System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting the position information of the senior, a means for learning the normal activity range of the senior, a means for collating the received position information with the normal activity range and detecting abnormal activity, a means for transmitting an alert to a family when detecting the abnormal activity, and a means for contacting the police when the family recognizes the abnormal activity.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's aging society, there has been an increase in cases of seniors going missing due to dementia or wandering, which is a major source of anxiety for seniors and their families. Going missing due to wandering in particular poses a life-threatening risk to the senior, so prompt and appropriate measures are necessary. However, current technology does not provide a complete system for monitoring seniors' range of movement in real time and detecting abnormal behavior. New technology is needed to ensure the safety of seniors while minimizing the burden on their families. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system that includes a means for collecting location information of seniors, a means for learning the senior's usual range of activity, a means for detecting abnormal behavior by comparing the received location information with the usual range of activity, a means for sending an alert to family members when abnormal behavior is detected, and a means for contacting the police if the family members recognize the abnormality. Specifically, the system uses a GPS module to collect the senior's location information, analyzes past behavioral patterns using a generative AI model, and defines the senior's usual range of activity. This allows abnormal behavior to be detected in real time and quickly notify the family members. Furthermore, if the family members recognize the abnormality, the system immediately cooperates with the police to ensure the senior's safety. This system can prevent seniors from wandering off or going missing, ensuring the safety of seniors while reducing the burden on families.

[0006] "Senior" refers to an elderly person who is socially and legally recognized as 65 years of age or older.

[0007] "Location information" refers to data that indicates a specific point on Earth using latitude and longitude.

[0008] "Learning" refers to the process of analyzing data collected over a period of time to identify patterns and trends.

[0009] "Verification" refers to the process of comparing received data with previously stored data to determine whether there is a match or mismatch.

[0010] "Abnormal behavior" refers to behavior that significantly deviates from the normal range or pattern of behavior.

[0011] An "alert" refers to a message or signal that notifies the user and draws their attention when some abnormality is detected.

[0012] "Family" refers to the senior's close relatives who are in a position to monitor or support the senior's health and safety.

[0013] "Police" refers to a public agency engaged in maintaining public safety and order, crime prevention and law enforcement.

[0014] "GPS Module" means an electronic component that uses the Global Positioning System to identify a specific location on Earth.

[0015] A "generative AI model" refers to an artificial intelligence algorithm that learns patterns from large amounts of data and makes predictions and classifications. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] The present invention is a system that collects location information of seniors, compares it with the range of their movements to detect abnormal behavior, sends an alert to their family, and contacts the police if necessary. An embodiment of the present invention will be described below.

[0038] 1. System Overview

[0039] This system is broadly composed of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. The user uses a smartphone application to monitor the senior's behavior.

[0040] 2. Device Features

[0041] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[0042] 3. Server Functions

[0043] The server receives and analyzes the data sent from the device, specifically through the following process:

[0044] 1. Data Receipt and Authentication

[0045] It receives data sent from the terminal and authenticates the terminal and the user.

[0046] 2. Learning the normal range of behavior

[0047] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[0048] 3. Anomaly Detection

[0049] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[0050] 4. Alert Notifications

[0051] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[0052] 5. Police Cooperation

[0053] If the family detects something unusual, the server will automatically contact the police, including the senior's current location and emergency contact information.

[0054] 4. User (family) operations

[0055] Through the smartphone application, the user performs the following operations:

[0056] 1. Monitoring

[0057] The application displays the senior's real-time location on a map.

[0058] 2. Receiving and checking alerts

[0059] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[0060] 3. Abnormality recognition

[0061] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[0062] Specific examples

[0063] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[0064] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[0065] The above is a specific embodiment for carrying out the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and respond promptly.

[0066] The processing flow will be explained below.

[0067] Server-side processing

[0068] Step 1:

[0069] The server receives real-time location and sensor data sent from the device.

[0070] The server receives GPS data and acceleration data and stores them in a database.

[0071] Step 2:

[0072] The server authenticates the terminal and the user.

[0073] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[0074] Step 3:

[0075] The server learns the usual range of movement based on the collected location data.

[0076] The collected data is input into a generative AI model to analyze the behavioral patterns of seniors.

[0077] The resulting normal behavior range is stored in a database.

[0078] Step 4:

[0079] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[0080] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[0081] If an anomaly is detected, an alert generation flag is set.

[0082] Step 5:

[0083] Once the server detects an abnormality, it sends an alert to the family.

[0084] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[0085] Step 6:

[0086] If the user detects an abnormality, the server will contact the police.

[0087] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's location, to the police.

[0088] Terminal side processing

[0089] Step 1:

[0090] The device collects the senior's location information.

[0091] The device's GPS module periodically obtains the current latitude and longitude.

[0092] The accelerometer also works at the same time to collect movement information.

[0093] Step 2:

[0094] The terminal transmits the collected data to the server.

[0095] The collected location data and sensor data are sent to a server at regular intervals.

[0096] If the data transmission fails, a retry process is performed.

[0097] User (family) operations

[0098] Step 1:

[0099] Users monitor the behavior of seniors through the application.

[0100] Users open the application and view real-time location information and past activity history.

[0101] Step 2:

[0102] The user receives an abnormality alert from the server.

[0103] An alert notification from the server will be displayed on your smartphone.

[0104] The alert will include current location and details of any unusual behavior.

[0105] Step 3:

[0106] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[0107] If an abnormality is detected, a button in the application is pressed to notify the server.

[0108] Step 4:

[0109] If the user detects an abnormality, the server contacts the police.

[0110] The server automatically provides the senior's current location and emergency contact information to the police and requests their response.

[0111] The above is a detailed description of the specific processing steps at the server, terminal, and user.

[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] In today's society, where the risk of elderly people wandering or going missing is increasing, there is a need for systems to ensure the safety of the elderly. In particular, there is a need for systems that can ensure the safety of the elderly by detecting abnormal behavior early and promptly notifying family members and the police. There is also a need for systems that can learn behavioral ranges and detect anomalies with higher accuracy by using GPS modules and generative AI models.

[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 periodically collecting location information of seniors, means for learning the senior's usual range of activity using a generative AI model, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to the family's communication device when abnormal behavior is detected, and means for contacting police authorities if the family recognizes the abnormality. This enables early detection of elderly people wandering or going missing and enables prompt response.

[0117] The "system" is a set of hardware and software that collects location information about seniors, detects abnormal behavior, and sends alerts to family members or the police if necessary.

[0118] "Location information" is information indicating the senior's current location, and is data including geographic coordinates such as latitude and longitude.

[0119] "Periodic collection means" refers to the methods and technical means for collecting senior location information at regular time intervals.

[0120] A "generative AI model" is an artificial intelligence algorithm and program that learns behavioral patterns based on large amounts of data and makes highly accurate predictions and detects anomalies.

[0121] "Usual range of activity" is information that indicates the geographical area in which seniors typically engage, and is data that includes the results learned by the generative AI model.

[0122] "Means for matching" refers to the technical means for comparing the received location information with the usual range of movement.

[0123] "Abnormal behavior" refers to cases in which a senior exhibits unnatural behavior that is different from the norm, such as going outside the normal range of behavior.

[0124] "Means for sending alerts" refers to the technical means for sending notifications to family members' communication devices when abnormal behavior is detected.

[0125] "Family communication devices" refers to communication devices such as smartphones and tablets used by family members.

[0126] "Means of contacting police authorities" refers to technological means to automatically notify the police of the senior's current location and situation if the family determines something is wrong.

[0127] MODE FOR CARRYING OUT THE INVENTION

[0128] This system collects and analyzes the location information of elderly people, detects abnormal behavior, and notifies their family and the police. This system consists of three main elements: a terminal, a server, and the user's communication terminal.

[0129] Device configuration and functions

[0130] The device is a wearable device worn by the elderly, and is equipped with a GPS module, an accelerometer, and other sensors. This hardware handles data collection and transmission, including:

[0131] 1. Collection of location information

[0132] The device uses a GPS module to periodically collect the elderly person's current location (latitude, longitude). For example, it can be set to obtain the current location every five minutes. This allows the elderly person's behavioral patterns to be understood in real time.

[0133] 2. Collecting sensor information

[0134] The device uses accelerometers and other sensors to collect information about the elderly person's movements and physical condition, including walking speed and fall detection.

[0135] 3. Data transmission

[0136] The device sends the collected location information and sensor information to a server at regular intervals. This data is analyzed in real time, enabling rapid detection of abnormalities.

[0137] Server configuration and functions

[0138] The server is a cloud-based computer system that receives and analyzes the data sent from the terminal.

[0139] 1. Data Receipt and Authentication

[0140] The server receives the data sent from the terminal and then authenticates the terminal and the user, ensuring the authenticity of the data.

[0141] 2. Initial learning of range of movement

[0142] During the initial setup period, the server analyzes location data sent from the device and uses a generative AI model to learn the typical range of movement of elderly people, enabling highly accurate learning of behavioral patterns.

[0143] 3. Anomaly Detection

[0144] The server compares the received current location data with the learned normal movement range to detect anomalies. For example, if the user moves significantly outside the normal movement range, it is deemed to be abnormal.

[0145] 4. Alert Notifications

[0146] If an abnormality is detected, the server sends an alert to the user's communication device, which includes the elderly person's current location and details of the abnormality.

[0147] 5. Police Cooperation

[0148] When the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police, which includes the elderly person's current location and emergency contact information.

[0149] User operations

[0150] Through the smartphone application, the user performs the following operations:

[0151] 1. Location monitoring

[0152] Through the application, users can view the real-time location information of the elderly person on a map, allowing them to instantly understand their current location.

[0153] 2. Receiving and checking alerts

[0154] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[0155] 3. Abnormality recognition

[0156] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[0157] Examples of concrete examples and prompts

[0158] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[0159] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[0160] Prompt Sentence Examples

[0161] "We designed a system that collects location information for seniors, detects abnormal behavior, and contacts their families and the police. In this system, seniors wear a wearable device that sends location information to a server using GPS and various sensors. The server uses a generative AI model to learn their usual range of behavior, and if it detects an abnormality, it sends an alert to their families and automatically contacts the police if necessary. This allows us to quickly ensure the safety of seniors."

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] System program processing flow

[0164] Terminal handling

[0165] Step 1:

[0166] The device uses a GPS module to periodically collect the senior's location information.

[0167] Input: GPS satellite signal

[0168] Data processing / calculation: Analyze signals to obtain latitude and longitude

[0169] Output: Latitude and longitude location data

[0170] Specific operation: The device obtains the current location data "Latitude: 35.6895, Longitude: 139.6917" every 5 minutes.

[0171] Step 2:

[0172] The device uses an accelerometer and other sensors to collect information about the senior's movements.

[0173] Input: Data from accelerometer and gyro sensor

[0174] Data processing / computation: Analyzing raw sensor data to identify movement patterns (walking, running, falling, etc.)

[0175] Output: Motion pattern data

[0176] Specific operation: The device collects data on "falls" and "walking."

[0177] Step 3:

[0178] The device sends the collected location information and sensor information to the server at regular intervals.

[0179] Input: Latitude and longitude location data, movement pattern data

[0180] Data processing / calculation: Convert data into a format for sending to the server

[0181] Output: Data packet sent to the server

[0182] Specific operation: At 8:05, send the data "Latitude: 35.6895, Longitude: 139.6917" and "Fall" to the server.

[0183] Server Processing

[0184] Step 4:

[0185] The server receives the data sent from the terminal and authenticates the terminal and the user.

[0186] Input: Data packet from the terminal

[0187] Data processing / calculation: Data packet analysis, authentication information verification

[0188] Output: Authentication result (success / failure)

[0189] Specific operation: The server checks the device's ID and authenticates the authenticity of the data.

[0190] Step 5:

[0191] During the initial setup period, the server analyzes location data sent from the device and learns the senior's usual range of movement using a generative AI model.

[0192] Input: Historical location data

[0193] Data processing / calculation: Generative AI models learn data and identify normal behavior ranges

[0194] Output: Learned normal behavior range data

[0195] Specific operation: The server uses two weeks' worth of location data to learn Mr. A's usual range of movement.

[0196] Step 6:

[0197] The server compares the received current location data with the learned normal range of movement to detect any anomalies.

[0198] Input: Current location data, learned movement range data

[0199] Data processing / calculation: Determine whether there are any abnormalities based on the matching algorithm

[0200] Output: Anomaly detection result (abnormal / normal)

[0201] Specific behavior: The server detects that "Latitude: 35.6895, Longitude: 139.6917" is outside the normal range of activity.

[0202] Step 7:

[0203] If an abnormality is detected, the server sends an alert to the user's communication terminal.

[0204] Input: Anomaly detection result, current location data

[0205] Data processing / calculation: Converting to a notification format for communication terminals

[0206] Output: An alert sent to the user's device

[0207] Specific operation: The server sends an alert to the family member's smartphone saying, "Senior A is outside his / her normal range of movement."

[0208] Step 8:

[0209] When the user checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police.

[0210] Input: User action (anomaly recognition)

[0211] Data processing / calculation: Emergency contact message generation

[0212] Output: Message to contact law enforcement

[0213] Specific operation: The family member presses the "Confirm Abnormality" button, and the server immediately starts contacting the police.

[0214] User operations

[0215] Step 9:

[0216] Through the application, users can view the real-time location information of elderly people on a map.

[0217] Input: Location data sent from the server

[0218] Data processing / calculation: Data conversion to map display format

[0219] Output: Current location displayed on a map

[0220] Specific actions: A family member checks the app to see that "Senior A is currently in the park."

[0221] Step 10:

[0222] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[0223] Input: Alert data sent from the server

[0224] Data processing / calculation: Alert display on communication terminal

[0225] Output: The alert information to be displayed

[0226] Specific actions: The family member opens the alert they received and checks the details: "Abnormality detected: Senior A is outside of normal activity range."

[0227] Step 11:

[0228] The family member checks the alert content and presses the "Abnormality Confirmation" button if necessary. This action causes the server to contact the police.

[0229] Input: User action (anomaly recognition)

[0230] Data processing / calculation: Generation of police contact messages

[0231] Output: Message to contact law enforcement

[0232] Specific operation: When the device is activated, it presses the "Abnormality Detection" button, and the server notifies the police of the elderly person's current location.

[0233] (Application example 1)

[0234] 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."

[0235] To ensure the safety of the elderly, there is a need for a system that can detect abnormalities early when seniors deviate from their normal range of movement and quickly notify family members and the police. It is also desirable to be able to constantly monitor the status of seniors by processing collected data in real time and effectively operating it in a cloud environment. However, it has been difficult for conventional systems to comprehensively realize such functions.

[0236] 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.

[0237] In this invention, the server includes a means for transmitting data to a cloud-based server, a means for receiving location information and behavioral data in real time, and a means for detecting abnormalities by comparing the received location information with the normal range of behavior learned using the generative AI model. This makes it possible to immediately detect abnormal behavior of elderly people and promptly notify their families and the police.

[0238] "Location information" is information that indicates where on Earth a particular person or object is located.

[0239] "Range of activity" refers to the area or region in which a person normally operates.

[0240] "Learning" is the process by which machines and algorithms automatically increase their knowledge by identifying patterns and trends based on data.

[0241] "Verification" refers to the act of comparing one piece of data with other data to check for matches or anomalies.

[0242] "Abnormal behavior" refers to behavior that significantly deviates from normal patterns of behavior.

[0243] An "alert" is a notification that immediately notifies you of an abnormality or important event.

[0244] A "cloud-based server" is a remote server accessible via the internet that stores and processes data.

[0245] "Real-time" refers to situations where data collection, processing, and notification occur immediately.

[0246] A "generative AI model" is a model that uses artificial intelligence to learn patterns and regularities from data and make future predictions and classifications.

[0247] The system for implementing this invention includes a cloud-based server, a wearable device worn by the senior, and a smartphone application used by family members. Each element is configured and functions in conjunction with the others as follows:

[0248] Server Features

[0249] The server is the main computer system deployed in the cloud-based environment that receives, analyzes, and notifies data. The server operates using the following hardware and software:

[0250] Hardware: A remote server with an internet connection

[0251] Software: Python program, HTTP request library (requests), location calculation library (geopy)

[0252] Main processing contents

[0253] 1. Data Receipt and Authentication:

[0254] The server receives the location information and operation data sent from the terminal and authenticates the sending terminal and the user.

[0255] 2. Learning normal behavior:

[0256] A generative AI model is used to learn and define normal ranges of movement based on location data collected during an initial setup period.

[0257] 3. Anomaly detection:

[0258] The received current location data is compared with the learned normal range of movement and anomalies are detected based on this.

[0259] 4. Alert Notification:

[0260] If an abnormality is detected, an alert is sent immediately to the family's smartphone application.

[0261] Device Features

[0262] The device is a wearable device that seniors wear at all times and is equipped with the following hardware:

[0263] GPS module: Obtains location information.

[0264] Accelerometer: Collects motion data.

[0265] Main processing contents

[0266] The terminal periodically collects the senior's current location (latitude, longitude) and movement information, and transmits this data to a server at regular intervals.

[0267] User operations

[0268] Through a smartphone application, family members can:

[0269] 1. Monitoring:

[0270] The application displays the senior's real-time location on a map.

[0271] 2. Receiving and confirming alerts:

[0272] Receive and review alerts from the server, which include the senior's current location and details about any abnormalities.

[0273] 3. Abnormality recognition:

[0274] Check the alert content and press the "Confirm Abnormality" button if necessary. This action will cause the server to contact the police and provide them with the senior's current location.

[0275] Specific examples

[0276] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use a generative AI model to learn this behavioral pattern and define the normal range of activity. If one day A significantly deviates from the normal range of activity, the device will detect this anomaly and send the location data to the server. The server will analyze this, determine it to be an anomaly, and send an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they can press the "Confirm Abnormality" button. This will cause the server to automatically contact the police and provide A's current location, ensuring prompt protection.

[0277] Prompt Sentence Examples

[0278] "Person A's current location is outside of his normal range. His current location is latitude: 35.6895, longitude: 139.6917. Would you like to send an alert?"

[0279] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0280] Step 1: Data collection

[0281] The terminal periodically collects the senior's current location (latitude, longitude) and movement data using the GPS module and acceleration sensor on the wearable device.

[0282] Input: Senior's GPS location and movement data

[0283] Output: Collected location and movement data

[0284] Specific operation: Obtains location information from the GPS module and movement data from the accelerometer and stores them in memory.

[0285] Step 2: Send data

[0286] The device periodically transmits the collected location and movement data to a cloud-based server.

[0287] Input: Collected location and movement data

[0288] Output: Location and movement data sent to the server

[0289] Specific operation: Uses the device's communication module to generate an HTTP request and send the collected data to the specified endpoint on the server.

[0290] Step 3: Data Receipt and Authentication

[0291] The server receives the data sent from the terminal and authenticates the terminal and the user.

[0292] Input: Location and movement data sent from your device

[0293] Output: Authenticated data or authentication error

[0294] Specific operation: The server analyzes the received data and performs authentication based on the device ID and user information. If authentication is successful, the data is saved and the process proceeds to the next step.

