system

Abstract

We provide the system. [Solution] A device means for acquiring location information, A device or means for learning past behavioral patterns, A device for detecting abnormal behavior, A device means for transmitting an alarm, A device and means for collecting information on dangerous areas, A device means for generating an alarm based on information about dangerous areas, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem of the elderly going missing or wandering is a serious social concern. In particular, wandering due to dementia or the like causes great anxiety and burden to the family. At present, means for effectively preventing this are limited, and there is a demand for the development of a system that can quickly and accurately detect abnormal behavior and notify the family. Furthermore, it is also important to prevent entry into dangerous areas in advance.

Means for Solving the Problems

[0005] This invention solves this problem by providing a device that uses GPS to acquire the location information of elderly people and learns their past behavioral patterns. If abnormal behavior is detected, it quickly generates an alarm and notifies relevant parties. Furthermore, it includes a means for periodically collecting information on dangerous areas via the internet and generating alarms based on this information. This system can ensure the safety of elderly people and alleviate the anxiety of their families.

[0006] A "device for acquiring location information" is a combination of hardware and software that uses GPS or other location-determining technologies to measure an individual's current location and acquire it as digital data.

[0007] A "device for learning past behavioral patterns" is a device that includes algorithms and processes that analyze acquired location information, record an individual's movement history over a specific period, and model their normal behavioral patterns.

[0008] A "device for detecting abnormal behavior" is a device equipped with an algorithm and warning system that compares real-time location information with past behavioral patterns to detect behavior that is different from the norm.

[0009] A "warning transmission device" is a device that has a communication interface for generating notifications and quickly transmitting information to designated recipients when abnormal behavior or entry into a dangerous area is detected.

[0010] A "device for collecting information on hazardous areas" is a device equipped with the function to acquire data on hazardous areas from the internet and other sources and update it in real time.

[0011] A "device that generates warnings based on hazardous area information" is a device that analyzes collected hazardous area information and has rules and logic to generate warnings when an individual approaches a hazardous area. [Brief explanation of the drawing]

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

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

[0017] In the following embodiments, a numbered 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.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0033] This invention is a senior monitoring system composed of collaboration between a terminal, a server, and a user. At the core of the system is location tracking using GPS technology, which enables users to live their daily lives with peace of mind. It mainly has the following functions:

[0034] The device acquires the senior's current location in real time. Location information is periodically sent to the server. The server stores the large amount of accumulated location information in a database and applies data analysis algorithms to learn the senior's normal behavior patterns. This allows for an understanding of movement trends based on time of day and day of the week.

[0035] The server continuously monitors location information received in real time or near real time and detects movement that deviates from known behavioral patterns. If an anomaly is detected, it has the function to promptly create an alert and send a notification. The notification is sent to the user or their related parties in the form of a text message, app notification, etc.

[0036] Furthermore, the server collects information about high-risk areas via the internet and stores it in a database. This information includes crime statistics and disaster predictions for specific areas. If a senior's current location matches this high-risk information, the server strengthens the alert and sends a notification. This feature ultimately enables seniors to avoid danger proactively.

[0037] For example, if a senior citizen stays outside their usual walking route for an extended period, the device continuously sends location information to the server. The server compares this data with existing behavioral patterns to measure the degree of abnormality. If the abnormality exceeds a certain threshold, an alert is immediately sent to the family. Furthermore, if the area entered is a dangerous zone, the alert can be made more detailed to encourage a quicker response.

[0038] This invention can improve the safety of seniors, reduce the burden on families and caregivers, and enhance a sense of security for society as a whole.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires the senior's location information via a GPS sensor. The acquired location information is then prepared to be sent to the server at predetermined time intervals.

[0042] Step 2:

[0043] The server receives location information transmitted from the terminal in real time. The received data is recorded in the database along with a timestamp.

[0044] Step 3:

[0045] The server analyzes accumulated location data and learns the typical behavioral patterns of seniors. Using machine learning algorithms, it models movement trends by time of day and day of the week of interest.

[0046] Step 4:

[0047] The server continuously acquires current location information and evaluates for anomalies by comparing it with previously learned behavioral patterns. If the conditions for an anomaly are met, an anomaly flag is set.

[0048] Step 5:

[0049] The server obtains the latest information on high-risk areas via the internet. This includes information on crime-ridden areas and accident-prone areas. The database is updated using this information to keep it current.

[0050] Step 6:

[0051] The server checks if the area the senior player is entering matches the hazardous area data. If they enter a hazardous area, a hazard warning flag is set.

[0052] Step 7:

[0053] The server generates an alarm when an anomaly flag or danger warning flag is set. The alarm includes the anomaly that occurred, location information, and, if necessary, information about the hazardous area.

[0054] Step 8:

[0055] The server notifies the user of any generated alarms. Notification methods include SMS, email, and push notifications via a dedicated app.

[0056] Step 9:

[0057] Users can review received alerts and, if they determine that an anomaly has occurred, take appropriate action as needed. They can also coordinate with the police and other relevant authorities through a dedicated platform.

[0058] (Example 1)

[0059] 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."

[0060] Ensuring the safety of seniors and providing timely information to relevant parties is crucial, but current technology is insufficient for real-time location tracking, anomaly detection, and rapid response to dangerous areas. Furthermore, there is a lack of a system to systematically manage and appropriately notify this information.

[0061] 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.

[0062] In this invention, the server includes functional means for acquiring location data, functional means for learning past movement patterns, and functional means for detecting abnormal movement. This enables real-time, safe monitoring of seniors and facilitates early detection and rapid response to abnormal situations.

[0063] The "location data acquisition function" refers to the function of accurately measuring and collecting the current location of an object using GPS or similar technology.

[0064] The "function to learn past movement patterns" is a function that analyzes accumulated location data to identify the target's usual movement routes and behavioral tendencies.

[0065] The "abnormal movement detection function" is a function that automatically identifies unnatural or unexpected movements by comparing real-time acquired location data with existing movement patterns.

[0066] The "function to create and communicate warnings" is a function that generates alarms for relevant parties based on detected anomalies and notifies them quickly.

[0067] The "function for collecting hazardous area information" is a function that collects data on high-risk areas from external sources and stores it as information for the system's decision-making.

[0068] The "function that generates warnings based on hazardous area information" is a function that strengthens the content of warnings and creates highly urgent information when a senior's current location is within a hazardous area.

[0069] The "function to receive and analyze location data in real time" refers to a function that instantly acquires constantly updated location information and performs the necessary analysis.

[0070] The senior monitoring system according to this invention consists of a terminal, a server, and a user, and has a location tracking function using GPS technology as its core. The terminal is a portable communication device with a built-in GPS module. This allows for the accurate acquisition of the senior's current location.

[0071] The server receives location data sent periodically from multiple terminals in real time and stores it in a database. The database uses an SQL-based system (e.g., MySQL®). The server utilizes programming languages ​​such as Python and machine learning libraries (e.g., scikit-learn) to analyze the received location data and learn past movement patterns. Through this process, the server understands the behavioral tendencies of seniors and generates warnings if abnormal movement is detected.

[0072] The server also has the capability to collect hazard zone information from external sources. This information may be collected using public APIs and news feeds from local governments. The server compares the collected hazard zone information with the senior's current location and strengthens the warning if it determines that the senior has entered a hazard zone.

[0073] Users can access the system via smartphones or computers to instantly detect any abnormalities in seniors. When an abnormality is detected, the server sends text messages or app notifications to the user. Communication protocols are used for communication, and commonly used APIs (e.g., Twilio and Firebase) are utilized for the notification service.

[0074] As a concrete example, let's simulate a scenario where a senior citizen deviates from their usual walking route and remains in a dangerous area for an extended period. In such a case, the device sends its location data to a server, which immediately analyzes the behavioral pattern to determine if there is an anomaly. If an anomaly is detected and the location falls within a dangerous area, the server can generate and send an enhanced warning to the user.

[0075] An example of a prompt sentence when asking a question about the generated AI model might be, "Please explain the details of the anomaly detection process in the senior monitoring system." Because the method for carrying out the invention is explained in detail in this way, it is possible to provide reassurance to both the user and the senior.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device acquires location data.

[0079] The input is satellite signals from a GPS sensor, and the output is position data in the form of latitude and longitude. This data is updated as needed and received periodically to maintain accuracy. Specifically, the device acquires and stores the position measured by the terminal every minute.

[0080] Step 2:

[0081] The device sends the acquired location data to the server.

[0082] The input is the location data obtained in step 1, and the output is a communication packet containing that data. This packet is sent to the server using 4G or Wi-Fi. A timestamp is added during transmission to clearly show the time chronological order of the data.

[0083] Step 3:

[0084] The server stores and analyzes the received location data.

[0085] The input consists of location data and timestamps, and the output is a model of typical movement patterns. Location data is stored using a database system, behavioral patterns are analyzed using a Python machine learning algorithm, and the model is refined. This process allows for the establishment of typical movement trends.

[0086] Step 4:

[0087] The server detects the abnormal movement.

[0088] The input is real-time updated location data, and the output is the result of anomaly detection. The server compares existing movement patterns with the current data and uses statistical methods to probabilistically evaluate deviations. If an anomaly is found, the information proceeds to the next processing step.

[0089] Step 5:

[0090] The server updates and compares the hazardous area information.

[0091] The input is hazardous area information collected from external sources, and the output is a hazard assessment. Hazard information is collected periodically via the API, and it is checked whether the current location falls within that area. If so, it is marked as a high-risk area.

[0092] Step 6:

[0093] The server generates a warning and notifies the user if an anomaly is detected.

[0094] The input consists of anomaly detection results, location data, and a risk assessment, while the output is a warning message. The message includes location, anomaly details, and risk level, and is sent to the user as a text message or app notification using an API. Specifically, services such as Twilio are used to ensure prompt notification.

[0095] (Application Example 1)

[0096] 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."

[0097] There is a need for effective systems to ensure the daily safety of the elderly, to detect abnormal behavior early, and to prevent them from approaching dangerous areas. In particular, it is important that relevant parties can quickly identify any abnormalities or dangers involving the elderly.

[0098] 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.

[0099] In this invention, the server includes information processing means for acquiring location information, information processing means for learning past behavioral patterns, and information processing means for transmitting notifications to a mobile communication terminal when an alarm is generated. This enables the detection of abnormal behavior and the generation of alarms when approaching dangerous areas based on the location information of elderly people, allowing relevant parties to respond quickly.

[0100] "Information processing means for acquiring location information" refers to technical means for measuring the current geographical location of elderly people and acquiring that data.

[0101] "Information processing methods for learning past behavioral patterns" refer to methods that analyze the typical movements and habits of elderly people, and accumulate and learn those patterns as data.

[0102] "Information processing means for detecting abnormal behavior" refers to means that have the function of detecting abnormalities when behavior that deviates from the normal behavior occurs in relation to learned behavioral patterns.

[0103] "Information processing means for transmitting warnings" refers to means for sending warnings or notifications to relevant parties when abnormal behavior or danger is detected.

[0104] "Information processing means for collecting information on dangerous areas" refers to means of collecting information from external sources about areas at risk of crime or natural disasters and storing it in a database.

[0105] "Information processing means for generating warnings based on dangerous area information" refers to means equipped with the function of creating warnings in real time when an elderly person approaches or enters an area deemed dangerous.

[0106] "Information processing means for transmitting notifications to mobile communication terminals when an alarm is generated" refers to technical means for promptly transmitting generated alarms and notifications to the mobile communication devices of relevant parties.

[0107] This invention is designed as a system to ensure the safety of the elderly, in which the server, terminal, and user cooperate as follows.

[0108] The device uses a built-in GPS receiver to acquire the elderly person's current location in real time. This information is continuously transmitted to a server, which stores the data and learns past behavioral patterns. An AI algorithm is used to learn behavioral patterns, analyzing typical travel routes and places of stay.

[0109] The server uses relevant algorithms to detect abnormal behavior. If abnormal behavior is detected, the server immediately generates an alarm and sends a notification to the relevant parties via mobile communication terminals. This allows for a swift response when an elderly person exhibits abnormal behavior.

[0110] Furthermore, the server regularly updates information on dangerous areas via the internet. If an elderly person approaches a dangerous area, the server generates a more enhanced alert and sends a notification containing detailed threat information. This function helps prevent danger and provides peace of mind to the elderly and their families.

[0111] As a concrete example, if an elderly person visiting a tourist spot in Tokyo stays in an unplanned location for an extended period, the server will detect this as an anomaly and send an alert to their family, including detailed location information. This allows for a swift response.

