System

Abstract

A system is provided.SOLUTION: A system for real-time understanding of the current location of a dementia patient, comprising: location information acquisition means; means for storing the acquired location information in a database; means for analyzing past location data and learning normal behavior patterns; means for analyzing new location data acquired in real time and detecting abnormal movement patterns; means for sending an alert when abnormal movement patterns are detected; and means for displaying the current patient location on a map.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] Currently, there is a lack of means to reduce the risk of dementia patients getting lost and ensure their safety. When a dementia patient gets lost, not only is their safety threatened, but it also causes great mental stress for their family and caregivers. To solve this problem, a system is needed that can track the patient's current location in real time, detect abnormal movement patterns, and notify them before a risk occurs. This will also reduce the burden on family and caregivers, and create a society in which dementia patients can live more independent lives. [Means for solving the problem]

[0005] The present invention provides a system for tracking the current location of a dementia patient in real time, including a location information acquisition unit, a database storage unit for storing the acquired location information, a database analysis unit for analyzing past location data and learning normal behavioral patterns, a real-time analysis unit for analyzing new location data acquired in real time to detect abnormal movement patterns, a real-time alert transmission unit for detecting abnormal movement patterns, and a map display unit for displaying the patient's current location. This system enables close monitoring of the behavior of a dementia patient and rapid response when an abnormality occurs. Furthermore, the system uses a wearable device to track the patient's movements in real time and also has the ability to send notifications to emergency contacts as needed. This system ensures the safety of the dementia patient while reducing the mental stress of family members and caregivers.

[0006] The "location information acquisition means" is a device or system for acquiring the current location of a dementia patient in real time.

[0007] A "database" is a storage device or system that stores acquired location information and makes it accessible as needed.

[0008] A "behavior pattern learning means" is a device or system for analyzing past location data to learn normal movement patterns.

[0009] A "new location data analysis means" is a device or system for analyzing new location data acquired in real time and detecting abnormal movement patterns.

[0010] An "alert sending means" is a device or system for sending a notification message to an emergency contact when an abnormality is detected.

[0011] The "current location display means" is a device or system for displaying the current location of a dementia patient on a map.

[0012] A "wearable device" is a terminal that can be worn by dementia patients at all times and is used to obtain location information.

[0013] "Emergency contacts" are contacts such as family members or caregivers who are set to receive notifications when an abnormality is detected. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is a location tracking system that aims to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[0036] System Configuration

[0037] 1. Wearable devices:

[0038] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0039] 2. Database:

[0040] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[0041] 3. Server:

[0042] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[0043] 4. Dedicated application (terminal):

[0044] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[0045] Feature Overview

[0046] The system provides the following main functions:

[0047] 1. User registration and device pairing

[0048] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0049] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0050] 2. Acquiring and saving location information

[0051] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[0052] Server: Receives data and records it in a database.

[0053] 3. Behavioral Pattern Analysis and Anomaly Detection

[0054] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[0055] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[0056] 4. Displaying location information

[0057] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[0058] Specific examples

[0059] Let's assume that the family of a dementia patient, Person B, will use this system. First, they enter Person B's information using a terminal and send it to the server. The server then stores Person B's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device worn by Person B.

[0060] When B leaves the house, the wearable device continues to collect GPS data and transmits it to the server at regular intervals. The server stores the data in a database and uses an AI model to learn his or her usual behavioral patterns.

[0061] If one day, Mr. B begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Using the device, his family can check Mr. B's current location on a map and respond quickly.

[0062] In this way, the system ensures the safety of dementia patients and reduces the burden on their families and caregivers.

[0063] The processing flow will be explained below.

[0064] User Registration and Device Pairing

[0065] Step 1:

[0066] The user installs the dedicated application and opens the account creation screen.

[0067] Step 2:

[0068] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[0069] Step 3:

[0070] The server stores the received patient information in a database and generates a unique user ID.

[0071] Step 4:

[0072] The server returns the generated user ID to the terminal.

[0073] Step 5:

[0074] The terminal displays the user ID on the screen and asks the user to confirm it.

[0075] Step 6:

[0076] The user prepares the wearable device and sets it to pairing mode.

[0077] Step 7:

[0078] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[0079] Step 8:

[0080] The server records the wearable device information and associates it with the user ID.

[0081] Step 9:

[0082] The device displays a screen informing the user that pairing was successful.

[0083] Acquiring and saving location information

[0084] Step 1:

[0085] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[0086] Step 2:

[0087] The server receives the GPS data sent from the wearable device.

[0088] Step 3:

[0089] The server stores the received GPS data in a database and associates it with the user ID.

[0090] Behavioral pattern analysis and anomaly detection

[0091] Step 1:

[0092] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[0093] Step 2:

[0094] Identify the server's normal range of activity and frequently visited locations.

[0095] Step 3:

[0096] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[0097] Step 4:

[0098] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[0099] Step 5:

[0100] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[0101] Displaying location information

[0102] Step 1:

[0103] The user launches the dedicated application on their smartphone or tablet.

[0104] Step 2:

[0105] The terminal displays a login screen and prompts the user to enter their user ID and password.

[0106] Step 3:

[0107] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[0108] Step 4:

[0109] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[0110] Step 5:

[0111] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[0112] Example 1

[0113] 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."

[0114] There is a high risk that patients with dementia will get lost, which could result in a threat to their safety. Furthermore, it is not always possible for family members or caregivers to monitor patients with dementia, placing a heavy burden on them. In these circumstances, a system is needed that can track the current location of patients with dementia in real time and respond quickly if any abnormal movements occur.

[0115] 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.

[0116] In this invention, the server includes means for saving the received basic information in a database and generating a unique user ID, means for receiving and saving GPS data acquired by the location information acquisition means, means for learning past location data using an AI model, analyzing new location data in real time, and detecting abnormal movement patterns, means for sending a notification message to an emergency contact when an abnormal movement pattern is detected, and means for displaying the current patient location on a map. This makes it possible to ensure the safety of dementia patients and reduce the burden on their families and caregivers.

[0117] A "user" is a person who inputs basic information about a dementia patient and sends it to the server via a dedicated application.

[0118] The "server" is a device that stores the received basic information in a database and generates a unique user ID.

[0119] A "database" is a storage device that records and manages location information and basic information received by the server.

[0120] A "unique user ID" is an identification number that is uniquely assigned to each dementia patient.

[0121] "Location information acquisition means" refers to a device or method that acquires the current location of a dementia patient using a wearable device.

[0122] A "wearable device" is a device worn by a dementia patient that collects GPS data and transmits it to a server.

[0123] An "AI model" is an artificial intelligence algorithm that learns from past location data and identifies normal behavioral patterns.

[0124] An "abnormal movement pattern" is location data that deviates significantly from normal behavioral patterns.

[0125] A "notification message" is a communication containing an alert sent to family members or caregivers when an abnormal movement pattern is detected.

[0126] The "dedicated application" is software that family members and caregivers can use to check the current location of a person with dementia.

[0127] "Real-time" is a term that refers to the immediate processing and display of current conditions and data without delay.

[0128] This invention is a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[0129] System Configuration

[0130] 1. Wearable device: A device that is always worn by a dementia patient and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0131] 2. Database: A storage device that records received location information. The server uses this database to manage and analyze past location data.

[0132] 3. Server: This is the central component that receives, stores, and analyzes location information. It uses AI models to learn from past location data and detect unusual movement patterns.

[0133] 4. Dedicated application (terminal): This application is for use by family members and caregivers and displays the patient's current location on a map, and notifies them if any abnormalities are detected.

[0134] Feature Overview

[0135] The system provides the following main functions:

[0136] User Registration and Device Pairing

[0137] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0138] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0139] Acquiring and sending location information

[0140] Wearable device: Uses the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes), and sends the acquired location information to a server.

[0141] Server: Processes the received location information and stores it in a database.

[0142] Behavioral pattern analysis and anomaly detection

[0143] Server: AI models are used to study past location data and identify normal patterns of behavior. As new location data arrives on the server, it is analyzed in real time to check for any unusual movement patterns.

[0144] Notification when an abnormality is detected

[0145] Server: If an anomaly is detected, a notification message is sent to emergency contacts, including the current location and the type of anomaly (e.g., unexpected location, significant movement, etc.).

[0146] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[0147] Displaying location information

[0148] Device: By opening the dedicated application, family members and caregivers can check the current location of the person with dementia on a map. The map display screen also shows real-time location information and past movement history.

[0149] Server: When it receives a location request from the device, it sends the latest location data to the device.

[0150] Specific examples of operation

[0151] Let's assume that a family member of a dementia patient uses this system. First, they enter the patient's information using a terminal and send it to the server. The server stores the patient's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device with the patient.

[0152] When the patient leaves the home, the wearable device continuously collects GPS data and transmits it to a server at regular intervals. The server stores the data in a database and uses an AI model to learn normal behavioral patterns.

[0153] If one day the patient begins to move far beyond their usual range of movement, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Family members can then use their devices to check the patient's current location on a map and respond quickly.

[0154] Prompt Sentence Examples

[0155] "Please explain in detail the four components of a location tracking system for dementia patients: a wearable device, a database, a server, and a dedicated application."

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1: User Registration

[0158] Device: The family member or caregiver launches the dedicated application and opens the user registration screen. They enter basic information such as the dementia patient's name, age, address, and emergency contact information, and click the "Register" button. This basic information becomes the input data.

[0159] Server: Receives the entered basic information and saves it in the database. After saving, it generates a unique user ID and sends it back to the terminal. This is the output data.

[0160] Step 2: Device pairing

[0161] Device: After receiving the unique user ID, you will be taken to a screen for pairing with the wearable device. Enter the user ID into the device and click the pairing button. The wearable device will then receive the user ID.

[0162] Wearable device: Recognizes the user ID and sends a message to the server indicating that pairing is complete.

[0163] Step 3: Obtaining location information

[0164] Wearable devices: Use the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes). The acquired location information becomes input data.

[0165] Wearable device: Sends the acquired location information to the server, which is the output data.

[0166] Step 4: Save location

[0167] Server: Stores the received location information in a database. The stored location information includes the location coordinates and acquisition time. This is the input data.

[0168] Server: Stored in a database for future analysis.

[0169] Step 5: Learning behavioral patterns

[0170] Server: Uses an AI model to learn from past location data and identify normal behavioral patterns. This is the input data.

[0171] Server: The analysis results include the range and patterns of normal behavior.

[0172] Step 6: Real-time analysis

[0173] Server: When new location data arrives at the server, it is analyzed in real time using an AI model. It compares it with past learning results to detect abnormal movement patterns. The new location data is the input data.

[0174] Server: If an anomaly is detected, send a notification message to emergency contacts. This is the output data.

[0175] Step 7: Notification when an anomaly is detected

[0176] Server: If an anomaly is detected, a notification message is sent to the registered emergency contacts. The message contains the current location and the type of anomaly.

[0177] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[0178] Step 8: View location information

[0179] Device: Open the dedicated application and go to the screen where you can check the current location of the dementia patient on a map.

[0180] Server: Receives location requests from devices and sends the latest location data to the devices. The request is the input data, and the location data is the output data.

[0181] Device: Real-time location information and past movement history are displayed on a map, allowing family members and caregivers to respond quickly.

[0182] (Application example 1)

[0183] 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."

[0184] There is a problem that dementia patients and food delivery drivers deviate from their normal range of movement, getting lost or behaving abnormally, threatening their safety. Such situations are very stressful for family members and managers, and require a quick response, but there is a lack of means to grasp location information in real time and detect abnormalities. Therefore, a system is needed that can grasp the current location of dementia patients and drivers in real time, and detect and notify abnormal movement patterns.

[0185] 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.

[0186] In this invention, the server includes a location information acquisition means, a means for storing the acquired location information in a database, a means for analyzing past location data and learning normal behavioral patterns using a machine learning model, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, and a means for displaying the current location of the subject on a map. This ensures the safety of the subject and enables a prompt response when an abnormality occurs. This reduces the burden on family members and administrators and enables effective monitoring of the subject's behavior.

[0187] A "dementia patient" refers to an adult who has impaired memory or cognitive abilities and requires assistance with daily living.

[0188] A "food delivery driver" is a person whose job is to drive a predetermined route to deliver requested food to customers.

[0189] "Current location" refers to the geographic location where a subject is located in real time.

[0190] "Real-time" refers to the instant information is processed and transmitted.

[0191] "Location information acquisition means" refers to a device or system that collects a subject's current location using GPS or other location identification technology.

[0192] A "database" refers to a system that systematically stores information such as saved location information and past movement data.

[0193] A "machine learning model" refers to a model that includes algorithms that allow a computer to learn data patterns and make future predictions or detect anomalies.

[0194] "Abnormal movement patterns" refer to movements that deviate significantly from normal patterns of behavior, including unexpected route changes and long periods of stoppage.

[0195] "Means for sending alerts" refers to a system or device for sending notifications or warnings to pre-designated recipients when abnormal movement is detected.

[0196] "Means for displaying on a map" refers to a system or application that visually displays collected location information, allowing users to easily check their current location.

[0197] A "wearable device" is an electronic device that can be worn by a subject and has the function of collecting location information, etc.

[0198] A "smartphone" refers to a mobile device based on a mobile operating system and equipped with multifunctional applications and communication functions.

[0199] "Emergency Contact" refers to a designated contact to be contacted in the event of abnormal movement.

[0200] This invention is a system for tracking the current locations of dementia patients and food delivery drivers in real time to ensure their safety. The system mainly consists of the following components: a location information acquisition means, a database, a server, and a dedicated application (terminal).

[0201] System Configuration

[0202] 1. Location information acquisition means:

[0203] It consists of a wearable device or smartphone that is constantly worn by dementia patients and food delivery drivers. This device has a built-in GPS module that measures their current location at regular intervals and transmits the data to a server.

[0204] 2. Database:

[0205] This is a storage device that records the location information received by the server. The server uses this database to manage and analyze past location data.

[0206] 3. Server:

[0207] It is the central component that receives, stores, and analyzes location information, and uses AI and machine learning models to learn from past location data and detect anomalous movement patterns.

[0208] 4. Dedicated application (terminal):

[0209] The application is intended for use by family members, caregivers, or food delivery system managers, and displays the current location of the person on a map and notifies them if any abnormalities are detected.

[0210] Program processing

[0211] Hardware and software used

[0212] Hardware: GPS-enabled smartphones, wearable devices

[0213] Software: Python (scripting language), Requests module, scikit-learn module, SMTP library

[0214] Data processing and calculation

[0215] 1. Location information acquisition:

[0216] Smartphones and wearable devices use GPS to obtain real-time location information, which is then sent to a server at regular intervals.

[0217] 2. Sending location information to the server:

[0218] The server stores the received data in a database and manages past location data. This location information is then used to feed the data into a machine learning model to learn normal behavioral patterns.

[0219] 3. Analysis by AI model:

[0220] A server-based machine learning model analyzes new location data in real time to detect unusual movement patterns, and if an anomaly is detected, it immediately sends a notification to emergency contacts or food delivery system administrators.

[0221] 4. Location display:

[0222] The application displays the received data on a map, allowing the user to view their current location and past movement history.

[0223] Specific examples

[0224] Cases of dementia patients

[0225] 1. Family members use a dedicated application to enter basic information about the person with dementia and send it to the server.

[0226] 2. The server stores the received information in a database and generates a unique user ID. Once the family member confirms this, they can pair the patient's wearable device.

[0227] 3. When the patient goes out, the wearable device continues to acquire GPS data and transmits it to the server at regular intervals.

[0228] 4. The server stores the received data in a database and uses machine learning models to learn normal behavioral patterns.

[0229] 5. If the patient begins to move significantly outside of their normal range of movement, the server will detect this as an abnormality and immediately send a notification to registered emergency contacts.

[0230] The case of a food delivery driver

[0231] 1. The administrator uses a dedicated application to enter driver information and send it to the server.

[0232] 2. The server stores the received information in a database and generates a unique user ID. After verifying this, the administrator pairs the driver's smartphone with the driver's smartphone.

[0233] 3. When the driver begins a delivery, the smartphone collects GPS data in real time and sends it to the server at regular intervals.

[0234] 4. The server stores the received data in a database and uses a machine learning model to learn the normal delivery route.

[0235] 5. If a driver significantly deviates from their normal delivery route, the server will detect this as an anomaly and immediately send a notification to the administrator.

[0236] Example prompt sentence:

[0237] "Build a system that tracks your drivers' locations and notifies you if they deviate significantly from their normal delivery routes."

[0238] This system ensures the safety of the subject and enables prompt response in the event of an abnormality, reducing the burden on family members and administrators and enabling effective monitoring of the subject's behavior.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] User Registration and Device Pairing

[0242] Using a dedicated application, users enter basic information about the dementia patient and food delivery driver (such as name, age, and emergency contact information) and send it to the server.

[0243] The server stores the received information in a database, generates a unique user ID, and returns this user ID to the device.

[0244] The user confirms their user ID and pairs with their wearable device or smartphone.

[0245] Input: Basic information of dementia patient and driver

[0246] Output: Information stored in the database and a unique user ID.

[0247] Step 2:

[0248] Obtaining location information

[0249] The location information acquisition means (wearable device or smartphone) acquires GPS data at regular intervals.

[0250] Input: Current location GPS data

[0251] Output: Acquired GPS data

[0252] Step 3:

[0253] Sending location information to the server

[0254] The GPS data acquired by the location information acquisition means is transmitted to a server.

[0255] The server stores the received GPS data in a database.

[0256] Input: Acquired GPS data

[0257] Output: GPS data stored in a database

[0258] Step 4:

[0259] Learning behavioral patterns

[0260] The server collects location data over a period of time and learns behavioral patterns using a machine learning model.

[0261] Identify typical behavioral patterns based on past location data.

[0262] Input: Location data stored in a database

[0263] Output: Learned normal behavior patterns

[0264] Step 5:

[0265] Real-time analytics and anomaly detection

[0266] The server receives new location data in real time and compares it with previously learned normal behavior patterns.

[0267] If an abnormal movement pattern is detected, it is determined to be abnormal.

[0268] Input: Real-time location data

[0269] Output: Determine if the movement pattern is abnormal

[0270] Step 6:

[0271] Sending alerts

[0272] The server will send a notification message to designated emergency contacts and administrators when an abnormal movement pattern is detected.