[0295] Step 4: Learn normal behavior

[0296] The server uses a generative AI model to learn and define typical ranges of movement based on location data collected during an initial setup period.

[0297] Input: Location data during the initialization period

[0298] Output: Learned normal behavior range

[0299] Specific operation: Location data is input into the generative AI model, and patterns are extracted and saved to learn normal behavioral ranges.

[0300] Step 5: Anomaly detection

[0301] The server compares the received current location data with the learned normal range of movement and detects any abnormalities.

[0302] Input: Current location data, learned normal range of movement

[0303] Output: Normal or abnormal result

[0304] Specific operation: The current location data is compared with the normal movement range, and if it is outside a certain range, it is determined to be abnormal and an abnormality flag is raised.

[0305] Step 6: Sending an alert

[0306] If an abnormality is detected, the server immediately sends an alert to the family's smartphone application.

[0307] Input: Abnormality detection result, senior's current location data

[0308] Output: Alert notification to family members

[0309] Specific operation: If an abnormality is detected, an alert message is created based on the generated prompt text and sent to the family's smartphone application using an HTTP request.

[0310] Step 7: Receiving and confirming alerts

[0311] The user receives an alert via a smartphone application and checks the details of the abnormality.

[0312] Input: Alert notification from the server

[0313] Output: Check the alert details and confirm the abnormality

[0314] Specific operation: When a user receives an alert, they view the message on their smartphone application and decide whether to press the abnormality acknowledgement button to contact the police.

[0315] Step 8: Contact the police

[0316] If a family member presses the abnormality detection button, the server automatically contacts the police and provides them with the senior's current location.

[0317] Input: Family abnormality certification operation

[0318] Output: Police contact and senior location data

[0319] Specific behavior: If an abnormality is detected, an HTTP request is generated to contact the police and send data including the senior's current location.

[0320] This will create a system that can quickly ensure the safety of seniors.

[0321] 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.

[0322] The present invention combines a system that collects location information of seniors, learns their normal range of activity, detects abnormal behavior, and sends alerts to family members, with an emotion engine that recognizes the emotions of users (family members). An embodiment of the present invention will be described below.

[0323] 1. System Overview

[0324] This system consists of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data, and also incorporates an emotion engine to recognize the user's emotions. The user uses a smartphone application to monitor the senior's behavior.

[0325] 2. Device Features

[0326] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[0327] 3. Server Functions

[0328] The server receives and analyzes the data sent from the device, specifically through the following process:

[0329] 1. Data Receipt and Authentication

[0330] It receives data sent from the terminal and authenticates the terminal and the user.

[0331] 2. Learning the normal range of behavior

[0332] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[0333] 3. Anomaly Detection

[0334] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[0335] 4. Alert Notifications

[0336] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[0337] When an alert is sent, the emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0338] 5. Police Cooperation

[0339] If the family determines that something is wrong, the server will automatically contact the police.

[0340] The communication will include the senior's current location and emergency contact information.

[0341] 4. Functions of the Emotion Engine

[0342] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method accordingly. For example, if the user is showing signs of stress or anxiety, the engine will increase the urgency of the alert and prompt a prompt response. By collecting emotion data over a long period of time and learning the user's reaction patterns, the engine can provide appropriate intervention and support.

[0343] 5. User (family) operations

[0344] Through the smartphone application, the user performs the following operations:

[0345] 1. Monitoring

[0346] The application displays the senior's real-time location on a map.

[0347] 2. Receiving and checking alerts

[0348] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[0349] 3. Abnormality recognition

[0350] Check the alert and press the "Confirm Abnormality" button if necessary. Based on the emotional state analyzed by the emotion engine, additional confirmation before pressing the button or a prompt to contact an emergency service may be provided.

[0351] 4. Notify the police

[0352] If the user detects an abnormality, the server automatically provides the senior's location information to the police for prompt protection.

[0353] Specific examples

[0354] For example, if senior citizen A has a habit of taking walks in the park near his or her home, the device will collect data on A's behavior and send it to the server. During the initial learning period, the server will use the generative AI model to learn A's usual range of behavior.

[0355] One day, when Person A walks far away, straying far from his usual range of movement, the device detects the abnormality and sends the location data to the server. The server analyzes the abnormality and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family) and sends a high level of urgency notification. If the user checks the alert and determines that there is an abnormality, they press the "Abnormality Confirmation" button, and the server automatically contacts the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly.

[0356] The above is a specific embodiment of the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and to take appropriate action according to the emotional state of the family.

[0357] The processing flow will be explained below.

[0358] Server-side processing

[0359] Step 1:

[0360] The server receives real-time location and sensor data sent from the device.

[0361] The GPS data and acceleration data sent by the device are received and stored in a database.

[0362] Step 2:

[0363] The server authenticates the terminal and the user.

[0364] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[0365] Step 3:

[0366] The server learns the usual range of movement based on the collected location data.

[0367] Data collected during the initial setup period is input into a generative AI model to analyze the behavioral patterns of seniors.

[0368] The analysis results, which indicate the normal range of behavior, are stored in a database.

[0369] Step 4:

[0370] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[0371] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[0372] If an anomaly is detected, an alert generation flag is set.

[0373] Step 5:

[0374] Once the server detects an abnormality, it sends an alert to the family.

[0375] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[0376] Step 6:

[0377] The server analyzes the user's emotional state using an emotion engine when an alert is sent.

[0378] The server runs an emotion engine based on the user's past behavioral data and current reactions to check their emotional state, such as stress or anxiety.

[0379] Adjust the content and presentation of alerts based on your emotional state.

[0380] Step 7:

[0381] If the user detects an abnormality, the server will contact the police.

[0382] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's current location, to the police.

[0383] Terminal side processing

[0384] Step 1:

[0385] The device collects the senior's location information.

[0386] The device's GPS module periodically obtains the current latitude and longitude.

[0387] The accelerometer also works at the same time to collect movement information.

[0388] Step 2:

[0389] The terminal transmits the collected data to the server.

[0390] The collected location data and sensor data are sent to a server at regular intervals.

[0391] If the data transmission fails, a retry process is performed.

[0392] User (family) operations

[0393] Step 1:

[0394] Users monitor the behavior of seniors through the application.

[0395] Open the application to see real-time location information and past activity history.

[0396] Step 2:

[0397] The user receives an abnormality alert from the server.

[0398] An alert notification from the server will be displayed on your smartphone.

[0399] The alert will include details of the senior's current location and any unusual behavior.

[0400] Step 3:

[0401] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[0402] When the user presses the confirm button, the emotion engine analyzes the user's emotional state.

[0403] If the user is showing signs of stress or anxiety, the server will increase the urgency of the alert, encouraging a quick response.

[0404] Step 4:

[0405] If the user detects an abnormality, the server automatically contacts the police.

[0406] The server sends the senior's location and emergency contact information to the police, requesting prompt protection.

[0407] Specific examples

[0408] For example, if senior citizen A usually walks in a park near his or her home, the device collects daily behavioral data and sends it to the server. During the initial learning period, the server uses a generative AI model to learn A's usual range of behavior. If one day A deviates significantly from his or her usual range and walks farther, the device detects the abnormality and sends location data to the server. The server analyzes this data, determines it to be an abnormality, and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family member) and adjusts the urgency of the alert. If the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with A's location information. In this way, a quick and effective response is possible.

[0409] Example 2

[0410] 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."

[0411] In an aging society, wandering and disappearance of seniors is a major social problem. Families and caregivers need to be able to quickly detect and respond when seniors deviate from their normal range of movement, but conventional systems lack real-time response and accuracy. Another problem is that it is difficult to respond appropriately while taking into account the emotional state of family members. The purpose of this invention is to solve these problems and provide a system that is highly secure and user-friendly.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0413] In this invention, the server includes means for collecting location information of seniors, means for learning the senior's usual range of activity, means for comparing the received location information with the usual range of activity to detect abnormal behavior, means for sending an alert to family members when abnormal behavior is detected, means for analyzing the user's emotional state at the time an abnormality is detected and adjusting the content and method of notification, and means for contacting the police if the family members determine that something is abnormal. This makes it possible to detect abnormal behavior of seniors in real time and provide appropriate notifications that take into account the emotional state of the family members.

[0414] "Senior" is a general term used to refer to older people.

[0415] "Location information" is data indicating a geographical location, and specifically consists of latitude and longitude.

[0416] A "GPS module" is hardware for obtaining location information using the Global Positioning System (GPS).

[0417] An "accelerometer" is a device that measures the acceleration of an object and is used to detect movement and tilt.

[0418] A "server" is a computer system that receives, analyzes, and provides data to other systems or users.

[0419] A "cloud-based computer system" is a system for storing, managing, and processing data over the Internet.

[0420] An "alert" is a notification or warning to notify of an abnormality or danger.

[0421] An "emotion engine" is a technology that analyzes a user's emotional state and responds appropriately based on the results.

[0422] "Abnormality determination" is the process of determining that a senior's behavior is outside the normal range.

[0423] A "generative AI model" is an artificial intelligence algorithm that learns patterns and anomalies from large amounts of data.

[0424] "Police" is a government agency tasked with maintaining public safety and order.

[0425] This system collects location information from seniors, learns their normal behavior ranges, detects abnormal behavior, and sends alerts to family members. This system includes three elements: a server, a device, and a user.

[0426] 1. System hardware configuration

[0427] Device:

[0428] The terminal is a wearable device worn by seniors. This terminal is equipped with a GPS module and an accelerometer to collect location and movement information. This data is sent to a server at regular intervals.

[0429] server:

[0430] The server is a cloud-based computer system that receives and analyzes data sent from the device. It also has an emotion engine built into it, which analyzes the user's emotional state.

[0431] User device:

[0432] Users (family members) use a smartphone application to monitor the behavior of seniors, which allows them to access real-time location information, receive alerts, and identify abnormalities.

[0433] 2. Software Configuration and Processing Flow

[0434] Data collection and transmission:

[0435] The device uses a GPS module and an accelerometer to collect location and movement information of the senior, and the collected data is sent to a server at regular intervals.

[0436] Data Receipt and Authentication:

[0437] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, data analysis begins.

[0438] Learning normal behavior:

[0439] Based on the data collected during the initial setup period, a generative AI model is used to learn the range of normal senior behavior, and the results of this learning are used to understand normal behavior patterns.

[0440] Anomaly detection:

[0441] The server compares the location data received in real time with the learned range of movement, and if the data deviates from the normal range of movement, it determines that the behavior is abnormal.

[0442] Alert Notifications:

[0443] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0444] Contact the police:

[0445] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[0446] 3. Specific Examples

[0447] Daily Activities:

[0448] For example, if senior citizen A regularly walks in the park near his or her home, this location data is sent from the device to the server. During the initial learning period, the server learns the user's usual range of movement based on this data.

[0449] Anomalous behavior detection:

[0450] If one day, Person A walks far beyond his usual range of movement, the device will detect the abnormality and send the location data to the server. The server will analyze the abnormality and send an alert to his family. At this time, the emotion engine will analyze the user's emotional state and send a high-level notification.

[0451] Prompt Sentence Examples

[0452] 1. Anomaly detection prompt

[0453] "Compare the senior's current location data with their usual range data. If any unusual behavior is detected, please report the details."

[0454] 2. Emotion Engine Prompts

[0455] "Analyze the user's emotional state when they receive the alert and promptly notify them if they are experiencing high levels of stress or anxiety."

[0456] This system will enable early detection of wandering or missing seniors, allowing families to respond appropriately based on their emotional state.

[0457] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0458] Step 1: Collect and send data

[0459] The terminal is a wearable device worn by the senior, and uses a GPS module and an accelerometer to collect the senior's location information (latitude and longitude) and movement information. The collected data is sent to a server at regular intervals.

[0460] Input: GPS module and accelerometer data

[0461] Output: Location and movement information sent to the server

[0462] Specific operation: When Mr. A is walking around his home, the device records his location and movements every minute and sends them to the server.

[0463] Step 2: Receiving and authenticating data

[0464] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, it begins analyzing the data.

[0465] Input: Location and movement information sent from your device

[0466] Output: Start of data analysis based on authentication results

[0467] Specific operation: The server receives the data "Device ID: 12345, Latitude: 35.6895, Longitude: 139.6917" and authenticates it by matching the device ID.

[0468] Step 3: Learn normal behavior

[0469] Based on the data collected during the initial setup period, the server uses a generative AI model to learn the typical range of behavior of seniors.

[0470] Input: Location data collected during the initialization period

[0471] Output: Normal range of movement for seniors

[0472] Specific operation: The server analyzes 30 days' worth of location data and determines that "Mr. A's usual range of movement is within 2 km of his home."

[0473] Step 4: Anomaly detection

[0474] The server compares the location data received in real time with the learned movement range to detect abnormal behavior. If the movement range deviates from the normal movement range, it is judged to be abnormal.

[0475] Input: Real-time incoming location data

[0476] Output: Abnormal behavior detection results

[0477] Specific operation: Person A is detected to be at "Latitude: 35.7000, Longitude: 139.8000" and is determined to be outside the normal range of movement.

[0478] Step 5: Alert Notification

[0479] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0480] Input: Abnormal behavior detection results, user emotional state data

[0481] Output: Alerts sent to family members

[0482] Specific behavior: An alert will be sent to the family saying "Person A is deviating from his / her normal behavior range," and if the user is under high stress, a high-urgency notification will be sent.

[0483] Step 6: Contact the police

[0484] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[0485] Input: Press the abnormality recognition button

[0486] Output: Police notification

[0487] Specific operation: When a family member presses the "Confirm Abnormality" button, the server instantly notifies the police that "Senior A is currently at latitude: 35.7000, longitude: 139.8000."

[0488] (Application example 2)

[0489] 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."

[0490] Systems already exist that monitor the behavior of elderly people in real time, instantly detect abnormal behavior, and notify family members, but these systems cannot adjust the urgency of the alerts received by family members or the notification method, which can cause stress and anxiety.Furthermore, because they do not take into account the emotional state of family members, it is difficult to respond at the appropriate time and in the appropriate way.

[0491] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting location information of the senior, means for learning the senior's usual range of activity, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to family members when abnormal behavior is detected, and means for analyzing the emotional state of family members and adjusting the urgency of the alert and the notification method. This makes it possible to detect abnormal behavior of the senior quickly and without burdening family members, and to respond appropriately.

[0492] "Senior location information" is latitude and longitude data indicating the location where the senior is currently active.

[0493] The "usual range of movement" is a geographical area defined by learning about the senior's daily movement patterns.

[0494] "Abnormal behavior" refers to a senior moving outside of their normal range of movement or behaving unnaturally.

[0495] An "alert" is a warning message that is sent to family members when abnormal behavior of a senior is detected.

[0496] "Emotional state" refers to the emotional reaction or psychological state of a family member when they receive an alert.

[0497] "Urgency" is a level that indicates the importance of the alert and the urgency of the response.

[0498] The "notification method" indicates the method by which the alert will be communicated to family members.

[0499] The system for implementing this invention is composed mainly of a server, a terminal, and a user. The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. It also incorporates an emotion engine to analyze the user's emotional state. The user uses a smartphone application to monitor the senior's behavior.

[0500] 1. Device Features

[0501] The device periodically obtains the senior's current location using a GPS module and collects movement information using an accelerometer, which is then sent to a server at regular intervals.

[0502] 2. Server Functions

[0503] The server receives and authenticates the data sent from the device. Based on the location data collected during the initial setup period, the server uses a generative AI model to learn the senior's usual range of movement. The server then compares the received current location data with the learned range of movement, and if it detects any abnormalities, it sends an alert to the family's smartphone application.

[0504] 3. Functions of the Emotion Engine

[0505] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method. By collecting user emotional data over a long period of time and learning reaction patterns, it can provide appropriate intervention and support.

[0506] 4. User (family) operations

[0507] Users can view the senior's real-time location information on a map through the smartphone application. They can also receive and check alerts from the server, which include the senior's current location and details of any abnormalities. When the user presses the "Confirm Abnormality" button, the server automatically provides the senior's location information to the police, who then make an emergency call. In particular, if the emotion engine determines that the user is experiencing high levels of stress or anxiety, the police will be notified promptly.

[0508] Specific examples

[0509] For example, if a senior normally takes frequent walks in the park near their home, the device will send that location data to the server. During the initial learning period, the server will use a generative AI model to learn the senior's normal range of movement. If one day the senior significantly deviates from their normal range of movement, the server will detect the abnormality and send an alert to the family. At this time, the emotion engine will analyze the family's emotional state and issue a high-level notification. If the user confirms the alert and determines that there is an abnormality, they can press the "Confirm Abnormality" button, and the server will contact the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly. This can prevent seniors from wandering or going missing, reducing the burden on families.

[0510] Prompt Sentence Examples

[0511] "It detects the range of normal activities and abnormal behavior of seniors. It uses location data from the past 30 days as input to learn the normal behavioral patterns of seniors and determines any deviations as abnormal."

[0512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0513] Step 1: The device collects the senior's location information

[0514] The device periodically obtains the senior's current location (latitude and longitude) using a GPS module. It also simultaneously collects movement data using an acceleration sensor. This collected data is sent to the server as packets at regular intervals. The input is location information and movement data, and the output is data packets sent to the server.

[0515] Step 2: The server receives the location information

[0516] The server receives data packets from the device and authenticates the device and user. The input is the data packet sent from the device, and the output is the location data after authentication is complete. At this stage, the location data is securely stored on the server.

[0517] Step 3: The server learns its normal behavior

[0518] During the initialization period, the server retrieves the accumulated location data and uses a generative AI model to learn the senior's normal behavior range. The input is the accumulated location data, and the output is the learned normal behavior range. This behavior range is used in the subsequent anomaly detection process.

[0519] Step 4: The server detects anomalous behavior

[0520] The server compares the received current location data with the learned normal behavior range to detect abnormal behavior. The input is the current location data and the learned normal behavior range, and the output is data on whether an abnormality exists. This data is used in the alert sending process if an abnormality is detected.

[0521] Step 5: The server sends an alert to the family

[0522] If an abnormality is detected, the server sends an alert to the family's smartphone application. At this time, the emotion engine analyzes the family's emotional state and adjusts the urgency and notification method. The input is the abnormality detection data and the family's emotional data, and the output is the adjusted alert message.

[0523] Step 6: User checks the alert and acknowledges the anomaly

[0524] The user checks the alert through the smartphone application and presses the "Abnormality Acknowledgement" button if they decide that it is an abnormality. The input is the alert message, and the output is the abnormality acknowledgement data. This data is sent to the server.

[0525] Step 7: The server contacts the police

[0526] When a user recognizes an abnormality, the server automatically provides the senior's location information to the police and makes an emergency contact. The input is the abnormality recognition data from the user, and the output is the contact data to the police. If the user's stress or anxiety is high due to the emotion engine, the police will be notified more quickly.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] [Second embodiment]

[0531] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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."