[0112] An example of an input prompt for a generating AI model is: "Please propose a program that simulates a system that sends a real-time alert to family members when it detects that a senior citizen is deviating from their usual movements."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device acquires the location information of the elderly person. The input is GPS data, which is acquired in real time to determine their geographical location. The output is location information in the form of location coordinates. This location information is transmitted to the server via a communication line.

[0116] Step 2:

[0117] The server receives location information. The input is the location coordinates sent from the terminal. This data is recorded and stored in a database along with past location data. The output is a message indicating that location information storage is complete. A data analysis algorithm is used to learn behavioral patterns.

[0118] Step 3:

[0119] The server detects abnormal behavior. The input consists of the received current location information and past behavior pattern data. An AI algorithm is applied to determine whether the current behavior deviates from the normal pattern. The output is the result of the abnormality determination. If an abnormality is detected, the system immediately proceeds to generate an alarm.

[0120] Step 4:

[0121] The server generates and transmits an alarm. The input consists of the anomaly detection result and information about the hazardous area. This information is used to generate specific alarm content. The output is an alarm message. The alarm is then notified to users and relevant parties via mobile communication terminals.

[0122] Step 5:

[0123] The server updates the hazardous area information. The input is hazardous area information collected periodically from the internet. This information is analyzed and stored in a database. The output is the updated hazardous area information. This information is used to enhance alarms when abnormal behavior is detected.

[0124] 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.

[0125] This invention is a monitoring system that enhances the safety of seniors, and in addition to real-time location tracking, it has the function of recognizing changes in emotions. The system consists of a terminal, a server, an emotion engine, and a user, and each element interacts to comprehensively evaluate the senior's state.

[0126] The device is equipped with a GPS sensor, microphone, and camera, and acquires not only the senior's location information but also voice and facial expression data. Using this data, the device analyzes the senior's emotional state via an emotion engine.

[0127] The server receives location information and emotional data transmitted from the terminal and stores them integrally in a database. The algorithm on the server is responsible not only for location tracking but also for monitoring seniors' typical behavioral patterns and emotional changes by incorporating the results of emotional analysis.

[0128] The emotion engine analyzes acquired voice and facial expression data to evaluate emotional states in real time. For example, it captures voice tone and facial expression changes that indicate stress or anxiety and reports them to the server. This data is sent to the server and analyzed in combination with location information and behavioral patterns.

[0129] The server compares previously learned behavioral patterns with current movements and emotional states, and generates an alarm if it detects abnormal behavior or significant emotional changes. The alarm includes information about the reason for the anomaly detection and the emotional change.

[0130] Users can review received alerts and choose appropriate actions as needed. Detailed information, including emotional states, makes it easier to determine urgency and priority.

[0131] For example, if a senior citizen deviates from their usual range of activity and exhibits restless voice or anxious facial expressions, the device immediately transmits this information. The server instantly analyzes these anomalies and issues an alert to the user. This alert includes both information about the deviation from normal behavior and the change in emotional state, allowing the user to quickly understand the senior citizen's situation and take appropriate action.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The device uses a GPS sensor to acquire the senior's location information. It also uses a voice sensor and camera to simultaneously record the senior's voice and facial expression data.

[0135] Step 2:

[0136] The device transmits the acquired location information, voice data, and facial expression data to the server. At this time, the data is packetized along with a timestamp.

[0137] Step 3:

[0138] The server receives location information, voice data, and facial expression data transmitted from the terminal and stores them in a database.

[0139] Step 4:

[0140] The server analyzes the senior's past behavioral patterns based on stored location information and compares them to their current movement patterns. This allows it to detect deviations from their behavior.

[0141] Step 5:

[0142] The server uses an emotion engine to analyze voice and facial expression data and evaluate the senior's emotional state. If emotions such as stress, anxiety, or excitement are detected, it determines if there is a possibility of abnormality.

[0143] Step 6:

[0144] The server comprehensively evaluates deviations in behavioral patterns and abnormal emotional states, and generates an alarm if an abnormality is detected. The alarm includes details of the specific behavioral deviations and emotional changes.

[0145] Step 7:

[0146] The server notifies the user of any generated alarms. These notifications may be sent via text message, email, or app push notifications.

[0147] Step 8:

[0148] Users check the received alerts and consider appropriate actions based on their content. For example, they might contact seniors or travel to the location.

[0149] Step 9:

[0150] Users will coordinate with the police and other emergency response agencies as needed. This information is based on the detailed information included in the alert.

[0151] (Example 2)

[0152] 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".

[0153] In monitoring systems for the elderly, there is a need not only to track location but also to detect changes in emotions and abnormal behavioral patterns in real time. However, conventional technologies have insufficient emotional state analysis, and there is a risk of missing sudden changes in condition, so improvements in safety were necessary.

[0154] 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.

[0155] In this invention, the server includes means for acquiring location information, means for analyzing emotional states, and means for learning past behavioral patterns. This makes it possible to comprehensively understand the location and condition of elderly people and to immediately issue an alarm if an abnormality is detected.

[0156] "Means of acquiring location information" refers to devices and software that detect and record an individual's geographical location in real time.

[0157] "Means for analyzing emotional states" refers to software or algorithms that analyze an individual's emotions based on voice and facial expression data and evaluate their emotional state.

[0158] "Means of learning past behavioral patterns" refers to technologies or systems that analyze and record an individual's usual behavioral patterns based on data collected in the past.

[0159] "Means for detecting abnormal behavior" refers to functions or programs that automatically recognize behaviors that deviate from normal behavioral patterns based on analyzed data.

[0160] "Means for acquiring voice and facial expression data" refers to sensors and devices for collecting voice and image data between an individual and their environment.

[0161] "Means of transmitting an alarm" refers to a device or program that electronically transmits a message to alert relevant parties when an abnormal condition is detected.

[0162] "Means for analyzing changes in behavior and emotions and generating alarms" refers to a system that continuously analyzes changes in an individual's behavior and emotional state and generates an alarm when an anomaly is detected.

[0163] "External data communication network" refers to public data networks such as the internet, and means infrastructure that enables data communication over a wide area.

[0164] This invention is designed as a monitoring system to ensure the safety of the elderly. The system includes a terminal, a server, an emotion engine, and a user, all of which work together in coordination.

[0165] The device is equipped with a GPS sensor, microphone, and camera. It acquires the elderly person's location information in real time and also collects voice and facial expression data. This data is analyzed using an emotion engine. The emotion engine has the function of evaluating emotional states such as stress and anxiety based on voice tone and changes in facial expression.

[0166] The server receives data sent from the terminal and stores it in a database. An algorithm on the server processes location information and sentiment analysis results, and detects anomalies by comparing them to normal behavioral patterns. When an anomaly is detected, an alarm is generated and the user is notified. This improves safety for the elderly.

[0167] Users receive alerts from the server through a dedicated application. These alerts include information about location deviations and changes in emotions, allowing users to take appropriate action. For example, they might immediately rush to the scene or contact relevant parties.

[0168] For example, if the device detects a deviation from the user's normal range of activity and signs of anxiety in their voice and facial expression, it sends this information to a server. The server immediately generates an alarm and notifies the user. This alarm allows the user to quickly understand the elderly person's condition and take appropriate measures.

[0169] In the emotion analysis section, which uses a generative AI model, prompts such as, "What should I do if a senior is leaving home and heading to an unfamiliar place? Also, what measures should be taken if the senior's voice contains a tone of tension?" are utilized. This enables advanced emotion analysis and emergency response.

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

[0171] Step 1:

[0172] The device uses a GPS sensor to acquire the elderly person's location information. The input is geographical coordinate data from the sensor, and the output is real-time location information sent to the server. Specifically, the device measures its current location every 30 seconds and packages that information into packets.

[0173] Step 2:

[0174] The device uses a microphone and camera to collect audio and facial data. The input is the voice and facial image of the elderly person, and the audio signal and image data are sent to the emotion engine as output. Specifically, the device records audio every 5 seconds and captures facial expressions with the camera.

[0175] Step 3:

[0176] The device processes data collected via an emotion engine. Inputs are audio signals and image data, and the emotion analysis algorithm evaluates stress and anxiety levels. Output is emotional state evaluation data, which is sent to the server. Specifically, the algorithm analyzes changes in voice tone and facial expression in real time and calculates an emotion score.

[0177] Step 4:

[0178] The server receives location information and sentiment evaluation data transmitted from the terminal and stores it in a database. The inputs are location data and sentiment scores, which are integrated as output for analysis performed after storage. Specifically, the server performs insertion operations on the database and records time-series data.

[0179] Step 5:

[0180] The server learns past behavioral patterns based on stored data and performs analysis to detect anomalies. The input is a dataset of past location history and sentiment scores, and the output is the result of anomaly detection. Specifically, pattern recognition is performed on the server using a machine learning algorithm to identify deviations from normal behavior and sentiment.

[0181] Step 6:

[0182] The server generates an alarm and sends a notification when an anomaly is detected. The input is the result of the anomaly detection, and the output is an alarm message to the user. Specifically, the alarm message is generated as text containing the details of the anomaly and recommended actions, and is sent to the user's mobile device.

[0183] Step 7:

[0184] The user reviews the received alarm and selects an appropriate response based on the situation. The input is the alarm message from the server, and the output is the confirmation result and the actual action plan. Specifically, the user views the alarm message through a dedicated app and takes actions such as checking on the elderly person at the scene or requesting assistance as needed.

[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 growing need to enhance safety by monitoring the behavior and emotional changes of seniors in real time. However, conventional systems are limited to acquiring location information and detecting simple behavioral abnormalities, and they cannot comprehensively consider emotional changes, making it difficult to respond quickly and accurately.

[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 functional means for acquiring location signals, functional means for analyzing voice and facial expression data to evaluate emotional states, and functional means for transmitting alarm signals. This enables comprehensive monitoring of not only behavioral abnormalities but also emotional changes in seniors, allowing for rapid and effective alarm notifications.

[0190] The "function to acquire location signals" refers to the function of determining the current location of an object in real time using GPS or other location measurement technologies.

[0191] The "function to learn behavioral history" is a function that accumulates and analyzes past behavioral data and learns normal behavioral patterns.

[0192] The "function to detect abnormal behavior" is a function that compares the learned normal behavior pattern with the current behavior and detects any deviation as abnormal.

[0193] The "alarm signal transmission function" is a function that sends notifications or alarms to the appropriate recipients when an abnormality or danger is detected.

[0194] The "function for collecting hazardous area information" refers to the function of gathering information on areas where danger is potential and managing it within the system.

[0195] The "function to generate alerts based on hazardous area information" is a function that uses collected hazardous area information to create an alert when applicable.

[0196] The "function to analyze voice and facial expression data to evaluate emotional state" is a function that analyzes acquired voice and facial expression data to evaluate the individual's emotional state in real time.

[0197] The system for implementing this invention mainly consists of a terminal, a server, an emotion analysis engine, and a user. The terminal is equipped with a GPS sensor, microphone, and camera, which enable the collection of location information, voice data, and facial expression data. This data is transmitted to the server using Bluetooth, Wi-Fi, or mobile data communication.

[0198] The server is built on a cloud platform and has the capability to receive and store data in real time. The server implements an emotion analysis engine using AI libraries such as TENSORFLOW® and PyTorch, which analyzes transmitted voice and facial expression data to evaluate emotional states. Furthermore, it compares behavioral history with current behavior and immediately generates an alarm signal if abnormal behavior or emotional changes are detected. The alarm includes location information, specific details of the anomaly, and changes in emotional state, and is sent to the user via push notification.

[0199] Users can receive alerts using a dedicated application on their smartphones or other devices. This application allows them to view detailed information and understand the current situation of the senior citizen. It also includes a function for quick contact when necessary.

[0200] For example, if a senior citizen moves to a different area than they normally frequent, and an anxious tone of voice is detected, the system will recognize this deviation from their usual pattern. Based on this information, family members and caregivers can respond quickly and make appropriate decisions to ensure their safety.

[0201] An example of a prompt to input into the generating AI model is, "Create a template that checks the latest location information and emotional state, and generates an alarm when an anomaly is detected." This ensures the system operates smoothly and the safety of seniors is maintained.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device uses a built-in GPS sensor, microphone, and camera to collect location information, voice data, and facial expression data of seniors in real time. Input is raw data acquired from the sensors, and output is location, voice, and image data temporarily stored within the device.

[0205] Step 2:

[0206] The device transmits the collected data to the server via Bluetooth or Wi-Fi. The input is the data acquired in step 1, and the output is the data converted into a format that can be sent to the server. During this process, data format conversion and compression are performed.

[0207] Step 3:

[0208] The server stores the received location information, audio data, and facial expression data in a cloud-based database. In this step, the input is the data sent from the terminal, and the output is the location and emotion data stored in the database.

