and emotional information and including a generation AI and emotional engine that learns the user's behavioral patterns and emotional patterns, means for detecting behaviors and abnormal emotional changes that deviate from the analyzed behavioral patterns and emotional patterns, means for notifying registered contacts when deviating behaviors and abnormal emotional changes are detected, and user terminal means for issuing audio alerts when abnormal behavior or abnormal emotional changes are detected. This makes it possible to detect abnormalities from both the user's behavioral patterns and emotional patterns and to quickly and accurately notify emergency contacts.

[0306] "User Location Information" means a user's current geographic location data obtained using GPS or other location measurement technology.

[0307] "Emotion information" is data that indicates the user's emotional state, obtained by analyzing the user's facial expression, voice, heart rate, and the like.

[0308] "Server" means a central processing unit that processes, stores, and analyzes data received from user terminals over a network.

[0309] "Generative AI" is a machine learning algorithm used to learn user behavior patterns and detect anomalous behavior.

[0310] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional information and detect changes in it.

[0311] The "behavior pattern" is a history of characteristic behaviors such as the user's daily travel routes and time periods.

[0312] "Abnormal behavior" is behavior that deviates from learned normal behavior patterns.

[0313] An "abnormal emotional change" is an emotional state that is abruptly changed from the user's normal emotional pattern.

[0314] "Registered contacts" are telephone numbers and email addresses registered in the system as contact points for emergency notifications.

[0315] "Notification" is an alert message sent to registered contacts when abnormal behavior or abnormal emotional changes are detected.

[0316] "Audio warning" is a function that allows the user device to issue an audio warning message when abnormal behavior or abnormal emotional changes are detected.

[0317] "Regular interval" refers to a set time interval for collecting and transmitting data periodically, such as every 10 minutes.

[0318] "Real-time" refers to data being processed and results provided immediately after it is generated, with almost no delay.

[0319] The present invention provides a system for detecting abnormal behavior based on location information and emotional information of a user and for quickly notifying registered contacts of the abnormal behavior. Hereinafter, a specific embodiment of the present invention will be described.

[0320] Overall system overview

[0321] This system is built around the user's device, a server, a generation AI, and an emotion engine. The user's device collects location information and emotion information and sends that data to the server. The server analyzes the data using the generation AI and emotion engine to detect abnormal behavior and abnormal emotional changes. If detected, a notification is sent to registered contacts.

[0322] Server Operation Details

[0323] The server works as follows:

[0324] 1. Data collection and storage

[0325] The server receives location information (e.g., GPS data) and emotional information sent from the user's device. This information is stored in a database. For example, MySQL or PostgreSQL can be used as a database management system to efficiently store and manage data.

[0326] 2. Learning behavioral and emotional patterns

[0327] The server is equipped with a generative AI and an emotion engine, which learns the user's daily behavioral and emotional patterns based on collected data. The generative AI analyzes the user's travel route and location, while the emotion engine analyzes the user's emotional state. For example, the generative AI model uses TensorFlow to perform real-time data analysis.

[0328] 3. Detecting Abnormal Behavior and Emotional Changes

[0329] The server monitors location and emotional information in real time, compares it with a trained model, and detects abnormal behavior or emotional changes. If an abnormality is detected, an emergency contact is immediately notified.

[0330] 4. Sending notifications

[0331] If abnormal behavior or emotional changes are detected, the server will send a notification to registered emergency contacts via SMS, email, or dedicated app notification, containing details of the user's current location, emotional state, and abnormal behavior.

[0332] User device operation details

[0333] The user terminal operates as follows:

[0334] 1. Data collection

[0335] The user's device periodically collects location information (using GPS) and emotional information. Emotional information is collected through the smartphone's camera, microphone, and specific apps. The emotion engine analyzes the user's emotional state using facial recognition software and voice analysis software.

[0336] 2. Data transmission

[0337] The collected location and emotion information is sent to a server at regular intervals (e.g., every 10 minutes).The system also has a real-time data transmission function, which immediately sends information to the server if the robot deviates from a specific area or if an abnormal change in emotion is detected.

[0338] 3. Audio warnings

[0339] If abnormal behavior or emotional changes are detected, the user device will issue a voice alert, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0340] Specific examples

[0341] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0342] When a user's child is on their way home from school, the device collects their current location and emotional information every 10 minutes and sends this information to the server. If the child takes a different route home than their usual route, the server recognizes this as an abnormality, and the emotion engine simultaneously detects that the child is feeling anxious or stressed. The server then recognizes this abnormal behavior and abnormal emotional changes and immediately sends an SMS notification to the registered emergency contact (e.g., parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state." Additionally, the user's device issues a voice warning, saying, "You are deviating from your usual route home. Please be careful," and "Your emotional state is unstable. Please take a deep breath."

[0343] Prompt Sentence Examples

[0344] Input prompt for the generative AI model:

[0345] "Please explain your system for detecting anomalous behavior based on user location and emotional information. Please also provide a detailed description of the hardware and software used, as well as the data processing method."

[0346] keyword

[0347] Generative AI model, prompt sentence

[0348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0349] System processing steps

[0350] Step 1: Collect data

[0351] User terminal operation

[0352] Users have devices such as smartphones. These devices collect location information using GPS and emotional information using cameras and microphones. Emotional information is analyzed using facial recognition software and voice analysis software. This data is temporarily stored in the device's internal memory.

[0353] Input: User's GPS location, raw data collected by camera and microphone

[0354] Output: Location data and analyzed emotion data

[0355] Step 2: Sending data

[0356] User terminal operation

[0357] The user device sends the collected location information and emotion information to the server at regular intervals (e.g., every 10 minutes). This transmission uses the HTTPS protocol to ensure security. In an emergency, the information is sent immediately when a specific event occurs (e.g., area departure, abnormal emotion change).

[0358] Input: Collected location data and analyzed emotion data

[0359] Output: Location and emotion data sent to the server

[0360] Step 3: Receiving and storing data

[0361] Server Operation

[0362] The server receives the location and emotion information sent from the user's device and stores it in a database. The database uses "MySQL" or "PostgreSQL" to manage data by user ID and timestamp, and processes it efficiently.

[0363] Input: Location data and emotional data sent from the user device

[0364] Output: Data stored in a database

[0365] Step 4: Analyze and train the data

[0366] Server Operation

[0367] The generative AI and emotion engine installed on the server use the stored data to learn the user's daily behavioral and emotional patterns. The generative AI uses TensorFlow to analyze the user's travel route and where they stay, and the emotion engine analyzes their emotional state.

[0368] Input: Stored location and emotion data

[0369] Output: Learned and updated behavioral and emotional pattern models

[0370] Step 5: Real-time monitoring and anomaly detection

[0371] Server Operation

[0372] The server monitors new location and emotion information in real time, compares it with the trained model, and detects abnormal behavior or abnormal emotional changes. If an anomaly is detected, the details are recorded in a log.

[0373] Input: Location and emotion data received in real time

[0374] Output: A log of any abnormal behaviors or emotional changes detected

[0375] Step 6: Sending notifications

[0376] Server Operation

[0377] If any abnormal behavior or emotional changes are detected, the server will immediately send a notification to registered emergency contacts via SMS, email, or dedicated app notification, and the message will include details of the user's current location, emotional state, and any abnormal behavior.

[0378] Input: Log of detected anomalous behavior or emotional changes

[0379] Output: Notification message to emergency contacts (e.g. SMS, email)

[0380] Step 7: Audio Reminder

[0381] User terminal operation

[0382] If abnormal behavior or emotional changes are detected, the user device will issue an audio alert, playing messages such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0383] Input: Notification of abnormal behavior or emotional changes from the server

[0384] Output: Audio alert on user device

[0385] (Application example 2)

[0386] 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."

[0387] The present invention aims to quickly detect abnormal behavior or abnormal emotional changes using a user's location information and emotional information, thereby effectively monitoring the user's safety. Conventional systems were able to detect abnormal behavior based solely on location information, but were unable to take the user's emotional state into account, making accurate anomaly detection difficult. Therefore, the present invention aims to provide a more accurate anomaly detection system by simultaneously analyzing the user's emotional information.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0389] In this invention, the server includes means for analyzing the collected location information and emotion information and including a generation AI and emotion engine that learns the user's behavioral and emotional patterns, means for detecting behavior that deviates from the analyzed behavioral and emotional patterns or abnormal emotional changes, and means for notifying registered contacts when a deviating behavior or abnormal emotional change is detected. This makes it possible to detect anomalies that take into account not only the user's location information but also their emotional information.

[0390] "User location information" means data that indicates the geographic location where a user is currently located.

[0391] A "server" is a remote computer system that receives, processes, stores, and transmits data over a network.

[0392] "Collected Location Information" means geographic location data obtained from a User Device and transmitted to a Server.

[0393] "Emotional information" refers to data that indicates changes in a user's psychological state or emotions.

[0394] "Generative AI" is an artificial intelligence system that learns user behavior patterns and detects abnormal behavior.

[0395] An "emotion engine" is a software system that analyzes a user's emotional information and detects abnormal emotional changes.

[0396] "Learning" is the process of analyzing collected data and understanding specific patterns and trends.

[0397] "Abnormal behavior" refers to user behavior that significantly deviates from normal behavior patterns.

[0398] "Abnormal emotional changes" refers to fluctuations in a user's emotions that significantly deviate from normal emotional patterns.

[0399] "Notification" refers to the act of transmitting information to pre-registered contacts when abnormal behavior or abnormal emotional changes are detected.

[0400] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and promptly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generation AI and an emotion engine. This system is built around a user terminal, a server, a generation AI, and an emotion engine.

[0401] User terminal operation

[0402] 1. Collecting location and emotional information

[0403] The user device periodically acquires GPS data and uses an emotion engine to collect the user's emotional information. This information is then sent to the server at regular intervals, such as every 10 minutes.

[0404] 2. Real-time data transmission

[0405] The user device also has the function of transmitting current location information and emotional information to the server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is immediately sent to the server.

[0406] 3. Audio warnings

[0407] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0408] Server Operation

[0409] 1. Collecting basic information, location information, and emotional information

[0410] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[0411] 2. Learning behavioral and emotional patterns

[0412] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[0413] 3. Detection of abnormal behavior and abnormal emotional changes

[0414] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[0415] 4. Sending notifications

[0416] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[0417] Hardware and software used

[0418] Hardware:

[0419] User device: Mobile device such as an Android smartphone

[0420] Server: A standard HTTP server (e.g. NGINX or Apache)

[0421] software:

[0422] Mobile application: Uses Android Location API

[0423] Server side: Using Python and the Hugging Face Transformers library

[0424] Sentiment Analysis: Transformers Library for Python (Hugging Face)

[0425] Specific examples

[0426] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0427] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[0428] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[0429] 3. The location information received by the server is analyzed by the generated AI, and it is recognized that the person has deviated from their usual route home.

[0430] 4. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[0431] 5. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[0432] 6. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[0433] Prompt Sentence Examples

[0434] "Analyze user comments to see if they indicate negative sentiment."

[0435] "Infer the user's current mental state from the provided text data."

[0436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0437] Step 1:

[0438] The user device periodically obtains location information using a GPS sensor. As input, a time interval (e.g., every 10 minutes) is set, and as output, the latest location information (latitude and longitude) is obtained.

[0439] Step 2:

[0440] The user device collects the user's emotional information using a built-in emotion engine. The input is the user's text input or the latest voice data, and the output is the user's emotional state (positive, negative, neutral, etc.).

[0441] Step 3:

[0442] The user terminal transmits the acquired location information and emotion information to the server. As input, a data packet is created that combines the location information and emotion information, and as output, the data packet is transmitted to the server.

[0443] Step 4:

[0444] The server stores the received location information and emotion information in a database.,As input, the server receives a data packet containing,location information and emotion information, and as output, stores the data in,a database.

[0445] Step 5:

[0446] The server analyzes location information using a generative AI and learns the user's behavioral patterns. It analyzes multiple location data as input and generates a model of normal behavioral patterns as output.

[0447] Step 6:

[0448] The server analyzes the emotional information using an emotion engine and learns the user's emotional patterns. It analyzes multiple pieces of emotional information data as input and generates a typical emotional pattern model as output.

[0449] Step 7:

[0450] The server compares the location and emotion information received in real time with a normal pattern model to detect abnormal behavior and abnormal emotional changes.The latest location and emotion information and the normal pattern model are used as input, and the detection results of abnormal behavior or abnormal emotional changes are obtained as output.

[0451] Step 8:

[0452] If the server detects abnormal behavior or emotional changes, it sends a notification to registered emergency contacts. The input is detailed information about the abnormality (current location, emotional state, description of the abnormal behavior, etc.), and the output is a notification via SMS, email, or a dedicated app.

[0453] Step 9:

[0454] If the user device detects abnormal behavior or abnormal emotional changes, it issues a voice warning. As input, it receives a warning notification from the server, and as output, it conveys a voice message to the user.

[0455] Through the above processing steps, this system is able to quickly detect abnormal behavior based on the user's location information and emotional information, and issue necessary notifications and warnings.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] The present invention is a system for detecting abnormal behavior based on location information of a user and immediately notifying registered contacts of the abnormal behavior. An embodiment of the present invention will be described below.

[0473] Overall system overview

[0474] This system is built around the user's device, a server, and a generation AI, and learns the user's behavioral patterns to detect abnormal behavior. Specifically, the system acquires the user's location information and sends it to the server, where the generation AI analyzes and learns from it to set criteria for detecting abnormal behavior. If abnormal behavior is detected, a notification is sent to registered contacts.

[0475] Server Operation

[0476] 1. Collection of basic information and location information

[0477] The server receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device. This data is stored in a database.

[0478] 2. Learning behavioral patterns

[0479] The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. The algorithm analyzes the user's route, location, time of day, etc. to identify typical behavioral patterns.

[0480] 3. Detecting Abnormal Behavior

[0481] The generative AI monitors location information transmitted in real time, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., movement outside of normal range, intentional power-off, etc.) is detected.

[0482] 4. Sending notifications

[0483] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[0484] User terminal operation

[0485] 1. Collection of location information

[0486] The user's device periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 minutes.

[0487] 2. Real-time data transmission

[0488] It also has the function of sending current location information to the server in real time. When the user is moving or deviates from a specific area, that information is sent to the server immediately.

[0489] 3. Audio warnings

[0490] If abnormal behavior is detected, the user device will issue an audio warning, such as a message telling the user, "You are outside your normal area, please be careful."

[0491] Specific examples

[0492] Scenario: Your child takes an unusual route home

[0493] User terminal operation

[0494] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0495] 2. Your child takes a different route home than usual.

[0496] Server Operation

[0497] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0498] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0499] User terminal operation

[0500] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0501] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0502] The processing flow will be explained below.

[0503] Step 1:

[0504] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting it up to allow collection of GPS data.

[0505] Step 2:

[0506] The device sends the basic information entered by the user to the server. If permission to collect GPS data is granted, the collection setting is enabled.

[0507] Step 3:

[0508] The server records the basic information received from the device in a database, and also sets up a table for collecting GPS data in the database.

[0509] Step 4:

[0510] The device periodically (for example, every 10 minutes) acquires the user's current location and sends the GPS data to the server.

[0511] Step 5:

[0512] The server stores the received GPS data in a database in chronological order.

