system

Abstract

We provide the system. [Solution] A means for periodically acquiring the user's location using a portable location information acquisition device, Means for transmitting acquired location information and operation information to a central processing unit, In the central processing unit, means for detecting anomalies by learning the user's normal behavior patterns and comparing them with the current behavior, A means of notifying registered contacts if an anomaly is detected, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, cases where vulnerable people such as children, elderly people with dementia, and pets go missing are occurring frequently. In such cases, it is important to quickly and accurately grasp the location information. However, conventional systems have limitations in their ability to efficiently detect deviations from normal behavior patterns and may not be able to notify anomalies promptly. Furthermore, when the notification is delayed, it becomes difficult to prevent accidents and incidents that could have been prevented. Therefore, a new monitoring system for improving safety is required.

Means for Solving the Problems

[0005] This invention is a system that periodically acquires the user's location and movements using a portable location information acquisition device and analyzes this data using a central processing unit to detect deviations from normal behavioral patterns. If an anomaly is detected, the system ensures the user's safety by promptly sending emails, text messages, or push notifications via applications to registered contacts. This system allows for comprehensive monitoring of movement outside a specific area, sudden changes in behavioral patterns, and unintended operation of the device.

[0006] A "portable location information acquisition device" is a highly portable device that periodically acquires the user's geographical coordinates and transmits them to a central processing unit.

[0007] "User" refers to an individual or object that carries this system and whose location and movement information is targeted.

[0008] A "central processing unit" is a computer device that receives acquired data and detects anomalies by learning and comparing it with the user's normal behavior patterns.

[0009] "Normal behavioral patterns" refer to a collection of data learned from the user's daily travel routes and actions.

[0010] An "abnormality" is a deviation from normal behavioral patterns, and refers to movements or situations detected under pre-defined conditions.

[0011] "Notification" refers to the act of communicating information about detected anomalies to registered contacts, and is done via email, text message, or push notification via application. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is implemented as an abnormal behavior detection system that utilizes the collection of location data by a portable location information acquisition device and data analysis by a central processing unit. The mechanism is described below in natural language.

[0034] The device collects the user's location information at regular intervals via a GPS module. This location information includes latitude and longitude, allowing for the precise identification of the user's current location. The device also collects motion information using an accelerometer and gyroscope to determine whether the user is walking or moving quickly.

[0035] The server receives location and movement information transmitted from the terminal. The generating AI within the server learns the user's usual behavior patterns based on previously accumulated data. These behavior patterns include information such as which routes the user typically takes and how fast they move.

[0036] The server compares the received current data with normal behavioral patterns. This comparison allows the server to detect anomalies if the user reaches an unexpected location, moves at an abnormal speed, or if the device itself is subjected to unnatural operations.

[0037] When an anomaly is detected, the server quickly notifies registered contacts. This notification is sent to important contacts, such as the user's parents or caregivers, via email, text message, or push notification through the application. The notification provides information about the time and location of the anomaly, allowing the contact to take immediate action.

[0038] For example, if the user is a child, and the child makes an unusual stop on their way home from school, this system can immediately detect the anomaly and notify the parent, enabling quick verification and response. In this way, the present invention improves safety.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device uses GPS functionality to acquire the user's current location at regular intervals. The acquired latitude and longitude information is stored in memory, and preparations for transmission to the server are made as needed.

[0042] Step 2:

[0043] The device uses an accelerometer and a gyroscope to detect the user's movements. This allows it to determine whether the user is walking, running, or stationary, and record this information as movement data.

[0044] Step 3:

[0045] The device sends location and activity information it collects to the server. This transmission is performed at pre-set time intervals, ensuring data integrity and security.

[0046] Step 4:

[0047] The server analyzes the location and movement information it receives. Using a generative AI, it learns the user's normal behavior patterns from past data and compares them with current behavior data.

[0048] Step 5:

[0049] The server determines whether there is an anomaly. An anomaly is determined if there is a significant deviation from the normal behavior pattern, if the behavior exceeds a predetermined range, or if there is a sudden change in operation.

[0050] Step 6:

[0051] When the server detects an anomaly, it activates a function to notify registered contacts. The notification, including the nature and location of the anomaly, is sent via email, SMS, or a dedicated application.

[0052] Step 7:

[0053] The system reviews notifications received by the user and takes appropriate action based on their content. If necessary, the user may travel to the location, contact service users, or report to relevant authorities.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern society, enhancing user safety is a crucial issue. In particular, there is a need for systems that can detect in advance when users move to unusual locations or deviate from their normal behavior patterns, and to respond quickly. However, existing systems have the problem of difficulty in analyzing user behavior in real time and effectively detecting anomalies.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for periodically collecting user location information using a positioning device, means for transmitting the collected location information and movement information to a central control unit, and means for the central control unit to learn the user's normal movement patterns using a generative AI model and detect deviations by comparing them with the current movement. This enables real-time monitoring of movement and detection of anomalies to enhance user safety.

[0059] A "positioning device" is a device used to collect a user's current location information in the form of latitude and longitude.

[0060] A "user" is a person or subject who uses this system and is subject to the collection of location and movement information.

[0061] "Location information" refers to geographical data such as latitude and longitude that indicates the user's current location.

[0062] "Dynamic information" refers to information that indicates the user's movement and actions, and may include acceleration data and rotation data.

[0063] The "central control unit" is a processing unit that receives and analyzes data transmitted from positioning devices.

[0064] A "generative AI model" is an algorithm that learns from a large amount of historical data and recognizes and evaluates normal behavioral patterns.

[0065] "Normal movement patterns" refer to typical behavioral characteristics of users, such as the routes they take and the speed at which they move on a daily basis.

[0066] "Deviation" refers to a state in which a user's behavior deviates from their normal behavioral patterns.

[0067] A "communication destination" is a contact point registered to receive notifications from the system.

[0068] A "press notification" is a real-time notification sent from the central control unit to a communication partner to inform them of an abnormal situation.

[0069] The present invention is a system for monitoring user behavior in real time and detecting deviations. Its embodiments are described in detail below.

[0070] The device uses a GPS module as a positioning device to collect the user's location information at regular intervals. This location information includes latitude and longitude, making it possible to accurately determine the user's current location. In addition, the device is equipped with an accelerometer and a gyroscope, which are used to collect information about the user's movements. For example, by analyzing acceleration data, it is possible to determine whether the user is walking or running.

[0071] The terminal transmits the collected location and movement information to a central control unit, which is a server. Within the server, a generative AI model is installed, which learns from previously accumulated user movement data. This AI model recognizes the user's normal movement patterns and detects deviations by comparing them to their current behavior.

[0072] The server promptly sends a notification to registered communication destinations if a user's behavior deviates from the norm. This notification is provided via electronic communication, text communication, or click notification via an application. The notification includes the time and location where the deviation occurred, allowing the recipient to take immediate action.

[0073] For example, if a user is a child and deviates from their usual route to school, the server will detect this as a deviation and notify the parent or guardian. This allows parents or guardians to quickly check on their child's safety.

[0074] An example of a prompt message might be, "Explain how you will be notified if your child uses an unfamiliar route to school."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal uses a GPS module to obtain the user's location information. This input information includes latitude and longitude, indicating the user's current location. The terminal converts the obtained location information into a data format and prepares for the next processing step. Specifically, at this stage, the terminal acquires location information at regular intervals and stores it in buffer memory.

[0078] Step 2:

[0079] The device collects motion information using an accelerometer and a gyroscope. This information includes changes in acceleration and direction of movement. Based on the input information, the device processes the data to determine motion, such as walking, running, or standing still, and prepares to send the results to the server. Specifically, this involves acquiring sensor values ​​in real time and analyzing them to identify the user's activity state.

[0080] Step 3:

[0081] The device transmits the collected location and movement information to the server. This transmission is performed using a secure and high-speed communication protocol, and data integrity is ensured. The input includes all the data collected by the device, and the output is the completion of the data transmission to the server.

[0082] Step 4:

[0083] The server analyzes the received location and movement information. Using a generative AI model, it learns normal movement patterns based on the user's past behavior patterns. The input information consists of the latest location and movement data sent from the terminal, and the output is the deviation from the normal pattern. This process involves high-speed data processing through the AI's algorithms.

[0084] Step 5:

[0085] The server compares the current data with normal dynamic patterns to determine if there are any deviations. Here, it performs a comparison operation with the input dynamic information, and if an anomaly is detected, it generates a result. This output data includes whether or not an anomaly exists, and detailed information about it.

[0086] Step 6:

[0087] The server notifies registered communication destinations when an anomaly is detected. The input includes the anomaly detection result, and the output is a generated notification message, which is sent via email, text message, or application. The specific operation involves a process of compiling the necessary information into a notification format and sending it.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Conventional anomaly detection systems using location and motion information have limitations in providing a quick and intuitive understanding of the situation when an anomaly is detected, potentially leading to delayed responses. Furthermore, the lack of clear location information in notifications meant that third parties could not immediately understand the actual location of the anomaly. Therefore, there was a need for a means to support rapid response by visually indicating the location of the anomaly.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for transmitting the acquired location information and operation information to a central processing unit, means for the central processing unit to learn the user's normal behavior patterns and detect anomalies by comparing them with current behavior, means for notifying registered contacts when an anomaly is detected, and means for providing map information that visually indicates the location of the anomaly when an anomaly is detected. This enables rapid detection of anomalies and intuitive understanding of the location where they occur.

[0093] A "portable location information acquisition device" is a portable device used to periodically collect location coordinate data.

[0094] "Location information" refers to data that indicates a specific location using geographical coordinates, including latitude and longitude.

[0095] "Motion information" refers to data used to measure the user's movement state and speed, and to capture the characteristics of their actions.

[0096] A "central processing unit" is a centralized processing unit that receives information from external sources and performs data analysis and decision-making.

[0097] "Normal behavioral patterns" refer to the user's tendencies in daily movement and behavior.

[0098] "Means for detecting anomalies" refers to methods or techniques for identifying unexpected situations or unusual behavior.

[0099] "Registered contact information" refers to the information of recipients who have been designated in advance to receive notifications in the event of an anomaly being detected.

[0100] "Map information" refers to data used to visually display specific geographical areas or locations.

[0101] "Means of notification" refers to methods used to convey information or warnings to recipients.

[0102] This invention provides a system for acquiring user location and movement information and detecting abnormal behavior. To implement this system, the terminal uses a portable location information acquisition device to record the user's latitude and longitude at regular intervals. In addition, the terminal uses an accelerometer and gyroscope to collect user movement information. This allows for obtaining detailed behavioral data, such as whether the user is walking or moving quickly.

[0103] The server receives location and movement information transmitted from the terminal. The server is equipped with a generative AI model for advanced data analysis, which learns the user's normal behavior patterns based on previously accumulated data. This generative AI model analyzes behavioral trends and estimates the normal travel route and speed.

[0104] The server compares real-time data received with normal behavioral patterns. It identifies an anomaly if the user reaches an unexpected location or moves at an unusually fast speed. When an anomaly is detected, the server immediately sends a notification to registered emergency contacts. The notification is sent via email or push notification and includes information about the specific time and location where the anomaly occurred. This allows emergency contacts to immediately confirm the anomaly and take prompt action.

[0105] As a concrete example, consider a situation where a child deviates from their usual route home from school. This system immediately notifies parents and provides map information to help them respond quickly to the anomaly. An example of a prompt message for the generated AI model would be: "This security assistant app analyzes the user's location in real time and notifies emergency contacts if it detects unusual behavior."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The device periodically acquires the user's current location using a portable location information acquisition device. It receives latitude and longitude data from a GPS module as input and outputs this as location information data. The device also acquires motion information using an accelerometer and gyroscope, and generates information such as speed and direction of movement.