[0543] The present invention is a system that collects location information of seniors, compares it with the range of their movements to detect abnormal behavior, sends an alert to their family, and contacts the police if necessary. An embodiment of the present invention will be described below.

[0544] 1. System Overview

[0545] This system is broadly composed of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. The user uses a smartphone application to monitor the senior's behavior.

[0546] 2. Device Features

[0547] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[0548] 3. Server Functions

[0549] The server receives and analyzes the data sent from the device, specifically through the following process:

[0550] 1. Data Receipt and Authentication

[0551] It receives data sent from the terminal and authenticates the terminal and the user.

[0552] 2. Learning the normal range of behavior

[0553] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[0554] 3. Anomaly Detection

[0555] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[0556] 4. Alert Notifications

[0557] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[0558] 5. Police Cooperation

[0559] If the family detects something unusual, the server will automatically contact the police, including the senior's current location and emergency contact information.

[0560] 4. User (family) operations

[0561] Through the smartphone application, the user performs the following operations:

[0562] 1. Monitoring

[0563] The application displays the senior's real-time location on a map.

[0564] 2. Receiving and checking alerts

[0565] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[0566] 3. Abnormality recognition

[0567] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[0568] Specific examples

[0569] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[0570] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[0571] The above is a specific embodiment for carrying out the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and respond promptly.

[0572] The processing flow will be explained below.

[0573] Server-side processing

[0574] Step 1:

[0575] The server receives real-time location and sensor data sent from the device.

[0576] The server receives GPS data and acceleration data and stores them in a database.

[0577] Step 2:

[0578] The server authenticates the terminal and the user.

[0579] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[0580] Step 3:

[0581] The server learns the usual range of movement based on the collected location data.

[0582] The collected data is input into a generative AI model to analyze the behavioral patterns of seniors.

[0583] The resulting normal behavior range is stored in a database.

[0584] Step 4:

[0585] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[0586] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[0587] If an anomaly is detected, an alert generation flag is set.

[0588] Step 5:

[0589] Once the server detects an abnormality, it sends an alert to the family.

[0590] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[0591] Step 6:

[0592] If the user detects an abnormality, the server will contact the police.

[0593] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's location, to the police.

[0594] Terminal side processing

[0595] Step 1:

[0596] The device collects the senior's location information.

[0597] The device's GPS module periodically obtains the current latitude and longitude.

[0598] The accelerometer also works at the same time to collect movement information.

[0599] Step 2:

[0600] The terminal transmits the collected data to the server.

[0601] The collected location data and sensor data are sent to a server at regular intervals.

[0602] If the data transmission fails, a retry process is performed.

[0603] User (family) operations

[0604] Step 1:

[0605] Users monitor the behavior of seniors through the application.

[0606] Users open the application and view real-time location information and past activity history.

[0607] Step 2:

[0608] The user receives an abnormality alert from the server.

[0609] An alert notification from the server will be displayed on your smartphone.

[0610] The alert will include current location and details of any unusual behavior.

[0611] Step 3:

[0612] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[0613] If an abnormality is detected, a button in the application is pressed to notify the server.

[0614] Step 4:

[0615] If the user detects an abnormality, the server contacts the police.

[0616] The server automatically provides the senior's current location and emergency contact information to the police and requests their response.

[0617] The above is a detailed description of the specific processing steps at the server, terminal, and user.

[0618] Example 1

[0619] 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."

[0620] In today's society, where the risk of elderly people wandering or going missing is increasing, there is a need for systems to ensure the safety of the elderly. In particular, there is a need for systems that can ensure the safety of the elderly by detecting abnormal behavior early and promptly notifying family members and the police. There is also a need for systems that can learn behavioral ranges and detect anomalies with higher accuracy by using GPS modules and generative AI models.

[0621] 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.

[0622] In this invention, the server includes means for periodically collecting location information of seniors, means for learning the senior's usual range of activity using a generative AI model, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to the family's communication device when abnormal behavior is detected, and means for contacting police authorities if the family recognizes the abnormality. This enables early detection of elderly people wandering or going missing and enables prompt response.

[0623] The "system" is a set of hardware and software that collects location information about seniors, detects abnormal behavior, and sends alerts to family members or the police if necessary.

[0624] "Location information" is information indicating the senior's current location, and is data including geographic coordinates such as latitude and longitude.

[0625] "Periodic collection means" refers to the methods and technical means for collecting senior location information at regular time intervals.

[0626] A "generative AI model" is an artificial intelligence algorithm and program that learns behavioral patterns based on large amounts of data and makes highly accurate predictions and detects anomalies.

[0627] "Usual range of activity" is information that indicates the geographical area in which seniors typically engage, and is data that includes the results learned by the generative AI model.

[0628] "Means for matching" refers to the technical means for comparing the received location information with the usual range of movement.

[0629] "Abnormal behavior" refers to cases in which a senior exhibits unnatural behavior that is different from the norm, such as going outside the normal range of behavior.

[0630] "Means for sending alerts" refers to the technical means for sending notifications to family members' communication devices when abnormal behavior is detected.

[0631] "Family communication devices" refers to communication devices such as smartphones and tablets used by family members.

[0632] "Means of contacting police authorities" refers to technological means to automatically notify the police of the senior's current location and situation if the family determines something is wrong.

[0633] MODE FOR CARRYING OUT THE INVENTION

[0634] This system collects and analyzes the location information of elderly people, detects abnormal behavior, and notifies their family and the police. This system consists of three main elements: a terminal, a server, and the user's communication terminal.

[0635] Device configuration and functions

[0636] The device is a wearable device worn by the elderly, and is equipped with a GPS module, an accelerometer, and other sensors. This hardware handles data collection and transmission, including:

[0637] 1. Collection of location information

[0638] The device uses a GPS module to periodically collect the elderly person's current location (latitude, longitude). For example, it can be set to obtain the current location every five minutes. This allows the elderly person's behavioral patterns to be understood in real time.

[0639] 2. Collecting sensor information

[0640] The device uses accelerometers and other sensors to collect information about the elderly person's movements and physical condition, including walking speed and fall detection.

[0641] 3. Data transmission

[0642] The device sends the collected location information and sensor information to a server at regular intervals. This data is analyzed in real time, enabling rapid detection of abnormalities.

[0643] Server configuration and functions

[0644] The server is a cloud-based computer system that receives and analyzes the data sent from the terminal.

[0645] 1. Data Receipt and Authentication

[0646] The server receives the data sent from the terminal and then authenticates the terminal and the user, ensuring the authenticity of the data.

[0647] 2. Initial learning of range of movement

[0648] During the initial setup period, the server analyzes location data sent from the device and uses a generative AI model to learn the typical range of movement of elderly people, enabling highly accurate learning of behavioral patterns.

[0649] 3. Anomaly Detection

[0650] The server compares the received current location data with the learned normal movement range to detect anomalies. For example, if the user moves significantly outside the normal movement range, it is deemed to be abnormal.

[0651] 4. Alert Notifications

[0652] If an abnormality is detected, the server sends an alert to the user's communication device, which includes the elderly person's current location and details of the abnormality.

[0653] 5. Police Cooperation

[0654] When the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police, which includes the elderly person's current location and emergency contact information.

[0655] User operations

[0656] Through the smartphone application, the user performs the following operations:

[0657] 1. Location monitoring

[0658] Through the application, users can view the real-time location information of the elderly person on a map, allowing them to instantly understand their current location.

[0659] 2. Receiving and checking alerts

[0660] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[0661] 3. Abnormality recognition

[0662] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[0663] Examples of concrete examples and prompts

[0664] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[0665] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[0666] Prompt Sentence Examples

[0667] "We designed a system that collects location information for seniors, detects abnormal behavior, and contacts their families and the police. In this system, seniors wear a wearable device that sends location information to a server using GPS and various sensors. The server uses a generative AI model to learn their usual range of behavior, and if it detects an abnormality, it sends an alert to their families and automatically contacts the police if necessary. This allows us to quickly ensure the safety of seniors."

[0668] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0669] System program processing flow

[0670] Terminal handling

[0671] Step 1:

[0672] The device uses a GPS module to periodically collect the senior's location information.

[0673] Input: GPS satellite signal

[0674] Data processing / calculation: Analyze signals to obtain latitude and longitude

[0675] Output: Latitude and longitude location data

[0676] Specific operation: The device obtains the current location data "Latitude: 35.6895, Longitude: 139.6917" every 5 minutes.

[0677] Step 2:

[0678] The device uses an accelerometer and other sensors to collect information about the senior's movements.

[0679] Input: Data from accelerometer and gyro sensor

[0680] Data processing / computation: Analyzing raw sensor data to identify movement patterns (walking, running, falling, etc.)

[0681] Output: Motion pattern data

[0682] Specific operation: The device collects data on "falls" and "walking."

[0683] Step 3:

[0684] The device sends the collected location information and sensor information to the server at regular intervals.

[0685] Input: Latitude and longitude location data, movement pattern data

[0686] Data processing / calculation: Convert data into a format for sending to the server

[0687] Output: Data packet sent to the server

[0688] Specific operation: At 8:05, send the data "Latitude: 35.6895, Longitude: 139.6917" and "Fall" to the server.

[0689] Server Processing

[0690] Step 4:

[0691] The server receives the data sent from the terminal and authenticates the terminal and the user.

[0692] Input: Data packet from the terminal

[0693] Data processing / calculation: Data packet analysis, authentication information verification

[0694] Output: Authentication result (success / failure)

[0695] Specific operation: The server checks the device's ID and authenticates the authenticity of the data.

[0696] Step 5:

[0697] During the initial setup period, the server analyzes location data sent from the device and learns the senior's usual range of movement using a generative AI model.

[0698] Input: Historical location data

[0699] Data processing / calculation: Generative AI models learn data and identify normal behavior ranges

[0700] Output: Learned normal behavior range data

[0701] Specific operation: The server uses two weeks' worth of location data to learn Mr. A's usual range of movement.

[0702] Step 6:

[0703] The server compares the received current location data with the learned normal range of movement to detect any anomalies.

[0704] Input: Current location data, learned movement range data

[0705] Data processing / calculation: Determine whether there are any abnormalities based on the matching algorithm

[0706] Output: Anomaly detection result (abnormal / normal)

[0707] Specific behavior: The server detects that "Latitude: 35.6895, Longitude: 139.6917" is outside the normal range of activity.

[0708] Step 7:

[0709] If an abnormality is detected, the server sends an alert to the user's communication terminal.

[0710] Input: Anomaly detection result, current location data

[0711] Data processing / calculation: Converting to a notification format for communication terminals

[0712] Output: An alert sent to the user's device

[0713] Specific operation: The server sends an alert to the family member's smartphone saying, "Senior A is outside his / her normal range of movement."

[0714] Step 8:

[0715] When the user checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police.

[0716] Input: User action (anomaly recognition)

[0717] Data processing / calculation: Emergency contact message generation

[0718] Output: Message to contact law enforcement

[0719] Specific operation: The family member presses the "Confirm Abnormality" button, and the server immediately starts contacting the police.

[0720] User operations

[0721] Step 9:

[0722] Through the application, users can view the real-time location information of elderly people on a map.

[0723] Input: Location data sent from the server

[0724] Data processing / calculation: Data conversion to map display format

[0725] Output: Current location displayed on a map

[0726] Specific actions: A family member checks the app to see that "Senior A is currently in the park."

[0727] Step 10:

[0728] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[0729] Input: Alert data sent from the server

[0730] Data processing / calculation: Alert display on communication terminal

[0731] Output: The alert information to be displayed

[0732] Specific actions: The family member opens the alert they received and checks the details: "Abnormality detected: Senior A is outside of normal activity range."

[0733] Step 11:

[0734] The family member checks the alert content and presses the "Abnormality Confirmation" button if necessary. This action causes the server to contact the police.

[0735] Input: User action (anomaly recognition)

[0736] Data processing / calculation: Generation of police contact messages

[0737] Output: Message to contact law enforcement

[0738] Specific operation: When the device is activated, it presses the "Abnormality Detection" button, and the server notifies the police of the elderly person's current location.

[0739] (Application example 1)

[0740] 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."

[0741] To ensure the safety of the elderly, there is a need for a system that can detect abnormalities early when seniors deviate from their normal range of movement and quickly notify family members and the police. It is also desirable to be able to constantly monitor the status of seniors by processing collected data in real time and effectively operating it in a cloud environment. However, it has been difficult for conventional systems to comprehensively realize such functions.

[0742] 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.

[0743] In this invention, the server includes a means for transmitting data to a cloud-based server, a means for receiving location information and behavioral data in real time, and a means for detecting abnormalities by comparing the received location information with the normal range of behavior learned using the generative AI model. This makes it possible to immediately detect abnormal behavior of elderly people and promptly notify their families and the police.

[0744] "Location information" is information that indicates where on Earth a particular person or object is located.

[0745] "Range of activity" refers to the area or region in which a person normally operates.

[0746] "Learning" is the process by which machines and algorithms automatically increase their knowledge by identifying patterns and trends based on data.

[0747] "Verification" refers to the act of comparing one piece of data with other data to check for matches or anomalies.

[0748] "Abnormal behavior" refers to behavior that significantly deviates from normal patterns of behavior.

[0749] An "alert" is a notification that immediately notifies you of an abnormality or important event.

[0750] A "cloud-based server" is a remote server accessible via the internet that stores and processes data.

[0751] "Real-time" refers to situations where data collection, processing, and notification occur immediately.

[0752] A "generative AI model" is a model that uses artificial intelligence to learn patterns and regularities from data and make future predictions and classifications.

[0753] The system for implementing this invention includes a cloud-based server, a wearable device worn by the senior, and a smartphone application used by family members. Each element is configured and functions in conjunction with the others as follows:

[0754] Server Features

[0755] The server is the main computer system deployed in the cloud-based environment that receives, analyzes, and notifies data. The server operates using the following hardware and software:

[0756] Hardware: A remote server with an internet connection

[0757] Software: Python program, HTTP request library (requests), location calculation library (geopy)

[0758] Main processing contents

[0759] 1. Data Receipt and Authentication:

[0760] The server receives the location information and operation data sent from the terminal and authenticates the sending terminal and the user.

[0761] 2. Learning normal behavior:

[0762] A generative AI model is used to learn and define normal ranges of movement based on location data collected during an initial setup period.

[0763] 3. Anomaly detection:

[0764] The received current location data is compared with the learned normal range of movement and anomalies are detected based on this.

[0765] 4. Alert Notification:

[0766] If an abnormality is detected, an alert is sent immediately to the family's smartphone application.

[0767] Device Features

[0768] The device is a wearable device that seniors wear at all times and is equipped with the following hardware:

[0769] GPS module: Obtains location information.

[0770] Accelerometer: Collects motion data.

[0771] Main processing contents

[0772] The terminal periodically collects the senior's current location (latitude, longitude) and movement information, and transmits this data to a server at regular intervals.

[0773] User operations

[0774] Through a smartphone application, family members can:

[0775] 1. Monitoring:

[0776] The application displays the senior's real-time location on a map.

[0777] 2. Receiving and confirming alerts:

[0778] Receive and review alerts from the server, which include the senior's current location and details about any abnormalities.

[0779] 3. Abnormality recognition:

[0780] Check the alert content and press the "Confirm Abnormality" button if necessary. This action will cause the server to contact the police and provide them with the senior's current location.

[0781] Specific examples

[0782] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use a generative AI model to learn this behavioral pattern and define the normal range of activity. If one day A significantly deviates from the normal range of activity, the device will detect this anomaly and send the location data to the server. The server will analyze this, determine it to be an anomaly, and send an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they can press the "Confirm Abnormality" button. This will cause the server to automatically contact the police and provide A's current location, ensuring prompt protection.

[0783] Prompt Sentence Examples

[0784] "Person A's current location is outside of his normal range. His current location is latitude: 35.6895, longitude: 139.6917. Would you like to send an alert?"

[0785] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0786] Step 1: Data collection

[0787] The terminal periodically collects the senior's current location (latitude, longitude) and movement data using the GPS module and acceleration sensor on the wearable device.

[0788] Input: Senior's GPS location and movement data

[0789] Output: Collected location and movement data

[0790] Specific operation: Obtains location information from the GPS module and movement data from the accelerometer and stores them in memory.

[0791] Step 2: Send data

[0792] The device periodically transmits the collected location and movement data to a cloud-based server.

[0793] Input: Collected location and movement data

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

[0795] Specific operation: Uses the device's communication module to generate an HTTP request and send the collected data to the specified endpoint on the server.

[0796] Step 3: Data Receipt and Authentication

[0797] The server receives the data sent from the terminal and authenticates the terminal and the user.

[0798] Input: Location and movement data sent from your device

[0799] Output: Authenticated data or authentication error

[0800] Specific operation: The server analyzes the received data and performs authentication based on the device ID and user information. If authentication is successful, the data is saved and the process proceeds to the next step.

[0801] Step 4: Learn normal behavior

[0802] The server uses a generative AI model to learn and define typical ranges of movement based on location data collected during an initial setup period.

[0803] Input: Location data during the initialization period

[0804] Output: Learned normal behavior range

[0805] Specific operation: Location data is input into the generative AI model, and patterns are extracted and saved to learn normal behavioral ranges.

[0806] Step 5: Anomaly detection

[0807] The server compares the received current location data with the learned normal range of movement and detects any abnormalities.

[0808] Input: Current location data, learned normal range of movement

[0809] Output: Normal or abnormal result

[0810] Specific operation: The current location data is compared with the normal movement range, and if it is outside a certain range, it is determined to be abnormal and an abnormality flag is raised.

[0811] Step 6: Sending an alert

[0812] If an abnormality is detected, the server immediately sends an alert to the family's smartphone application.

[0813] Input: Abnormality detection result, senior's current location data

[0814] Output: Alert notification to family members

[0815] Specific operation: If an abnormality is detected, an alert message is created based on the generated prompt text and sent to the family's smartphone application using an HTTP request.

[0816] Step 7: Receiving and confirming alerts

[0817] The user receives an alert via a smartphone application and checks the details of the abnormality.

[0818] Input: Alert notification from the server

[0819] Output: Check the alert details and confirm the abnormality

[0820] Specific operation: When a user receives an alert, they view the message on their smartphone application and decide whether to press the abnormality acknowledgement button to contact the police.

[0821] Step 8: Contact the police

[0822] If a family member presses the abnormality detection button, the server automatically contacts the police and provides them with the senior's current location.

[0823] Input: Family abnormality certification operation

[0824] Output: Police contact and senior location data

[0825] Specific behavior: If an abnormality is detected, an HTTP request is generated to contact the police and send data including the senior's current location.

[0826] This will create a system that can quickly ensure the safety of seniors.

[0827] 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.

[0828] The present invention combines a system that collects location information of seniors, learns their normal range of activity, detects abnormal behavior, and sends alerts to family members, with an emotion engine that recognizes the emotions of users (family members). An embodiment of the present invention will be described below.

[0829] 1. System Overview

[0830] This system consists of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data, and also incorporates an emotion engine to recognize the user's emotions. The user uses a smartphone application to monitor the senior's behavior.