[0513] Step 6:

[0514] The server periodically collects GPS data and provides it to the AI ​​generator, which analyzes and learns the user's daily behavior patterns. The AI ​​generator identifies normal behavior patterns.

[0515] Step 7:

[0516] The device continues to send its current location information to the server in real time, even if the user intentionally turns it off.

[0517] Step 8:

[0518] The server analyzes the real-time location information received, and the AI ​​compares it with normal behavior patterns to detect abnormal behavior, such as moving outside the designated area or turning the power off.

[0519] Step 9:

[0520] If the server detects any abnormal behavior, it extracts details (such as the current location and the nature of the abnormality) and sends a notification to the registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[0521] Step 10:

[0522] If the device detects abnormal behavior, it will issue a voice warning to the user, for example, "You are outside the normal area, please be careful."

[0523] Through these steps, the system can detect abnormal user behavior in real time and respond quickly.

[0524] Example 1

[0525] 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."

[0526] There is a need for a method to quickly detect abnormal behavior based on the user's location information and immediately notify the user and their emergency contacts. In particular, a system that can respond effectively and quickly when a user deviates from their usual range of movement or intentionally turns off the power is required. There is also a need for a means to make users themselves aware of abnormal behavior and improve safety.

[0527] 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.

[0528] In this invention, the server includes means for collecting user location information, means for transmitting the collected location information to the server, means for analyzing the collected location information and having a machine learning algorithm for learning the user's behavioral patterns, means for detecting behavior that deviates from the analyzed behavioral patterns, means for sending a notification to registered contacts when deviating behavior is detected, and means for issuing a voice alert from the terminal when abnormal behavior is detected. This makes it possible to quickly detect abnormal user behavior, immediately notify emergency contacts, and also alert the user themselves.

[0529] "Collecting user location information" refers to the user's device acquiring GPS data and recording that location information periodically or in real time.

[0530] "Sending the collected location information to the server" refers to transferring the location information acquired on the user's device to the server using a communication protocol such as an HTTP request.

[0531] "Equipped with a machine learning algorithm that analyzes collected location information and learns user behavior patterns" refers to the operation of a machine learning model on the server that uses collected location information data to identify and learn daily behavior patterns.

[0532] "Detecting behavior that deviates from analyzed behavioral patterns" refers to comparing learned normal behavioral patterns with location information collected in real time and identifying behavior that deviates from them.

[0533] "Send a notification to registered contacts when deviant behavior is detected" means that when abnormal behavior is detected, the server will send a warning message to registered emergency contacts (e.g., parents or administrators) via email, SMS, app notification, etc.

[0534] "When abnormal behavior is detected, the device issues a voice message to warn the user" refers to playing a voice message to warn the user when the user's device detects abnormal behavior.

[0535] This invention is a system that detects abnormal behavior based on a user's location information and immediately notifies registered contacts. This system is built around the user's terminal, a server, and a generation AI. The following describes an embodiment of the system.

[0536] Server Operation

[0537] The server has several important functions. First, it receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device and stores them in a database. This database stores each user's past location information. The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. This generative AI analyzes information such as travel routes, locations, and time periods to identify normal behavioral patterns.

[0538] The real-time location information sent is monitored and compared with the normal behavioral patterns that the generation AI has learned. If abnormal behavior is detected, the server immediately sends a notification to the registered emergency contacts. The notification includes the current location information and details of the abnormal behavior. The notification is sent via SMS, email, or dedicated app notification.

[0539] User terminal operation

[0540] The user's device periodically acquires GPS data and sends that location information to the server. This transmission frequency is set at regular intervals, such as every 10 minutes. It also has a function that sends current location information to the server in real time when the user is moving or deviates from a specific area. If abnormal behavior is detected, the user's device will issue an audio warning. Specific messages include, "You are outside the normal area, please be careful."

[0541] Specific examples

[0542] Scenario: Your child takes an unusual route home

[0543] User terminal behavior:

[0544] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0545] 2. Along the way, your child takes a different route home than usual.

[0546] Server behavior:

[0547] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0548] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0549] User terminal behavior:

[0550] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0551] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0552] Prompt Sentence Examples

[0553] "Design a program to notify users when they deviate from their usual range of activity."

[0554] "Write a program that implements an algorithm that analyzes a user's GPS data and detects anomalous behavior."

[0555] The present invention can be specifically implemented using the above-described configuration and procedures.

[0556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0557] Step 1:

[0558] The user terminal acquires location information.

[0559] Specifically, the device's GPS sensor measures the current location and obtains data in the format "longitude: 35.6895, latitude: 139.6917". This data is obtained every 10 minutes.

[0560] Input: "GPS sensor"

[0561] Output: "Current location information (longitude and latitude)"

[0562] Step 2:

[0563] The user terminal transmits the collected location information to the server.

[0564] The device periodically sends the data "longitude: 35.6895, latitude: 139.6917" to the server using an HTTP POST request.

[0565] Input: "Current location information (longitude and latitude)"

[0566] Output: "Send location data to server"

[0567] Specific operation: The user device sends data to the server in the format "POST / location HTTP / 1.1 Host: server.com Content-Type: application / json {"longitude": 35.6895, "latitude": 139.6917}".

[0568] Step 3:

[0569] The server receives the location information and stores it in a database.

[0570] The server receives the location information sent from the terminal and stores it in a database.

[0571] Input: "Location information sent from the device"

[0572] Output: "Saved to database"

[0573] Specific operation: The location information received by the server is saved in the database in the format "INSERT INTO location_table (longitude, latitude, timestamp) VALUES (35.6895, 139.6917, CURRENT_TIMESTAMP)".

[0574] Step 4:

[0575] The server's generated AI learns behavioral patterns.

[0576] The server analyzes the location information stored in the database and learns daily behavior patterns.

[0577] Input: "Past location information in database"

[0578] Output: "Learned behavioral patterns"

[0579] Specific operation: The generation AI uses location data from the past three months to learn travel routes and time periods for each day of the week, and extracts a "pattern for traveling from station A to station B at 7 a.m. on a weekday."

[0580] Step 5:

[0581] The server-generated AI detects abnormal behavior in real time.

[0582] The generative AI monitors the location information transmitted in real time, compares it with learned behavioral patterns, and flags any abnormal behavior it detects.

[0583] Input: "Location information sent in real time" "Learned behavioral patterns"

[0584] Output: "Flag of abnormal behavior"

[0585] Specific behavior: If a user is at an unusual station C at 8:00 AM, the generation AI will flag this as "abnormal behavior."

[0586] Step 6:

[0587] Sends notifications when the server detects abnormal behavior.

[0588] When abnormal behavior is detected, the server sends a warning message to registered emergency contacts via email, SMS, or dedicated app notification.

[0589] Input: "Flag for abnormal behavior"

[0590] Output: "Notification sent to emergency contacts"

[0591] Specific behavior: The server sends an SMS to the parent with the message "Your child is taking an unusual route home."

[0592] Step 7:

[0593] The user terminal issues an audio warning.

[0594] If abnormal behavior is detected, the user's device will issue an audio warning.

[0595] Input: "Flag for abnormal behavior"

[0596] Output: "Audio warning message"

[0597] Action: A recorded message will be played saying, "You are off your normal route home, be careful."

[0598] (Application example 1)

[0599] 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."

[0600] In autonomous vehicles, the lack of driver intervention makes it difficult to immediately detect and respond to abnormal driving behavior and associated emergency situations. Furthermore, it is necessary to quickly notify abnormalities using multiple communication methods. Under these circumstances, there is a demand for a system that can detect abnormal driving behavior in real time and take appropriate action.

[0601] 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.

[0602] In this invention, the server includes a means for collecting user location information, a means for transmitting the collected location information to the server, and a means for analyzing the collected location information and having a generation AI that learns the user's behavioral patterns, thereby enabling a means for detecting behavior that deviates from the analyzed behavioral patterns, a means for notifying registered contacts when a deviating behavior is detected, a means for detecting abnormal vehicle driving behavior, and a means for issuing an audio warning to vehicle passengers based on the detected abnormal driving behavior.

[0603] "Means for collecting user location information" refers to devices or systems that obtain the user's current location using GPS or other location information technology.

[0604] The "means for transmitting collected location information to a server" refers to a communication means for transferring location information from a user terminal to a server in real time or at regular intervals.

[0605] "Means equipped with a generative AI that analyzes collected location information and learns user behavior patterns" refers to a system equipped with artificial intelligence that can learn daily behavior patterns using location data collected from users and detect abnormalities.

[0606] The "means for detecting behavior that deviates from the analyzed behavior pattern" is a technology that identifies abnormal behavior by comparing it with learned normal behavior patterns.

[0607] "Means for notifying registered contacts when deviant behavior is detected" is a function that sends a notification to pre-registered emergency contacts when abnormal behavior is detected.

[0608] "Means for detecting abnormal vehicle driving behavior" refers to a system that monitors vehicle location and behavior data and identifies behavior that deviates from normal driving patterns.

[0609] "Means for issuing an audio warning to vehicle passengers based on detected abnormal driving behavior" refers to a function that uses speakers in the vehicle to audibly warn passengers when abnormal driving behavior is detected.

[0610] Overall system overview

[0611] This invention is a system that can detect abnormal driving behavior in autonomous vehicles and respond immediately. The system consists of vehicle sensors, a terminal that transmits location information, a server that processes the data, and a generative AI model. It collects user location information and driving data, detects abnormal behavior based on that, and issues warnings as necessary.

[0612] Server Operation

[0613] 1. Collection of basic information and location information

[0614] The server receives basic information (vehicle ID, driver ID, emergency contact information, etc.) and location information (GPS data) sent from the vehicle's sensors. This data is stored in a database.

[0615] 2. Learning behavioral patterns

[0616] The server is equipped with a generation AI that learns the vehicle's daily driving patterns based on the collected location information. The generation AI analyzes the vehicle's driving route, stopping locations, driving time zones, etc. to identify normal driving patterns.

[0617] 3. Detecting Abnormal Behavior

[0618] The generative AI monitors real-time location and driving data, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., sudden braking or erratic steering) is detected.

[0619] 4. Sending notifications

[0620] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[0621] 5. Audio warnings

[0622] If abnormal behavior is detected, an audio warning will be issued using the vehicle's speakers, with a message such as "Abnormal driving behavior detected, please be careful" being conveyed to passengers.

[0623] User terminal operation

[0624] 1. Collection of location information

[0625] The user's device (a communication device in the vehicle) periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 seconds.

[0626] 2. Real-time data transmission

[0627] It also has the function of transmitting current location information and driving data to a server in real time. When the vehicle is moving or deviates from a specific area, the information is sent to the server immediately.

[0628] Hardware and software used

[0629] GPS module: Uses the GPS module installed in the vehicle to obtain location information.

[0630] Base server: Uses cloud services such as Amazon Web Services (AWS).

[0631] Generative AI models: Use deep learning platforms such as TensorFlow to learn behavioral patterns and detect anomalous behavior.

[0632] Notification system: Uses communication methods such as Twilio (SMS and email notifications) and Firebase (app notifications).

[0633] Voice output: When abnormal behavior is detected, a warning voice is generated using a speech synthesis service such as AWS Polly and broadcast through the vehicle's speakers.

[0634] Specific examples

[0635] Scenario: An autonomous vehicle makes a sharp turn

[0636] User terminal behavior:

[0637] 1. A communication device in the vehicle acquires GPS data and driving data in real time and transmits it to a server.

[0638] 2. If the vehicle makes a sudden turn, the data is sent to the server immediately.

[0639] Server behavior:

[0640] 1. The server analyzes the location information and sudden change of direction data received, and the generating AI detects abnormal driving behavior.

[0641] 2. The server recognizes the abnormal behavior and sends an SMS notification to the registered emergency contacts stating, "The vehicle has made a sudden turn. Please check."

[0642] 3. At the same time, the server uses AWS Polly to generate an audio warning that plays over the vehicle's speaker: "Abnormal driving behavior detected, please be careful."

[0643] Examples of prompt statements

[0644] An example of a prompt to be input to the generative AI model when the vehicle detects abnormal driving behavior is as follows:

[0645] "A sudden turn has been detected. We will analyze the abnormal behavior based on detailed location and driving data and send you a notification."

[0646] This will significantly improve the safety of self-driving vehicles and enable rapid response in the event of an abnormality.

[0647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0648] Step 1:

[0649] Input: Basic information of the user (driver and vehicle) and location information (GPS data)

[0650] Processing: The device collects location and basic information from sensors and GPS modules in the vehicle.

[0651] Output: Acquired location information and basic information data

[0652] The terminal obtains the vehicle's current location from the GPS module and collects basic information such as the vehicle ID and driver ID, and prepares to send this information to the server.

[0653] Step 2:

[0654] Input: Location and basic information data collected on your device

[0655] Processing: The device sends this information to the server at regular intervals (e.g., every 10 seconds).

[0656] Output: Location and basic information data sent to the server

[0657] The device periodically sends the collected location information and basic information to a server, and transfers the data to a cloud server via the Internet.

[0658] Step 3:

[0659] Input: Location and basic information data sent to the server

[0660] Processing: The server stores the received location information and basic information in a database.

[0661] Output: Location and basic information stored in a database

[0662] The server stores the received location information and basic information in a cloud service database (e.g., AWS RDS), making this data easily accessible and analyzable.

[0663] Step 4:

[0664] Input: Location data stored in a database

[0665] Processing: The server uses a generative AI model to learn the vehicle's daily driving patterns from the location information.

[0666] Output: Learned normal driving pattern model

[0667] The server uses deep learning platforms such as TensorFlow to learn typical driving patterns based on historical location data, including driving routes, stopping locations, and driving times.

[0668] Step 5:

[0669] Input: Real-time location and driving data

[0670] Processing: The server compares the data received in real time with learned driving patterns to detect abnormal behavior.

[0671] Output: Detected abnormal behavior information

[0672] The server compares real-time data with previously learned patterns to determine if there are any irregular driving behaviors (e.g., hard braking, sudden turns). If an anomaly is detected, the information is logged.

[0673] Step 6:

[0674] Input: Detected abnormal behavior information

[0675] Action: If abnormal activity is detected, the server will send a notification to the registered emergency contacts, including the current location and details of the abnormal activity.

[0676] Output: Notification to emergency contacts (SMS, email, app notification)

[0677] The server uses the Twilio API or Firebase to send a notification of abnormal behavior to emergency contacts, such as a message saying, "Vehicle has made a sudden turn. Please check."

[0678] Step 7:

[0679] Input: Detected abnormal behavior information

[0680] Processing: If abnormal behavior is detected, the server generates an audio alert and plays a warning message through the vehicle's speaker.

[0681] Output: Audio warning played in the vehicle

[0682] The server uses AWS Polly to generate a voice message warning the driver, saying, "Abnormal driving behavior detected. Please be careful." This voice message is then played through the vehicle's speakers.

[0683] This makes it possible to detect abnormal operating behavior in real time and respond quickly.

[0684] 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.

[0685] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and quickly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generative AI and an emotion engine. The following describes an embodiment of the invention.

[0686] Overall system overview

[0687] This system is built around the user's device, server, generation AI, and emotion engine, and learns the user's behavioral patterns and emotions to detect abnormal behavior. Specifically, it acquires the user's location and emotional information and sends it to the server. The generation AI analyzes and learns from this information to set criteria for detecting abnormal behavior, and the emotion engine analyzes the emotional information to detect abnormal emotional changes. If abnormal behavior or abnormal emotional changes are detected, a notification is sent to registered contacts.