[0109] Step 2:

[0110] The terminal transmits acquired location and movement information to the server. It takes location and movement data stored within the terminal as input and outputs it as data packets transmitted to the server via the network. To prevent data errors, an appropriate protocol is used for transmission.

[0111] Step 3:

[0112] The server analyzes the received location and motion information. It receives location and motion data transmitted from the terminal as input and derives the analysis results as output. A generative AI model is used here, which learns normal behavior patterns based on past behavior data and then performs anomaly monitoring.

[0113] Step 4:

[0114] The server detects anomalies when current behavior does not match normal behavior patterns. It compares the analyzed behavior data as input with a baseline pattern and generates an anomaly detection flag as output. Specifically, it executes algorithms to detect speeds and movement routes outside acceptable ranges.

[0115] Step 5:

[0116] The server notifies registered emergency contacts when an anomaly is detected. Based on the time and location of the anomaly as input, it sends out email and application push notifications as output. These notifications include a map link visualizing the location and are attached with information to support a quick response.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention is implemented as an anomaly detection system that combines a portable location information acquisition device with an emotion engine that recognizes the user's emotional state. This system coordinates the terminal, server, and emotion engine to comprehensively monitor the user's location information and emotional state.

[0119] The device uses GPS functionality to periodically acquire the user's location, and simultaneously uses a microphone and camera to capture voice and facial expressions. This allows it to acquire data related to the user's emotions, preparing it for analysis by an emotion engine.

[0120] The server not only compares location information transmitted from the terminal with normal behavioral patterns, but also receives emotional data analyzed by the emotion engine. This emotional data indicates the user's psychological state through changes in voice tone and facial expressions, and is analyzed by the emotion engine. The emotion engine uses machine learning algorithms to learn the user's normal emotional patterns and detect abnormal emotional states.

[0121] When the server detects unusual behavior or emotions, it sends a notification to pre-registered contacts. The notification states that a deviation from normal behavioral or emotional patterns has been observed, and includes specific location information and details of the emotional state. This allows recipients to respond quickly and accurately to the user's situation.

[0122] For example, if a user who is normally in a stable mood suddenly deviates from their usual range of movement and displays signs of anxiety or fear, the system will recognize this as an anomaly and the server will immediately send a notification. Parents and caregivers can then check the notification and take appropriate action to ensure the user's safety. This system enhances safety by considering psychological abnormalities in addition to physical location information.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device uses a GPS module to periodically acquire the user's current location. It also simultaneously captures the user's voice and facial expression data using its built-in microphone and camera. This data is later used as material for analysis by an emotion engine.

[0126] Step 2:

[0127] The device transmits location information, voice data, and facial expression data acquired by the device to the server at pre-set time intervals. Encrypted communication is used to protect data integrity and privacy.

[0128] Step 3:

[0129] The server compares the received location information with the user's known normal behavior patterns. When using a generation AI to detect anomalies based on past behavior data, an alert is generated if the location information falls outside the normal range.

[0130] Step 4:

[0131] The server invokes the emotion engine to analyze voice and facial expression data. Based on this data, the emotion engine evaluates the user's emotional state and detects anomalies if there are any unusual emotional changes.

[0132] Step 5:

[0133] If the server detects an anomaly in either location data, emotion data, or both, it will notify pre-registered contacts. The notification will include the current location, emotion status, and specific details of the anomaly.

[0134] Step 6:

[0135] The user (the recipient of the notification) reviews the information received and takes necessary actions based on its content. These actions may include on-site verification by the user, direct contact with the contact person, or reporting to a specialized agency.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] This system comprehensively monitors the user's current location and emotional state, quickly detecting anomalies and notifying relevant parties. Conventional technologies often rely solely on location information for monitoring, making it difficult to detect anomalies that take emotional state changes into account. This issue needs to be addressed.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for acquiring emotional data using a device for capturing voice and facial expressions, and means for transmitting the acquired location information and emotional data to a central processing unit. This makes it possible to learn the user's normal behavior and emotional patterns, detect anomalies by comparing them with the current behavior and emotional state, and quickly notify relevant parties.

[0141] A "portable location information acquisition device" is a portable device used to periodically acquire the user's current location.

[0142] "Emotional data" refers to data about the user's psychological state obtained from their voice and facial expressions.

[0143] A "central processing unit" is a computer system that receives and analyzes acquired location information and emotional data.

[0144] "Normal behavioral patterns" refer to a dataset that shows behavioral tendencies formed based on the user's travel history and daily behavior.

[0145] "Emotional patterns" refer to a dataset that shows the user's typical psychological state, analyzed based on their voice and facial expressions.

[0146] "Abnormal" refers to a state in which the user's current behavior and emotional state differ significantly from their normal behavioral and emotional patterns.

[0147] This invention provides an anomaly detection system that comprehensively monitors the user's location information and emotional state. The main components consist of a portable location information acquisition device, an emotional data acquisition device, and a central processing unit.

[0148] The device functions as a portable location information acquisition device, periodically acquiring the user's location (latitude and longitude) using GPS. In addition, it is equipped with a microphone for capturing voice data and a camera for capturing facial expression data, collecting the user's emotional data as well.

[0149] The terminal preprocesses the data before transmitting it to the central processing unit. This preprocessing includes denoising location data and normalizing audio data. This process ensures that the data is securely transmitted to the central processing unit in an analyzable format.

[0150] The server functions as a central processing unit, analyzing received location and sentiment data. This analysis utilizes machine learning algorithms to learn the user's typical behavioral and emotional patterns, comparing them to current data to detect anomalies. To achieve this, it accumulates historical data and continuously updates the model.

[0151] For example, if a user who doesn't usually go out suddenly moves to a distant location one day and emotional patterns indicating anxiety or fear are recorded, the system will determine this to be abnormal. The server will immediately send a notification so that relevant parties can respond promptly.

[0152] Examples of prompt statements to input into a generative AI model are as follows:

[0153] "Please explain how the emotion engine detects an anomaly when a user displays an anxious expression after exceeding their normal range of movement."

[0154] In this way, the system enhances user safety and helps stakeholders take swift and appropriate action.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The device periodically acquires the user's location using GPS functionality. It takes current latitude and longitude information as input and records it as location data. In addition, it captures audio and facial expression data using a microphone and camera. The output consists of location data and emotion data. Specifically, the device tracks the user's movements in their environment and monitors their behavior.

[0158] Step 2:

[0159] The device preprocesses the acquired location and sentiment data. It receives the raw data as input, removes unwanted noise from the location information, and normalizes the audio data. This process converts the data into a format suitable for analysis. The output is a clean dataset. Specifically, the device utilizes its processing power to filter the data in real time.

[0160] Step 3:

[0161] The terminal sends pre-processed data to the server. A clean dataset is used as input. As output, the data is passed to the server via a secure communication protocol. Specifically, the terminal encrypts the data and sends it to the server over the internet.

[0162] Step 4:

[0163] The server compares the received location data with the user's normal behavior patterns. The input consists of location data and pre-learned normal behavior patterns. The output is a determination of whether the current location is normal or abnormal. Specifically, the server performs analysis by referring to past behavior data stored in a database.

[0164] Step 5:

[0165] The server detects emotional changes by analyzing emotional data. The input consists of emotional data containing voice tone and facial features. The output is a comparison with normal emotional patterns. Specifically, the server applies machine learning algorithms to determine emotional abnormalities.

[0166] Step 6:

[0167] The server detects anomalies by integrating behavioral and emotional data. The input includes the output results from steps 4 and 5. The output is a determination of whether an anomaly has been detected. Specifically, the server performs a comprehensive anomaly assessment using statistical analysis methods.

[0168] Step 7:

[0169] When the server detects an anomaly, it notifies registered contacts. Input includes the anomaly detection result and its detailed information. Output is a notification sent via email or SMS. Specifically, the server activates a rapid notification sending function to share the situation with relevant parties.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] In modern society, ensuring individual safety is a critical issue. In particular, there is a need for safety monitoring systems that consider not only location information but also emotional states. However, conventional systems primarily focus on acquiring location information and lack the technology to detect anomalies by linking them with emotional states. Therefore, a comprehensive anomaly detection system that also considers the user's psychological state is necessary.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition function, means for transmitting the acquired location information, voice data, and facial expression data to a central processing unit, and means for the central processing unit to learn the user's normal behavioral patterns and emotional patterns and detect anomalies by comparing them with the current behavior and emotions. This makes it possible to comprehensively monitor the user's location information and emotions and to quickly notify the user when an anomaly is detected.

[0175] "Portable location information acquisition function" refers to a technology incorporated into a personal, portable device that identifies and acquires the user's geographical location.

[0176] A "central processing unit" is a central computing device or system used to collect and analyze various types of information.

[0177] "Behavioral patterns" refer to the tendencies and characteristics of a series of actions that a particular user exhibits on a daily basis.

[0178] "Emotional patterns" refer to the tendencies and characteristics of emotions that a particular user exhibits on a daily basis.

[0179] "Means for detecting anomalies" refer to methods and algorithms for analyzing states that deviate from normal behavioral and emotional patterns.

[0180] "Means of notification" refers to methods or technologies for transmitting relevant information to designated contacts based on detected anomalies.

[0181] The system implementing this invention consists primarily of a portable device (terminal), a central processing unit (server), and an emotion analysis engine. The terminal uses GPS to periodically acquire the user's location information and is equipped with a microphone and camera to simultaneously capture voice and facial expression data. Voice data is analyzed using Google® Cloud Speech-to-Text API, and facial expression data is analyzed using OpenCV.

[0182] Data acquired by the device is sent to a central processing unit (server). The server uses machine learning algorithms to learn normal behavioral and emotional patterns and compares them with the currently acquired data. If an anomaly is detected, communication technologies such as Twilio are used to immediately notify registered contacts. The notification includes the current location information and details of the emotional state at that time.

[0183] For example, if a user stays in an unfamiliar location for an extended period and stress or anxiety is detected, the system will recognize this as an anomaly and quickly send an alert to a security company or other designated contacts. This helps those involved to take immediate action.

[0184] Examples of prompt messages include: "Design a program that monitors the user's current behavior and emotional patterns and provides immediate notifications if anomalies are detected. For example, include how to handle situations where the user deviates from their usual route or shows signs of tension or anxiety on their face."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The device uses GPS to acquire the user's location information and captures voice and facial expression data using a microphone and camera. The input consists of location information, voice data, and facial expression data, which are collected for subsequent data processing.

[0188] Step 2:

[0189] The acquired audio data is converted into text data using the Google Cloud Speech-to-Text API on the device, and the emotional state is inferred by analyzing the tone of voice from this text data. The input is the audio data, and the output is the analyzed emotional parameters.

[0190] Step 3:

[0191] The device analyzes images acquired by its camera using OpenCV and evaluates the user's emotions from their facial expressions. This evaluation yields specific emotional patterns. The input is a facial image, and the output is emotional data determined from the facial expression.

[0192] Step 4:

[0193] The terminal transmits acquired location information and analyzed sentiment data to a central processing unit (server). The transmission is triggered periodically or when an event suspected of being abnormal is detected.

[0194] Step 5:

[0195] The server processes the received location and sentiment data using a machine learning algorithm and compares it to the user's normal behavior and sentiment patterns to detect anomalies. The input is location and sentiment data, and the output is the result of the anomaly detection.

