system

Abstract

We provide the system. [Solution] A sensor device for acquiring location information, A communication device for transmitting the aforementioned location information, A data processing means for analyzing the received location information and detecting deviations from normal behavioral patterns, A notification means for notifying pre-registered contacts when such deviation 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to monitor an individual's safety and the location information of important objects (such as elderly people with dementia or pets) and to suppress crimes and prevent people from going missing, a system that can immediately detect movements deviating from normal behavior patterns and notify relevant persons is essential. However, current monitoring systems are often difficult to immediately grasp abnormal behavior and lack real-time performance and accuracy. Therefore, it is required to solve these problems.

Means for Solving the Problems

[0005] To solve the above-mentioned problems, the present invention provides a system comprising a sensor device for acquiring location information, a communication device for transmitting location information, a data processing means for analyzing the location information to detect deviations from normal behavioral patterns, and a notification means for notifying pre-registered contacts when a deviation is detected. This system learns normal behavioral patterns based on past behavioral data and can detect anomalies in real time. Furthermore, the notification means generates a message including the type of anomaly and current location information, thereby providing timely and accurate information to relevant parties.

[0006] A "sensor device" is a device used to acquire location information, and includes GPS sensors.

[0007] A "communication device" is a device used to transmit location information acquired by a sensor device to an external source.

[0008] "Data processing means" refers to means for analyzing received location information and detecting deviations from normal behavioral patterns.

[0009] A "notification method" is a means of transmitting information to pre-registered contacts when an anomaly is detected.

[0010] A "behavioral pattern" is a collection of data that shows the tendencies of movement and activities that a subject typically engages in.

[0011] "Deviation" refers to behavior that is significantly different from normal behavioral patterns.

[0012] "Contact information" refers to the information of individuals or organizations that have been registered in the system in advance to receive notifications.

[0013] "Location information" refers to data that indicates the current location of an object, including its geographical coordinates. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] 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 applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for acquiring location information of individuals or pets and providing notifications when they deviate from their normal behavioral patterns. The following describes embodiments for carrying out this invention.

[0036] First, a sensor device attached to the terminal periodically acquires the target's location information using GPS. The acquired location information is transmitted to the server via the terminal's communication device. The server receives this data and stores it in a database.

[0037] The server's data processing mechanism compares previously accumulated behavioral data with the latest location information to learn normal behavioral patterns. It uses generative AI to perform pattern analysis and evaluate deviations from predicted movements. If a deviation from normal behavioral patterns is detected, the server uses notification mechanisms to send information to pre-registered contacts.

[0038] The notification system generates a detailed message including the type of anomaly and the user's current location. This message is delivered to the user via email or phone notification. The user receives this notification and can quickly respond to the anomaly.

[0039] As a concrete example, consider a scenario where a dementia patient is being monitored. If a patient carrying a device leaves their usual living area and begins moving towards an unknown location, the GPS immediately recognizes this and sends the location information to a server. When the server detects behavior that deviates from normal movements, a notification is sent to registered family members or caregivers to alert them of the abnormality. This system allows users to intervene in real time.

[0040] By implementing this invention, it is possible to provide a system that ensures the safety of the subject and is useful in situations where a quick response is required.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device uses a GPS sensor to acquire its current location information at regular intervals. The acquired location information is immediately stored within the device.

[0044] Step 2:

[0045] The device transmits the stored location information to the server via a communication device. This process is performed in real time and is repeated until the data transmission is complete.

[0046] Step 3:

[0047] The server receives location information transmitted from the terminal and stores it in the database. When the data is stored, it is standardized according to the time axis, taking into account its relationship with past data.

[0048] Step 4:

[0049] The server's data processing system activates a generative AI using the stored location information. The generative AI begins to learn normal behavioral patterns and detects anomalies by comparing them with past data.

[0050] Step 5:

[0051] The generating AI analyzes whether the current location information deviates from normal behavioral patterns and determines it to be abnormal if it exceeds a certain threshold.

[0052] Step 6:

[0053] If the server detects an anomaly, it activates a notification system and generates a notification message to pre-registered contacts. This message includes the type of anomaly and the current location information.

[0054] Step 7:

[0055] The server sends notifications to users via email or telephone. This allows users to check for anomalies in real time and take appropriate action.

[0056] (Example 1)

[0057] 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."

[0058] Conventional personal and pet location monitoring systems have problems in appropriately and quickly detecting deviations from normal behavioral patterns and responding immediately to unintended crises. In particular, they were unable to accurately predict abnormal movement or behavioral patterns and notify of anomalies, so there was a need for more effective monitoring and notification methods.

[0059] 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.

[0060] In this invention, the server includes a device for detecting location, information processing means for analyzing the received location, and means for using a generated model to monitor the movement path. This makes it possible to immediately detect deviations from the normal behavioral patterns of individuals or pets and to quickly notify when an anomaly is detected.

[0061] A "location detection device" is a device used to accurately identify the current location of an individual or pet and to acquire that location information.

[0062] A "device for transmitting location" is a device that transmits acquired location information to a server or other receiving device using communication means.

[0063] "Information processing means" refers to a device that includes an electronic or computational process for analyzing received location information, learning normal behavior patterns by comparing them with past behavior data, and detecting deviations.

[0064] A "notification means" is a device or system that has the function of sending a notification to a pre-set destination when an abnormality is detected.

[0065] A "model generated to monitor movement paths" is an analytical model that uses the target's normal movement paths, learned by a generating AI, to monitor behavioral patterns in real time.

[0066] This invention is designed as a location information monitoring system, and in particular has the function of monitoring the location information of individuals and pets in real time, detecting abnormal behavioral patterns, and promptly notifying users.

[0067] System Overview

[0068] 1. The role of the terminal

[0069] The device has a built-in GPS sensor that periodically acquires location information for individuals and pets. This location information includes latitude, longitude, and time of acquisition.

[0070] This location information is transmitted to the server via the device's communication equipment. Wi-Fi and cellular networks are used for communication.

[0071] 2. Server Role

[0072] The server immediately stores the received location information in a database and compares it with past behavioral patterns. This allows it to detect deviations from normal behavior.

[0073] The server uses a generated AI model to analyze these behavioral patterns. This allows for the rapid detection of any movements that deviate from normal behavior.

[0074] 3. Means of notification

[0075] The notification system will send a detailed notification message to registered contacts as soon as an anomaly is detected. The message will include the type of anomaly and the current location information.

[0076] Technology used

[0077] Hardware: GPS sensor device, communication device.

[0078] Software: An analysis system for utilizing generative AI models.

[0079] Specific example

[0080] As a concrete example, consider monitoring the areas that elderly people with dementia move around in on a daily basis. If they move outside their usual living environment, this system can immediately detect the anomaly and notify family members or caregivers. This allows for the prevention of unforeseen incidents and enables a swift response.

[0081] Example of a prompt

[0082] Prompt messages such as "Please tell me specifically how to be notified when abnormal behavior is detected" can be input into the AI ​​model to collect data for improving behavioral pattern analysis.

[0083] In this way, location-based monitoring systems enhance the safety of individuals and pets, while also enabling rapid crisis response.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The device uses a GPS sensor to acquire location information for individuals and pets. This location information includes latitude and longitude, and is recorded along with the time of acquisition. The input is location coordinates and time information, and the output is a location data set containing this data. The device periodically generates new data from this set.

[0087] Step 2:

[0088] The terminal transmits the acquired location information to the server via a communication device. Encryption technology is used for this transmission for security purposes. The input is the previously acquired location information dataset, and the output is a confirmation of data arrival at the server, including a transmission log. After sending the data, the terminal receives a response to confirm the completion of the transmission.

[0089] Step 3:

[0090] The server stores the received location information in a database. The input is location information transferred from the terminal, and the output is the update of the record in the database. The server performs the operation of accumulating the latest information in the appropriate format while comparing it with existing data.

[0091] Step 4:

[0092] The server uses a generated AI model to compare the received location information with historical data and analyzes normal behavior patterns. The input is stored historical behavior data and the latest location information, and the output is the analysis result determining whether or not there is a deviation. The server performs pattern analysis using AI and carries out the process of detecting outliers.

[0093] Step 5:

[0094] The server, upon detecting an anomaly, uses a notification mechanism to send a notification message to pre-registered contacts. The input is the analysis result that identified the anomaly, and the output is the content to be sent as a notification message. The server generates the message content and transmits the information via the appropriate communication method (email or telephone).

[0095] Step 6:

[0096] The user receives notifications from the server and makes situational judgments and takes emergency actions as needed. Input is notification messages from the server, and output is the user's confirmation and response actions. The user receives the message and takes action based on the information provided to make the best decision.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern society, monitoring the elderly and ensuring the safety of pets are crucial issues. However, systems that can detect deviations from an individual's usual range of activity in real time and provide rapid notification are still lacking. To address this challenge, a mechanism is needed that can instantly detect deviations from normal behavior patterns and notify relevant parties.

[0100] 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.

[0101] In this invention, the server includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for analyzing the received location information and detecting deviations from normal behavior patterns. This ensures personal safety and enables a rapid response when a person deviates from their normal range of activity.

[0102] A "detection device for acquiring location information" is a device that uses GPS or other location information technologies to identify the current location of an object and acquire it as data.

[0103] A "communication device for transmitting location information" is a communication device that has the function of transmitting acquired location information to a server or other device.

[0104] "Information processing means for detecting deviations from normal behavior patterns" refers to a device or system that analyzes the differences between normal behavior patterns learned based on past behavioral data and performs processing to determine deviations.

[0105] A "notification means" is a device or means that has the function of notifying registered contacts when an abnormality is detected.

[0106] A "learning model" is an algorithm or system that automatically learns behavioral patterns based on data analysis and predicts future behavior.

[0107] "A means of communication for generating and sending notification messages" refers to a system that, when an anomaly is detected, automatically generates a message containing detailed information and communicates it to the relevant parties.

[0108] The present invention is a system that periodically acquires location information of individuals or pets and promptly notifies when a deviation from normal behavior is detected. This system includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for detecting deviations from normal behavior.

[0109] The server uses the GPS built into the smartphone as a detection device to periodically acquire the current location of individuals and pets. The smartphone, acting as a communication device, sends location information to the cloud server using an HTTP API. In this case, it is recommended to use the SSL / TLS protocol to ensure the reliability of the communication.

[0110] The server uses a generative AI model to analyze accumulated historical location data and learn normal behavior patterns. Machine learning libraries such as TENSORFLOW® are often used for this purpose. The AI ​​model improves the accuracy of predicting target behavior through continuous learning and updating of behavioral patterns.

[0111] If a deviation from the user's predicted behavior is detected, notifications will be sent via SMS or email to registered family members and related parties. Digital communication methods, such as those implemented through the Twilio API, will be used for notifications. An automatically generated notification message will be sent based on the type of anomaly and the user's current location.

[0112] As a concrete example, consider a scenario where an individual with dementia deviates from their usual range of activity. This system allows family members to receive real-time notifications of the anomaly and confirm that the individual is safe.

[0113] Examples of prompt statements are as follows:

[0114] "Generate behavioral patterns based on the following location data: Location data. Also, describe the process for evaluating whether the location deviates from the normal range and for notifying if a deviation is detected."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The device uses its built-in GPS sensor to obtain the location information of the individual or pet. This location information is output as latitude and longitude data.

[0118] Step 2:

[0119] The device transmits the acquired location information to the server via a communication device. Here, the HTTP API is used, and the data is protected by the SSL / TLS protocol. The input is location data, and the purpose is to securely reach the server.