[0688] Server Operation

[0689] 1. Collecting basic information, location information, and emotional information

[0690] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[0691] 2. Learning behavioral and emotional patterns

[0692] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[0693] 3. Detection of abnormal behavior and abnormal emotional changes

[0694] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[0695] 4. Sending notifications

[0696] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[0697] User terminal operation

[0698] 1. Collecting location and emotional information

[0699] The user's device periodically acquires GPS data, uses an emotion engine to collect the user's emotional information, and sends this information to the server at regular intervals, such as every 10 minutes.

[0700] 2. Real-time data transmission

[0701] It also has the ability to send current location and emotional information to a server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is sent to the server immediately.

[0702] 3. Audio warnings

[0703] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0704] Specific examples

[0705] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0706] User terminal operation

[0707] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[0708] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[0709] Server Operation

[0710] 1. The location information received by the server is analyzed by the generation AI, and it is recognized that the person has deviated from their usual route home.

[0711] 2. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[0712] 3. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[0713] User terminal operation

[0714] 1. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[0715] This allows parents to immediately recognize abnormal behavior and emotional changes and respond quickly, and also alerts the child themselves, further improving safety.

[0716] The processing flow will be explained below.

[0717] Step 1:

[0718] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting up the device to allow collection of GPS data and emotional data.

[0719] Step 2:

[0720] The device sends the basic information entered by the user to the server. If permission to collect GPS data and emotion data is granted, the collection settings are enabled.

[0721] Step 3:

[0722] The server records the basic information received from the device in a database, and also sets up tables in the database for collecting GPS data and emotion data.

[0723] Step 4:

[0724] The device periodically (for example, every 10 minutes) acquires the user's current location and emotion data and transmits this information to the server.

[0725] Step 5:

[0726] The server stores the received GPS data and emotion data in a database in chronological order.

[0727] Step 6:

[0728] The server periodically collects GPS data and emotional data and provides it to the generative AI and emotion engine, which then analyzes and learns the user's daily behavioral and emotional patterns. The generative AI and emotion engine then identify normal behavioral and emotional patterns.

[0729] Step 7:

[0730] The device continuously transmits its current location and emotional information to the server in real time. If the user intentionally turns off the device, that information is also sent to the server.

[0731] Step 8:

[0732] The server analyzes the real-time location and emotional information received, and the AI ​​compares it with the user's usual behavior and emotional patterns to detect abnormal behavior or emotional changes. Criteria for abnormal behavior include moving outside the designated area, turning off the power, and abnormal emotional changes.

[0733] Step 9:

[0734] If the server detects abnormal behavior or emotional changes, it extracts details (current location, emotional state, details of the abnormality, etc.) and sends a notification to registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[0735] Step 10:

[0736] If the device detects abnormal behavior or emotional changes, it will issue a voice warning to the user, such as "You are outside your normal range, be careful" or "Your emotional state is abnormal, please take a deep breath."

[0737] Example 2

[0738] 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."

[0739] Conventional systems detect abnormal behavior based solely on the user's location information, which limits their ability to accurately identify abnormal behavior patterns or dangerous situations. Furthermore, when sending notifications to emergency contacts, information about the user's emotional state is not included, making it difficult to quickly recognize true abnormal situations and take appropriate action.

[0740] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user location information and emotional information, means for transmitting the collected location information and emotional information to the server, means for analyzing the collected location information and emotional information and including a generation AI and emotional engine that learns the user's behavioral patterns and emotional patterns, means for detecting behaviors and abnormal emotional changes that deviate from the analyzed behavioral patterns and emotional patterns, means for notifying registered contacts when deviating behaviors and abnormal emotional changes are detected, and user terminal means for issuing audio alerts when abnormal behavior or abnormal emotional changes are detected. This makes it possible to detect abnormalities from both the user's behavioral patterns and emotional patterns and to quickly and accurately notify emergency contacts.

[0741] "User Location Information" means a user's current geographic location data obtained using GPS or other location measurement technology.

[0742] "Emotion information" is data that indicates the user's emotional state, obtained by analyzing the user's facial expression, voice, heart rate, and the like.

[0743] "Server" means a central processing unit that processes, stores, and analyzes data received from user terminals over a network.

[0744] "Generative AI" is a machine learning algorithm used to learn user behavior patterns and detect anomalous behavior.

[0745] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional information and detect changes in it.

[0746] The "behavior pattern" is a history of characteristic behaviors such as the user's daily travel routes and time periods.

[0747] "Abnormal behavior" is behavior that deviates from learned normal behavior patterns.

[0748] An "abnormal emotional change" is an emotional state that is abruptly changed from the user's normal emotional pattern.

[0749] "Registered contacts" are telephone numbers and email addresses registered in the system as contact points for emergency notifications.

[0750] "Notification" is an alert message sent to registered contacts when abnormal behavior or abnormal emotional changes are detected.

[0751] "Audio warning" is a function that allows the user device to issue an audio warning message when abnormal behavior or abnormal emotional changes are detected.

[0752] "Regular interval" refers to a set time interval for collecting and transmitting data periodically, such as every 10 minutes.

[0753] "Real-time" refers to data being processed and results provided immediately after it is generated, with almost no delay.

[0754] The present invention provides a system for detecting abnormal behavior based on location information and emotional information of a user and for quickly notifying registered contacts of the abnormal behavior. Hereinafter, a specific embodiment of the present invention will be described.

[0755] Overall system overview

[0756] This system is built around the user's device, a server, a generation AI, and an emotion engine. The user's device collects location information and emotion information and sends that data to the server. The server analyzes the data using the generation AI and emotion engine to detect abnormal behavior and abnormal emotional changes. If detected, a notification is sent to registered contacts.

[0757] Server Operation Details

[0758] The server works as follows:

[0759] 1. Data collection and storage

[0760] The server receives location information (e.g., GPS data) and emotional information sent from the user's device. This information is stored in a database. For example, MySQL or PostgreSQL can be used as a database management system to efficiently store and manage data.

[0761] 2. Learning behavioral and emotional patterns

[0762] The server is equipped with a generative AI and an emotion engine, which learns the user's daily behavioral and emotional patterns based on collected data. The generative AI analyzes the user's travel route and location, while the emotion engine analyzes the user's emotional state. For example, the generative AI model uses TensorFlow to perform real-time data analysis.

[0763] 3. Detecting Abnormal Behavior and Emotional Changes

[0764] The server monitors location and emotional information in real time, compares it with a trained model, and detects abnormal behavior or emotional changes. If an abnormality is detected, an emergency contact is immediately notified.

[0765] 4. Sending notifications

[0766] If abnormal behavior or emotional changes are detected, the server will send a notification to registered emergency contacts via SMS, email, or dedicated app notification, containing details of the user's current location, emotional state, and abnormal behavior.

[0767] User device operation details

[0768] The user terminal operates as follows:

[0769] 1. Data collection

[0770] The user's device periodically collects location information (using GPS) and emotional information. Emotional information is collected through the smartphone's camera, microphone, and specific apps. The emotion engine analyzes the user's emotional state using facial recognition software and voice analysis software.

[0771] 2. Data transmission

[0772] The collected location and emotion information is sent to a server at regular intervals (e.g., every 10 minutes).The system also has a real-time data transmission function, which immediately sends information to the server if the robot deviates from a specific area or if an abnormal change in emotion is detected.

[0773] 3. Audio warnings

[0774] If abnormal behavior or emotional changes are detected, the user device will issue a voice alert, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0775] Specific examples

[0776] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0777] When a user's child is on their way home from school, the device collects their current location and emotional information every 10 minutes and sends this information to the server. If the child takes a different route home than their usual route, the server recognizes this as an abnormality, and the emotion engine simultaneously detects that the child is feeling anxious or stressed. The server then recognizes this abnormal behavior and abnormal emotional changes and immediately sends an SMS notification to the registered emergency contact (e.g., parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state." Additionally, the user's device issues a voice warning, saying, "You are deviating from your usual route home. Please be careful," and "Your emotional state is unstable. Please take a deep breath."

[0778] Prompt Sentence Examples

[0779] Input prompt for the generative AI model:

[0780] "Please explain your system for detecting anomalous behavior based on user location and emotional information. Please also provide a detailed description of the hardware and software used, as well as the data processing method."

[0781] keyword

[0782] Generative AI model, prompt sentence

[0783] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0784] System processing steps

[0785] Step 1: Collect data

[0786] User terminal operation

[0787] Users have devices such as smartphones. These devices collect location information using GPS and emotional information using cameras and microphones. Emotional information is analyzed using facial recognition software and voice analysis software. This data is temporarily stored in the device's internal memory.

[0788] Input: User's GPS location, raw data collected by camera and microphone

[0789] Output: Location data and analyzed emotion data

[0790] Step 2: Sending data

[0791] User terminal operation

[0792] The user device sends the collected location information and emotion information to the server at regular intervals (e.g., every 10 minutes). This transmission uses the HTTPS protocol to ensure security. In an emergency, the information is sent immediately when a specific event occurs (e.g., area departure, abnormal emotion change).

[0793] Input: Collected location data and analyzed emotion data

[0794] Output: Location and emotion data sent to the server

[0795] Step 3: Receiving and storing data

[0796] Server Operation

[0797] The server receives the location and emotion information sent from the user's device and stores it in a database. The database uses "MySQL" or "PostgreSQL" to manage data by user ID and timestamp, and processes it efficiently.

[0798] Input: Location data and emotional data sent from the user device

[0799] Output: Data stored in a database

[0800] Step 4: Analyze and train the data

[0801] Server Operation

[0802] The generative AI and emotion engine installed on the server use the stored data to learn the user's daily behavioral and emotional patterns. The generative AI uses TensorFlow to analyze the user's travel route and where they stay, and the emotion engine analyzes their emotional state.

[0803] Input: Stored location and emotion data

[0804] Output: Learned and updated behavioral and emotional pattern models

[0805] Step 5: Real-time monitoring and anomaly detection

[0806] Server Operation

[0807] The server monitors new location and emotion information in real time, compares it with the trained model, and detects abnormal behavior or abnormal emotional changes. If an anomaly is detected, the details are recorded in a log.

[0808] Input: Location and emotion data received in real time

[0809] Output: A log of any abnormal behaviors or emotional changes detected

[0810] Step 6: Sending notifications

[0811] Server Operation

[0812] If any abnormal behavior or emotional changes are detected, the server will immediately send a notification to registered emergency contacts via SMS, email, or dedicated app notification, and the message will include details of the user's current location, emotional state, and any abnormal behavior.

[0813] Input: Log of detected anomalous behavior or emotional changes

[0814] Output: Notification message to emergency contacts (e.g. SMS, email)

[0815] Step 7: Audio Reminder

[0816] User terminal operation

[0817] If abnormal behavior or emotional changes are detected, the user device will issue an audio alert, playing messages such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0818] Input: Notification of abnormal behavior or emotional changes from the server

[0819] Output: Audio alert on user device

[0820] (Application example 2)

[0821] 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."

[0822] The present invention aims to quickly detect abnormal behavior or abnormal emotional changes using a user's location information and emotional information, thereby effectively monitoring the user's safety. Conventional systems were able to detect abnormal behavior based solely on location information, but were unable to take the user's emotional state into account, making accurate anomaly detection difficult. Therefore, the present invention aims to provide a more accurate anomaly detection system by simultaneously analyzing the user's emotional information.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0824] In this invention, the server includes means for analyzing the collected location information and emotion information and including a generation AI and emotion engine that learns the user's behavioral and emotional patterns, means for detecting behavior that deviates from the analyzed behavioral and emotional patterns or abnormal emotional changes, and means for notifying registered contacts when a deviating behavior or abnormal emotional change is detected. This makes it possible to detect anomalies that take into account not only the user's location information but also their emotional information.

[0825] "User location information" means data that indicates the geographic location where a user is currently located.

[0826] A "server" is a remote computer system that receives, processes, stores, and transmits data over a network.

[0827] "Collected Location Information" means geographic location data obtained from a User Device and transmitted to a Server.

[0828] "Emotional information" refers to data that indicates changes in a user's psychological state or emotions.

[0829] "Generative AI" is an artificial intelligence system that learns user behavior patterns and detects abnormal behavior.

[0830] An "emotion engine" is a software system that analyzes a user's emotional information and detects abnormal emotional changes.

[0831] "Learning" is the process of analyzing collected data and understanding specific patterns and trends.

[0832] "Abnormal behavior" refers to user behavior that significantly deviates from normal behavior patterns.

[0833] "Abnormal emotional changes" refers to fluctuations in a user's emotions that significantly deviate from normal emotional patterns.

[0834] "Notification" refers to the act of transmitting information to pre-registered contacts when abnormal behavior or abnormal emotional changes are detected.

[0835] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and promptly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generation AI and an emotion engine. This system is built around a user terminal, a server, a generation AI, and an emotion engine.

[0836] User terminal operation

[0837] 1. Collecting location and emotional information

[0838] The user device periodically acquires GPS data and uses an emotion engine to collect the user's emotional information. This information is then sent to the server at regular intervals, such as every 10 minutes.

[0839] 2. Real-time data transmission

[0840] The user device also has the function of transmitting current location information and emotional information to the server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is immediately sent to the server.

[0841] 3. Audio warnings

[0842] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[0843] Server Operation

[0844] 1. Collecting basic information, location information, and emotional information

[0845] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[0846] 2. Learning behavioral and emotional patterns

[0847] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[0848] 3. Detection of abnormal behavior and abnormal emotional changes

[0849] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[0850] 4. Sending notifications

[0851] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[0852] Hardware and software used

[0853] Hardware:

[0854] User device: Mobile device such as an Android smartphone

[0855] Server: A standard HTTP server (e.g. NGINX or Apache)

[0856] software:

[0857] Mobile application: Uses Android Location API

[0858] Server side: Using Python and the Hugging Face Transformers library

[0859] Sentiment Analysis: Transformers Library for Python (Hugging Face)

[0860] Specific examples

[0861] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[0862] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[0863] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[0864] 3. The location information received by the server is analyzed by the generated AI, and it is recognized that the person has deviated from their usual route home.

[0865] 4. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[0866] 5. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[0867] 6. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[0868] Prompt Sentence Examples

[0869] "Analyze user comments to see if they indicate negative sentiment."

[0870] "Infer the user's current mental state from the provided text data."

[0871] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0872] Step 1:

[0873] The user device periodically obtains location information using a GPS sensor. As input, a time interval (e.g., every 10 minutes) is set, and as output, the latest location information (latitude and longitude) is obtained.

[0874] Step 2:

[0875] The user device collects the user's emotional information using a built-in emotion engine. The input is the user's text input or the latest voice data, and the output is the user's emotional state (positive, negative, neutral, etc.).

[0876] Step 3:

[0877] The user terminal transmits the acquired location information and emotion information to the server. As input, a data packet is created that combines the location information and emotion information, and as output, the data packet is transmitted to the server.

[0878] Step 4:

[0879] The server stores the received location information and emotion information in a database.,As input, the server receives a data packet containing,location information and emotion information, and as output, stores the data in,a database.

[0880] Step 5:

[0881] The server analyzes location information using a generative AI and learns the user's behavioral patterns. It analyzes multiple location data as input and generates a model of normal behavioral patterns as output.