[0273] Input: Abnormal movement pattern detection results

[0274] Output: Send notification message

[0275] Step 7:

[0276] Displaying location information

[0277] The device visually displays the received location data on a map, allowing the user to verify the location.

[0278] Input: Real-time location data

[0279] Output: Current location and past movement history displayed on a map

[0280] Through the above processing steps, this system is able to monitor the current locations of dementia patients and food delivery drivers in real time to ensure their safety.

[0281] 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.

[0282] This invention is a system that combines a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety, with an emotion engine that recognizes the user's emotions. The system mainly includes the following components: a wearable device, a database, a server, a dedicated application (terminal), and an emotion engine.

[0283] System Configuration

[0284] 1. Wearable devices:

[0285] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0286] 2. Database:

[0287] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[0288] 3. Server:

[0289] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[0290] 4. Dedicated application (terminal):

[0291] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[0292] 5. Emotion Engine:

[0293] This is a device or system that recognizes the emotions of users (dementia patients, family members, and caregivers) and analyzes their emotions from their movements, facial expressions, and voice.

[0294] Feature Overview

[0295] The system provides the following main functions:

[0296] 1. User registration and device pairing

[0297] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0298] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0299] 2. Acquiring and saving location information

[0300] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[0301] Server: Receives data and records it in a database.

[0302] 3. Behavioral Pattern Analysis and Anomaly Detection

[0303] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[0304] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[0305] 4. Displaying location information

[0306] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[0307] 5. Emotion Recognition and Alerts

[0308] Emotion engine: Analyzes emotions from the movements, facial expressions, and voice of patients and caregivers, and sends the data to a server.

[0309] Server: Analyzes emotion data and generates alerts when abnormal emotion changes are detected.

[0310] Server: When an alert is generated, it sends a notification message to emergency contacts.

[0311] Specific examples

[0312] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they will enter Mr. C's information using a terminal and send it to the server. The server will then store Mr. C's information in a database and generate a unique user ID. After confirming this, the family members will pair the wearable device worn by Mr. C.

[0313] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[0314] If one day Mr. C begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. His family can then use their devices to check Mr. C's current location on a map and respond quickly.

[0315] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in daily life, such as sudden anxiety or excitement, the server will analyze them and send an alert to emergency contacts if necessary. This not only ensures the overall safety of dementia patients, but also enables quick response to emotional changes.

[0316] The processing flow will be explained below.

[0317] User Registration and Device Pairing

[0318] Step 1:

[0319] The user installs the dedicated application and opens the account creation screen.

[0320] Step 2:

[0321] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[0322] Step 3:

[0323] The server stores the received patient information in a database and generates a unique user ID.

[0324] Step 4:

[0325] The server returns the generated user ID to the terminal.

[0326] Step 5:

[0327] The terminal displays the user ID on the screen and asks the user to confirm it.

[0328] Step 6:

[0329] The user prepares the wearable device and sets it to pairing mode.

[0330] Step 7:

[0331] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[0332] Step 8:

[0333] The server records the wearable device information and associates it with the user ID.

[0334] Step 9:

[0335] The device displays a screen informing the user that pairing was successful.

[0336] Acquiring and saving location information

[0337] Step 1:

[0338] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[0339] Step 2:

[0340] The server receives the GPS data sent from the wearable device.

[0341] Step 3:

[0342] The server stores the received GPS data in a database and associates it with the user ID.

[0343] Behavioral pattern analysis and anomaly detection

[0344] Step 1:

[0345] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[0346] Step 2:

[0347] Identify the server's normal range of activity and frequently visited locations.

[0348] Step 3:

[0349] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[0350] Step 4:

[0351] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[0352] Step 5:

[0353] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[0354] Displaying location information

[0355] Step 1:

[0356] The user launches the dedicated application on their smartphone or tablet.

[0357] Step 2:

[0358] The terminal displays a login screen and prompts the user to enter their user ID and password.

[0359] Step 3:

[0360] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[0361] Step 4:

[0362] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[0363] Step 5:

[0364] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[0365] Emotion Recognition and Alerts

[0366] Step 1:

[0367] The emotion engine analyzes emotions from the movements, facial expressions, and voice of patients and caregivers.

[0368] Step 2:

[0369] The emotion engine periodically sends the analysis results to the server.

[0370] Step 3:

[0371] The server receives the emotion data and stores it in a database.

[0372] Step 4:

[0373] The server analyzes the emotion data and generates an alert if an abnormal emotional change (e.g., sudden anxiety, excitement, or sadness) is detected.

[0374] Step 5:

[0375] If the server detects an abnormality, it will send an alert about the emotional change to registered emergency contacts.

[0376] Through these processing steps, the system can monitor not only the location information of dementia patients but also their emotional changes in real time, and respond quickly when an abnormality occurs, thereby ensuring the overall safety of patients and reducing the burden on their families and caregivers.

[0377] Example 2

[0378] 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."

[0379] The risk of dementia patients wandering or getting lost is a serious problem, and real-time location monitoring is essential to ensure their safety. Furthermore, it is necessary not only to know their location but also to respond quickly to changes in the patient's emotions. Current location tracking systems lack the ability to respond to changes in the patient's emotions, and safety improvements are needed.

[0380] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes. This makes it possible to not only monitor location information but also respond to emotional changes in the dementia patient in real time.

[0381] The "location information acquisition means" is a device or system that acquires the current location of a dementia patient in real time.

[0382] A "database" is a storage system for storing and managing acquired location information and emotion data.

[0383] A "means for learning behavioral patterns" is a method or system for analyzing historical location data and identifying typical behavioral patterns of a person with dementia.

[0384] A "means for detecting anomalous movement patterns" is a method or system that analyzes new location data acquired in real time to detect anomalous movement that deviates from normal patterns of behavior.

[0385] An "alert sending means" is a device or system that sends a notification to pre-defined emergency contacts when an abnormal movement pattern or emotional change is detected.

[0386] The "means for displaying on a map" is a device or system for visually displaying the obtained current location information on a map.

[0387] "Emotion recognition means" refers to a device or system that analyzes the user's movements, facial expressions, and voice data to detect changes in emotions.

[0388] The "means for detecting abnormal emotional changes" is a method or system that analyzes emotional data and detects abnormal emotional changes that deviate from normal states.

[0389] This invention is a location tracking system for ensuring the safety of dementia patients by reducing the risk of them wandering or getting lost. This system includes a location information acquisition means for determining the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current location of the patient on a map, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes.

[0390] System Configuration

[0391] 1. Wearable devices

[0392] Wearable devices are devices that dementia patients wear at all times and contain a built-in GPS module. They measure their current location at regular intervals and send that location information to a server. For example, commercially available GPS trackers are used for wearable devices.

[0393] 2. Database

[0394] The database is a storage device for recording received location information and emotion data. The server uses this database to manage and analyze past location data and emotion data. A general relational database system (RDBMS) is used for the database.

[0395] 3. Server

[0396] The server is the central component that receives, stores, and analyzes location information. It uses an AI model to learn from past location data and detect unusual movement patterns. The server also analyzes emotion data and generates alerts if abnormal emotion changes are detected. The servers used include general cloud servers.

[0397] 4. Dedicated application (terminal)

[0398] The dedicated application is intended for use by family members and caregivers, and provides a function to display the patient's current location on a map. The application also has a function to notify users if an abnormality is detected. For example, this application can be installed on a smartphone or tablet.

[0399] 5. Emotion Engine

[0400] The emotion engine is a device or system that analyzes the emotions of users (dementia patients, family members, and caregivers) from their movements, facial expressions, and voices. This allows it to monitor emotional changes in patients and caregivers in real time, and if any abnormalities are detected, the data is sent to a server.

[0401] Specific examples

[0402] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they enter Mr. C's information using a dedicated application and send it to the server. The server then saves Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair the wearable device worn by Mr. C.

[0403] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[0404] If one day Mr. C begins to move far outside his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. Using a dedicated application, his family can check Mr. C's current location on a map and respond quickly.

[0405] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in his daily life, such as sudden anxiety or excitement, the server will analyze it and, if necessary, send an alert to emergency contacts.

[0406] Prompt Sentence Examples

[0407] "Let's say the family of a dementia patient, Mr. C, uses this system. First, they enter information about Mr. C using a terminal and send it to the server. The server then stores Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair it with the wearable device worn by Mr. C."

[0408] This invention will dramatically improve the safety of dementia patients and reduce the burden on their families and caregivers.

[0409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0410] Program processing flow

[0411] Step 1:

[0412] Device: The family member or caregiver launches the dedicated application and enters the patient's basic information (name, age, address, emergency contact information, etc.), then presses the "Send" button to send the entered information to the server.

[0413] Input: Patient basic information.

[0414] Output: Basic patient information sent to the server.

[0415] Processing: The application formats the information entered, encrypts it, and sends it to the server.

[0416] Step 2:

[0417] Server: Analyzes the received patient information and saves it in a database. After saving, it generates a unique user ID and returns it to the terminal.

[0418] Input: Basic patient information submitted.

[0419] Output: A unique user ID.

[0420] Processing: The server stores the information in a database and generates a unique user ID that is sent back to the device.

[0421] Step 3:

[0422] Device: The device checks the received user ID and pairs with the wearable device.

[0423] Input: A unique user ID.

[0424] Output: Pairing completion status.

[0425] Processing: The terminal associates the user ID with the wearable device and performs the pairing process.

[0426] Step 4:

[0427] Wearable device: Acquires GPS data at regular intervals (for example, every 5 minutes) and sends it to a server.

[0428] Input: Current location.

[0429] Output: Location data sent to the server.

[0430] Processing: The GPS module acquires the current location, encrypts the data, and sends it to the server.

[0431] Step 5:

[0432] Server: Stores the received location data in a database. It sequentially analyzes past location data and uses an AI model to learn normal behavioral patterns.

[0433] Input: Location data.

[0434] Output: Location information stored in a database and a learned behavioral pattern model.

[0435] Processing: The location information is stored in a database and an AI model is used to learn normal behavior patterns.

[0436] Step 6:

[0437] Server: Analyzes new location data in real time to detect unusual movement patterns and, if an anomaly is detected, sends a notification to emergency contacts.

[0438] Input: Real-time location information.

[0439] Output: Detection and notification of anomalous movement patterns.

[0440] Processing: Analyze the new location data and send a notification to emergency contacts if an anomaly is detected.

[0441] Step 7:

[0442] Terminal: Family members and caregivers can check the patient's current location on a map using a dedicated application.

[0443] Input: A real-time location request.

[0444] Output: Current location displayed on a map.

[0445] Processing: The device retrieves the latest location information from the server and displays it on a map.

[0446] Step 8:

[0447] Emotion engine: Collects movement, facial expression, and voice data from patients and caregivers and analyzes their emotions.

[0448] Input: movement, facial expression, and voice data.

[0449] Output: Sentiment analysis results.

[0450] Processing: The emotion engine analyzes movement, facial expression, and voice data to assess the emotional state.

[0451] Step 9:

[0452] Server: Receives and analyzes emotion data. If abnormal emotion changes are detected, an alert is generated and a notification is sent to emergency contacts.

[0453] Input: Sentiment analysis result data.

[0454] Output: Detection and notification of abnormal emotional changes.

[0455] Processing: The server analyzes the emotion data and sends a notification to emergency contacts if it detects any abnormal changes.

[0456] This series of processes makes it possible to monitor the location information of dementia patients and respond quickly to emotional changes.

[0457] (Application example 2)

[0458] 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."

[0459] In the daily lives of dementia patients, the risk of getting lost and sudden emotional changes have become social issues. Ensuring safety is particularly important when going out or delivering meals, but conventional technology has not been able to adequately monitor not only the location information of dementia patients and delivery personnel, but also emotional changes. This has meant that sufficient consideration cannot be given to the safety of both patients and delivery personnel, making it difficult to reduce the risks for both.

[0460] 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.

[0461] In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current patient location on a map, a means for acquiring and recording the location information of the delivery person, a means for analyzing the emotions of both the delivery person and the dementia patient and detecting abnormal emotional changes, and a means for sending an alert when an abnormal emotional change is detected. This makes it possible to monitor the location information and emotional changes of both the dementia patient and the delivery person in real time and respond immediately when an abnormality occurs.

[0462] "Location information acquisition means" refers to devices or technologies used to determine the current location of dementia patients or delivery personnel in real time.

[0463] A "database" is an information system for recording and storing information such as acquired location information and emotional data.

[0464] A "behavioral pattern learning means" is a device or technology used to analyze historical location data and identify typical behavioral patterns.

[0465] "Abnormal movement pattern detection means" refers to devices or techniques used to analyze location data acquired in real time and detect abnormal movements that differ from normal behavior patterns.

[0466] An "alert sending means" is a device or technology that sends a notification to emergency contacts when an abnormal movement pattern or emotional change is detected.

[0467] "Real-time display means" refers to the device or technology used to display the current location of patients and delivery personnel on a map.

[0468] "Emotion analysis means" refers to devices and technologies used to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery personnel.

[0469] An "abnormal emotion detection means" is a device or technology used to detect abnormal changes in the emotions of dementia patients or delivery personnel.

[0470] This invention is a system for ensuring the safety of dementia patients and delivery personnel, and for monitoring abnormal movement patterns and emotional changes in real time. To implement this system, the following components and means are used.

[0471] System Configuration

[0472] 1. Location information acquisition means:

[0473] The wearable devices carried by dementia patients and delivery workers acquire location information. These devices have a built-in GPS module that measures their current location at regular intervals and transmits the information to a server.

[0474] 2. Database:

[0475] The server stores location information and emotion data in a database, using a database management system such as PostgreSQL.

[0476] 3. Behavioral pattern learning methods:

[0477] The server collects and analyzes historical location data to identify typical behavioral patterns, using machine learning frameworks such as TENSORFLOW®.

[0478] 4. Abnormal movement pattern detection method:

[0479] The server analyzes new location data in real time to detect anomalous movements that deviate from normal patterns of behavior, again using TensorFlow.

[0480] 5. Alert delivery method:

[0481] The server sends notifications to emergency contacts when unusual movement patterns or emotional changes are detected, for example, via SMS or push notifications.

[0482] 6. Real-time display means:

[0483] Family members and caregivers can check the current location of dementia patients and delivery personnel on a map through a dedicated application. The application runs on iOS and ANDROID (registered trademark) and is developed in Swift and Kotlin.

[0484] 7. Sentiment analysis tools:

[0485] The server uses an emotion engine to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery workers, using OpenCV and Google Cloud Speech-to-Text API.

[0486] 8. Abnormal Emotion Detection Methods:

[0487] The server analyzes the emotional data and generates an alert if it detects any abnormal emotional changes, which is also sent to emergency contacts.

[0488] Specific examples

[0489] Suppose a patient with dementia uses a meal delivery service. The wearable devices carried by the patient and delivery person collect location information and send it to a server. The server stores this information in a database and uses a machine learning model to learn normal behavioral patterns. If an abnormality is detected, a notification is sent to emergency contacts. Through a dedicated application, family members and caregivers can monitor the current location of the patient and delivery person in real time. Furthermore, an emotion engine analyzes the emotions of the patient and delivery person and generates an alert if an abnormality is detected.

[0490] Prompt Sentence Examples

[0491] Generate code suitable for building a location tracking and emotion recognition system like this:

[0492] The wearable device acquires GPS data at regular intervals and sends it to the server.

[0493] The server receives location information and detects anomalies using an AI model

[0494] The emotion engine analyzes emotions from facial expressions and voice, and generates an alert if there is an abnormality.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] Acquiring and sending location information

[0498] A wearable device carried by a user (in this case, a dementia patient or a delivery person) acquires GPS data at regular intervals. The acquired location data (input) is sent from the wearable device to a server (output). At this time, location measurement is performed using the GPS module in the device.

[0499] Step 2:

[0500] Save location information

[0501] The server stores the received location data (input) in a database (output), using a database management system such as PostgreSQL, and the stored data is used for later analysis.

[0502] Step 3:

[0503] Learning behavioral patterns

[0504] The server uses past location data (input) to learn normal behavioral patterns using a machine learning framework such as TensorFlow (output). The learned model is then used to identify the user's normal behavioral patterns.

[0505] Step 4:

[0506] Analyzing real-time location data

[0507] The server analyzes newly received location data (input) in real time and compares it with previously learned behavioral pattern models. If an abnormal movement pattern is detected, the information (output) is sent to the next step. TensorFlow is used to compare the current data with the model and detect anomalies.

[0508] Step 5:

[0509] Sending alerts

[0510] The server sends a notification message to emergency contacts when an abnormal movement pattern is detected (output). This notification can be sent via SMS or push notification. The data triggers the notification system when an abnormality is detected.

[0511] Step 6:

[0512] Real-time location information display

[0513] The device uses a dedicated application to display the user's current location data (input) on a map (output). By checking the map, the user can understand the current location of the dementia patient or delivery person.

[0514] Step 7:

[0515] Acquiring and analyzing emotion data

[0516] Wearable devices and other sensors capture the movements, facial expressions, and voice data (input) of dementia patients and delivery workers. The server receives this data, analyzes emotions using OpenCV and Google Cloud Speech-to-Text API, and obtains the results (output).

[0517] Step 8:

[0518] Detecting abnormal emotional changes

[0519] The server detects abnormal emotional changes based on the analysis results (input). If an abnormality is detected, the information (output) is sent to the next step. To detect an abnormality, the pattern of emotional changes is compared with pre-set criteria.

[0520] Step 9:

[0521] Emotional abnormality alert sending

[0522] When an abnormal emotion change is detected, the server sends a notification message to emergency contacts (output). This is also sent via SMS or push notification. The abnormal emotion data triggers the notification system.

[0523] 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.

[0524] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0525] 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.

[0526] [Second embodiment]

[0527] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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).

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] 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."

[0539] This invention is a location tracking system that aims to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[0540] System Configuration

[0541] 1. Wearable devices:

[0542] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0543] 2. Database:

[0544] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[0545] 3. Server:

[0546] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[0547] 4. Dedicated application (terminal):

[0548] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[0549] Feature Overview

[0550] The system provides the following main functions:

[0551] 1. User registration and device pairing

[0552] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0553] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0554] 2. Acquiring and saving location information

[0555] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[0556] Server: Receives data and records it in a database.