[0196] Step 6:

[0197] If an anomaly is detected, the server will use communication methods such as Twilio to send an alert notification to registered contacts. The notification will include the current location and emotional state. This notification will allow recipients to take immediate action.

[0198] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0214] This invention is implemented as an abnormal behavior detection system that utilizes the collection of location data by a portable location information acquisition device and data analysis by a central processing unit. The mechanism is described below in natural language.

[0215] The device collects the user's location information at regular intervals via a GPS module. This location information includes latitude and longitude, allowing for the precise identification of the user's current location. The device also collects motion information using an accelerometer and gyroscope to determine whether the user is walking or moving quickly.

[0216] The server receives location and movement information transmitted from the terminal. The generating AI within the server learns the user's usual behavior patterns based on previously accumulated data. These behavior patterns include information such as which routes the user typically takes and how fast they move.

[0217] The server compares the received current data with normal behavioral patterns. This comparison allows the server to detect anomalies if the user reaches an unexpected location, moves at an abnormal speed, or if the device itself is subjected to unnatural operations.

[0218] When an anomaly is detected, the server quickly notifies registered contacts. This notification is sent to important contacts, such as the user's parents or caregivers, via email, text message, or push notification through the application. The notification provides information about the time and location of the anomaly, allowing the contact to take immediate action.

[0219] For example, if the user is a child, and the child makes an unusual stop on their way home from school, this system can immediately detect the anomaly and notify the parent, enabling quick verification and response. In this way, the present invention improves safety.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The device uses GPS functionality to acquire the user's current location at regular intervals. The acquired latitude and longitude information is stored in memory, and preparations for transmission to the server are made as needed.

[0223] Step 2:

[0224] The device uses an accelerometer and a gyroscope to detect the user's movements. This allows it to determine whether the user is walking, running, or stationary, and record this information as movement data.

[0225] Step 3:

[0226] The device sends location and activity information it collects to the server. This transmission is performed at pre-set time intervals, ensuring data integrity and security.

[0227] Step 4:

[0228] The server analyzes the location and movement information it receives. Using a generative AI, it learns the user's normal behavior patterns from past data and compares them with current behavior data.

[0229] Step 5:

[0230] The server determines whether there is an anomaly. An anomaly is determined if there is a significant deviation from the normal behavior pattern, if the behavior exceeds a predetermined range, or if there is a sudden change in operation.

[0231] Step 6:

[0232] When the server detects an anomaly, it activates a function to notify registered contacts. The notification, including the nature and location of the anomaly, is sent via email, SMS, or a dedicated application.

[0233] Step 7:

[0234] The system reviews notifications received by the user and takes appropriate action based on their content. If necessary, the user may travel to the location, contact service users, or report to relevant authorities.

[0235] (Example 1)

[0236] Next, we will describe Example 1. 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."

[0237] In modern society, enhancing user safety is a crucial issue. In particular, there is a need for systems that can detect in advance when users move to unusual locations or deviate from their normal behavior patterns, and to respond quickly. However, existing systems have the problem of difficulty in analyzing user behavior in real time and effectively detecting anomalies.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for periodically collecting user location information using a positioning device, means for transmitting the collected location information and movement information to a central control unit, and means for the central control unit to learn the user's normal movement patterns using a generative AI model and detect deviations by comparing them with the current movement. This enables real-time monitoring of movement and detection of anomalies to enhance user safety.

[0240] A "positioning device" is a device used to collect a user's current location information in the form of latitude and longitude.

[0241] A "user" is a person or subject who uses this system and is subject to the collection of location and movement information.

[0242] "Location information" refers to geographical data such as latitude and longitude that indicates the user's current location.

[0243] "Dynamic information" refers to information that indicates the user's movement and actions, and may include acceleration data and rotation data.

[0244] The "central control unit" is a processing unit that receives and analyzes data transmitted from positioning devices.

[0245] A "generative AI model" is an algorithm that learns from a large amount of historical data and recognizes and evaluates normal behavioral patterns.

[0246] "Normal movement patterns" refer to typical behavioral characteristics of users, such as the routes they take and the speed at which they move on a daily basis.

[0247] "Deviation" refers to a state in which a user's behavior deviates from their normal behavioral patterns.

[0248] A "communication destination" is a contact point registered to receive notifications from the system.

[0249] A "press notification" is a real-time notification sent from the central control unit to a communication partner to inform them of an abnormal situation.

[0250] The present invention is a system for monitoring user behavior in real time and detecting deviations. Its embodiments are described in detail below.

[0251] The device uses a GPS module as a positioning device to collect the user's location information at regular intervals. This location information includes latitude and longitude, making it possible to accurately determine the user's current location. In addition, the device is equipped with an accelerometer and a gyroscope, which are used to collect information about the user's movements. For example, by analyzing acceleration data, it is possible to determine whether the user is walking or running.

[0252] The terminal transmits the collected location and movement information to a central control unit, which is a server. Within the server, a generative AI model is installed, which learns from previously accumulated user movement data. This AI model recognizes the user's normal movement patterns and detects deviations by comparing them to their current behavior.

[0253] The server promptly sends a notification to registered communication destinations if a user's behavior deviates from the norm. This notification is provided via electronic communication, text communication, or click notification via an application. The notification includes the time and location where the deviation occurred, allowing the recipient to take immediate action.

[0254] For example, if a user is a child and deviates from their usual route to school, the server will detect this as a deviation and notify the parent or guardian. This allows parents or guardians to quickly check on their child's safety.

[0255] An example of a prompt message might be, "Explain how you will be notified if your child uses an unfamiliar route to school."

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The terminal uses a GPS module to obtain the user's location information. This input information includes latitude and longitude, indicating the user's current location. The terminal converts the obtained location information into a data format and prepares for the next processing step. Specifically, at this stage, the terminal acquires location information at regular intervals and stores it in buffer memory.

[0259] Step 2:

[0260] The device collects motion information using an accelerometer and a gyroscope. This information includes changes in acceleration and direction of movement. Based on the input information, the device processes the data to determine motion, such as walking, running, or standing still, and prepares to send the results to the server. Specifically, this involves acquiring sensor values ​​in real time and analyzing them to identify the user's activity state.

[0261] Step 3:

[0262] The device transmits the collected location and movement information to the server. This transmission is performed using a secure and high-speed communication protocol, and data integrity is ensured. The input includes all the data collected by the device, and the output is the completion of the data transmission to the server.

[0263] Step 4:

[0264] The server analyzes the received location and movement information. Using a generative AI model, it learns normal movement patterns based on the user's past behavior patterns. The input information consists of the latest location and movement data sent from the terminal, and the output is the deviation from the normal pattern. This process involves high-speed data processing through the AI's algorithms.

[0265] Step 5:

[0266] The server compares the current data with normal dynamic patterns to determine if there are any deviations. Here, it performs a comparison operation with the input dynamic information, and if an anomaly is detected, it generates a result. This output data includes whether or not an anomaly exists, and detailed information about it.

[0267] Step 6:

[0268] The server notifies registered communication destinations when an anomaly is detected. The input includes the anomaly detection result, and the output is a generated notification message, which is sent via email, text message, or application. The specific operation involves a process of compiling the necessary information into a notification format and sending it.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] Conventional anomaly detection systems using location and motion information have limitations in providing a quick and intuitive understanding of the situation when an anomaly is detected, potentially leading to delayed responses. Furthermore, the lack of clear location information in notifications meant that third parties could not immediately understand the actual location of the anomaly. Therefore, there was a need for a means to support rapid response by visually indicating the location of the anomaly.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for transmitting the acquired location information and operation information to a central processing unit, means for the central processing unit to learn the user's normal behavior patterns and detect anomalies by comparing them with current behavior, means for notifying registered contacts when an anomaly is detected, and means for providing map information that visually indicates the location of the anomaly when an anomaly is detected. This enables rapid detection of anomalies and intuitive understanding of the location where they occur.

[0274] A "portable location information acquisition device" is a portable device used to periodically collect location coordinate data.

[0275] "Location information" refers to data that indicates a specific location using geographical coordinates, including latitude and longitude.

[0276] "Motion information" refers to data used to measure the user's movement state and speed, and to capture the characteristics of their actions.

[0277] The "central processing unit" is a centralized processing device that receives information from the outside and performs data analysis and judgment.

[0278] The "normal behavior pattern" represents the tendencies of the movements and actions that the user performs daily.

[0279] The "means for detecting an abnormality" is a method or technology for identifying unexpected situations or actions different from normal.

[0280] The "registered contact" is the information of the recipient who is specified in advance to receive a notification when an abnormality is detected.

[0281] The "map information" is data for visually displaying a specific geographical area or location.

[0282] The "means for sending a notification" is a method used to transmit information or a warning to the recipient.

[0283] In this invention, a system for acquiring the position and movement information of a user and detecting abnormal behavior is provided. To implement this system, the terminal uses a portable position information acquisition device to record the latitude and longitude of the user at regular intervals. Also, an acceleration sensor and a gyro sensor provided in the terminal are used to collect the movement information of the user. As a result, detailed behavior data such as whether the user is walking or moving hurriedly can be obtained.

[0284] The server receives the position information and movement information transmitted from the terminal. The server is equipped with a generative AI model for performing advanced data analysis and learns the normal behavior pattern of the user based on the data accumulated in the past. This generative AI model analyzes the tendency of the behavior and estimates the normal movement route and speed.

[0285] The server compares the currently received data in real time with normal behavior patterns. If the user reaches an unexpected location or is moving at an abnormally high speed, it is determined as an anomaly. When an anomaly is detected, the server immediately sends a notification to the registered emergency contacts. The notification is sent in a way such as an email or a push notification and includes information about the specific time and location where the anomaly occurred. Thus, the emergency contacts can immediately confirm the anomaly and take prompt countermeasures.

[0286] As a specific example, consider the case where a child deviates from the normal route home from school. This system immediately notifies the guardian and provides map information to assist the guardian in promptly responding to the anomaly. Examples of prompt texts for the generative AI model include sentences such as "This security assistant app analyzes the user's location information in real time and sends a notification to the emergency contacts when abnormal behavior is detected."

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The terminal uses a portable location information acquisition device to periodically acquire the user's current location. It receives latitude and longitude data from the GPS module as input and uses this as location information data as output. Also, the terminal uses an acceleration sensor and a gyro sensor to acquire motion information and generates information such as speed and direction of movement.

[0290] Step 2:

[0291] The terminal sends the acquired location information and motion information to the server. It takes the location and motion data stored in the terminal as input and uses this as data packets to be sent to the server via the network as output. At this time, the transmission is performed using an appropriate protocol to prevent data errors.

[0292] Step 3:

[0293] The server analyzes the received location and motion information. It receives location and motion data transmitted from the terminal as input and derives the analysis results as output. A generative AI model is used here, which learns normal behavior patterns based on past behavior data and then performs anomaly monitoring.

[0294] Step 4:

[0295] The server detects anomalies when current behavior does not match normal behavior patterns. It compares the analyzed behavior data as input with a baseline pattern and generates an anomaly detection flag as output. Specifically, it executes algorithms to detect speeds and movement routes outside acceptable ranges.

[0296] Step 5:

[0297] The server notifies registered emergency contacts when an anomaly is detected. Based on the time and location of the anomaly as input, it sends out email and application push notifications as output. These notifications include a map link visualizing the location and are attached with information to support a quick response.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention is implemented as an anomaly detection system that combines a portable location information acquisition device with an emotion engine that recognizes the user's emotional state. This system coordinates the terminal, server, and emotion engine to comprehensively monitor the user's location information and emotional state.