[0882] Step 6:

[0883] The server analyzes the emotional information using an emotion engine and learns the user's emotional patterns. It analyzes multiple pieces of emotional information data as input and generates a typical emotional pattern model as output.

[0884] Step 7:

[0885] The server compares the location and emotion information received in real time with a normal pattern model to detect abnormal behavior and abnormal emotional changes.The latest location and emotion information and the normal pattern model are used as input, and the detection results of abnormal behavior or abnormal emotional changes are obtained as output.

[0886] Step 8:

[0887] If the server detects abnormal behavior or emotional changes, it sends a notification to registered emergency contacts. The input is detailed information about the abnormality (current location, emotional state, description of the abnormal behavior, etc.), and the output is a notification via SMS, email, or a dedicated app.

[0888] Step 9:

[0889] If the user device detects abnormal behavior or abnormal emotional changes, it issues a voice warning. As input, it receives a warning notification from the server, and as output, it conveys a voice message to the user.

[0890] Through the above processing steps, this system is able to quickly detect abnormal behavior based on the user's location information and emotional information, and issue necessary notifications and warnings.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] [Third embodiment]

[0895] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0896] 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.

[0897] 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).

[0898] 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.

[0899] 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.

[0900] 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).

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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."

[0907] The present invention is a system for detecting abnormal behavior based on location information of a user and immediately notifying registered contacts of the abnormal behavior. An embodiment of the present invention will be described below.

[0908] Overall system overview

[0909] This system is built around the user's device, a server, and a generation AI, and learns the user's behavioral patterns to detect abnormal behavior. Specifically, the system acquires the user's location information and sends it to the server, where the generation AI analyzes and learns from it to set criteria for detecting abnormal behavior. If abnormal behavior is detected, a notification is sent to registered contacts.

[0910] Server Operation

[0911] 1. Collection of basic information and location information

[0912] The server receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device. This data is stored in a database.

[0913] 2. Learning behavioral patterns

[0914] The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. The algorithm analyzes the user's route, location, time of day, etc. to identify typical behavioral patterns.

[0915] 3. Detecting Abnormal Behavior

[0916] The generative AI monitors location information transmitted in real time, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., movement outside of normal range, intentional power-off, etc.) is detected.

[0917] 4. Sending notifications

[0918] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[0919] User terminal operation

[0920] 1. Collection of location information

[0921] The user's device periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 minutes.

[0922] 2. Real-time data transmission

[0923] It also has the function of sending current location information to the server in real time. When the user is moving or deviates from a specific area, that information is sent to the server immediately.

[0924] 3. Audio warnings

[0925] If abnormal behavior is detected, the user device will issue an audio warning, such as a message telling the user, "You are outside your normal area, please be careful."

[0926] Specific examples

[0927] Scenario: Your child takes an unusual route home

[0928] User terminal operation

[0929] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0930] 2. Your child takes a different route home than usual.

[0931] Server Operation

[0932] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0933] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0934] User terminal operation

[0935] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0936] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0937] The processing flow will be explained below.

[0938] Step 1:

[0939] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting it up to allow collection of GPS data.

[0940] Step 2:

[0941] The device sends the basic information entered by the user to the server. If permission to collect GPS data is granted, the collection setting is enabled.

[0942] Step 3:

[0943] The server records the basic information received from the device in a database, and also sets up a table for collecting GPS data in the database.

[0944] Step 4:

[0945] The device periodically (for example, every 10 minutes) acquires the user's current location and sends the GPS data to the server.

[0946] Step 5:

[0947] The server stores the received GPS data in a database in chronological order.

[0948] Step 6:

[0949] The server periodically collects GPS data and provides it to the AI ​​generator, which analyzes and learns the user's daily behavior patterns. The AI ​​generator identifies normal behavior patterns.

[0950] Step 7:

[0951] The device continues to send its current location information to the server in real time, even if the user intentionally turns it off.

[0952] Step 8:

[0953] The server analyzes the real-time location information received, and the AI ​​compares it with normal behavior patterns to detect abnormal behavior, such as moving outside the designated area or turning the power off.

[0954] Step 9:

[0955] If the server detects any abnormal behavior, it extracts details (such as the current location and the nature of the abnormality) and sends a notification to the registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[0956] Step 10:

[0957] If the device detects abnormal behavior, it will issue a voice warning to the user, for example, "You are outside the normal area, please be careful."

[0958] Through these steps, the system can detect abnormal user behavior in real time and respond quickly.

[0959] Example 1

[0960] 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."

[0961] There is a need for a method to quickly detect abnormal behavior based on the user's location information and immediately notify the user and their emergency contacts. In particular, a system that can respond effectively and quickly when a user deviates from their usual range of movement or intentionally turns off the power is required. There is also a need for a means to make users themselves aware of abnormal behavior and improve safety.

[0962] 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.

[0963] In this invention, the server includes means for collecting user location information, means for transmitting the collected location information to the server, means for analyzing the collected location information and having a machine learning algorithm for learning the user's behavioral patterns, means for detecting behavior that deviates from the analyzed behavioral patterns, means for sending a notification to registered contacts when deviating behavior is detected, and means for issuing a voice alert from the terminal when abnormal behavior is detected. This makes it possible to quickly detect abnormal user behavior, immediately notify emergency contacts, and also alert the user themselves.

[0964] "Collecting user location information" refers to the user's device acquiring GPS data and recording that location information periodically or in real time.

[0965] "Sending the collected location information to the server" refers to transferring the location information acquired on the user's device to the server using a communication protocol such as an HTTP request.

[0966] "Equipped with a machine learning algorithm that analyzes collected location information and learns user behavior patterns" refers to the operation of a machine learning model on the server that uses collected location information data to identify and learn daily behavior patterns.

[0967] "Detecting behavior that deviates from analyzed behavioral patterns" refers to comparing learned normal behavioral patterns with location information collected in real time and identifying behavior that deviates from them.

[0968] "Send a notification to registered contacts when deviant behavior is detected" means that when abnormal behavior is detected, the server will send a warning message to registered emergency contacts (e.g., parents or administrators) via email, SMS, app notification, etc.

[0969] "When abnormal behavior is detected, the device issues a voice message to warn the user" refers to playing a voice message to warn the user when the user's device detects abnormal behavior.

[0970] This invention is a system that detects abnormal behavior based on a user's location information and immediately notifies registered contacts. This system is built around the user's terminal, a server, and a generation AI. The following describes an embodiment of the system.

[0971] Server Operation

[0972] The server has several important functions. First, it receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device and stores them in a database. This database stores each user's past location information. The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. This generative AI analyzes information such as travel routes, locations, and time periods to identify normal behavioral patterns.

[0973] The real-time location information sent is monitored and compared with the normal behavioral patterns that the generation AI has learned. If abnormal behavior is detected, the server immediately sends a notification to the registered emergency contacts. The notification includes the current location information and details of the abnormal behavior. The notification is sent via SMS, email, or dedicated app notification.

[0974] User terminal operation

[0975] The user's device periodically acquires GPS data and sends that location information to the server. This transmission frequency is set at regular intervals, such as every 10 minutes. It also has a function that sends current location information to the server in real time when the user is moving or deviates from a specific area. If abnormal behavior is detected, the user's device will issue an audio warning. Specific messages include, "You are outside the normal area, please be careful."

[0976] Specific examples

[0977] Scenario: Your child takes an unusual route home

[0978] User terminal behavior:

[0979] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[0980] 2. Along the way, your child takes a different route home than usual.

[0981] Server behavior:

[0982] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[0983] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[0984] User terminal behavior:

[0985] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[0986] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[0987] Prompt Sentence Examples

[0988] "Design a program to notify users when they deviate from their usual range of activity."

[0989] "Write a program that implements an algorithm that analyzes a user's GPS data and detects anomalous behavior."

[0990] The present invention can be specifically implemented using the above-described configuration and procedures.

[0991] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0992] Step 1:

[0993] The user terminal acquires location information.

[0994] Specifically, the device's GPS sensor measures the current location and obtains data in the format "longitude: 35.6895, latitude: 139.6917". This data is obtained every 10 minutes.

[0995] Input: "GPS sensor"

[0996] Output: "Current location information (longitude and latitude)"

[0997] Step 2:

[0998] The user terminal transmits the collected location information to the server.

[0999] The device periodically sends the data "longitude: 35.6895, latitude: 139.6917" to the server using an HTTP POST request.

[1000] Input: "Current location information (longitude and latitude)"

[1001] Output: "Send location data to server"

[1002] Specific operation: The user device sends data to the server in the format "POST / location HTTP / 1.1 Host: server.com Content-Type: application / json {"longitude": 35.6895, "latitude": 139.6917}".

[1003] Step 3:

[1004] The server receives the location information and stores it in a database.

[1005] The server receives the location information sent from the terminal and stores it in a database.

[1006] Input: "Location information sent from the device"

[1007] Output: "Saved to database"

[1008] Specific operation: The location information received by the server is saved in the database in the format "INSERT INTO location_table (longitude, latitude, timestamp) VALUES (35.6895, 139.6917, CURRENT_TIMESTAMP)".

[1009] Step 4:

[1010] The server's generated AI learns behavioral patterns.

[1011] The server analyzes the location information stored in the database and learns daily behavior patterns.

[1012] Input: "Past location information in database"

[1013] Output: "Learned behavioral patterns"

[1014] Specific operation: The generation AI uses location data from the past three months to learn travel routes and time periods for each day of the week, and extracts a "pattern for traveling from station A to station B at 7 a.m. on a weekday."

[1015] Step 5:

[1016] The server-generated AI detects abnormal behavior in real time.

[1017] The generative AI monitors the location information transmitted in real time, compares it with learned behavioral patterns, and flags any abnormal behavior it detects.

[1018] Input: "Location information sent in real time" "Learned behavioral patterns"

[1019] Output: "Flag of abnormal behavior"

[1020] Specific behavior: If a user is at an unusual station C at 8:00 AM, the generation AI will flag this as "abnormal behavior."

[1021] Step 6:

[1022] Sends notifications when the server detects abnormal behavior.

[1023] When abnormal behavior is detected, the server sends a warning message to registered emergency contacts via email, SMS, or dedicated app notification.

[1024] Input: "Flag for abnormal behavior"

[1025] Output: "Notification sent to emergency contacts"

[1026] Specific behavior: The server sends an SMS to the parent with the message "Your child is taking an unusual route home."

[1027] Step 7:

[1028] The user terminal issues an audio warning.

[1029] If abnormal behavior is detected, the user's device will issue an audio warning.

[1030] Input: "Flag for abnormal behavior"

[1031] Output: "Audio warning message"

[1032] Action: A recorded message will be played saying, "You are off your normal route home, be careful."

[1033] (Application example 1)

[1034] 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."

[1035] In autonomous vehicles, the lack of driver intervention makes it difficult to immediately detect and respond to abnormal driving behavior and associated emergency situations. Furthermore, it is necessary to quickly notify abnormalities using multiple communication methods. Under these circumstances, there is a demand for a system that can detect abnormal driving behavior in real time and take appropriate action.

[1036] 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.

[1037] In this invention, the server includes a means for collecting user location information, a means for transmitting the collected location information to the server, and a means for analyzing the collected location information and having a generation AI that learns the user's behavioral patterns, thereby enabling a means for detecting behavior that deviates from the analyzed behavioral patterns, a means for notifying registered contacts when a deviating behavior is detected, a means for detecting abnormal vehicle driving behavior, and a means for issuing an audio warning to vehicle passengers based on the detected abnormal driving behavior.

[1038] "Means for collecting user location information" refers to devices or systems that obtain the user's current location using GPS or other location information technology.

[1039] The "means for transmitting collected location information to a server" refers to a communication means for transferring location information from a user terminal to a server in real time or at regular intervals.

[1040] "Means equipped with a generative AI that analyzes collected location information and learns user behavior patterns" refers to a system equipped with artificial intelligence that can learn daily behavior patterns using location data collected from users and detect abnormalities.

[1041] The "means for detecting behavior that deviates from the analyzed behavior pattern" is a technology that identifies abnormal behavior by comparing it with learned normal behavior patterns.

[1042] "Means for notifying registered contacts when deviant behavior is detected" is a function that sends a notification to pre-registered emergency contacts when abnormal behavior is detected.

[1043] "Means for detecting abnormal vehicle driving behavior" refers to a system that monitors vehicle location and behavior data and identifies behavior that deviates from normal driving patterns.

[1044] "Means for issuing an audio warning to vehicle passengers based on detected abnormal driving behavior" refers to a function that uses speakers in the vehicle to audibly warn passengers when abnormal driving behavior is detected.

[1045] Overall system overview

[1046] This invention is a system that can detect abnormal driving behavior in autonomous vehicles and respond immediately. The system consists of vehicle sensors, a terminal that transmits location information, a server that processes the data, and a generative AI model. It collects user location information and driving data, detects abnormal behavior based on that, and issues warnings as necessary.

[1047] Server Operation

[1048] 1. Collection of basic information and location information

[1049] The server receives basic information (vehicle ID, driver ID, emergency contact information, etc.) and location information (GPS data) sent from the vehicle's sensors. This data is stored in a database.

[1050] 2. Learning behavioral patterns

[1051] The server is equipped with a generation AI that learns the vehicle's daily driving patterns based on the collected location information. The generation AI analyzes the vehicle's driving route, stopping locations, driving time zones, etc. to identify normal driving patterns.

[1052] 3. Detecting Abnormal Behavior

[1053] The generative AI monitors real-time location and driving data, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., sudden braking or erratic steering) is detected.

[1054] 4. Sending notifications

[1055] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[1056] 5. Audio warnings

[1057] If abnormal behavior is detected, an audio warning will be issued using the vehicle's speakers, with a message such as "Abnormal driving behavior detected, please be careful" being conveyed to passengers.

[1058] User terminal operation

[1059] 1. Collection of location information

[1060] The user's device (a communication device in the vehicle) periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 seconds.

[1061] 2. Real-time data transmission

[1062] It also has the function of transmitting current location information and driving data to a server in real time. When the vehicle is moving or deviates from a specific area, the information is sent to the server immediately.

[1063] Hardware and software used

[1064] GPS module: Uses the GPS module installed in the vehicle to obtain location information.

[1065] Base server: Uses cloud services such as Amazon Web Services (AWS).

[1066] Generative AI models: Use deep learning platforms such as TensorFlow to learn behavioral patterns and detect anomalous behavior.

[1067] Notification system: Uses communication methods such as Twilio (SMS and email notifications) and Firebase (app notifications).

[1068] Voice output: When abnormal behavior is detected, a warning voice is generated using a speech synthesis service such as AWS Polly and broadcast through the vehicle's speakers.

[1069] Specific examples

[1070] Scenario: An autonomous vehicle makes a sharp turn

[1071] User terminal behavior:

[1072] 1. A communication device in the vehicle acquires GPS data and driving data in real time and transmits it to a server.

[1073] 2. If the vehicle makes a sudden turn, the data is sent to the server immediately.

[1074] Server behavior:

[1075] 1. The server analyzes the location information and sudden change of direction data received, and the generating AI detects abnormal driving behavior.

[1076] 2. The server recognizes the abnormal behavior and sends an SMS notification to the registered emergency contacts stating, "The vehicle has made a sudden turn. Please check."

[1077] 3. At the same time, the server uses AWS Polly to generate an audio warning that plays over the vehicle's speaker: "Abnormal driving behavior detected, please be careful."