[0831] 2. Device Features

[0832] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[0833] 3. Server Functions

[0834] The server receives and analyzes the data sent from the device, specifically through the following process:

[0835] 1. Data Receipt and Authentication

[0836] It receives data sent from the terminal and authenticates the terminal and the user.

[0837] 2. Learning the normal range of behavior

[0838] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[0839] 3. Anomaly Detection

[0840] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[0841] 4. Alert Notifications

[0842] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[0843] When an alert is sent, the emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0844] 5. Police Cooperation

[0845] If the family determines that something is wrong, the server will automatically contact the police.

[0846] The communication will include the senior's current location and emergency contact information.

[0847] 4. Functions of the Emotion Engine

[0848] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method accordingly. For example, if the user is showing signs of stress or anxiety, the engine will increase the urgency of the alert and prompt a prompt response. By collecting emotion data over a long period of time and learning the user's reaction patterns, the engine can provide appropriate intervention and support.

[0849] 5. User (family) operations

[0850] Through the smartphone application, the user performs the following operations:

[0851] 1. Monitoring

[0852] The application displays the senior's real-time location on a map.

[0853] 2. Receiving and checking alerts

[0854] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[0855] 3. Abnormality recognition

[0856] Check the alert and press the "Confirm Abnormality" button if necessary. Based on the emotional state analyzed by the emotion engine, additional confirmation before pressing the button or a prompt to contact an emergency service may be provided.

[0857] 4. Notify the police

[0858] If the user detects an abnormality, the server automatically provides the senior's location information to the police for prompt protection.

[0859] Specific examples

[0860] For example, if senior citizen A has a habit of taking walks in the park near his or her home, the device will collect data on A's behavior and send it to the server. During the initial learning period, the server will use the generative AI model to learn A's usual range of behavior.

[0861] One day, when Person A walks far away, straying far from his usual range of movement, the device detects the abnormality and sends the location data to the server. The server analyzes the abnormality and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family) and sends a high level of urgency notification. If the user checks the alert and determines that there is an abnormality, they press the "Abnormality Confirmation" button, and the server automatically contacts the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly.

[0862] The above is a specific embodiment of the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and to take appropriate action according to the emotional state of the family.

[0863] The processing flow will be explained below.

[0864] Server-side processing

[0865] Step 1:

[0866] The server receives real-time location and sensor data sent from the device.

[0867] The GPS data and acceleration data sent by the device are received and stored in a database.

[0868] Step 2:

[0869] The server authenticates the terminal and the user.

[0870] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[0871] Step 3:

[0872] The server learns the usual range of movement based on the collected location data.

[0873] Data collected during the initial setup period is input into a generative AI model to analyze the behavioral patterns of seniors.

[0874] The analysis results, which indicate the normal range of behavior, are stored in a database.

[0875] Step 4:

[0876] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[0877] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[0878] If an anomaly is detected, an alert generation flag is set.

[0879] Step 5:

[0880] Once the server detects an abnormality, it sends an alert to the family.

[0881] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[0882] Step 6:

[0883] The server analyzes the user's emotional state using an emotion engine when an alert is sent.

[0884] The server runs an emotion engine based on the user's past behavioral data and current reactions to check their emotional state, such as stress or anxiety.

[0885] Adjust the content and presentation of alerts based on your emotional state.

[0886] Step 7:

[0887] If the user detects an abnormality, the server will contact the police.

[0888] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's current location, to the police.

[0889] Terminal side processing

[0890] Step 1:

[0891] The device collects the senior's location information.

[0892] The device's GPS module periodically obtains the current latitude and longitude.

[0893] The accelerometer also works at the same time to collect movement information.

[0894] Step 2:

[0895] The terminal transmits the collected data to the server.

[0896] The collected location data and sensor data are sent to a server at regular intervals.

[0897] If the data transmission fails, a retry process is performed.

[0898] User (family) operations

[0899] Step 1:

[0900] Users monitor the behavior of seniors through the application.

[0901] Open the application to see real-time location information and past activity history.

[0902] Step 2:

[0903] The user receives an abnormality alert from the server.

[0904] An alert notification from the server will be displayed on your smartphone.

[0905] The alert will include details of the senior's current location and any unusual behavior.

[0906] Step 3:

[0907] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[0908] When the user presses the confirm button, the emotion engine analyzes the user's emotional state.

[0909] If the user is showing signs of stress or anxiety, the server will increase the urgency of the alert, encouraging a quick response.

[0910] Step 4:

[0911] If the user detects an abnormality, the server automatically contacts the police.

[0912] The server sends the senior's location and emergency contact information to the police, requesting prompt protection.

[0913] Specific examples

[0914] For example, if senior citizen A usually walks in a park near his or her home, the device collects daily behavioral data and sends it to the server. During the initial learning period, the server uses a generative AI model to learn A's usual range of behavior. If one day A deviates significantly from his or her usual range and walks farther, the device detects the abnormality and sends location data to the server. The server analyzes this data, determines it to be an abnormality, and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family member) and adjusts the urgency of the alert. If the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with A's location information. In this way, a quick and effective response is possible.

[0915] Example 2

[0916] 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."

[0917] In an aging society, wandering and disappearance of seniors is a major social problem. Families and caregivers need to be able to quickly detect and respond when seniors deviate from their normal range of movement, but conventional systems lack real-time response and accuracy. Another problem is that it is difficult to respond appropriately while taking into account the emotional state of family members. The purpose of this invention is to solve these problems and provide a system that is highly secure and user-friendly.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0919] In this invention, the server includes means for collecting location information of seniors, means for learning the senior's usual range of activity, means for comparing the received location information with the usual range of activity to detect abnormal behavior, means for sending an alert to family members when abnormal behavior is detected, means for analyzing the user's emotional state at the time an abnormality is detected and adjusting the content and method of notification, and means for contacting the police if the family members determine that something is abnormal. This makes it possible to detect abnormal behavior of seniors in real time and provide appropriate notifications that take into account the emotional state of the family members.

[0920] "Senior" is a general term used to refer to older people.

[0921] "Location information" is data indicating a geographical location, and specifically consists of latitude and longitude.

[0922] A "GPS module" is hardware for obtaining location information using the Global Positioning System (GPS).

[0923] An "accelerometer" is a device that measures the acceleration of an object and is used to detect movement and tilt.

[0924] A "server" is a computer system that receives, analyzes, and provides data to other systems or users.

[0925] A "cloud-based computer system" is a system for storing, managing, and processing data over the Internet.

[0926] An "alert" is a notification or warning to notify of an abnormality or danger.

[0927] An "emotion engine" is a technology that analyzes a user's emotional state and responds appropriately based on the results.

[0928] "Abnormality determination" is the process of determining that a senior's behavior is outside the normal range.

[0929] A "generative AI model" is an artificial intelligence algorithm that learns patterns and anomalies from large amounts of data.

[0930] "Police" is a government agency tasked with maintaining public safety and order.

[0931] This system collects location information from seniors, learns their normal behavior ranges, detects abnormal behavior, and sends alerts to family members. This system includes three elements: a server, a device, and a user.

[0932] 1. System hardware configuration

[0933] Device:

[0934] The terminal is a wearable device worn by seniors. This terminal is equipped with a GPS module and an accelerometer to collect location and movement information. This data is sent to a server at regular intervals.

[0935] server:

[0936] The server is a cloud-based computer system that receives and analyzes data sent from the device. It also has an emotion engine built into it, which analyzes the user's emotional state.

[0937] User device:

[0938] Users (family members) use a smartphone application to monitor the behavior of seniors, which allows them to access real-time location information, receive alerts, and identify abnormalities.

[0939] 2. Software Configuration and Processing Flow

[0940] Data collection and transmission:

[0941] The device uses a GPS module and an accelerometer to collect location and movement information of the senior, and the collected data is sent to a server at regular intervals.

[0942] Data Receipt and Authentication:

[0943] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, data analysis begins.

[0944] Learning normal behavior:

[0945] Based on the data collected during the initial setup period, a generative AI model is used to learn the range of normal senior behavior, and the results of this learning are used to understand normal behavior patterns.

[0946] Anomaly detection:

[0947] The server compares the location data received in real time with the learned range of movement, and if the data deviates from the normal range of movement, it determines that the behavior is abnormal.

[0948] Alert Notifications:

[0949] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0950] Contact the police:

[0951] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[0952] 3. Specific Examples

[0953] Daily Activities:

[0954] For example, if senior citizen A regularly walks in the park near his or her home, this location data is sent from the device to the server. During the initial learning period, the server learns the user's usual range of movement based on this data.

[0955] Anomalous behavior detection:

[0956] If one day, Person A walks far beyond his usual range of movement, the device will detect the abnormality and send the location data to the server. The server will analyze the abnormality and send an alert to his family. At this time, the emotion engine will analyze the user's emotional state and send a high-level notification.

[0957] Prompt Sentence Examples

[0958] 1. Anomaly detection prompt

[0959] "Compare the senior's current location data with their usual range data. If any unusual behavior is detected, please report the details."

[0960] 2. Emotion Engine Prompts

[0961] "Analyze the user's emotional state when they receive the alert and promptly notify them if they are experiencing high levels of stress or anxiety."

[0962] This system will enable early detection of wandering or missing seniors, allowing families to respond appropriately based on their emotional state.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Step 1: Collect and send data

[0965] The terminal is a wearable device worn by the senior, and uses a GPS module and an accelerometer to collect the senior's location information (latitude and longitude) and movement information. The collected data is sent to a server at regular intervals.

[0966] Input: GPS module and accelerometer data

[0967] Output: Location and movement information sent to the server

[0968] Specific operation: When Mr. A is walking around his home, the device records his location and movements every minute and sends them to the server.

[0969] Step 2: Receiving and authenticating data

[0970] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, it begins analyzing the data.

[0971] Input: Location and movement information sent from your device

[0972] Output: Start of data analysis based on authentication results

[0973] Specific operation: The server receives the data "Device ID: 12345, Latitude: 35.6895, Longitude: 139.6917" and authenticates it by matching the device ID.

[0974] Step 3: Learn normal behavior

[0975] Based on the data collected during the initial setup period, the server uses a generative AI model to learn the typical range of behavior of seniors.

[0976] Input: Location data collected during the initialization period

[0977] Output: Normal range of movement for seniors

[0978] Specific operation: The server analyzes 30 days' worth of location data and determines that "Mr. A's usual range of movement is within 2 km of his home."

[0979] Step 4: Anomaly detection

[0980] The server compares the location data received in real time with the learned movement range to detect abnormal behavior. If the movement range deviates from the normal movement range, it is judged to be abnormal.

[0981] Input: Real-time incoming location data

[0982] Output: Abnormal behavior detection results

[0983] Specific operation: Person A is detected to be at "Latitude: 35.7000, Longitude: 139.8000" and is determined to be outside the normal range of movement.

[0984] Step 5: Alert Notification

[0985] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[0986] Input: Abnormal behavior detection results, user emotional state data

[0987] Output: Alerts sent to family members

[0988] Specific behavior: An alert will be sent to the family saying "Person A is deviating from his / her normal behavior range," and if the user is under high stress, a high-urgency notification will be sent.

[0989] Step 6: Contact the police

[0990] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[0991] Input: Press the abnormality recognition button

[0992] Output: Police notification

[0993] Specific operation: When a family member presses the "Confirm Abnormality" button, the server instantly notifies the police that "Senior A is currently at latitude: 35.7000, longitude: 139.8000."

[0994] (Application example 2)

[0995] 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."

[0996] Systems already exist that monitor the behavior of elderly people in real time, instantly detect abnormal behavior, and notify family members, but these systems cannot adjust the urgency of the alerts received by family members or the notification method, which can cause stress and anxiety.Furthermore, because they do not take into account the emotional state of family members, it is difficult to respond at the appropriate time and in the appropriate way.

[0997] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting location information of the senior, means for learning the senior's usual range of activity, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to family members when abnormal behavior is detected, and means for analyzing the emotional state of family members and adjusting the urgency of the alert and the notification method. This makes it possible to detect abnormal behavior of the senior quickly and without burdening family members, and to respond appropriately.

[0998] "Senior location information" is latitude and longitude data indicating the location where the senior is currently active.

[0999] The "usual range of movement" is a geographical area defined by learning about the senior's daily movement patterns.

[1000] "Abnormal behavior" refers to a senior moving outside of their normal range of movement or behaving unnaturally.

[1001] An "alert" is a warning message that is sent to family members when abnormal behavior of a senior is detected.

[1002] "Emotional state" refers to the emotional reaction or psychological state of a family member when they receive an alert.

[1003] "Urgency" is a level that indicates the importance of the alert and the urgency of the response.

[1004] The "notification method" indicates the method by which the alert will be communicated to family members.

[1005] The system for implementing this invention is composed mainly of a server, a terminal, and a user. The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. It also incorporates an emotion engine to analyze the user's emotional state. The user uses a smartphone application to monitor the senior's behavior.

[1006] 1. Device Features

[1007] The device periodically obtains the senior's current location using a GPS module and collects movement information using an accelerometer, which is then sent to a server at regular intervals.

[1008] 2. Server Functions

[1009] The server receives and authenticates the data sent from the device. Based on the location data collected during the initial setup period, the server uses a generative AI model to learn the senior's usual range of movement. The server then compares the received current location data with the learned range of movement, and if it detects any abnormalities, it sends an alert to the family's smartphone application.

[1010] 3. Functions of the Emotion Engine

[1011] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method. By collecting user emotional data over a long period of time and learning reaction patterns, it can provide appropriate intervention and support.

[1012] 4. User (family) operations

[1013] Users can view the senior's real-time location information on a map through the smartphone application. They can also receive and check alerts from the server, which include the senior's current location and details of any abnormalities. When the user presses the "Confirm Abnormality" button, the server automatically provides the senior's location information to the police, who then make an emergency call. In particular, if the emotion engine determines that the user is experiencing high levels of stress or anxiety, the police will be notified promptly.

[1014] Specific examples

[1015] For example, if a senior normally takes frequent walks in the park near their home, the device will send that location data to the server. During the initial learning period, the server will use a generative AI model to learn the senior's normal range of movement. If one day the senior significantly deviates from their normal range of movement, the server will detect the abnormality and send an alert to the family. At this time, the emotion engine will analyze the family's emotional state and issue a high-level notification. If the user confirms the alert and determines that there is an abnormality, they can press the "Confirm Abnormality" button, and the server will contact the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly. This can prevent seniors from wandering or going missing, reducing the burden on families.

[1016] Prompt Sentence Examples

[1017] "It detects the range of normal activities and abnormal behavior of seniors. It uses location data from the past 30 days as input to learn the normal behavioral patterns of seniors and determines any deviations as abnormal."

[1018] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1019] Step 1: The device collects the senior's location information

[1020] The device periodically obtains the senior's current location (latitude and longitude) using a GPS module. It also simultaneously collects movement data using an acceleration sensor. This collected data is sent to the server as packets at regular intervals. The input is location information and movement data, and the output is data packets sent to the server.

[1021] Step 2: The server receives the location information

[1022] The server receives data packets from the device and authenticates the device and user. The input is the data packet sent from the device, and the output is the location data after authentication is complete. At this stage, the location data is securely stored on the server.

[1023] Step 3: The server learns its normal behavior

[1024] During the initialization period, the server retrieves the accumulated location data and uses a generative AI model to learn the senior's normal behavior range. The input is the accumulated location data, and the output is the learned normal behavior range. This behavior range is used in the subsequent anomaly detection process.

[1025] Step 4: The server detects anomalous behavior

[1026] The server compares the received current location data with the learned normal behavior range to detect abnormal behavior. The input is the current location data and the learned normal behavior range, and the output is data on whether an abnormality exists. This data is used in the alert sending process if an abnormality is detected.

[1027] Step 5: The server sends an alert to the family

[1028] If an abnormality is detected, the server sends an alert to the family's smartphone application. At this time, the emotion engine analyzes the family's emotional state and adjusts the urgency and notification method. The input is the abnormality detection data and the family's emotional data, and the output is the adjusted alert message.

[1029] Step 6: User checks the alert and acknowledges the anomaly

[1030] The user checks the alert through the smartphone application and presses the "Abnormality Acknowledgement" button if they decide that it is an abnormality. The input is the alert message, and the output is the abnormality acknowledgement data. This data is sent to the server.

[1031] Step 7: The server contacts the police

[1032] When a user recognizes an abnormality, the server automatically provides the senior's location information to the police and makes an emergency contact. The input is the abnormality recognition data from the user, and the output is the contact data to the police. If the user's stress or anxiety is high due to the emotion engine, the police will be notified more quickly.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] [Third embodiment]

[1037] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1038] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1039] 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).

[1040] 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.

[1041] 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.

[1042] 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).

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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."

[1049] The present invention is a system that collects location information of seniors, compares it with the range of their movements to detect abnormal behavior, sends an alert to their family, and contacts the police if necessary. An embodiment of the present invention will be described below.

[1050] 1. System Overview

[1051] This system is broadly composed of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. The user uses a smartphone application to monitor the senior's behavior.

[1052] 2. Device Features

[1053] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[1054] 3. Server Functions

[1055] The server receives and analyzes the data sent from the device, specifically through the following process:

[1056] 1. Data Receipt and Authentication

[1057] It receives data sent from the terminal and authenticates the terminal and the user.

[1058] 2. Learning the normal range of behavior

[1059] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[1060] 3. Anomaly Detection

[1061] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[1062] 4. Alert Notifications

[1063] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[1064] 5. Police Cooperation

[1065] If the family detects something unusual, the server will automatically contact the police, including the senior's current location and emergency contact information.

[1066] 4. User (family) operations

[1067] Through the smartphone application, the user performs the following operations:

[1068] 1. Monitoring

[1069] The application displays the senior's real-time location on a map.

[1070] 2. Receiving and checking alerts

[1071] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[1072] 3. Abnormality recognition

[1073] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[1074] Specific examples

[1075] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[1076] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[1077] The above is a specific embodiment for carrying out the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and respond promptly.

[1078] The processing flow will be explained below.

[1079] Server-side processing

[1080] Step 1:

[1081] The server receives real-time location and sensor data sent from the device.

[1082] The server receives GPS data and acceleration data and stores them in a database.

[1083] Step 2:

[1084] The server authenticates the terminal and the user.

[1085] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[1086] Step 3:

[1087] The server learns the usual range of movement based on the collected location data.

[1088] The collected data is input into a generative AI model to analyze the behavioral patterns of seniors.

[1089] The resulting normal behavior range is stored in a database.

[1090] Step 4:

[1091] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[1092] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[1093] If an anomaly is detected, an alert generation flag is set.

[1094] Step 5:

[1095] Once the server detects an abnormality, it sends an alert to the family.

[1096] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[1097] Step 6:

[1098] If the user detects an abnormality, the server will contact the police.

[1099] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's location, to the police.

[1100] Terminal side processing

[1101] Step 1:

[1102] The device collects the senior's location information.

[1103] The device's GPS module periodically obtains the current latitude and longitude.