[0300] The device uses GPS functionality to periodically acquire the user's location, and simultaneously uses a microphone and camera to capture voice and facial expressions. This allows it to acquire data related to the user's emotions, preparing it for analysis by an emotion engine.

[0301] The server not only compares location information transmitted from the terminal with normal behavioral patterns, but also receives emotional data analyzed by the emotion engine. This emotional data indicates the user's psychological state through changes in voice tone and facial expressions, and is analyzed by the emotion engine. The emotion engine uses machine learning algorithms to learn the user's normal emotional patterns and detect abnormal emotional states.

[0302] When the server detects unusual behavior or emotions, it sends a notification to pre-registered contacts. The notification states that a deviation from normal behavioral or emotional patterns has been observed, and includes specific location information and details of the emotional state. This allows recipients to respond quickly and accurately to the user's situation.

[0303] For example, if a user who is normally in a stable mood suddenly deviates from their usual range of movement and displays signs of anxiety or fear, the system will recognize this as an anomaly and the server will immediately send a notification. Parents and caregivers can then check the notification and take appropriate action to ensure the user's safety. This system enhances safety by considering psychological abnormalities in addition to physical location information.

[0304] The following describes the processing flow.

[0305] Step 1:

[0306] The device uses a GPS module to periodically acquire the user's current location. It also simultaneously captures the user's voice and facial expression data using its built-in microphone and camera. This data is later used as material for analysis by an emotion engine.

[0307] Step 2:

[0308] The terminal transmits the acquired location information, voice, and audio-visual data to the server at preset time intervals. At this time, encrypted communication is used to protect data integrity and privacy.

[0309] Step 3:

[0310] The server compares the received location information with the known normal behavior patterns of the user. When detecting anomalies based on past behavior data using generative AI, an alert is generated if the location information deviates from the normal range.

[0311] Step 4:

[0312] The server calls the emotion engine to analyze the voice and audio-visual data. Based on these data, the emotion engine evaluates the user's emotional state and detects anomalies if there are emotional changes different from normal.

[0313] Step 5:

[0314] If the server detects an anomaly in either the location information or the emotion data, or both, it notifies the pre-registered contacts. The notification includes the current location, emotional state, and specific details of the anomaly.

[0315] Step 6:

[0316] The user (recipient of the notification) checks the received information and takes necessary measures based on the content. Such responses may include on-site verification by the user, direct contact with the contacts, reporting to a specialized agency, etc.

[0317] (Example 2)

[0318] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0319] This system comprehensively monitors the user's current location and emotional state, quickly detecting anomalies and notifying relevant parties. Conventional technologies often rely solely on location information for monitoring, making it difficult to detect anomalies that take emotional state changes into account. This issue needs to be addressed.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for acquiring emotional data using a device for capturing voice and facial expressions, and means for transmitting the acquired location information and emotional data to a central processing unit. This makes it possible to learn the user's normal behavior and emotional patterns, detect anomalies by comparing them with the current behavior and emotional state, and quickly notify relevant parties.

[0322] A "portable location information acquisition device" is a portable device used to periodically acquire the user's current location.

[0323] "Emotional data" refers to data about the user's psychological state obtained from their voice and facial expressions.

[0324] A "central processing unit" is a computer system that receives and analyzes acquired location information and emotional data.

[0325] "Normal behavioral patterns" refer to a dataset that shows behavioral tendencies formed based on the user's travel history and daily behavior.

[0326] "Emotional patterns" refer to a dataset that shows the user's typical psychological state, analyzed based on their voice and facial expressions.

[0327] "Abnormal" refers to a state in which the user's current behavior and emotional state differ significantly from their normal behavioral and emotional patterns.

[0328] This invention provides an anomaly detection system that comprehensively monitors the user's location information and emotional state. The main components consist of a portable location information acquisition device, an emotional data acquisition device, and a central processing unit.

[0329] The device functions as a portable location information acquisition device, periodically acquiring the user's location (latitude and longitude) using GPS. In addition, it is equipped with a microphone for capturing voice data and a camera for capturing facial expression data, collecting the user's emotional data as well.

[0330] The terminal preprocesses the data before transmitting it to the central processing unit. This preprocessing includes denoising location data and normalizing audio data. This process ensures that the data is securely transmitted to the central processing unit in an analyzable format.

[0331] The server functions as a central processing unit, analyzing received location and sentiment data. This analysis utilizes machine learning algorithms to learn the user's typical behavioral and emotional patterns, comparing them to current data to detect anomalies. To achieve this, it accumulates historical data and continuously updates the model.

[0332] For example, if a user who doesn't usually go out suddenly moves to a distant location one day and emotional patterns indicating anxiety or fear are recorded, the system will determine this to be abnormal. The server will immediately send a notification so that relevant parties can respond promptly.

[0333] Examples of prompt statements to input into a generative AI model are as follows:

[0334] "Please explain how the emotion engine detects an anomaly when a user displays an anxious expression after exceeding their normal range of movement."

[0335] In this way, the system enhances user safety and helps stakeholders take swift and appropriate action.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The device periodically acquires the user's location using GPS functionality. It takes current latitude and longitude information as input and records it as location data. In addition, it captures audio and facial expression data using a microphone and camera. The output consists of location data and emotion data. Specifically, the device tracks the user's movements in their environment and monitors their behavior.

[0339] Step 2:

[0340] The device preprocesses the acquired location and sentiment data. It receives the raw data as input, removes unwanted noise from the location information, and normalizes the audio data. This process converts the data into a format suitable for analysis. The output is a clean dataset. Specifically, the device utilizes its processing power to filter the data in real time.

[0341] Step 3:

[0342] The terminal sends pre-processed data to the server. A clean dataset is used as input. As output, the data is passed to the server via a secure communication protocol. Specifically, the terminal encrypts the data and sends it to the server over the internet.

[0343] Step 4:

[0344] The server compares the received location data with the user's normal behavior patterns. The input consists of location data and pre-learned normal behavior patterns. The output is a determination of whether the current location is normal or abnormal. Specifically, the server performs analysis by referring to past behavior data stored in a database.

[0345] Step 5:

[0346] The server detects emotional changes by analyzing emotional data. The input consists of emotional data containing voice tone and facial features. The output is a comparison with normal emotional patterns. Specifically, the server applies machine learning algorithms to determine emotional abnormalities.

[0347] Step 6:

[0348] The server detects anomalies by integrating behavioral and emotional data. The input includes the output results from steps 4 and 5. The output is a determination of whether an anomaly has been detected. Specifically, the server performs a comprehensive anomaly assessment using statistical analysis methods.

[0349] Step 7:

[0350] When the server detects an anomaly, it notifies registered contacts. Input includes the anomaly detection result and its detailed information. Output is a notification sent via email or SMS. Specifically, the server activates a rapid notification sending function to share the situation with relevant parties.

[0351] (Application Example 2)

[0352] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0353] In modern society, ensuring individual safety is a critical issue. In particular, there is a need for safety monitoring systems that consider not only location information but also emotional states. However, conventional systems primarily focus on acquiring location information and lack the technology to detect anomalies by linking them with emotional states. Therefore, a comprehensive anomaly detection system that also considers the user's psychological state is necessary.

[0354] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0355] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition function, means for transmitting the acquired location information, voice data, and facial expression data to a central processing unit, and means for the central processing unit to learn the user's normal behavioral patterns and emotional patterns and detect anomalies by comparing them with the current behavior and emotions. This makes it possible to comprehensively monitor the user's location information and emotions and to quickly notify the user when an anomaly is detected.

[0356] "Portable location information acquisition function" refers to a technology incorporated into a personal, portable device that identifies and acquires the user's geographical location.

[0357] A "central processing unit" is a central computing device or system used to collect and analyze various types of information.

[0358] "Behavioral patterns" refer to the tendencies and characteristics of a series of actions that a particular user exhibits on a daily basis.

[0359] "Emotional patterns" refer to the tendencies and characteristics of emotions that a particular user exhibits on a daily basis.

[0360] "Means for detecting anomalies" refer to methods and algorithms for analyzing states that deviate from normal behavioral and emotional patterns.

[0361] "Means of notification" refers to methods or technologies for transmitting relevant information to designated contacts based on detected anomalies.

[0362] The system implementing this invention consists primarily of a portable device (terminal), a central processing unit (server), and an emotion analysis engine. The terminal uses GPS to periodically acquire the user's location information and is equipped with a microphone and camera to simultaneously capture voice and facial expression data. The voice data is analyzed using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using OpenCV.

[0363] Data acquired by the device is sent to a central processing unit (server). The server uses machine learning algorithms to learn normal behavioral and emotional patterns and compares them with the currently acquired data. If an anomaly is detected, communication technologies such as Twilio are used to immediately notify registered contacts. The notification includes the current location information and details of the emotional state at that time.

[0364] For example, if a user stays in an unfamiliar location for an extended period and stress or anxiety is detected, the system will recognize this as an anomaly and quickly send an alert to a security company or other designated contacts. This helps those involved to take immediate action.

[0365] Examples of prompt messages include: "Design a program that monitors the user's current behavior and emotional patterns and provides immediate notifications if anomalies are detected. For example, include how to handle situations where the user deviates from their usual route or shows signs of tension or anxiety on their face."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device uses GPS to acquire the user's location information and captures voice and facial expression data using a microphone and camera. The input consists of location information, voice data, and facial expression data, which are collected for subsequent data processing.

[0369] Step 2:

[0370] The acquired audio data is converted into text data using the Google Cloud Speech-to-Text API on the device, and the emotional state is inferred by analyzing the tone of voice from this text data. The input is the audio data, and the output is the analyzed emotional parameters.

[0371] Step 3:

[0372] The device analyzes images acquired by its camera using OpenCV and evaluates the user's emotions from their facial expressions. This evaluation yields specific emotional patterns. The input is a facial image, and the output is emotional data determined from the facial expression.

[0373] Step 4:

[0374] The terminal transmits acquired location information and analyzed sentiment data to a central processing unit (server). The transmission is triggered periodically or when an event suspected of being abnormal is detected.

[0375] Step 5:

[0376] The server processes the received location and sentiment data using a machine learning algorithm and compares it to the user's normal behavior and sentiment patterns to detect anomalies. The input is location and sentiment data, and the output is the result of the anomaly detection.

[0377] Step 6:

[0378] If an anomaly is detected, the server will use communication methods such as Twilio to send an alert notification to registered contacts. The notification will include the current location and emotional state. This notification will allow recipients to take immediate action.

[0379] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention is implemented as an abnormal behavior detection system that utilizes the collection of location data by a portable location information acquisition device and data analysis by a central processing unit. The mechanism is described below in natural language.

[0396] The device collects the user's location information at regular intervals via a GPS module. This location information includes latitude and longitude, allowing for the precise identification of the user's current location. The device also collects motion information using an accelerometer and gyroscope to determine whether the user is walking or moving quickly.

[0397] The server receives location and movement information transmitted from the terminal. The generating AI within the server learns the user's usual behavior patterns based on previously accumulated data. These behavior patterns include information such as which routes the user typically takes and how fast they move.

[0398] The server compares the received current data with normal behavioral patterns. This comparison allows the server to detect anomalies if the user reaches an unexpected location, moves at an abnormal speed, or if the device itself is subjected to unnatural operations.

[0399] When an anomaly is detected, the server quickly notifies registered contacts. This notification is sent to important contacts, such as the user's parents or caregivers, via email, text message, or push notification through the application. The notification provides information about the time and location of the anomaly, allowing the contact to take immediate action.