[1078] Examples of prompt statements

[1079] An example of a prompt to be input to the generative AI model when the vehicle detects abnormal driving behavior is as follows:

[1080] "A sudden turn has been detected. We will analyze the abnormal behavior based on detailed location and driving data and send you a notification."

[1081] This will significantly improve the safety of self-driving vehicles and enable rapid response in the event of an abnormality.

[1082] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1083] Step 1:

[1084] Input: Basic information of the user (driver and vehicle) and location information (GPS data)

[1085] Processing: The device collects location and basic information from sensors and GPS modules in the vehicle.

[1086] Output: Acquired location information and basic information data

[1087] The terminal obtains the vehicle's current location from the GPS module and collects basic information such as the vehicle ID and driver ID, and prepares to send this information to the server.

[1088] Step 2:

[1089] Input: Location and basic information data collected on your device

[1090] Processing: The device sends this information to the server at regular intervals (e.g., every 10 seconds).

[1091] Output: Location and basic information data sent to the server

[1092] The device periodically sends the collected location information and basic information to a server, and transfers the data to a cloud server via the Internet.

[1093] Step 3:

[1094] Input: Location and basic information data sent to the server

[1095] Processing: The server stores the received location information and basic information in a database.

[1096] Output: Location and basic information stored in a database

[1097] The server stores the received location information and basic information in a cloud service database (e.g., AWS RDS), making this data easily accessible and analyzable.

[1098] Step 4:

[1099] Input: Location data stored in a database

[1100] Processing: The server uses a generative AI model to learn the vehicle's daily driving patterns from the location information.

[1101] Output: Learned normal driving pattern model

[1102] The server uses deep learning platforms such as TensorFlow to learn typical driving patterns based on historical location data, including driving routes, stopping locations, and driving times.

[1103] Step 5:

[1104] Input: Real-time location and driving data

[1105] Processing: The server compares the data received in real time with learned driving patterns to detect abnormal behavior.

[1106] Output: Detected abnormal behavior information

[1107] The server compares real-time data with previously learned patterns to determine if there are any irregular driving behaviors (e.g., hard braking, sudden turns). If an anomaly is detected, the information is logged.

[1108] Step 6:

[1109] Input: Detected abnormal behavior information

[1110] Action: If abnormal activity is detected, the server will send a notification to the registered emergency contacts, including the current location and details of the abnormal activity.

[1111] Output: Notification to emergency contacts (SMS, email, app notification)

[1112] The server uses the Twilio API or Firebase to send a notification of abnormal behavior to emergency contacts, such as a message saying, "Vehicle has made a sudden turn. Please check."

[1113] Step 7:

[1114] Input: Detected abnormal behavior information

[1115] Processing: If abnormal behavior is detected, the server generates an audio alert and plays a warning message through the vehicle's speaker.

[1116] Output: Audio warning played in the vehicle

[1117] The server uses AWS Polly to generate a voice message warning the driver, saying, "Abnormal driving behavior detected. Please be careful." This voice message is then played through the vehicle's speakers.

[1118] This makes it possible to detect abnormal operating behavior in real time and respond quickly.

[1119] 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.

[1120] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and quickly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generative AI and an emotion engine. The following describes an embodiment of the invention.

[1121] Overall system overview

[1122] This system is built around the user's device, server, generation AI, and emotion engine, and learns the user's behavioral patterns and emotions to detect abnormal behavior. Specifically, it acquires the user's location and emotional information and sends it to the server. The generation AI analyzes and learns from this information to set criteria for detecting abnormal behavior, and the emotion engine analyzes the emotional information to detect abnormal emotional changes. If abnormal behavior or abnormal emotional changes are detected, a notification is sent to registered contacts.

[1123] Server Operation

[1124] 1. Collecting basic information, location information, and emotional information

[1125] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[1126] 2. Learning behavioral and emotional patterns

[1127] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[1128] 3. Detection of abnormal behavior and abnormal emotional changes

[1129] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[1130] 4. Sending notifications

[1131] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[1132] User terminal operation

[1133] 1. Collecting location and emotional information

[1134] The user's device periodically acquires GPS data, uses an emotion engine to collect the user's emotional information, and sends this information to the server at regular intervals, such as every 10 minutes.

[1135] 2. Real-time data transmission

[1136] It also has the ability to send current location and emotional information to a server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is sent to the server immediately.

[1137] 3. Audio warnings

[1138] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1139] Specific examples

[1140] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1141] User terminal operation

[1142] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[1143] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[1144] Server Operation

[1145] 1. The location information received by the server is analyzed by the generation AI, and it is recognized that the person has deviated from their usual route home.

[1146] 2. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[1147] 3. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[1148] User terminal operation

[1149] 1. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[1150] This allows parents to immediately recognize abnormal behavior and emotional changes and respond quickly, and also alerts the child themselves, further improving safety.

[1151] The processing flow will be explained below.

[1152] Step 1:

[1153] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting up the device to allow collection of GPS data and emotional data.

[1154] Step 2:

[1155] The device sends the basic information entered by the user to the server. If permission to collect GPS data and emotion data is granted, the collection settings are enabled.

[1156] Step 3:

[1157] The server records the basic information received from the device in a database, and also sets up tables in the database for collecting GPS data and emotion data.

[1158] Step 4:

[1159] The device periodically (for example, every 10 minutes) acquires the user's current location and emotion data and transmits this information to the server.

[1160] Step 5:

[1161] The server stores the received GPS data and emotion data in a database in chronological order.

[1162] Step 6:

[1163] The server periodically collects GPS data and emotional data and provides it to the generative AI and emotion engine, which then analyzes and learns the user's daily behavioral and emotional patterns. The generative AI and emotion engine then identify normal behavioral and emotional patterns.

[1164] Step 7:

[1165] The device continuously transmits its current location and emotional information to the server in real time. If the user intentionally turns off the device, that information is also sent to the server.

[1166] Step 8:

[1167] The server analyzes the real-time location and emotional information received, and the AI ​​compares it with the user's usual behavior and emotional patterns to detect abnormal behavior or emotional changes. Criteria for abnormal behavior include moving outside the designated area, turning off the power, and abnormal emotional changes.

[1168] Step 9:

[1169] If the server detects abnormal behavior or emotional changes, it extracts details (current location, emotional state, details of the abnormality, etc.) and sends a notification to registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[1170] Step 10:

[1171] If the device detects abnormal behavior or emotional changes, it will issue a voice warning to the user, such as "You are outside your normal range, be careful" or "Your emotional state is abnormal, please take a deep breath."

[1172] Example 2

[1173] 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."

[1174] Conventional systems detect abnormal behavior based solely on the user's location information, which limits their ability to accurately identify abnormal behavior patterns or dangerous situations. Furthermore, when sending notifications to emergency contacts, information about the user's emotional state is not included, making it difficult to quickly recognize true abnormal situations and take appropriate action.

[1175] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user location information and emotional information, means for transmitting the collected location information and emotional information to the server, means for analyzing the collected location information and emotional information and including a generation AI and emotional engine that learns the user's behavioral patterns and emotional patterns, means for detecting behaviors and abnormal emotional changes that deviate from the analyzed behavioral patterns and emotional patterns, means for notifying registered contacts when deviating behaviors and abnormal emotional changes are detected, and user terminal means for issuing audio alerts when abnormal behavior or abnormal emotional changes are detected. This makes it possible to detect abnormalities from both the user's behavioral patterns and emotional patterns and to quickly and accurately notify emergency contacts.

[1176] "User Location Information" means a user's current geographic location data obtained using GPS or other location measurement technology.

[1177] "Emotion information" is data that indicates the user's emotional state, obtained by analyzing the user's facial expression, voice, heart rate, and the like.

[1178] "Server" means a central processing unit that processes, stores, and analyzes data received from user terminals over a network.

[1179] "Generative AI" is a machine learning algorithm used to learn user behavior patterns and detect anomalous behavior.

[1180] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional information and detect changes in it.

[1181] The "behavior pattern" is a history of characteristic behaviors such as the user's daily travel routes and time periods.

[1182] "Abnormal behavior" is behavior that deviates from learned normal behavior patterns.

[1183] An "abnormal emotional change" is an emotional state that is abruptly changed from the user's normal emotional pattern.

[1184] "Registered contacts" are telephone numbers and email addresses registered in the system as contact points for emergency notifications.

[1185] "Notification" is an alert message sent to registered contacts when abnormal behavior or abnormal emotional changes are detected.

[1186] "Audio warning" is a function that allows the user device to issue an audio warning message when abnormal behavior or abnormal emotional changes are detected.

[1187] "Regular interval" refers to a set time interval for collecting and transmitting data periodically, such as every 10 minutes.

[1188] "Real-time" refers to data being processed and results provided immediately after it is generated, with almost no delay.

[1189] The present invention provides a system for detecting abnormal behavior based on location information and emotional information of a user and for quickly notifying registered contacts of the abnormal behavior. Hereinafter, a specific embodiment of the present invention will be described.

[1190] Overall system overview

[1191] This system is built around the user's device, a server, a generation AI, and an emotion engine. The user's device collects location information and emotion information and sends that data to the server. The server analyzes the data using the generation AI and emotion engine to detect abnormal behavior and abnormal emotional changes. If detected, a notification is sent to registered contacts.

[1192] Server Operation Details

[1193] The server works as follows:

[1194] 1. Data collection and storage

[1195] The server receives location information (e.g., GPS data) and emotional information sent from the user's device. This information is stored in a database. For example, MySQL or PostgreSQL can be used as a database management system to efficiently store and manage data.

[1196] 2. Learning behavioral and emotional patterns

[1197] The server is equipped with a generative AI and an emotion engine, which learns the user's daily behavioral and emotional patterns based on collected data. The generative AI analyzes the user's travel route and location, while the emotion engine analyzes the user's emotional state. For example, the generative AI model uses TensorFlow to perform real-time data analysis.

[1198] 3. Detecting Abnormal Behavior and Emotional Changes

[1199] The server monitors location and emotional information in real time, compares it with a trained model, and detects abnormal behavior or emotional changes. If an abnormality is detected, an emergency contact is immediately notified.

[1200] 4. Sending notifications

[1201] If abnormal behavior or emotional changes are detected, the server will send a notification to registered emergency contacts via SMS, email, or dedicated app notification, containing details of the user's current location, emotional state, and abnormal behavior.

[1202] User device operation details

[1203] The user terminal operates as follows:

[1204] 1. Data collection

[1205] The user's device periodically collects location information (using GPS) and emotional information. Emotional information is collected through the smartphone's camera, microphone, and specific apps. The emotion engine analyzes the user's emotional state using facial recognition software and voice analysis software.

[1206] 2. Data transmission

[1207] The collected location and emotion information is sent to a server at regular intervals (e.g., every 10 minutes).The system also has a real-time data transmission function, which immediately sends information to the server if the robot deviates from a specific area or if an abnormal change in emotion is detected.

[1208] 3. Audio warnings

[1209] If abnormal behavior or emotional changes are detected, the user device will issue a voice alert, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1210] Specific examples

[1211] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1212] When a user's child is on their way home from school, the device collects their current location and emotional information every 10 minutes and sends this information to the server. If the child takes a different route home than their usual route, the server recognizes this as an abnormality, and the emotion engine simultaneously detects that the child is feeling anxious or stressed. The server then recognizes this abnormal behavior and abnormal emotional changes and immediately sends an SMS notification to the registered emergency contact (e.g., parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state." Additionally, the user's device issues a voice warning, saying, "You are deviating from your usual route home. Please be careful," and "Your emotional state is unstable. Please take a deep breath."

[1213] Prompt Sentence Examples

[1214] Input prompt for the generative AI model:

[1215] "Please explain your system for detecting anomalous behavior based on user location and emotional information. Please also provide a detailed description of the hardware and software used, as well as the data processing method."

[1216] keyword

[1217] Generative AI model, prompt sentence

[1218] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1219] System processing steps

[1220] Step 1: Collect data

[1221] User terminal operation

[1222] Users have devices such as smartphones. These devices collect location information using GPS and emotional information using cameras and microphones. Emotional information is analyzed using facial recognition software and voice analysis software. This data is temporarily stored in the device's internal memory.

[1223] Input: User's GPS location, raw data collected by camera and microphone

[1224] Output: Location data and analyzed emotion data

[1225] Step 2: Sending data

[1226] User terminal operation

[1227] The user device sends the collected location information and emotion information to the server at regular intervals (e.g., every 10 minutes). This transmission uses the HTTPS protocol to ensure security. In an emergency, the information is sent immediately when a specific event occurs (e.g., area departure, abnormal emotion change).

[1228] Input: Collected location data and analyzed emotion data

[1229] Output: Location and emotion data sent to the server

[1230] Step 3: Receiving and storing data

[1231] Server Operation

[1232] The server receives the location and emotion information sent from the user's device and stores it in a database. The database uses "MySQL" or "PostgreSQL" to manage data by user ID and timestamp, and processes it efficiently.

[1233] Input: Location data and emotional data sent from the user device

[1234] Output: Data stored in a database

[1235] Step 4: Analyze and train the data

[1236] Server Operation

[1237] The generative AI and emotion engine installed on the server use the stored data to learn the user's daily behavioral and emotional patterns. The generative AI uses TensorFlow to analyze the user's travel route and where they stay, and the emotion engine analyzes their emotional state.

[1238] Input: Stored location and emotion data

[1239] Output: Learned and updated behavioral and emotional pattern models

[1240] Step 5: Real-time monitoring and anomaly detection

[1241] Server Operation

[1242] The server monitors new location and emotion information in real time, compares it with the trained model, and detects abnormal behavior or abnormal emotional changes. If an anomaly is detected, the details are recorded in a log.

[1243] Input: Location and emotion data received in real time

[1244] Output: A log of any abnormal behaviors or emotional changes detected

[1245] Step 6: Sending notifications

[1246] Server Operation

[1247] If any abnormal behavior or emotional changes are detected, the server will immediately send a notification to registered emergency contacts via SMS, email, or dedicated app notification, and the message will include details of the user's current location, emotional state, and any abnormal behavior.

[1248] Input: Log of detected anomalous behavior or emotional changes

[1249] Output: Notification message to emergency contacts (e.g. SMS, email)

[1250] Step 7: Audio Reminder

[1251] User terminal operation

[1252] If abnormal behavior or emotional changes are detected, the user device will issue an audio alert, playing messages such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1253] Input: Notification of abnormal behavior or emotional changes from the server

[1254] Output: Audio alert on user device

[1255] (Application example 2)

[1256] 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."

[1257] The present invention aims to quickly detect abnormal behavior or abnormal emotional changes using a user's location information and emotional information, thereby effectively monitoring the user's safety. Conventional systems were able to detect abnormal behavior based solely on location information, but were unable to take the user's emotional state into account, making accurate anomaly detection difficult. Therefore, the present invention aims to provide a more accurate anomaly detection system by simultaneously analyzing the user's emotional information.

[1258] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1259] In this invention, the server includes means for analyzing the collected location information and emotion information and including a generation AI and emotion engine that learns the user's behavioral and emotional patterns, means for detecting behavior that deviates from the analyzed behavioral and emotional patterns or abnormal emotional changes, and means for notifying registered contacts when a deviating behavior or abnormal emotional change is detected. This makes it possible to detect anomalies that take into account not only the user's location information but also their emotional information.