[1104] The accelerometer also works at the same time to collect movement information.

[1105] Step 2:

[1106] The terminal transmits the collected data to the server.

[1107] The collected location data and sensor data are sent to a server at regular intervals.

[1108] If the data transmission fails, a retry process is performed.

[1109] User (family) operations

[1110] Step 1:

[1111] Users monitor the behavior of seniors through the application.

[1112] Users open the application and view real-time location information and past activity history.

[1113] Step 2:

[1114] The user receives an abnormality alert from the server.

[1115] An alert notification from the server will be displayed on your smartphone.

[1116] The alert will include current location and details of any unusual behavior.

[1117] Step 3:

[1118] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[1119] If an abnormality is detected, a button in the application is pressed to notify the server.

[1120] Step 4:

[1121] If the user detects an abnormality, the server contacts the police.

[1122] The server automatically provides the senior's current location and emergency contact information to the police and requests their response.

[1123] The above is a detailed description of the specific processing steps at the server, terminal, and user.

[1124] Example 1

[1125] 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."

[1126] In today's society, where the risk of elderly people wandering or going missing is increasing, there is a need for systems to ensure the safety of the elderly. In particular, there is a need for systems that can ensure the safety of the elderly by detecting abnormal behavior early and promptly notifying family members and the police. There is also a need for systems that can learn behavioral ranges and detect anomalies with higher accuracy by using GPS modules and generative AI models.

[1127] 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.

[1128] In this invention, the server includes means for periodically collecting location information of seniors, means for learning the senior's usual range of activity using a generative AI model, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to the family's communication device when abnormal behavior is detected, and means for contacting police authorities if the family recognizes the abnormality. This enables early detection of elderly people wandering or going missing and enables prompt response.

[1129] The "system" is a set of hardware and software that collects location information about seniors, detects abnormal behavior, and sends alerts to family members or the police if necessary.

[1130] "Location information" is information indicating the senior's current location, and is data including geographic coordinates such as latitude and longitude.

[1131] "Periodic collection means" refers to the methods and technical means for collecting senior location information at regular time intervals.

[1132] A "generative AI model" is an artificial intelligence algorithm and program that learns behavioral patterns based on large amounts of data and makes highly accurate predictions and detects anomalies.

[1133] "Usual range of activity" is information that indicates the geographical area in which seniors typically engage, and is data that includes the results learned by the generative AI model.

[1134] "Means for matching" refers to the technical means for comparing the received location information with the usual range of movement.

[1135] "Abnormal behavior" refers to cases in which a senior exhibits unnatural behavior that is different from the norm, such as going outside the normal range of behavior.

[1136] "Means for sending alerts" refers to the technical means for sending notifications to family members' communication devices when abnormal behavior is detected.

[1137] "Family communication devices" refers to communication devices such as smartphones and tablets used by family members.

[1138] "Means of contacting police authorities" refers to technological means to automatically notify the police of the senior's current location and situation if the family determines something is wrong.

[1139] MODE FOR CARRYING OUT THE INVENTION

[1140] This system collects and analyzes the location information of elderly people, detects abnormal behavior, and notifies their family and the police. This system consists of three main elements: a terminal, a server, and the user's communication terminal.

[1141] Device configuration and functions

[1142] The device is a wearable device worn by the elderly, and is equipped with a GPS module, an accelerometer, and other sensors. This hardware handles data collection and transmission, including:

[1143] 1. Collection of location information

[1144] The device uses a GPS module to periodically collect the elderly person's current location (latitude, longitude). For example, it can be set to obtain the current location every five minutes. This allows the elderly person's behavioral patterns to be understood in real time.

[1145] 2. Collecting sensor information

[1146] The device uses accelerometers and other sensors to collect information about the elderly person's movements and physical condition, including walking speed and fall detection.

[1147] 3. Data transmission

[1148] The device sends the collected location information and sensor information to a server at regular intervals. This data is analyzed in real time, enabling rapid detection of abnormalities.

[1149] Server configuration and functions

[1150] The server is a cloud-based computer system that receives and analyzes the data sent from the terminal.

[1151] 1. Data Receipt and Authentication

[1152] The server receives the data sent from the terminal and then authenticates the terminal and the user, ensuring the authenticity of the data.

[1153] 2. Initial learning of range of movement

[1154] During the initial setup period, the server analyzes location data sent from the device and uses a generative AI model to learn the typical range of movement of elderly people, enabling highly accurate learning of behavioral patterns.

[1155] 3. Anomaly Detection

[1156] The server compares the received current location data with the learned normal movement range to detect anomalies. For example, if the user moves significantly outside the normal movement range, it is deemed to be abnormal.

[1157] 4. Alert Notifications

[1158] If an abnormality is detected, the server sends an alert to the user's communication device, which includes the elderly person's current location and details of the abnormality.

[1159] 5. Police Cooperation

[1160] When the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police, which includes the elderly person's current location and emergency contact information.

[1161] User operations

[1162] Through the smartphone application, the user performs the following operations:

[1163] 1. Location monitoring

[1164] Through the application, users can view the real-time location information of the elderly person on a map, allowing them to instantly understand their current location.

[1165] 2. Receiving and checking alerts

[1166] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[1167] 3. Abnormality recognition

[1168] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[1169] Examples of concrete examples and prompts

[1170] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[1171] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[1172] Prompt Sentence Examples

[1173] "We designed a system that collects location information for seniors, detects abnormal behavior, and contacts their families and the police. In this system, seniors wear a wearable device that sends location information to a server using GPS and various sensors. The server uses a generative AI model to learn their usual range of behavior, and if it detects an abnormality, it sends an alert to their families and automatically contacts the police if necessary. This allows us to quickly ensure the safety of seniors."

[1174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1175] System program processing flow

[1176] Terminal handling

[1177] Step 1:

[1178] The device uses a GPS module to periodically collect the senior's location information.

[1179] Input: GPS satellite signal

[1180] Data processing / calculation: Analyze signals to obtain latitude and longitude

[1181] Output: Latitude and longitude location data

[1182] Specific operation: The device obtains the current location data "Latitude: 35.6895, Longitude: 139.6917" every 5 minutes.

[1183] Step 2:

[1184] The device uses an accelerometer and other sensors to collect information about the senior's movements.

[1185] Input: Data from accelerometer and gyro sensor

[1186] Data processing / computation: Analyzing raw sensor data to identify movement patterns (walking, running, falling, etc.)

[1187] Output: Motion pattern data

[1188] Specific operation: The device collects data on "falls" and "walking."

[1189] Step 3:

[1190] The device sends the collected location information and sensor information to the server at regular intervals.

[1191] Input: Latitude and longitude location data, movement pattern data

[1192] Data processing / calculation: Convert data into a format for sending to the server

[1193] Output: Data packet sent to the server

[1194] Specific operation: At 8:05, send the data "Latitude: 35.6895, Longitude: 139.6917" and "Fall" to the server.

[1195] Server Processing

[1196] Step 4:

[1197] The server receives the data sent from the terminal and authenticates the terminal and the user.

[1198] Input: Data packet from the terminal

[1199] Data processing / calculation: Data packet analysis, authentication information verification

[1200] Output: Authentication result (success / failure)

[1201] Specific operation: The server checks the device's ID and authenticates the authenticity of the data.

[1202] Step 5:

[1203] During the initial setup period, the server analyzes location data sent from the device and learns the senior's usual range of movement using a generative AI model.

[1204] Input: Historical location data

[1205] Data processing / calculation: Generative AI models learn data and identify normal behavior ranges

[1206] Output: Learned normal behavior range data

[1207] Specific operation: The server uses two weeks' worth of location data to learn Mr. A's usual range of movement.

[1208] Step 6:

[1209] The server compares the received current location data with the learned normal range of movement to detect any anomalies.

[1210] Input: Current location data, learned movement range data

[1211] Data processing / calculation: Determine whether there are any abnormalities based on the matching algorithm

[1212] Output: Anomaly detection result (abnormal / normal)

[1213] Specific behavior: The server detects that "Latitude: 35.6895, Longitude: 139.6917" is outside the normal range of activity.

[1214] Step 7:

[1215] If an abnormality is detected, the server sends an alert to the user's communication terminal.

[1216] Input: Anomaly detection result, current location data

[1217] Data processing / calculation: Converting to a notification format for communication terminals

[1218] Output: An alert sent to the user's device

[1219] Specific operation: The server sends an alert to the family member's smartphone saying, "Senior A is outside his / her normal range of movement."

[1220] Step 8:

[1221] When the user checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police.

[1222] Input: User action (anomaly recognition)

[1223] Data processing / calculation: Emergency contact message generation

[1224] Output: Message to contact law enforcement

[1225] Specific operation: The family member presses the "Confirm Abnormality" button, and the server immediately starts contacting the police.

[1226] User operations

[1227] Step 9:

[1228] Through the application, users can view the real-time location information of elderly people on a map.

[1229] Input: Location data sent from the server

[1230] Data processing / calculation: Data conversion to map display format

[1231] Output: Current location displayed on a map

[1232] Specific actions: A family member checks the app to see that "Senior A is currently in the park."

[1233] Step 10:

[1234] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[1235] Input: Alert data sent from the server

[1236] Data processing / calculation: Alert display on communication terminal

[1237] Output: The alert information to be displayed

[1238] Specific actions: The family member opens the alert they received and checks the details: "Abnormality detected: Senior A is outside of normal activity range."

[1239] Step 11:

[1240] The family member checks the alert content and presses the "Abnormality Confirmation" button if necessary. This action causes the server to contact the police.

[1241] Input: User action (anomaly recognition)

[1242] Data processing / calculation: Generation of police contact messages

[1243] Output: Message to contact law enforcement

[1244] Specific operation: When the device is activated, it presses the "Abnormality Detection" button, and the server notifies the police of the elderly person's current location.

[1245] (Application example 1)

[1246] 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."

[1247] To ensure the safety of the elderly, there is a need for a system that can detect abnormalities early when seniors deviate from their normal range of movement and quickly notify family members and the police. It is also desirable to be able to constantly monitor the status of seniors by processing collected data in real time and effectively operating it in a cloud environment. However, it has been difficult for conventional systems to comprehensively realize such functions.

[1248] 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.

[1249] In this invention, the server includes a means for transmitting data to a cloud-based server, a means for receiving location information and behavioral data in real time, and a means for detecting abnormalities by comparing the received location information with the normal range of behavior learned using the generative AI model. This makes it possible to immediately detect abnormal behavior of elderly people and promptly notify their families and the police.

[1250] "Location information" is information that indicates where on Earth a particular person or object is located.

[1251] "Range of activity" refers to the area or region in which a person normally operates.

[1252] "Learning" is the process by which machines and algorithms automatically increase their knowledge by identifying patterns and trends based on data.

[1253] "Verification" refers to the act of comparing one piece of data with other data to check for matches or anomalies.

[1254] "Abnormal behavior" refers to behavior that significantly deviates from normal patterns of behavior.

[1255] An "alert" is a notification that immediately notifies you of an abnormality or important event.

[1256] A "cloud-based server" is a remote server accessible via the internet that stores and processes data.

[1257] "Real-time" refers to situations where data collection, processing, and notification occur immediately.

[1258] A "generative AI model" is a model that uses artificial intelligence to learn patterns and regularities from data and make future predictions and classifications.

[1259] The system for implementing this invention includes a cloud-based server, a wearable device worn by the senior, and a smartphone application used by family members. Each element is configured and functions in conjunction with the others as follows:

[1260] Server Features

[1261] The server is the main computer system deployed in the cloud-based environment that receives, analyzes, and notifies data. The server operates using the following hardware and software:

[1262] Hardware: A remote server with an internet connection

[1263] Software: Python program, HTTP request library (requests), location calculation library (geopy)

[1264] Main processing contents

[1265] 1. Data Receipt and Authentication:

[1266] The server receives the location information and operation data sent from the terminal and authenticates the sending terminal and the user.

[1267] 2. Learning normal behavior:

[1268] A generative AI model is used to learn and define normal ranges of movement based on location data collected during an initial setup period.

[1269] 3. Anomaly detection:

[1270] The received current location data is compared with the learned normal range of movement and anomalies are detected based on this.

[1271] 4. Alert Notification:

[1272] If an abnormality is detected, an alert is sent immediately to the family's smartphone application.

[1273] Device Features

[1274] The device is a wearable device that seniors wear at all times and is equipped with the following hardware:

[1275] GPS module: Obtains location information.

[1276] Accelerometer: Collects motion data.

[1277] Main processing contents

[1278] The terminal periodically collects the senior's current location (latitude, longitude) and movement information, and transmits this data to a server at regular intervals.

[1279] User operations

[1280] Through a smartphone application, family members can:

[1281] 1. Monitoring:

[1282] The application displays the senior's real-time location on a map.

[1283] 2. Receiving and confirming alerts:

[1284] Receive and review alerts from the server, which include the senior's current location and details about any abnormalities.

[1285] 3. Abnormality recognition:

[1286] Check the alert content and press the "Confirm Abnormality" button if necessary. This action will cause the server to contact the police and provide them with the senior's current location.

[1287] Specific examples

[1288] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use a generative AI model to learn this behavioral pattern and define the normal range of activity. If one day A significantly deviates from the normal range of activity, the device will detect this anomaly and send the location data to the server. The server will analyze this, determine it to be an anomaly, and send an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they can press the "Confirm Abnormality" button. This will cause the server to automatically contact the police and provide A's current location, ensuring prompt protection.

[1289] Prompt Sentence Examples

[1290] "Person A's current location is outside of his normal range. His current location is latitude: 35.6895, longitude: 139.6917. Would you like to send an alert?"

[1291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1292] Step 1: Data collection

[1293] The terminal periodically collects the senior's current location (latitude, longitude) and movement data using the GPS module and acceleration sensor on the wearable device.

[1294] Input: Senior's GPS location and movement data

[1295] Output: Collected location and movement data

[1296] Specific operation: Obtains location information from the GPS module and movement data from the accelerometer and stores them in memory.

[1297] Step 2: Send data

[1298] The device periodically transmits the collected location and movement data to a cloud-based server.

[1299] Input: Collected location and movement data

[1300] Output: Location and movement data sent to the server

[1301] Specific operation: Uses the device's communication module to generate an HTTP request and send the collected data to the specified endpoint on the server.

[1302] Step 3: Data Receipt and Authentication

[1303] The server receives the data sent from the terminal and authenticates the terminal and the user.

[1304] Input: Location and movement data sent from your device

[1305] Output: Authenticated data or authentication error

[1306] Specific operation: The server analyzes the received data and performs authentication based on the device ID and user information. If authentication is successful, the data is saved and the process proceeds to the next step.

[1307] Step 4: Learn normal behavior

[1308] The server uses a generative AI model to learn and define typical ranges of movement based on location data collected during an initial setup period.

[1309] Input: Location data during the initialization period

[1310] Output: Learned normal behavior range

[1311] Specific operation: Location data is input into the generative AI model, and patterns are extracted and saved to learn normal behavioral ranges.

[1312] Step 5: Anomaly detection

[1313] The server compares the received current location data with the learned normal range of movement and detects any abnormalities.

[1314] Input: Current location data, learned normal range of movement

[1315] Output: Normal or abnormal result

[1316] Specific operation: The current location data is compared with the normal movement range, and if it is outside a certain range, it is determined to be abnormal and an abnormality flag is raised.

[1317] Step 6: Sending an alert

[1318] If an abnormality is detected, the server immediately sends an alert to the family's smartphone application.

[1319] Input: Abnormality detection result, senior's current location data

[1320] Output: Alert notification to family members

[1321] Specific operation: If an abnormality is detected, an alert message is created based on the generated prompt text and sent to the family's smartphone application using an HTTP request.

[1322] Step 7: Receiving and confirming alerts

[1323] The user receives an alert via a smartphone application and checks the details of the abnormality.

[1324] Input: Alert notification from the server

[1325] Output: Check the alert details and confirm the abnormality

[1326] Specific operation: When a user receives an alert, they view the message on their smartphone application and decide whether to press the abnormality acknowledgement button to contact the police.

[1327] Step 8: Contact the police

[1328] If a family member presses the abnormality detection button, the server automatically contacts the police and provides them with the senior's current location.

[1329] Input: Family abnormality certification operation

[1330] Output: Police contact and senior location data

[1331] Specific behavior: If an abnormality is detected, an HTTP request is generated to contact the police and send data including the senior's current location.

[1332] This will create a system that can quickly ensure the safety of seniors.

[1333] 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.

[1334] The present invention combines a system that collects location information of seniors, learns their normal range of activity, detects abnormal behavior, and sends alerts to family members, with an emotion engine that recognizes the emotions of users (family members). An embodiment of the present invention will be described below.

[1335] 1. System Overview

[1336] This system consists of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data, and also incorporates an emotion engine to recognize the user's emotions. The user uses a smartphone application to monitor the senior's behavior.

[1337] 2. Device Features

[1338] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[1339] 3. Server Functions

[1340] The server receives and analyzes the data sent from the device, specifically through the following process:

[1341] 1. Data Receipt and Authentication

[1342] It receives data sent from the terminal and authenticates the terminal and the user.

[1343] 2. Learning the normal range of behavior

[1344] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[1345] 3. Anomaly Detection

[1346] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[1347] 4. Alert Notifications

[1348] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[1349] When an alert is sent, the emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1350] 5. Police Cooperation

[1351] If the family determines that something is wrong, the server will automatically contact the police.

[1352] The communication will include the senior's current location and emergency contact information.

[1353] 4. Functions of the Emotion Engine

[1354] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method accordingly. For example, if the user is showing signs of stress or anxiety, the engine will increase the urgency of the alert and prompt a prompt response. By collecting emotion data over a long period of time and learning the user's reaction patterns, the engine can provide appropriate intervention and support.

[1355] 5. User (family) operations

[1356] Through the smartphone application, the user performs the following operations:

[1357] 1. Monitoring

[1358] The application displays the senior's real-time location on a map.

[1359] 2. Receiving and checking alerts

[1360] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[1361] 3. Abnormality recognition

[1362] Check the alert and press the "Confirm Abnormality" button if necessary. Based on the emotional state analyzed by the emotion engine, additional confirmation before pressing the button or a prompt to contact an emergency service may be provided.

[1363] 4. Notify the police

[1364] If the user detects an abnormality, the server automatically provides the senior's location information to the police for prompt protection.

[1365] Specific examples

[1366] For example, if senior citizen A has a habit of taking walks in the park near his or her home, the device will collect data on A's behavior and send it to the server. During the initial learning period, the server will use the generative AI model to learn A's usual range of behavior.

[1367] One day, when Person A walks far away, straying far from his usual range of movement, the device detects the abnormality and sends the location data to the server. The server analyzes the abnormality and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family) and sends a high level of urgency notification. If the user checks the alert and determines that there is an abnormality, they press the "Abnormality Confirmation" button, and the server automatically contacts the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly.

[1368] The above is a specific embodiment of the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and to take appropriate action according to the emotional state of the family.

[1369] The processing flow will be explained below.