[0209] Step 4:

[0210] The server processes voice and facial expression data using an emotion analysis engine to evaluate the senior's emotional state. The input is the voice and facial expression data saved in step 3, and the output is the result of the emotional state evaluation. Specifically, a model using TensorFlow or PyTorch analyzes changes in voice pitch, tone, and facial expression.

[0211] Step 5:

[0212] The server integrates the emotional state evaluation results and location information, compares them with past behavioral history, and detects anomalies. The input is the emotional evaluation results and location information obtained in step 4, and the output is the result regarding whether or not an anomaly was detected. The algorithm detects deviations from normal behavioral patterns.

[0213] Step 6:

[0214] If an anomaly is detected, the server generates an alarm signal and sends a push notification to the user's device. The input is the anomaly information detected in step 5, and the output is the alarm signal. Specifically, the notification content is constructed and immediately delivered to the user.

[0215] Step 7:

[0216] Users use a dedicated application on a device such as a smartphone to check the content of received alarms. The input is the alarm signal sent from the server, and the output is the alarm information displayed on the screen. Based on this, users decide on the appropriate response.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a senior monitoring system composed of collaboration between a terminal, a server, and a user. At the core of the system is location tracking using GPS technology, which enables users to live their daily lives with peace of mind. It mainly has the following functions:

[0234] The device acquires the senior's current location in real time. Location information is periodically sent to the server. The server stores the large amount of accumulated location information in a database and applies data analysis algorithms to learn the senior's normal behavior patterns. This allows for an understanding of movement trends based on time of day and day of the week.

[0235] The server continuously monitors location information received in real time or near real time and detects movement that deviates from known behavioral patterns. If an anomaly is detected, it has the function to promptly create an alert and send a notification. The notification is sent to the user or their related parties in the form of a text message, app notification, etc.

[0236] Furthermore, the server collects information about high-risk areas via the internet and stores it in a database. This information includes crime statistics and disaster predictions for specific areas. If a senior's current location matches this high-risk information, the server strengthens the alert and sends a notification. This feature ultimately enables seniors to avoid danger proactively.

[0237] For example, if a senior citizen stays outside their usual walking route for an extended period, the device continuously sends location information to the server. The server compares this data with existing behavioral patterns to measure the degree of abnormality. If the abnormality exceeds a certain threshold, an alert is immediately sent to the family. Furthermore, if the area entered is a dangerous zone, the alert can be made more detailed to encourage a quicker response.

[0238] This invention can improve the safety of seniors, reduce the burden on families and caregivers, and enhance a sense of security for society as a whole.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The device acquires the senior's location information via a GPS sensor. The acquired location information is then prepared to be sent to the server at predetermined time intervals.

[0242] Step 2:

[0243] The server receives location information transmitted from the terminal in real time. The received data is recorded in the database along with a timestamp.

[0244] Step 3:

[0245] The server analyzes accumulated location data and learns the typical behavioral patterns of seniors. Using machine learning algorithms, it models movement trends by time of day and day of the week of interest.

[0246] Step 4:

[0247] The server continuously acquires current location information and evaluates for anomalies by comparing it with previously learned behavioral patterns. If the conditions for an anomaly are met, an anomaly flag is set.

[0248] Step 5:

[0249] The server obtains the latest information on high-risk areas via the internet. This includes information on crime-ridden areas and accident-prone areas. The database is updated using this information to keep it current.

[0250] Step 6:

[0251] The server checks if the area the senior player is entering matches the hazardous area data. If they enter a hazardous area, a hazard warning flag is set.

[0252] Step 7:

[0253] The server generates an alarm when an anomaly flag or danger warning flag is set. The alarm includes the anomaly that occurred, location information, and, if necessary, information about the hazardous area.

[0254] Step 8:

[0255] The server notifies the user of any generated alarms. Notification methods include SMS, email, and push notifications via a dedicated app.

[0256] Step 9:

[0257] Users can review received alerts and, if they determine that an anomaly has occurred, take appropriate action as needed. They can also coordinate with the police and other relevant authorities through a dedicated platform.

[0258] (Example 1)

[0259] 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."

[0260] Ensuring the safety of seniors and providing timely information to relevant parties is crucial, but current technology is insufficient for real-time location tracking, anomaly detection, and rapid response to dangerous areas. Furthermore, there is a lack of a system to systematically manage and appropriately notify this information.

[0261] 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.

[0262] In this invention, the server includes functional means for acquiring location data, functional means for learning past movement patterns, and functional means for detecting abnormal movement. This enables real-time, safe monitoring of seniors and facilitates early detection and rapid response to abnormal situations.

[0263] The "location data acquisition function" refers to the function of accurately measuring and collecting the current location of an object using GPS or similar technology.

[0264] The "function to learn past movement patterns" is a function that analyzes accumulated location data to identify the target's usual movement routes and behavioral tendencies.

[0265] The "abnormal movement detection function" is a function that automatically identifies unnatural or unexpected movements by comparing real-time acquired location data with existing movement patterns.

[0266] The "function to create and communicate warnings" is a function that generates alarms for relevant parties based on detected anomalies and notifies them quickly.

[0267] The "function for collecting hazardous area information" is a function that collects data on high-risk areas from external sources and stores it as information for the system's decision-making.

[0268] The "function that generates warnings based on hazardous area information" is a function that strengthens the content of warnings and creates highly urgent information when a senior's current location is within a hazardous area.

[0269] The "function to receive and analyze location data in real time" refers to a function that instantly acquires constantly updated location information and performs the necessary analysis.

[0270] The senior monitoring system according to this invention consists of a terminal, a server, and a user, and has a location tracking function using GPS technology as its core. The terminal is a portable communication device with a built-in GPS module. This allows for the accurate acquisition of the senior's current location.

[0271] The server receives location data sent periodically from multiple terminals in real time and stores it in a database. The database uses an SQL-based system (e.g., MySQL). The server utilizes programming languages ​​such as Python and machine learning libraries (e.g., scikit-learn) to analyze the received location data and learn past movement patterns. Through this process, the server understands the behavioral tendencies of seniors and generates warnings if abnormal movement is detected.

[0272] The server also has the capability to collect hazard zone information from external sources. This information may be collected using public APIs and news feeds from local governments. The server compares the collected hazard zone information with the senior's current location and strengthens the warning if it determines that the senior has entered a hazard zone.

[0273] Users can access the system via smartphones or computers to instantly detect any abnormalities in seniors. When an abnormality is detected, the server sends text messages or app notifications to the user. Communication protocols are used for communication, and commonly used APIs (e.g., Twilio and Firebase) are utilized for the notification service.

[0274] As a concrete example, let's simulate a scenario where a senior citizen deviates from their usual walking route and remains in a dangerous area for an extended period. In such a case, the device sends its location data to a server, which immediately analyzes the behavioral pattern to determine if there is an anomaly. If an anomaly is detected and the location falls within a dangerous area, the server can generate and send an enhanced warning to the user.

[0275] An example of a prompt sentence when asking a question about the generated AI model might be, "Please explain the details of the anomaly detection process in the senior monitoring system." Because the method for carrying out the invention is explained in detail in this way, it is possible to provide reassurance to both the user and the senior.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The device acquires location data.

[0279] The input is a satellite signal from a GPS sensor, and the output is position data in the form of latitude and longitude. This data is updated each time and is received periodically to maintain accuracy. Specifically, the terminal acquires and holds the position measured every minute.

[0280] Step 2:

[0281] The terminal sends the acquired position data to the server.

[0282] The input is the position data obtained in Step 1, and the output is a communication packet containing that data. This packet is sent to the server using 4G or Wi-Fi. A timestamp is added during transmission to clarify the time series of the data.

[0283] Step 3:

[0284] The server saves and analyzes the received position data.

[0285] The input is the position data and the timestamp, and the output is a model of the normal movement pattern. The position data is saved using a database system, and the behavior pattern is analyzed and the model is adjusted using a machine learning algorithm in Python. Through this process, the normal movement tendency can be established.

[0286] Step 4:

[0287] The server detects abnormal movement.

[0288] The input is the position data updated in real time, and the output is the detection result of an anomaly. The server compares the existing movement pattern with the current data and probabilistically evaluates the deviation using statistical methods. If an anomaly is found, that information proceeds to subsequent processing.

[0289] Step 5:

[0290] The server updates and compares the dangerous area information.

[0291] The input is hazardous area information collected from external sources, and the output is a hazard assessment. Hazard information is collected periodically via the API, and it is checked whether the current location falls within that area. If so, it is marked as a high-risk area.

[0292] Step 6:

[0293] The server generates a warning and notifies the user if an anomaly is detected.

[0294] The input consists of anomaly detection results, location data, and a risk assessment, while the output is a warning message. The message includes location, anomaly details, and risk level, and is sent to the user as a text message or app notification using an API. Specifically, services such as Twilio are used to ensure prompt notification.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] There is a need for effective systems to ensure the daily safety of the elderly, to detect abnormal behavior early, and to prevent them from approaching dangerous areas. In particular, it is important that relevant parties can quickly identify any abnormalities or dangers involving the elderly.

[0298] 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.

[0299] In this invention, the server includes information processing means for acquiring location information, information processing means for learning past behavioral patterns, and information processing means for transmitting notifications to a mobile communication terminal when an alarm is generated. This enables the detection of abnormal behavior and the generation of alarms when approaching dangerous areas based on the location information of elderly people, allowing relevant parties to respond quickly.

[0300] The "information processing means for obtaining location information" is a technical means for measuring the current geographical location of the elderly and obtaining the data thereof.

[0301] The "information processing means for learning past behavior patterns" is a means for analyzing the normal movements and habits of the elderly, accumulating and learning the patterns as data.

[0302] The "information processing means for detecting abnormal behavior" is a means having a function of detecting an abnormality when an abnormal behavior occurs with respect to the learned behavior pattern.

[0303] The "information processing means for transmitting an alarm" is a means for transmitting a warning or notification to related persons when an abnormal behavior or a danger is detected.

[0304] The "information processing means for collecting dangerous area information" is a means for collecting information on areas at risk of crime or natural disasters from the outside and storing it in a database.

[0305] The "information processing means for generating an alarm based on dangerous area information" is a means having a function of creating an alarm in real time when the elderly approach or enter a dangerous area.

[0306] The "information processing means for transmitting a notification to a mobile communication terminal when an alarm is generated" is a technical means for promptly transmitting the generated alarm or notification to the mobile communication devices of related persons.

[0307] The present invention is designed as a system for ensuring the safety of the elderly, and in this system, the server, the terminal, and the user cooperate as follows.

[0308] The device uses a built-in GPS receiver to acquire the elderly person's current location in real time. This information is continuously transmitted to a server, which stores the data and learns past behavioral patterns. An AI algorithm is used to learn behavioral patterns, analyzing typical travel routes and places of stay.

[0309] The server uses relevant algorithms to detect abnormal behavior. If abnormal behavior is detected, the server immediately generates an alarm and sends a notification to the relevant parties via mobile communication terminals. This allows for a swift response when an elderly person exhibits abnormal behavior.

[0310] Furthermore, the server regularly updates information on dangerous areas via the internet. If an elderly person approaches a dangerous area, the server generates a more enhanced alert and sends a notification containing detailed threat information. This function helps prevent danger and provides peace of mind to the elderly and their families.

[0311] As a concrete example, if an elderly person visiting a tourist spot in Tokyo stays in an unplanned location for an extended period, the server will detect this as an anomaly and send an alert to their family, including detailed location information. This allows for a swift response.

[0312] An example of an input prompt for a generating AI model is: "Please propose a program that simulates a system that sends a real-time alert to family members when it detects that a senior citizen is deviating from their usual movements."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The device acquires the location information of the elderly person. The input is GPS data, which is acquired in real time to determine their geographical location. The output is location information in the form of location coordinates. This location information is transmitted to the server via a communication line.

[0316] Step 2:

[0317] The server receives location information. The input is the location coordinates sent from the terminal. This data is recorded and stored in a database along with past location data. The output is a message indicating that location information storage is complete. A data analysis algorithm is used to learn behavioral patterns.

[0318] Step 3:

[0319] The server detects abnormal behavior. The input consists of the received current location information and past behavior pattern data. An AI algorithm is applied to determine whether the current behavior deviates from the normal pattern. The output is the result of the abnormality determination. If an abnormality is detected, the system immediately proceeds to generate an alarm.

[0320] Step 4:

[0321] The server generates and transmits an alarm. The input consists of the anomaly detection result and information about the hazardous area. This information is used to generate specific alarm content. The output is an alarm message. The alarm is then notified to users and relevant parties via mobile communication terminals.