[0557] 3. Behavioral Pattern Analysis and Anomaly Detection

[0558] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[0559] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[0560] 4. Displaying location information

[0561] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[0562] Specific examples

[0563] Let's assume that the family of a dementia patient, Person B, will use this system. First, they enter Person B's information using a terminal and send it to the server. The server then stores Person B's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device worn by Person B.

[0564] When B leaves the house, the wearable device continues to collect GPS data and transmits it to the server at regular intervals. The server stores the data in a database and uses an AI model to learn his or her usual behavioral patterns.

[0565] If one day, Mr. B begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Using the device, his family can check Mr. B's current location on a map and respond quickly.

[0566] In this way, the system ensures the safety of dementia patients and reduces the burden on their families and caregivers.

[0567] The processing flow will be explained below.

[0568] User Registration and Device Pairing

[0569] Step 1:

[0570] The user installs the dedicated application and opens the account creation screen.

[0571] Step 2:

[0572] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[0573] Step 3:

[0574] The server stores the received patient information in a database and generates a unique user ID.

[0575] Step 4:

[0576] The server returns the generated user ID to the terminal.

[0577] Step 5:

[0578] The terminal displays the user ID on the screen and asks the user to confirm it.

[0579] Step 6:

[0580] The user prepares the wearable device and sets it to pairing mode.

[0581] Step 7:

[0582] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[0583] Step 8:

[0584] The server records the wearable device information and associates it with the user ID.

[0585] Step 9:

[0586] The device displays a screen informing the user that pairing was successful.

[0587] Acquiring and saving location information

[0588] Step 1:

[0589] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[0590] Step 2:

[0591] The server receives the GPS data sent from the wearable device.

[0592] Step 3:

[0593] The server stores the received GPS data in a database and associates it with the user ID.

[0594] Behavioral pattern analysis and anomaly detection

[0595] Step 1:

[0596] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[0597] Step 2:

[0598] Identify the server's normal range of activity and frequently visited locations.

[0599] Step 3:

[0600] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[0601] Step 4:

[0602] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[0603] Step 5:

[0604] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[0605] Displaying location information

[0606] Step 1:

[0607] The user launches the dedicated application on their smartphone or tablet.

[0608] Step 2:

[0609] The terminal displays a login screen and prompts the user to enter their user ID and password.

[0610] Step 3:

[0611] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[0612] Step 4:

[0613] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[0614] Step 5:

[0615] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[0616] Example 1

[0617] 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."

[0618] There is a high risk that patients with dementia will get lost, which could result in a threat to their safety. Furthermore, it is not always possible for family members or caregivers to monitor patients with dementia, placing a heavy burden on them. In these circumstances, a system is needed that can track the current location of patients with dementia in real time and respond quickly if any abnormal movements occur.

[0619] 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.

[0620] In this invention, the server includes means for saving the received basic information in a database and generating a unique user ID, means for receiving and saving GPS data acquired by the location information acquisition means, means for learning past location data using an AI model, analyzing new location data in real time, and detecting abnormal movement patterns, means for sending a notification message to an emergency contact when an abnormal movement pattern is detected, and means for displaying the current patient location on a map. This makes it possible to ensure the safety of dementia patients and reduce the burden on their families and caregivers.

[0621] A "user" is a person who inputs basic information about a dementia patient and sends it to the server via a dedicated application.

[0622] The "server" is a device that stores the received basic information in a database and generates a unique user ID.

[0623] A "database" is a storage device that records and manages location information and basic information received by the server.

[0624] A "unique user ID" is an identification number that is uniquely assigned to each dementia patient.

[0625] "Location information acquisition means" refers to a device or method that acquires the current location of a dementia patient using a wearable device.

[0626] A "wearable device" is a device worn by a dementia patient that collects GPS data and transmits it to a server.

[0627] An "AI model" is an artificial intelligence algorithm that learns from past location data and identifies normal behavioral patterns.

[0628] An "abnormal movement pattern" is location data that deviates significantly from normal behavioral patterns.

[0629] A "notification message" is a communication containing an alert sent to family members or caregivers when an abnormal movement pattern is detected.

[0630] The "dedicated application" is software that family members and caregivers can use to check the current location of a person with dementia.

[0631] "Real-time" is a term that refers to the immediate processing and display of current conditions and data without delay.

[0632] This invention is a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[0633] System Configuration

[0634] 1. Wearable device: A device that is always worn by a dementia patient and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0635] 2. Database: A storage device that records received location information. The server uses this database to manage and analyze past location data.

[0636] 3. Server: This is the central component that receives, stores, and analyzes location information. It uses AI models to learn from past location data and detect unusual movement patterns.

[0637] 4. Dedicated application (terminal): This application is for use by family members and caregivers and displays the patient's current location on a map, and notifies them if any abnormalities are detected.

[0638] Feature Overview

[0639] The system provides the following main functions:

[0640] User Registration and Device Pairing

[0641] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0642] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0643] Acquiring and sending location information

[0644] Wearable device: Uses the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes), and sends the acquired location information to a server.

[0645] Server: Processes the received location information and stores it in a database.

[0646] Behavioral pattern analysis and anomaly detection

[0647] Server: AI models are used to study past location data and identify normal patterns of behavior. As new location data arrives on the server, it is analyzed in real time to check for any unusual movement patterns.

[0648] Notification when an abnormality is detected

[0649] Server: If an anomaly is detected, a notification message is sent to emergency contacts, including the current location and the type of anomaly (e.g., unexpected location, significant movement, etc.).

[0650] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[0651] Displaying location information

[0652] Device: By opening the dedicated application, family members and caregivers can check the current location of the person with dementia on a map. The map display screen also shows real-time location information and past movement history.

[0653] Server: When it receives a location request from the device, it sends the latest location data to the device.

[0654] Specific examples of operation

[0655] Let's assume that a family member of a dementia patient uses this system. First, they enter the patient's information using a terminal and send it to the server. The server stores the patient's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device with the patient.

[0656] When the patient leaves the home, the wearable device continuously collects GPS data and transmits it to a server at regular intervals. The server stores the data in a database and uses an AI model to learn normal behavioral patterns.

[0657] If one day the patient begins to move far beyond their usual range of movement, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Family members can then use their devices to check the patient's current location on a map and respond quickly.

[0658] Prompt Sentence Examples

[0659] "Please explain in detail the four components of a location tracking system for dementia patients: a wearable device, a database, a server, and a dedicated application."

[0660] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0661] Step 1: User Registration

[0662] Device: The family member or caregiver launches the dedicated application and opens the user registration screen. They enter basic information such as the dementia patient's name, age, address, and emergency contact information, and click the "Register" button. This basic information becomes the input data.

[0663] Server: Receives the entered basic information and saves it in the database. After saving, it generates a unique user ID and sends it back to the terminal. This is the output data.

[0664] Step 2: Device pairing

[0665] Device: After receiving the unique user ID, you will be taken to a screen for pairing with the wearable device. Enter the user ID into the device and click the pairing button. The wearable device will then receive the user ID.

[0666] Wearable device: Recognizes the user ID and sends a message to the server indicating that pairing is complete.

[0667] Step 3: Obtaining location information

[0668] Wearable devices: Use the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes). The acquired location information becomes input data.

[0669] Wearable device: Sends the acquired location information to the server, which is the output data.

[0670] Step 4: Save location

[0671] Server: Stores the received location information in a database. The stored location information includes the location coordinates and acquisition time. This is the input data.

[0672] Server: Stored in a database for future analysis.

[0673] Step 5: Learning behavioral patterns

[0674] Server: Uses an AI model to learn from past location data and identify normal behavioral patterns. This is the input data.

[0675] Server: The analysis results include the range and patterns of normal behavior.

[0676] Step 6: Real-time analysis

[0677] Server: When new location data arrives at the server, it is analyzed in real time using an AI model. It compares it with past learning results to detect abnormal movement patterns. The new location data is the input data.

[0678] Server: If an anomaly is detected, send a notification message to emergency contacts. This is the output data.

[0679] Step 7: Notification when an anomaly is detected

[0680] Server: If an anomaly is detected, a notification message is sent to the registered emergency contacts. The message contains the current location and the type of anomaly.

[0681] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[0682] Step 8: View location information

[0683] Device: Open the dedicated application and go to the screen where you can check the current location of the dementia patient on a map.

[0684] Server: Receives location requests from devices and sends the latest location data to the devices. The request is the input data, and the location data is the output data.

[0685] Device: Real-time location information and past movement history are displayed on a map, allowing family members and caregivers to respond quickly.

[0686] (Application example 1)

[0687] 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."

[0688] There is a problem that dementia patients and food delivery drivers deviate from their normal range of movement, getting lost or behaving abnormally, threatening their safety. Such situations are very stressful for family members and managers, and require a quick response, but there is a lack of means to grasp location information in real time and detect abnormalities. Therefore, a system is needed that can grasp the current location of dementia patients and drivers in real time, and detect and notify abnormal movement patterns.

[0689] 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.

[0690] In this invention, the server includes a location information acquisition means, a means for storing the acquired location information in a database, a means for analyzing past location data and learning normal behavioral patterns using a machine learning model, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, and a means for displaying the current location of the subject on a map. This ensures the safety of the subject and enables a prompt response when an abnormality occurs. This reduces the burden on family members and administrators and enables effective monitoring of the subject's behavior.

[0691] A "dementia patient" refers to an adult who has impaired memory or cognitive abilities and requires assistance with daily living.

[0692] A "food delivery driver" is a person whose job is to drive a predetermined route to deliver requested food to customers.

[0693] "Current location" refers to the geographic location where a subject is located in real time.

[0694] "Real-time" refers to the instant information is processed and transmitted.

[0695] "Location information acquisition means" refers to a device or system that collects a subject's current location using GPS or other location identification technology.

[0696] A "database" refers to a system that systematically stores information such as saved location information and past movement data.

[0697] A "machine learning model" refers to a model that includes algorithms that allow a computer to learn data patterns and make future predictions or detect anomalies.

[0698] "Abnormal movement patterns" refer to movements that deviate significantly from normal patterns of behavior, including unexpected route changes and long periods of stoppage.

[0699] "Means for sending alerts" refers to a system or device for sending notifications or warnings to pre-designated recipients when abnormal movement is detected.

[0700] "Means for displaying on a map" refers to a system or application that visually displays collected location information, allowing users to easily check their current location.

[0701] A "wearable device" is an electronic device that can be worn by a subject and has the function of collecting location information, etc.

[0702] A "smartphone" refers to a mobile device based on a mobile operating system and equipped with multifunctional applications and communication functions.

[0703] "Emergency Contact" refers to a designated contact to be contacted in the event of abnormal movement.

[0704] This invention is a system for tracking the current locations of dementia patients and food delivery drivers in real time to ensure their safety. The system mainly consists of the following components: a location information acquisition means, a database, a server, and a dedicated application (terminal).

[0705] System Configuration

[0706] 1. Location information acquisition means:

[0707] It consists of a wearable device or smartphone that is constantly worn by dementia patients and food delivery drivers. This device has a built-in GPS module that measures their current location at regular intervals and transmits the data to a server.

[0708] 2. Database:

[0709] This is a storage device that records the location information received by the server. The server uses this database to manage and analyze past location data.

[0710] 3. Server:

[0711] It is the central component that receives, stores, and analyzes location information, and uses AI and machine learning models to learn from past location data and detect anomalous movement patterns.

[0712] 4. Dedicated application (terminal):

[0713] The application is intended for use by family members, caregivers, or food delivery system managers, and displays the current location of the person on a map and notifies them if any abnormalities are detected.

[0714] Program processing

[0715] Hardware and software used

[0716] Hardware: GPS-enabled smartphones, wearable devices

[0717] Software: Python (scripting language), Requests module, scikit-learn module, SMTP library

[0718] Data processing and calculation

[0719] 1. Location information acquisition:

[0720] Smartphones and wearable devices use GPS to obtain real-time location information, which is then sent to a server at regular intervals.

[0721] 2. Sending location information to the server:

[0722] The server stores the received data in a database and manages past location data. This location information is then used to feed the data into a machine learning model to learn normal behavioral patterns.

[0723] 3. Analysis by AI model:

[0724] A server-based machine learning model analyzes new location data in real time to detect unusual movement patterns, and if an anomaly is detected, it immediately sends a notification to emergency contacts or food delivery system administrators.

[0725] 4. Location display:

[0726] The application displays the received data on a map, allowing the user to view their current location and past movement history.

[0727] Specific examples

[0728] Cases of dementia patients

[0729] 1. Family members use a dedicated application to enter basic information about the person with dementia and send it to the server.

[0730] 2. The server stores the received information in a database and generates a unique user ID. Once the family member confirms this, they can pair the patient's wearable device.

[0731] 3. When the patient goes out, the wearable device continues to acquire GPS data and transmits it to the server at regular intervals.

[0732] 4. The server stores the received data in a database and uses machine learning models to learn normal behavioral patterns.

[0733] 5. If the patient begins to move significantly outside of their normal range of movement, the server will detect this as an abnormality and immediately send a notification to registered emergency contacts.

[0734] The case of a food delivery driver

[0735] 1. The administrator uses a dedicated application to enter driver information and send it to the server.

[0736] 2. The server stores the received information in a database and generates a unique user ID. After verifying this, the administrator pairs the driver's smartphone with the driver's smartphone.

[0737] 3. When the driver begins a delivery, the smartphone collects GPS data in real time and sends it to the server at regular intervals.

[0738] 4. The server stores the received data in a database and uses a machine learning model to learn the normal delivery route.

[0739] 5. If a driver significantly deviates from their normal delivery route, the server will detect this as an anomaly and immediately send a notification to the administrator.

[0740] Example prompt sentence:

[0741] "Build a system that tracks your drivers' locations and notifies you if they deviate significantly from their normal delivery routes."

[0742] This system ensures the safety of the subject and enables prompt response in the event of an abnormality, reducing the burden on family members and administrators and enabling effective monitoring of the subject's behavior.

[0743] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0744] Step 1:

[0745] User Registration and Device Pairing

[0746] Using a dedicated application, users enter basic information about the dementia patient and food delivery driver (such as name, age, and emergency contact information) and send it to the server.

[0747] The server stores the received information in a database, generates a unique user ID, and returns this user ID to the device.

[0748] The user confirms their user ID and pairs with their wearable device or smartphone.

[0749] Input: Basic information of dementia patient and driver

[0750] Output: Information stored in the database and a unique user ID.

[0751] Step 2:

[0752] Obtaining location information

[0753] The location information acquisition means (wearable device or smartphone) acquires GPS data at regular intervals.

[0754] Input: Current location GPS data

[0755] Output: Acquired GPS data

[0756] Step 3:

[0757] Sending location information to the server

[0758] The GPS data acquired by the location information acquisition means is transmitted to a server.

[0759] The server stores the received GPS data in a database.

[0760] Input: Acquired GPS data

[0761] Output: GPS data stored in a database

[0762] Step 4:

[0763] Learning behavioral patterns

[0764] The server collects location data over a period of time and learns behavioral patterns using a machine learning model.

[0765] Identify typical behavioral patterns based on past location data.

[0766] Input: Location data stored in a database

[0767] Output: Learned normal behavior patterns

[0768] Step 5:

[0769] Real-time analytics and anomaly detection

[0770] The server receives new location data in real time and compares it with previously learned normal behavior patterns.

[0771] If an abnormal movement pattern is detected, it is determined to be abnormal.

[0772] Input: Real-time location data

[0773] Output: Determine if the movement pattern is abnormal

[0774] Step 6:

[0775] Sending alerts

[0776] The server will send a notification message to designated emergency contacts and administrators when an abnormal movement pattern is detected.

[0777] Input: Abnormal movement pattern detection results

[0778] Output: Send notification message

[0779] Step 7:

[0780] Displaying location information

[0781] The device visually displays the received location data on a map, allowing the user to verify the location.

[0782] Input: Real-time location data

[0783] Output: Current location and past movement history displayed on a map

[0784] Through the above processing steps, this system is able to monitor the current locations of dementia patients and food delivery drivers in real time to ensure their safety.

[0785] 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.

[0786] This invention is a system that combines a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety, with an emotion engine that recognizes the user's emotions. The system mainly includes the following components: a wearable device, a database, a server, a dedicated application (terminal), and an emotion engine.

[0787] System Configuration

[0788] 1. Wearable devices:

[0789] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[0790] 2. Database:

[0791] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[0792] 3. Server:

[0793] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[0794] 4. Dedicated application (terminal):

[0795] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[0796] 5. Emotion Engine:

[0797] This is a device or system that recognizes the emotions of users (dementia patients, family members, and caregivers) and analyzes their emotions from their movements, facial expressions, and voice.

[0798] Feature Overview

[0799] The system provides the following main functions:

[0800] 1. User registration and device pairing

[0801] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[0802] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[0803] 2. Acquiring and saving location information

[0804] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[0805] Server: Receives data and records it in a database.

[0806] 3. Behavioral Pattern Analysis and Anomaly Detection

[0807] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[0808] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[0809] 4. Displaying location information

[0810] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[0811] 5. Emotion Recognition and Alerts

[0812] Emotion engine: Analyzes emotions from the movements, facial expressions, and voice of patients and caregivers, and sends the data to a server.

[0813] Server: Analyzes emotion data and generates alerts when abnormal emotion changes are detected.

[0814] Server: When an alert is generated, it sends a notification message to emergency contacts.

[0815] Specific examples

[0816] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they will enter Mr. C's information using a terminal and send it to the server. The server will then store Mr. C's information in a database and generate a unique user ID. After confirming this, the family members will pair the wearable device worn by Mr. C.

[0817] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[0818] If one day Mr. C begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. His family can then use their devices to check Mr. C's current location on a map and respond quickly.

[0819] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in daily life, such as sudden anxiety or excitement, the server will analyze them and send an alert to emergency contacts if necessary. This not only ensures the overall safety of dementia patients, but also enables quick response to emotional changes.

[0820] The processing flow will be explained below.

[0821] User Registration and Device Pairing

[0822] Step 1:

[0823] The user installs the dedicated application and opens the account creation screen.

[0824] Step 2:

[0825] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[0826] Step 3:

[0827] The server stores the received patient information in a database and generates a unique user ID.

[0828] Step 4:

[0829] The server returns the generated user ID to the terminal.