[0400] For example, if the user is a child, and the child makes an unusual stop on their way home from school, this system can immediately detect the anomaly and notify the parent, enabling quick verification and response. In this way, the present invention improves safety.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The device uses GPS functionality to acquire the user's current location at regular intervals. The acquired latitude and longitude information is stored in memory, and preparations for transmission to the server are made as needed.

[0404] Step 2:

[0405] The device uses an accelerometer and a gyroscope to detect the user's movements. This allows it to determine whether the user is walking, running, or stationary, and record this information as movement data.

[0406] Step 3:

[0407] The device sends location and activity information it collects to the server. This transmission is performed at pre-set time intervals, ensuring data integrity and security.

[0408] Step 4:

[0409] The server analyzes the location and movement information it receives. Using a generative AI, it learns the user's normal behavior patterns from past data and compares them with current behavior data.

[0410] Step 5:

[0411] The server determines whether there is an anomaly. An anomaly is determined if there is a significant deviation from the normal behavior pattern, if the behavior exceeds a predetermined range, or if there is a sudden change in operation.

[0412] Step 6:

[0413] When the server detects an anomaly, it activates a function to notify registered contacts. The notification, including the nature and location of the anomaly, is sent via email, SMS, or a dedicated application.

[0414] Step 7:

[0415] The system reviews notifications received by the user and takes appropriate action based on their content. If necessary, the user may travel to the location, contact service users, or report to relevant authorities.

[0416] (Example 1)

[0417] Next, we will describe Example 1. 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."

[0418] In modern society, enhancing user safety is a crucial issue. In particular, there is a need for systems that can detect in advance when users move to unusual locations or deviate from their normal behavior patterns, and to respond quickly. However, existing systems have the problem of difficulty in analyzing user behavior in real time and effectively detecting anomalies.

[0419] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0420] In this invention, the server includes means for periodically collecting user location information using a positioning device, means for transmitting the collected location information and movement information to a central control unit, and means for the central control unit to learn the user's normal movement patterns using a generative AI model and detect deviations by comparing them with the current movement. This enables real-time monitoring of movement and detection of anomalies to enhance user safety.

[0421] A "positioning device" is a device used to collect a user's current location information in the form of latitude and longitude.

[0422] A "user" is a person or subject who uses this system and is subject to the collection of location and movement information.

[0423] "Location information" refers to geographical data such as latitude and longitude that indicates the user's current location.

[0424] "Dynamic information" refers to information that indicates the user's movement and actions, and may include acceleration data and rotation data.

[0425] The "central control unit" is a processing unit that receives and analyzes data transmitted from positioning devices.

[0426] A "generative AI model" is an algorithm that learns from a large amount of historical data and recognizes and evaluates normal behavioral patterns.

[0427] "Normal movement patterns" refer to typical behavioral characteristics of users, such as the routes they take and the speed at which they move on a daily basis.

[0428] "Deviation" refers to a state in which a user's behavior deviates from their normal behavioral patterns.

[0429] A "communication destination" is a contact point registered to receive notifications from the system.

[0430] A "press notification" is a real-time notification sent from the central control unit to a communication partner to inform them of an abnormal situation.

[0431] The present invention is a system for monitoring user behavior in real time and detecting deviations. Its embodiments are described in detail below.

[0432] The device uses a GPS module as a positioning device to collect the user's location information at regular intervals. This location information includes latitude and longitude, making it possible to accurately determine the user's current location. In addition, the device is equipped with an accelerometer and a gyroscope, which are used to collect information about the user's movements. For example, by analyzing acceleration data, it is possible to determine whether the user is walking or running.

[0433] The terminal transmits the collected location and movement information to a central control unit, which is a server. Within the server, a generative AI model is installed, which learns from previously accumulated user movement data. This AI model recognizes the user's normal movement patterns and detects deviations by comparing them to their current behavior.

[0434] The server promptly sends a notification to registered communication destinations if a user's behavior deviates from the norm. This notification is provided via electronic communication, text communication, or click notification via an application. The notification includes the time and location where the deviation occurred, allowing the recipient to take immediate action.

[0435] For example, if a user is a child and deviates from their usual route to school, the server will detect this as a deviation and notify the parent or guardian. This allows parents or guardians to quickly check on their child's safety.

[0436] An example of a prompt message might be, "Explain how you will be notified if your child uses an unfamiliar route to school."

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The terminal uses a GPS module to obtain the user's location information. This input information includes latitude and longitude, indicating the user's current location. The terminal converts the obtained location information into a data format and prepares for the next processing step. Specifically, at this stage, the terminal acquires location information at regular intervals and stores it in buffer memory.

[0440] Step 2:

[0441] The device collects motion information using an accelerometer and a gyroscope. This information includes changes in acceleration and direction of movement. Based on the input information, the device processes the data to determine motion, such as walking, running, or standing still, and prepares to send the results to the server. Specifically, this involves acquiring sensor values ​​in real time and analyzing them to identify the user's activity state.

[0442] Step 3:

[0443] The device transmits the collected location and movement information to the server. This transmission is performed using a secure and high-speed communication protocol, and data integrity is ensured. The input includes all the data collected by the device, and the output is the completion of the data transmission to the server.

[0444] Step 4:

[0445] The server analyzes the received location and movement information. Using a generative AI model, it learns normal movement patterns based on the user's past behavior patterns. The input information consists of the latest location and movement data sent from the terminal, and the output is the deviation from the normal pattern. This process involves high-speed data processing through the AI's algorithms.

[0446] Step 5:

[0447] The server compares the current data with normal dynamic patterns to determine if there are any deviations. Here, it performs a comparison operation with the input dynamic information, and if an anomaly is detected, it generates a result. This output data includes whether or not an anomaly exists, and detailed information about it.

[0448] Step 6:

[0449] The server notifies registered communication destinations when an anomaly is detected. The input includes the anomaly detection result, and the output is a generated notification message, which is sent via email, text message, or application. The specific operation involves a process of compiling the necessary information into a notification format and sending it.

[0450] (Application Example 1)

[0451] Next, we will explain Application Example 1. In the following explanation, 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."

[0452] Conventional anomaly detection systems using location and motion information have limitations in providing a quick and intuitive understanding of the situation when an anomaly is detected, potentially leading to delayed responses. Furthermore, the lack of clear location information in notifications meant that third parties could not immediately understand the actual location of the anomaly. Therefore, there was a need for a means to support rapid response by visually indicating the location of the anomaly.

[0453] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0454] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for transmitting the acquired location information and operation information to a central processing unit, means for the central processing unit to learn the user's normal behavior patterns and detect anomalies by comparing them with current behavior, means for notifying registered contacts when an anomaly is detected, and means for providing map information that visually indicates the location of the anomaly when an anomaly is detected. This enables rapid detection of anomalies and intuitive understanding of the location where they occur.

[0455] A "portable location information acquisition device" is a portable device used to periodically collect location coordinate data.

[0456] "Location information" refers to data that indicates a specific location using geographical coordinates, including latitude and longitude.

[0457] "Motion information" refers to data used to measure the user's movement state and speed, and to capture the characteristics of their actions.

[0458] A "central processing unit" is a centralized processing unit that receives information from external sources and performs data analysis and decision-making.

[0459] "Normal behavioral patterns" refer to the user's tendencies in daily movement and behavior.

[0460] "Means for detecting anomalies" refers to methods or techniques for identifying unexpected situations or unusual behavior.

[0461] "Registered contact information" refers to the information of recipients who have been designated in advance to receive notifications in the event of an anomaly being detected.

[0462] "Map information" refers to data used to visually display specific geographical areas or locations.

[0463] "Means of notification" refers to methods used to convey information or warnings to recipients.

[0464] This invention provides a system for acquiring user location and movement information and detecting abnormal behavior. To implement this system, the terminal uses a portable location information acquisition device to record the user's latitude and longitude at regular intervals. In addition, the terminal uses an accelerometer and gyroscope to collect user movement information. This allows for obtaining detailed behavioral data, such as whether the user is walking or moving quickly.

[0465] The server receives location and movement information transmitted from the terminal. The server is equipped with a generative AI model for advanced data analysis, which learns the user's normal behavior patterns based on previously accumulated data. This generative AI model analyzes behavioral trends and estimates the normal travel route and speed.

[0466] The server compares real-time data received with normal behavioral patterns. It identifies an anomaly if the user reaches an unexpected location or moves at an unusually fast speed. When an anomaly is detected, the server immediately sends a notification to registered emergency contacts. The notification is sent via email or push notification and includes information about the specific time and location where the anomaly occurred. This allows emergency contacts to immediately confirm the anomaly and take prompt action.

[0467] As a concrete example, consider a situation where a child deviates from their usual route home from school. This system immediately notifies parents and provides map information to help them respond quickly to the anomaly. An example of a prompt message for the generated AI model would be: "This security assistant app analyzes the user's location in real time and notifies emergency contacts if it detects unusual behavior."

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The device periodically acquires the user's current location using a portable location information acquisition device. It receives latitude and longitude data from a GPS module as input and outputs this as location information data. The device also acquires motion information using an accelerometer and gyroscope, and generates information such as speed and direction of movement.

[0471] Step 2:

[0472] The terminal transmits acquired location and movement information to the server. It takes location and movement data stored within the terminal as input and outputs it as data packets transmitted to the server via the network. To prevent data errors, an appropriate protocol is used for transmission.

[0473] Step 3:

[0474] The server analyzes the received location and motion information. It receives location and motion data transmitted from the terminal as input and derives the analysis results as output. A generative AI model is used here, which learns normal behavior patterns based on past behavior data and then performs anomaly monitoring.

[0475] Step 4:

[0476] The server detects anomalies when current behavior does not match normal behavior patterns. It compares the analyzed behavior data as input with a baseline pattern and generates an anomaly detection flag as output. Specifically, it executes algorithms to detect speeds and movement routes outside acceptable ranges.

[0477] Step 5:

[0478] The server notifies registered emergency contacts when an anomaly is detected. Based on the time and location of the anomaly as input, it sends out email and application push notifications as output. These notifications include a map link visualizing the location and are attached with information to support a quick response.

[0479] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0480] This invention is implemented as an anomaly detection system that combines a portable location information acquisition device with an emotion engine that recognizes the user's emotional state. This system coordinates the terminal, server, and emotion engine to comprehensively monitor the user's location information and emotional state.

[0481] The device uses GPS functionality to periodically acquire the user's location, and simultaneously uses a microphone and camera to capture voice and facial expressions. This allows it to acquire data related to the user's emotions, preparing it for analysis by an emotion engine.

[0482] The server not only compares location information transmitted from the terminal with normal behavioral patterns, but also receives emotional data analyzed by the emotion engine. This emotional data indicates the user's psychological state through changes in voice tone and facial expressions, and is analyzed by the emotion engine. The emotion engine uses machine learning algorithms to learn the user's normal emotional patterns and detect abnormal emotional states.

[0483] When the server detects unusual behavior or emotions, it sends a notification to pre-registered contacts. The notification states that a deviation from normal behavioral or emotional patterns has been observed, and includes specific location information and details of the emotional state. This allows recipients to respond quickly and accurately to the user's situation.

[0484] For example, if a user who is normally in a stable mood suddenly deviates from their usual range of movement and displays signs of anxiety or fear, the system will recognize this as an anomaly and the server will immediately send a notification. Parents and caregivers can then check the notification and take appropriate action to ensure the user's safety. This system enhances safety by considering psychological abnormalities in addition to physical location information.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The device uses a GPS module to periodically acquire the user's current location. It also simultaneously captures the user's voice and facial expression data using its built-in microphone and camera. This data is later used as material for analysis by an emotion engine.