[0120] Step 3:

[0121] The server stores the received location information in a database along with past data. The data is stored with the target ID and timestamp, thereby forming a temporal history of the user's movements.

[0122] Step 4:

[0123] The server learns normal behavior patterns using a generative AI model based on accumulated data. The input here is past location history, and the output is a statistically predicted model of normal behavior areas and their movements.

[0124] Step 5:

[0125] The server compares the location information received in real time with learned behavioral patterns. This process involves data calculations to evaluate the likelihood of deviation, and as a result, an output is generated indicating whether or not it deviates from the normal pattern.

[0126] Step 6:

[0127] If a deviation is detected, the server generates a notification message through a notification mechanism. The generated message includes the type of anomaly and the current location information, which is used for notification.

[0128] Step 7:

[0129] The server uses communication methods to send the generated notification message to registered family members and related contacts. This allows users to receive real-time notifications via SMS or email.

[0130] 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.

[0131] This invention is a system that combines location-based behavioral monitoring with emotional state recognition using an emotion engine to ensure user safety. A specific implementation of this system is described below.

[0132] First, the device periodically acquires the user's location information using a GPS sensor. This location information is transmitted to the server by the device's communication device. Upon receiving the location data, the server begins analysis using data processing equipment and detects deviations from normal behavior patterns.

[0133] In parallel, the device is equipped with an emotion engine to analyze the user's voice and facial expressions. The emotion engine analyzes the user's voice tone and changes in facial expressions to recognize their current emotional state. The emotional data is sent to a server separately from location information and integrated into the analysis of the data processing system.

[0134] When the server detects deviations from location or sentiment data, the system sends a notification to pre-registered contacts via a notification mechanism. This notification includes not only behavioral anomalies but also the user's emotional state, allowing stakeholders to understand the situation in more detail.

[0135] For example, if a user deviates from their usual range of activity and feelings of anxiety or confusion are detected, the system will generate a notification message such as, "The user has deviated from their usual range of activity and may be emotionally unstable." The user's family and related parties can then use this information to take prompt action.

[0136] By implementing this invention, comprehensive monitoring that takes into account both physical behavior and emotional state becomes possible, thereby enhancing user safety.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The device activates its GPS sensor to periodically acquire the user's current location. The acquired location information is temporarily stored within the device.

[0140] Step 2:

[0141] The device uses a built-in emotion engine to analyze the user's voice and facial expressions to recognize their emotional state. During this process, sensors detect changes in voice tone and facial expressions and convert them into data.

[0142] Step 3:

[0143] The device organizes location information and sentiment data and sends it to the server via a communication device. This transmission occurs in real time, and the data arrives at the server as a continuous stream.

[0144] Step 4:

[0145] The server receives location and emotion data from the terminal and records it in a database. At this time, the data is standardized along with time information and stored in a way that facilitates analysis.

[0146] Step 5:

[0147] The server activates the data processing system and analyzes the received data. It compares normal behavioral patterns with emotional patterns using a generating AI and detects anomalies that do not match past data. This analysis is performed in real time.

[0148] Step 6:

[0149] After detecting an anomaly, the server prepares to notify pre-configured contacts. The system generates a notification message that details the nature of the anomaly, its location, and emotional state.

[0150] Step 7:

[0151] The server sends the generated notification to the user's family or related parties via email or voice call. This allows related parties to understand the user's unusual situation and consider necessary actions.

[0152] (Example 2)

[0153] 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".

[0154] Current monitoring systems rely solely on user location data and do not take into account their emotional state, making it difficult to effectively ensure true user safety. It is necessary to go beyond simply detecting location deviations and integrate analysis of the user's emotional state to enable more accurate anomaly detection and faster response.

[0155] 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.

[0156] In this invention, the server includes a transmission means for transmitting location information, an emotion analysis means for acquiring and analyzing the user's voice and facial expressions, and an information processing means for analyzing the received location information and emotion information and detecting deviations from normal behavior and emotion patterns. This makes it possible to comprehensively monitor the user's physical movements and emotional state, enabling more accurate anomaly detection and appropriate notification.

[0157] A "positioning device" is a device used to determine a geographical location, and typically uses satellites to obtain latitude and longitude data.

[0158] "Transmission means" refers to a device or method for transmitting data, typically using wireless communication technology to send location information and sentiment data to a server.

[0159] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's voice and facial expressions and evaluating their psychological state.

[0160] "Information processing means" refers to a device or system that includes algorithms or programs for analyzing received location information and emotional information to detect deviations from normal behavioral and emotional patterns.

[0161] A "notification method" is a mechanism for notifying pre-registered contacts of detected anomalies, and can use email or messaging services.

[0162] This invention describes a system that comprehensively ensures user safety by monitoring the user's location information and emotional state. Specifically, it includes a positioning device for acquiring location information, a transmission means for transmitting location information and emotional information, an emotion analysis means for analyzing the user's voice and facial expressions, and an information processing means for processing this data and detecting anomalies.

[0163] Hardware and software

[0164] The server uses programming languages ​​such as Python and R to analyze received location and emotion data, and utilizes database management systems such as MySQL® for data storage and processing. The terminal is equipped with a GPS sensor for acquiring location information, and a microphone and camera for analyzing voice and facial expressions. Machine learning frameworks such as TensorFlow and OpenCV are used for the emotion engine.

[0165] Specific example

[0166] For example, if a user moves beyond their usual range of activity and simultaneously, emotion analysis detects feelings of anxiety, the system will send a notification to registered contacts stating, "The user has moved outside their usual range of activity and may be in an unstable emotional state." This notification will be delivered quickly via email or messaging services.

[0167] Example of a prompt

[0168] "Design a system that evaluates safety based on the user's location and emotional state. Location information will be obtained from GPS, and emotional state will be recognized by analyzing voice tone and facial expression changes."

[0169] This system allows for real-time monitoring of both the user's physical behavior and emotional state, thereby improving safety.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device periodically acquires the user's location information using a GPS sensor. It takes the current geographical location as input and generates latitude and longitude data as output. This data is temporarily stored within the device and also prepared for transmission to the server. Specifically, location information is requested at regular intervals, and the sensor data is read.

[0173] Step 2:

[0174] The device uses its built-in microphone and camera to collect the user's voice and facial expressions. The input is the user's real-time voice and video, and the output is digital audio and video data used by the emotion engine for analysis. Specifically, the audio is recorded in PCM format, and the video is captured at regular intervals.

[0175] Step 3:

[0176] The device transmits location information and sentiment data to the server using a communication module. The input consists of acquired location information and sentiment data, while the output is the transmission of this data to the server. During this process, the data is encrypted for secure transmission.

[0177] Step 4:

[0178] The server stores the received location information in a database and performs analysis by applying a data processing algorithm. The input is location information transmitted from the terminal, and the output is a result indicating whether a deviation from the normal behavior pattern was detected. Specifically, the location information is stored as time-series data and compared with past patterns.

[0179] Step 5:

[0180] The server receives emotion data and executes an emotion analysis algorithm based on the extracted features. The input is user voice and video data, and the output is a recognition of the user's emotional state. This process involves analysis based on machine learning models.

[0181] Step 6:

[0182] The server integrates location data and sentiment data to perform comprehensive anomaly detection. Inputs include detection results for deviations from location information and changes in sentiment state, while output is a judgment on the presence or absence of anomalies. Specifically, it evaluates the correlation between the two datasets and scores the anomaly level.

[0183] Step 7:

[0184] When the server detects an anomaly, it generates and sends a notification message to registered contacts. Inputs include the type of anomaly, the user's current location, and their emotional state. The output is a comprehensive notification message sent via email or SMS. The specific operation involves assembling a message based on a template and sending it over the communication network.

[0185] (Application Example 2)

[0186] 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".

[0187] There is a need for a monitoring system that can help elderly people and dementia patients live safely in their daily lives by accurately detecting deviations from normal movement patterns and emotional instability, and responding quickly to prevent unexpected accidents and confusion. Conventional technologies only monitor location information and cannot comprehensively assess the user's emotional state, which poses a challenge in ensuring safety.

[0188] 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.

[0189] In this invention, the server includes a detection device for acquiring location information, an emotion analysis means for analyzing the user's voice and facial expressions to recognize their emotional state, and a notification means for notifying pre-registered contacts when the deviation or emotional state is recognized as abnormal. This makes it possible to quickly and accurately detect deviations from the usual movement patterns or abnormal emotional states of elderly people and dementia patients, and to provide necessary support in a timely manner.

[0190] A "detection device" is a device used to acquire location information, and is primarily a device that includes GPS functionality.

[0191] An "information and communication device" is a device that has a communication function for transmitting acquired location information to a server.

[0192] "Information processing means" refers to a mechanism that analyzes received location information and detects deviations from pre-recorded normal movement patterns.

[0193] An "emotion analysis device" is a device that analyzes the user's voice and facial expressions to recognize their emotional state in real time.

[0194] A "notification method" is a means of sending an alert to pre-registered contacts when deviations or abnormal emotional states are detected.

[0195] "Usual movement patterns" refer to a collection of data that shows the user's usual range of activity and routes.

[0196] "Registered contact information" refers to recipient information that has been recorded in the system in advance for the purpose of notifying users in the event of an anomaly.

[0197] "Classification of abnormalities" refers to criteria used to identify and classify deviations from normal behavior and emotional states.

[0198] This invention is a monitoring system aimed at protecting the safety of the elderly and people with dementia. It is a system that provides multifaceted support to users through the interplay of servers, terminals such as smartphones, and acquired data.

[0199] The server receives data transmitted from location-acquiring detection devices in order to analyze location information in real time. This data is used for information processing to detect deviations from normal movement patterns. Specifically, the server uses the Google® Maps API to analyze location information and determine whether the user has left their usual range of activity.

[0200] The device also features an emotion analysis system that analyzes the user's voice and facial expressions. This function collects the user's voice tone and changes in facial expressions through the microphone and camera, and uses emotion analysis libraries such as Microsoft® Emotion API to recognize their real-world emotional state. The emotion data, along with location data, is sent to a server for comprehensive analysis.

[0201] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts using a notification system. This notification will include the user's current location information along with an analysis of their emotional state.

[0202] For example, if a 77-year-old grandmother gets lost in an unfamiliar place and feels anxious, the system will quickly inform her family using a prompt message that says, "Based on your current behavior patterns and emotional state, we will generate a monitoring notification." This allows the family to respond quickly.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device periodically acquires the user's location information using its GPS function. This location information is collected as latitude and longitude data. This becomes the input data. The device transmits this data to the server via an information and communication device. The output is the transmission of location information.

[0206] Step 2:

[0207] The server analyzes the received location information using the Google Maps API. The input is the location data sent in step 1. The server compares this data with past, typical movement patterns to determine if there is a deviation. The output indicates whether or not there is a deviation from the normal pattern.

[0208] Step 3:

[0209] The device uses its built-in microphone and camera to acquire user voice and facial expression data. The input consists of voice and video data. The device inputs this information into an emotion analysis library, such as the Microsoft Emotion API, to analyze the user's emotional state. The output is the user's emotional state data.

[0210] Step 4:

[0211] The server integrates the deviation information from step 2 and the emotional state data from step 3. The inputs are the presence or absence of deviations from normal movement patterns and emotional state data. The server analyzes this data and, if an anomaly is detected, makes an overall judgment based on that information. The output is the result of the anomaly detection.