[0322] Step 5:

[0323] The server updates the hazardous area information. The input is hazardous area information collected periodically from the internet. This information is analyzed and stored in a database. The output is the updated hazardous area information. This information is used to enhance alarms when abnormal behavior is detected.

[0324] 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.

[0325] This invention is a monitoring system that enhances the safety of seniors, and in addition to real-time location tracking, it has the function of recognizing changes in emotions. The system consists of a terminal, a server, an emotion engine, and a user, and each element interacts to comprehensively evaluate the senior's state.

[0326] The device is equipped with a GPS sensor, microphone, and camera, and acquires not only the senior's location information but also voice and facial expression data. Using this data, the device analyzes the senior's emotional state via an emotion engine.

[0327] The server receives location information and emotional data transmitted from the terminal and stores them integrally in a database. The algorithm on the server is responsible not only for location tracking but also for monitoring seniors' typical behavioral patterns and emotional changes by incorporating the results of emotional analysis.

[0328] The emotion engine analyzes acquired voice and facial expression data to evaluate emotional states in real time. For example, it captures voice tone and facial expression changes that indicate stress or anxiety and reports them to the server. This data is sent to the server and analyzed in combination with location information and behavioral patterns.

[0329] The server compares previously learned behavioral patterns with current movements and emotional states, and generates an alarm if it detects abnormal behavior or significant emotional changes. The alarm includes information about the reason for the anomaly detection and the emotional change.

[0330] Users can review received alerts and choose appropriate actions as needed. Detailed information, including emotional states, makes it easier to determine urgency and priority.

[0331] For example, if a senior citizen deviates from their usual range of activity and exhibits restless voice or anxious facial expressions, the device immediately transmits this information. The server instantly analyzes these anomalies and issues an alert to the user. This alert includes both information about the deviation from normal behavior and the change in emotional state, allowing the user to quickly understand the senior citizen's situation and take appropriate action.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The device uses a GPS sensor to acquire the senior's location information. It also uses a voice sensor and camera to simultaneously record the senior's voice and facial expression data.

[0335] Step 2:

[0336] The device transmits the acquired location information, voice data, and facial expression data to the server. At this time, the data is packetized along with a timestamp.

[0337] Step 3:

[0338] The server receives location information, voice data, and facial expression data transmitted from the terminal and stores them in a database.

[0339] Step 4:

[0340] The server analyzes the senior's past behavioral patterns based on stored location information and compares them to their current movement patterns. This allows it to detect deviations from their behavior.

[0341] Step 5:

[0342] The server uses an emotion engine to analyze voice and facial expression data and evaluate the senior's emotional state. If emotions such as stress, anxiety, or excitement are detected, it determines if there is a possibility of abnormality.

[0343] Step 6:

[0344] The server comprehensively evaluates deviations in behavioral patterns and abnormal emotional states, and generates an alarm if an abnormality is detected. The alarm includes details of the specific behavioral deviations and emotional changes.

[0345] Step 7:

[0346] The server notifies the user of any generated alarms. These notifications may be sent via text message, email, or app push notifications.

[0347] Step 8:

[0348] Users check the received alerts and consider appropriate actions based on their content. For example, they might contact seniors or travel to the location.

[0349] Step 9:

[0350] Users will coordinate with the police and other emergency response agencies as needed. This information is based on the detailed information included in the alert.

[0351] (Example 2)

[0352] 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".

[0353] In monitoring systems for the elderly, there is a need not only to track location but also to detect changes in emotions and abnormal behavioral patterns in real time. However, conventional technologies have insufficient emotional state analysis, and there is a risk of missing sudden changes in condition, so improvements in safety were necessary.

[0354] 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.

[0355] In this invention, the server includes means for acquiring location information, means for analyzing emotional states, and means for learning past behavioral patterns. This makes it possible to comprehensively understand the location and condition of elderly people and to immediately issue an alarm if an abnormality is detected.

[0356] "Means of acquiring location information" refers to devices and software that detect and record an individual's geographical location in real time.

[0357] "Means for analyzing emotional states" refers to software or algorithms that analyze an individual's emotions based on voice and facial expression data and evaluate their emotional state.

[0358] "Means of learning past behavioral patterns" refers to technologies or systems that analyze and record an individual's usual behavioral patterns based on data collected in the past.

[0359] "Means for detecting abnormal behavior" refers to functions or programs that automatically recognize behaviors that deviate from normal behavioral patterns based on analyzed data.

[0360] "Means for acquiring voice and facial expression data" refers to sensors and devices for collecting voice and image data between an individual and their environment.

[0361] "Means of transmitting an alarm" refers to a device or program that electronically transmits a message to alert relevant parties when an abnormal condition is detected.

[0362] "Means for analyzing changes in behavior and emotions and generating alarms" refers to a system that continuously analyzes changes in an individual's behavior and emotional state and generates an alarm when an anomaly is detected.

[0363] "External data communication network" refers to public data networks such as the internet, and means infrastructure that enables data communication over a wide area.

[0364] This invention is designed as a monitoring system to ensure the safety of the elderly. The system includes a terminal, a server, an emotion engine, and a user, all of which work together in coordination.

[0365] The device is equipped with a GPS sensor, microphone, and camera. It acquires the elderly person's location information in real time and also collects voice and facial expression data. This data is analyzed using an emotion engine. The emotion engine has the function of evaluating emotional states such as stress and anxiety based on voice tone and changes in facial expression.

[0366] The server receives data sent from the terminal and stores it in a database. An algorithm on the server processes location information and sentiment analysis results, and detects anomalies by comparing them to normal behavioral patterns. When an anomaly is detected, an alarm is generated and the user is notified. This improves safety for the elderly.

[0367] Users receive alerts from the server through a dedicated application. These alerts include information about location deviations and changes in emotions, allowing users to take appropriate action. For example, they might immediately rush to the scene or contact relevant parties.

[0368] For example, if the device detects a deviation from the user's normal range of activity and signs of anxiety in their voice and facial expression, it sends this information to a server. The server immediately generates an alarm and notifies the user. This alarm allows the user to quickly understand the elderly person's condition and take appropriate measures.

[0369] In the emotion analysis section, which uses a generative AI model, prompts such as, "What should I do if a senior is leaving home and heading to an unfamiliar place? Also, what measures should be taken if the senior's voice contains a tone of tension?" are utilized. This enables advanced emotion analysis and emergency response.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device uses a GPS sensor to acquire the elderly person's location information. The input is geographical coordinate data from the sensor, and the output is real-time location information sent to the server. Specifically, the device measures its current location every 30 seconds and packages that information into packets.

[0373] Step 2:

[0374] The device uses a microphone and camera to collect audio and facial data. The input is the voice and facial image of the elderly person, and the audio signal and image data are sent to the emotion engine as output. Specifically, the device records audio every 5 seconds and captures facial expressions with the camera.

[0375] Step 3:

[0376] The device processes data collected via an emotion engine. Inputs are audio signals and image data, and the emotion analysis algorithm evaluates stress and anxiety levels. Output is emotional state evaluation data, which is sent to the server. Specifically, the algorithm analyzes changes in voice tone and facial expression in real time and calculates an emotion score.

[0377] Step 4:

[0378] The server receives location information and sentiment evaluation data transmitted from the terminal and stores it in a database. The inputs are location data and sentiment scores, which are integrated as output for analysis performed after storage. Specifically, the server performs insertion operations on the database and records time-series data.

[0379] Step 5:

[0380] The server learns past behavioral patterns based on stored data and performs analysis to detect anomalies. The input is a dataset of past location history and sentiment scores, and the output is the result of anomaly detection. Specifically, pattern recognition is performed on the server using a machine learning algorithm to identify deviations from normal behavior and sentiment.

[0381] Step 6:

[0382] The server generates an alarm and sends a notification when an anomaly is detected. The input is the result of the anomaly detection, and the output is an alarm message to the user. Specifically, the alarm message is generated as text containing the details of the anomaly and recommended actions, and is sent to the user's mobile device.

[0383] Step 7:

[0384] The user reviews the received alarm and selects an appropriate response based on the situation. The input is the alarm message from the server, and the output is the confirmation result and the actual action plan. Specifically, the user views the alarm message through a dedicated app and takes actions such as checking on the elderly person at the scene or requesting assistance as needed.

[0385] (Application Example 2)

[0386] 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."

[0387] There is a growing need to enhance safety by monitoring the behavior and emotional changes of seniors in real time. However, conventional systems are limited to acquiring location information and detecting simple behavioral abnormalities, and they cannot comprehensively consider emotional changes, making it difficult to respond quickly and accurately.

[0388] 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.

[0389] In this invention, the server includes functional means for acquiring location signals, functional means for analyzing voice and facial expression data to evaluate emotional states, and functional means for transmitting alarm signals. This enables comprehensive monitoring of not only behavioral abnormalities but also emotional changes in seniors, allowing for rapid and effective alarm notifications.

[0390] The "function to acquire location signals" refers to the function of determining the current location of an object in real time using GPS or other location measurement technologies.

[0391] The "function to learn behavioral history" is a function that accumulates and analyzes past behavioral data and learns normal behavioral patterns.

[0392] The "function to detect abnormal behavior" is a function that compares the learned normal behavior pattern with the current behavior and detects any deviation as abnormal.

[0393] The "alarm signal transmission function" is a function that sends notifications or alarms to the appropriate recipients when an abnormality or danger is detected.

[0394] The "function for collecting hazardous area information" refers to the function of gathering information on areas where danger is potential and managing it within the system.

[0395] The "function to generate alerts based on hazardous area information" is a function that uses collected hazardous area information to create an alert when applicable.

[0396] The "function to analyze voice and facial expression data to evaluate emotional state" is a function that analyzes acquired voice and facial expression data to evaluate the individual's emotional state in real time.

[0397] The system for implementing this invention mainly consists of a terminal, a server, an emotion analysis engine, and a user. The terminal is equipped with a GPS sensor, microphone, and camera, which enable the collection of location information, voice data, and facial expression data. This data is transmitted to the server using Bluetooth, Wi-Fi, or mobile data communication.

[0398] The server is built on a cloud platform and has the capability to receive and store data in real time. The server implements an emotion analysis engine using AI libraries such as TensorFlow and PyTorch, which analyzes transmitted voice and facial expression data to evaluate emotional states. Furthermore, it compares behavioral history with current behavior and immediately generates an alarm signal if abnormal behavior or emotional changes are detected. The alarm includes location information, the specific nature of the anomaly, and the change in emotional state, and is sent to the user via push notification.

[0399] Users can receive alerts using a dedicated application on their smartphones or other devices. This application allows them to view detailed information and understand the current situation of the senior citizen. It also includes a function for quick contact when necessary.

[0400] For example, if a senior citizen moves to a different area than they normally frequent, and an anxious tone of voice is detected, the system will recognize this deviation from their usual pattern. Based on this information, family members and caregivers can respond quickly and make appropriate decisions to ensure their safety.

[0401] An example of a prompt to input into the generating AI model is, "Create a template that checks the latest location information and emotional state, and generates an alarm when an anomaly is detected." This ensures the system operates smoothly and the safety of seniors is maintained.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The device uses a built-in GPS sensor, microphone, and camera to collect location information, voice data, and facial expression data of seniors in real time. Input is raw data acquired from the sensors, and output is location, voice, and image data temporarily stored within the device.

[0405] Step 2:

[0406] The device transmits the collected data to the server via Bluetooth or Wi-Fi. The input is the data acquired in step 1, and the output is the data converted into a format that can be sent to the server. During this process, data format conversion and compression are performed.

[0407] Step 3:

[0408] The server stores the received location information, audio data, and facial expression data in a cloud-based database. In this step, the input is the data sent from the terminal, and the output is the location and emotion data stored in the database.

[0409] Step 4:

[0410] The server processes voice and facial expression data using an emotion analysis engine to evaluate the senior's emotional state. The input is the voice and facial expression data saved in step 3, and the output is the result of the emotional state evaluation. Specifically, a model using TensorFlow or PyTorch analyzes changes in voice pitch, tone, and facial expression.

[0411] Step 5:

[0412] The server integrates the emotional state evaluation results and location information, compares them with past behavioral history, and detects anomalies. The input is the emotional evaluation results and location information obtained in step 4, and the output is the result regarding whether or not an anomaly was detected. The algorithm detects deviations from normal behavioral patterns.

[0413] Step 6:

[0414] If an anomaly is detected, the server generates an alarm signal and sends a push notification to the user's device. The input is the anomaly information detected in step 5, and the output is the alarm signal. Specifically, the notification content is constructed and immediately delivered to the user.