[1260] "User location information" means data that indicates the geographic location where a user is currently located.

[1261] A "server" is a remote computer system that receives, processes, stores, and transmits data over a network.

[1262] "Collected Location Information" means geographic location data obtained from a User Device and transmitted to a Server.

[1263] "Emotional information" refers to data that indicates changes in a user's psychological state or emotions.

[1264] "Generative AI" is an artificial intelligence system that learns user behavior patterns and detects abnormal behavior.

[1265] An "emotion engine" is a software system that analyzes a user's emotional information and detects abnormal emotional changes.

[1266] "Learning" is the process of analyzing collected data and understanding specific patterns and trends.

[1267] "Abnormal behavior" refers to user behavior that significantly deviates from normal behavior patterns.

[1268] "Abnormal emotional changes" refers to fluctuations in a user's emotions that significantly deviate from normal emotional patterns.

[1269] "Notification" refers to the act of transmitting information to pre-registered contacts when abnormal behavior or abnormal emotional changes are detected.

[1270] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and promptly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generation AI and an emotion engine. This system is built around a user terminal, a server, a generation AI, and an emotion engine.

[1271] User terminal operation

[1272] 1. Collecting location and emotional information

[1273] The user device periodically acquires GPS data and uses an emotion engine to collect the user's emotional information. This information is then sent to the server at regular intervals, such as every 10 minutes.

[1274] 2. Real-time data transmission

[1275] The user device also has the function of transmitting current location information and emotional information to the server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is immediately sent to the server.

[1276] 3. Audio warnings

[1277] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1278] Server Operation

[1279] 1. Collecting basic information, location information, and emotional information

[1280] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[1281] 2. Learning behavioral and emotional patterns

[1282] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[1283] 3. Detection of abnormal behavior and abnormal emotional changes

[1284] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[1285] 4. Sending notifications

[1286] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[1287] Hardware and software used

[1288] Hardware:

[1289] User device: Mobile device such as an Android smartphone

[1290] Server: A standard HTTP server (e.g. NGINX or Apache)

[1291] software:

[1292] Mobile application: Uses Android Location API

[1293] Server side: Using Python and the Hugging Face Transformers library

[1294] Sentiment Analysis: Transformers Library for Python (Hugging Face)

[1295] Specific examples

[1296] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1297] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[1298] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[1299] 3. The location information received by the server is analyzed by the generated AI, and it is recognized that the person has deviated from their usual route home.

[1300] 4. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[1301] 5. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[1302] 6. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[1303] Prompt Sentence Examples

[1304] "Analyze user comments to see if they indicate negative sentiment."

[1305] "Infer the user's current mental state from the provided text data."

[1306] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1307] Step 1:

[1308] The user device periodically obtains location information using a GPS sensor. As input, a time interval (e.g., every 10 minutes) is set, and as output, the latest location information (latitude and longitude) is obtained.

[1309] Step 2:

[1310] The user device collects the user's emotional information using a built-in emotion engine. The input is the user's text input or the latest voice data, and the output is the user's emotional state (positive, negative, neutral, etc.).

[1311] Step 3:

[1312] The user terminal transmits the acquired location information and emotion information to the server. As input, a data packet is created that combines the location information and emotion information, and as output, the data packet is transmitted to the server.

[1313] Step 4:

[1314] The server stores the received location information and emotion information in a database.,As input, the server receives a data packet containing,location information and emotion information, and as output, stores the data in,a database.

[1315] Step 5:

[1316] The server analyzes location information using a generative AI and learns the user's behavioral patterns. It analyzes multiple location data as input and generates a model of normal behavioral patterns as output.

[1317] Step 6:

[1318] The server analyzes the emotional information using an emotion engine and learns the user's emotional patterns. It analyzes multiple pieces of emotional information data as input and generates a typical emotional pattern model as output.

[1319] Step 7:

[1320] The server compares the location and emotion information received in real time with a normal pattern model to detect abnormal behavior and abnormal emotional changes.The latest location and emotion information and the normal pattern model are used as input, and the detection results of abnormal behavior or abnormal emotional changes are obtained as output.

[1321] Step 8:

[1322] If the server detects abnormal behavior or emotional changes, it sends a notification to registered emergency contacts. The input is detailed information about the abnormality (current location, emotional state, description of the abnormal behavior, etc.), and the output is a notification via SMS, email, or a dedicated app.

[1323] Step 9:

[1324] If the user device detects abnormal behavior or abnormal emotional changes, it issues a voice warning. As input, it receives a warning notification from the server, and as output, it conveys a voice message to the user.

[1325] Through the above processing steps, this system is able to quickly detect abnormal behavior based on the user's location information and emotional information, and issue necessary notifications and warnings.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] [Fourth embodiment]

[1330] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1331] 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.

[1332] 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).

[1333] 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.

[1334] 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.

[1335] 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).

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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."

[1343] The present invention is a system for detecting abnormal behavior based on location information of a user and immediately notifying registered contacts of the abnormal behavior. An embodiment of the present invention will be described below.

[1344] Overall system overview

[1345] This system is built around the user's device, a server, and a generation AI, and learns the user's behavioral patterns to detect abnormal behavior. Specifically, the system acquires the user's location information and sends it to the server, where the generation AI analyzes and learns from it to set criteria for detecting abnormal behavior. If abnormal behavior is detected, a notification is sent to registered contacts.

[1346] Server Operation

[1347] 1. Collection of basic information and location information

[1348] The server receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device. This data is stored in a database.

[1349] 2. Learning behavioral patterns

[1350] The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. The algorithm analyzes the user's route, location, time of day, etc. to identify typical behavioral patterns.

[1351] 3. Detecting Abnormal Behavior

[1352] The generative AI monitors location information transmitted in real time, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., movement outside of normal range, intentional power-off, etc.) is detected.

[1353] 4. Sending notifications

[1354] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[1355] User terminal operation

[1356] 1. Collection of location information

[1357] The user's device periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 minutes.

[1358] 2. Real-time data transmission

[1359] It also has the function of sending current location information to the server in real time. When the user is moving or deviates from a specific area, that information is sent to the server immediately.

[1360] 3. Audio warnings

[1361] If abnormal behavior is detected, the user device will issue an audio warning, such as a message telling the user, "You are outside your normal area, please be careful."

[1362] Specific examples

[1363] Scenario: Your child takes an unusual route home

[1364] User terminal operation

[1365] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[1366] 2. Your child takes a different route home than usual.

[1367] Server Operation

[1368] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[1369] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[1370] User terminal operation

[1371] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[1372] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[1373] The processing flow will be explained below.

[1374] Step 1:

[1375] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting it up to allow collection of GPS data.

[1376] Step 2:

[1377] The device sends the basic information entered by the user to the server. If permission to collect GPS data is granted, the collection setting is enabled.

[1378] Step 3:

[1379] The server records the basic information received from the device in a database, and also sets up a table for collecting GPS data in the database.

[1380] Step 4:

[1381] The device periodically (for example, every 10 minutes) acquires the user's current location and sends the GPS data to the server.

[1382] Step 5:

[1383] The server stores the received GPS data in a database in chronological order.

[1384] Step 6:

[1385] The server periodically collects GPS data and provides it to the AI ​​generator, which analyzes and learns the user's daily behavior patterns. The AI ​​generator identifies normal behavior patterns.

[1386] Step 7:

[1387] The device continues to send its current location information to the server in real time, even if the user intentionally turns it off.

[1388] Step 8:

[1389] The server analyzes the real-time location information received, and the AI ​​compares it with normal behavior patterns to detect abnormal behavior, such as moving outside the designated area or turning the power off.

[1390] Step 9:

[1391] If the server detects any abnormal behavior, it extracts details (such as the current location and the nature of the abnormality) and sends a notification to the registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[1392] Step 10:

[1393] If the device detects abnormal behavior, it will issue a voice warning to the user, for example, "You are outside the normal area, please be careful."

[1394] Through these steps, the system can detect abnormal user behavior in real time and respond quickly.

[1395] Example 1

[1396] 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."

[1397] There is a need for a method to quickly detect abnormal behavior based on the user's location information and immediately notify the user and their emergency contacts. In particular, a system that can respond effectively and quickly when a user deviates from their usual range of movement or intentionally turns off the power is required. There is also a need for a means to make users themselves aware of abnormal behavior and improve safety.

[1398] 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.

[1399] In this invention, the server includes means for collecting user location information, means for transmitting the collected location information to the server, means for analyzing the collected location information and having a machine learning algorithm for learning the user's behavioral patterns, means for detecting behavior that deviates from the analyzed behavioral patterns, means for sending a notification to registered contacts when deviating behavior is detected, and means for issuing a voice alert from the terminal when abnormal behavior is detected. This makes it possible to quickly detect abnormal user behavior, immediately notify emergency contacts, and also alert the user themselves.

[1400] "Collecting user location information" refers to the user's device acquiring GPS data and recording that location information periodically or in real time.

[1401] "Sending the collected location information to the server" refers to transferring the location information acquired on the user's device to the server using a communication protocol such as an HTTP request.

[1402] "Equipped with a machine learning algorithm that analyzes collected location information and learns user behavior patterns" refers to the operation of a machine learning model on the server that uses collected location information data to identify and learn daily behavior patterns.

[1403] "Detecting behavior that deviates from analyzed behavioral patterns" refers to comparing learned normal behavioral patterns with location information collected in real time and identifying behavior that deviates from them.

[1404] "Send a notification to registered contacts when deviant behavior is detected" means that when abnormal behavior is detected, the server will send a warning message to registered emergency contacts (e.g., parents or administrators) via email, SMS, app notification, etc.

[1405] "When abnormal behavior is detected, the device issues a voice message to warn the user" refers to playing a voice message to warn the user when the user's device detects abnormal behavior.

[1406] This invention is a system that detects abnormal behavior based on a user's location information and immediately notifies registered contacts. This system is built around the user's terminal, a server, and a generation AI. The following describes an embodiment of the system.

[1407] Server Operation

[1408] The server has several important functions. First, it receives basic information (such as name, age, and emergency contact information) and location information (GPS data) sent from the user's device and stores them in a database. This database stores each user's past location information. The server is equipped with a generative AI that learns the user's daily behavioral patterns based on the collected location information. This generative AI analyzes information such as travel routes, locations, and time periods to identify normal behavioral patterns.

[1409] The real-time location information sent is monitored and compared with the normal behavioral patterns that the generation AI has learned. If abnormal behavior is detected, the server immediately sends a notification to the registered emergency contacts. The notification includes the current location information and details of the abnormal behavior. The notification is sent via SMS, email, or dedicated app notification.

[1410] User terminal operation

[1411] The user's device periodically acquires GPS data and sends that location information to the server. This transmission frequency is set at regular intervals, such as every 10 minutes. It also has a function that sends current location information to the server in real time when the user is moving or deviates from a specific area. If abnormal behavior is detected, the user's device will issue an audio warning. Specific messages include, "You are outside the normal area, please be careful."

[1412] Specific examples

[1413] Scenario: Your child takes an unusual route home

[1414] User terminal behavior:

[1415] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes and sends it to the server.

[1416] 2. Along the way, your child takes a different route home than usual.

[1417] Server behavior:

[1418] 1. The generated AI analyzes the location information received by the server and detects any deviation from the usual route home.

[1419] 2. The server recognizes the abnormal behavior and immediately sends an SMS to the registered emergency contact (parent) informing them that their child is taking an unusual route home.

[1420] User terminal behavior:

[1421] 1. The user device issues a voice alert to the child saying, "You are deviating from your usual route home, please be careful."

[1422] This allows parents to immediately recognize abnormal behavior and take prompt action, and also alerts the child to the danger, improving safety.

[1423] Prompt Sentence Examples

[1424] "Design a program to notify users when they deviate from their usual range of activity."

[1425] "Write a program that implements an algorithm that analyzes a user's GPS data and detects anomalous behavior."

[1426] The present invention can be specifically implemented using the above-described configuration and procedures.

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Step 1:

[1429] The user terminal acquires location information.

[1430] Specifically, the device's GPS sensor measures the current location and obtains data in the format "longitude: 35.6895, latitude: 139.6917". This data is obtained every 10 minutes.

[1431] Input: "GPS sensor"

[1432] Output: "Current location information (longitude and latitude)"

[1433] Step 2:

[1434] The user terminal transmits the collected location information to the server.

[1435] The device periodically sends the data "longitude: 35.6895, latitude: 139.6917" to the server using an HTTP POST request.

[1436] Input: "Current location information (longitude and latitude)"

[1437] Output: "Send location data to server"

[1438] Specific operation: The user device sends data to the server in the format "POST / location HTTP / 1.1 Host: server.com Content-Type: application / json {"longitude": 35.6895, "latitude": 139.6917}".

[1439] Step 3:

[1440] The server receives the location information and stores it in a database.

[1441] The server receives the location information sent from the terminal and stores it in a database.

[1442] Input: "Location information sent from the device"

[1443] Output: "Saved to database"

[1444] Specific operation: The location information received by the server is saved in the database in the format "INSERT INTO location_table (longitude, latitude, timestamp) VALUES (35.6895, 139.6917, CURRENT_TIMESTAMP)".

[1445] Step 4:

[1446] The server's generated AI learns behavioral patterns.

[1447] The server analyzes the location information stored in the database and learns daily behavior patterns.

[1448] Input: "Past location information in database"

[1449] Output: "Learned behavioral patterns"

[1450] Specific operation: The generation AI uses location data from the past three months to learn travel routes and time periods for each day of the week, and extracts a "pattern for traveling from station A to station B at 7 a.m. on a weekday."

[1451] Step 5:

[1452] The server-generated AI detects abnormal behavior in real time.

[1453] The generative AI monitors the location information transmitted in real time, compares it with learned behavioral patterns, and flags any abnormal behavior it detects.

[1454] Input: "Location information sent in real time" "Learned behavioral patterns"

[1455] Output: "Flag of abnormal behavior"

[1456] Specific behavior: If a user is at an unusual station C at 8:00 AM, the generation AI will flag this as "abnormal behavior."

[1457] Step 6:

[1458] Sends notifications when the server detects abnormal behavior.

[1459] When abnormal behavior is detected, the server sends a warning message to registered emergency contacts via email, SMS, or dedicated app notification.

[1460] Input: "Flag for abnormal behavior"

[1461] Output: "Notification sent to emergency contacts"

[1462] Specific behavior: The server sends an SMS to the parent with the message "Your child is taking an unusual route home."

[1463] Step 7:

[1464] The user terminal issues an audio warning.

[1465] If abnormal behavior is detected, the user's device will issue an audio warning.

[1466] Input: "Flag for abnormal behavior"

[1467] Output: "Audio warning message"

[1468] Action: A recorded message will be played saying, "You are off your normal route home, be careful."

[1469] (Application example 1)

[1470] 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."

[1471] In autonomous vehicles, the lack of driver intervention makes it difficult to immediately detect and respond to abnormal driving behavior and associated emergency situations. Furthermore, it is necessary to quickly notify abnormalities using multiple communication methods. Under these circumstances, there is a demand for a system that can detect abnormal driving behavior in real time and take appropriate action.

[1472] 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.

[1473] In this invention, the server includes a means for collecting user location information, a means for transmitting the collected location information to the server, and a means for analyzing the collected location information and having a generation AI that learns the user's behavioral patterns, thereby enabling a means for detecting behavior that deviates from the analyzed behavioral patterns, a means for notifying registered contacts when a deviating behavior is detected, a means for detecting abnormal vehicle driving behavior, and a means for issuing an audio warning to vehicle passengers based on the detected abnormal driving behavior.