[0212] Step 5:

[0213] If an anomaly is detected, the server generates and sends a specific notification message to pre-registered contacts. The input is the result of the anomaly detection in step 4, and the output is the sent notification message. The notification uses a generation AI model to add a detailed explanation using the prompt phrase "Generate a monitoring notification based on current behavioral patterns and emotional state."

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention is a system for acquiring location information of individuals or pets and providing notifications when they deviate from their normal behavioral patterns. The following describes embodiments for carrying out this invention.

[0231] First, a sensor device attached to the terminal periodically acquires the target's location information using GPS. The acquired location information is transmitted to the server via the terminal's communication device. The server receives this data and stores it in a database.

[0232] The server's data processing mechanism compares previously accumulated behavioral data with the latest location information to learn normal behavioral patterns. It uses generative AI to perform pattern analysis and evaluate deviations from predicted movements. If a deviation from normal behavioral patterns is detected, the server uses notification mechanisms to send information to pre-registered contacts.

[0233] The notification system generates a detailed message including the type of anomaly and the user's current location. This message is delivered to the user via email or phone notification. The user receives this notification and can quickly respond to the anomaly.

[0234] As a concrete example, consider a scenario where a dementia patient is being monitored. If a patient carrying a device leaves their usual living area and begins moving towards an unknown location, the GPS immediately recognizes this and sends the location information to a server. When the server detects behavior that deviates from normal movements, a notification is sent to registered family members or caregivers to alert them of the abnormality. This system allows users to intervene in real time.

[0235] By implementing this invention, it is possible to provide a system that ensures the safety of the subject and is useful in situations where a quick response is required.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The device uses a GPS sensor to acquire its current location information at regular intervals. The acquired location information is immediately stored within the device.

[0239] Step 2:

[0240] The device transmits the stored location information to the server via a communication device. This process is performed in real time and is repeated until the data transmission is complete.

[0241] Step 3:

[0242] The server receives location information transmitted from the terminal and stores it in the database. When the data is stored, it is standardized according to the time axis, taking into account its relationship with past data.

[0243] Step 4:

[0244] The server's data processing system activates a generative AI using the stored location information. The generative AI begins to learn normal behavioral patterns and detects anomalies by comparing them with past data.

[0245] Step 5:

[0246] The generating AI analyzes whether the current location information deviates from normal behavioral patterns and determines it to be abnormal if it exceeds a certain threshold.

[0247] Step 6:

[0248] If the server detects an anomaly, it activates a notification system and generates a notification message to pre-registered contacts. This message includes the type of anomaly and the current location information.

[0249] Step 7:

[0250] The server sends notifications to users via email or telephone. This allows users to check for anomalies in real time and take appropriate action.

[0251] (Example 1)

[0252] 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."

[0253] Conventional personal and pet location monitoring systems have problems in appropriately and quickly detecting deviations from normal behavioral patterns and responding immediately to unintended crises. In particular, they were unable to accurately predict abnormal movement or behavioral patterns and notify of anomalies, so there was a need for more effective monitoring and notification methods.

[0254] 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.

[0255] In this invention, the server includes a device for detecting location, information processing means for analyzing the received location, and means for using a generated model to monitor the movement path. This makes it possible to immediately detect deviations from the normal behavioral patterns of individuals or pets and to quickly notify when an anomaly is detected.

[0256] A "location detection device" is a device used to accurately identify the current location of an individual or pet and to acquire that location information.

[0257] A "device for transmitting location" is a device that transmits acquired location information to a server or other receiving device using communication means.

[0258] "Information processing means" refers to a device that includes an electronic or computational process for analyzing received location information, learning normal behavior patterns by comparing them with past behavior data, and detecting deviations.

[0259] A "notification means" is a device or system that has the function of sending a notification to a pre-set destination when an abnormality is detected.

[0260] A "model generated to monitor movement paths" is an analytical model that uses the target's normal movement paths, learned by a generating AI, to monitor behavioral patterns in real time.

[0261] This invention is designed as a location information monitoring system, and in particular has the function of monitoring the location information of individuals and pets in real time, detecting abnormal behavioral patterns, and promptly notifying users.

[0262] System Overview

[0263] 1. The role of the terminal

[0264] The device has a built-in GPS sensor that periodically acquires location information for individuals and pets. This location information includes latitude, longitude, and time of acquisition.

[0265] This location information is transmitted to the server via the device's communication equipment. Wi-Fi and cellular networks are used for communication.

[0266] 2. Server Role

[0267] The server immediately stores the received location information in a database and compares it with past behavioral patterns. This allows it to detect deviations from normal behavior.

[0268] The server uses a generated AI model to analyze these behavioral patterns. This allows for the rapid detection of any movements that deviate from normal behavior.

[0269] 3. Means of notification

[0270] The notification system will send a detailed notification message to registered contacts as soon as an anomaly is detected. The message will include the type of anomaly and the current location information.

[0271] Technology used

[0272] Hardware: GPS sensor device, communication device.

[0273] Software: An analysis system for utilizing generative AI models.

[0274] Specific example

[0275] As a specific example, consider the case of monitoring the area where elderly dementia patients move daily. When they move beyond the normal living environment, this system can immediately detect abnormalities and notify family members or caregivers. This makes it possible to prevent unexpected situations and take prompt action.

[0276] Examples of prompt sentences

[0277] By inputting prompt sentences such as "Please specifically explain the notification method when abnormal behavior is detected." into the generative AI model, data can be collected to improve behavior pattern analysis.

[0278] In this way, the location information monitoring system enhances the safety of individuals and pets and enables prompt crisis response.

[0279] The flow of the specific process in Example 1 will be described using FIG. 11.

[0280] Step 1:

[0281] The terminal uses a GPS sensor to obtain the location information of an individual or a pet. This location information includes latitude and longitude and is recorded together with the acquisition time. The input is the location coordinates and time information, and the output is a location information dataset containing those data. The terminal periodically generates this as new data.

[0282] Step 2:

[0283] The terminal transmits the acquired location information to the server via a communication device. For this transmission, encryption technology is used considering security. The input is the previously acquired location information dataset, and the output is the confirmation of data arrival at the server including the transmission log. The terminal performs the operation of receiving a response for confirming transmission completion after data transmission.

[0284] Step 3:

[0285] The server stores the received location information in the database. The input is the location information transferred from the terminal, and the output is the update of the record in the database. The server performs the operation of accumulating the latest information in an appropriate format while comparing with the existing data.

[0286] Step 4:

[0287] The server compares the received location information with the past data using the generative AI model and analyzes the normal behavior pattern. The input is the stored past behavior data and the latest location information, and the output is the analysis result for determining the presence or absence of deviation. The server performs pattern analysis by AI and conducts the detection process of abnormal values.

[0288] Step 5:

[0289] When an abnormality is detected, the server uses the notification means to send a notification message to the pre-registered contact. The input is the analysis result determined to be abnormal, and the output is the content sent as the notification message. The server generates the message content and performs the operation of transmitting the information by the required communication means (such as email or phone).

[0290] Step 6:

[0291] The user receives the notification from the server and makes a situation judgment and emergency response as necessary. The input is the notification message from the server, and the output is the user's confirmation and response measures. The user accepts the message and takes the action of making an optimal judgment based on the provided information.

[0292] (Application Example 1)

[0293] Next, Application Example 1 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".

[0294] In modern society, monitoring the elderly and ensuring the safety of pets are crucial issues. However, systems that can detect deviations from an individual's usual range of activity in real time and provide rapid notification are still lacking. To address this challenge, a mechanism is needed that can instantly detect deviations from normal behavior patterns and notify relevant parties.

[0295] 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.

[0296] In this invention, the server includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for analyzing the received location information and detecting deviations from normal behavior patterns. This ensures personal safety and enables a rapid response when a person deviates from their normal range of activity.

[0297] A "detection device for acquiring location information" is a device that uses GPS or other location information technologies to identify the current location of an object and acquire it as data.

[0298] A "communication device for transmitting location information" is a communication device that has the function of transmitting acquired location information to a server or other device.

[0299] "Information processing means for detecting deviations from normal behavior patterns" refers to a device or system that analyzes the differences between normal behavior patterns learned based on past behavioral data and performs processing to determine deviations.

[0300] A "notification means" is a device or means that has the function of notifying registered contacts when an abnormality is detected.

[0301] A "learning model" is an algorithm or system that automatically learns behavioral patterns based on data analysis and predicts future behavior.

[0302] "Communication means for generating and transmitting notification messages" refers to a system that automatically generates a message containing detailed information and transmits it to relevant parties when an abnormality is detected.

[0303] The present invention is a system that periodically acquires the location information of individuals and pets and quickly notifies when a deviation from the normal behavior pattern is detected. This system includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for detecting a deviation from the normal behavior pattern.

[0304] The server uses the built-in GPS of the smartphone as a detection device to periodically acquire the current location of individuals and pets. The smartphone as a communication device transmits location information to the server on the cloud using the HTTP API. In this case, it is recommended to use the SSL / TLS protocol to ensure the reliability of communication.

[0305] The server uses a generative AI model to analyze the accumulated past location information and learn the normal behavior pattern. Machine learning libraries such as TensorFlow are often used for this. The AI model improves the accuracy of behavior prediction for the target through continuous learning and updating of the behavior pattern.

[0306] When a deviation from the predicted behavior pattern is detected for the user, notifications are sent to family members and relevant parties registered as contacts via SMS or email. Digital communication means implemented through APIs such as Twilio are used as the notification means. An automatically generated notification message is sent based on the type of abnormality and the current location information.

[0307] As a specific example, consider the scenario when an individual with dementia deviates from their usual activity range. With this system, the family can receive real-time notifications of the abnormality and confirm that the individual is in a safe state.

[0308] Examples of prompt sentences are as follows:

[0309] "Generate behavioral patterns based on the following location data: Location data. Also, describe the process for evaluating whether the location deviates from the normal range and for notifying if a deviation is detected."

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The device uses its built-in GPS sensor to obtain the location information of the individual or pet. This location information is output as latitude and longitude data.

[0313] Step 2:

[0314] The device transmits the acquired location information to the server via a communication device. Here, the HTTP API is used, and the data is protected by the SSL / TLS protocol. The input is location data, and the purpose is to securely reach the server.

[0315] Step 3:

[0316] The server stores the received location information in a database along with past data. The data is stored with the target ID and timestamp, thereby forming a temporal history of the user's movements.

[0317] Step 4:

[0318] The server learns normal behavior patterns using a generative AI model based on accumulated data. The input here is past location history, and the output is a statistically predicted model of normal behavior areas and their movements.

[0319] Step 5:

[0320] The server compares the location information received in real time with learned behavioral patterns. This process involves data calculations to evaluate the likelihood of deviation, and as a result, an output is generated indicating whether or not it deviates from the normal pattern.

[0321] Step 6:

[0322] If a deviation is detected, the server generates a notification message through a notification mechanism. The generated message includes the type of anomaly and the current location information, which is used for notification.

[0323] Step 7:

[0324] The server uses communication methods to send the generated notification message to registered family members and related contacts. This allows users to receive real-time notifications via SMS or email.

[0325] 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.

[0326] This invention is a system that combines location-based behavioral monitoring with emotional state recognition using an emotion engine to ensure user safety. A specific implementation of this system is described below.

[0327] First, the device periodically acquires the user's location information using a GPS sensor. This location information is transmitted to the server by the device's communication device. Upon receiving the location data, the server begins analysis using data processing equipment and detects deviations from normal behavior patterns.