[0415] Step 7:

[0416] Users use a dedicated application on a device such as a smartphone to check the content of received alarms. The input is the alarm signal sent from the server, and the output is the alarm information displayed on the screen. Based on this, users decide on the appropriate response.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a senior monitoring system composed of collaboration between a terminal, a server, and a user. At the core of the system is location tracking using GPS technology, which enables users to live their daily lives with peace of mind. It mainly has the following functions:

[0434] The device acquires the senior's current location in real time. Location information is periodically sent to the server. The server stores the large amount of accumulated location information in a database and applies data analysis algorithms to learn the senior's normal behavior patterns. This allows for an understanding of movement trends based on time of day and day of the week.

[0435] The server continuously monitors location information received in real time or near real time and detects movement that deviates from known behavioral patterns. If an anomaly is detected, it has the function to promptly create an alert and send a notification. The notification is sent to the user or their related parties in the form of a text message, app notification, etc.

[0436] Furthermore, the server collects information about high-risk areas via the internet and stores it in a database. This information includes crime statistics and disaster predictions for specific areas. If a senior's current location matches this high-risk information, the server strengthens the alert and sends a notification. This feature ultimately enables seniors to avoid danger proactively.

[0437] For example, if a senior citizen stays outside their usual walking route for an extended period, the device continuously sends location information to the server. The server compares this data with existing behavioral patterns to measure the degree of abnormality. If the abnormality exceeds a certain threshold, an alert is immediately sent to the family. Furthermore, if the area entered is a dangerous zone, the alert can be made more detailed to encourage a quicker response.

[0438] This invention can improve the safety of seniors, reduce the burden on families and caregivers, and enhance a sense of security for society as a whole.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The device acquires the senior's location information via a GPS sensor. The acquired location information is then prepared to be sent to the server at predetermined time intervals.

[0442] Step 2:

[0443] The server receives location information transmitted from the terminal in real time. The received data is recorded in the database along with a timestamp.

[0444] Step 3:

[0445] The server analyzes accumulated location data and learns the typical behavioral patterns of seniors. Using machine learning algorithms, it models movement trends by time of day and day of the week of interest.

[0446] Step 4:

[0447] The server continuously acquires current location information and evaluates for anomalies by comparing it with previously learned behavioral patterns. If the conditions for an anomaly are met, an anomaly flag is set.

[0448] Step 5:

[0449] The server obtains the latest information on high-risk areas via the internet. This includes information on crime-ridden areas and accident-prone areas. The database is updated using this information to keep it current.

[0450] Step 6:

[0451] The server checks if the area the senior player is entering matches the hazardous area data. If they enter a hazardous area, a hazard warning flag is set.

[0452] Step 7:

[0453] The server generates an alarm when an anomaly flag or danger warning flag is set. The alarm includes the anomaly that occurred, location information, and, if necessary, information about the hazardous area.

[0454] Step 8:

[0455] The server notifies the user of any generated alarms. Notification methods include SMS, email, and push notifications via a dedicated app.

[0456] Step 9:

[0457] Users can review received alerts and, if they determine that an anomaly has occurred, take appropriate action as needed. They can also coordinate with the police and other relevant authorities through a dedicated platform.

[0458] (Example 1)

[0459] 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."

[0460] Ensuring the safety of seniors and providing timely information to relevant parties is crucial, but current technology is insufficient for real-time location tracking, anomaly detection, and rapid response to dangerous areas. Furthermore, there is a lack of a system to systematically manage and appropriately notify this information.

[0461] 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.

[0462] In this invention, the server includes functional means for acquiring location data, functional means for learning past movement patterns, and functional means for detecting abnormal movement. This enables real-time, safe monitoring of seniors and facilitates early detection and rapid response to abnormal situations.

[0463] The "location data acquisition function" refers to the function of accurately measuring and collecting the current location of an object using GPS or similar technology.

[0464] The "function to learn past movement patterns" is a function that analyzes accumulated location data to identify the target's usual movement routes and behavioral tendencies.

[0465] The "abnormal movement detection function" is a function that automatically identifies unnatural or unexpected movements by comparing real-time acquired location data with existing movement patterns.

[0466] The "function to create and communicate warnings" is a function that generates alarms for relevant parties based on detected anomalies and notifies them quickly.

[0467] The "function for collecting hazardous area information" is a function that collects data on high-risk areas from external sources and stores it as information for the system's decision-making.

[0468] The "function that generates warnings based on hazardous area information" is a function that strengthens the content of warnings and creates highly urgent information when a senior's current location is within a hazardous area.

[0469] The "function to receive and analyze location data in real time" refers to a function that instantly acquires constantly updated location information and performs the necessary analysis.

[0470] The senior monitoring system according to this invention consists of a terminal, a server, and a user, and has a location tracking function using GPS technology as its core. The terminal is a portable communication device with a built-in GPS module. This allows for the accurate acquisition of the senior's current location.

[0471] The server receives location data sent periodically from multiple terminals in real time and stores it in a database. The database uses an SQL-based system (e.g., MySQL). The server utilizes programming languages ​​such as Python and machine learning libraries (e.g., scikit-learn) to analyze the received location data and learn past movement patterns. Through this process, the server understands the behavioral tendencies of seniors and generates warnings if abnormal movement is detected.

[0472] The server also has the capability to collect hazard zone information from external sources. This information may be collected using public APIs and news feeds from local governments. The server compares the collected hazard zone information with the senior's current location and strengthens the warning if it determines that the senior has entered a hazard zone.

[0473] Users can access the system via smartphones or computers to instantly detect any abnormalities in seniors. When an abnormality is detected, the server sends text messages or app notifications to the user. Communication protocols are used for communication, and commonly used APIs (e.g., Twilio and Firebase) are utilized for the notification service.

[0474] As a concrete example, let's simulate a scenario where a senior citizen deviates from their usual walking route and remains in a dangerous area for an extended period. In such a case, the device sends its location data to a server, which immediately analyzes the behavioral pattern to determine if there is an anomaly. If an anomaly is detected and the location falls within a dangerous area, the server can generate and send an enhanced warning to the user.

[0475] An example of a prompt sentence when asking a question about the generated AI model might be, "Please explain the details of the anomaly detection process in the senior monitoring system." Because the method for carrying out the invention is explained in detail in this way, it is possible to provide reassurance to both the user and the senior.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device acquires location data.

[0479] The input is satellite signals from a GPS sensor, and the output is position data in the form of latitude and longitude. This data is updated as needed and received periodically to maintain accuracy. Specifically, the device acquires and stores the position measured by the terminal every minute.

[0480] Step 2:

[0481] The device sends the acquired location data to the server.

[0482] The input is the location data obtained in step 1, and the output is a communication packet containing that data. This packet is sent to the server using 4G or Wi-Fi. A timestamp is added during transmission to clearly show the time chronological order of the data.

[0483] Step 3:

[0484] The server stores and analyzes the received location data.

[0485] The input consists of location data and timestamps, and the output is a model of typical movement patterns. Location data is stored using a database system, behavioral patterns are analyzed using a Python machine learning algorithm, and the model is refined. This process allows for the establishment of typical movement trends.

[0486] Step 4:

[0487] The server detects the abnormal movement.

[0488] The input is real-time updated location data, and the output is the result of anomaly detection. The server compares existing movement patterns with the current data and uses statistical methods to probabilistically evaluate deviations. If an anomaly is found, the information proceeds to the next processing step.

[0489] Step 5:

[0490] The server updates and compares the hazardous area information.

[0491] The input is hazardous area information collected from external sources, and the output is a hazard assessment. Hazard information is collected periodically via the API, and it is checked whether the current location falls within that area. If so, it is marked as a high-risk area.

[0492] Step 6:

[0493] The server generates a warning and notifies the user if an anomaly is detected.

[0494] The input consists of anomaly detection results, location data, and a risk assessment, while the output is a warning message. The message includes location, anomaly details, and risk level, and is sent to the user as a text message or app notification using an API. Specifically, services such as Twilio are used to ensure prompt notification.

[0495] (Application Example 1)

[0496] 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."

[0497] There is a need for effective systems to ensure the daily safety of the elderly, to detect abnormal behavior early, and to prevent them from approaching dangerous areas. In particular, it is important that relevant parties can quickly identify any abnormalities or dangers involving the elderly.

[0498] 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.

[0499] In this invention, the server includes information processing means for acquiring location information, information processing means for learning past behavioral patterns, and information processing means for transmitting notifications to a mobile communication terminal when an alarm is generated. This enables the detection of abnormal behavior and the generation of alarms when approaching dangerous areas based on the location information of elderly people, allowing relevant parties to respond quickly.

[0500] "Information processing means for acquiring location information" refers to technical means for measuring the current geographical location of elderly people and acquiring that data.

[0501] "Information processing methods for learning past behavioral patterns" refer to methods that analyze the typical movements and habits of elderly people, and accumulate and learn those patterns as data.

[0502] "Information processing means for detecting abnormal behavior" refers to means that have the function of detecting abnormalities when behavior that deviates from the normal behavior occurs in relation to learned behavioral patterns.

[0503] "Information processing means for transmitting warnings" refers to means for sending warnings or notifications to relevant parties when abnormal behavior or danger is detected.

[0504] "Information processing means for collecting information on dangerous areas" refers to means of collecting information from external sources about areas at risk of crime or natural disasters and storing it in a database.

[0505] "Information processing means for generating warnings based on dangerous area information" refers to means equipped with the function of creating warnings in real time when an elderly person approaches or enters an area deemed dangerous.

[0506] "Information processing means for transmitting notifications to mobile communication terminals when an alarm is generated" refers to technical means for promptly transmitting generated alarms and notifications to the mobile communication devices of relevant parties.

[0507] This invention is designed as a system to ensure the safety of the elderly, in which the server, terminal, and user cooperate as follows.

[0508] The device uses a built-in GPS receiver to acquire the elderly person's current location in real time. This information is continuously transmitted to a server, which stores the data and learns past behavioral patterns. An AI algorithm is used to learn behavioral patterns, analyzing typical travel routes and places of stay.

[0509] The server uses relevant algorithms to detect abnormal behavior. If abnormal behavior is detected, the server immediately generates an alarm and sends a notification to the relevant parties via mobile communication terminals. This allows for a swift response when an elderly person exhibits abnormal behavior.

[0510] Furthermore, the server regularly updates information on dangerous areas via the internet. If an elderly person approaches a dangerous area, the server generates a more enhanced alert and sends a notification containing detailed threat information. This function helps prevent danger and provides peace of mind to the elderly and their families.

[0511] As a concrete example, if an elderly person visiting a tourist spot in Tokyo stays in an unplanned location for an extended period, the server will detect this as an anomaly and send an alert to their family, including detailed location information. This allows for a swift response.

[0512] An example of an input prompt for a generating AI model is: "Please propose a program that simulates a system that sends a real-time alert to family members when it detects that a senior citizen is deviating from their usual movements."

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The device acquires the location information of the elderly person. The input is GPS data, which is acquired in real time to determine their geographical location. The output is location information in the form of location coordinates. This location information is transmitted to the server via a communication line.

[0516] Step 2:

[0517] The server receives location information. The input is the location coordinates sent from the terminal. This data is recorded and stored in a database along with past location data. The output is a message indicating that location information storage is complete. A data analysis algorithm is used to learn behavioral patterns.

[0518] Step 3:

[0519] The server detects abnormal behavior. The input consists of the received current location information and past behavior pattern data. An AI algorithm is applied to determine whether the current behavior deviates from the normal pattern. The output is the result of the abnormality determination. If an abnormality is detected, the system immediately proceeds to generate an alarm.

[0520] Step 4:

[0521] The server generates and transmits an alarm. The input consists of the anomaly detection result and information about the hazardous area. This information is used to generate specific alarm content. The output is an alarm message. The alarm is then notified to users and relevant parties via mobile communication terminals.

[0522] Step 5:

[0523] The server updates the hazardous area information. The input is hazardous area information collected periodically from the internet. This information is analyzed and stored in a database. The output is the updated hazardous area information. This information is used to enhance alarms when abnormal behavior is detected.

[0524] 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.

[0525] This invention is a monitoring system that enhances the safety of seniors, and in addition to real-time location tracking, it has the function of recognizing changes in emotions. The system consists of a terminal, a server, an emotion engine, and a user, and each element interacts to comprehensively evaluate the senior's state.

[0526] The device is equipped with a GPS sensor, microphone, and camera, and acquires not only the senior's location information but also voice and facial expression data. Using this data, the device analyzes the senior's emotional state via an emotion engine.

[0527] The server receives location information and emotional data transmitted from the terminal and stores them integrally in a database. The algorithm on the server is responsible not only for location tracking but also for monitoring seniors' typical behavioral patterns and emotional changes by incorporating the results of emotional analysis.