[1474] "Means for collecting user location information" refers to devices or systems that obtain the user's current location using GPS or other location information technology.

[1475] The "means for transmitting collected location information to a server" refers to a communication means for transferring location information from a user terminal to a server in real time or at regular intervals.

[1476] "Means equipped with a generative AI that analyzes collected location information and learns user behavior patterns" refers to a system equipped with artificial intelligence that can learn daily behavior patterns using location data collected from users and detect abnormalities.

[1477] The "means for detecting behavior that deviates from the analyzed behavior pattern" is a technology that identifies abnormal behavior by comparing it with learned normal behavior patterns.

[1478] "Means for notifying registered contacts when deviant behavior is detected" is a function that sends a notification to pre-registered emergency contacts when abnormal behavior is detected.

[1479] "Means for detecting abnormal vehicle driving behavior" refers to a system that monitors vehicle location and behavior data and identifies behavior that deviates from normal driving patterns.

[1480] "Means for issuing an audio warning to vehicle passengers based on detected abnormal driving behavior" refers to a function that uses speakers in the vehicle to audibly warn passengers when abnormal driving behavior is detected.

[1481] Overall system overview

[1482] This invention is a system that can detect abnormal driving behavior in autonomous vehicles and respond immediately. The system consists of vehicle sensors, a terminal that transmits location information, a server that processes the data, and a generative AI model. It collects user location information and driving data, detects abnormal behavior based on that, and issues warnings as necessary.

[1483] Server Operation

[1484] 1. Collection of basic information and location information

[1485] The server receives basic information (vehicle ID, driver ID, emergency contact information, etc.) and location information (GPS data) sent from the vehicle's sensors. This data is stored in a database.

[1486] 2. Learning behavioral patterns

[1487] The server is equipped with a generation AI that learns the vehicle's daily driving patterns based on the collected location information. The generation AI analyzes the vehicle's driving route, stopping locations, driving time zones, etc. to identify normal driving patterns.

[1488] 3. Detecting Abnormal Behavior

[1489] The generative AI monitors real-time location and driving data, compares it with learned normal behavior patterns, and immediately responds if abnormal behavior (e.g., sudden braking or erratic steering) is detected.

[1490] 4. Sending notifications

[1491] When abnormal behavior is detected, the server will send a notification to the registered emergency contacts. The notification will include the current location information and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notification.

[1492] 5. Audio warnings

[1493] If abnormal behavior is detected, an audio warning will be issued using the vehicle's speakers, with a message such as "Abnormal driving behavior detected, please be careful" being conveyed to passengers.

[1494] User terminal operation

[1495] 1. Collection of location information

[1496] The user's device (a communication device in the vehicle) periodically acquires GPS data and transmits the location information to the server. This transmission is performed at regular intervals, such as every 10 seconds.

[1497] 2. Real-time data transmission

[1498] It also has the function of transmitting current location information and driving data to a server in real time. When the vehicle is moving or deviates from a specific area, the information is sent to the server immediately.

[1499] Hardware and software used

[1500] GPS module: Uses the GPS module installed in the vehicle to obtain location information.

[1501] Base server: Uses cloud services such as Amazon Web Services (AWS).

[1502] Generative AI models: Use deep learning platforms such as TensorFlow to learn behavioral patterns and detect anomalous behavior.

[1503] Notification system: Uses communication methods such as Twilio (SMS and email notifications) and Firebase (app notifications).

[1504] Voice output: When abnormal behavior is detected, a warning voice is generated using a speech synthesis service such as AWS Polly and broadcast through the vehicle's speakers.

[1505] Specific examples

[1506] Scenario: An autonomous vehicle makes a sharp turn

[1507] User terminal behavior:

[1508] 1. A communication device in the vehicle acquires GPS data and driving data in real time and transmits it to a server.

[1509] 2. If the vehicle makes a sudden turn, the data is sent to the server immediately.

[1510] Server behavior:

[1511] 1. The server analyzes the location information and sudden change of direction data received, and the generating AI detects abnormal driving behavior.

[1512] 2. The server recognizes the abnormal behavior and sends an SMS notification to the registered emergency contacts stating, "The vehicle has made a sudden turn. Please check."

[1513] 3. At the same time, the server uses AWS Polly to generate an audio warning that plays over the vehicle's speaker: "Abnormal driving behavior detected, please be careful."

[1514] Examples of prompt statements

[1515] An example of a prompt to be input to the generative AI model when the vehicle detects abnormal driving behavior is as follows:

[1516] "A sudden turn has been detected. We will analyze the abnormal behavior based on detailed location and driving data and send you a notification."

[1517] This will significantly improve the safety of self-driving vehicles and enable rapid response in the event of an abnormality.

[1518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1519] Step 1:

[1520] Input: Basic information of the user (driver and vehicle) and location information (GPS data)

[1521] Processing: The device collects location and basic information from sensors and GPS modules in the vehicle.

[1522] Output: Acquired location information and basic information data

[1523] The terminal obtains the vehicle's current location from the GPS module and collects basic information such as the vehicle ID and driver ID, and prepares to send this information to the server.

[1524] Step 2:

[1525] Input: Location and basic information data collected on your device

[1526] Processing: The device sends this information to the server at regular intervals (e.g., every 10 seconds).

[1527] Output: Location and basic information data sent to the server

[1528] The device periodically sends the collected location information and basic information to a server, and transfers the data to a cloud server via the Internet.

[1529] Step 3:

[1530] Input: Location and basic information data sent to the server

[1531] Processing: The server stores the received location information and basic information in a database.

[1532] Output: Location and basic information stored in a database

[1533] The server stores the received location information and basic information in a cloud service database (e.g., AWS RDS), making this data easily accessible and analyzable.

[1534] Step 4:

[1535] Input: Location data stored in a database

[1536] Processing: The server uses a generative AI model to learn the vehicle's daily driving patterns from the location information.

[1537] Output: Learned normal driving pattern model

[1538] The server uses deep learning platforms such as TensorFlow to learn typical driving patterns based on historical location data, including driving routes, stopping locations, and driving times.

[1539] Step 5:

[1540] Input: Real-time location and driving data

[1541] Processing: The server compares the data received in real time with learned driving patterns to detect abnormal behavior.

[1542] Output: Detected abnormal behavior information

[1543] The server compares real-time data with previously learned patterns to determine if there are any irregular driving behaviors (e.g., hard braking, sudden turns). If an anomaly is detected, the information is logged.

[1544] Step 6:

[1545] Input: Detected abnormal behavior information

[1546] Action: If abnormal activity is detected, the server will send a notification to the registered emergency contacts, including the current location and details of the abnormal activity.

[1547] Output: Notification to emergency contacts (SMS, email, app notification)

[1548] The server uses the Twilio API or Firebase to send a notification of abnormal behavior to emergency contacts, such as a message saying, "Vehicle has made a sudden turn. Please check."

[1549] Step 7:

[1550] Input: Detected abnormal behavior information

[1551] Processing: If abnormal behavior is detected, the server generates an audio alert and plays a warning message through the vehicle's speaker.

[1552] Output: Audio warning played in the vehicle

[1553] The server uses AWS Polly to generate a voice message warning the driver, saying, "Abnormal driving behavior detected. Please be careful." This voice message is then played through the vehicle's speakers.

[1554] This makes it possible to detect abnormal operating behavior in real time and respond quickly.

[1555] 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.

[1556] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and quickly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generative AI and an emotion engine. The following describes an embodiment of the invention.

[1557] Overall system overview

[1558] This system is built around the user's device, server, generation AI, and emotion engine, and learns the user's behavioral patterns and emotions to detect abnormal behavior. Specifically, it acquires the user's location and emotional information and sends it to the server. The generation AI analyzes and learns from this information to set criteria for detecting abnormal behavior, and the emotion engine analyzes the emotional information to detect abnormal emotional changes. If abnormal behavior or abnormal emotional changes are detected, a notification is sent to registered contacts.

[1559] Server Operation

[1560] 1. Collecting basic information, location information, and emotional information

[1561] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[1562] 2. Learning behavioral and emotional patterns

[1563] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[1564] 3. Detection of abnormal behavior and abnormal emotional changes

[1565] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[1566] 4. Sending notifications

[1567] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[1568] User terminal operation

[1569] 1. Collecting location and emotional information

[1570] The user's device periodically acquires GPS data, uses an emotion engine to collect the user's emotional information, and sends this information to the server at regular intervals, such as every 10 minutes.

[1571] 2. Real-time data transmission

[1572] It also has the ability to send current location and emotional information to a server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is sent to the server immediately.

[1573] 3. Audio warnings

[1574] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1575] Specific examples

[1576] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1577] User terminal operation

[1578] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[1579] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[1580] Server Operation

[1581] 1. The location information received by the server is analyzed by the generation AI, and it is recognized that the person has deviated from their usual route home.

[1582] 2. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[1583] 3. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[1584] User terminal operation

[1585] 1. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[1586] This allows parents to immediately recognize abnormal behavior and emotional changes and respond quickly, and also alerts the child themselves, further improving safety.

[1587] The processing flow will be explained below.

[1588] Step 1:

[1589] The user initially configures the system by entering basic information (name, age, emergency contact information) into the device and setting up the device to allow collection of GPS data and emotional data.

[1590] Step 2:

[1591] The device sends the basic information entered by the user to the server. If permission to collect GPS data and emotion data is granted, the collection settings are enabled.

[1592] Step 3:

[1593] The server records the basic information received from the device in a database, and also sets up tables in the database for collecting GPS data and emotion data.

[1594] Step 4:

[1595] The device periodically (for example, every 10 minutes) acquires the user's current location and emotion data and transmits this information to the server.

[1596] Step 5:

[1597] The server stores the received GPS data and emotion data in a database in chronological order.

[1598] Step 6:

[1599] The server periodically collects GPS data and emotional data and provides it to the generative AI and emotion engine, which then analyzes and learns the user's daily behavioral and emotional patterns. The generative AI and emotion engine then identify normal behavioral and emotional patterns.

[1600] Step 7:

[1601] The device continuously transmits its current location and emotional information to the server in real time. If the user intentionally turns off the device, that information is also sent to the server.

[1602] Step 8:

[1603] The server analyzes the real-time location and emotional information received, and the AI ​​compares it with the user's usual behavior and emotional patterns to detect abnormal behavior or emotional changes. Criteria for abnormal behavior include moving outside the designated area, turning off the power, and abnormal emotional changes.

[1604] Step 9:

[1605] If the server detects abnormal behavior or emotional changes, it extracts details (current location, emotional state, details of the abnormality, etc.) and sends a notification to registered emergency contacts. Notification methods include SMS, email, and dedicated app notifications.

[1606] Step 10:

[1607] If the device detects abnormal behavior or emotional changes, it will issue a voice warning to the user, such as "You are outside your normal range, be careful" or "Your emotional state is abnormal, please take a deep breath."

[1608] Example 2

[1609] 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."

[1610] Conventional systems detect abnormal behavior based solely on the user's location information, which limits their ability to accurately identify abnormal behavior patterns or dangerous situations. Furthermore, when sending notifications to emergency contacts, information about the user's emotional state is not included, making it difficult to quickly recognize true abnormal situations and take appropriate action.

[1611] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user location information and emotional information, means for transmitting the collected location information and emotional information to the server, means for analyzing the collected location information and emotional information and including a generation AI and emotional engine that learns the user's behavioral patterns and emotional patterns, means for detecting behaviors and abnormal emotional changes that deviate from the analyzed behavioral patterns and emotional patterns, means for notifying registered contacts when deviating behaviors and abnormal emotional changes are detected, and user terminal means for issuing audio alerts when abnormal behavior or abnormal emotional changes are detected. This makes it possible to detect abnormalities from both the user's behavioral patterns and emotional patterns and to quickly and accurately notify emergency contacts.

[1612] "User Location Information" means a user's current geographic location data obtained using GPS or other location measurement technology.

[1613] "Emotion information" is data that indicates the user's emotional state, obtained by analyzing the user's facial expression, voice, heart rate, and the like.

[1614] "Server" means a central processing unit that processes, stores, and analyzes data received from user terminals over a network.

[1615] "Generative AI" is a machine learning algorithm used to learn user behavior patterns and detect anomalous behavior.

[1616] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional information and detect changes in it.

[1617] The "behavior pattern" is a history of characteristic behaviors such as the user's daily travel routes and time periods.

[1618] "Abnormal behavior" is behavior that deviates from learned normal behavior patterns.

[1619] An "abnormal emotional change" is an emotional state that is abruptly changed from the user's normal emotional pattern.

[1620] "Registered contacts" are telephone numbers and email addresses registered in the system as contact points for emergency notifications.

[1621] "Notification" is an alert message sent to registered contacts when abnormal behavior or abnormal emotional changes are detected.

[1622] "Audio warning" is a function that allows the user device to issue an audio warning message when abnormal behavior or abnormal emotional changes are detected.

[1623] "Regular interval" refers to a set time interval for collecting and transmitting data periodically, such as every 10 minutes.

[1624] "Real-time" refers to data being processed and results provided immediately after it is generated, with almost no delay.

[1625] The present invention provides a system for detecting abnormal behavior based on location information and emotional information of a user and for quickly notifying registered contacts of the abnormal behavior. Hereinafter, a specific embodiment of the present invention will be described.

[1626] Overall system overview

[1627] This system is built around the user's device, a server, a generation AI, and an emotion engine. The user's device collects location information and emotion information and sends that data to the server. The server analyzes the data using the generation AI and emotion engine to detect abnormal behavior and abnormal emotional changes. If detected, a notification is sent to registered contacts.

[1628] Server Operation Details

[1629] The server works as follows:

[1630] 1. Data collection and storage

[1631] The server receives location information (e.g., GPS data) and emotional information sent from the user's device. This information is stored in a database. For example, MySQL or PostgreSQL can be used as a database management system to efficiently store and manage data.

[1632] 2. Learning behavioral and emotional patterns

[1633] The server is equipped with a generative AI and an emotion engine, which learns the user's daily behavioral and emotional patterns based on collected data. The generative AI analyzes the user's travel route and location, while the emotion engine analyzes the user's emotional state. For example, the generative AI model uses TensorFlow to perform real-time data analysis.

[1634] 3. Detecting Abnormal Behavior and Emotional Changes

[1635] The server monitors location and emotional information in real time, compares it with a trained model, and detects abnormal behavior or emotional changes. If an abnormality is detected, an emergency contact is immediately notified.

[1636] 4. Sending notifications

[1637] If abnormal behavior or emotional changes are detected, the server will send a notification to registered emergency contacts via SMS, email, or dedicated app notification, containing details of the user's current location, emotional state, and abnormal behavior.

[1638] User device operation details

[1639] The user terminal operates as follows:

[1640] 1. Data collection

[1641] The user's device periodically collects location information (using GPS) and emotional information. Emotional information is collected through the smartphone's camera, microphone, and specific apps. The emotion engine analyzes the user's emotional state using facial recognition software and voice analysis software.

[1642] 2. Data transmission

[1643] The collected location and emotion information is sent to a server at regular intervals (e.g., every 10 minutes).The system also has a real-time data transmission function, which immediately sends information to the server if the robot deviates from a specific area or if an abnormal change in emotion is detected.