[0488] Step 2:

[0489] The device transmits location information, voice data, and facial expression data acquired by the device to the server at pre-set time intervals. Encrypted communication is used to protect data integrity and privacy.

[0490] Step 3:

[0491] The server compares the received location information with the user's known normal behavior patterns. When using a generation AI to detect anomalies based on past behavior data, an alert is generated if the location information falls outside the normal range.

[0492] Step 4:

[0493] The server invokes the emotion engine to analyze voice and facial expression data. Based on this data, the emotion engine evaluates the user's emotional state and detects anomalies if there are any unusual emotional changes.

[0494] Step 5:

[0495] If the server detects an anomaly in either location data, emotion data, or both, it will notify pre-registered contacts. The notification will include the current location, emotion status, and specific details of the anomaly.

[0496] Step 6:

[0497] The user (the recipient of the notification) reviews the information received and takes necessary actions based on its content. These actions may include on-site verification by the user, direct contact with the contact person, or reporting to a specialized agency.

[0498] (Example 2)

[0499] Next, we will describe Example 2. 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."

[0500] This system comprehensively monitors the user's current location and emotional state, quickly detecting anomalies and notifying relevant parties. Conventional technologies often rely solely on location information for monitoring, making it difficult to detect anomalies that take emotional state changes into account. This issue needs to be addressed.

[0501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0502] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for acquiring emotional data using a device for capturing voice and facial expressions, and means for transmitting the acquired location information and emotional data to a central processing unit. This makes it possible to learn the user's normal behavior and emotional patterns, detect anomalies by comparing them with the current behavior and emotional state, and quickly notify relevant parties.

[0503] A "portable location information acquisition device" is a portable device used to periodically acquire the user's current location.

[0504] "Emotional data" refers to data about the user's psychological state obtained from their voice and facial expressions.

[0505] A "central processing unit" is a computer system that receives and analyzes acquired location information and emotional data.

[0506] "Normal behavioral patterns" refer to a dataset that shows behavioral tendencies formed based on the user's travel history and daily behavior.

[0507] "Emotional patterns" refer to a dataset that shows the user's typical psychological state, analyzed based on their voice and facial expressions.

[0508] "Abnormal" refers to a state in which the user's current behavior and emotional state differ significantly from their normal behavioral and emotional patterns.

[0509] This invention provides an anomaly detection system that comprehensively monitors the user's location information and emotional state. The main components consist of a portable location information acquisition device, an emotional data acquisition device, and a central processing unit.

[0510] The device functions as a portable location information acquisition device, periodically acquiring the user's location (latitude and longitude) using GPS. In addition, it is equipped with a microphone for capturing voice data and a camera for capturing facial expression data, collecting the user's emotional data as well.

[0511] The terminal preprocesses the data before transmitting it to the central processing unit. This preprocessing includes denoising location data and normalizing audio data. This process ensures that the data is securely transmitted to the central processing unit in an analyzable format.

[0512] The server functions as a central processing unit, analyzing received location and sentiment data. This analysis utilizes machine learning algorithms to learn the user's typical behavioral and emotional patterns, comparing them to current data to detect anomalies. To achieve this, it accumulates historical data and continuously updates the model.

[0513] For example, if a user who doesn't usually go out suddenly moves to a distant location one day and emotional patterns indicating anxiety or fear are recorded, the system will determine this to be abnormal. The server will immediately send a notification so that relevant parties can respond promptly.

[0514] Examples of prompt statements to input into a generative AI model are as follows:

[0515] "Please explain how the emotion engine detects an anomaly when a user displays an anxious expression after exceeding their normal range of movement."

[0516] In this way, the system enhances user safety and helps stakeholders take swift and appropriate action.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The device periodically acquires the user's location using GPS functionality. It takes current latitude and longitude information as input and records it as location data. In addition, it captures audio and facial expression data using a microphone and camera. The output consists of location data and emotion data. Specifically, the device tracks the user's movements in their environment and monitors their behavior.

[0520] Step 2:

[0521] The device preprocesses the acquired location and sentiment data. It receives the raw data as input, removes unwanted noise from the location information, and normalizes the audio data. This process converts the data into a format suitable for analysis. The output is a clean dataset. Specifically, the device utilizes its processing power to filter the data in real time.

[0522] Step 3:

[0523] The terminal sends pre-processed data to the server. A clean dataset is used as input. As output, the data is passed to the server via a secure communication protocol. Specifically, the terminal encrypts the data and sends it to the server over the internet.

[0524] Step 4:

[0525] The server compares the received location data with the user's normal behavior patterns. The input consists of location data and pre-learned normal behavior patterns. The output is a determination of whether the current location is normal or abnormal. Specifically, the server performs analysis by referring to past behavior data stored in a database.

[0526] Step 5:

[0527] The server detects emotional changes by analyzing emotional data. The input consists of emotional data containing voice tone and facial features. The output is a comparison with normal emotional patterns. Specifically, the server applies machine learning algorithms to determine emotional abnormalities.

[0528] Step 6:

[0529] The server detects anomalies by integrating behavioral and emotional data. The input includes the output results from steps 4 and 5. The output is a determination of whether an anomaly has been detected. Specifically, the server performs a comprehensive anomaly assessment using statistical analysis methods.

[0530] Step 7:

[0531] When the server detects an anomaly, it notifies registered contacts. Input includes the anomaly detection result and its detailed information. Output is a notification sent via email or SMS. Specifically, the server activates a rapid notification sending function to share the situation with relevant parties.

[0532] (Application Example 2)

[0533] Next, we will explain application example 2. In the following explanation, 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."

[0534] In modern society, ensuring individual safety is a critical issue. In particular, there is a need for safety monitoring systems that consider not only location information but also emotional states. However, conventional systems primarily focus on acquiring location information and lack the technology to detect anomalies by linking them with emotional states. Therefore, a comprehensive anomaly detection system that also considers the user's psychological state is necessary.

[0535] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0536] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition function, means for transmitting the acquired location information, voice data, and facial expression data to a central processing unit, and means for the central processing unit to learn the user's normal behavioral patterns and emotional patterns and detect anomalies by comparing them with the current behavior and emotions. This makes it possible to comprehensively monitor the user's location information and emotions and to quickly notify the user when an anomaly is detected.

[0537] "Portable location information acquisition function" refers to a technology incorporated into a personal, portable device that identifies and acquires the user's geographical location.

[0538] A "central processing unit" is a central computing device or system used to collect and analyze various types of information.

[0539] "Behavioral patterns" refer to the tendencies and characteristics of a series of actions that a particular user exhibits on a daily basis.

[0540] "Emotional patterns" refer to the tendencies and characteristics of emotions that a particular user exhibits on a daily basis.

[0541] "Means for detecting anomalies" refer to methods and algorithms for analyzing states that deviate from normal behavioral and emotional patterns.

[0542] "Means of notification" refers to methods or technologies for transmitting relevant information to designated contacts based on detected anomalies.

[0543] The system implementing this invention consists primarily of a portable device (terminal), a central processing unit (server), and an emotion analysis engine. The terminal uses GPS to periodically acquire the user's location information and is equipped with a microphone and camera to simultaneously capture voice and facial expression data. The voice data is analyzed using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using OpenCV.

[0544] Data acquired by the device is sent to a central processing unit (server). The server uses machine learning algorithms to learn normal behavioral and emotional patterns and compares them with the currently acquired data. If an anomaly is detected, communication technologies such as Twilio are used to immediately notify registered contacts. The notification includes the current location information and details of the emotional state at that time.

[0545] For example, if a user stays in an unfamiliar location for an extended period and stress or anxiety is detected, the system will recognize this as an anomaly and quickly send an alert to a security company or other designated contacts. This helps those involved to take immediate action.

[0546] Examples of prompt messages include: "Design a program that monitors the user's current behavior and emotional patterns and provides immediate notifications if anomalies are detected. For example, include how to handle situations where the user deviates from their usual route or shows signs of tension or anxiety on their face."

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The device uses GPS to acquire the user's location information and captures voice and facial expression data using a microphone and camera. The input consists of location information, voice data, and facial expression data, which are collected for subsequent data processing.

[0550] Step 2:

[0551] The acquired audio data is converted into text data using the Google Cloud Speech-to-Text API on the device, and the emotional state is inferred by analyzing the tone of voice from this text data. The input is the audio data, and the output is the analyzed emotional parameters.

[0552] Step 3:

[0553] The device analyzes images acquired by its camera using OpenCV and evaluates the user's emotions from their facial expressions. This evaluation yields specific emotional patterns. The input is a facial image, and the output is emotional data determined from the facial expression.

[0554] Step 4:

[0555] The terminal transmits acquired location information and analyzed sentiment data to a central processing unit (server). The transmission is triggered periodically or when an event suspected of being abnormal is detected.

[0556] Step 5:

[0557] The server processes the received location and sentiment data using a machine learning algorithm and compares it to the user's normal behavior and sentiment patterns to detect anomalies. The input is location and sentiment data, and the output is the result of the anomaly detection.

[0558] Step 6:

[0559] If an anomaly is detected, the server will use communication methods such as Twilio to send an alert notification to registered contacts. The notification will include the current location and emotional state. This notification will allow recipients to take immediate action.

[0560] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] As shown in Figure 7, the 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.

[0566] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0567] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0568] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0569] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0570] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0571] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0572] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0573] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0574] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0575] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0576] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0577] This invention is implemented as an abnormal behavior detection system that utilizes the collection of location data by a portable location information acquisition device and data analysis by a central processing unit. The mechanism is described below in natural language.

[0578] The device collects the user's location information at regular intervals via a GPS module. This location information includes latitude and longitude, allowing for the precise identification of the user's current location. The device also collects motion information using an accelerometer and gyroscope to determine whether the user is walking or moving quickly.

[0579] The server receives location and movement information transmitted from the terminal. The generating AI within the server learns the user's usual behavior patterns based on previously accumulated data. These behavior patterns include information such as which routes the user typically takes and how fast they move.

[0580] The server compares the received current data with normal behavioral patterns. This comparison allows the server to detect anomalies if the user reaches an unexpected location, moves at an abnormal speed, or if the device itself is subjected to unnatural operations.

[0581] When an anomaly is detected, the server quickly notifies registered contacts. This notification is sent to important contacts, such as the user's parents or caregivers, via email, text message, or push notification through the application. The notification provides information about the time and location of the anomaly, allowing the contact to take immediate action.

[0582] For example, if the user is a child, and the child makes an unusual stop on their way home from school, this system can immediately detect the anomaly and notify the parent, enabling quick verification and response. In this way, the present invention improves safety.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The device uses GPS functionality to acquire the user's current location at regular intervals. The acquired latitude and longitude information is stored in memory, and preparations for transmission to the server are made as needed.

[0586] Step 2:

[0587] The device uses an accelerometer and a gyroscope to detect the user's movements. This allows it to determine whether the user is walking, running, or stationary, and record this information as movement data.

[0588] Step 3:

[0589] The device sends location and activity information it collects to the server. This transmission is performed at pre-set time intervals, ensuring data integrity and security.

[0590] Step 4:

[0591] The server analyzes the location and movement information it receives. Using a generative AI, it learns the user's normal behavior patterns from past data and compares them with current behavior data.

[0592] Step 5:

[0593] The server determines whether there is an anomaly. An anomaly is determined if there is a significant deviation from the normal behavior pattern, if the behavior exceeds a predetermined range, or if there is a sudden change in operation.

[0594] Step 6:

[0595] When the server detects an anomaly, it activates a function to notify registered contacts. The notification, including the nature and location of the anomaly, is sent via email, SMS, or a dedicated application.