[0328] In parallel, the device is equipped with an emotion engine to analyze the user's voice and facial expressions. The emotion engine analyzes the user's voice tone and changes in facial expressions to recognize their current emotional state. The emotional data is sent to a server separately from location information and integrated into the analysis of the data processing system.

[0329] When the server detects deviations from location or sentiment data, the system sends a notification to pre-registered contacts via a notification mechanism. This notification includes not only behavioral anomalies but also the user's emotional state, allowing stakeholders to understand the situation in more detail.

[0330] For example, if a user deviates from their usual range of activity and feelings of anxiety or confusion are detected, the system will generate a notification message such as, "The user has deviated from their usual range of activity and may be emotionally unstable." The user's family and related parties can then use this information to take prompt action.

[0331] By implementing this invention, comprehensive monitoring that takes into account both physical behavior and emotional state becomes possible, thereby enhancing user safety.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The device activates its GPS sensor to periodically acquire the user's current location. The acquired location information is temporarily stored within the device.

[0335] Step 2:

[0336] The device uses a built-in emotion engine to analyze the user's voice and facial expressions to recognize their emotional state. During this process, sensors detect changes in voice tone and facial expressions and convert them into data.

[0337] Step 3:

[0338] The device organizes location information and sentiment data and sends it to the server via a communication device. This transmission occurs in real time, and the data arrives at the server as a continuous stream.

[0339] Step 4:

[0340] The server receives location and emotion data from the terminal and records it in a database. At this time, the data is standardized along with time information and stored in a way that facilitates analysis.

[0341] Step 5:

[0342] The server activates the data processing system and analyzes the received data. It compares normal behavioral patterns with emotional patterns using a generating AI and detects anomalies that do not match past data. This analysis is performed in real time.

[0343] Step 6:

[0344] After detecting an anomaly, the server prepares to notify pre-configured contacts. The system generates a notification message that details the nature of the anomaly, its location, and emotional state.

[0345] Step 7:

[0346] The server sends the generated notification to the user's family or related parties via email or voice call. This allows related parties to understand the user's unusual situation and consider necessary actions.

[0347] (Example 2)

[0348] 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 glasses 214 will be referred to as the "terminal".

[0349] Current monitoring systems rely solely on user location data and do not take into account their emotional state, making it difficult to effectively ensure true user safety. It is necessary to go beyond simply detecting location deviations and integrate analysis of the user's emotional state to enable more accurate anomaly detection and faster response.

[0350] 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.

[0351] In this invention, the server includes a transmission means for transmitting location information, an emotion analysis means for acquiring and analyzing the user's voice and facial expressions, and an information processing means for analyzing the received location information and emotion information and detecting deviations from normal behavior and emotion patterns. This makes it possible to comprehensively monitor the user's physical movements and emotional state, enabling more accurate anomaly detection and appropriate notification.

[0352] A "positioning device" is a device used to determine a geographical location, and typically uses satellites to obtain latitude and longitude data.

[0353] "Transmission means" refers to a device or method for transmitting data, typically using wireless communication technology to send location information and sentiment data to a server.

[0354] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's voice and facial expressions and evaluating their psychological state.

[0355] "Information processing means" refers to a device or system that includes algorithms or programs for analyzing received location information and emotional information to detect deviations from normal behavioral and emotional patterns.

[0356] A "notification method" is a mechanism for notifying pre-registered contacts of detected anomalies, and can use email or messaging services.

[0357] This invention describes a system that comprehensively ensures user safety by monitoring the user's location information and emotional state. Specifically, it includes a positioning device for acquiring location information, a transmission means for transmitting location information and emotional information, an emotion analysis means for analyzing the user's voice and facial expressions, and an information processing means for processing this data and detecting anomalies.

[0358] Hardware and software

[0359] The server uses programming languages ​​such as Python and R to analyze received location and sentiment data, and utilizes database management systems such as MySQL for data storage and processing. The terminal is equipped with a GPS sensor to acquire location information, and a microphone and camera to analyze voice and facial expressions. Machine learning frameworks such as TensorFlow and OpenCV are used for the sentiment engine.

[0360] Specific example

[0361] For example, if a user moves beyond their usual range of activity and simultaneously, emotion analysis detects feelings of anxiety, the system will send a notification to registered contacts stating, "The user has moved outside their usual range of activity and may be in an unstable emotional state." This notification will be delivered quickly via email or messaging services.

[0362] Example of a prompt

[0363] "Design a system that evaluates safety based on the user's location and emotional state. Location information will be obtained from GPS, and emotional state will be recognized by analyzing voice tone and facial expression changes."

[0364] This system allows for real-time monitoring of both the user's physical behavior and emotional state, thereby improving safety.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The device periodically acquires the user's location information using a GPS sensor. It takes the current geographical location as input and generates latitude and longitude data as output. This data is temporarily stored within the device and also prepared for transmission to the server. Specifically, location information is requested at regular intervals, and the sensor data is read.

[0368] Step 2:

[0369] The device uses its built-in microphone and camera to collect the user's voice and facial expressions. The input is the user's real-time voice and video, and the output is digital audio and video data used by the emotion engine for analysis. Specifically, the audio is recorded in PCM format, and the video is captured at regular intervals.

[0370] Step 3:

[0371] The device transmits location information and sentiment data to the server using a communication module. The input consists of acquired location information and sentiment data, while the output is the transmission of this data to the server. During this process, the data is encrypted for secure transmission.

[0372] Step 4:

[0373] The server stores the received location information in a database and performs analysis by applying a data processing algorithm. The input is location information transmitted from the terminal, and the output is a result indicating whether a deviation from the normal behavior pattern was detected. Specifically, the location information is stored as time-series data and compared with past patterns.

[0374] Step 5:

[0375] The server receives emotion data and executes an emotion analysis algorithm based on the extracted features. The input is user voice and video data, and the output is a recognition of the user's emotional state. This process involves analysis based on machine learning models.

[0376] Step 6:

[0377] The server integrates location data and sentiment data to perform comprehensive anomaly detection. Inputs include detection results for deviations from location information and changes in sentiment state, while output is a judgment on the presence or absence of anomalies. Specifically, it evaluates the correlation between the two datasets and scores the anomaly level.

[0378] Step 7:

[0379] When the server detects an anomaly, it generates and sends a notification message to registered contacts. Inputs include the type of anomaly, the user's current location, and their emotional state. The output is a comprehensive notification message sent via email or SMS. The specific operation involves assembling a message based on a template and sending it over the communication network.

[0380] (Application Example 2)

[0381] 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."

[0382] There is a need for a monitoring system that can help elderly people and dementia patients live safely in their daily lives by accurately detecting deviations from normal movement patterns and emotional instability, and responding quickly to prevent unexpected accidents and confusion. Conventional technologies only monitor location information and cannot comprehensively assess the user's emotional state, which poses a challenge in ensuring safety.

[0383] 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.

[0384] In this invention, the server includes a detection device for acquiring location information, an emotion analysis means for analyzing the user's voice and facial expressions to recognize their emotional state, and a notification means for notifying pre-registered contacts when the deviation or emotional state is recognized as abnormal. This makes it possible to quickly and accurately detect deviations from the usual movement patterns or abnormal emotional states of elderly people and dementia patients, and to provide necessary support in a timely manner.

[0385] A "detection device" is a device used to acquire location information, and is primarily a device that includes GPS functionality.

[0386] An "information and communication device" is a device that has a communication function for transmitting acquired location information to a server.

[0387] "Information processing means" refers to a mechanism that analyzes received location information and detects deviations from pre-recorded normal movement patterns.

[0388] An "emotion analysis device" is a device that analyzes the user's voice and facial expressions to recognize their emotional state in real time.

[0389] A "notification method" is a means of sending an alert to pre-registered contacts when deviations or abnormal emotional states are detected.

[0390] "Usual movement patterns" refer to a collection of data that shows the user's usual range of activity and routes.

[0391] "Registered contact information" refers to recipient information that has been recorded in the system in advance for the purpose of notifying users in the event of an anomaly.

[0392] "Classification of abnormalities" refers to criteria used to identify and classify deviations from normal behavior and emotional states.

[0393] This invention is a monitoring system aimed at protecting the safety of the elderly and people with dementia. It is a system that provides multifaceted support to users through the interplay of servers, terminals such as smartphones, and acquired data.

[0394] The server receives data transmitted from location-acquiring detection devices in order to analyze location information in real time. This data is used for information processing to detect deviations from normal movement patterns. Specifically, the server uses the Google Maps API to analyze location information and determine whether the user has left their usual range of activity.

[0395] The device also features an emotion analysis system that analyzes the user's voice and facial expressions. This function collects the user's voice tone and changes in facial expressions through the microphone and camera, and uses emotion analysis libraries such as the Microsoft Emotion API to recognize their real-world emotional state. The emotion data, along with location data, is sent to a server for comprehensive analysis.

[0396] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts using a notification system. This notification will include the user's current location information along with an analysis of their emotional state.

[0397] For example, if a 77-year-old grandmother gets lost in an unfamiliar place and feels anxious, the system will quickly inform her family using a prompt message that says, "Based on your current behavior patterns and emotional state, we will generate a monitoring notification." This allows the family to respond quickly.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The device periodically acquires the user's location information using its GPS function. This location information is collected as latitude and longitude data. This becomes the input data. The device transmits this data to the server via an information and communication device. The output is the transmission of location information.

[0401] Step 2:

[0402] The server analyzes the received location information using the Google Maps API. The input is the location data sent in step 1. The server compares this data with past, typical movement patterns to determine if there is a deviation. The output indicates whether or not there is a deviation from the normal pattern.

[0403] Step 3:

[0404] The device uses its built-in microphone and camera to acquire user voice and facial expression data. The input consists of voice and video data. The device inputs this information into an emotion analysis library, such as the Microsoft Emotion API, to analyze the user's emotional state. The output is the user's emotional state data.

[0405] Step 4:

[0406] The server integrates the deviation information from step 2 and the emotional state data from step 3. The inputs are the presence or absence of deviations from normal movement patterns and emotional state data. The server analyzes this data and, if an anomaly is detected, makes an overall judgment based on that information. The output is the result of the anomaly detection.

[0407] Step 5:

[0408] If an anomaly is detected, the server generates and sends a specific notification message to pre-registered contacts. The input is the result of the anomaly detection in step 4, and the output is the sent notification message. The notification uses a generation AI model to add a detailed explanation using the prompt phrase "Generate a monitoring notification based on current behavioral patterns and emotional state."

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention is a system for acquiring location information of individuals or pets and providing notifications when they deviate from their normal behavioral patterns. The following describes embodiments for carrying out this invention.

[0426] First, a sensor device attached to the terminal periodically acquires the target's location information using GPS. The acquired location information is transmitted to the server via the terminal's communication device. The server receives this data and stores it in a database.

[0427] The server's data processing mechanism compares previously accumulated behavioral data with the latest location information to learn normal behavioral patterns. It uses generative AI to perform pattern analysis and evaluate deviations from predicted movements. If a deviation from normal behavioral patterns is detected, the server uses notification mechanisms to send information to pre-registered contacts.

[0428] The notification system generates a detailed message including the type of anomaly and the user's current location. This message is delivered to the user via email or phone notification. The user receives this notification and can quickly respond to the anomaly.