[0830] Step 5:

[0831] The terminal displays the user ID on the screen and asks the user to confirm it.

[0832] Step 6:

[0833] The user prepares the wearable device and sets it to pairing mode.

[0834] Step 7:

[0835] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[0836] Step 8:

[0837] The server records the wearable device information and associates it with the user ID.

[0838] Step 9:

[0839] The device displays a screen informing the user that pairing was successful.

[0840] Acquiring and saving location information

[0841] Step 1:

[0842] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[0843] Step 2:

[0844] The server receives the GPS data sent from the wearable device.

[0845] Step 3:

[0846] The server stores the received GPS data in a database and associates it with the user ID.

[0847] Behavioral pattern analysis and anomaly detection

[0848] Step 1:

[0849] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[0850] Step 2:

[0851] Identify the server's normal range of activity and frequently visited locations.

[0852] Step 3:

[0853] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[0854] Step 4:

[0855] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[0856] Step 5:

[0857] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[0858] Displaying location information

[0859] Step 1:

[0860] The user launches the dedicated application on their smartphone or tablet.

[0861] Step 2:

[0862] The terminal displays a login screen and prompts the user to enter their user ID and password.

[0863] Step 3:

[0864] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[0865] Step 4:

[0866] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[0867] Step 5:

[0868] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[0869] Emotion Recognition and Alerts

[0870] Step 1:

[0871] The emotion engine analyzes emotions from the movements, facial expressions, and voice of patients and caregivers.

[0872] Step 2:

[0873] The emotion engine periodically sends the analysis results to the server.

[0874] Step 3:

[0875] The server receives the emotion data and stores it in a database.

[0876] Step 4:

[0877] The server analyzes the emotion data and generates an alert if an abnormal emotional change (e.g., sudden anxiety, excitement, or sadness) is detected.

[0878] Step 5:

[0879] If the server detects an abnormality, it will send an alert about the emotional change to registered emergency contacts.

[0880] Through these processing steps, the system can monitor not only the location information of dementia patients but also their emotional changes in real time, and respond quickly when an abnormality occurs, thereby ensuring the overall safety of patients and reducing the burden on their families and caregivers.

[0881] Example 2

[0882] 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."

[0883] The risk of dementia patients wandering or getting lost is a serious problem, and real-time location monitoring is essential to ensure their safety. Furthermore, it is necessary not only to know their location but also to respond quickly to changes in the patient's emotions. Current location tracking systems lack the ability to respond to changes in the patient's emotions, and safety improvements are needed.

[0884] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes. This makes it possible to not only monitor location information but also respond to emotional changes in the dementia patient in real time.

[0885] The "location information acquisition means" is a device or system that acquires the current location of a dementia patient in real time.

[0886] A "database" is a storage system for storing and managing acquired location information and emotion data.

[0887] A "means for learning behavioral patterns" is a method or system for analyzing historical location data and identifying typical behavioral patterns of a person with dementia.

[0888] A "means for detecting anomalous movement patterns" is a method or system that analyzes new location data acquired in real time to detect anomalous movement that deviates from normal patterns of behavior.

[0889] An "alert sending means" is a device or system that sends a notification to pre-defined emergency contacts when an abnormal movement pattern or emotional change is detected.

[0890] The "means for displaying on a map" is a device or system for visually displaying the obtained current location information on a map.

[0891] "Emotion recognition means" refers to a device or system that analyzes the user's movements, facial expressions, and voice data to detect changes in emotions.

[0892] The "means for detecting abnormal emotional changes" is a method or system that analyzes emotional data and detects abnormal emotional changes that deviate from normal states.

[0893] This invention is a location tracking system for ensuring the safety of dementia patients by reducing the risk of them wandering or getting lost. This system includes a location information acquisition means for determining the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current location of the patient on a map, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes.

[0894] System Configuration

[0895] 1. Wearable devices

[0896] Wearable devices are devices that dementia patients wear at all times and contain a built-in GPS module. They measure their current location at regular intervals and send that location information to a server. For example, commercially available GPS trackers are used for wearable devices.

[0897] 2. Database

[0898] The database is a storage device for recording received location information and emotion data. The server uses this database to manage and analyze past location data and emotion data. A general relational database system (RDBMS) is used for the database.

[0899] 3. Server

[0900] The server is the central component that receives, stores, and analyzes location information. It uses an AI model to learn from past location data and detect unusual movement patterns. The server also analyzes emotion data and generates alerts if abnormal emotion changes are detected. The servers used include general cloud servers.

[0901] 4. Dedicated application (terminal)

[0902] The dedicated application is intended for use by family members and caregivers, and provides a function to display the patient's current location on a map. The application also has a function to notify users if an abnormality is detected. For example, this application can be installed on a smartphone or tablet.

[0903] 5. Emotion Engine

[0904] The emotion engine is a device or system that analyzes the emotions of users (dementia patients, family members, and caregivers) from their movements, facial expressions, and voices. This allows it to monitor emotional changes in patients and caregivers in real time, and if any abnormalities are detected, the data is sent to a server.

[0905] Specific examples

[0906] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they enter Mr. C's information using a dedicated application and send it to the server. The server then saves Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair the wearable device worn by Mr. C.

[0907] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[0908] If one day Mr. C begins to move far outside his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. Using a dedicated application, his family can check Mr. C's current location on a map and respond quickly.

[0909] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in his daily life, such as sudden anxiety or excitement, the server will analyze it and, if necessary, send an alert to emergency contacts.

[0910] Prompt Sentence Examples

[0911] "Let's say the family of a dementia patient, Mr. C, uses this system. First, they enter information about Mr. C using a terminal and send it to the server. The server then stores Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair it with the wearable device worn by Mr. C."

[0912] This invention will dramatically improve the safety of dementia patients and reduce the burden on their families and caregivers.

[0913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0914] Program processing flow

[0915] Step 1:

[0916] Device: The family member or caregiver launches the dedicated application and enters the patient's basic information (name, age, address, emergency contact information, etc.), then presses the "Send" button to send the entered information to the server.

[0917] Input: Patient basic information.

[0918] Output: Basic patient information sent to the server.

[0919] Processing: The application formats the information entered, encrypts it, and sends it to the server.

[0920] Step 2:

[0921] Server: Analyzes the received patient information and saves it in a database. After saving, it generates a unique user ID and returns it to the terminal.

[0922] Input: Basic patient information submitted.

[0923] Output: A unique user ID.

[0924] Processing: The server stores the information in a database and generates a unique user ID that is sent back to the device.

[0925] Step 3:

[0926] Device: The device checks the received user ID and pairs with the wearable device.

[0927] Input: A unique user ID.

[0928] Output: Pairing completion status.

[0929] Processing: The terminal associates the user ID with the wearable device and performs the pairing process.

[0930] Step 4:

[0931] Wearable device: Acquires GPS data at regular intervals (for example, every 5 minutes) and sends it to a server.

[0932] Input: Current location.

[0933] Output: Location data sent to the server.

[0934] Processing: The GPS module acquires the current location, encrypts the data, and sends it to the server.

[0935] Step 5:

[0936] Server: Stores the received location data in a database. It sequentially analyzes past location data and uses an AI model to learn normal behavioral patterns.

[0937] Input: Location data.

[0938] Output: Location information stored in a database and a learned behavioral pattern model.

[0939] Processing: The location information is stored in a database and an AI model is used to learn normal behavior patterns.

[0940] Step 6:

[0941] Server: Analyzes new location data in real time to detect unusual movement patterns and, if an anomaly is detected, sends a notification to emergency contacts.

[0942] Input: Real-time location information.

[0943] Output: Detection and notification of anomalous movement patterns.

[0944] Processing: Analyze the new location data and send a notification to emergency contacts if an anomaly is detected.

[0945] Step 7:

[0946] Terminal: Family members and caregivers can check the patient's current location on a map using a dedicated application.

[0947] Input: A real-time location request.

[0948] Output: Current location displayed on a map.

[0949] Processing: The device retrieves the latest location information from the server and displays it on a map.

[0950] Step 8:

[0951] Emotion engine: Collects movement, facial expression, and voice data from patients and caregivers and analyzes their emotions.

[0952] Input: movement, facial expression, and voice data.

[0953] Output: Sentiment analysis results.

[0954] Processing: The emotion engine analyzes movement, facial expression, and voice data to assess the emotional state.

[0955] Step 9:

[0956] Server: Receives and analyzes emotion data. If abnormal emotion changes are detected, an alert is generated and a notification is sent to emergency contacts.

[0957] Input: Sentiment analysis result data.

[0958] Output: Detection and notification of abnormal emotional changes.

[0959] Processing: The server analyzes the emotion data and sends a notification to emergency contacts if it detects any abnormal changes.

[0960] This series of processes makes it possible to monitor the location information of dementia patients and respond quickly to emotional changes.

[0961] (Application example 2)

[0962] 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."

[0963] In the daily lives of dementia patients, the risk of getting lost and sudden emotional changes have become social issues. Ensuring safety is particularly important when going out or delivering meals, but conventional technology has not been able to adequately monitor not only the location information of dementia patients and delivery personnel, but also emotional changes. This has meant that sufficient consideration cannot be given to the safety of both patients and delivery personnel, making it difficult to reduce the risks for both.

[0964] 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.

[0965] In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current patient location on a map, a means for acquiring and recording the location information of the delivery person, a means for analyzing the emotions of both the delivery person and the dementia patient and detecting abnormal emotional changes, and a means for sending an alert when an abnormal emotional change is detected. This makes it possible to monitor the location information and emotional changes of both the dementia patient and the delivery person in real time and respond immediately when an abnormality occurs.

[0966] "Location information acquisition means" refers to devices or technologies used to determine the current location of dementia patients or delivery personnel in real time.

[0967] A "database" is an information system for recording and storing information such as acquired location information and emotional data.

[0968] A "behavioral pattern learning means" is a device or technology used to analyze historical location data and identify typical behavioral patterns.

[0969] "Abnormal movement pattern detection means" refers to devices or techniques used to analyze location data acquired in real time and detect abnormal movements that differ from normal behavior patterns.

[0970] An "alert sending means" is a device or technology that sends a notification to emergency contacts when an abnormal movement pattern or emotional change is detected.

[0971] "Real-time display means" refers to the device or technology used to display the current location of patients and delivery personnel on a map.

[0972] "Emotion analysis means" refers to devices and technologies used to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery personnel.

[0973] An "abnormal emotion detection means" is a device or technology used to detect abnormal changes in the emotions of dementia patients or delivery personnel.

[0974] This invention is a system for ensuring the safety of dementia patients and delivery personnel, and for monitoring abnormal movement patterns and emotional changes in real time. To implement this system, the following components and means are used.

[0975] System Configuration

[0976] 1. Location information acquisition means:

[0977] The wearable devices carried by dementia patients and delivery workers acquire location information. These devices have a built-in GPS module that measures their current location at regular intervals and transmits the information to a server.

[0978] 2. Database:

[0979] The server stores location information and emotion data in a database, using a database management system such as PostgreSQL.

[0980] 3. Behavioral pattern learning methods:

[0981] The server collects and analyzes historical location data to identify typical patterns of behavior, using machine learning frameworks such as TensorFlow.

[0982] 4. Abnormal movement pattern detection method:

[0983] The server analyzes new location data in real time to detect anomalous movements that deviate from normal patterns of behavior, again using TensorFlow.

[0984] 5. Alert delivery method:

[0985] The server sends notifications to emergency contacts when unusual movement patterns or emotional changes are detected, for example, via SMS or push notifications.

[0986] 6. Real-time display means:

[0987] Family members and caregivers can check the current location of dementia patients and delivery personnel on a map through a dedicated application, which runs on iOS and Android and is developed in Swift and Kotlin.

[0988] 7. Sentiment analysis tools:

[0989] The server uses an emotion engine to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery workers, using OpenCV and Google Cloud Speech-to-Text API.

[0990] 8. Abnormal Emotion Detection Methods:

[0991] The server analyzes the emotional data and generates an alert if it detects any abnormal emotional changes, which is also sent to emergency contacts.

[0992] Specific examples

[0993] Suppose a patient with dementia uses a meal delivery service. The wearable devices carried by the patient and delivery person collect location information and send it to a server. The server stores this information in a database and uses a machine learning model to learn normal behavioral patterns. If an abnormality is detected, a notification is sent to emergency contacts. Through a dedicated application, family members and caregivers can monitor the current location of the patient and delivery person in real time. Furthermore, an emotion engine analyzes the emotions of the patient and delivery person and generates an alert if an abnormality is detected.

[0994] Prompt Sentence Examples

[0995] Generate code suitable for building a location tracking and emotion recognition system like this:

[0996] The wearable device acquires GPS data at regular intervals and sends it to the server.

[0997] The server receives location information and detects anomalies using an AI model

[0998] The emotion engine analyzes emotions from facial expressions and voice, and generates an alert if there is an abnormality.

[0999] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1000] Step 1:

[1001] Acquiring and sending location information

[1002] A wearable device carried by a user (in this case, a dementia patient or a delivery person) acquires GPS data at regular intervals. The acquired location data (input) is sent from the wearable device to a server (output). At this time, location measurement is performed using the GPS module in the device.

[1003] Step 2:

[1004] Save location information

[1005] The server stores the received location data (input) in a database (output), using a database management system such as PostgreSQL, and the stored data is used for later analysis.

[1006] Step 3:

[1007] Learning behavioral patterns

[1008] The server uses past location data (input) to learn normal behavioral patterns using a machine learning framework such as TensorFlow (output). The learned model is then used to identify the user's normal behavioral patterns.

[1009] Step 4:

[1010] Analyzing real-time location data

[1011] The server analyzes newly received location data (input) in real time and compares it with previously learned behavioral pattern models. If an abnormal movement pattern is detected, the information (output) is sent to the next step. TensorFlow is used to compare the current data with the model and detect anomalies.

[1012] Step 5:

[1013] Sending alerts

[1014] The server sends a notification message to emergency contacts when an abnormal movement pattern is detected (output). This notification can be sent via SMS or push notification. The data triggers the notification system when an abnormality is detected.

[1015] Step 6:

[1016] Real-time location information display

[1017] The device uses a dedicated application to display the user's current location data (input) on a map (output). By checking the map, the user can understand the current location of the dementia patient or delivery person.

[1018] Step 7:

[1019] Acquiring and analyzing emotion data

[1020] Wearable devices and other sensors capture the movements, facial expressions, and voice data (input) of dementia patients and delivery workers. The server receives this data, analyzes emotions using OpenCV and Google Cloud Speech-to-Text API, and obtains the results (output).

[1021] Step 8:

[1022] Detecting abnormal emotional changes

[1023] The server detects abnormal emotional changes based on the analysis results (input). If an abnormality is detected, the information (output) is sent to the next step. To detect an abnormality, the pattern of emotional changes is compared with pre-set criteria.

[1024] Step 9:

[1025] Emotional abnormality alert sending

[1026] When an abnormal emotion change is detected, the server sends a notification message to emergency contacts (output). This is also sent via SMS or push notification. The abnormal emotion data triggers the notification system.

[1027] 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.

[1028] 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.

[1029] 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.

[1030] [Third embodiment]

[1031] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1032] 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.

[1033] 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).

[1034] 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.

[1035] 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.

[1036] 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).

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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."

[1043] This invention is a location tracking system that aims to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[1044] System Configuration

[1045] 1. Wearable devices:

[1046] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1047] 2. Database:

[1048] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[1049] 3. Server:

[1050] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[1051] 4. Dedicated application (terminal):

[1052] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[1053] Feature Overview

[1054] The system provides the following main functions:

[1055] 1. User registration and device pairing

[1056] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1057] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1058] 2. Acquiring and saving location information

[1059] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[1060] Server: Receives data and records it in a database.

[1061] 3. Behavioral Pattern Analysis and Anomaly Detection

[1062] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[1063] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[1064] 4. Displaying location information

[1065] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[1066] Specific examples

[1067] Let's assume that the family of a dementia patient, Person B, will use this system. First, they enter Person B's information using a terminal and send it to the server. The server then stores Person B's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device worn by Person B.

[1068] When B leaves the house, the wearable device continues to collect GPS data and transmits it to the server at regular intervals. The server stores the data in a database and uses an AI model to learn his or her usual behavioral patterns.

[1069] If one day, Mr. B begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Using the device, his family can check Mr. B's current location on a map and respond quickly.

[1070] In this way, the system ensures the safety of dementia patients and reduces the burden on their families and caregivers.

[1071] The processing flow will be explained below.

[1072] User Registration and Device Pairing

[1073] Step 1:

[1074] The user installs the dedicated application and opens the account creation screen.

[1075] Step 2:

[1076] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[1077] Step 3:

[1078] The server stores the received patient information in a database and generates a unique user ID.

[1079] Step 4:

[1080] The server returns the generated user ID to the terminal.

[1081] Step 5:

[1082] The terminal displays the user ID on the screen and asks the user to confirm it.

[1083] Step 6:

[1084] The user prepares the wearable device and sets it to pairing mode.

[1085] Step 7:

[1086] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[1087] Step 8:

[1088] The server records the wearable device information and associates it with the user ID.

[1089] Step 9:

[1090] The device displays a screen informing the user that pairing was successful.

[1091] Acquiring and saving location information

[1092] Step 1:

[1093] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[1094] Step 2:

[1095] The server receives the GPS data sent from the wearable device.

[1096] Step 3:

[1097] The server stores the received GPS data in a database and associates it with the user ID.

[1098] Behavioral pattern analysis and anomaly detection

[1099] Step 1:

[1100] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[1101] Step 2:

[1102] Identify the server's normal range of activity and frequently visited locations.

[1103] Step 3:

[1104] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[1105] Step 4:

[1106] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[1107] Step 5:

[1108] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[1109] Displaying location information

[1110] Step 1:

[1111] The user launches the dedicated application on their smartphone or tablet.

[1112] Step 2:

[1113] The terminal displays a login screen and prompts the user to enter their user ID and password.

[1114] Step 3:

[1115] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[1116] Step 4:

[1117] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[1118] Step 5:

[1119] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[1120] Example 1

[1121] 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."

[1122] There is a high risk that patients with dementia will get lost, which could result in a threat to their safety. Furthermore, it is not always possible for family members or caregivers to monitor patients with dementia, placing a heavy burden on them. In these circumstances, a system is needed that can track the current location of patients with dementia in real time and respond quickly if any abnormal movements occur.