[0596] Step 7:

[0597] The system reviews notifications received by the user and takes appropriate action based on their content. If necessary, the user may travel to the location, contact service users, or report to relevant authorities.

[0598] (Example 1)

[0599] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] In modern society, enhancing user safety is a crucial issue. In particular, there is a need for systems that can detect in advance when users move to unusual locations or deviate from their normal behavior patterns, and to respond quickly. However, existing systems have the problem of difficulty in analyzing user behavior in real time and effectively detecting anomalies.

[0601] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0602] In this invention, the server includes means for periodically collecting user location information using a positioning device, means for transmitting the collected location information and movement information to a central control unit, and means for the central control unit to learn the user's normal movement patterns using a generative AI model and detect deviations by comparing them with the current movement. This enables real-time monitoring of movement and detection of anomalies to enhance user safety.

[0603] A "positioning device" is a device used to collect a user's current location information in the form of latitude and longitude.

[0604] A "user" is a person or subject who uses this system and is subject to the collection of location and movement information.

[0605] "Location information" refers to geographical data such as latitude and longitude that indicates the user's current location.

[0606] "Dynamic information" refers to information that indicates the user's movement and actions, and may include acceleration data and rotation data.

[0607] The "central control unit" is a processing unit that receives and analyzes data transmitted from positioning devices.

[0608] A "generative AI model" is an algorithm that learns from a large amount of historical data and recognizes and evaluates normal behavioral patterns.

[0609] "Normal movement patterns" refer to typical behavioral characteristics of users, such as the routes they take and the speed at which they move on a daily basis.

[0610] "Deviation" refers to a state in which a user's behavior deviates from their normal behavioral patterns.

[0611] A "communication destination" is a contact point registered to receive notifications from the system.

[0612] A "press notification" is a real-time notification sent from the central control unit to a communication partner to inform them of an abnormal situation.

[0613] The present invention is a system for monitoring user behavior in real time and detecting deviations. Its embodiments are described in detail below.

[0614] The device uses a GPS module as a positioning device to collect the user's location information at regular intervals. This location information includes latitude and longitude, making it possible to accurately determine the user's current location. In addition, the device is equipped with an accelerometer and a gyroscope, which are used to collect information about the user's movements. For example, by analyzing acceleration data, it is possible to determine whether the user is walking or running.

[0615] The terminal transmits the collected location and movement information to a central control unit, which is a server. Within the server, a generative AI model is installed, which learns from previously accumulated user movement data. This AI model recognizes the user's normal movement patterns and detects deviations by comparing them to their current behavior.

[0616] The server promptly sends a notification to registered communication destinations if a user's behavior deviates from the norm. This notification is provided via electronic communication, text communication, or click notification via an application. The notification includes the time and location where the deviation occurred, allowing the recipient to take immediate action.

[0617] For example, if a user is a child and deviates from their usual route to school, the server will detect this as a deviation and notify the parent or guardian. This allows parents or guardians to quickly check on their child's safety.

[0618] An example of a prompt message might be, "Explain how you will be notified if your child uses an unfamiliar route to school."

[0619] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0620] Step 1:

[0621] The terminal uses a GPS module to obtain the user's location information. This input information includes latitude and longitude, indicating the user's current location. The terminal converts the obtained location information into a data format and prepares for the next processing step. Specifically, at this stage, the terminal acquires location information at regular intervals and stores it in buffer memory.

[0622] Step 2:

[0623] The device collects motion information using an accelerometer and a gyroscope. This information includes changes in acceleration and direction of movement. Based on the input information, the device processes the data to determine motion, such as walking, running, or standing still, and prepares to send the results to the server. Specifically, this involves acquiring sensor values ​​in real time and analyzing them to identify the user's activity state.

[0624] Step 3:

[0625] The device transmits the collected location and movement information to the server. This transmission is performed using a secure and high-speed communication protocol, and data integrity is ensured. The input includes all the data collected by the device, and the output is the completion of the data transmission to the server.

[0626] Step 4:

[0627] The server analyzes the received location and movement information. Using a generative AI model, it learns normal movement patterns based on the user's past behavior patterns. The input information consists of the latest location and movement data sent from the terminal, and the output is the deviation from the normal pattern. This process involves high-speed data processing through the AI's algorithms.

[0628] Step 5:

[0629] The server compares the current data with normal dynamic patterns to determine if there are any deviations. Here, it performs a comparison operation with the input dynamic information, and if an anomaly is detected, it generates a result. This output data includes whether or not an anomaly exists, and detailed information about it.

[0630] Step 6:

[0631] The server notifies registered communication destinations when an anomaly is detected. The input includes the anomaly detection result, and the output is a generated notification message, which is sent via email, text message, or application. The specific operation involves a process of compiling the necessary information into a notification format and sending it.

[0632] (Application Example 1)

[0633] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] Conventional anomaly detection systems using location and motion information have limitations in providing a quick and intuitive understanding of the situation when an anomaly is detected, potentially leading to delayed responses. Furthermore, the lack of clear location information in notifications meant that third parties could not immediately understand the actual location of the anomaly. Therefore, there was a need for a means to support rapid response by visually indicating the location of the anomaly.

[0635] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0636] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for transmitting the acquired location information and operation information to a central processing unit, means for the central processing unit to learn the user's normal behavior patterns and detect anomalies by comparing them with current behavior, means for notifying registered contacts when an anomaly is detected, and means for providing map information that visually indicates the location of the anomaly when an anomaly is detected. This enables rapid detection of anomalies and intuitive understanding of the location where they occur.

[0637] A "portable location information acquisition device" is a portable device used to periodically collect location coordinate data.

[0638] "Location information" refers to data that indicates a specific location using geographical coordinates, including latitude and longitude.

[0639] "Motion information" refers to data used to measure the user's movement state and speed, and to capture the characteristics of their actions.

[0640] A "central processing unit" is a centralized processing unit that receives information from external sources and performs data analysis and decision-making.

[0641] "Normal behavioral patterns" refer to the user's tendencies in daily movement and behavior.

[0642] "Means for detecting anomalies" refers to methods or techniques for identifying unexpected situations or unusual behavior.

[0643] "Registered contact information" refers to the information of recipients who have been designated in advance to receive notifications in the event of an anomaly being detected.

[0644] "Map information" refers to data used to visually display specific geographical areas or locations.

[0645] "Means of notification" refers to methods used to convey information or warnings to recipients.

[0646] This invention provides a system for acquiring user location and movement information and detecting abnormal behavior. To implement this system, the terminal uses a portable location information acquisition device to record the user's latitude and longitude at regular intervals. In addition, the terminal uses an accelerometer and gyroscope to collect user movement information. This allows for obtaining detailed behavioral data, such as whether the user is walking or moving quickly.

[0647] The server receives location and movement information transmitted from the terminal. The server is equipped with a generative AI model for advanced data analysis, which learns the user's normal behavior patterns based on previously accumulated data. This generative AI model analyzes behavioral trends and estimates the normal travel route and speed.

[0648] The server compares real-time data received with normal behavioral patterns. It identifies an anomaly if the user reaches an unexpected location or moves at an unusually fast speed. When an anomaly is detected, the server immediately sends a notification to registered emergency contacts. The notification is sent via email or push notification and includes information about the specific time and location where the anomaly occurred. This allows emergency contacts to immediately confirm the anomaly and take prompt action.

[0649] As a concrete example, consider a situation where a child deviates from their usual route home from school. This system immediately notifies parents and provides map information to help them respond quickly to the anomaly. An example of a prompt message for the generated AI model would be: "This security assistant app analyzes the user's location in real time and notifies emergency contacts if it detects unusual behavior."

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The device periodically acquires the user's current location using a portable location information acquisition device. It receives latitude and longitude data from a GPS module as input and outputs this as location information data. The device also acquires motion information using an accelerometer and gyroscope, and generates information such as speed and direction of movement.

[0653] Step 2:

[0654] The terminal transmits acquired location and movement information to the server. It takes location and movement data stored within the terminal as input and outputs it as data packets transmitted to the server via the network. To prevent data errors, an appropriate protocol is used for transmission.

[0655] Step 3:

[0656] The server analyzes the received location and motion information. It receives location and motion data transmitted from the terminal as input and derives the analysis results as output. A generative AI model is used here, which learns normal behavior patterns based on past behavior data and then performs anomaly monitoring.

[0657] Step 4:

[0658] The server detects anomalies when current behavior does not match normal behavior patterns. It compares the analyzed behavior data as input with a baseline pattern and generates an anomaly detection flag as output. Specifically, it executes algorithms to detect speeds and movement routes outside acceptable ranges.

[0659] Step 5:

[0660] The server notifies registered emergency contacts when an anomaly is detected. Based on the time and location of the anomaly as input, it sends out email and application push notifications as output. These notifications include a map link visualizing the location and are attached with information to support a quick response.

[0661] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0662] This invention is implemented as an anomaly detection system that combines a portable location information acquisition device with an emotion engine that recognizes the user's emotional state. This system coordinates the terminal, server, and emotion engine to comprehensively monitor the user's location information and emotional state.

[0663] The device uses GPS functionality to periodically acquire the user's location, and simultaneously uses a microphone and camera to capture voice and facial expressions. This allows it to acquire data related to the user's emotions, preparing it for analysis by an emotion engine.

[0664] The server not only compares location information transmitted from the terminal with normal behavioral patterns, but also receives emotional data analyzed by the emotion engine. This emotional data indicates the user's psychological state through changes in voice tone and facial expressions, and is analyzed by the emotion engine. The emotion engine uses machine learning algorithms to learn the user's normal emotional patterns and detect abnormal emotional states.

[0665] When the server detects unusual behavior or emotions, it sends a notification to pre-registered contacts. The notification states that a deviation from normal behavioral or emotional patterns has been observed, and includes specific location information and details of the emotional state. This allows recipients to respond quickly and accurately to the user's situation.

[0666] For example, if a user who is normally in a stable mood suddenly deviates from their usual range of movement and displays signs of anxiety or fear, the system will recognize this as an anomaly and the server will immediately send a notification. Parents and caregivers can then check the notification and take appropriate action to ensure the user's safety. This system enhances safety by considering psychological abnormalities in addition to physical location information.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The device uses a GPS module to periodically acquire the user's current location. It also simultaneously captures the user's voice and facial expression data using its built-in microphone and camera. This data is later used as material for analysis by an emotion engine.

[0670] Step 2:

[0671] The device transmits location information, voice data, and facial expression data acquired by the device to the server at pre-set time intervals. Encrypted communication is used to protect data integrity and privacy.

[0672] Step 3:

[0673] The server compares the received location information with the user's known normal behavior patterns. When using a generation AI to detect anomalies based on past behavior data, an alert is generated if the location information falls outside the normal range.

[0674] Step 4:

[0675] The server invokes the emotion engine to analyze voice and facial expression data. Based on this data, the emotion engine evaluates the user's emotional state and detects anomalies if there are any unusual emotional changes.

[0676] Step 5:

[0677] If the server detects an anomaly in either location data, emotion data, or both, it will notify pre-registered contacts. The notification will include the current location, emotion status, and specific details of the anomaly.

[0678] Step 6:

[0679] The user (the recipient of the notification) reviews the information received and takes necessary actions based on its content. These actions may include on-site verification by the user, direct contact with the contact person, or reporting to a specialized agency.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] This system comprehensively monitors the user's current location and emotional state, quickly detecting anomalies and notifying relevant parties. Conventional technologies often rely solely on location information for monitoring, making it difficult to detect anomalies that take emotional state changes into account. This issue needs to be addressed.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition device, means for acquiring emotional data using a device for capturing voice and facial expressions, and means for transmitting the acquired location information and emotional data to a central processing unit. This makes it possible to learn the user's normal behavior and emotional patterns, detect anomalies by comparing them with the current behavior and emotional state, and quickly notify relevant parties.