[0429] As a concrete example, consider a scenario where a dementia patient is being monitored. If a patient carrying a device leaves their usual living area and begins moving towards an unknown location, the GPS immediately recognizes this and sends the location information to a server. When the server detects behavior that deviates from normal movements, a notification is sent to registered family members or caregivers to alert them of the abnormality. This system allows users to intervene in real time.

[0430] By implementing this invention, it is possible to provide a system that ensures the safety of the subject and is useful in situations where a quick response is required.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The device uses a GPS sensor to acquire its current location information at regular intervals. The acquired location information is immediately stored within the device.

[0434] Step 2:

[0435] The device transmits the stored location information to the server via a communication device. This process is performed in real time and is repeated until the data transmission is complete.

[0436] Step 3:

[0437] The server receives location information transmitted from the terminal and stores it in the database. When the data is stored, it is standardized according to the time axis, taking into account its relationship with past data.

[0438] Step 4:

[0439] The server's data processing system activates a generative AI using the stored location information. The generative AI begins to learn normal behavioral patterns and detects anomalies by comparing them with past data.

[0440] Step 5:

[0441] The generating AI analyzes whether the current location information deviates from normal behavioral patterns and determines it to be abnormal if it exceeds a certain threshold.

[0442] Step 6:

[0443] If the server detects an anomaly, it activates a notification system and generates a notification message to pre-registered contacts. This message includes the type of anomaly and the current location information.

[0444] Step 7:

[0445] The server sends notifications to users via email or telephone. This allows users to check for anomalies in real time and take appropriate action.

[0446] (Example 1)

[0447] 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."

[0448] Conventional personal and pet location monitoring systems have problems in appropriately and quickly detecting deviations from normal behavioral patterns and responding immediately to unintended crises. In particular, they were unable to accurately predict abnormal movement or behavioral patterns and notify of anomalies, so there was a need for more effective monitoring and notification methods.

[0449] 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.

[0450] In this invention, the server includes a device for detecting location, information processing means for analyzing the received location, and means for using a generated model to monitor the movement path. This makes it possible to immediately detect deviations from the normal behavioral patterns of individuals or pets and to quickly notify when an anomaly is detected.

[0451] A "location detection device" is a device used to accurately identify the current location of an individual or pet and to acquire that location information.

[0452] A "device for transmitting location" is a device that transmits acquired location information to a server or other receiving device using communication means.

[0453] "Information processing means" refers to a device that includes an electronic or computational process for analyzing received location information, learning normal behavior patterns by comparing them with past behavior data, and detecting deviations.

[0454] A "notification means" is a device or system that has the function of sending a notification to a pre-set destination when an abnormality is detected.

[0455] A "model generated to monitor movement paths" is an analytical model that uses the target's normal movement paths, learned by a generating AI, to monitor behavioral patterns in real time.

[0456] This invention is designed as a location information monitoring system, and in particular has the function of monitoring the location information of individuals and pets in real time, detecting abnormal behavioral patterns, and promptly notifying users.

[0457] System Overview

[0458] 1. The role of the terminal

[0459] The device has a built-in GPS sensor that periodically acquires location information for individuals and pets. This location information includes latitude, longitude, and time of acquisition.

[0460] This location information is transmitted to the server via the device's communication equipment. Wi-Fi and cellular networks are used for communication.

[0461] 2. Server Role

[0462] The server immediately stores the received location information in a database and compares it with past behavioral patterns. This allows it to detect deviations from normal behavior.

[0463] The server uses a generated AI model to analyze these behavioral patterns. This allows for the rapid detection of any movements that deviate from normal behavior.

[0464] 3. Means of notification

[0465] The notification system will send a detailed notification message to registered contacts as soon as an anomaly is detected. The message will include the type of anomaly and the current location information.

[0466] Technology used

[0467] Hardware: GPS sensor device, communication device.

[0468] Software: An analysis system for utilizing generative AI models.

[0469] Specific example

[0470] As a concrete example, consider monitoring the areas that elderly people with dementia move around in on a daily basis. If they move outside their usual living environment, this system can immediately detect the anomaly and notify family members or caregivers. This allows for the prevention of unforeseen incidents and enables a swift response.

[0471] Example of a prompt

[0472] Prompt messages such as "Please tell me specifically how to be notified when abnormal behavior is detected" can be input into the AI ​​model to collect data for improving behavioral pattern analysis.

[0473] In this way, location-based monitoring systems enhance the safety of individuals and pets, while also enabling rapid crisis response.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device uses a GPS sensor to acquire location information for individuals and pets. This location information includes latitude and longitude, and is recorded along with the time of acquisition. The input is location coordinates and time information, and the output is a location data set containing this data. The device periodically generates new data from this set.

[0477] Step 2:

[0478] The terminal transmits the acquired location information to the server via a communication device. Encryption technology is used for this transmission for security purposes. The input is the previously acquired location information dataset, and the output is a confirmation of data arrival at the server, including a transmission log. After sending the data, the terminal receives a response to confirm the completion of the transmission.

[0479] Step 3:

[0480] The server stores the received location information in a database. The input is location information transferred from the terminal, and the output is the update of the record in the database. The server performs the operation of accumulating the latest information in the appropriate format while comparing it with existing data.

[0481] Step 4:

[0482] The server uses a generated AI model to compare the received location information with historical data and analyzes normal behavior patterns. The input is stored historical behavior data and the latest location information, and the output is the analysis result determining whether or not there is a deviation. The server performs pattern analysis using AI and carries out the process of detecting outliers.

[0483] Step 5:

[0484] The server, upon detecting an anomaly, uses a notification mechanism to send a notification message to pre-registered contacts. The input is the analysis result that identified the anomaly, and the output is the content to be sent as a notification message. The server generates the message content and transmits the information via the appropriate communication method (email or telephone).

[0485] Step 6:

[0486] The user receives notifications from the server and makes situational judgments and takes emergency actions as needed. Input is notification messages from the server, and output is the user's confirmation and response actions. The user receives the message and takes action based on the information provided to make the best decision.

[0487] (Application Example 1)

[0488] 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."

[0489] In modern society, monitoring the elderly and ensuring the safety of pets are crucial issues. However, systems that can detect deviations from an individual's usual range of activity in real time and provide rapid notification are still lacking. To address this challenge, a mechanism is needed that can instantly detect deviations from normal behavior patterns and notify relevant parties.

[0490] 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.

[0491] In this invention, the server includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for analyzing the received location information and detecting deviations from normal behavior patterns. This ensures personal safety and enables a rapid response when a person deviates from their normal range of activity.

[0492] A "detection device for acquiring location information" is a device that uses GPS or other location information technologies to identify the current location of an object and acquire it as data.

[0493] A "communication device for transmitting location information" is a communication device that has the function of transmitting acquired location information to a server or other device.

[0494] "Information processing means for detecting deviations from normal behavior patterns" refers to a device or system that analyzes the differences between normal behavior patterns learned based on past behavioral data and performs processing to determine deviations.

[0495] A "notification means" is a device or means that has the function of notifying registered contacts when an abnormality is detected.

[0496] A "learning model" is an algorithm or system that automatically learns behavioral patterns based on data analysis and predicts future behavior.

[0497] "A means of communication for generating and sending notification messages" refers to a system that, when an anomaly is detected, automatically generates a message containing detailed information and communicates it to the relevant parties.

[0498] The present invention is a system that periodically acquires location information of individuals or pets and promptly notifies when a deviation from normal behavior is detected. This system includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for detecting deviations from normal behavior.

[0499] The server uses the GPS built into the smartphone as a detection device to periodically acquire the current location of individuals and pets. The smartphone, acting as a communication device, sends location information to the cloud server using an HTTP API. In this case, it is recommended to use the SSL / TLS protocol to ensure the reliability of the communication.

[0500] The server uses a generative AI model to analyze accumulated historical location data and learn typical behavioral patterns. Machine learning libraries such as TensorFlow are often used for this purpose. The AI ​​model improves the accuracy of predicting target behavior through continuous learning and updating of behavioral patterns.

[0501] If a deviation from the user's predicted behavior is detected, notifications will be sent via SMS or email to registered family members and related parties. Digital communication methods, such as those implemented through the Twilio API, will be used for notifications. An automatically generated notification message will be sent based on the type of anomaly and the user's current location.

[0502] As a concrete example, consider a scenario where an individual with dementia deviates from their usual range of activity. This system allows family members to receive real-time notifications of the anomaly and confirm that the individual is safe.

[0503] Examples of prompt statements are as follows:

[0504] "Generate behavioral patterns based on the following location data: Location data. Also, describe the process for evaluating whether the location deviates from the normal range and for notifying if a deviation is detected."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The device uses its built-in GPS sensor to obtain the location information of the individual or pet. This location information is output as latitude and longitude data.

[0508] Step 2:

[0509] The device transmits the acquired location information to the server via a communication device. Here, the HTTP API is used, and the data is protected by the SSL / TLS protocol. The input is location data, and the purpose is to securely reach the server.

[0510] Step 3:

[0511] The server stores the received location information in a database along with past data. The data is stored with the target ID and timestamp, thereby forming a temporal history of the user's movements.

[0512] Step 4:

[0513] The server learns normal behavior patterns using a generative AI model based on accumulated data. The input here is past location history, and the output is a statistically predicted model of normal behavior areas and their movements.

[0514] Step 5:

[0515] The server compares the location information received in real time with learned behavioral patterns. This process involves data calculations to evaluate the likelihood of deviation, and as a result, an output is generated indicating whether or not it deviates from the normal pattern.

[0516] Step 6:

[0517] If a deviation is detected, the server generates a notification message through a notification mechanism. The generated message includes the type of anomaly and the current location information, which is used for notification.

[0518] Step 7:

[0519] The server uses communication methods to send the generated notification message to registered family members and related contacts. This allows users to receive real-time notifications via SMS or email.

[0520] 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.

[0521] This invention is a system that combines location-based behavioral monitoring with emotional state recognition using an emotion engine to ensure user safety. A specific implementation of this system is described below.

[0522] First, the device periodically acquires the user's location information using a GPS sensor. This location information is transmitted to the server by the device's communication device. Upon receiving the location data, the server begins analysis using data processing equipment and detects deviations from normal behavior patterns.

[0523] In parallel, the device is equipped with an emotion engine to analyze the user's voice and facial expressions. The emotion engine analyzes the user's voice tone and changes in facial expressions to recognize their current emotional state. The emotional data is sent to a server separately from location information and integrated into the analysis of the data processing system.

[0524] When the server detects deviations from location or sentiment data, the system sends a notification to pre-registered contacts via a notification mechanism. This notification includes not only behavioral anomalies but also the user's emotional state, allowing stakeholders to understand the situation in more detail.

[0525] For example, if a user deviates from their usual range of activity and feelings of anxiety or confusion are detected, the system will generate a notification message such as, "The user has deviated from their usual range of activity and may be emotionally unstable." The user's family and related parties can then use this information to take prompt action.

[0526] By implementing this invention, comprehensive monitoring that takes into account both physical behavior and emotional state becomes possible, thereby enhancing user safety.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The device activates its GPS sensor to periodically acquire the user's current location. The acquired location information is temporarily stored within the device.

[0530] Step 2:

[0531] The device uses a built-in emotion engine to analyze the user's voice and facial expressions to recognize their emotional state. During this process, sensors detect changes in voice tone and facial expressions and convert them into data.

[0532] Step 3:

[0533] The device organizes location information and sentiment data and sends it to the server via a communication device. This transmission occurs in real time, and the data arrives at the server as a continuous stream.