[1123] 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.

[1124] In this invention, the server includes means for saving the received basic information in a database and generating a unique user ID, means for receiving and saving GPS data acquired by the location information acquisition means, means for learning past location data using an AI model, analyzing new location data in real time, and detecting abnormal movement patterns, means for sending a notification message to an emergency contact when an abnormal movement pattern is detected, and means for displaying the current patient location on a map. This makes it possible to ensure the safety of dementia patients and reduce the burden on their families and caregivers.

[1125] A "user" is a person who inputs basic information about a dementia patient and sends it to the server via a dedicated application.

[1126] The "server" is a device that stores the received basic information in a database and generates a unique user ID.

[1127] A "database" is a storage device that records and manages location information and basic information received by the server.

[1128] A "unique user ID" is an identification number that is uniquely assigned to each dementia patient.

[1129] "Location information acquisition means" refers to a device or method that acquires the current location of a dementia patient using a wearable device.

[1130] A "wearable device" is a device worn by a dementia patient that collects GPS data and transmits it to a server.

[1131] An "AI model" is an artificial intelligence algorithm that learns from past location data and identifies normal behavioral patterns.

[1132] An "abnormal movement pattern" is location data that deviates significantly from normal behavioral patterns.

[1133] A "notification message" is a communication containing an alert sent to family members or caregivers when an abnormal movement pattern is detected.

[1134] The "dedicated application" is software that family members and caregivers can use to check the current location of a person with dementia.

[1135] "Real-time" is a term that refers to the immediate processing and display of current conditions and data without delay.

[1136] This invention is a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[1137] System Configuration

[1138] 1. Wearable device: A device that is always worn by a dementia patient and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1139] 2. Database: A storage device that records received location information. The server uses this database to manage and analyze past location data.

[1140] 3. Server: This is the central component that receives, stores, and analyzes location information. It uses AI models to learn from past location data and detect unusual movement patterns.

[1141] 4. Dedicated application (terminal): This application is for use by family members and caregivers and displays the patient's current location on a map, and notifies them if any abnormalities are detected.

[1142] Feature Overview

[1143] The system provides the following main functions:

[1144] User Registration and Device Pairing

[1145] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1146] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1147] Acquiring and sending location information

[1148] Wearable device: Uses the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes), and sends the acquired location information to a server.

[1149] Server: Processes the received location information and stores it in a database.

[1150] Behavioral pattern analysis and anomaly detection

[1151] Server: AI models are used to study past location data and identify normal patterns of behavior. As new location data arrives on the server, it is analyzed in real time to check for any unusual movement patterns.

[1152] Notification when an abnormality is detected

[1153] Server: If an anomaly is detected, a notification message is sent to emergency contacts, including the current location and the type of anomaly (e.g., unexpected location, significant movement, etc.).

[1154] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[1155] Displaying location information

[1156] Device: By opening the dedicated application, family members and caregivers can check the current location of the person with dementia on a map. The map display screen also shows real-time location information and past movement history.

[1157] Server: When it receives a location request from the device, it sends the latest location data to the device.

[1158] Specific examples of operation

[1159] Let's assume that a family member of a dementia patient uses this system. First, they enter the patient's information using a terminal and send it to the server. The server stores the patient's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device with the patient.

[1160] When the patient leaves the home, the wearable device continuously collects GPS data and transmits it to a server at regular intervals. The server stores the data in a database and uses an AI model to learn normal behavioral patterns.

[1161] If one day the patient begins to move far beyond their usual range of movement, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Family members can then use their devices to check the patient's current location on a map and respond quickly.

[1162] Prompt Sentence Examples

[1163] "Please explain in detail the four components of a location tracking system for dementia patients: a wearable device, a database, a server, and a dedicated application."

[1164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1165] Step 1: User Registration

[1166] Device: The family member or caregiver launches the dedicated application and opens the user registration screen. They enter basic information such as the dementia patient's name, age, address, and emergency contact information, and click the "Register" button. This basic information becomes the input data.

[1167] Server: Receives the entered basic information and saves it in the database. After saving, it generates a unique user ID and sends it back to the terminal. This is the output data.

[1168] Step 2: Device pairing

[1169] Device: After receiving the unique user ID, you will be taken to a screen for pairing with the wearable device. Enter the user ID into the device and click the pairing button. The wearable device will then receive the user ID.

[1170] Wearable device: Recognizes the user ID and sends a message to the server indicating that pairing is complete.

[1171] Step 3: Obtaining location information

[1172] Wearable devices: Use the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes). The acquired location information becomes input data.

[1173] Wearable device: Sends the acquired location information to the server, which is the output data.

[1174] Step 4: Save location

[1175] Server: Stores the received location information in a database. The stored location information includes the location coordinates and acquisition time. This is the input data.

[1176] Server: Stored in a database for future analysis.

[1177] Step 5: Learning behavioral patterns

[1178] Server: Uses an AI model to learn from past location data and identify normal behavioral patterns. This is the input data.

[1179] Server: The analysis results include the range and patterns of normal behavior.

[1180] Step 6: Real-time analysis

[1181] Server: When new location data arrives at the server, it is analyzed in real time using an AI model. It compares it with past learning results to detect abnormal movement patterns. The new location data is the input data.

[1182] Server: If an anomaly is detected, send a notification message to emergency contacts. This is the output data.

[1183] Step 7: Notification when an anomaly is detected

[1184] Server: If an anomaly is detected, a notification message is sent to the registered emergency contacts. The message contains the current location and the type of anomaly.

[1185] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[1186] Step 8: View location information

[1187] Device: Open the dedicated application and go to the screen where you can check the current location of the dementia patient on a map.

[1188] Server: Receives location requests from devices and sends the latest location data to the devices. The request is the input data, and the location data is the output data.

[1189] Device: Real-time location information and past movement history are displayed on a map, allowing family members and caregivers to respond quickly.

[1190] (Application example 1)

[1191] 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."

[1192] There is a problem that dementia patients and food delivery drivers deviate from their normal range of movement, getting lost or behaving abnormally, threatening their safety. Such situations are very stressful for family members and managers, and require a quick response, but there is a lack of means to grasp location information in real time and detect abnormalities. Therefore, a system is needed that can grasp the current location of dementia patients and drivers in real time, and detect and notify abnormal movement patterns.

[1193] 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.

[1194] In this invention, the server includes a location information acquisition means, a means for storing the acquired location information in a database, a means for analyzing past location data and learning normal behavioral patterns using a machine learning model, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, and a means for displaying the current location of the subject on a map. This ensures the safety of the subject and enables a prompt response when an abnormality occurs. This reduces the burden on family members and administrators and enables effective monitoring of the subject's behavior.

[1195] A "dementia patient" refers to an adult who has impaired memory or cognitive abilities and requires assistance with daily living.

[1196] A "food delivery driver" is a person whose job is to drive a predetermined route to deliver requested food to customers.

[1197] "Current location" refers to the geographic location where a subject is located in real time.

[1198] "Real-time" refers to the instant information is processed and transmitted.

[1199] "Location information acquisition means" refers to a device or system that collects a subject's current location using GPS or other location identification technology.

[1200] A "database" refers to a system that systematically stores information such as saved location information and past movement data.

[1201] A "machine learning model" refers to a model that includes algorithms that allow a computer to learn data patterns and make future predictions or detect anomalies.

[1202] "Abnormal movement patterns" refer to movements that deviate significantly from normal patterns of behavior, including unexpected route changes and long periods of stoppage.

[1203] "Means for sending alerts" refers to a system or device for sending notifications or warnings to pre-designated recipients when abnormal movement is detected.

[1204] "Means for displaying on a map" refers to a system or application that visually displays collected location information, allowing users to easily check their current location.

[1205] A "wearable device" is an electronic device that can be worn by a subject and has the function of collecting location information, etc.

[1206] A "smartphone" refers to a mobile device based on a mobile operating system and equipped with multifunctional applications and communication functions.

[1207] "Emergency Contact" refers to a designated contact to be contacted in the event of abnormal movement.

[1208] This invention is a system for tracking the current locations of dementia patients and food delivery drivers in real time to ensure their safety. The system mainly consists of the following components: a location information acquisition means, a database, a server, and a dedicated application (terminal).

[1209] System Configuration

[1210] 1. Location information acquisition means:

[1211] It consists of a wearable device or smartphone that is constantly worn by dementia patients and food delivery drivers. This device has a built-in GPS module that measures their current location at regular intervals and transmits the data to a server.

[1212] 2. Database:

[1213] This is a storage device that records the location information received by the server. The server uses this database to manage and analyze past location data.

[1214] 3. Server:

[1215] It is the central component that receives, stores, and analyzes location information, and uses AI and machine learning models to learn from past location data and detect anomalous movement patterns.

[1216] 4. Dedicated application (terminal):

[1217] The application is intended for use by family members, caregivers, or food delivery system managers, and displays the current location of the person on a map and notifies them if any abnormalities are detected.

[1218] Program processing

[1219] Hardware and software used

[1220] Hardware: GPS-enabled smartphones, wearable devices

[1221] Software: Python (scripting language), Requests module, scikit-learn module, SMTP library

[1222] Data processing and calculation

[1223] 1. Location information acquisition:

[1224] Smartphones and wearable devices use GPS to obtain real-time location information, which is then sent to a server at regular intervals.

[1225] 2. Sending location information to the server:

[1226] The server stores the received data in a database and manages past location data. This location information is then used to feed the data into a machine learning model to learn normal behavioral patterns.

[1227] 3. Analysis by AI model:

[1228] A server-based machine learning model analyzes new location data in real time to detect unusual movement patterns, and if an anomaly is detected, it immediately sends a notification to emergency contacts or food delivery system administrators.

[1229] 4. Location display:

[1230] The application displays the received data on a map, allowing the user to view their current location and past movement history.

[1231] Specific examples

[1232] Cases of dementia patients

[1233] 1. Family members use a dedicated application to enter basic information about the person with dementia and send it to the server.

[1234] 2. The server stores the received information in a database and generates a unique user ID. Once the family member confirms this, they can pair the patient's wearable device.

[1235] 3. When the patient goes out, the wearable device continues to acquire GPS data and transmits it to the server at regular intervals.

[1236] 4. The server stores the received data in a database and uses machine learning models to learn normal behavioral patterns.

[1237] 5. If the patient begins to move significantly outside of their normal range of movement, the server will detect this as an abnormality and immediately send a notification to registered emergency contacts.

[1238] The case of a food delivery driver

[1239] 1. The administrator uses a dedicated application to enter driver information and send it to the server.

[1240] 2. The server stores the received information in a database and generates a unique user ID. After verifying this, the administrator pairs the driver's smartphone with the driver's smartphone.

[1241] 3. When the driver begins a delivery, the smartphone collects GPS data in real time and sends it to the server at regular intervals.

[1242] 4. The server stores the received data in a database and uses a machine learning model to learn the normal delivery route.

[1243] 5. If a driver significantly deviates from their normal delivery route, the server will detect this as an anomaly and immediately send a notification to the administrator.

[1244] Example prompt sentence:

[1245] "Build a system that tracks your drivers' locations and notifies you if they deviate significantly from their normal delivery routes."

[1246] This system ensures the safety of the subject and enables prompt response in the event of an abnormality, reducing the burden on family members and administrators and enabling effective monitoring of the subject's behavior.

[1247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1248] Step 1:

[1249] User Registration and Device Pairing

[1250] Using a dedicated application, users enter basic information about the dementia patient and food delivery driver (such as name, age, and emergency contact information) and send it to the server.

[1251] The server stores the received information in a database, generates a unique user ID, and returns this user ID to the device.

[1252] The user confirms their user ID and pairs with their wearable device or smartphone.

[1253] Input: Basic information of dementia patient and driver

[1254] Output: Information stored in the database and a unique user ID.

[1255] Step 2:

[1256] Obtaining location information

[1257] The location information acquisition means (wearable device or smartphone) acquires GPS data at regular intervals.

[1258] Input: Current location GPS data

[1259] Output: Acquired GPS data

[1260] Step 3:

[1261] Sending location information to the server

[1262] The GPS data acquired by the location information acquisition means is transmitted to a server.

[1263] The server stores the received GPS data in a database.

[1264] Input: Acquired GPS data

[1265] Output: GPS data stored in a database

[1266] Step 4:

[1267] Learning behavioral patterns

[1268] The server collects location data over a period of time and learns behavioral patterns using a machine learning model.

[1269] Identify typical behavioral patterns based on past location data.

[1270] Input: Location data stored in a database

[1271] Output: Learned normal behavior patterns

[1272] Step 5:

[1273] Real-time analytics and anomaly detection

[1274] The server receives new location data in real time and compares it with previously learned normal behavior patterns.

[1275] If an abnormal movement pattern is detected, it is determined to be abnormal.

[1276] Input: Real-time location data

[1277] Output: Determine if the movement pattern is abnormal

[1278] Step 6:

[1279] Sending alerts

[1280] The server will send a notification message to designated emergency contacts and administrators when an abnormal movement pattern is detected.

[1281] Input: Abnormal movement pattern detection results

[1282] Output: Send notification message

[1283] Step 7:

[1284] Displaying location information

[1285] The device visually displays the received location data on a map, allowing the user to verify the location.

[1286] Input: Real-time location data

[1287] Output: Current location and past movement history displayed on a map

[1288] Through the above processing steps, this system is able to monitor the current locations of dementia patients and food delivery drivers in real time to ensure their safety.

[1289] 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.

[1290] This invention is a system that combines a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety, with an emotion engine that recognizes the user's emotions. The system mainly includes the following components: a wearable device, a database, a server, a dedicated application (terminal), and an emotion engine.

[1291] System Configuration

[1292] 1. Wearable devices:

[1293] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1294] 2. Database:

[1295] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[1296] 3. Server:

[1297] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[1298] 4. Dedicated application (terminal):

[1299] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[1300] 5. Emotion Engine:

[1301] This is a device or system that recognizes the emotions of users (dementia patients, family members, and caregivers) and analyzes their emotions from their movements, facial expressions, and voice.

[1302] Feature Overview

[1303] The system provides the following main functions:

[1304] 1. User registration and device pairing

[1305] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1306] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1307] 2. Acquiring and saving location information

[1308] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[1309] Server: Receives data and records it in a database.

[1310] 3. Behavioral Pattern Analysis and Anomaly Detection

[1311] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[1312] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[1313] 4. Displaying location information

[1314] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[1315] 5. Emotion Recognition and Alerts

[1316] Emotion engine: Analyzes emotions from the movements, facial expressions, and voice of patients and caregivers, and sends the data to a server.

[1317] Server: Analyzes emotion data and generates alerts when abnormal emotion changes are detected.

[1318] Server: When an alert is generated, it sends a notification message to emergency contacts.

[1319] Specific examples

[1320] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they will enter Mr. C's information using a terminal and send it to the server. The server will then store Mr. C's information in a database and generate a unique user ID. After confirming this, the family members will pair the wearable device worn by Mr. C.

[1321] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[1322] If one day Mr. C begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. His family can then use their devices to check Mr. C's current location on a map and respond quickly.

[1323] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in daily life, such as sudden anxiety or excitement, the server will analyze them and send an alert to emergency contacts if necessary. This not only ensures the overall safety of dementia patients, but also enables quick response to emotional changes.

[1324] The processing flow will be explained below.

[1325] User Registration and Device Pairing

[1326] Step 1:

[1327] The user installs the dedicated application and opens the account creation screen.

[1328] Step 2:

[1329] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[1330] Step 3:

[1331] The server stores the received patient information in a database and generates a unique user ID.

[1332] Step 4:

[1333] The server returns the generated user ID to the terminal.

[1334] Step 5:

[1335] The terminal displays the user ID on the screen and asks the user to confirm it.

[1336] Step 6:

[1337] The user prepares the wearable device and sets it to pairing mode.

[1338] Step 7:

[1339] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[1340] Step 8:

[1341] The server records the wearable device information and associates it with the user ID.

[1342] Step 9:

[1343] The device displays a screen informing the user that pairing was successful.

[1344] Acquiring and saving location information

[1345] Step 1:

[1346] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[1347] Step 2:

[1348] The server receives the GPS data sent from the wearable device.

[1349] Step 3:

[1350] The server stores the received GPS data in a database and associates it with the user ID.

[1351] Behavioral pattern analysis and anomaly detection

[1352] Step 1:

[1353] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[1354] Step 2:

[1355] Identify the server's normal range of activity and frequently visited locations.

[1356] Step 3:

[1357] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[1358] Step 4:

[1359] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[1360] Step 5:

[1361] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[1362] Displaying location information

[1363] Step 1:

[1364] The user launches the dedicated application on their smartphone or tablet.

[1365] Step 2:

[1366] The terminal displays a login screen and prompts the user to enter their user ID and password.

[1367] Step 3:

[1368] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[1369] Step 4:

[1370] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[1371] Step 5:

[1372] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[1373] Emotion Recognition and Alerts

[1374] Step 1:

[1375] The emotion engine analyzes emotions from the movements, facial expressions, and voice of patients and caregivers.

[1376] Step 2:

[1377] The emotion engine periodically sends the analysis results to the server.

[1378] Step 3:

[1379] The server receives the emotion data and stores it in a database.

[1380] Step 4:

[1381] The server analyzes the emotion data and generates an alert if an abnormal emotional change (e.g., sudden anxiety, excitement, or sadness) is detected.

[1382] Step 5:

[1383] If the server detects an abnormality, it will send an alert about the emotional change to registered emergency contacts.

[1384] Through these processing steps, the system can monitor not only the location information of dementia patients but also their emotional changes in real time, and respond quickly when an abnormality occurs, thereby ensuring the overall safety of patients and reducing the burden on their families and caregivers.

[1385] Example 2

[1386] 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."

[1387] The risk of dementia patients wandering or getting lost is a serious problem, and real-time location monitoring is essential to ensure their safety. Furthermore, it is necessary not only to know their location but also to respond quickly to changes in the patient's emotions. Current location tracking systems lack the ability to respond to changes in the patient's emotions, and safety improvements are needed.

[1388] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes. This makes it possible to not only monitor location information but also respond to emotional changes in the dementia patient in real time.

[1389] The "location information acquisition means" is a device or system that acquires the current location of a dementia patient in real time.