[0528] The emotion engine analyzes acquired voice and facial expression data to evaluate emotional states in real time. For example, it captures voice tone and facial expression changes that indicate stress or anxiety and reports them to the server. This data is sent to the server and analyzed in combination with location information and behavioral patterns.

[0529] The server compares previously learned behavioral patterns with current movements and emotional states, and generates an alarm if it detects abnormal behavior or significant emotional changes. The alarm includes information about the reason for the anomaly detection and the emotional change.

[0530] Users can review received alerts and choose appropriate actions as needed. Detailed information, including emotional states, makes it easier to determine urgency and priority.

[0531] For example, if a senior citizen deviates from their usual range of activity and exhibits restless voice or anxious facial expressions, the device immediately transmits this information. The server instantly analyzes these anomalies and issues an alert to the user. This alert includes both information about the deviation from normal behavior and the change in emotional state, allowing the user to quickly understand the senior citizen's situation and take appropriate action.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The device uses a GPS sensor to acquire the senior's location information. It also uses a voice sensor and camera to simultaneously record the senior's voice and facial expression data.

[0535] Step 2:

[0536] The device transmits the acquired location information, voice data, and facial expression data to the server. At this time, the data is packetized along with a timestamp.

[0537] Step 3:

[0538] The server receives location information, voice data, and facial expression data transmitted from the terminal and stores them in a database.

[0539] Step 4:

[0540] The server analyzes the senior's past behavioral patterns based on stored location information and compares them to their current movement patterns. This allows it to detect deviations from their behavior.

[0541] Step 5:

[0542] The server uses an emotion engine to analyze voice and facial expression data and evaluate the senior's emotional state. If emotions such as stress, anxiety, or excitement are detected, it determines if there is a possibility of abnormality.

[0543] Step 6:

[0544] The server comprehensively evaluates deviations in behavioral patterns and abnormal emotional states, and generates an alarm if an abnormality is detected. The alarm includes details of the specific behavioral deviations and emotional changes.

[0545] Step 7:

[0546] The server notifies the user of any generated alarms. These notifications may be sent via text message, email, or app push notifications.

[0547] Step 8:

[0548] Users check the received alerts and consider appropriate actions based on their content. For example, they might contact seniors or travel to the location.

[0549] Step 9:

[0550] Users will coordinate with the police and other emergency response agencies as needed. This information is based on the detailed information included in the alert.

[0551] (Example 2)

[0552] 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."

[0553] In monitoring systems for the elderly, there is a need not only to track location but also to detect changes in emotions and abnormal behavioral patterns in real time. However, conventional technologies have insufficient emotional state analysis, and there is a risk of missing sudden changes in condition, so improvements in safety were necessary.

[0554] 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.

[0555] In this invention, the server includes means for acquiring location information, means for analyzing emotional states, and means for learning past behavioral patterns. This makes it possible to comprehensively understand the location and condition of elderly people and to immediately issue an alarm if an abnormality is detected.

[0556] "Means of acquiring location information" refers to devices and software that detect and record an individual's geographical location in real time.

[0557] "Means for analyzing emotional states" refers to software or algorithms that analyze an individual's emotions based on voice and facial expression data and evaluate their emotional state.

[0558] "Means of learning past behavioral patterns" refers to technologies or systems that analyze and record an individual's usual behavioral patterns based on data collected in the past.

[0559] "Means for detecting abnormal behavior" refers to functions or programs that automatically recognize behaviors that deviate from normal behavioral patterns based on analyzed data.

[0560] "Means for acquiring voice and facial expression data" refers to sensors and devices for collecting voice and image data between an individual and their environment.

[0561] "Means of transmitting an alarm" refers to a device or program that electronically transmits a message to alert relevant parties when an abnormal condition is detected.

[0562] "Means for analyzing changes in behavior and emotions and generating alarms" refers to a system that continuously analyzes changes in an individual's behavior and emotional state and generates an alarm when an anomaly is detected.

[0563] "External data communication network" refers to public data networks such as the internet, and means infrastructure that enables data communication over a wide area.

[0564] This invention is designed as a monitoring system to ensure the safety of the elderly. The system includes a terminal, a server, an emotion engine, and a user, all of which work together in coordination.

[0565] The device is equipped with a GPS sensor, microphone, and camera. It acquires the elderly person's location information in real time and also collects voice and facial expression data. This data is analyzed using an emotion engine. The emotion engine has the function of evaluating emotional states such as stress and anxiety based on voice tone and changes in facial expression.

[0566] The server receives data sent from the terminal and stores it in a database. An algorithm on the server processes location information and sentiment analysis results, and detects anomalies by comparing them to normal behavioral patterns. When an anomaly is detected, an alarm is generated and the user is notified. This improves safety for the elderly.

[0567] Users receive alerts from the server through a dedicated application. These alerts include information about location deviations and changes in emotions, allowing users to take appropriate action. For example, they might immediately rush to the scene or contact relevant parties.

[0568] For example, if the device detects a deviation from the user's normal range of activity and signs of anxiety in their voice and facial expression, it sends this information to a server. The server immediately generates an alarm and notifies the user. This alarm allows the user to quickly understand the elderly person's condition and take appropriate measures.

[0569] In the emotion analysis section, which uses a generative AI model, prompts such as, "What should I do if a senior is leaving home and heading to an unfamiliar place? Also, what measures should be taken if the senior's voice contains a tone of tension?" are utilized. This enables advanced emotion analysis and emergency response.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The device uses a GPS sensor to acquire the elderly person's location information. The input is geographical coordinate data from the sensor, and the output is real-time location information sent to the server. Specifically, the device measures its current location every 30 seconds and packages that information into packets.

[0573] Step 2:

[0574] The device uses a microphone and camera to collect audio and facial data. The input is the voice and facial image of the elderly person, and the audio signal and image data are sent to the emotion engine as output. Specifically, the device records audio every 5 seconds and captures facial expressions with the camera.

[0575] Step 3:

[0576] The device processes data collected via an emotion engine. Inputs are audio signals and image data, and the emotion analysis algorithm evaluates stress and anxiety levels. Output is emotional state evaluation data, which is sent to the server. Specifically, the algorithm analyzes changes in voice tone and facial expression in real time and calculates an emotion score.

[0577] Step 4:

[0578] The server receives location information and sentiment evaluation data transmitted from the terminal and stores it in a database. The inputs are location data and sentiment scores, which are integrated as output for analysis performed after storage. Specifically, the server performs insertion operations on the database and records time-series data.

[0579] Step 5:

[0580] The server learns past behavioral patterns based on stored data and performs analysis to detect anomalies. The input is a dataset of past location history and sentiment scores, and the output is the result of anomaly detection. Specifically, pattern recognition is performed on the server using a machine learning algorithm to identify deviations from normal behavior and sentiment.

[0581] Step 6:

[0582] The server generates an alarm and sends a notification when an anomaly is detected. The input is the result of the anomaly detection, and the output is an alarm message to the user. Specifically, the alarm message is generated as text containing the details of the anomaly and recommended actions, and is sent to the user's mobile device.

[0583] Step 7:

[0584] The user reviews the received alarm and selects an appropriate response based on the situation. The input is the alarm message from the server, and the output is the confirmation result and the actual action plan. Specifically, the user views the alarm message through a dedicated app and takes actions such as checking on the elderly person at the scene or requesting assistance as needed.

[0585] (Application Example 2)

[0586] 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."

[0587] There is a growing need to enhance safety by monitoring the behavior and emotional changes of seniors in real time. However, conventional systems are limited to acquiring location information and detecting simple behavioral abnormalities, and they cannot comprehensively consider emotional changes, making it difficult to respond quickly and accurately.

[0588] 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.

[0589] In this invention, the server includes functional means for acquiring location signals, functional means for analyzing voice and facial expression data to evaluate emotional states, and functional means for transmitting alarm signals. This enables comprehensive monitoring of not only behavioral abnormalities but also emotional changes in seniors, allowing for rapid and effective alarm notifications.

[0590] The "function to acquire location signals" refers to the function of determining the current location of an object in real time using GPS or other location measurement technologies.

[0591] The "function to learn behavioral history" is a function that accumulates and analyzes past behavioral data and learns normal behavioral patterns.

[0592] The "function to detect abnormal behavior" is a function that compares the learned normal behavior pattern with the current behavior and detects any deviation as abnormal.

[0593] The "alarm signal transmission function" is a function that sends notifications or alarms to the appropriate recipients when an abnormality or danger is detected.

[0594] The "function for collecting hazardous area information" refers to the function of gathering information on areas where danger is potential and managing it within the system.

[0595] The "function to generate alerts based on hazardous area information" is a function that uses collected hazardous area information to create an alert when applicable.

[0596] The "function to analyze voice and facial expression data to evaluate emotional state" is a function that analyzes acquired voice and facial expression data to evaluate the individual's emotional state in real time.

[0597] The system for implementing this invention mainly consists of a terminal, a server, an emotion analysis engine, and a user. The terminal is equipped with a GPS sensor, microphone, and camera, which enable the collection of location information, voice data, and facial expression data. This data is transmitted to the server using Bluetooth, Wi-Fi, or mobile data communication.

[0598] The server is built on a cloud platform and has the capability to receive and store data in real time. The server implements an emotion analysis engine using AI libraries such as TensorFlow and PyTorch, which analyzes transmitted voice and facial expression data to evaluate emotional states. Furthermore, it compares behavioral history with current behavior and immediately generates an alarm signal if abnormal behavior or emotional changes are detected. The alarm includes location information, the specific nature of the anomaly, and the change in emotional state, and is sent to the user via push notification.

[0599] Users can receive alerts using a dedicated application on their smartphones or other devices. This application allows them to view detailed information and understand the current situation of the senior citizen. It also includes a function for quick contact when necessary.

[0600] For example, if a senior citizen moves to a different area than they normally frequent, and an anxious tone of voice is detected, the system will recognize this deviation from their usual pattern. Based on this information, family members and caregivers can respond quickly and make appropriate decisions to ensure their safety.

[0601] An example of a prompt to input into the generating AI model is, "Create a template that checks the latest location information and emotional state, and generates an alarm when an anomaly is detected." This ensures the system operates smoothly and the safety of seniors is maintained.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The device uses a built-in GPS sensor, microphone, and camera to collect location information, voice data, and facial expression data of seniors in real time. Input is raw data acquired from the sensors, and output is location, voice, and image data temporarily stored within the device.

[0605] Step 2:

[0606] The device transmits the collected data to the server via Bluetooth or Wi-Fi. The input is the data acquired in step 1, and the output is the data converted into a format that can be sent to the server. During this process, data format conversion and compression are performed.

[0607] Step 3:

[0608] The server stores the received location information, audio data, and facial expression data in a cloud-based database. In this step, the input is the data sent from the terminal, and the output is the location and emotion data stored in the database.

[0609] Step 4:

[0610] The server processes voice and facial expression data using an emotion analysis engine to evaluate the senior's emotional state. The input is the voice and facial expression data saved in step 3, and the output is the result of the emotional state evaluation. Specifically, a model using TensorFlow or PyTorch analyzes changes in voice pitch, tone, and facial expression.

[0611] Step 5:

[0612] The server integrates the emotional state evaluation results and location information, compares them with past behavioral history, and detects anomalies. The input is the emotional evaluation results and location information obtained in step 4, and the output is the result regarding whether or not an anomaly was detected. The algorithm detects deviations from normal behavioral patterns.

[0613] Step 6:

[0614] If an anomaly is detected, the server generates an alarm signal and sends a push notification to the user's device. The input is the anomaly information detected in step 5, and the output is the alarm signal. Specifically, the notification content is constructed and immediately delivered to the user.

[0615] Step 7:

[0616] Users use a dedicated application on a device such as a smartphone to check the content of received alarms. The input is the alarm signal sent from the server, and the output is the alarm information displayed on the screen. Based on this, users decide on the appropriate response.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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".

[0634] This invention is a senior monitoring system composed of collaboration between a terminal, a server, and a user. At the core of the system is location tracking using GPS technology, which enables users to live their daily lives with peace of mind. It mainly has the following functions:

[0635] The device acquires the senior's current location in real time. Location information is periodically sent to the server. The server stores the large amount of accumulated location information in a database and applies data analysis algorithms to learn the senior's normal behavior patterns. This allows for an understanding of movement trends based on time of day and day of the week.

[0636] The server continuously monitors location information received in real time or near real time and detects movement that deviates from known behavioral patterns. If an anomaly is detected, it has the function to promptly create an alert and send a notification. The notification is sent to the user or their related parties in the form of a text message, app notification, etc.

[0637] Furthermore, the server collects information about high-risk areas via the internet and stores it in a database. This information includes crime statistics and disaster predictions for specific areas. If a senior's current location matches this high-risk information, the server strengthens the alert and sends a notification. This feature ultimately enables seniors to avoid danger proactively.