[0534] Step 4:

[0535] The server receives location and emotion data from the terminal and records it in a database. At this time, the data is standardized along with time information and stored in a way that facilitates analysis.

[0536] Step 5:

[0537] The server activates the data processing system and analyzes the received data. It compares normal behavioral patterns with emotional patterns using a generating AI and detects anomalies that do not match past data. This analysis is performed in real time.

[0538] Step 6:

[0539] After detecting an anomaly, the server prepares to notify pre-configured contacts. The system generates a notification message that details the nature of the anomaly, its location, and emotional state.

[0540] Step 7:

[0541] The server sends the generated notification to the user's family or related parties via email or voice call. This allows related parties to understand the user's unusual situation and consider necessary actions.

[0542] (Example 2)

[0543] 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."

[0544] Current monitoring systems rely solely on user location data and do not take into account their emotional state, making it difficult to effectively ensure true user safety. It is necessary to go beyond simply detecting location deviations and integrate analysis of the user's emotional state to enable more accurate anomaly detection and faster response.

[0545] 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.

[0546] In this invention, the server includes a transmission means for transmitting location information, an emotion analysis means for acquiring and analyzing the user's voice and facial expressions, and an information processing means for analyzing the received location information and emotion information and detecting deviations from normal behavior and emotion patterns. This makes it possible to comprehensively monitor the user's physical movements and emotional state, enabling more accurate anomaly detection and appropriate notification.

[0547] A "positioning device" is a device used to determine a geographical location, and typically uses satellites to obtain latitude and longitude data.

[0548] "Transmission means" refers to a device or method for transmitting data, typically using wireless communication technology to send location information and sentiment data to a server.

[0549] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's voice and facial expressions and evaluating their psychological state.

[0550] "Information processing means" refers to a device or system that includes algorithms or programs for analyzing received location information and emotional information to detect deviations from normal behavioral and emotional patterns.

[0551] A "notification method" is a mechanism for notifying pre-registered contacts of detected anomalies, and can use email or messaging services.

[0552] This invention describes a system that comprehensively ensures user safety by monitoring the user's location information and emotional state. Specifically, it includes a positioning device for acquiring location information, a transmission means for transmitting location information and emotional information, an emotion analysis means for analyzing the user's voice and facial expressions, and an information processing means for processing this data and detecting anomalies.

[0553] Hardware and software

[0554] The server uses programming languages ​​such as Python and R to analyze received location and sentiment data, and utilizes database management systems such as MySQL for data storage and processing. The terminal is equipped with a GPS sensor to acquire location information, and a microphone and camera to analyze voice and facial expressions. Machine learning frameworks such as TensorFlow and OpenCV are used for the sentiment engine.

[0555] Specific example

[0556] For example, if a user moves beyond their usual range of activity and simultaneously, emotion analysis detects feelings of anxiety, the system will send a notification to registered contacts stating, "The user has moved outside their usual range of activity and may be in an unstable emotional state." This notification will be delivered quickly via email or messaging services.

[0557] Example of a prompt

[0558] "Design a system that evaluates safety based on the user's location and emotional state. Location information will be obtained from GPS, and emotional state will be recognized by analyzing voice tone and facial expression changes."

[0559] This system allows for real-time monitoring of both the user's physical behavior and emotional state, thereby improving safety.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The device periodically acquires the user's location information using a GPS sensor. It takes the current geographical location as input and generates latitude and longitude data as output. This data is temporarily stored within the device and also prepared for transmission to the server. Specifically, location information is requested at regular intervals, and the sensor data is read.

[0563] Step 2:

[0564] The device uses its built-in microphone and camera to collect the user's voice and facial expressions. The input is the user's real-time voice and video, and the output is digital audio and video data used by the emotion engine for analysis. Specifically, the audio is recorded in PCM format, and the video is captured at regular intervals.

[0565] Step 3:

[0566] The device transmits location information and sentiment data to the server using a communication module. The input consists of acquired location information and sentiment data, while the output is the transmission of this data to the server. During this process, the data is encrypted for secure transmission.

[0567] Step 4:

[0568] The server stores the received location information in a database and performs analysis by applying a data processing algorithm. The input is location information transmitted from the terminal, and the output is a result indicating whether a deviation from the normal behavior pattern was detected. Specifically, the location information is stored as time-series data and compared with past patterns.

[0569] Step 5:

[0570] The server receives emotion data and executes an emotion analysis algorithm based on the extracted features. The input is user voice and video data, and the output is a recognition of the user's emotional state. This process involves analysis based on machine learning models.

[0571] Step 6:

[0572] The server integrates location data and sentiment data to perform comprehensive anomaly detection. Inputs include detection results for deviations from location information and changes in sentiment state, while output is a judgment on the presence or absence of anomalies. Specifically, it evaluates the correlation between the two datasets and scores the anomaly level.

[0573] Step 7:

[0574] When the server detects an anomaly, it generates and sends a notification message to registered contacts. Inputs include the type of anomaly, the user's current location, and their emotional state. The output is a comprehensive notification message sent via email or SMS. The specific operation involves assembling a message based on a template and sending it over the communication network.

[0575] (Application Example 2)

[0576] 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."

[0577] There is a need for a monitoring system that can help elderly people and dementia patients live safely in their daily lives by accurately detecting deviations from normal movement patterns and emotional instability, and responding quickly to prevent unexpected accidents and confusion. Conventional technologies only monitor location information and cannot comprehensively assess the user's emotional state, which poses a challenge in ensuring safety.

[0578] 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.

[0579] In this invention, the server includes a detection device for acquiring location information, an emotion analysis means for analyzing the user's voice and facial expressions to recognize their emotional state, and a notification means for notifying pre-registered contacts when the deviation or emotional state is recognized as abnormal. This makes it possible to quickly and accurately detect deviations from the usual movement patterns or abnormal emotional states of elderly people and dementia patients, and to provide necessary support in a timely manner.

[0580] A "detection device" is a device used to acquire location information, and is primarily a device that includes GPS functionality.

[0581] An "information and communication device" is a device that has a communication function for transmitting acquired location information to a server.

[0582] "Information processing means" refers to a mechanism that analyzes received location information and detects deviations from pre-recorded normal movement patterns.

[0583] An "emotion analysis device" is a device that analyzes the user's voice and facial expressions to recognize their emotional state in real time.

[0584] A "notification method" is a means of sending an alert to pre-registered contacts when deviations or abnormal emotional states are detected.

[0585] "Usual movement patterns" refer to a collection of data that shows the user's usual range of activity and routes.

[0586] "Registered contact information" refers to recipient information that has been recorded in the system in advance for the purpose of notifying users in the event of an anomaly.

[0587] "Classification of abnormalities" refers to criteria used to identify and classify deviations from normal behavior and emotional states.

[0588] This invention is a monitoring system aimed at protecting the safety of the elderly and people with dementia. It is a system that provides multifaceted support to users through the interplay of servers, terminals such as smartphones, and acquired data.

[0589] The server receives data transmitted from location-acquiring detection devices in order to analyze location information in real time. This data is used for information processing to detect deviations from normal movement patterns. Specifically, the server uses the Google Maps API to analyze location information and determine whether the user has left their usual range of activity.

[0590] The device also features an emotion analysis system that analyzes the user's voice and facial expressions. This function collects the user's voice tone and changes in facial expressions through the microphone and camera, and uses emotion analysis libraries such as the Microsoft Emotion API to recognize their real-world emotional state. The emotion data, along with location data, is sent to a server for comprehensive analysis.

[0591] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts using a notification system. This notification will include the user's current location information along with an analysis of their emotional state.

[0592] For example, if a 77-year-old grandmother gets lost in an unfamiliar place and feels anxious, the system will quickly inform her family using a prompt message that says, "Based on your current behavior patterns and emotional state, we will generate a monitoring notification." This allows the family to respond quickly.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The device periodically acquires the user's location information using its GPS function. This location information is collected as latitude and longitude data. This becomes the input data. The device transmits this data to the server via an information and communication device. The output is the transmission of location information.

[0596] Step 2:

[0597] The server analyzes the received location information using the Google Maps API. The input is the location data sent in step 1. The server compares this data with past, typical movement patterns to determine if there is a deviation. The output indicates whether or not there is a deviation from the normal pattern.

[0598] Step 3:

[0599] The device uses its built-in microphone and camera to acquire user voice and facial expression data. The input consists of voice and video data. The device inputs this information into an emotion analysis library, such as the Microsoft Emotion API, to analyze the user's emotional state. The output is the user's emotional state data.

[0600] Step 4:

[0601] The server integrates the deviation information from step 2 and the emotional state data from step 3. The inputs are the presence or absence of deviations from normal movement patterns and emotional state data. The server analyzes this data and, if an anomaly is detected, makes an overall judgment based on that information. The output is the result of the anomaly detection.

[0602] Step 5:

[0603] If an anomaly is detected, the server generates and sends a specific notification message to pre-registered contacts. The input is the result of the anomaly detection in step 4, and the output is the sent notification message. The notification uses a generation AI model to add a detailed explanation using the prompt phrase "Generate a monitoring notification based on current behavioral patterns and emotional state."

[0604] 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.

[0605] 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.

[0606] 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.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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".

[0621] This invention is a system for acquiring location information of individuals or pets and providing notifications when they deviate from their normal behavioral patterns. The following describes embodiments for carrying out this invention.

[0622] First, a sensor device attached to the terminal periodically acquires the target's location information using GPS. The acquired location information is transmitted to the server via the terminal's communication device. The server receives this data and stores it in a database.

[0623] The server's data processing mechanism compares previously accumulated behavioral data with the latest location information to learn normal behavioral patterns. It uses generative AI to perform pattern analysis and evaluate deviations from predicted movements. If a deviation from normal behavioral patterns is detected, the server uses notification mechanisms to send information to pre-registered contacts.

[0624] The notification system generates a detailed message including the type of anomaly and the user's current location. This message is delivered to the user via email or phone notification. The user receives this notification and can quickly respond to the anomaly.

[0625] As a concrete example, consider a scenario where a dementia patient is being monitored. If a patient carrying a device leaves their usual living area and begins moving towards an unknown location, the GPS immediately recognizes this and sends the location information to a server. When the server detects behavior that deviates from normal movements, a notification is sent to registered family members or caregivers to alert them of the abnormality. This system allows users to intervene in real time.

[0626] By implementing this invention, it is possible to provide a system that ensures the safety of the subject and is useful in situations where a quick response is required.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The device uses a GPS sensor to acquire its current location information at regular intervals. The acquired location information is immediately stored within the device.

[0630] Step 2:

[0631] The device transmits the stored location information to the server via a communication device. This process is performed in real time and is repeated until the data transmission is complete.

[0632] Step 3:

[0633] The server receives location information transmitted from the terminal and stores it in the database. When the data is stored, it is standardized according to the time axis, taking into account its relationship with past data.

[0634] Step 4:

[0635] The server's data processing system activates a generative AI using the stored location information. The generative AI begins to learn normal behavioral patterns and detects anomalies by comparing them with past data.

[0636] Step 5:

[0637] The generating AI analyzes whether the current location information deviates from normal behavioral patterns and determines it to be abnormal if it exceeds a certain threshold.

[0638] Step 6:

[0639] If the server detects an anomaly, it activates a notification system and generates a notification message to pre-registered contacts. This message includes the type of anomaly and the current location information.