[1370] Server-side processing

[1371] Step 1:

[1372] The server receives real-time location and sensor data sent from the device.

[1373] The GPS data and acceleration data sent by the device are received and stored in a database.

[1374] Step 2:

[1375] The server authenticates the terminal and the user.

[1376] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[1377] Step 3:

[1378] The server learns the usual range of movement based on the collected location data.

[1379] Data collected during the initial setup period is input into a generative AI model to analyze the behavioral patterns of seniors.

[1380] The analysis results, which indicate the normal range of behavior, are stored in a database.

[1381] Step 4:

[1382] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[1383] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[1384] If an anomaly is detected, an alert generation flag is set.

[1385] Step 5:

[1386] Once the server detects an abnormality, it sends an alert to the family.

[1387] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[1388] Step 6:

[1389] The server analyzes the user's emotional state using an emotion engine when an alert is sent.

[1390] The server runs an emotion engine based on the user's past behavioral data and current reactions to check their emotional state, such as stress or anxiety.

[1391] Adjust the content and presentation of alerts based on your emotional state.

[1392] Step 7:

[1393] If the user detects an abnormality, the server will contact the police.

[1394] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's current location, to the police.

[1395] Terminal side processing

[1396] Step 1:

[1397] The device collects the senior's location information.

[1398] The device's GPS module periodically obtains the current latitude and longitude.

[1399] The accelerometer also works at the same time to collect movement information.

[1400] Step 2:

[1401] The terminal transmits the collected data to the server.

[1402] The collected location data and sensor data are sent to a server at regular intervals.

[1403] If the data transmission fails, a retry process is performed.

[1404] User (family) operations

[1405] Step 1:

[1406] Users monitor the behavior of seniors through the application.

[1407] Open the application to see real-time location information and past activity history.

[1408] Step 2:

[1409] The user receives an abnormality alert from the server.

[1410] An alert notification from the server will be displayed on your smartphone.

[1411] The alert will include details of the senior's current location and any unusual behavior.

[1412] Step 3:

[1413] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[1414] When the user presses the confirm button, the emotion engine analyzes the user's emotional state.

[1415] If the user is showing signs of stress or anxiety, the server will increase the urgency of the alert, encouraging a quick response.

[1416] Step 4:

[1417] If the user detects an abnormality, the server automatically contacts the police.

[1418] The server sends the senior's location and emergency contact information to the police, requesting prompt protection.

[1419] Specific examples

[1420] For example, if senior citizen A usually walks in a park near his or her home, the device collects daily behavioral data and sends it to the server. During the initial learning period, the server uses a generative AI model to learn A's usual range of behavior. If one day A deviates significantly from his or her usual range and walks farther, the device detects the abnormality and sends location data to the server. The server analyzes this data, determines it to be an abnormality, and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family member) and adjusts the urgency of the alert. If the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with A's location information. In this way, a quick and effective response is possible.

[1421] Example 2

[1422] 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."

[1423] In an aging society, wandering and disappearance of seniors is a major social problem. Families and caregivers need to be able to quickly detect and respond when seniors deviate from their normal range of movement, but conventional systems lack real-time response and accuracy. Another problem is that it is difficult to respond appropriately while taking into account the emotional state of family members. The purpose of this invention is to solve these problems and provide a system that is highly secure and user-friendly.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1425] In this invention, the server includes means for collecting location information of seniors, means for learning the senior's usual range of activity, means for comparing the received location information with the usual range of activity to detect abnormal behavior, means for sending an alert to family members when abnormal behavior is detected, means for analyzing the user's emotional state at the time an abnormality is detected and adjusting the content and method of notification, and means for contacting the police if the family members determine that something is abnormal. This makes it possible to detect abnormal behavior of seniors in real time and provide appropriate notifications that take into account the emotional state of the family members.

[1426] "Senior" is a general term used to refer to older people.

[1427] "Location information" is data indicating a geographical location, and specifically consists of latitude and longitude.

[1428] A "GPS module" is hardware for obtaining location information using the Global Positioning System (GPS).

[1429] An "accelerometer" is a device that measures the acceleration of an object and is used to detect movement and tilt.

[1430] A "server" is a computer system that receives, analyzes, and provides data to other systems or users.

[1431] A "cloud-based computer system" is a system for storing, managing, and processing data over the Internet.

[1432] An "alert" is a notification or warning to notify of an abnormality or danger.

[1433] An "emotion engine" is a technology that analyzes a user's emotional state and responds appropriately based on the results.

[1434] "Abnormality determination" is the process of determining that a senior's behavior is outside the normal range.

[1435] A "generative AI model" is an artificial intelligence algorithm that learns patterns and anomalies from large amounts of data.

[1436] "Police" is a government agency tasked with maintaining public safety and order.

[1437] This system collects location information from seniors, learns their normal behavior ranges, detects abnormal behavior, and sends alerts to family members. This system includes three elements: a server, a device, and a user.

[1438] 1. System hardware configuration

[1439] Device:

[1440] The terminal is a wearable device worn by seniors. This terminal is equipped with a GPS module and an accelerometer to collect location and movement information. This data is sent to a server at regular intervals.

[1441] server:

[1442] The server is a cloud-based computer system that receives and analyzes data sent from the device. It also has an emotion engine built into it, which analyzes the user's emotional state.

[1443] User device:

[1444] Users (family members) use a smartphone application to monitor the behavior of seniors, which allows them to access real-time location information, receive alerts, and identify abnormalities.

[1445] 2. Software Configuration and Processing Flow

[1446] Data collection and transmission:

[1447] The device uses a GPS module and an accelerometer to collect location and movement information of the senior, and the collected data is sent to a server at regular intervals.

[1448] Data Receipt and Authentication:

[1449] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, data analysis begins.

[1450] Learning normal behavior:

[1451] Based on the data collected during the initial setup period, a generative AI model is used to learn the range of normal senior behavior, and the results of this learning are used to understand normal behavior patterns.

[1452] Anomaly detection:

[1453] The server compares the location data received in real time with the learned range of movement, and if the data deviates from the normal range of movement, it determines that the behavior is abnormal.

[1454] Alert Notifications:

[1455] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1456] Contact the police:

[1457] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[1458] 3. Specific Examples

[1459] Daily Activities:

[1460] For example, if senior citizen A regularly walks in the park near his or her home, this location data is sent from the device to the server. During the initial learning period, the server learns the user's usual range of movement based on this data.

[1461] Anomalous behavior detection:

[1462] If one day, Person A walks far beyond his usual range of movement, the device will detect the abnormality and send the location data to the server. The server will analyze the abnormality and send an alert to his family. At this time, the emotion engine will analyze the user's emotional state and send a high-level notification.

[1463] Prompt Sentence Examples

[1464] 1. Anomaly detection prompt

[1465] "Compare the senior's current location data with their usual range data. If any unusual behavior is detected, please report the details."

[1466] 2. Emotion Engine Prompts

[1467] "Analyze the user's emotional state when they receive the alert and promptly notify them if they are experiencing high levels of stress or anxiety."

[1468] This system will enable early detection of wandering or missing seniors, allowing families to respond appropriately based on their emotional state.

[1469] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1470] Step 1: Collect and send data

[1471] The terminal is a wearable device worn by the senior, and uses a GPS module and an accelerometer to collect the senior's location information (latitude and longitude) and movement information. The collected data is sent to a server at regular intervals.

[1472] Input: GPS module and accelerometer data

[1473] Output: Location and movement information sent to the server

[1474] Specific operation: When Mr. A is walking around his home, the device records his location and movements every minute and sends them to the server.

[1475] Step 2: Receiving and authenticating data

[1476] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, it begins analyzing the data.

[1477] Input: Location and movement information sent from your device

[1478] Output: Start of data analysis based on authentication results

[1479] Specific operation: The server receives the data "Device ID: 12345, Latitude: 35.6895, Longitude: 139.6917" and authenticates it by matching the device ID.

[1480] Step 3: Learn normal behavior

[1481] Based on the data collected during the initial setup period, the server uses a generative AI model to learn the typical range of behavior of seniors.

[1482] Input: Location data collected during the initialization period

[1483] Output: Normal range of movement for seniors

[1484] Specific operation: The server analyzes 30 days' worth of location data and determines that "Mr. A's usual range of movement is within 2 km of his home."

[1485] Step 4: Anomaly detection

[1486] The server compares the location data received in real time with the learned movement range to detect abnormal behavior. If the movement range deviates from the normal movement range, it is judged to be abnormal.

[1487] Input: Real-time incoming location data

[1488] Output: Abnormal behavior detection results

[1489] Specific operation: Person A is detected to be at "Latitude: 35.7000, Longitude: 139.8000" and is determined to be outside the normal range of movement.

[1490] Step 5: Alert Notification

[1491] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1492] Input: Abnormal behavior detection results, user emotional state data

[1493] Output: Alerts sent to family members

[1494] Specific behavior: An alert will be sent to the family saying "Person A is deviating from his / her normal behavior range," and if the user is under high stress, a high-urgency notification will be sent.

[1495] Step 6: Contact the police

[1496] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[1497] Input: Press the abnormality recognition button

[1498] Output: Police notification

[1499] Specific operation: When a family member presses the "Confirm Abnormality" button, the server instantly notifies the police that "Senior A is currently at latitude: 35.7000, longitude: 139.8000."

[1500] (Application example 2)

[1501] 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."

[1502] Systems already exist that monitor the behavior of elderly people in real time, instantly detect abnormal behavior, and notify family members, but these systems cannot adjust the urgency of the alerts received by family members or the notification method, which can cause stress and anxiety.Furthermore, because they do not take into account the emotional state of family members, it is difficult to respond at the appropriate time and in the appropriate way.

[1503] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting location information of the senior, means for learning the senior's usual range of activity, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to family members when abnormal behavior is detected, and means for analyzing the emotional state of family members and adjusting the urgency of the alert and the notification method. This makes it possible to detect abnormal behavior of the senior quickly and without burdening family members, and to respond appropriately.

[1504] "Senior location information" is latitude and longitude data indicating the location where the senior is currently active.

[1505] The "usual range of movement" is a geographical area defined by learning about the senior's daily movement patterns.

[1506] "Abnormal behavior" refers to a senior moving outside of their normal range of movement or behaving unnaturally.

[1507] An "alert" is a warning message that is sent to family members when abnormal behavior of a senior is detected.

[1508] "Emotional state" refers to the emotional reaction or psychological state of a family member when they receive an alert.

[1509] "Urgency" is a level that indicates the importance of the alert and the urgency of the response.

[1510] The "notification method" indicates the method by which the alert will be communicated to family members.

[1511] The system for implementing this invention is composed mainly of a server, a terminal, and a user. The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. It also incorporates an emotion engine to analyze the user's emotional state. The user uses a smartphone application to monitor the senior's behavior.

[1512] 1. Device Features

[1513] The device periodically obtains the senior's current location using a GPS module and collects movement information using an accelerometer, which is then sent to a server at regular intervals.

[1514] 2. Server Functions

[1515] The server receives and authenticates the data sent from the device. Based on the location data collected during the initial setup period, the server uses a generative AI model to learn the senior's usual range of movement. The server then compares the received current location data with the learned range of movement, and if it detects any abnormalities, it sends an alert to the family's smartphone application.

[1516] 3. Functions of the Emotion Engine

[1517] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method. By collecting user emotional data over a long period of time and learning reaction patterns, it can provide appropriate intervention and support.

[1518] 4. User (family) operations

[1519] Users can view the senior's real-time location information on a map through the smartphone application. They can also receive and check alerts from the server, which include the senior's current location and details of any abnormalities. When the user presses the "Confirm Abnormality" button, the server automatically provides the senior's location information to the police, who then make an emergency call. In particular, if the emotion engine determines that the user is experiencing high levels of stress or anxiety, the police will be notified promptly.

[1520] Specific examples

[1521] For example, if a senior normally takes frequent walks in the park near their home, the device will send that location data to the server. During the initial learning period, the server will use a generative AI model to learn the senior's normal range of movement. If one day the senior significantly deviates from their normal range of movement, the server will detect the abnormality and send an alert to the family. At this time, the emotion engine will analyze the family's emotional state and issue a high-level notification. If the user confirms the alert and determines that there is an abnormality, they can press the "Confirm Abnormality" button, and the server will contact the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly. This can prevent seniors from wandering or going missing, reducing the burden on families.

[1522] Prompt Sentence Examples

[1523] "It detects the range of normal activities and abnormal behavior of seniors. It uses location data from the past 30 days as input to learn the normal behavioral patterns of seniors and determines any deviations as abnormal."

[1524] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1525] Step 1: The device collects the senior's location information

[1526] The device periodically obtains the senior's current location (latitude and longitude) using a GPS module. It also simultaneously collects movement data using an acceleration sensor. This collected data is sent to the server as packets at regular intervals. The input is location information and movement data, and the output is data packets sent to the server.

[1527] Step 2: The server receives the location information

[1528] The server receives data packets from the device and authenticates the device and user. The input is the data packet sent from the device, and the output is the location data after authentication is complete. At this stage, the location data is securely stored on the server.

[1529] Step 3: The server learns its normal behavior

[1530] During the initialization period, the server retrieves the accumulated location data and uses a generative AI model to learn the senior's normal behavior range. The input is the accumulated location data, and the output is the learned normal behavior range. This behavior range is used in the subsequent anomaly detection process.

[1531] Step 4: The server detects anomalous behavior

[1532] The server compares the received current location data with the learned normal behavior range to detect abnormal behavior. The input is the current location data and the learned normal behavior range, and the output is data on whether an abnormality exists. This data is used in the alert sending process if an abnormality is detected.

[1533] Step 5: The server sends an alert to the family

[1534] If an abnormality is detected, the server sends an alert to the family's smartphone application. At this time, the emotion engine analyzes the family's emotional state and adjusts the urgency and notification method. The input is the abnormality detection data and the family's emotional data, and the output is the adjusted alert message.

[1535] Step 6: User checks the alert and acknowledges the anomaly

[1536] The user checks the alert through the smartphone application and presses the "Abnormality Acknowledgement" button if they decide that it is an abnormality. The input is the alert message, and the output is the abnormality acknowledgement data. This data is sent to the server.

[1537] Step 7: The server contacts the police

[1538] When a user recognizes an abnormality, the server automatically provides the senior's location information to the police and makes an emergency contact. The input is the abnormality recognition data from the user, and the output is the contact data to the police. If the user's stress or anxiety is high due to the emotion engine, the police will be notified more quickly.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] [Fourth embodiment]

[1543] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1544] 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.

[1545] 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).

[1546] 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.

[1547] 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.

[1548] 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).

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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."

[1556] The present invention is a system that collects location information of seniors, compares it with the range of their movements to detect abnormal behavior, sends an alert to their family, and contacts the police if necessary. An embodiment of the present invention will be described below.

[1557] 1. System Overview

[1558] This system is broadly composed of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. The user uses a smartphone application to monitor the senior's behavior.

[1559] 2. Device Features

[1560] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[1561] 3. Server Functions

[1562] The server receives and analyzes the data sent from the device, specifically through the following process:

[1563] 1. Data Receipt and Authentication

[1564] It receives data sent from the terminal and authenticates the terminal and the user.

[1565] 2. Learning the normal range of behavior

[1566] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[1567] 3. Anomaly Detection

[1568] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[1569] 4. Alert Notifications

[1570] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[1571] 5. Police Cooperation

[1572] If the family detects something unusual, the server will automatically contact the police, including the senior's current location and emergency contact information.

[1573] 4. User (family) operations

[1574] Through the smartphone application, the user performs the following operations:

[1575] 1. Monitoring

[1576] The application displays the senior's real-time location on a map.

[1577] 2. Receiving and checking alerts

[1578] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[1579] 3. Abnormality recognition

[1580] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[1581] Specific examples

[1582] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[1583] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[1584] The above is a specific embodiment for carrying out the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and respond promptly.

[1585] The processing flow will be explained below.

[1586] Server-side processing

[1587] Step 1:

[1588] The server receives real-time location and sensor data sent from the device.

[1589] The server receives GPS data and acceleration data and stores them in a database.

[1590] Step 2:

[1591] The server authenticates the terminal and the user.

[1592] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[1593] Step 3:

[1594] The server learns the usual range of movement based on the collected location data.

[1595] The collected data is input into a generative AI model to analyze the behavioral patterns of seniors.

[1596] The resulting normal behavior range is stored in a database.

[1597] Step 4:

[1598] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[1599] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[1600] If an anomaly is detected, an alert generation flag is set.

[1601] Step 5:

[1602] Once the server detects an abnormality, it sends an alert to the family.

[1603] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[1604] Step 6:

[1605] If the user detects an abnormality, the server will contact the police.

[1606] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's location, to the police.

[1607] Terminal side processing

[1608] Step 1:

[1609] The device collects the senior's location information.

[1610] The device's GPS module periodically obtains the current latitude and longitude.

[1611] The accelerometer also works at the same time to collect movement information.

[1612] Step 2:

[1613] The terminal transmits the collected data to the server.

[1614] The collected location data and sensor data are sent to a server at regular intervals.

[1615] If the data transmission fails, a retry process is performed.

[1616] User (family) operations

[1617] Step 1:

[1618] Users monitor the behavior of seniors through the application.

[1619] Users open the application and view real-time location information and past activity history.

[1620] Step 2:

[1621] The user receives an abnormality alert from the server.

[1622] An alert notification from the server will be displayed on your smartphone.

[1623] The alert will include current location and details of any unusual behavior.

[1624] Step 3:

[1625] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[1626] If an abnormality is detected, a button in the application is pressed to notify the server.

[1627] Step 4:

[1628] If the user detects an abnormality, the server contacts the police.

[1629] The server automatically provides the senior's current location and emergency contact information to the police and requests their response.

[1630] The above is a detailed description of the specific processing steps at the server, terminal, and user.

[1631] Example 1

[1632] 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."

[1633] In today's society, where the risk of elderly people wandering or going missing is increasing, there is a need for systems to ensure the safety of the elderly. In particular, there is a need for systems that can ensure the safety of the elderly by detecting abnormal behavior early and promptly notifying family members and the police. There is also a need for systems that can learn behavioral ranges and detect anomalies with higher accuracy by using GPS modules and generative AI models.

[1634] 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.

[1635] In this invention, the server includes means for periodically collecting location information of seniors, means for learning the senior's usual range of activity using a generative AI model, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to the family's communication device when abnormal behavior is detected, and means for contacting police authorities if the family recognizes the abnormality. This enables early detection of elderly people wandering or going missing and enables prompt response.

[1636] The "system" is a set of hardware and software that collects location information about seniors, detects abnormal behavior, and sends alerts to family members or the police if necessary.

[1637] "Location information" is information indicating the senior's current location, and is data including geographic coordinates such as latitude and longitude.

[1638] "Periodic collection means" refers to the methods and technical means for collecting senior location information at regular time intervals.

[1639] A "generative AI model" is an artificial intelligence algorithm and program that learns behavioral patterns based on large amounts of data and makes highly accurate predictions and detects anomalies.