[1390] A "database" is a storage system for storing and managing acquired location information and emotion data.

[1391] A "means for learning behavioral patterns" is a method or system for analyzing historical location data and identifying typical behavioral patterns of a person with dementia.

[1392] A "means for detecting anomalous movement patterns" is a method or system that analyzes new location data acquired in real time to detect anomalous movement that deviates from normal patterns of behavior.

[1393] An "alert sending means" is a device or system that sends a notification to pre-defined emergency contacts when an abnormal movement pattern or emotional change is detected.

[1394] The "means for displaying on a map" is a device or system for visually displaying the obtained current location information on a map.

[1395] "Emotion recognition means" refers to a device or system that analyzes the user's movements, facial expressions, and voice data to detect changes in emotions.

[1396] The "means for detecting abnormal emotional changes" is a method or system that analyzes emotional data and detects abnormal emotional changes that deviate from normal states.

[1397] This invention is a location tracking system for ensuring the safety of dementia patients by reducing the risk of them wandering or getting lost. This system includes a location information acquisition means for determining the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current location of the patient on a map, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes.

[1398] System Configuration

[1399] 1. Wearable devices

[1400] Wearable devices are devices that dementia patients wear at all times and contain a built-in GPS module. They measure their current location at regular intervals and send that location information to a server. For example, commercially available GPS trackers are used for wearable devices.

[1401] 2. Database

[1402] The database is a storage device for recording received location information and emotion data. The server uses this database to manage and analyze past location data and emotion data. A general relational database system (RDBMS) is used for the database.

[1403] 3. Server

[1404] The server is the central component that receives, stores, and analyzes location information. It uses an AI model to learn from past location data and detect unusual movement patterns. The server also analyzes emotion data and generates alerts if abnormal emotion changes are detected. The servers used include general cloud servers.

[1405] 4. Dedicated application (terminal)

[1406] The dedicated application is intended for use by family members and caregivers, and provides a function to display the patient's current location on a map. The application also has a function to notify users if an abnormality is detected. For example, this application can be installed on a smartphone or tablet.

[1407] 5. Emotion Engine

[1408] The emotion engine is a device or system that analyzes the emotions of users (dementia patients, family members, and caregivers) from their movements, facial expressions, and voices. This allows it to monitor emotional changes in patients and caregivers in real time, and if any abnormalities are detected, the data is sent to a server.

[1409] Specific examples

[1410] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they enter Mr. C's information using a dedicated application and send it to the server. The server then saves Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair the wearable device worn by Mr. C.

[1411] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[1412] If one day Mr. C begins to move far outside his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. Using a dedicated application, his family can check Mr. C's current location on a map and respond quickly.

[1413] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in his daily life, such as sudden anxiety or excitement, the server will analyze it and, if necessary, send an alert to emergency contacts.

[1414] Prompt Sentence Examples

[1415] "Let's say the family of a dementia patient, Mr. C, uses this system. First, they enter information about Mr. C using a terminal and send it to the server. The server then stores Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair it with the wearable device worn by Mr. C."

[1416] This invention will dramatically improve the safety of dementia patients and reduce the burden on their families and caregivers.

[1417] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1418] Program processing flow

[1419] Step 1:

[1420] Device: The family member or caregiver launches the dedicated application and enters the patient's basic information (name, age, address, emergency contact information, etc.), then presses the "Send" button to send the entered information to the server.

[1421] Input: Patient basic information.

[1422] Output: Basic patient information sent to the server.

[1423] Processing: The application formats the information entered, encrypts it, and sends it to the server.

[1424] Step 2:

[1425] Server: Analyzes the received patient information and saves it in a database. After saving, it generates a unique user ID and returns it to the terminal.

[1426] Input: Basic patient information submitted.

[1427] Output: A unique user ID.

[1428] Processing: The server stores the information in a database and generates a unique user ID that is sent back to the device.

[1429] Step 3:

[1430] Device: The device checks the received user ID and pairs with the wearable device.

[1431] Input: A unique user ID.

[1432] Output: Pairing completion status.

[1433] Processing: The terminal associates the user ID with the wearable device and performs the pairing process.

[1434] Step 4:

[1435] Wearable device: Acquires GPS data at regular intervals (for example, every 5 minutes) and sends it to a server.

[1436] Input: Current location.

[1437] Output: Location data sent to the server.

[1438] Processing: The GPS module acquires the current location, encrypts the data, and sends it to the server.

[1439] Step 5:

[1440] Server: Stores the received location data in a database. It sequentially analyzes past location data and uses an AI model to learn normal behavioral patterns.

[1441] Input: Location data.

[1442] Output: Location information stored in a database and a learned behavioral pattern model.

[1443] Processing: The location information is stored in a database and an AI model is used to learn normal behavior patterns.

[1444] Step 6:

[1445] Server: Analyzes new location data in real time to detect unusual movement patterns and, if an anomaly is detected, sends a notification to emergency contacts.

[1446] Input: Real-time location information.

[1447] Output: Detection and notification of anomalous movement patterns.

[1448] Processing: Analyze the new location data and send a notification to emergency contacts if an anomaly is detected.

[1449] Step 7:

[1450] Terminal: Family members and caregivers can check the patient's current location on a map using a dedicated application.

[1451] Input: A real-time location request.

[1452] Output: Current location displayed on a map.

[1453] Processing: The device retrieves the latest location information from the server and displays it on a map.

[1454] Step 8:

[1455] Emotion engine: Collects movement, facial expression, and voice data from patients and caregivers and analyzes their emotions.

[1456] Input: movement, facial expression, and voice data.

[1457] Output: Sentiment analysis results.

[1458] Processing: The emotion engine analyzes movement, facial expression, and voice data to assess the emotional state.

[1459] Step 9:

[1460] Server: Receives and analyzes emotion data. If abnormal emotion changes are detected, an alert is generated and a notification is sent to emergency contacts.

[1461] Input: Sentiment analysis result data.

[1462] Output: Detection and notification of abnormal emotional changes.

[1463] Processing: The server analyzes the emotion data and sends a notification to emergency contacts if it detects any abnormal changes.

[1464] This series of processes makes it possible to monitor the location information of dementia patients and respond quickly to emotional changes.

[1465] (Application example 2)

[1466] 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."

[1467] In the daily lives of dementia patients, the risk of getting lost and sudden emotional changes have become social issues. Ensuring safety is particularly important when going out or delivering meals, but conventional technology has not been able to adequately monitor not only the location information of dementia patients and delivery personnel, but also emotional changes. This has meant that sufficient consideration cannot be given to the safety of both patients and delivery personnel, making it difficult to reduce the risks for both.

[1468] 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.

[1469] In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current patient location on a map, a means for acquiring and recording the location information of the delivery person, a means for analyzing the emotions of both the delivery person and the dementia patient and detecting abnormal emotional changes, and a means for sending an alert when an abnormal emotional change is detected. This makes it possible to monitor the location information and emotional changes of both the dementia patient and the delivery person in real time and respond immediately when an abnormality occurs.

[1470] "Location information acquisition means" refers to devices or technologies used to determine the current location of dementia patients or delivery personnel in real time.

[1471] A "database" is an information system for recording and storing information such as acquired location information and emotional data.

[1472] A "behavioral pattern learning means" is a device or technology used to analyze historical location data and identify typical behavioral patterns.

[1473] "Abnormal movement pattern detection means" refers to devices or techniques used to analyze location data acquired in real time and detect abnormal movements that differ from normal behavior patterns.

[1474] An "alert sending means" is a device or technology that sends a notification to emergency contacts when an abnormal movement pattern or emotional change is detected.

[1475] "Real-time display means" refers to the device or technology used to display the current location of patients and delivery personnel on a map.

[1476] "Emotion analysis means" refers to devices and technologies used to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery personnel.

[1477] An "abnormal emotion detection means" is a device or technology used to detect abnormal changes in the emotions of dementia patients or delivery personnel.

[1478] This invention is a system for ensuring the safety of dementia patients and delivery personnel, and for monitoring abnormal movement patterns and emotional changes in real time. To implement this system, the following components and means are used.

[1479] System Configuration

[1480] 1. Location information acquisition means:

[1481] The wearable devices carried by dementia patients and delivery workers acquire location information. These devices have a built-in GPS module that measures their current location at regular intervals and transmits the information to a server.

[1482] 2. Database:

[1483] The server stores location information and emotion data in a database, using a database management system such as PostgreSQL.

[1484] 3. Behavioral pattern learning methods:

[1485] The server collects and analyzes historical location data to identify typical patterns of behavior, using machine learning frameworks such as TensorFlow.

[1486] 4. Abnormal movement pattern detection method:

[1487] The server analyzes new location data in real time to detect anomalous movements that deviate from normal patterns of behavior, again using TensorFlow.

[1488] 5. Alert delivery method:

[1489] The server sends notifications to emergency contacts when unusual movement patterns or emotional changes are detected, for example, via SMS or push notifications.

[1490] 6. Real-time display means:

[1491] Family members and caregivers can check the current location of dementia patients and delivery personnel on a map through a dedicated application, which runs on iOS and Android and is developed in Swift and Kotlin.

[1492] 7. Sentiment analysis tools:

[1493] The server uses an emotion engine to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery workers, using OpenCV and Google Cloud Speech-to-Text API.

[1494] 8. Abnormal Emotion Detection Methods:

[1495] The server analyzes the emotional data and generates an alert if it detects any abnormal emotional changes, which is also sent to emergency contacts.

[1496] Specific examples

[1497] Suppose a patient with dementia uses a meal delivery service. The wearable devices carried by the patient and delivery person collect location information and send it to a server. The server stores this information in a database and uses a machine learning model to learn normal behavioral patterns. If an abnormality is detected, a notification is sent to emergency contacts. Through a dedicated application, family members and caregivers can monitor the current location of the patient and delivery person in real time. Furthermore, an emotion engine analyzes the emotions of the patient and delivery person and generates an alert if an abnormality is detected.

[1498] Prompt Sentence Examples

[1499] Generate code suitable for building a location tracking and emotion recognition system like this:

[1500] The wearable device acquires GPS data at regular intervals and sends it to the server.

[1501] The server receives location information and detects anomalies using an AI model

[1502] The emotion engine analyzes emotions from facial expressions and voice, and generates an alert if there is an abnormality.

[1503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1504] Step 1:

[1505] Acquiring and sending location information

[1506] A wearable device carried by a user (in this case, a dementia patient or a delivery person) acquires GPS data at regular intervals. The acquired location data (input) is sent from the wearable device to a server (output). At this time, location measurement is performed using the GPS module in the device.

[1507] Step 2:

[1508] Save location information

[1509] The server stores the received location data (input) in a database (output), using a database management system such as PostgreSQL, and the stored data is used for later analysis.

[1510] Step 3:

[1511] Learning behavioral patterns

[1512] The server uses past location data (input) to learn normal behavioral patterns using a machine learning framework such as TensorFlow (output). The learned model is then used to identify the user's normal behavioral patterns.

[1513] Step 4:

[1514] Analyzing real-time location data

[1515] The server analyzes newly received location data (input) in real time and compares it with previously learned behavioral pattern models. If an abnormal movement pattern is detected, the information (output) is sent to the next step. TensorFlow is used to compare the current data with the model and detect anomalies.

[1516] Step 5:

[1517] Sending alerts

[1518] The server sends a notification message to emergency contacts when an abnormal movement pattern is detected (output). This notification can be sent via SMS or push notification. The data triggers the notification system when an abnormality is detected.

[1519] Step 6:

[1520] Real-time location information display

[1521] The device uses a dedicated application to display the user's current location data (input) on a map (output). By checking the map, the user can understand the current location of the dementia patient or delivery person.

[1522] Step 7:

[1523] Acquiring and analyzing emotion data

[1524] Wearable devices and other sensors capture the movements, facial expressions, and voice data (input) of dementia patients and delivery workers. The server receives this data, analyzes emotions using OpenCV and Google Cloud Speech-to-Text API, and obtains the results (output).

[1525] Step 8:

[1526] Detecting abnormal emotional changes

[1527] The server detects abnormal emotional changes based on the analysis results (input). If an abnormality is detected, the information (output) is sent to the next step. To detect an abnormality, the pattern of emotional changes is compared with pre-set criteria.

[1528] Step 9:

[1529] Emotional abnormality alert sending

[1530] When an abnormal emotion change is detected, the server sends a notification message to emergency contacts (output). This is also sent via SMS or push notification. The abnormal emotion data triggers the notification system.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] [Fourth embodiment]

[1535] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1536] 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.

[1537] 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).

[1538] 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.

[1539] 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.

[1540] 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).

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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.

[1547] 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."

[1548] This invention is a location tracking system that aims to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[1549] System Configuration

[1550] 1. Wearable devices:

[1551] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1552] 2. Database:

[1553] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[1554] 3. Server:

[1555] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[1556] 4. Dedicated application (terminal):

[1557] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[1558] Feature Overview

[1559] The system provides the following main functions:

[1560] 1. User registration and device pairing

[1561] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1562] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1563] 2. Acquiring and saving location information

[1564] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[1565] Server: Receives data and records it in a database.

[1566] 3. Behavioral Pattern Analysis and Anomaly Detection

[1567] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[1568] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[1569] 4. Displaying location information

[1570] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[1571] Specific examples

[1572] Let's assume that the family of a dementia patient, Person B, will use this system. First, they enter Person B's information using a terminal and send it to the server. The server then stores Person B's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device worn by Person B.

[1573] When B leaves the house, the wearable device continues to collect GPS data and transmits it to the server at regular intervals. The server stores the data in a database and uses an AI model to learn his or her usual behavioral patterns.

[1574] If one day, Mr. B begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Using the device, his family can check Mr. B's current location on a map and respond quickly.

[1575] In this way, the system ensures the safety of dementia patients and reduces the burden on their families and caregivers.

[1576] The processing flow will be explained below.

[1577] User Registration and Device Pairing

[1578] Step 1:

[1579] The user installs the dedicated application and opens the account creation screen.

[1580] Step 2:

[1581] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[1582] Step 3:

[1583] The server stores the received patient information in a database and generates a unique user ID.

[1584] Step 4:

[1585] The server returns the generated user ID to the terminal.

[1586] Step 5:

[1587] The terminal displays the user ID on the screen and asks the user to confirm it.

[1588] Step 6:

[1589] The user prepares the wearable device and sets it to pairing mode.

[1590] Step 7:

[1591] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[1592] Step 8:

[1593] The server records the wearable device information and associates it with the user ID.

[1594] Step 9:

[1595] The device displays a screen informing the user that pairing was successful.

[1596] Acquiring and saving location information

[1597] Step 1:

[1598] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[1599] Step 2:

[1600] The server receives the GPS data sent from the wearable device.

[1601] Step 3:

[1602] The server stores the received GPS data in a database and associates it with the user ID.

[1603] Behavioral pattern analysis and anomaly detection

[1604] Step 1:

[1605] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[1606] Step 2:

[1607] Identify the server's normal range of activity and frequently visited locations.

[1608] Step 3:

[1609] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[1610] Step 4:

[1611] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[1612] Step 5:

[1613] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[1614] Displaying location information

[1615] Step 1:

[1616] The user launches the dedicated application on their smartphone or tablet.

[1617] Step 2:

[1618] The terminal displays a login screen and prompts the user to enter their user ID and password.

[1619] Step 3:

[1620] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[1621] Step 4:

[1622] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[1623] Step 5:

[1624] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[1625] Example 1

[1626] 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."

[1627] There is a high risk that patients with dementia will get lost, which could result in a threat to their safety. Furthermore, it is not always possible for family members or caregivers to monitor patients with dementia, placing a heavy burden on them. In these circumstances, a system is needed that can track the current location of patients with dementia in real time and respond quickly if any abnormal movements occur.

[1628] 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.

[1629] In this invention, the server includes means for saving the received basic information in a database and generating a unique user ID, means for receiving and saving GPS data acquired by the location information acquisition means, means for learning past location data using an AI model, analyzing new location data in real time, and detecting abnormal movement patterns, means for sending a notification message to an emergency contact when an abnormal movement pattern is detected, and means for displaying the current patient location on a map. This makes it possible to ensure the safety of dementia patients and reduce the burden on their families and caregivers.

[1630] A "user" is a person who inputs basic information about a dementia patient and sends it to the server via a dedicated application.

[1631] The "server" is a device that stores the received basic information in a database and generates a unique user ID.

[1632] A "database" is a storage device that records and manages location information and basic information received by the server.

[1633] A "unique user ID" is an identification number that is uniquely assigned to each dementia patient.

[1634] "Location information acquisition means" refers to a device or method that acquires the current location of a dementia patient using a wearable device.

[1635] A "wearable device" is a device worn by a dementia patient that collects GPS data and transmits it to a server.

[1636] An "AI model" is an artificial intelligence algorithm that learns from past location data and identifies normal behavioral patterns.

[1637] An "abnormal movement pattern" is location data that deviates significantly from normal behavioral patterns.

[1638] A "notification message" is a communication containing an alert sent to family members or caregivers when an abnormal movement pattern is detected.

[1639] The "dedicated application" is software that family members and caregivers can use to check the current location of a person with dementia.

[1640] "Real-time" is a term that refers to the immediate processing and display of current conditions and data without delay.

[1641] This invention is a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety. The system mainly includes the following components: a wearable device, a database, a server, and a dedicated application (terminal).

[1642] System Configuration

[1643] 1. Wearable device: A device that is always worn by a dementia patient and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1644] 2. Database: A storage device that records received location information. The server uses this database to manage and analyze past location data.

[1645] 3. Server: This is the central component that receives, stores, and analyzes location information. It uses AI models to learn from past location data and detect unusual movement patterns.

[1646] 4. Dedicated application (terminal): This application is for use by family members and caregivers and displays the patient's current location on a map, and notifies them if any abnormalities are detected.

[1647] Feature Overview

[1648] The system provides the following main functions:

[1649] User Registration and Device Pairing

[1650] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1651] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1652] Acquiring and sending location information

[1653] Wearable device: Uses the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes), and sends the acquired location information to a server.

[1654] Server: Processes the received location information and stores it in a database.

[1655] Behavioral pattern analysis and anomaly detection

[1656] Server: AI models are used to study past location data and identify normal patterns of behavior. As new location data arrives on the server, it is analyzed in real time to check for any unusual movement patterns.