[0640] Step 7:

[0641] The server sends notifications to users via email or telephone. This allows users to check for anomalies in real time and take appropriate action.

[0642] (Example 1)

[0643] 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".

[0644] Conventional personal and pet location monitoring systems have problems in appropriately and quickly detecting deviations from normal behavioral patterns and responding immediately to unintended crises. In particular, they were unable to accurately predict abnormal movement or behavioral patterns and notify of anomalies, so there was a need for more effective monitoring and notification methods.

[0645] 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.

[0646] In this invention, the server includes a device for detecting location, information processing means for analyzing the received location, and means for using a generated model to monitor the movement path. This makes it possible to immediately detect deviations from the normal behavioral patterns of individuals or pets and to quickly notify when an anomaly is detected.

[0647] A "location detection device" is a device used to accurately identify the current location of an individual or pet and to acquire that location information.

[0648] A "device for transmitting location" is a device that transmits acquired location information to a server or other receiving device using communication means.

[0649] "Information processing means" refers to a device that includes an electronic or computational process for analyzing received location information, learning normal behavior patterns by comparing them with past behavior data, and detecting deviations.

[0650] A "notification means" is a device or system that has the function of sending a notification to a pre-set destination when an abnormality is detected.

[0651] A "model generated to monitor movement paths" is an analytical model that uses the target's normal movement paths, learned by a generating AI, to monitor behavioral patterns in real time.

[0652] This invention is designed as a location information monitoring system, and in particular has the function of monitoring the location information of individuals and pets in real time, detecting abnormal behavioral patterns, and promptly notifying users.

[0653] System Overview

[0654] 1. The role of the terminal

[0655] The device has a built-in GPS sensor that periodically acquires location information for individuals and pets. This location information includes latitude, longitude, and time of acquisition.

[0656] This location information is transmitted to the server via the device's communication equipment. Wi-Fi and cellular networks are used for communication.

[0657] 2. Server Role

[0658] The server immediately stores the received location information in a database and compares it with past behavioral patterns. This allows it to detect deviations from normal behavior.

[0659] The server uses a generated AI model to analyze these behavioral patterns. This allows for the rapid detection of any movements that deviate from normal behavior.

[0660] 3. Means of notification

[0661] The notification system will send a detailed notification message to registered contacts as soon as an anomaly is detected. The message will include the type of anomaly and the current location information.

[0662] Technology used

[0663] Hardware: GPS sensor device, communication device.

[0664] Software: An analysis system for utilizing generative AI models.

[0665] Specific example

[0666] As a concrete example, consider monitoring the areas that elderly people with dementia move around in on a daily basis. If they move outside their usual living environment, this system can immediately detect the anomaly and notify family members or caregivers. This allows for the prevention of unforeseen incidents and enables a swift response.

[0667] Example of a prompt

[0668] Prompt messages such as "Please tell me specifically how to be notified when abnormal behavior is detected" can be input into the AI ​​model to collect data for improving behavioral pattern analysis.

[0669] In this way, location-based monitoring systems enhance the safety of individuals and pets, while also enabling rapid crisis response.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The device uses a GPS sensor to acquire location information for individuals and pets. This location information includes latitude and longitude, and is recorded along with the time of acquisition. The input is location coordinates and time information, and the output is a location data set containing this data. The device periodically generates new data from this set.

[0673] Step 2:

[0674] The terminal transmits the acquired location information to the server via a communication device. Encryption technology is used for this transmission for security purposes. The input is the previously acquired location information dataset, and the output is a confirmation of data arrival at the server, including a transmission log. After sending the data, the terminal receives a response to confirm the completion of the transmission.

[0675] Step 3:

[0676] The server stores the received location information in a database. The input is location information transferred from the terminal, and the output is the update of the record in the database. The server performs the operation of accumulating the latest information in the appropriate format while comparing it with existing data.

[0677] Step 4:

[0678] The server uses a generated AI model to compare the received location information with historical data and analyzes normal behavior patterns. The input is stored historical behavior data and the latest location information, and the output is the analysis result determining whether or not there is a deviation. The server performs pattern analysis using AI and carries out the process of detecting outliers.

[0679] Step 5:

[0680] The server, upon detecting an anomaly, uses a notification mechanism to send a notification message to pre-registered contacts. The input is the analysis result that identified the anomaly, and the output is the content to be sent as a notification message. The server generates the message content and transmits the information via the appropriate communication method (email or telephone).

[0681] Step 6:

[0682] The user receives notifications from the server and makes situational judgments and takes emergency actions as needed. Input is notification messages from the server, and output is the user's confirmation and response actions. The user receives the message and takes action based on the information provided to make the best decision.

[0683] (Application Example 1)

[0684] 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".

[0685] In modern society, monitoring the elderly and ensuring the safety of pets are crucial issues. However, systems that can detect deviations from an individual's usual range of activity in real time and provide rapid notification are still lacking. To address this challenge, a mechanism is needed that can instantly detect deviations from normal behavior patterns and notify relevant parties.

[0686] 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.

[0687] In this invention, the server includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for analyzing the received location information and detecting deviations from normal behavior patterns. This ensures personal safety and enables a rapid response when a person deviates from their normal range of activity.

[0688] A "detection device for acquiring location information" is a device that uses GPS or other location information technologies to identify the current location of an object and acquire it as data.

[0689] A "communication device for transmitting location information" is a communication device that has the function of transmitting acquired location information to a server or other device.

[0690] "Information processing means for detecting deviations from normal behavior patterns" refers to a device or system that analyzes the differences between normal behavior patterns learned based on past behavioral data and performs processing to determine deviations.

[0691] A "notification means" is a device or means that has the function of notifying registered contacts when an abnormality is detected.

[0692] A "learning model" is an algorithm or system that automatically learns behavioral patterns based on data analysis and predicts future behavior.

[0693] "A means of communication for generating and sending notification messages" refers to a system that, when an anomaly is detected, automatically generates a message containing detailed information and communicates it to the relevant parties.

[0694] The present invention is a system that periodically acquires location information of individuals or pets and promptly notifies when a deviation from normal behavior is detected. This system includes a detection device for acquiring location information, a communication device for transmitting location information, and information processing means for detecting deviations from normal behavior.

[0695] The server uses the GPS built into the smartphone as a detection device to periodically acquire the current location of individuals and pets. The smartphone, acting as a communication device, sends location information to the cloud server using an HTTP API. In this case, it is recommended to use the SSL / TLS protocol to ensure the reliability of the communication.

[0696] The server uses a generative AI model to analyze accumulated historical location data and learn typical behavioral patterns. Machine learning libraries such as TensorFlow are often used for this purpose. The AI ​​model improves the accuracy of predicting target behavior through continuous learning and updating of behavioral patterns.

[0697] If a deviation from the user's predicted behavior is detected, notifications will be sent via SMS or email to registered family members and related parties. Digital communication methods, such as those implemented through the Twilio API, will be used for notifications. An automatically generated notification message will be sent based on the type of anomaly and the user's current location.

[0698] As a concrete example, consider a scenario where an individual with dementia deviates from their usual range of activity. This system allows family members to receive real-time notifications of the anomaly and confirm that the individual is safe.

[0699] Examples of prompt statements are as follows:

[0700] "Generate behavioral patterns based on the following location data: Location data. Also, describe the process for evaluating whether the location deviates from the normal range and for notifying if a deviation is detected."

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] The device uses its built-in GPS sensor to obtain the location information of the individual or pet. This location information is output as latitude and longitude data.

[0704] Step 2:

[0705] The device transmits the acquired location information to the server via a communication device. Here, the HTTP API is used, and the data is protected by the SSL / TLS protocol. The input is location data, and the purpose is to securely reach the server.

[0706] Step 3:

[0707] The server stores the received location information in a database along with past data. The data is stored with the target ID and timestamp, thereby forming a temporal history of the user's movements.

[0708] Step 4:

[0709] The server learns normal behavior patterns using a generative AI model based on accumulated data. The input here is past location history, and the output is a statistically predicted model of normal behavior areas and their movements.

[0710] Step 5:

[0711] The server compares the location information received in real time with learned behavioral patterns. This process involves data calculations to evaluate the likelihood of deviation, and as a result, an output is generated indicating whether or not it deviates from the normal pattern.

[0712] Step 6:

[0713] If a deviation is detected, the server generates a notification message through a notification mechanism. The generated message includes the type of anomaly and the current location information, which is used for notification.

[0714] Step 7:

[0715] The server uses communication methods to send the generated notification message to registered family members and related contacts. This allows users to receive real-time notifications via SMS or email.

[0716] 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.

[0717] This invention is a system that combines location-based behavioral monitoring with emotional state recognition using an emotion engine to ensure user safety. A specific implementation of this system is described below.

[0718] First, the device periodically acquires the user's location information using a GPS sensor. This location information is transmitted to the server by the device's communication device. Upon receiving the location data, the server begins analysis using data processing equipment and detects deviations from normal behavior patterns.

[0719] In parallel, the device is equipped with an emotion engine to analyze the user's voice and facial expressions. The emotion engine analyzes the user's voice tone and changes in facial expressions to recognize their current emotional state. The emotional data is sent to a server separately from location information and integrated into the analysis of the data processing system.

[0720] When the server detects deviations from location or sentiment data, the system sends a notification to pre-registered contacts via a notification mechanism. This notification includes not only behavioral anomalies but also the user's emotional state, allowing stakeholders to understand the situation in more detail.

[0721] For example, if a user deviates from their usual range of activity and feelings of anxiety or confusion are detected, the system will generate a notification message such as, "The user has deviated from their usual range of activity and may be emotionally unstable." The user's family and related parties can then use this information to take prompt action.

[0722] By implementing this invention, comprehensive monitoring that takes into account both physical behavior and emotional state becomes possible, thereby enhancing user safety.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The device activates its GPS sensor to periodically acquire the user's current location. The acquired location information is temporarily stored within the device.

[0726] Step 2:

[0727] The device uses a built-in emotion engine to analyze the user's voice and facial expressions to recognize their emotional state. During this process, sensors detect changes in voice tone and facial expressions and convert them into data.

[0728] Step 3:

[0729] The device organizes location information and sentiment data and sends it to the server via a communication device. This transmission occurs in real time, and the data arrives at the server as a continuous stream.

[0730] Step 4:

[0731] The server receives location and emotion data from the terminal and records it in a database. At this time, the data is standardized along with time information and stored in a way that facilitates analysis.

[0732] Step 5:

[0733] The server activates the data processing system and analyzes the received data. It compares normal behavioral patterns with emotional patterns using a generating AI and detects anomalies that do not match past data. This analysis is performed in real time.

[0734] Step 6:

[0735] After detecting an anomaly, the server prepares to notify pre-configured contacts. The system generates a notification message that details the nature of the anomaly, its location, and emotional state.

[0736] Step 7:

[0737] The server sends the generated notification to the user's family or related parties via email or voice call. This allows related parties to understand the user's unusual situation and consider necessary actions.

[0738] (Example 2)

[0739] 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".

[0740] Current monitoring systems rely solely on user location data and do not take into account their emotional state, making it difficult to effectively ensure true user safety. It is necessary to go beyond simply detecting location deviations and integrate analysis of the user's emotional state to enable more accurate anomaly detection and faster response.

[0741] 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.

[0742] In this invention, the server includes a transmission means for transmitting location information, an emotion analysis means for acquiring and analyzing the user's voice and facial expressions, and an information processing means for analyzing the received location information and emotion information and detecting deviations from normal behavior and emotion patterns. This makes it possible to comprehensively monitor the user's physical movements and emotional state, enabling more accurate anomaly detection and appropriate notification.