[1640] "Usual range of activity" is information that indicates the geographical area in which seniors typically engage, and is data that includes the results learned by the generative AI model.

[1641] "Means for matching" refers to the technical means for comparing the received location information with the usual range of movement.

[1642] "Abnormal behavior" refers to cases in which a senior exhibits unnatural behavior that is different from the norm, such as going outside the normal range of behavior.

[1643] "Means for sending alerts" refers to the technical means for sending notifications to family members' communication devices when abnormal behavior is detected.

[1644] "Family communication devices" refers to communication devices such as smartphones and tablets used by family members.

[1645] "Means of contacting police authorities" refers to technological means to automatically notify the police of the senior's current location and situation if the family determines something is wrong.

[1646] MODE FOR CARRYING OUT THE INVENTION

[1647] This system collects and analyzes the location information of elderly people, detects abnormal behavior, and notifies their family and the police. This system consists of three main elements: a terminal, a server, and the user's communication terminal.

[1648] Device configuration and functions

[1649] The device is a wearable device worn by the elderly, and is equipped with a GPS module, an accelerometer, and other sensors. This hardware handles data collection and transmission, including:

[1650] 1. Collection of location information

[1651] The device uses a GPS module to periodically collect the elderly person's current location (latitude, longitude). For example, it can be set to obtain the current location every five minutes. This allows the elderly person's behavioral patterns to be understood in real time.

[1652] 2. Collecting sensor information

[1653] The device uses accelerometers and other sensors to collect information about the elderly person's movements and physical condition, including walking speed and fall detection.

[1654] 3. Data transmission

[1655] The device sends the collected location information and sensor information to a server at regular intervals. This data is analyzed in real time, enabling rapid detection of abnormalities.

[1656] Server configuration and functions

[1657] The server is a cloud-based computer system that receives and analyzes the data sent from the terminal.

[1658] 1. Data Receipt and Authentication

[1659] The server receives the data sent from the terminal and then authenticates the terminal and the user, ensuring the authenticity of the data.

[1660] 2. Initial learning of range of movement

[1661] During the initial setup period, the server analyzes location data sent from the device and uses a generative AI model to learn the typical range of movement of elderly people, enabling highly accurate learning of behavioral patterns.

[1662] 3. Anomaly Detection

[1663] The server compares the received current location data with the learned normal movement range to detect anomalies. For example, if the user moves significantly outside the normal movement range, it is deemed to be abnormal.

[1664] 4. Alert Notifications

[1665] If an abnormality is detected, the server sends an alert to the user's communication device, which includes the elderly person's current location and details of the abnormality.

[1666] 5. Police Cooperation

[1667] When the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police, which includes the elderly person's current location and emergency contact information.

[1668] User operations

[1669] Through the smartphone application, the user performs the following operations:

[1670] 1. Location monitoring

[1671] Through the application, users can view the real-time location information of the elderly person on a map, allowing them to instantly understand their current location.

[1672] 2. Receiving and checking alerts

[1673] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[1674] 3. Abnormality recognition

[1675] Check the alert content and press the "Abnormality Confirmation" button if necessary. This action will cause the server to contact the police.

[1676] Examples of concrete examples and prompts

[1677] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use this behavioral pattern to learn and define A's usual range of movement using a generative AI model.

[1678] One day, suppose Person A deviates significantly from his usual range of movement and walks into the city center. The device detects this anomaly and sends the location data to the server. The server analyzes it, determines it to be an anomaly, and sends an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they press the "Abnormality Confirmation" button. This causes the server to automatically contact the police and provide Person A's current location, ensuring prompt protection.

[1679] Prompt Sentence Examples

[1680] "We designed a system that collects location information for seniors, detects abnormal behavior, and contacts their families and the police. In this system, seniors wear a wearable device that sends location information to a server using GPS and various sensors. The server uses a generative AI model to learn their usual range of behavior, and if it detects an abnormality, it sends an alert to their families and automatically contacts the police if necessary. This allows us to quickly ensure the safety of seniors."

[1681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1682] System program processing flow

[1683] Terminal handling

[1684] Step 1:

[1685] The device uses a GPS module to periodically collect the senior's location information.

[1686] Input: GPS satellite signal

[1687] Data processing / calculation: Analyze signals to obtain latitude and longitude

[1688] Output: Latitude and longitude location data

[1689] Specific operation: The device obtains the current location data "Latitude: 35.6895, Longitude: 139.6917" every 5 minutes.

[1690] Step 2:

[1691] The device uses an accelerometer and other sensors to collect information about the senior's movements.

[1692] Input: Data from accelerometer and gyro sensor

[1693] Data processing / computation: Analyzing raw sensor data to identify movement patterns (walking, running, falling, etc.)

[1694] Output: Motion pattern data

[1695] Specific operation: The device collects data on "falls" and "walking."

[1696] Step 3:

[1697] The device sends the collected location information and sensor information to the server at regular intervals.

[1698] Input: Latitude and longitude location data, movement pattern data

[1699] Data processing / calculation: Convert data into a format for sending to the server

[1700] Output: Data packet sent to the server

[1701] Specific operation: At 8:05, send the data "Latitude: 35.6895, Longitude: 139.6917" and "Fall" to the server.

[1702] Server Processing

[1703] Step 4:

[1704] The server receives the data sent from the terminal and authenticates the terminal and the user.

[1705] Input: Data packet from the terminal

[1706] Data processing / calculation: Data packet analysis, authentication information verification

[1707] Output: Authentication result (success / failure)

[1708] Specific operation: The server checks the device's ID and authenticates the authenticity of the data.

[1709] Step 5:

[1710] During the initial setup period, the server analyzes location data sent from the device and learns the senior's usual range of movement using a generative AI model.

[1711] Input: Historical location data

[1712] Data processing / calculation: Generative AI models learn data and identify normal behavior ranges

[1713] Output: Learned normal behavior range data

[1714] Specific operation: The server uses two weeks' worth of location data to learn Mr. A's usual range of movement.

[1715] Step 6:

[1716] The server compares the received current location data with the learned normal range of movement to detect any anomalies.

[1717] Input: Current location data, learned movement range data

[1718] Data processing / calculation: Determine whether there are any abnormalities based on the matching algorithm

[1719] Output: Anomaly detection result (abnormal / normal)

[1720] Specific behavior: The server detects that "Latitude: 35.6895, Longitude: 139.6917" is outside the normal range of activity.

[1721] Step 7:

[1722] If an abnormality is detected, the server sends an alert to the user's communication terminal.

[1723] Input: Anomaly detection result, current location data

[1724] Data processing / calculation: Converting to a notification format for communication terminals

[1725] Output: An alert sent to the user's device

[1726] Specific operation: The server sends an alert to the family member's smartphone saying, "Senior A is outside his / her normal range of movement."

[1727] Step 8:

[1728] When the user checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police.

[1729] Input: User action (anomaly recognition)

[1730] Data processing / calculation: Emergency contact message generation

[1731] Output: Message to contact law enforcement

[1732] Specific operation: The family member presses the "Confirm Abnormality" button, and the server immediately starts contacting the police.

[1733] User operations

[1734] Step 9:

[1735] Through the application, users can view the real-time location information of elderly people on a map.

[1736] Input: Location data sent from the server

[1737] Data processing / calculation: Data conversion to map display format

[1738] Output: Current location displayed on a map

[1739] Specific actions: A family member checks the app to see that "Senior A is currently in the park."

[1740] Step 10:

[1741] Receive alerts from the server and check their contents. The alerts include the elderly person's current location and details of any abnormalities.

[1742] Input: Alert data sent from the server

[1743] Data processing / calculation: Alert display on communication terminal

[1744] Output: The alert information to be displayed

[1745] Specific actions: The family member opens the alert they received and checks the details: "Abnormality detected: Senior A is outside of normal activity range."

[1746] Step 11:

[1747] The family member checks the alert content and presses the "Abnormality Confirmation" button if necessary. This action causes the server to contact the police.

[1748] Input: User action (anomaly recognition)

[1749] Data processing / calculation: Generation of police contact messages

[1750] Output: Message to contact law enforcement

[1751] Specific operation: When the device is activated, it presses the "Abnormality Detection" button, and the server notifies the police of the elderly person's current location.

[1752] (Application example 1)

[1753] 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."

[1754] To ensure the safety of the elderly, there is a need for a system that can detect abnormalities early when seniors deviate from their normal range of movement and quickly notify family members and the police. It is also desirable to be able to constantly monitor the status of seniors by processing collected data in real time and effectively operating it in a cloud environment. However, it has been difficult for conventional systems to comprehensively realize such functions.

[1755] 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.

[1756] In this invention, the server includes a means for transmitting data to a cloud-based server, a means for receiving location information and behavioral data in real time, and a means for detecting abnormalities by comparing the received location information with the normal range of behavior learned using the generative AI model. This makes it possible to immediately detect abnormal behavior of elderly people and promptly notify their families and the police.

[1757] "Location information" is information that indicates where on Earth a particular person or object is located.

[1758] "Range of activity" refers to the area or region in which a person normally operates.

[1759] "Learning" is the process by which machines and algorithms automatically increase their knowledge by identifying patterns and trends based on data.

[1760] "Verification" refers to the act of comparing one piece of data with other data to check for matches or anomalies.

[1761] "Abnormal behavior" refers to behavior that significantly deviates from normal patterns of behavior.

[1762] An "alert" is a notification that immediately notifies you of an abnormality or important event.

[1763] A "cloud-based server" is a remote server accessible via the internet that stores and processes data.

[1764] "Real-time" refers to situations where data collection, processing, and notification occur immediately.

[1765] A "generative AI model" is a model that uses artificial intelligence to learn patterns and regularities from data and make future predictions and classifications.

[1766] The system for implementing this invention includes a cloud-based server, a wearable device worn by the senior, and a smartphone application used by family members. Each element is configured and functions in conjunction with the others as follows:

[1767] Server Features

[1768] The server is the main computer system deployed in the cloud-based environment that receives, analyzes, and notifies data. The server operates using the following hardware and software:

[1769] Hardware: A remote server with an internet connection

[1770] Software: Python program, HTTP request library (requests), location calculation library (geopy)

[1771] Main processing contents

[1772] 1. Data Receipt and Authentication:

[1773] The server receives the location information and operation data sent from the terminal and authenticates the sending terminal and the user.

[1774] 2. Learning normal behavior:

[1775] A generative AI model is used to learn and define normal ranges of movement based on location data collected during an initial setup period.

[1776] 3. Anomaly detection:

[1777] The received current location data is compared with the learned normal range of movement and anomalies are detected based on this.

[1778] 4. Alert Notification:

[1779] If an abnormality is detected, an alert is sent immediately to the family's smartphone application.

[1780] Device Features

[1781] The device is a wearable device that seniors wear at all times and is equipped with the following hardware:

[1782] GPS module: Obtains location information.

[1783] Accelerometer: Collects motion data.

[1784] Main processing contents

[1785] The terminal periodically collects the senior's current location (latitude, longitude) and movement information, and transmits this data to a server at regular intervals.

[1786] User operations

[1787] Through a smartphone application, family members can:

[1788] 1. Monitoring:

[1789] The application displays the senior's real-time location on a map.

[1790] 2. Receiving and confirming alerts:

[1791] Receive and review alerts from the server, which include the senior's current location and details about any abnormalities.

[1792] 3. Abnormality recognition:

[1793] Check the alert content and press the "Confirm Abnormality" button if necessary. This action will cause the server to contact the police and provide them with the senior's current location.

[1794] Specific examples

[1795] For example, if senior citizen A usually walks in the park near his home, the device will collect A's daily location information and send it to the server. During the initial learning period, the server will use a generative AI model to learn this behavioral pattern and define the normal range of activity. If one day A significantly deviates from the normal range of activity, the device will detect this anomaly and send the location data to the server. The server will analyze this, determine it to be an anomaly, and send an alert to the family member's smartphone. If the family member checks the alert and recognizes it as an anomaly, they can press the "Confirm Abnormality" button. This will cause the server to automatically contact the police and provide A's current location, ensuring prompt protection.

[1796] Prompt Sentence Examples

[1797] "Person A's current location is outside of his normal range. His current location is latitude: 35.6895, longitude: 139.6917. Would you like to send an alert?"

[1798] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1799] Step 1: Data collection

[1800] The terminal periodically collects the senior's current location (latitude, longitude) and movement data using the GPS module and acceleration sensor on the wearable device.

[1801] Input: Senior's GPS location and movement data

[1802] Output: Collected location and movement data

[1803] Specific operation: Obtains location information from the GPS module and movement data from the accelerometer and stores them in memory.

[1804] Step 2: Send data

[1805] The device periodically transmits the collected location and movement data to a cloud-based server.

[1806] Input: Collected location and movement data

[1807] Output: Location and movement data sent to the server

[1808] Specific operation: Uses the device's communication module to generate an HTTP request and send the collected data to the specified endpoint on the server.

[1809] Step 3: Data Receipt and Authentication

[1810] The server receives the data sent from the terminal and authenticates the terminal and the user.

[1811] Input: Location and movement data sent from your device

[1812] Output: Authenticated data or authentication error

[1813] Specific operation: The server analyzes the received data and performs authentication based on the device ID and user information. If authentication is successful, the data is saved and the process proceeds to the next step.

[1814] Step 4: Learn normal behavior

[1815] The server uses a generative AI model to learn and define typical ranges of movement based on location data collected during an initial setup period.

[1816] Input: Location data during the initialization period

[1817] Output: Learned normal behavior range

[1818] Specific operation: Location data is input into the generative AI model, and patterns are extracted and saved to learn normal behavioral ranges.

[1819] Step 5: Anomaly detection

[1820] The server compares the received current location data with the learned normal range of movement and detects any abnormalities.

[1821] Input: Current location data, learned normal range of movement

[1822] Output: Normal or abnormal result

[1823] Specific operation: The current location data is compared with the normal movement range, and if it is outside a certain range, it is determined to be abnormal and an abnormality flag is raised.

[1824] Step 6: Sending an alert

[1825] If an abnormality is detected, the server immediately sends an alert to the family's smartphone application.

[1826] Input: Abnormality detection result, senior's current location data

[1827] Output: Alert notification to family members

[1828] Specific operation: If an abnormality is detected, an alert message is created based on the generated prompt text and sent to the family's smartphone application using an HTTP request.

[1829] Step 7: Receiving and confirming alerts

[1830] The user receives an alert via a smartphone application and checks the details of the abnormality.

[1831] Input: Alert notification from the server

[1832] Output: Check the alert details and confirm the abnormality

[1833] Specific operation: When a user receives an alert, they view the message on their smartphone application and decide whether to press the abnormality acknowledgement button to contact the police.

[1834] Step 8: Contact the police

[1835] If a family member presses the abnormality detection button, the server automatically contacts the police and provides them with the senior's current location.

[1836] Input: Family abnormality certification operation

[1837] Output: Police contact and senior location data

[1838] Specific behavior: If an abnormality is detected, an HTTP request is generated to contact the police and send data including the senior's current location.

[1839] This will create a system that can quickly ensure the safety of seniors.

[1840] 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.

[1841] The present invention combines a system that collects location information of seniors, learns their normal range of activity, detects abnormal behavior, and sends alerts to family members, with an emotion engine that recognizes the emotions of users (family members). An embodiment of the present invention will be described below.

[1842] 1. System Overview

[1843] This system consists of three elements: a server, a terminal, and a user (family member). The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data, and also incorporates an emotion engine to recognize the user's emotions. The user uses a smartphone application to monitor the senior's behavior.

[1844] 2. Device Features

[1845] The device periodically collects the senior's location information and sends it to the server. Specifically, it uses a GPS module to obtain the senior's current location (latitude and longitude), and collects movement information using an accelerometer and other sensors. This data is then sent to the server at regular intervals.

[1846] 3. Server Functions

[1847] The server receives and analyzes the data sent from the device, specifically through the following process:

[1848] 1. Data Receipt and Authentication

[1849] It receives data sent from the terminal and authenticates the terminal and the user.

[1850] 2. Learning the normal range of behavior

[1851] Based on location data collected during the initial setup period, a generative AI model learns the typical range of movement of seniors.

[1852] 3. Anomaly Detection

[1853] The current location data received is compared with the learned normal range of movement to detect abnormalities.

[1854] 4. Alert Notifications

[1855] If an abnormality is detected, an alert will be sent to the family's smartphone application.

[1856] When an alert is sent, the emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1857] 5. Police Cooperation

[1858] If the family determines that something is wrong, the server will automatically contact the police.

[1859] The communication will include the senior's current location and emergency contact information.

[1860] 4. Functions of the Emotion Engine

[1861] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method accordingly. For example, if the user is showing signs of stress or anxiety, the engine will increase the urgency of the alert and prompt a prompt response. By collecting emotion data over a long period of time and learning the user's reaction patterns, the engine can provide appropriate intervention and support.

[1862] 5. User (family) operations

[1863] Through the smartphone application, the user performs the following operations:

[1864] 1. Monitoring

[1865] The application displays the senior's real-time location on a map.

[1866] 2. Receiving and checking alerts

[1867] Receive and check alerts from the server, which include the senior's current location and details of any abnormalities.

[1868] 3. Abnormality recognition

[1869] Check the alert and press the "Confirm Abnormality" button if necessary. Based on the emotional state analyzed by the emotion engine, additional confirmation before pressing the button or a prompt to contact an emergency service may be provided.

[1870] 4. Notify the police

[1871] If the user detects an abnormality, the server automatically provides the senior's location information to the police for prompt protection.

[1872] Specific examples

[1873] For example, if senior citizen A has a habit of taking walks in the park near his or her home, the device will collect data on A's behavior and send it to the server. During the initial learning period, the server will use the generative AI model to learn A's usual range of behavior.

[1874] One day, when Person A walks far away, straying far from his usual range of movement, the device detects the abnormality and sends the location data to the server. The server analyzes the abnormality and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family) and sends a high level of urgency notification. If the user checks the alert and determines that there is an abnormality, they press the "Abnormality Confirmation" button, and the server automatically contacts the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly.

[1875] The above is a specific embodiment of the present invention. This system makes it possible to detect wandering or missing seniors at an early stage and to take appropriate action according to the emotional state of the family.

[1876] The processing flow will be explained below.

[1877] Server-side processing

[1878] Step 1:

[1879] The server receives real-time location and sensor data sent from the device.

[1880] The GPS data and acceleration data sent by the device are received and stored in a database.

[1881] Step 2:

[1882] The server authenticates the terminal and the user.

[1883] The server compares the device's unique ID with the user account information to confirm whether it is a legitimate device.

[1884] Step 3:

[1885] The server learns the usual range of movement based on the collected location data.

[1886] Data collected during the initial setup period is input into a generative AI model to analyze the behavioral patterns of seniors.

[1887] The analysis results, which indicate the normal range of behavior, are stored in a database.

[1888] Step 4:

[1889] The server compares the received current location data with the user's normal range of movement to detect any abnormalities.

[1890] Current location data is compared with the learned range of normal behavior to determine if there are any deviations.