[1657] Notification when an abnormality is detected

[1658] Server: If an anomaly is detected, a notification message is sent to emergency contacts, including the current location and the type of anomaly (e.g., unexpected location, significant movement, etc.).

[1659] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[1660] Displaying location information

[1661] Device: By opening the dedicated application, family members and caregivers can check the current location of the person with dementia on a map. The map display screen also shows real-time location information and past movement history.

[1662] Server: When it receives a location request from the device, it sends the latest location data to the device.

[1663] Specific examples of operation

[1664] Let's assume that a family member of a dementia patient uses this system. First, they enter the patient's information using a terminal and send it to the server. The server stores the patient's information in a database and generates a unique user ID. After confirming this, the family member pairs the wearable device with the patient.

[1665] When the patient leaves the home, the wearable device continuously collects GPS data and transmits it to a server at regular intervals. The server stores the data in a database and uses an AI model to learn normal behavioral patterns.

[1666] If one day the patient begins to move far beyond their usual range of movement, the server will detect this as an abnormality and immediately notify the registered emergency contacts. Family members can then use their devices to check the patient's current location on a map and respond quickly.

[1667] Prompt Sentence Examples

[1668] "Please explain in detail the four components of a location tracking system for dementia patients: a wearable device, a database, a server, and a dedicated application."

[1669] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1670] Step 1: User Registration

[1671] Device: The family member or caregiver launches the dedicated application and opens the user registration screen. They enter basic information such as the dementia patient's name, age, address, and emergency contact information, and click the "Register" button. This basic information becomes the input data.

[1672] Server: Receives the entered basic information and saves it in the database. After saving, it generates a unique user ID and sends it back to the terminal. This is the output data.

[1673] Step 2: Device pairing

[1674] Device: After receiving the unique user ID, you will be taken to a screen for pairing with the wearable device. Enter the user ID into the device and click the pairing button. The wearable device will then receive the user ID.

[1675] Wearable device: Recognizes the user ID and sends a message to the server indicating that pairing is complete.

[1676] Step 3: Obtaining location information

[1677] Wearable devices: Use the built-in GPS module to acquire current location information at regular intervals (e.g., every 5 minutes). The acquired location information becomes input data.

[1678] Wearable device: Sends the acquired location information to the server, which is the output data.

[1679] Step 4: Save location

[1680] Server: Stores the received location information in a database. The stored location information includes the location coordinates and acquisition time. This is the input data.

[1681] Server: Stored in a database for future analysis.

[1682] Step 5: Learning behavioral patterns

[1683] Server: Uses an AI model to learn from past location data and identify normal behavioral patterns. This is the input data.

[1684] Server: The analysis results include the range and patterns of normal behavior.

[1685] Step 6: Real-time analysis

[1686] Server: When new location data arrives at the server, it is analyzed in real time using an AI model. It compares it with past learning results to detect abnormal movement patterns. The new location data is the input data.

[1687] Server: If an anomaly is detected, send a notification message to emergency contacts. This is the output data.

[1688] Step 7: Notification when an anomaly is detected

[1689] Server: If an anomaly is detected, a notification message is sent to the registered emergency contacts. The message contains the current location and the type of anomaly.

[1690] On your device: Receive notification messages and notify family members or caregivers with sounds or vibrations.

[1691] Step 8: View location information

[1692] Device: Open the dedicated application and go to the screen where you can check the current location of the dementia patient on a map.

[1693] Server: Receives location requests from devices and sends the latest location data to the devices. The request is the input data, and the location data is the output data.

[1694] Device: Real-time location information and past movement history are displayed on a map, allowing family members and caregivers to respond quickly.

[1695] (Application example 1)

[1696] 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."

[1697] There is a problem that dementia patients and food delivery drivers deviate from their normal range of movement, getting lost or behaving abnormally, threatening their safety. Such situations are very stressful for family members and managers, and require a quick response, but there is a lack of means to grasp location information in real time and detect abnormalities. Therefore, a system is needed that can grasp the current location of dementia patients and drivers in real time, and detect and notify abnormal movement patterns.

[1698] 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.

[1699] In this invention, the server includes a location information acquisition means, a means for storing the acquired location information in a database, a means for analyzing past location data and learning normal behavioral patterns using a machine learning model, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, and a means for displaying the current location of the subject on a map. This ensures the safety of the subject and enables a prompt response when an abnormality occurs. This reduces the burden on family members and administrators and enables effective monitoring of the subject's behavior.

[1700] A "dementia patient" refers to an adult who has impaired memory or cognitive abilities and requires assistance with daily living.

[1701] A "food delivery driver" is a person whose job is to drive a predetermined route to deliver requested food to customers.

[1702] "Current location" refers to the geographic location where a subject is located in real time.

[1703] "Real-time" refers to the instant information is processed and transmitted.

[1704] "Location information acquisition means" refers to a device or system that collects a subject's current location using GPS or other location identification technology.

[1705] A "database" refers to a system that systematically stores information such as saved location information and past movement data.

[1706] A "machine learning model" refers to a model that includes algorithms that allow a computer to learn data patterns and make future predictions or detect anomalies.

[1707] "Abnormal movement patterns" refer to movements that deviate significantly from normal patterns of behavior, including unexpected route changes and long periods of stoppage.

[1708] "Means for sending alerts" refers to a system or device for sending notifications or warnings to pre-designated recipients when abnormal movement is detected.

[1709] "Means for displaying on a map" refers to a system or application that visually displays collected location information, allowing users to easily check their current location.

[1710] A "wearable device" is an electronic device that can be worn by a subject and has the function of collecting location information, etc.

[1711] A "smartphone" refers to a mobile device based on a mobile operating system and equipped with multifunctional applications and communication functions.

[1712] "Emergency Contact" refers to a designated contact to be contacted in the event of abnormal movement.

[1713] This invention is a system for tracking the current locations of dementia patients and food delivery drivers in real time to ensure their safety. The system mainly consists of the following components: a location information acquisition means, a database, a server, and a dedicated application (terminal).

[1714] System Configuration

[1715] 1. Location information acquisition means:

[1716] It consists of a wearable device or smartphone that is constantly worn by dementia patients and food delivery drivers. This device has a built-in GPS module that measures their current location at regular intervals and transmits the data to a server.

[1717] 2. Database:

[1718] This is a storage device that records the location information received by the server. The server uses this database to manage and analyze past location data.

[1719] 3. Server:

[1720] It is the central component that receives, stores, and analyzes location information, and uses AI and machine learning models to learn from past location data and detect anomalous movement patterns.

[1721] 4. Dedicated application (terminal):

[1722] The application is intended for use by family members, caregivers, or food delivery system managers, and displays the current location of the person on a map and notifies them if any abnormalities are detected.

[1723] Program processing

[1724] Hardware and software used

[1725] Hardware: GPS-enabled smartphones, wearable devices

[1726] Software: Python (scripting language), Requests module, scikit-learn module, SMTP library

[1727] Data processing and calculation

[1728] 1. Location information acquisition:

[1729] Smartphones and wearable devices use GPS to obtain real-time location information, which is then sent to a server at regular intervals.

[1730] 2. Sending location information to the server:

[1731] The server stores the received data in a database and manages past location data. This location information is then used to feed the data into a machine learning model to learn normal behavioral patterns.

[1732] 3. Analysis by AI model:

[1733] A server-based machine learning model analyzes new location data in real time to detect unusual movement patterns, and if an anomaly is detected, it immediately sends a notification to emergency contacts or food delivery system administrators.

[1734] 4. Location display:

[1735] The application displays the received data on a map, allowing the user to view their current location and past movement history.

[1736] Specific examples

[1737] Cases of dementia patients

[1738] 1. Family members use a dedicated application to enter basic information about the person with dementia and send it to the server.

[1739] 2. The server stores the received information in a database and generates a unique user ID. Once the family member confirms this, they can pair the patient's wearable device.

[1740] 3. When the patient goes out, the wearable device continues to acquire GPS data and transmits it to the server at regular intervals.

[1741] 4. The server stores the received data in a database and uses machine learning models to learn normal behavioral patterns.

[1742] 5. If the patient begins to move significantly outside of their normal range of movement, the server will detect this as an abnormality and immediately send a notification to registered emergency contacts.

[1743] The case of a food delivery driver

[1744] 1. The administrator uses a dedicated application to enter driver information and send it to the server.

[1745] 2. The server stores the received information in a database and generates a unique user ID. After verifying this, the administrator pairs the driver's smartphone with the driver's smartphone.

[1746] 3. When the driver begins a delivery, the smartphone collects GPS data in real time and sends it to the server at regular intervals.

[1747] 4. The server stores the received data in a database and uses a machine learning model to learn the normal delivery route.

[1748] 5. If a driver significantly deviates from their normal delivery route, the server will detect this as an anomaly and immediately send a notification to the administrator.

[1749] Example prompt sentence:

[1750] "Build a system that tracks your drivers' locations and notifies you if they deviate significantly from their normal delivery routes."

[1751] This system ensures the safety of the subject and enables prompt response in the event of an abnormality, reducing the burden on family members and administrators and enabling effective monitoring of the subject's behavior.

[1752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1753] Step 1:

[1754] User Registration and Device Pairing

[1755] Using a dedicated application, users enter basic information about the dementia patient and food delivery driver (such as name, age, and emergency contact information) and send it to the server.

[1756] The server stores the received information in a database, generates a unique user ID, and returns this user ID to the device.

[1757] The user confirms their user ID and pairs with their wearable device or smartphone.

[1758] Input: Basic information of dementia patient and driver

[1759] Output: Information stored in the database and a unique user ID.

[1760] Step 2:

[1761] Obtaining location information

[1762] The location information acquisition means (wearable device or smartphone) acquires GPS data at regular intervals.

[1763] Input: Current location GPS data

[1764] Output: Acquired GPS data

[1765] Step 3:

[1766] Sending location information to the server

[1767] The GPS data acquired by the location information acquisition means is transmitted to a server.

[1768] The server stores the received GPS data in a database.

[1769] Input: Acquired GPS data

[1770] Output: GPS data stored in a database

[1771] Step 4:

[1772] Learning behavioral patterns

[1773] The server collects location data over a period of time and learns behavioral patterns using a machine learning model.

[1774] Identify typical behavioral patterns based on past location data.

[1775] Input: Location data stored in a database

[1776] Output: Learned normal behavior patterns

[1777] Step 5:

[1778] Real-time analytics and anomaly detection

[1779] The server receives new location data in real time and compares it with previously learned normal behavior patterns.

[1780] If an abnormal movement pattern is detected, it is determined to be abnormal.

[1781] Input: Real-time location data

[1782] Output: Determine if the movement pattern is abnormal

[1783] Step 6:

[1784] Sending alerts

[1785] The server will send a notification message to designated emergency contacts and administrators when an abnormal movement pattern is detected.

[1786] Input: Abnormal movement pattern detection results

[1787] Output: Send notification message

[1788] Step 7:

[1789] Displaying location information

[1790] The device visually displays the received location data on a map, allowing the user to verify the location.

[1791] Input: Real-time location data

[1792] Output: Current location and past movement history displayed on a map

[1793] Through the above processing steps, this system is able to monitor the current locations of dementia patients and food delivery drivers in real time to ensure their safety.

[1794] 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.

[1795] This invention is a system that combines a location tracking system to reduce the risk of dementia patients getting lost and ensure their safety, with an emotion engine that recognizes the user's emotions. The system mainly includes the following components: a wearable device, a database, a server, a dedicated application (terminal), and an emotion engine.

[1796] System Configuration

[1797] 1. Wearable devices:

[1798] This device is worn by dementia patients at all times and acquires their current location information. It has a built-in GPS module that measures their current location at regular intervals and sends the information to a server.

[1799] 2. Database:

[1800] This is a storage device that records received location information. The server uses this database to manage and analyze past location data.

[1801] 3. Server:

[1802] It is the central component that receives, stores, and analyzes location information, and uses AI models to learn from past location data and detect anomalous movement patterns.

[1803] 4. Dedicated application (terminal):

[1804] The application is intended for use by family members and caregivers, and displays the patient's current location on a map and notifies them if any abnormalities are detected.

[1805] 5. Emotion Engine:

[1806] This is a device or system that recognizes the emotions of users (dementia patients, family members, and caregivers) and analyzes their emotions from their movements, facial expressions, and voice.

[1807] Feature Overview

[1808] The system provides the following main functions:

[1809] 1. User registration and device pairing

[1810] Device: Family members or caregivers use a dedicated application to enter basic information about the dementia patient (such as name, age, address, and emergency contact information) and send it to the server.

[1811] Server: Stores the received information in a database, generates a unique user ID, and sends it back to the device to pair it with the wearable device.

[1812] 2. Acquiring and saving location information

[1813] Wearable device: Acquires GPS data at regular intervals and sends it to a server.

[1814] Server: Receives data and records it in a database.

[1815] 3. Behavioral Pattern Analysis and Anomaly Detection

[1816] Server: Uses AI models to learn from past location data to identify normal patterns of behavior, and analyzes new location data in real time to detect anomalous movement patterns.

[1817] Server: If an abnormality is detected, a notification message is sent to emergency contacts.

[1818] 4. Displaying location information

[1819] Terminal: Users can check the current patient location on a map through a dedicated application. The application provides the ability to display real-time location information as well as past records.

[1820] 5. Emotion Recognition and Alerts

[1821] Emotion engine: Analyzes emotions from the movements, facial expressions, and voice of patients and caregivers, and sends the data to a server.

[1822] Server: Analyzes emotion data and generates alerts when abnormal emotion changes are detected.

[1823] Server: When an alert is generated, it sends a notification message to emergency contacts.

[1824] Specific examples

[1825] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they will enter Mr. C's information using a terminal and send it to the server. The server will then store Mr. C's information in a database and generate a unique user ID. After confirming this, the family members will pair the wearable device worn by Mr. C.

[1826] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[1827] If one day Mr. C begins to move far beyond his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. His family can then use their devices to check Mr. C's current location on a map and respond quickly.

[1828] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in daily life, such as sudden anxiety or excitement, the server will analyze them and send an alert to emergency contacts if necessary. This not only ensures the overall safety of dementia patients, but also enables quick response to emotional changes.

[1829] The processing flow will be explained below.

[1830] User Registration and Device Pairing

[1831] Step 1:

[1832] The user installs the dedicated application and opens the account creation screen.

[1833] Step 2:

[1834] The user enters basic information about the dementia patient (name, age, address, emergency contact, etc.) and presses the send button.

[1835] Step 3:

[1836] The server stores the received patient information in a database and generates a unique user ID.

[1837] Step 4:

[1838] The server returns the generated user ID to the terminal.

[1839] Step 5:

[1840] The terminal displays the user ID on the screen and asks the user to confirm it.

[1841] Step 6:

[1842] The user prepares the wearable device and sets it to pairing mode.

[1843] Step 7:

[1844] The user opens the device's pairing settings within the dedicated app and enters the device's serial number and pairing code.

[1845] Step 8:

[1846] The server records the wearable device information and associates it with the user ID.

[1847] Step 9:

[1848] The device displays a screen informing the user that pairing was successful.

[1849] Acquiring and saving location information

[1850] Step 1:

[1851] The wearable device acquires GPS data at regular intervals (for example, every 60 seconds) and sends it to the server.

[1852] Step 2:

[1853] The server receives the GPS data sent from the wearable device.

[1854] Step 3:

[1855] The server stores the received GPS data in a database and associates it with the user ID.

[1856] Behavioral pattern analysis and anomaly detection

[1857] Step 1:

[1858] The server analyzes historical location data and trains an AI model to learn normal behavioral patterns.

[1859] Step 2:

[1860] Identify the server's normal range of activity and frequently visited locations.

[1861] Step 3:

[1862] The server inputs new location data obtained in real time into the AI ​​model and analyzes it for any anomalies.

[1863] Step 4:

[1864] If the server detects an abnormal movement pattern (e.g., sudden distance travel, movement outside of normal activity range), it will generate an alert.

[1865] Step 5:

[1866] If the server detects an abnormality, it will send a notification message containing the alert details to registered emergency contacts.

[1867] Displaying location information

[1868] Step 1:

[1869] The user launches the dedicated application on their smartphone or tablet.

[1870] Step 2:

[1871] The terminal displays a login screen and prompts the user to enter their user ID and password.

[1872] Step 3:

[1873] Once the server has successfully logged in, it will retrieve the current GPS data and send it to the device.

[1874] Step 4:

[1875] The device will display the patient's current location on a map and also allow the patient's movement history to be checked.

[1876] Step 5:

[1877] The device will display the current status (e.g., safety, abnormality detection) on the screen, and also provide a function to view past location data.

[1878] Emotion Recognition and Alerts

[1879] Step 1:

[1880] The emotion engine analyzes emotions from the movements, facial expressions, and voice of patients and caregivers.

[1881] Step 2:

[1882] The emotion engine periodically sends the analysis results to the server.

[1883] Step 3:

[1884] The server receives the emotion data and stores it in a database.

[1885] Step 4:

[1886] The server analyzes the emotion data and generates an alert if an abnormal emotional change (e.g., sudden anxiety, excitement, or sadness) is detected.

[1887] Step 5:

[1888] If the server detects an abnormality, it will send an alert about the emotional change to registered emergency contacts.

[1889] Through these processing steps, the system can monitor not only the location information of dementia patients but also their emotional changes in real time, and respond quickly when an abnormality occurs, thereby ensuring the overall safety of patients and reducing the burden on their families and caregivers.

[1890] Example 2

[1891] 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."

[1892] The risk of dementia patients wandering or getting lost is a serious problem, and real-time location monitoring is essential to ensure their safety. Furthermore, it is necessary not only to know their location but also to respond quickly to changes in the patient's emotions. Current location tracking systems lack the ability to respond to changes in the patient's emotions, and safety improvements are needed.

[1893] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes. This makes it possible to not only monitor location information but also respond to emotional changes in the dementia patient in real time.

[1894] The "location information acquisition means" is a device or system that acquires the current location of a dementia patient in real time.

[1895] A "database" is a storage system for storing and managing acquired location information and emotion data.

[1896] A "means for learning behavioral patterns" is a method or system for analyzing historical location data and identifying typical behavioral patterns of a person with dementia.