[0743] A "positioning device" is a device used to determine a geographical location, and typically uses satellites to obtain latitude and longitude data.

[0744] "Transmission means" refers to a device or method for transmitting data, typically using wireless communication technology to send location information and sentiment data to a server.

[0745] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's voice and facial expressions and evaluating their psychological state.

[0746] "Information processing means" refers to a device or system that includes algorithms or programs for analyzing received location information and emotional information to detect deviations from normal behavioral and emotional patterns.

[0747] A "notification method" is a mechanism for notifying pre-registered contacts of detected anomalies, and can use email or messaging services.

[0748] This invention describes a system that comprehensively ensures user safety by monitoring the user's location information and emotional state. Specifically, it includes a positioning device for acquiring location information, a transmission means for transmitting location information and emotional information, an emotion analysis means for analyzing the user's voice and facial expressions, and an information processing means for processing this data and detecting anomalies.

[0749] Hardware and software

[0750] The server uses programming languages ​​such as Python and R to analyze received location and sentiment data, and utilizes database management systems such as MySQL for data storage and processing. The terminal is equipped with a GPS sensor to acquire location information, and a microphone and camera to analyze voice and facial expressions. Machine learning frameworks such as TensorFlow and OpenCV are used for the sentiment engine.

[0751] Specific example

[0752] For example, if a user moves beyond their usual range of activity and simultaneously, emotion analysis detects feelings of anxiety, the system will send a notification to registered contacts stating, "The user has moved outside their usual range of activity and may be in an unstable emotional state." This notification will be delivered quickly via email or messaging services.

[0753] Example of a prompt

[0754] "Design a system that evaluates safety based on the user's location and emotional state. Location information will be obtained from GPS, and emotional state will be recognized by analyzing voice tone and facial expression changes."

[0755] This system allows for real-time monitoring of both the user's physical behavior and emotional state, thereby improving safety.

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1:

[0758] The device periodically acquires the user's location information using a GPS sensor. It takes the current geographical location as input and generates latitude and longitude data as output. This data is temporarily stored within the device and also prepared for transmission to the server. Specifically, location information is requested at regular intervals, and the sensor data is read.

[0759] Step 2:

[0760] The device uses its built-in microphone and camera to collect the user's voice and facial expressions. The input is the user's real-time voice and video, and the output is digital audio and video data used by the emotion engine for analysis. Specifically, the audio is recorded in PCM format, and the video is captured at regular intervals.

[0761] Step 3:

[0762] The device transmits location information and sentiment data to the server using a communication module. The input consists of acquired location information and sentiment data, while the output is the transmission of this data to the server. During this process, the data is encrypted for secure transmission.

[0763] Step 4:

[0764] The server stores the received location information in a database and performs analysis by applying a data processing algorithm. The input is location information transmitted from the terminal, and the output is a result indicating whether a deviation from the normal behavior pattern was detected. Specifically, the location information is stored as time-series data and compared with past patterns.

[0765] Step 5:

[0766] The server receives emotion data and executes an emotion analysis algorithm based on the extracted features. The input is user voice and video data, and the output is a recognition of the user's emotional state. This process involves analysis based on machine learning models.

[0767] Step 6:

[0768] The server integrates location data and sentiment data to perform comprehensive anomaly detection. Inputs include detection results for deviations from location information and changes in sentiment state, while output is a judgment on the presence or absence of anomalies. Specifically, it evaluates the correlation between the two datasets and scores the anomaly level.

[0769] Step 7:

[0770] When the server detects an anomaly, it generates and sends a notification message to registered contacts. Inputs include the type of anomaly, the user's current location, and their emotional state. The output is a comprehensive notification message sent via email or SMS. The specific operation involves assembling a message based on a template and sending it over the communication network.

[0771] (Application Example 2)

[0772] 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".

[0773] There is a need for a monitoring system that can help elderly people and dementia patients live safely in their daily lives by accurately detecting deviations from normal movement patterns and emotional instability, and responding quickly to prevent unexpected accidents and confusion. Conventional technologies only monitor location information and cannot comprehensively assess the user's emotional state, which poses a challenge in ensuring safety.

[0774] 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.

[0775] In this invention, the server includes a detection device for acquiring location information, an emotion analysis means for analyzing the user's voice and facial expressions to recognize their emotional state, and a notification means for notifying pre-registered contacts when the deviation or emotional state is recognized as abnormal. This makes it possible to quickly and accurately detect deviations from the usual movement patterns or abnormal emotional states of elderly people and dementia patients, and to provide necessary support in a timely manner.

[0776] A "detection device" is a device used to acquire location information, and is primarily a device that includes GPS functionality.

[0777] An "information and communication device" is a device that has a communication function for transmitting acquired location information to a server.

[0778] "Information processing means" refers to a mechanism that analyzes received location information and detects deviations from pre-recorded normal movement patterns.

[0779] An "emotion analysis device" is a device that analyzes the user's voice and facial expressions to recognize their emotional state in real time.

[0780] A "notification method" is a means of sending an alert to pre-registered contacts when deviations or abnormal emotional states are detected.

[0781] "Usual movement patterns" refer to a collection of data that shows the user's usual range of activity and routes.

[0782] "Registered contact information" refers to recipient information that has been recorded in the system in advance for the purpose of notifying users in the event of an anomaly.

[0783] "Classification of abnormalities" refers to criteria used to identify and classify deviations from normal behavior and emotional states.

[0784] This invention is a monitoring system aimed at protecting the safety of the elderly and people with dementia. It is a system that provides multifaceted support to users through the interplay of servers, terminals such as smartphones, and acquired data.

[0785] The server receives data transmitted from location-acquiring detection devices in order to analyze location information in real time. This data is used for information processing to detect deviations from normal movement patterns. Specifically, the server uses the Google Maps API to analyze location information and determine whether the user has left their usual range of activity.

[0786] The device also features an emotion analysis system that analyzes the user's voice and facial expressions. This function collects the user's voice tone and changes in facial expressions through the microphone and camera, and uses emotion analysis libraries such as the Microsoft Emotion API to recognize their real-world emotional state. The emotion data, along with location data, is sent to a server for comprehensive analysis.

[0787] If an anomaly is detected, the server will automatically send a notification to pre-registered contacts using a notification system. This notification will include the user's current location information along with an analysis of their emotional state.

[0788] For example, if a 77-year-old grandmother gets lost in an unfamiliar place and feels anxious, the system will quickly inform her family using a prompt message that says, "Based on your current behavior patterns and emotional state, we will generate a monitoring notification." This allows the family to respond quickly.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The device periodically acquires the user's location information using its GPS function. This location information is collected as latitude and longitude data. This becomes the input data. The device transmits this data to the server via an information and communication device. The output is the transmission of location information.

[0792] Step 2:

[0793] The server analyzes the received location information using the Google Maps API. The input is the location data sent in step 1. The server compares this data with past, typical movement patterns to determine if there is a deviation. The output indicates whether or not there is a deviation from the normal pattern.

[0794] Step 3:

[0795] The device uses its built-in microphone and camera to acquire user voice and facial expression data. The input consists of voice and video data. The device inputs this information into an emotion analysis library, such as the Microsoft Emotion API, to analyze the user's emotional state. The output is the user's emotional state data.

[0796] Step 4:

[0797] The server integrates the deviation information from step 2 and the emotional state data from step 3. The inputs are the presence or absence of deviations from normal movement patterns and emotional state data. The server analyzes this data and, if an anomaly is detected, makes an overall judgment based on that information. The output is the result of the anomaly detection.

[0798] Step 5:

[0799] If an anomaly is detected, the server generates and sends a specific notification message to pre-registered contacts. The input is the result of the anomaly detection in step 4, and the output is the sent notification message. The notification uses a generation AI model to add a detailed explanation using the prompt phrase "Generate a monitoring notification based on current behavioral patterns and emotional state."

[0800] 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.

[0801] 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.

[0802] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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."

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] The following is further disclosed regarding the embodiments described above.

[0822] (Claim 1)

[0823] A sensor device for acquiring location information,

[0824] A communication device for transmitting the aforementioned location information,

[0825] A data processing means for analyzing the received location information and detecting deviations from normal behavioral patterns,

[0826] A notification means for notifying pre-registered contacts when such deviation is detected,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, characterized in that the data processing means learns normal behavioral patterns by comparing and analyzing past behavioral data.

[0830] (Claim 3)

[0831] The system according to claim 1, characterized in that the notification means generates a notification message including the type of anomaly and current location information.

[0832] "Example 1"

[0833] (Claim 1)

[0834] A device for detecting position,

[0835] A device for transmitting the aforementioned position,

[0836] Information processing means for analyzing the received location and detecting deviations from general behavioral patterns,

[0837] A notification means for notifying a pre-registered recipient when such deviation is detected,

[0838] A means of using a model generated to monitor the movement path,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, characterized in that the information processing means learns general behavioral patterns by comparing and analyzing past behavioral information.

[0842] (Claim 3)

[0843] The system according to claim 1, characterized in that the notification means generates a notification message including the type of abnormality and the current location.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] A detection device for acquiring location information,

[0847] A communication device for transmitting the aforementioned location information,

[0848] Information processing means for analyzing the received location information and detecting deviations from normal behavior patterns,

[0849] A notification means for notifying a pre-registered contact when the aforementioned deviation is detected,

[0850] Analysis of behavioral patterns using learning models,

[0851] A means of communication for generating and sending notification messages,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the information processing means automatically learns normal behavior patterns by comparing and analyzing past behavioral information.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the notification means automatically generates a notification message including the type of anomaly and current location information.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A positioning device for acquiring location information,

[0860] A transmission means for transmitting the aforementioned location information,

[0861] An emotion analysis method that acquires and analyzes the user's voice and facial expressions,

[0862] Information processing means for analyzing the received location information and emotional information and detecting deviations from normal behavioral and emotional patterns,

[0863] A notification means for notifying a pre-registered contact when the aforementioned deviation is detected,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized in that the information processing means learns normal behavioral and emotional patterns by comparing and analyzing past behavioral and emotional data.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized in that the notification means generates a notification message including the type of anomaly, current location information, and emotional state.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] A detection device for acquiring location information,

[0872] An information communication device for transmitting the aforementioned location information,

[0873] Information processing means for analyzing the received location information and detecting deviations from the usual movement pattern,

[0874] An emotion analysis means for analyzing the user's voice and facial expressions to recognize their emotional state,

[0875] A notification means for notifying pre-registered contacts when the aforementioned deviation and emotional state are recognized as abnormal,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, characterized in that the information processing means learns the user's usual movement patterns by comparing and analyzing past behavioral data, and also takes into account the user's emotional state.

[0879] (Claim 3)

[0880] The system according to claim 1, characterized in that the notification means generates a notification message including an abnormality classification, current location information, and emotional state. [Explanation of symbols]

[0881] 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 sensor device for acquiring location information, A communication device for transmitting the aforementioned location information, A data processing means for analyzing the received location information and detecting deviations from normal behavioral patterns, A notification means for notifying pre-registered contacts when such deviation is detected, A system that includes this.

2. The system according to claim 1, characterized in that the data processing means learns normal behavioral patterns by comparing and analyzing past behavioral data.

3. The system according to claim 1, characterized in that the notification means generates a notification message including the type of anomaly and current location information.

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