[1891] If an anomaly is detected, an alert generation flag is set.

[1892] Step 5:

[1893] Once the server detects an abnormality, it sends an alert to the family.

[1894] Based on the alert generation flag set, a push notification is sent to the family's smartphone app.

[1895] Step 6:

[1896] The server analyzes the user's emotional state using an emotion engine when an alert is sent.

[1897] The server runs an emotion engine based on the user's past behavioral data and current reactions to check their emotional state, such as stress or anxiety.

[1898] Adjust the content and presentation of alerts based on your emotional state.

[1899] Step 7:

[1900] If the user detects an abnormality, the server will contact the police.

[1901] When a user presses the "Confirm Abnormality" button on the app, the server automatically sends emergency contact information, including the senior's current location, to the police.

[1902] Terminal side processing

[1903] Step 1:

[1904] The device collects the senior's location information.

[1905] The device's GPS module periodically obtains the current latitude and longitude.

[1906] The accelerometer also works at the same time to collect movement information.

[1907] Step 2:

[1908] The terminal transmits the collected data to the server.

[1909] The collected location data and sensor data are sent to a server at regular intervals.

[1910] If the data transmission fails, a retry process is performed.

[1911] User (family) operations

[1912] Step 1:

[1913] Users monitor the behavior of seniors through the application.

[1914] Open the application to see real-time location information and past activity history.

[1915] Step 2:

[1916] The user receives an abnormality alert from the server.

[1917] An alert notification from the server will be displayed on your smartphone.

[1918] The alert will include details of the senior's current location and any unusual behavior.

[1919] Step 3:

[1920] The user checks the alert and presses the "Abnormality Confirmation" button if necessary.

[1921] When the user presses the confirm button, the emotion engine analyzes the user's emotional state.

[1922] If the user is showing signs of stress or anxiety, the server will increase the urgency of the alert, encouraging a quick response.

[1923] Step 4:

[1924] If the user detects an abnormality, the server automatically contacts the police.

[1925] The server sends the senior's location and emergency contact information to the police, requesting prompt protection.

[1926] Specific examples

[1927] For example, if senior citizen A usually walks in a park near his or her home, the device collects daily behavioral data and sends it to the server. During the initial learning period, the server uses a generative AI model to learn A's usual range of behavior. If one day A deviates significantly from his or her usual range and walks farther, the device detects the abnormality and sends location data to the server. The server analyzes this data, determines it to be an abnormality, and sends an alert to the family. At this time, the emotion engine analyzes the emotional state of the user (family member) and adjusts the urgency of the alert. If the user confirms the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with A's location information. In this way, a quick and effective response is possible.

[1928] Example 2

[1929] 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."

[1930] In an aging society, wandering and disappearance of seniors is a major social problem. Families and caregivers need to be able to quickly detect and respond when seniors deviate from their normal range of movement, but conventional systems lack real-time response and accuracy. Another problem is that it is difficult to respond appropriately while taking into account the emotional state of family members. The purpose of this invention is to solve these problems and provide a system that is highly secure and user-friendly.

[1931] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1932] In this invention, the server includes means for collecting location information of seniors, means for learning the senior's usual range of activity, means for comparing the received location information with the usual range of activity to detect abnormal behavior, means for sending an alert to family members when abnormal behavior is detected, means for analyzing the user's emotional state at the time an abnormality is detected and adjusting the content and method of notification, and means for contacting the police if the family members determine that something is abnormal. This makes it possible to detect abnormal behavior of seniors in real time and provide appropriate notifications that take into account the emotional state of the family members.

[1933] "Senior" is a general term used to refer to older people.

[1934] "Location information" is data indicating a geographical location, and specifically consists of latitude and longitude.

[1935] A "GPS module" is hardware for obtaining location information using the Global Positioning System (GPS).

[1936] An "accelerometer" is a device that measures the acceleration of an object and is used to detect movement and tilt.

[1937] A "server" is a computer system that receives, analyzes, and provides data to other systems or users.

[1938] A "cloud-based computer system" is a system for storing, managing, and processing data over the Internet.

[1939] An "alert" is a notification or warning to notify of an abnormality or danger.

[1940] An "emotion engine" is a technology that analyzes a user's emotional state and responds appropriately based on the results.

[1941] "Abnormality determination" is the process of determining that a senior's behavior is outside the normal range.

[1942] A "generative AI model" is an artificial intelligence algorithm that learns patterns and anomalies from large amounts of data.

[1943] "Police" is a government agency tasked with maintaining public safety and order.

[1944] This system collects location information from seniors, learns their normal behavior ranges, detects abnormal behavior, and sends alerts to family members. This system includes three elements: a server, a device, and a user.

[1945] 1. System hardware configuration

[1946] Device:

[1947] The terminal is a wearable device worn by seniors. This terminal is equipped with a GPS module and an accelerometer to collect location and movement information. This data is sent to a server at regular intervals.

[1948] server:

[1949] The server is a cloud-based computer system that receives and analyzes data sent from the device. It also has an emotion engine built into it, which analyzes the user's emotional state.

[1950] User device:

[1951] Users (family members) use a smartphone application to monitor the behavior of seniors, which allows them to access real-time location information, receive alerts, and identify abnormalities.

[1952] 2. Software Configuration and Processing Flow

[1953] Data collection and transmission:

[1954] The device uses a GPS module and an accelerometer to collect location and movement information of the senior, and the collected data is sent to a server at regular intervals.

[1955] Data Receipt and Authentication:

[1956] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, data analysis begins.

[1957] Learning normal behavior:

[1958] Based on the data collected during the initial setup period, a generative AI model is used to learn the range of normal senior behavior, and the results of this learning are used to understand normal behavior patterns.

[1959] Anomaly detection:

[1960] The server compares the location data received in real time with the learned range of movement, and if the data deviates from the normal range of movement, it determines that the behavior is abnormal.

[1961] Alert Notifications:

[1962] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1963] Contact the police:

[1964] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[1965] 3. Specific Examples

[1966] Daily Activities:

[1967] For example, if senior citizen A regularly walks in the park near his or her home, this location data is sent from the device to the server. During the initial learning period, the server learns the user's usual range of movement based on this data.

[1968] Anomalous behavior detection:

[1969] If one day, Person A walks far beyond his usual range of movement, the device will detect the abnormality and send the location data to the server. The server will analyze the abnormality and send an alert to his family. At this time, the emotion engine will analyze the user's emotional state and send a high-level notification.

[1970] Prompt Sentence Examples

[1971] 1. Anomaly detection prompt

[1972] "Compare the senior's current location data with their usual range data. If any unusual behavior is detected, please report the details."

[1973] 2. Emotion Engine Prompts

[1974] "Analyze the user's emotional state when they receive the alert and promptly notify them if they are experiencing high levels of stress or anxiety."

[1975] This system will enable early detection of wandering or missing seniors, allowing families to respond appropriately based on their emotional state.

[1976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1977] Step 1: Collect and send data

[1978] The terminal is a wearable device worn by the senior, and uses a GPS module and an accelerometer to collect the senior's location information (latitude and longitude) and movement information. The collected data is sent to a server at regular intervals.

[1979] Input: GPS module and accelerometer data

[1980] Output: Location and movement information sent to the server

[1981] Specific operation: When Mr. A is walking around his home, the device records his location and movements every minute and sends them to the server.

[1982] Step 2: Receiving and authenticating data

[1983] The server receives the data sent from the device in real time and authenticates the device and user. If authentication is successful, it begins analyzing the data.

[1984] Input: Location and movement information sent from your device

[1985] Output: Start of data analysis based on authentication results

[1986] Specific operation: The server receives the data "Device ID: 12345, Latitude: 35.6895, Longitude: 139.6917" and authenticates it by matching the device ID.

[1987] Step 3: Learn normal behavior

[1988] Based on the data collected during the initial setup period, the server uses a generative AI model to learn the typical range of behavior of seniors.

[1989] Input: Location data collected during the initialization period

[1990] Output: Normal range of movement for seniors

[1991] Specific operation: The server analyzes 30 days' worth of location data and determines that "Mr. A's usual range of movement is within 2 km of his home."

[1992] Step 4: Anomaly detection

[1993] The server compares the location data received in real time with the learned movement range to detect abnormal behavior. If the movement range deviates from the normal movement range, it is judged to be abnormal.

[1994] Input: Real-time incoming location data

[1995] Output: Abnormal behavior detection results

[1996] Specific operation: Person A is detected to be at "Latitude: 35.7000, Longitude: 139.8000" and is determined to be outside the normal range of movement.

[1997] Step 5: Alert Notification

[1998] When abnormal behavior is detected, the server sends an alert to the family member's smartphone application. The emotion engine analyzes the user's emotional state and adjusts the content and method of the notification.

[1999] Input: Abnormal behavior detection results, user emotional state data

[2000] Output: Alerts sent to family members

[2001] Specific behavior: An alert will be sent to the family saying "Person A is deviating from his / her normal behavior range," and if the user is under high stress, a high-urgency notification will be sent.

[2002] Step 6: Contact the police

[2003] When a family member checks the alert and presses the "Confirm Abnormality" button, the server automatically contacts the police and provides them with the senior's current location information.

[2004] Input: Press the abnormality recognition button

[2005] Output: Police notification

[2006] Specific operation: When a family member presses the "Confirm Abnormality" button, the server instantly notifies the police that "Senior A is currently at latitude: 35.7000, longitude: 139.8000."

[2007] (Application example 2)

[2008] 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."

[2009] Systems already exist that monitor the behavior of elderly people in real time, instantly detect abnormal behavior, and notify family members, but these systems cannot adjust the urgency of the alerts received by family members or the notification method, which can cause stress and anxiety.Furthermore, because they do not take into account the emotional state of family members, it is difficult to respond at the appropriate time and in the appropriate way.

[2010] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting location information of the senior, means for learning the senior's usual range of activity, means for detecting abnormal behavior by comparing the received location information with the usual range of activity, means for sending an alert to family members when abnormal behavior is detected, and means for analyzing the emotional state of family members and adjusting the urgency of the alert and the notification method. This makes it possible to detect abnormal behavior of the senior quickly and without burdening family members, and to respond appropriately.

[2011] "Senior location information" is latitude and longitude data indicating the location where the senior is currently active.

[2012] The "usual range of movement" is a geographical area defined by learning about the senior's daily movement patterns.

[2013] "Abnormal behavior" refers to a senior moving outside of their normal range of movement or behaving unnaturally.

[2014] An "alert" is a warning message that is sent to family members when abnormal behavior of a senior is detected.

[2015] "Emotional state" refers to the emotional reaction or psychological state of a family member when they receive an alert.

[2016] "Urgency" is a level that indicates the importance of the alert and the urgency of the response.

[2017] The "notification method" indicates the method by which the alert will be communicated to family members.

[2018] The system for implementing this invention is composed mainly of a server, a terminal, and a user. The terminal is a wearable device worn by the senior and is equipped with a GPS module and various sensors. The server is a cloud-based computer system that receives, analyzes, and notifies data. It also incorporates an emotion engine to analyze the user's emotional state. The user uses a smartphone application to monitor the senior's behavior.

[2019] 1. Device Features

[2020] The device periodically obtains the senior's current location using a GPS module and collects movement information using an accelerometer, which is then sent to a server at regular intervals.

[2021] 2. Server Functions

[2022] The server receives and authenticates the data sent from the device. Based on the location data collected during the initial setup period, the server uses a generative AI model to learn the senior's usual range of movement. The server then compares the received current location data with the learned range of movement, and if it detects any abnormalities, it sends an alert to the family's smartphone application.

[2023] 3. Functions of the Emotion Engine

[2024] The emotion engine recognizes the user's emotional state when receiving an alert and adjusts the urgency of the alert and notification method. By collecting user emotional data over a long period of time and learning reaction patterns, it can provide appropriate intervention and support.

[2025] 4. User (family) operations

[2026] Users can view the senior's real-time location information on a map through the smartphone application. They can also receive and check alerts from the server, which include the senior's current location and details of any abnormalities. When the user presses the "Confirm Abnormality" button, the server automatically provides the senior's location information to the police, who then make an emergency call. In particular, if the emotion engine determines that the user is experiencing high levels of stress or anxiety, the police will be notified promptly.

[2027] Specific examples

[2028] For example, if a senior normally takes frequent walks in the park near their home, the device will send that location data to the server. During the initial learning period, the server will use a generative AI model to learn the senior's normal range of movement. If one day the senior significantly deviates from their normal range of movement, the server will detect the abnormality and send an alert to the family. At this time, the emotion engine will analyze the family's emotional state and issue a high-level notification. If the user confirms the alert and determines that there is an abnormality, they can press the "Confirm Abnormality" button, and the server will contact the police. If the emotion engine determines that the user is highly stressed or anxious, the police will be notified even more quickly. This can prevent seniors from wandering or going missing, reducing the burden on families.

[2029] Prompt Sentence Examples

[2030] "It detects the range of normal activities and abnormal behavior of seniors. It uses location data from the past 30 days as input to learn the normal behavioral patterns of seniors and determines any deviations as abnormal."

[2031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2032] Step 1: The device collects the senior's location information

[2033] The device periodically obtains the senior's current location (latitude and longitude) using a GPS module. It also simultaneously collects movement data using an acceleration sensor. This collected data is sent to the server as packets at regular intervals. The input is location information and movement data, and the output is data packets sent to the server.

[2034] Step 2: The server receives the location information

[2035] The server receives data packets from the device and authenticates the device and user. The input is the data packet sent from the device, and the output is the location data after authentication is complete. At this stage, the location data is securely stored on the server.

[2036] Step 3: The server learns its normal behavior

[2037] During the initialization period, the server retrieves the accumulated location data and uses a generative AI model to learn the senior's normal behavior range. The input is the accumulated location data, and the output is the learned normal behavior range. This behavior range is used in the subsequent anomaly detection process.

[2038] Step 4: The server detects anomalous behavior

[2039] The server compares the received current location data with the learned normal behavior range to detect abnormal behavior. The input is the current location data and the learned normal behavior range, and the output is data on whether an abnormality exists. This data is used in the alert sending process if an abnormality is detected.

[2040] Step 5: The server sends an alert to the family

[2041] If an abnormality is detected, the server sends an alert to the family's smartphone application. At this time, the emotion engine analyzes the family's emotional state and adjusts the urgency and notification method. The input is the abnormality detection data and the family's emotional data, and the output is the adjusted alert message.

[2042] Step 6: User checks the alert and acknowledges the anomaly

[2043] The user checks the alert through the smartphone application and presses the "Abnormality Acknowledgement" button if they decide that it is an abnormality. The input is the alert message, and the output is the abnormality acknowledgement data. This data is sent to the server.

[2044] Step 7: The server contacts the police

[2045] When a user recognizes an abnormality, the server automatically provides the senior's location information to the police and makes an emergency contact. The input is the abnormality recognition data from the user, and the output is the contact data to the police. If the user's stress or anxiety is high due to the emotion engine, the police will be notified more quickly.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] 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.

[2052] 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).

[2053] 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.

[2054] 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."

[2055] 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.

[2056] 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).

[2057] 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.

[2058] 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.

[2059] 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.

[2060] 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.

[2061] 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.

[2062] 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.

[2063] 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.

[2064] 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.

[2065] 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.

[2066] 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.

[2067] The following is further disclosed regarding the above embodiment.

[2068] (Claim 1)

[2069] a means for collecting location information of the senior;

[2070] A means of learning the normal range of activities of seniors;

[2071] A means for detecting abnormal behavior by comparing the received location information with a normal range of movement;

[2072] A means of sending alerts to family members when abnormal behavior is detected;

[2073] A way to contact the police if the family determines something is wrong, and

[2074] A system including:

[2075] (Claim 2)

[2076] 10. The system of claim 1, wherein a GPS module is used to collect location information of the senior.

[2077] (Claim 3)

[2078] 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges.

[2079] "Example 1"

[2080] (Claim 1)

[2081] a means of periodically collecting location information of the senior;

[2082] A means of learning the range of normal activities of seniors using a generative AI model;

[2083] A means for detecting abnormal behavior by comparing the received location information with a normal range of movement;

[2084] A means for sending an alert to a family member's communication device when abnormal behavior is detected;

[2085] A way for the family to contact law enforcement if they detect something abnormal, and

[2086] A system including:

[2087] (Claim 2)

[2088] 10. The system of claim 1, wherein the system uses a satellite positioning system module to collect the senior's location information.

[2089] (Claim 3)

[2090] 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges.

[2091] "Application Example 1"

[2092] (Claim 1)

[2093] a means for collecting location information of the senior;

[2094] A means of learning the normal range of activities of seniors;

[2095] A means for detecting abnormal behavior by comparing the received location information with a normal range of movement;

[2096] A means of sending alerts to family members when abnormal behavior is detected;

[2097] A way to contact the police if the family determines something is wrong, and

[2098] a means for transmitting data to a cloud-based server;

[2099] means for receiving real-time location and behavioral data;

[2100] A system including:

[2101] (Claim 2)

[2102] 10. The system of claim 1, wherein a GPS module is used to collect location information of the senior.

[2103] (Claim 3)

[2104] 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges.

[2105] "Example 2: Combining Emotion Engines"

[2106] (Claim 1)

[2107] a means for collecting location information of the senior;

[2108] A means of learning the normal range of activities of seniors;

[2109] A means for detecting abnormal behavior by comparing the received location information with a normal range of movement;

[2110] A means of sending alerts to family members when abnormal behavior is detected;

[2111] A means for analyzing the user's emotional state when an abnormality is detected and adjusting the content and method of notification;

[2112] A way to contact the police if the family determines something is wrong, and

[2113] A system including:

[2114] (Claim 2)

[2115] 10. The system of claim 1, wherein a plurality of sensors and a GPS module are used to collect location information of the senior.

[2116] (Claim 3)

[2117] 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges.

[2118] "Application example 2 when combining emotion engines"

[2119] (Claim 1)

[2120] a means for collecting location information of the senior;

[2121] A means of learning the normal range of activities of seniors;

[2122] A means for detecting abnormal behavior by comparing the received location information with a normal range of movement;

[2123] A means of sending alerts to family members when abnormal behavior is detected;

[2124] A way to contact the police if the family determines something is wrong, and

[2125] A means to analyze the emotional state of family members and adjust the urgency of alerts and notification methods,

[2126] A system including:

[2127] (Claim 2)

[2128] 10. The system of claim 1, wherein a GPS module is used to collect location information of the senior.

[2129] (Claim 3)

[2130] 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges. [Explanation of symbols]

[2131] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting location information of the senior; A means of learning the normal range of activities of seniors; A means for detecting abnormal behavior by comparing the received location information with a normal range of movement; A means of sending alerts to family members when abnormal behavior is detected; A way to contact the police if the family determines something is wrong, and A system including:

2. 2. The system of claim 1, wherein a GPS module is used to collect location information of the senior.

3. 10. The system of claim 1, including a generative AI model for analyzing senior behavior patterns and defining normal behavior ranges.

Images