[1897] A "means for detecting anomalous movement patterns" is a method or system that analyzes new location data acquired in real time to detect anomalous movement that deviates from normal patterns of behavior.

[1898] An "alert sending means" is a device or system that sends a notification to pre-defined emergency contacts when an abnormal movement pattern or emotional change is detected.

[1899] The "means for displaying on a map" is a device or system for visually displaying the obtained current location information on a map.

[1900] "Emotion recognition means" refers to a device or system that analyzes the user's movements, facial expressions, and voice data to detect changes in emotions.

[1901] The "means for detecting abnormal emotional changes" is a method or system that analyzes emotional data and detects abnormal emotional changes that deviate from normal states.

[1902] This invention is a location tracking system for ensuring the safety of dementia patients by reducing the risk of them wandering or getting lost. This system includes a location information acquisition means for determining the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current location of the patient on a map, an emotion recognition means for analyzing the user's movements, facial expressions, and voice data, and a means for analyzing the emotion data and detecting abnormal emotional changes.

[1903] System Configuration

[1904] 1. Wearable devices

[1905] Wearable devices are devices that dementia patients wear at all times and contain a built-in GPS module. They measure their current location at regular intervals and send that location information to a server. For example, commercially available GPS trackers are used for wearable devices.

[1906] 2. Database

[1907] The database is a storage device for recording received location information and emotion data. The server uses this database to manage and analyze past location data and emotion data. A general relational database system (RDBMS) is used for the database.

[1908] 3. Server

[1909] The server is the central component that receives, stores, and analyzes location information. It uses an AI model to learn from past location data and detect unusual movement patterns. The server also analyzes emotion data and generates alerts if abnormal emotion changes are detected. The servers used include general cloud servers.

[1910] 4. Dedicated application (terminal)

[1911] The dedicated application is intended for use by family members and caregivers, and provides a function to display the patient's current location on a map. The application also has a function to notify users if an abnormality is detected. For example, this application can be installed on a smartphone or tablet.

[1912] 5. Emotion Engine

[1913] The emotion engine is a device or system that analyzes the emotions of users (dementia patients, family members, and caregivers) from their movements, facial expressions, and voices. This allows it to monitor emotional changes in patients and caregivers in real time, and if any abnormalities are detected, the data is sent to a server.

[1914] Specific examples

[1915] Let's assume that the family of a dementia patient, Mr. C, will use this system. First, they enter Mr. C's information using a dedicated application and send it to the server. The server then saves Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair the wearable device worn by Mr. C.

[1916] When Mr. C goes out, the wearable device continuously collects GPS data and sends it to the server at regular intervals. The server stores the received data in a database and uses an AI model to learn his usual behavioral patterns.

[1917] If one day Mr. C begins to move far outside his usual range, the server will detect this as an abnormality and immediately notify his registered emergency contacts. Using a dedicated application, his family can check Mr. C's current location on a map and respond quickly.

[1918] In addition, if the emotion engine detects any abnormalities in Mr. C's emotions in his daily life, such as sudden anxiety or excitement, the server will analyze it and, if necessary, send an alert to emergency contacts.

[1919] Prompt Sentence Examples

[1920] "Let's say the family of a dementia patient, Mr. C, uses this system. First, they enter information about Mr. C using a terminal and send it to the server. The server then stores Mr. C's information in a database and generates a unique user ID. After confirming this, the family members pair it with the wearable device worn by Mr. C."

[1921] This invention will dramatically improve the safety of dementia patients and reduce the burden on their families and caregivers.

[1922] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1923] Program processing flow

[1924] Step 1:

[1925] Device: The family member or caregiver launches the dedicated application and enters the patient's basic information (name, age, address, emergency contact information, etc.), then presses the "Send" button to send the entered information to the server.

[1926] Input: Patient basic information.

[1927] Output: Basic patient information sent to the server.

[1928] Processing: The application formats the information entered, encrypts it, and sends it to the server.

[1929] Step 2:

[1930] Server: Analyzes the received patient information and saves it in a database. After saving, it generates a unique user ID and returns it to the terminal.

[1931] Input: Basic patient information submitted.

[1932] Output: A unique user ID.

[1933] Processing: The server stores the information in a database and generates a unique user ID that is sent back to the device.

[1934] Step 3:

[1935] Device: The device checks the received user ID and pairs with the wearable device.

[1936] Input: A unique user ID.

[1937] Output: Pairing completion status.

[1938] Processing: The terminal associates the user ID with the wearable device and performs the pairing process.

[1939] Step 4:

[1940] Wearable device: Acquires GPS data at regular intervals (for example, every 5 minutes) and sends it to a server.

[1941] Input: Current location.

[1942] Output: Location data sent to the server.

[1943] Processing: The GPS module acquires the current location, encrypts the data, and sends it to the server.

[1944] Step 5:

[1945] Server: Stores the received location data in a database. It sequentially analyzes past location data and uses an AI model to learn normal behavioral patterns.

[1946] Input: Location data.

[1947] Output: Location information stored in a database and a learned behavioral pattern model.

[1948] Processing: The location information is stored in a database and an AI model is used to learn normal behavior patterns.

[1949] Step 6:

[1950] Server: Analyzes new location data in real time to detect unusual movement patterns and, if an anomaly is detected, sends a notification to emergency contacts.

[1951] Input: Real-time location information.

[1952] Output: Detection and notification of anomalous movement patterns.

[1953] Processing: Analyze the new location data and send a notification to emergency contacts if an anomaly is detected.

[1954] Step 7:

[1955] Terminal: Family members and caregivers can check the patient's current location on a map using a dedicated application.

[1956] Input: A real-time location request.

[1957] Output: Current location displayed on a map.

[1958] Processing: The device retrieves the latest location information from the server and displays it on a map.

[1959] Step 8:

[1960] Emotion engine: Collects movement, facial expression, and voice data from patients and caregivers and analyzes their emotions.

[1961] Input: movement, facial expression, and voice data.

[1962] Output: Sentiment analysis results.

[1963] Processing: The emotion engine analyzes movement, facial expression, and voice data to assess the emotional state.

[1964] Step 9:

[1965] Server: Receives and analyzes emotion data. If abnormal emotion changes are detected, an alert is generated and a notification is sent to emergency contacts.

[1966] Input: Sentiment analysis result data.

[1967] Output: Detection and notification of abnormal emotional changes.

[1968] Processing: The server analyzes the emotion data and sends a notification to emergency contacts if it detects any abnormal changes.

[1969] This series of processes makes it possible to monitor the location information of dementia patients and respond quickly to emotional changes.

[1970] (Application example 2)

[1971] 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."

[1972] In the daily lives of dementia patients, the risk of getting lost and sudden emotional changes have become social issues. Ensuring safety is particularly important when going out or delivering meals, but conventional technology has not been able to adequately monitor not only the location information of dementia patients and delivery personnel, but also emotional changes. This has meant that sufficient consideration cannot be given to the safety of both patients and delivery personnel, making it difficult to reduce the risks for both.

[1973] 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.

[1974] In this invention, the server includes a location information acquisition means for grasping the current location of the dementia patient in real time, a means for saving the acquired location information in a database, a means for analyzing past location data and learning normal behavior patterns, a means for analyzing new location data acquired in real time and detecting abnormal movement patterns, a means for sending an alert when an abnormal movement pattern is detected, a means for displaying the current patient location on a map, a means for acquiring and recording the location information of the delivery person, a means for analyzing the emotions of both the delivery person and the dementia patient and detecting abnormal emotional changes, and a means for sending an alert when an abnormal emotional change is detected. This makes it possible to monitor the location information and emotional changes of both the dementia patient and the delivery person in real time and respond immediately when an abnormality occurs.

[1975] "Location information acquisition means" refers to devices or technologies used to determine the current location of dementia patients or delivery personnel in real time.

[1976] A "database" is an information system for recording and storing information such as acquired location information and emotional data.

[1977] A "behavioral pattern learning means" is a device or technology used to analyze historical location data and identify typical behavioral patterns.

[1978] "Abnormal movement pattern detection means" refers to devices or techniques used to analyze location data acquired in real time and detect abnormal movements that differ from normal behavior patterns.

[1979] An "alert sending means" is a device or technology that sends a notification to emergency contacts when an abnormal movement pattern or emotional change is detected.

[1980] "Real-time display means" refers to the device or technology used to display the current location of patients and delivery personnel on a map.

[1981] "Emotion analysis means" refers to devices and technologies used to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery personnel.

[1982] An "abnormal emotion detection means" is a device or technology used to detect abnormal changes in the emotions of dementia patients or delivery personnel.

[1983] This invention is a system for ensuring the safety of dementia patients and delivery personnel, and for monitoring abnormal movement patterns and emotional changes in real time. To implement this system, the following components and means are used.

[1984] System Configuration

[1985] 1. Location information acquisition means:

[1986] The wearable devices carried by dementia patients and delivery workers acquire location information. These devices have a built-in GPS module that measures their current location at regular intervals and transmits the information to a server.

[1987] 2. Database:

[1988] The server stores location information and emotion data in a database, using a database management system such as PostgreSQL.

[1989] 3. Behavioral pattern learning methods:

[1990] The server collects and analyzes historical location data to identify typical patterns of behavior, using machine learning frameworks such as TensorFlow.

[1991] 4. Abnormal movement pattern detection method:

[1992] The server analyzes new location data in real time to detect anomalous movements that deviate from normal patterns of behavior, again using TensorFlow.

[1993] 5. Alert delivery method:

[1994] The server sends notifications to emergency contacts when unusual movement patterns or emotional changes are detected, for example, via SMS or push notifications.

[1995] 6. Real-time display means:

[1996] Family members and caregivers can check the current location of dementia patients and delivery personnel on a map through a dedicated application, which runs on iOS and Android and is developed in Swift and Kotlin.

[1997] 7. Sentiment analysis tools:

[1998] The server uses an emotion engine to recognize and analyze emotions from the movements, facial expressions, and voices of dementia patients and delivery workers, using OpenCV and Google Cloud Speech-to-Text API.

[1999] 8. Abnormal Emotion Detection Methods:

[2000] The server analyzes the emotional data and generates an alert if it detects any abnormal emotional changes, which is also sent to emergency contacts.

[2001] Specific examples

[2002] Suppose a patient with dementia uses a meal delivery service. The wearable devices carried by the patient and delivery person collect location information and send it to a server. The server stores this information in a database and uses a machine learning model to learn normal behavioral patterns. If an abnormality is detected, a notification is sent to emergency contacts. Through a dedicated application, family members and caregivers can monitor the current location of the patient and delivery person in real time. Furthermore, an emotion engine analyzes the emotions of the patient and delivery person and generates an alert if an abnormality is detected.

[2003] Prompt Sentence Examples

[2004] Generate code suitable for building a location tracking and emotion recognition system like this:

[2005] The wearable device acquires GPS data at regular intervals and sends it to the server.

[2006] The server receives location information and detects anomalies using an AI model

[2007] The emotion engine analyzes emotions from facial expressions and voice, and generates an alert if there is an abnormality.

[2008] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2009] Step 1:

[2010] Acquiring and sending location information

[2011] A wearable device carried by a user (in this case, a dementia patient or a delivery person) acquires GPS data at regular intervals. The acquired location data (input) is sent from the wearable device to a server (output). At this time, location measurement is performed using the GPS module in the device.

[2012] Step 2:

[2013] Save location information

[2014] The server stores the received location data (input) in a database (output), using a database management system such as PostgreSQL, and the stored data is used for later analysis.

[2015] Step 3:

[2016] Learning behavioral patterns

[2017] The server uses past location data (input) to learn normal behavioral patterns using a machine learning framework such as TensorFlow (output). The learned model is then used to identify the user's normal behavioral patterns.

[2018] Step 4:

[2019] Analyzing real-time location data

[2020] The server analyzes newly received location data (input) in real time and compares it with previously learned behavioral pattern models. If an abnormal movement pattern is detected, the information (output) is sent to the next step. TensorFlow is used to compare the current data with the model and detect anomalies.

[2021] Step 5:

[2022] Sending alerts

[2023] The server sends a notification message to emergency contacts when an abnormal movement pattern is detected (output). This notification can be sent via SMS or push notification. The data triggers the notification system when an abnormality is detected.

[2024] Step 6:

[2025] Real-time location information display

[2026] The device uses a dedicated application to display the user's current location data (input) on a map (output). By checking the map, the user can understand the current location of the dementia patient or delivery person.

[2027] Step 7:

[2028] Acquiring and analyzing emotion data

[2029] Wearable devices and other sensors capture the movements, facial expressions, and voice data (input) of dementia patients and delivery workers. The server receives this data, analyzes emotions using OpenCV and Google Cloud Speech-to-Text API, and obtains the results (output).

[2030] Step 8:

[2031] Detecting abnormal emotional changes

[2032] The server detects abnormal emotional changes based on the analysis results (input). If an abnormality is detected, the information (output) is sent to the next step. To detect an abnormality, the pattern of emotional changes is compared with pre-set criteria.

[2033] Step 9:

[2034] Emotional abnormality alert sending

[2035] When an abnormal emotion change is detected, the server sends a notification message to emergency contacts (output). This is also sent via SMS or push notification. The abnormal emotion data triggers the notification system.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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.

[2040] 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.

[2041] 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.

[2042] 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).

[2043] 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.

[2044] 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."

[2045] 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.

[2046] 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).

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] 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.

[2052] 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.

[2053] 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.

[2054] 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.

[2055] 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.

[2056] 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.

[2057] The following is further disclosed regarding the above embodiment.

[2058] (Claim 1)

[2059] A location information acquisition means for grasping the current location of a dementia patient in real time;

[2060] a means for storing the acquired location information in a database;

[2061] A means of analyzing past location data and learning normal behavior patterns;

[2062] means for analyzing new location data obtained in real time to detect anomalous movement patterns;

[2063] means for sending an alert if an abnormal movement pattern is detected;

[2064] means for displaying the current patient location on a map;

[2065] A system including:

[2066] (Claim 2)

[2067] 2. The system according to claim 1, wherein the location information acquisition means is configured by a wearable device.

[2068] (Claim 3)

[2069] 10. The system of claim 1, wherein the means for sending an alert is configured to send a notification message to an emergency contact.

[2070] "Example 1"

[2071] (Claim 1)

[2072] A registration means for a user to input basic information of a dementia patient and transmit the information to a server;

[2073] The server stores the received basic information in a database and generates a unique user ID.

[2074] A means for transmitting the acquired GPS data to a server by the location information acquisition means;

[2075] a means for storing the received location information in a database by the server;

[2076] A means for the server to learn past location data using an AI model;

[2077] a means for the server to analyze new location data in real time to detect anomalous movement patterns;

[2078] means for sending a notification message to an emergency contact if an abnormal movement pattern is detected;

[2079] means for displaying the current patient location on a map;

[2080] A system including:

[2081] (Claim 2)

[2082] 2. The system according to claim 1, wherein the location information acquisition means is configured by a wearable device.

[2083] (Claim 3)

[2084] 10. The system of claim 1, further comprising a dedicated application for determining current patient location.

[2085] "Application Example 1"

[2086] (Claim 1)

[2087] A location information acquisition method to grasp the current location of dementia patients and food delivery drivers in real time,

[2088] a means for storing the acquired location information in a database;

[2089] A means of analyzing past location data and learning normal behavior patterns using machine learning models;

[2090] means for analyzing new location data obtained in real time to detect anomalous movement patterns;

[2091] means for sending an alert if an abnormal movement pattern is detected;

[2092] means for displaying the current location of the subject on a map;

[2093] A system including:

[2094] (Claim 2)

[2095] 2. The system according to claim 1, wherein the location information acquisition means is configured by a wearable device or a smartphone.

[2096] (Claim 3)

[2097] 10. The system of claim 1, wherein the means for sending an alert is configured to send a notification message to an emergency contact or an administrator.

[2098] "Example 2: Combining Emotion Engines"

[2099] (Claim 1)

[2100] A location information acquisition means for grasping the current location of a dementia patient in real time;

[2101] a means for storing the acquired location information in a database;

[2102] A means of analyzing past location data and learning normal behavior patterns;

[2103] means for analyzing new location data obtained in real time to detect anomalous movement patterns;

[2104] means for sending an alert if an abnormal movement pattern is detected;

[2105] means for displaying the current patient location on a map;

[2106] An emotion recognition means for analyzing user's movements, facial expressions, and voice data;

[2107] means for analyzing emotion data and detecting abnormal emotion changes;

[2108] means for sending an alert when an abnormal emotional change is detected;

[2109] A system including:

[2110] (Claim 2)

[2111] 2. The system according to claim 1, wherein the location information acquisition means is configured by a wearable device.

[2112] (Claim 3)

[2113] 10. The system of claim 1, wherein the means for sending an alert is configured to send a notification message to an emergency contact.

[2114] "Application example 2 when combining emotion engines"

[2115] (Claim 1)

[2116] A location information acquisition means for grasping the current location of a dementia patient in real time;

[2117] a means for storing the acquired location information in a database;

[2118] A means of analyzing past location data and learning normal behavior patterns;

[2119] means for analyzing new location data obtained in real time to detect anomalous movement patterns;

[2120] means for sending an alert if an abnormal movement pattern is detected;

[2121] means for displaying the current patient location on a map;

[2122] A means for acquiring and recording location information of a delivery person;

[2123] A method for analyzing the emotions of both delivery workers and dementia patients and detecting abnormal emotional changes.

[2124] means for sending an alert when an abnormal emotional change is detected;

[2125] A system including:

[2126] (Claim 2)

[2127] 2. The system according to claim 1, wherein the location information acquisition means is configured by a wearable device.

[2128] (Claim 3)

[2129] 10. The system of claim 1, wherein the means for sending an alert is configured to send a notification message to an emergency contact. [Explanation of symbols]

[2130] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A location information acquisition means for grasping the current location of a dementia patient in real time; a means for storing the acquired location information in a database; A means of analyzing past location data and learning normal behavior patterns; means for analyzing new location data obtained in real time to detect anomalous movement patterns; means for sending an alert if an abnormal movement pattern is detected; means for displaying the current patient location on a map; A system including:

2. The system according to claim 1 , wherein the location information acquisition means is configured by a wearable device.

3. 10. The system of claim 1, wherein the means for sending an alert is configured to send a notification message to an emergency contact.

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