[0638] For example, if a senior citizen stays outside their usual walking route for an extended period, the device continuously sends location information to the server. The server compares this data with existing behavioral patterns to measure the degree of abnormality. If the abnormality exceeds a certain threshold, an alert is immediately sent to the family. Furthermore, if the area entered is a dangerous zone, the alert can be made more detailed to encourage a quicker response.

[0639] This invention can improve the safety of seniors, reduce the burden on families and caregivers, and enhance a sense of security for society as a whole.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The device acquires the senior's location information via a GPS sensor. The acquired location information is then prepared to be sent to the server at predetermined time intervals.

[0643] Step 2:

[0644] The server receives location information transmitted from the terminal in real time. The received data is recorded in the database along with a timestamp.

[0645] Step 3:

[0646] The server analyzes accumulated location data and learns the typical behavioral patterns of seniors. Using machine learning algorithms, it models movement trends by time of day and day of the week of interest.

[0647] Step 4:

[0648] The server continuously acquires current location information and evaluates for anomalies by comparing it with previously learned behavioral patterns. If the conditions for an anomaly are met, an anomaly flag is set.

[0649] Step 5:

[0650] The server obtains the latest information on high-risk areas via the internet. This includes information on crime-ridden areas and accident-prone areas. The database is updated using this information to keep it current.

[0651] Step 6:

[0652] The server checks if the area the senior player is entering matches the hazardous area data. If they enter a hazardous area, a hazard warning flag is set.

[0653] Step 7:

[0654] The server generates an alarm when an anomaly flag or danger warning flag is set. The alarm includes the anomaly that occurred, location information, and, if necessary, information about the hazardous area.

[0655] Step 8:

[0656] The server notifies the user of any generated alarms. Notification methods include SMS, email, and push notifications via a dedicated app.

[0657] Step 9:

[0658] Users can review received alerts and, if they determine that an anomaly has occurred, take appropriate action as needed. They can also coordinate with the police and other relevant authorities through a dedicated platform.

[0659] (Example 1)

[0660] 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".

[0661] Ensuring the safety of seniors and providing timely information to relevant parties is crucial, but current technology is insufficient for real-time location tracking, anomaly detection, and rapid response to dangerous areas. Furthermore, there is a lack of a system to systematically manage and appropriately notify this information.

[0662] 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.

[0663] In this invention, the server includes functional means for acquiring location data, functional means for learning past movement patterns, and functional means for detecting abnormal movement. This enables real-time, safe monitoring of seniors and facilitates early detection and rapid response to abnormal situations.

[0664] The "location data acquisition function" refers to the function of accurately measuring and collecting the current location of an object using GPS or similar technology.

[0665] The "function to learn past movement patterns" is a function that analyzes accumulated location data to identify the target's usual movement routes and behavioral tendencies.

[0666] The "abnormal movement detection function" is a function that automatically identifies unnatural or unexpected movements by comparing real-time acquired location data with existing movement patterns.

[0667] The "function to create and communicate warnings" is a function that generates alarms for relevant parties based on detected anomalies and notifies them quickly.

[0668] The "function for collecting hazardous area information" is a function that collects data on high-risk areas from external sources and stores it as information for the system's decision-making.

[0669] The "function that generates warnings based on hazardous area information" is a function that strengthens the content of warnings and creates highly urgent information when a senior's current location is within a hazardous area.

[0670] The "function to receive and analyze location data in real time" refers to a function that instantly acquires constantly updated location information and performs the necessary analysis.

[0671] The senior monitoring system according to this invention consists of a terminal, a server, and a user, and has a location tracking function using GPS technology as its core. The terminal is a portable communication device with a built-in GPS module. This allows for the accurate acquisition of the senior's current location.

[0672] The server receives location data sent periodically from multiple terminals in real time and stores it in a database. The database uses an SQL-based system (e.g., MySQL). The server utilizes programming languages ​​such as Python and machine learning libraries (e.g., scikit-learn) to analyze the received location data and learn past movement patterns. Through this process, the server understands the behavioral tendencies of seniors and generates warnings if abnormal movement is detected.

[0673] The server also has the capability to collect hazard zone information from external sources. This information may be collected using public APIs and news feeds from local governments. The server compares the collected hazard zone information with the senior's current location and strengthens the warning if it determines that the senior has entered a hazard zone.

[0674] Users can access the system via smartphones or computers to instantly detect any abnormalities in seniors. When an abnormality is detected, the server sends text messages or app notifications to the user. Communication protocols are used for communication, and commonly used APIs (e.g., Twilio and Firebase) are utilized for the notification service.

[0675] As a concrete example, let's simulate a scenario where a senior citizen deviates from their usual walking route and remains in a dangerous area for an extended period. In such a case, the device sends its location data to a server, which immediately analyzes the behavioral pattern to determine if there is an anomaly. If an anomaly is detected and the location falls within a dangerous area, the server can generate and send an enhanced warning to the user.

[0676] An example of a prompt sentence when asking a question about the generated AI model might be, "Please explain the details of the anomaly detection process in the senior monitoring system." Because the method for carrying out the invention is explained in detail in this way, it is possible to provide reassurance to both the user and the senior.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The device acquires location data.

[0680] The input is satellite signals from a GPS sensor, and the output is position data in the form of latitude and longitude. This data is updated as needed and received periodically to maintain accuracy. Specifically, the device acquires and stores the position measured by the terminal every minute.

[0681] Step 2:

[0682] The device sends the acquired location data to the server.

[0683] The input is the location data obtained in step 1, and the output is a communication packet containing that data. This packet is sent to the server using 4G or Wi-Fi. A timestamp is added during transmission to clearly show the time chronological order of the data.

[0684] Step 3:

[0685] The server stores and analyzes the received location data.

[0686] The input consists of location data and timestamps, and the output is a model of typical movement patterns. Location data is stored using a database system, behavioral patterns are analyzed using a Python machine learning algorithm, and the model is refined. This process allows for the establishment of typical movement trends.

[0687] Step 4:

[0688] The server detects the abnormal movement.

[0689] The input is real-time updated location data, and the output is the result of anomaly detection. The server compares existing movement patterns with the current data and uses statistical methods to probabilistically evaluate deviations. If an anomaly is found, the information proceeds to the next processing step.

[0690] Step 5:

[0691] The server updates and compares the hazardous area information.

[0692] The input is hazardous area information collected from external sources, and the output is a hazard assessment. Hazard information is collected periodically via the API, and it is checked whether the current location falls within that area. If so, it is marked as a high-risk area.

[0693] Step 6:

[0694] The server generates a warning and notifies the user if an anomaly is detected.

[0695] The input consists of anomaly detection results, location data, and a risk assessment, while the output is a warning message. The message includes location, anomaly details, and risk level, and is sent to the user as a text message or app notification using an API. Specifically, services such as Twilio are used to ensure prompt notification.

[0696] (Application Example 1)

[0697] 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".

[0698] There is a need for effective systems to ensure the daily safety of the elderly, to detect abnormal behavior early, and to prevent them from approaching dangerous areas. In particular, it is important that relevant parties can quickly identify any abnormalities or dangers involving the elderly.

[0699] 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.

[0700] In this invention, the server includes information processing means for acquiring location information, information processing means for learning past behavioral patterns, and information processing means for transmitting notifications to a mobile communication terminal when an alarm is generated. This enables the detection of abnormal behavior and the generation of alarms when approaching dangerous areas based on the location information of elderly people, allowing relevant parties to respond quickly.

[0701] "Information processing means for acquiring location information" refers to technical means for measuring the current geographical location of elderly people and acquiring that data.

[0702] "Information processing methods for learning past behavioral patterns" refer to methods that analyze the typical movements and habits of elderly people, and accumulate and learn those patterns as data.

[0703] "Information processing means for detecting abnormal behavior" refers to means that have the function of detecting abnormalities when behavior that deviates from the normal behavior occurs in relation to learned behavioral patterns.

[0704] "Information processing means for transmitting warnings" refers to means for sending warnings or notifications to relevant parties when abnormal behavior or danger is detected.

[0705] "Information processing means for collecting information on dangerous areas" refers to means of collecting information from external sources about areas at risk of crime or natural disasters and storing it in a database.

[0706] "Information processing means for generating warnings based on dangerous area information" refers to means equipped with the function of creating warnings in real time when an elderly person approaches or enters an area deemed dangerous.

[0707] "Information processing means for transmitting notifications to mobile communication terminals when an alarm is generated" refers to technical means for promptly transmitting generated alarms and notifications to the mobile communication devices of relevant parties.

[0708] This invention is designed as a system to ensure the safety of the elderly, in which the server, terminal, and user cooperate as follows.

[0709] The device uses a built-in GPS receiver to acquire the elderly person's current location in real time. This information is continuously transmitted to a server, which stores the data and learns past behavioral patterns. An AI algorithm is used to learn behavioral patterns, analyzing typical travel routes and places of stay.

[0710] The server uses relevant algorithms to detect abnormal behavior. If abnormal behavior is detected, the server immediately generates an alarm and sends a notification to the relevant parties via mobile communication terminals. This allows for a swift response when an elderly person exhibits abnormal behavior.

[0711] Furthermore, the server regularly updates information on dangerous areas via the internet. If an elderly person approaches a dangerous area, the server generates a more enhanced alert and sends a notification containing detailed threat information. This function helps prevent danger and provides peace of mind to the elderly and their families.

[0712] As a concrete example, if an elderly person visiting a tourist spot in Tokyo stays in an unplanned location for an extended period, the server will detect this as an anomaly and send an alert to their family, including detailed location information. This allows for a swift response.

[0713] An example of an input prompt for a generating AI model is: "Please propose a program that simulates a system that sends a real-time alert to family members when it detects that a senior citizen is deviating from their usual movements."

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The device acquires the location information of the elderly person. The input is GPS data, which is acquired in real time to determine their geographical location. The output is location information in the form of location coordinates. This location information is transmitted to the server via a communication line.

[0717] Step 2:

[0718] The server receives location information. The input is the location coordinates sent from the terminal. This data is recorded and stored in a database along with past location data. The output is a message indicating that location information storage is complete. A data analysis algorithm is used to learn behavioral patterns.

[0719] Step 3:

[0720] The server detects abnormal behavior. The input consists of the received current location information and past behavior pattern data. An AI algorithm is applied to determine whether the current behavior deviates from the normal pattern. The output is the result of the abnormality determination. If an abnormality is detected, the system immediately proceeds to generate an alarm.

[0721] Step 4:

[0722] The server generates and transmits an alarm. The input consists of the anomaly detection result and information about the hazardous area. This information is used to generate specific alarm content. The output is an alarm message. The alarm is then notified to users and relevant parties via mobile communication terminals.

[0723] Step 5:

[0724] The server updates the hazardous area information. The input is hazardous area information collected periodically from the internet. This information is analyzed and stored in a database. The output is the updated hazardous area information. This information is used to enhance alarms when abnormal behavior is detected.

[0725] 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.

[0726] This invention is a monitoring system that enhances the safety of seniors, and in addition to real-time location tracking, it has the function of recognizing changes in emotions. The system consists of a terminal, a server, an emotion engine, and a user, and each element interacts to comprehensively evaluate the senior's state.

[0727] The device is equipped with a GPS sensor, microphone, and camera, and acquires not only the senior's location information but also voice and facial expression data. Using this data, the device analyzes the senior's emotional state via an emotion engine.

[0728] The server receives location information and emotional data transmitted from the terminal and stores them integrally in a database. The algorithm on the server is responsible not only for location tracking but also for monitoring seniors' typical behavioral patterns and emotional changes by incorporating the results of emotional analysis.

[0729] The emotion engine analyzes acquired voice and facial expression data to evaluate emotional states in real time. For example, it captures voice tone and facial expression changes that indicate stress or anxiety and reports them to the server. This data is sent to the server and analyzed in combination with location information and behavioral patterns.

[0730] The server compares previously learned behavioral patterns with current movements and emotional states, and generates an alarm if it detects abnormal behavior or significant emotional changes. The alarm includes information about the reason for the anomaly detection and the emotional change.

[0731] Users can review received alerts and choose appropriate actions as needed. Detailed information, including emotional states, makes it easier to determine urgency and priority.

[0732] For example, if a senior citizen deviates from their usual range of activity and exhibits restless voice or anxious facial expressions, the device immediately transmits this information. The server instantly analyzes these anomalies and issues an alert to the user. This alert includes both information about the deviation from normal behavior and the change in emotional state, allowing the user to quickly understand the senior citizen's situation and take appropriate action.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The device uses a GPS sensor to acquire the senior's location information. It also uses a voice sensor and camera to simultaneously record the senior's voice and facial expression data.