[1644] 3. Audio warnings

[1645] If abnormal behavior or emotional changes are detected, the user device will issue a voice alert, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1646] Specific examples

[1647] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1648] When a user's child is on their way home from school, the device collects their current location and emotional information every 10 minutes and sends this information to the server. If the child takes a different route home than their usual route, the server recognizes this as an abnormality, and the emotion engine simultaneously detects that the child is feeling anxious or stressed. The server then recognizes this abnormal behavior and abnormal emotional changes and immediately sends an SMS notification to the registered emergency contact (e.g., parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state." Additionally, the user's device issues a voice warning, saying, "You are deviating from your usual route home. Please be careful," and "Your emotional state is unstable. Please take a deep breath."

[1649] Prompt Sentence Examples

[1650] Input prompt for the generative AI model:

[1651] "Please explain your system for detecting anomalous behavior based on user location and emotional information. Please also provide a detailed description of the hardware and software used, as well as the data processing method."

[1652] keyword

[1653] Generative AI model, prompt sentence

[1654] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1655] System processing steps

[1656] Step 1: Collect data

[1657] User terminal operation

[1658] Users have devices such as smartphones. These devices collect location information using GPS and emotional information using cameras and microphones. Emotional information is analyzed using facial recognition software and voice analysis software. This data is temporarily stored in the device's internal memory.

[1659] Input: User's GPS location, raw data collected by camera and microphone

[1660] Output: Location data and analyzed emotion data

[1661] Step 2: Sending data

[1662] User terminal operation

[1663] The user device sends the collected location information and emotion information to the server at regular intervals (e.g., every 10 minutes). This transmission uses the HTTPS protocol to ensure security. In an emergency, the information is sent immediately when a specific event occurs (e.g., area departure, abnormal emotion change).

[1664] Input: Collected location data and analyzed emotion data

[1665] Output: Location and emotion data sent to the server

[1666] Step 3: Receiving and storing data

[1667] Server Operation

[1668] The server receives the location and emotion information sent from the user's device and stores it in a database. The database uses "MySQL" or "PostgreSQL" to manage data by user ID and timestamp, and processes it efficiently.

[1669] Input: Location data and emotional data sent from the user device

[1670] Output: Data stored in a database

[1671] Step 4: Analyze and train the data

[1672] Server Operation

[1673] The generative AI and emotion engine installed on the server use the stored data to learn the user's daily behavioral and emotional patterns. The generative AI uses TensorFlow to analyze the user's travel route and where they stay, and the emotion engine analyzes their emotional state.

[1674] Input: Stored location and emotion data

[1675] Output: Learned and updated behavioral and emotional pattern models

[1676] Step 5: Real-time monitoring and anomaly detection

[1677] Server Operation

[1678] The server monitors new location and emotion information in real time, compares it with the trained model, and detects abnormal behavior or abnormal emotional changes. If an anomaly is detected, the details are recorded in a log.

[1679] Input: Location and emotion data received in real time

[1680] Output: A log of any abnormal behaviors or emotional changes detected

[1681] Step 6: Sending notifications

[1682] Server Operation

[1683] If any abnormal behavior or emotional changes are detected, the server will immediately send a notification to registered emergency contacts via SMS, email, or dedicated app notification, and the message will include details of the user's current location, emotional state, and any abnormal behavior.

[1684] Input: Log of detected anomalous behavior or emotional changes

[1685] Output: Notification message to emergency contacts (e.g. SMS, email)

[1686] Step 7: Audio Reminder

[1687] User terminal operation

[1688] If abnormal behavior or emotional changes are detected, the user device will issue an audio alert, playing messages such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1689] Input: Notification of abnormal behavior or emotional changes from the server

[1690] Output: Audio alert on user device

[1691] (Application example 2)

[1692] 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."

[1693] The present invention aims to quickly detect abnormal behavior or abnormal emotional changes using a user's location information and emotional information, thereby effectively monitoring the user's safety. Conventional systems were able to detect abnormal behavior based solely on location information, but were unable to take the user's emotional state into account, making accurate anomaly detection difficult. Therefore, the present invention aims to provide a more accurate anomaly detection system by simultaneously analyzing the user's emotional information.

[1694] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1695] In this invention, the server includes means for analyzing the collected location information and emotion information and including a generation AI and emotion engine that learns the user's behavioral and emotional patterns, means for detecting behavior that deviates from the analyzed behavioral and emotional patterns or abnormal emotional changes, and means for notifying registered contacts when a deviating behavior or abnormal emotional change is detected. This makes it possible to detect anomalies that take into account not only the user's location information but also their emotional information.

[1696] "User location information" means data that indicates the geographic location where a user is currently located.

[1697] A "server" is a remote computer system that receives, processes, stores, and transmits data over a network.

[1698] "Collected Location Information" means geographic location data obtained from a User Device and transmitted to a Server.

[1699] "Emotional information" refers to data that indicates changes in a user's psychological state or emotions.

[1700] "Generative AI" is an artificial intelligence system that learns user behavior patterns and detects abnormal behavior.

[1701] An "emotion engine" is a software system that analyzes a user's emotional information and detects abnormal emotional changes.

[1702] "Learning" is the process of analyzing collected data and understanding specific patterns and trends.

[1703] "Abnormal behavior" refers to user behavior that significantly deviates from normal behavior patterns.

[1704] "Abnormal emotional changes" refers to fluctuations in a user's emotions that significantly deviate from normal emotional patterns.

[1705] "Notification" refers to the act of transmitting information to pre-registered contacts when abnormal behavior or abnormal emotional changes are detected.

[1706] This invention is a system that detects abnormal behavior based on a user's location information and emotional information and promptly notifies registered contacts, and is characterized by its ability to comprehensively monitor a user's behavior and emotions by combining a generation AI and an emotion engine. This system is built around a user terminal, a server, a generation AI, and an emotion engine.

[1707] User terminal operation

[1708] 1. Collecting location and emotional information

[1709] The user device periodically acquires GPS data and uses an emotion engine to collect the user's emotional information. This information is then sent to the server at regular intervals, such as every 10 minutes.

[1710] 2. Real-time data transmission

[1711] The user device also has the function of transmitting current location information and emotional information to the server in real time. If the user is moving, deviates from a specific area, or shows abnormal emotional changes, the information is immediately sent to the server.

[1712] 3. Audio warnings

[1713] If abnormal behavior or emotional changes are detected, the user device will issue a voice warning, such as "You are outside your normal range, please be careful" or "Your emotional state is abnormal, please take a deep breath."

[1714] Server Operation

[1715] 1. Collecting basic information, location information, and emotional information

[1716] The server receives basic information (such as name, age, and emergency contact information), location information (GPS data), and emotional information sent from the user's device. This data is stored in a database.

[1717] 2. Learning behavioral and emotional patterns

[1718] The server is equipped with a generative AI and emotion engine that learns the user's daily behavioral and emotional patterns based on the collected location and emotional information. The generative AI and emotion engine comprehensively analyze the user's travel route, location, time of day, emotional state, etc. to identify normal behavioral and emotional patterns.

[1719] 3. Detection of abnormal behavior and abnormal emotional changes

[1720] The generative AI monitors location information transmitted in real time and compares it with learned normal behavior patterns to detect abnormal behavior. Similarly, the emotion engine analyzes emotional information in real time to detect abnormal emotional changes.

[1721] 4. Sending notifications

[1722] If abnormal behavior or emotional changes are detected, the server will send a notification to the registered emergency contacts. The notification will include the current location, emotional state, and details of the abnormal behavior. Notification methods can be selected as SMS, email, or dedicated app notifications.

[1723] Hardware and software used

[1724] Hardware:

[1725] User device: Mobile device such as an Android smartphone

[1726] Server: A standard HTTP server (e.g. NGINX or Apache)

[1727] software:

[1728] Mobile application: Uses Android Location API

[1729] Server side: Using Python and the Hugging Face Transformers library

[1730] Sentiment Analysis: Transformers Library for Python (Hugging Face)

[1731] Specific examples

[1732] Scenario: A child takes an unusual route home and exhibits unusual emotional changes.

[1733] 1. When a child is on their way home from school, the device obtains their current location information every 10 minutes, the emotion engine evaluates their emotional state, and sends this information to the server.

[1734] 2. The child takes a different route home than usual, and the emotion engine detects that the child is feeling anxious or stressed.

[1735] 3. The location information received by the server is analyzed by the generated AI, and it is recognized that the person has deviated from their usual route home.

[1736] 4. The emotion engine detects abnormal emotion changes and confirms that the user's emotional state is unstable.

[1737] 5. The server recognizes abnormal behavior and emotional changes and immediately sends an SMS to the registered emergency contact (parent) informing them that "your child has taken an unusual route home and is in an abnormal emotional state."

[1738] 6. Provide voice alerts to your child saying, "You are off your normal route home, be careful" and "Your emotional state is unstable, take a deep breath."

[1739] Prompt Sentence Examples

[1740] "Analyze user comments to see if they indicate negative sentiment."

[1741] "Infer the user's current mental state from the provided text data."

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] The user device periodically obtains location information using a GPS sensor. As input, a time interval (e.g., every 10 minutes) is set, and as output, the latest location information (latitude and longitude) is obtained.

[1745] Step 2:

[1746] The user device collects the user's emotional information using a built-in emotion engine. The input is the user's text input or the latest voice data, and the output is the user's emotional state (positive, negative, neutral, etc.).

[1747] Step 3:

[1748] The user terminal transmits the acquired location information and emotion information to the server. As input, a data packet is created that combines the location information and emotion information, and as output, the data packet is transmitted to the server.

[1749] Step 4:

[1750] The server stores the received location information and emotion information in a database.,As input, the server receives a data packet containing,location information and emotion information, and as output, stores the data in,a database.

[1751] Step 5:

[1752] The server analyzes location information using a generative AI and learns the user's behavioral patterns. It analyzes multiple location data as input and generates a model of normal behavioral patterns as output.

[1753] Step 6:

[1754] The server analyzes the emotional information using an emotion engine and learns the user's emotional patterns. It analyzes multiple pieces of emotional information data as input and generates a typical emotional pattern model as output.

[1755] Step 7:

[1756] The server compares the location and emotion information received in real time with a normal pattern model to detect abnormal behavior and abnormal emotional changes.The latest location and emotion information and the normal pattern model are used as input, and the detection results of abnormal behavior or abnormal emotional changes are obtained as output.

[1757] Step 8:

[1758] If the server detects abnormal behavior or emotional changes, it sends a notification to registered emergency contacts. The input is detailed information about the abnormality (current location, emotional state, description of the abnormal behavior, etc.), and the output is a notification via SMS, email, or a dedicated app.

[1759] Step 9:

[1760] If the user device detects abnormal behavior or abnormal emotional changes, it issues a voice warning. As input, it receives a warning notification from the server, and as output, it conveys a voice message to the user.

[1761] Through the above processing steps, this system is able to quickly detect abnormal behavior based on the user's location information and emotional information, and issue necessary notifications and warnings.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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).

[1769] 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.

[1770] 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."

[1771] 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.

[1772] 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).

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] The following is further disclosed regarding the above embodiment.

[1784] (Claim 1)

[1785] means for collecting user location information;

[1786] means for transmitting the collected location information to a server;

[1787] A means for analyzing the collected location information and providing a generating AI that learns the user's behavioral patterns;

[1788] means for detecting behavior that deviates from the analyzed behavioral patterns;

[1789] A means of notifying registered contacts when deviations are detected;

[1790] A system including:

[1791] (Claim 2)

[1792] The system of claim 1, wherein the system detects that the user's location information is outside a registration area.

[1793] (Claim 3)

[1794] 10. The system of claim 1, wherein the system collects user location information at regular intervals.

[1795] "Example 1"

[1796] (Claim 1)

[1797] means for collecting user location information;

[1798] means for transmitting the collected location information to a server;

[1799] a means for analyzing the collected location information and providing a machine learning algorithm for learning user behavior patterns;

[1800] means for detecting behavior that deviates from the analyzed behavioral patterns;

[1801] a means for sending notifications to registered contacts when deviant behavior is detected;

[1802] A means for issuing a voice warning from the terminal when abnormal behavior is detected;

[1803] A system including:

[1804] (Claim 2)

[1805] The system of claim 1, wherein the system detects that the user's location information is outside a registration area.

[1806] (Claim 3)

[1807] 10. The system of claim 1, wherein the system collects user location information at regular intervals.

[1808] "Application Example 1"

[1809] (Claim 1)

[1810] means for collecting user location information;

[1811] means for transmitting the collected location information to a server;

[1812] A means for analyzing the collected location information and providing a generating AI that learns the user's behavioral patterns;

[1813] means for detecting behavior that deviates from the analyzed behavioral patterns;

[1814] A means of notifying registered contacts when deviations are detected;

[1815] means for detecting abnormal vehicle driving behavior;

[1816] means for issuing an audio warning to a vehicle passenger based on the detected abnormal driving behavior;

[1817] A system including:

[1818] (Claim 2)

[1819] The system of claim 1, wherein the system detects that the user's location information is outside a registration area.

[1820] (Claim 3)

[1821] 10. The system of claim 1, wherein the system collects user location information at regular intervals.

[1822] "Example 2: Combining Emotion Engines"

[1823] (Claim 1)

[1824] means for collecting location information and emotion information of a user;

[1825] means for transmitting the collected location information and emotion information to a server;

[1826] A means for analyzing the collected location information and emotion information and providing a generation AI and emotion engine for learning the user's behavioral patterns and emotion patterns;

[1827] means for detecting behaviors and abnormal emotional changes that deviate from the analyzed behavioral and emotional patterns;

[1828] means for notifying registered contacts when deviant behavior and abnormal emotional changes are detected;

[1829] a user terminal means for issuing a voice warning when abnormal behavior or abnormal emotional changes are detected;

[1830] A system including:

[1831] (Claim 2)

[1832] 10. The system of claim 1, wherein the system collects and transmits the user's location information and emotion information at regular intervals.

[1833] (Claim 3)

[1834] The system of claim 1, wherein the system detects whether the user's location information and emotional information are outside a registered area or in an abnormal emotional state.

[1835] "Application example 2 when combining emotion engines"

[1836] (Claim 1)

[1837] means for collecting user location information;

[1838] means for transmitting the collected location information to a server;

[1839] A means for analyzing the collected location information and emotion information and providing a generation AI and emotion engine for learning the user's behavioral patterns and emotion patterns;

[1840] means for detecting behaviors or abnormal emotional changes that deviate from the analyzed behavioral and emotional patterns;

[1841] means for notifying registered contacts when deviant behavior or abnormal emotional changes are detected;

[1842] A system including:

[1843] (Claim 2)

[1844] The system of claim 1, wherein the system detects that the user's location information is outside a registration area.

[1845] (Claim 3)

[1846] 10. The system of claim 1, wherein location information and emotion information of the user are collected at regular intervals. [Explanation of symbols]

[1847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting user location information; means for transmitting the collected location information to a server; A means for analyzing the collected location information and providing a generating AI that learns the user's behavioral patterns; means for detecting behavior that deviates from the analyzed behavioral patterns; A means of notifying registered contacts when deviations are detected; A system including:

2. The system according to claim 1, wherein the system detects that the user's location information is outside a registration area.

3. The system according to claim 1 , wherein the system collects user location information at regular intervals.

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