[0685] A "portable location information acquisition device" is a portable device used to periodically acquire the user's current location.

[0686] "Emotional data" refers to data about the user's psychological state obtained from their voice and facial expressions.

[0687] A "central processing unit" is a computer system that receives and analyzes acquired location information and emotional data.

[0688] "Normal behavioral patterns" refer to a dataset that shows behavioral tendencies formed based on the user's travel history and daily behavior.

[0689] "Emotional patterns" refer to a dataset that shows the user's typical psychological state, analyzed based on their voice and facial expressions.

[0690] "Abnormal" refers to a state in which the user's current behavior and emotional state differ significantly from their normal behavioral and emotional patterns.

[0691] This invention provides an anomaly detection system that comprehensively monitors the user's location information and emotional state. The main components consist of a portable location information acquisition device, an emotional data acquisition device, and a central processing unit.

[0692] The device functions as a portable location information acquisition device, periodically acquiring the user's location (latitude and longitude) using GPS. In addition, it is equipped with a microphone for capturing voice data and a camera for capturing facial expression data, collecting the user's emotional data as well.

[0693] The terminal preprocesses the data before transmitting it to the central processing unit. This preprocessing includes denoising location data and normalizing audio data. This process ensures that the data is securely transmitted to the central processing unit in an analyzable format.

[0694] The server functions as a central processing unit, analyzing received location and sentiment data. This analysis utilizes machine learning algorithms to learn the user's typical behavioral and emotional patterns, comparing them to current data to detect anomalies. To achieve this, it accumulates historical data and continuously updates the model.

[0695] For example, if a user who doesn't usually go out suddenly moves to a distant location one day and emotional patterns indicating anxiety or fear are recorded, the system will determine this to be abnormal. The server will immediately send a notification so that relevant parties can respond promptly.

[0696] Examples of prompt statements to input into a generative AI model are as follows:

[0697] "Please explain how the emotion engine detects an anomaly when a user displays an anxious expression after exceeding their normal range of movement."

[0698] In this way, the system enhances user safety and helps stakeholders take swift and appropriate action.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1:

[0701] The device periodically acquires the user's location using GPS functionality. It takes current latitude and longitude information as input and records it as location data. In addition, it captures audio and facial expression data using a microphone and camera. The output consists of location data and emotion data. Specifically, the device tracks the user's movements in their environment and monitors their behavior.

[0702] Step 2:

[0703] The device preprocesses the acquired location and sentiment data. It receives the raw data as input, removes unwanted noise from the location information, and normalizes the audio data. This process converts the data into a format suitable for analysis. The output is a clean dataset. Specifically, the device utilizes its processing power to filter the data in real time.

[0704] Step 3:

[0705] The terminal sends pre-processed data to the server. A clean dataset is used as input. As output, the data is passed to the server via a secure communication protocol. Specifically, the terminal encrypts the data and sends it to the server over the internet.

[0706] Step 4:

[0707] The server compares the received location data with the user's normal behavior patterns. The input consists of location data and pre-learned normal behavior patterns. The output is a determination of whether the current location is normal or abnormal. Specifically, the server performs analysis by referring to past behavior data stored in a database.

[0708] Step 5:

[0709] The server detects emotional changes by analyzing emotional data. The input consists of emotional data containing voice tone and facial features. The output is a comparison with normal emotional patterns. Specifically, the server applies machine learning algorithms to determine emotional abnormalities.

[0710] Step 6:

[0711] The server detects anomalies by integrating behavioral and emotional data. The input includes the output results from steps 4 and 5. The output is a determination of whether an anomaly has been detected. Specifically, the server performs a comprehensive anomaly assessment using statistical analysis methods.

[0712] Step 7:

[0713] When the server detects an anomaly, it notifies registered contacts. Input includes the anomaly detection result and its detailed information. Output is a notification sent via email or SMS. Specifically, the server activates a rapid notification sending function to share the situation with relevant parties.

[0714] (Application Example 2)

[0715] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] In modern society, ensuring individual safety is a critical issue. In particular, there is a need for safety monitoring systems that consider not only location information but also emotional states. However, conventional systems primarily focus on acquiring location information and lack the technology to detect anomalies by linking them with emotional states. Therefore, a comprehensive anomaly detection system that also considers the user's psychological state is necessary.

[0717] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0718] In this invention, the server includes means for periodically acquiring the user's location using a portable location information acquisition function, means for transmitting the acquired location information, voice data, and facial expression data to a central processing unit, and means for the central processing unit to learn the user's normal behavioral patterns and emotional patterns and detect anomalies by comparing them with the current behavior and emotions. This makes it possible to comprehensively monitor the user's location information and emotions and to quickly notify the user when an anomaly is detected.

[0719] "Portable location information acquisition function" refers to a technology incorporated into a personal, portable device that identifies and acquires the user's geographical location.

[0720] A "central processing unit" is a central computing device or system used to collect and analyze various types of information.

[0721] "Behavioral patterns" refer to the tendencies and characteristics of a series of actions that a particular user exhibits on a daily basis.

[0722] "Emotional patterns" refer to the tendencies and characteristics of emotions that a particular user exhibits on a daily basis.

[0723] "Means for detecting anomalies" refer to methods and algorithms for analyzing states that deviate from normal behavioral and emotional patterns.

[0724] "Means of notification" refers to methods or technologies for transmitting relevant information to designated contacts based on detected anomalies.

[0725] The system implementing this invention consists primarily of a portable device (terminal), a central processing unit (server), and an emotion analysis engine. The terminal uses GPS to periodically acquire the user's location information and is equipped with a microphone and camera to simultaneously capture voice and facial expression data. The voice data is analyzed using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using OpenCV.

[0726] Data acquired by the device is sent to a central processing unit (server). The server uses machine learning algorithms to learn normal behavioral and emotional patterns and compares them with the currently acquired data. If an anomaly is detected, communication technologies such as Twilio are used to immediately notify registered contacts. The notification includes the current location information and details of the emotional state at that time.

[0727] For example, if a user stays in an unfamiliar location for an extended period and stress or anxiety is detected, the system will recognize this as an anomaly and quickly send an alert to a security company or other designated contacts. This helps those involved to take immediate action.

[0728] Examples of prompt messages include: "Design a program that monitors the user's current behavior and emotional patterns and provides immediate notifications if anomalies are detected. For example, include how to handle situations where the user deviates from their usual route or shows signs of tension or anxiety on their face."

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The device uses GPS to acquire the user's location information and captures voice and facial expression data using a microphone and camera. The input consists of location information, voice data, and facial expression data, which are collected for subsequent data processing.

[0732] Step 2:

[0733] The acquired audio data is converted into text data using the Google Cloud Speech-to-Text API on the device, and the emotional state is inferred by analyzing the tone of voice from this text data. The input is the audio data, and the output is the analyzed emotional parameters.

[0734] Step 3:

[0735] The device analyzes images acquired by its camera using OpenCV and evaluates the user's emotions from their facial expressions. This evaluation yields specific emotional patterns. The input is a facial image, and the output is emotional data determined from the facial expression.

[0736] Step 4:

[0737] The terminal transmits acquired location information and analyzed sentiment data to a central processing unit (server). The transmission is triggered periodically or when an event suspected of being abnormal is detected.

[0738] Step 5:

[0739] The server processes the received location and sentiment data using a machine learning algorithm and compares it to the user's normal behavior and sentiment patterns to detect anomalies. The input is location and sentiment data, and the output is the result of the anomaly detection.

[0740] Step 6:

[0741] If an anomaly is detected, the server will use communication methods such as Twilio to send an alert notification to registered contacts. The notification will include the current location and emotional state. This notification will allow recipients to take immediate action.

[0742] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0748] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0749] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0750] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0755] 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.

[0756] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0757] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0758] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0759] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0760] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0761] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means for periodically acquiring the user's location using a portable location information acquisition device,

[0766] Means for transmitting acquired location information and operation information to a central processing unit,

[0767] In the central processing unit, means for detecting anomalies by learning the user's normal behavior patterns and comparing them with the current behavior,

[0768] A means of notifying registered contacts if an anomaly is detected,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the detection of an anomaly includes movement outside the user's pre-set range of movement, a sudden change from a known behavioral pattern, or unintended operation of the terminal.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the notification means provides information via email, text message, or push notification via application.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A means of periodically collecting the user's location information using a positioning device,

[0777] Means for transmitting collected location information and dynamic information to a central control unit,

[0778] In the central control unit, a means for detecting deviations by learning the user's normal movement patterns using a generative AI model and comparing them with the current movement,

[0779] A means of notifying registered communication destinations when a deviation is detected,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein deviation detection includes movement outside a pre-set range of movement by the user, a sudden change from a known movement pattern, or unintended operation of the terminal.

[0783] (Claim 3)

[0784] The system according to claim 1, wherein the notification means provides information via electronic communication, text communication, or application-based press notification.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] A means for periodically acquiring the user's location using a portable location information acquisition device,

[0788] Means for transmitting acquired location information and operation information to a central processing unit,

[0789] In the central processing unit, means for detecting anomalies by learning the user's normal behavior patterns and comparing them with the current behavior,

[0790] A means of notifying registered contacts if an anomaly is detected,

[0791] A means for providing map information that visually indicates the location of the anomaly when an anomaly is detected,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the detection of an anomaly includes movement outside the user's pre-set range of movement, a sudden change from a known behavioral pattern, or unintended operation of the terminal.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the notification means provides information via email, text message, or push notification via an application, and further provides visual information through a map function.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A means for periodically acquiring the user's location using a portable location information acquisition device,

[0800] A means for acquiring emotional data using a device for capturing voice and facial expressions,

[0801] Means for transmitting acquired location information and emotional data to a central processing unit,

[0802] In the central processing unit, means for detecting anomalies by learning the user's normal behavior and emotional patterns and comparing them with the current behavior and emotional state,

[0803] A means of notifying registered contacts if an anomaly is detected,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the detection of an anomaly includes movement outside the user's pre-set range of movement, a sudden change in known behavioral and emotional patterns, or unintended operation of the terminal.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the notification means provides information via email, text message, or push notification via application.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A means of periodically acquiring the user's location using a portable location information acquisition function,

[0812] Means for transmitting acquired location information, voice data, and facial expression data to a central processing unit,

[0813] A central processing unit has means for detecting anomalies by learning the user's normal behavioral and emotional patterns and comparing them with the current behavior and emotions.

[0814] A means of notifying registered contacts, including details of their current location and emotional state, when an anomaly is detected.

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the detection of an anomaly includes the user moving outside a pre-set movement area, a sudden change in existing behavioral and emotional patterns, or unintended operation of the terminal.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the notification means provides information via digital communication means, text messages, or push notifications by application software. [Explanation of symbols]

[0820] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for periodically acquiring the user's location using a portable location information acquisition device, Means for transmitting acquired location information and operation information to a central processing unit, In the central processing unit, means for detecting anomalies by learning the user's normal behavior patterns and comparing them with the current behavior, A means of notifying registered contacts if an anomaly is detected, A system that includes this.

2. The system according to claim 1, wherein the detection of an anomaly includes movement outside the user's pre-set range of movement, a sudden change from a known behavioral pattern, or unintended operation of the terminal.

3. The system according to claim 1, wherein the notification means provides information via email, text message, or push notification by application.

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