[0736] Step 2:

[0737] The device transmits the acquired location information, voice data, and facial expression data to the server. At this time, the data is packetized along with a timestamp.

[0738] Step 3:

[0739] The server receives location information, voice data, and facial expression data transmitted from the terminal and stores them in a database.

[0740] Step 4:

[0741] The server analyzes the senior's past behavioral patterns based on stored location information and compares them to their current movement patterns. This allows it to detect deviations from their behavior.

[0742] Step 5:

[0743] The server uses an emotion engine to analyze voice and facial expression data and evaluate the senior's emotional state. If emotions such as stress, anxiety, or excitement are detected, it determines if there is a possibility of abnormality.

[0744] Step 6:

[0745] The server comprehensively evaluates deviations in behavioral patterns and abnormal emotional states, and generates an alarm if an abnormality is detected. The alarm includes details of the specific behavioral deviations and emotional changes.

[0746] Step 7:

[0747] The server notifies the user of any generated alarms. These notifications may be sent via text message, email, or app push notifications.

[0748] Step 8:

[0749] Users check the received alerts and consider appropriate actions based on their content. For example, they might contact seniors or travel to the location.

[0750] Step 9:

[0751] Users will coordinate with the police and other emergency response agencies as needed. This information is based on the detailed information included in the alert.

[0752] (Example 2)

[0753] 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".

[0754] In monitoring systems for the elderly, there is a need not only to track location but also to detect changes in emotions and abnormal behavioral patterns in real time. However, conventional technologies have insufficient emotional state analysis, and there is a risk of missing sudden changes in condition, so improvements in safety were necessary.

[0755] 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.

[0756] In this invention, the server includes means for acquiring location information, means for analyzing emotional states, and means for learning past behavioral patterns. This makes it possible to comprehensively understand the location and condition of elderly people and to immediately issue an alarm if an abnormality is detected.

[0757] "Means of acquiring location information" refers to devices and software that detect and record an individual's geographical location in real time.

[0758] "Means for analyzing emotional states" refers to software or algorithms that analyze an individual's emotions based on voice and facial expression data and evaluate their emotional state.

[0759] "Means of learning past behavioral patterns" refers to technologies or systems that analyze and record an individual's usual behavioral patterns based on data collected in the past.

[0760] "Means for detecting abnormal behavior" refers to functions or programs that automatically recognize behaviors that deviate from normal behavioral patterns based on analyzed data.

[0761] "Means for acquiring voice and facial expression data" refers to sensors and devices for collecting voice and image data between an individual and their environment.

[0762] "Means of transmitting an alarm" refers to a device or program that electronically transmits a message to alert relevant parties when an abnormal condition is detected.

[0763] "Means for analyzing changes in behavior and emotions and generating alarms" refers to a system that continuously analyzes changes in an individual's behavior and emotional state and generates an alarm when an anomaly is detected.

[0764] "External data communication network" refers to public data networks such as the internet, and means infrastructure that enables data communication over a wide area.

[0765] This invention is designed as a monitoring system to ensure the safety of the elderly. The system includes a terminal, a server, an emotion engine, and a user, all of which work together in coordination.

[0766] The device is equipped with a GPS sensor, microphone, and camera. It acquires the elderly person's location information in real time and also collects voice and facial expression data. This data is analyzed using an emotion engine. The emotion engine has the function of evaluating emotional states such as stress and anxiety based on voice tone and changes in facial expression.

[0767] The server receives data sent from the terminal and stores it in a database. An algorithm on the server processes location information and sentiment analysis results, and detects anomalies by comparing them to normal behavioral patterns. When an anomaly is detected, an alarm is generated and the user is notified. This improves safety for the elderly.

[0768] Users receive alerts from the server through a dedicated application. These alerts include information about location deviations and changes in emotions, allowing users to take appropriate action. For example, they might immediately rush to the scene or contact relevant parties.

[0769] For example, if the device detects a deviation from the user's normal range of activity and signs of anxiety in their voice and facial expression, it sends this information to a server. The server immediately generates an alarm and notifies the user. This alarm allows the user to quickly understand the elderly person's condition and take appropriate measures.

[0770] In the emotion analysis section, which uses a generative AI model, prompts such as, "What should I do if a senior is leaving home and heading to an unfamiliar place? Also, what measures should be taken if the senior's voice contains a tone of tension?" are utilized. This enables advanced emotion analysis and emergency response.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The device uses a GPS sensor to acquire the elderly person's location information. The input is geographical coordinate data from the sensor, and the output is real-time location information sent to the server. Specifically, the device measures its current location every 30 seconds and packages that information into packets.

[0774] Step 2:

[0775] The device uses a microphone and camera to collect audio and facial data. The input is the voice and facial image of the elderly person, and the audio signal and image data are sent to the emotion engine as output. Specifically, the device records audio every 5 seconds and captures facial expressions with the camera.

[0776] Step 3:

[0777] The device processes data collected via an emotion engine. Inputs are audio signals and image data, and the emotion analysis algorithm evaluates stress and anxiety levels. Output is emotional state evaluation data, which is sent to the server. Specifically, the algorithm analyzes changes in voice tone and facial expression in real time and calculates an emotion score.

[0778] Step 4:

[0779] The server receives location information and sentiment evaluation data transmitted from the terminal and stores it in a database. The inputs are location data and sentiment scores, which are integrated as output for analysis performed after storage. Specifically, the server performs insertion operations on the database and records time-series data.

[0780] Step 5:

[0781] The server learns past behavioral patterns based on stored data and performs analysis to detect anomalies. The input is a dataset of past location history and sentiment scores, and the output is the result of anomaly detection. Specifically, pattern recognition is performed on the server using a machine learning algorithm to identify deviations from normal behavior and sentiment.

[0782] Step 6:

[0783] The server generates an alarm and sends a notification when an anomaly is detected. The input is the result of the anomaly detection, and the output is an alarm message to the user. Specifically, the alarm message is generated as text containing the details of the anomaly and recommended actions, and is sent to the user's mobile device.

[0784] Step 7:

[0785] The user reviews the received alarm and selects an appropriate response based on the situation. The input is the alarm message from the server, and the output is the confirmation result and the actual action plan. Specifically, the user views the alarm message through a dedicated app and takes actions such as checking on the elderly person at the scene or requesting assistance as needed.

[0786] (Application Example 2)

[0787] 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".

[0788] There is a growing need to enhance safety by monitoring the behavior and emotional changes of seniors in real time. However, conventional systems are limited to acquiring location information and detecting simple behavioral abnormalities, and they cannot comprehensively consider emotional changes, making it difficult to respond quickly and accurately.

[0789] 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.

[0790] In this invention, the server includes functional means for acquiring location signals, functional means for analyzing voice and facial expression data to evaluate emotional states, and functional means for transmitting alarm signals. This enables comprehensive monitoring of not only behavioral abnormalities but also emotional changes in seniors, allowing for rapid and effective alarm notifications.

[0791] The "function to acquire location signals" refers to the function of determining the current location of an object in real time using GPS or other location measurement technologies.

[0792] The "function to learn behavioral history" is a function that accumulates and analyzes past behavioral data and learns normal behavioral patterns.

[0793] The "function to detect abnormal behavior" is a function that compares the learned normal behavior pattern with the current behavior and detects any deviation as abnormal.

[0794] The "alarm signal transmission function" is a function that sends notifications or alarms to the appropriate recipients when an abnormality or danger is detected.

[0795] The "function for collecting hazardous area information" refers to the function of gathering information on areas where danger is potential and managing it within the system.

[0796] The "function to generate alerts based on hazardous area information" is a function that uses collected hazardous area information to create an alert when applicable.

[0797] The "function to analyze voice and facial expression data to evaluate emotional state" is a function that analyzes acquired voice and facial expression data to evaluate the individual's emotional state in real time.

[0798] The system for implementing this invention mainly consists of a terminal, a server, an emotion analysis engine, and a user. The terminal is equipped with a GPS sensor, microphone, and camera, which enable the collection of location information, voice data, and facial expression data. This data is transmitted to the server using Bluetooth, Wi-Fi, or mobile data communication.

[0799] The server is built on a cloud platform and has the capability to receive and store data in real time. The server implements an emotion analysis engine using AI libraries such as TensorFlow and PyTorch, which analyzes transmitted voice and facial expression data to evaluate emotional states. Furthermore, it compares behavioral history with current behavior and immediately generates an alarm signal if abnormal behavior or emotional changes are detected. The alarm includes location information, the specific nature of the anomaly, and the change in emotional state, and is sent to the user via push notification.

[0800] Users can receive alerts using a dedicated application on their smartphones or other devices. This application allows them to view detailed information and understand the current situation of the senior citizen. It also includes a function for quick contact when necessary.

[0801] For example, if a senior citizen moves to a different area than they normally frequent, and an anxious tone of voice is detected, the system will recognize this deviation from their usual pattern. Based on this information, family members and caregivers can respond quickly and make appropriate decisions to ensure their safety.

[0802] An example of a prompt to input into the generating AI model is, "Create a template that checks the latest location information and emotional state, and generates an alarm when an anomaly is detected." This ensures the system operates smoothly and the safety of seniors is maintained.

[0803] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0804] Step 1:

[0805] The device uses a built-in GPS sensor, microphone, and camera to collect location information, voice data, and facial expression data of seniors in real time. Input is raw data acquired from the sensors, and output is location, voice, and image data temporarily stored within the device.

[0806] Step 2:

[0807] The device transmits the collected data to the server via Bluetooth or Wi-Fi. The input is the data acquired in step 1, and the output is the data converted into a format that can be sent to the server. During this process, data format conversion and compression are performed.

[0808] Step 3:

[0809] The server stores the received location information, audio data, and facial expression data in a cloud-based database. In this step, the input is the data sent from the terminal, and the output is the location and emotion data stored in the database.

[0810] Step 4:

[0811] The server processes voice and facial expression data using an emotion analysis engine to evaluate the senior's emotional state. The input is the voice and facial expression data saved in step 3, and the output is the result of the emotional state evaluation. Specifically, a model using TensorFlow or PyTorch analyzes changes in voice pitch, tone, and facial expression.

[0812] Step 5:

[0813] The server integrates the emotional state evaluation results and location information, compares them with past behavioral history, and detects anomalies. The input is the emotional evaluation results and location information obtained in step 4, and the output is the result regarding whether or not an anomaly was detected. The algorithm detects deviations from normal behavioral patterns.

[0814] Step 6:

[0815] If an anomaly is detected, the server generates an alarm signal and sends a push notification to the user's device. The input is the anomaly information detected in step 5, and the output is the alarm signal. Specifically, the notification content is constructed and immediately delivered to the user.

[0816] Step 7:

[0817] Users use a dedicated application on a device such as a smartphone to check the content of received alarms. The input is the alarm signal sent from the server, and the output is the alarm information displayed on the screen. Based on this, users decide on the appropriate response.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] I apologize, but I cannot create a specific description of the patent claims here. However, the following is an example of a draft patent claims in a general form.

[0841] (Claim 1)

[0842] A device means for acquiring location information,

[0843] A device or means for learning past behavioral patterns,

[0844] A device for detecting abnormal behavior,

[0845] A device means for transmitting an alarm,

[0846] A device and means for collecting information on dangerous areas,

[0847] A device means for generating an alarm based on information about dangerous areas,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein if the aforementioned alarm is determined to be an abnormal behavior, the relevant parties are notified via a communication device.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the aforementioned hazardous area information is periodically updated from an external network.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A functional means for acquiring location data,

[0856] A functional means for learning past movement patterns,

[0857] A functional means for detecting abnormal movement,

[0858] A functional means for creating and communicating warnings,

[0859] A functional means for collecting information on hazardous areas,

[0860] A functional means for generating warnings based on hazardous area information,

[0861] A functional means for receiving and analyzing location data in real time,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the warning is transmitted to the relevant parties via a communication medium if it is identified as an abnormal movement.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the aforementioned hazardous area information is periodically updated from an external communication network.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] Information processing means for acquiring location information,

[0870] Information processing means for learning past behavioral patterns,

[0871] Information processing means for detecting abnormal behavior,

[0872] Information processing means for transmitting an alarm,

[0873] Information processing means for collecting information on dangerous areas,

[0874] Information processing means for generating warnings based on information about dangerous areas,

[0875] Information processing means for transmitting a notification to a mobile communication terminal when an alarm is generated,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein if the aforementioned alarm is determined to be an abnormal behavior, the relevant parties are notified via a communication device.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein the aforementioned dangerous area information is periodically updated from an external network and the information is presented to the user via a mobile communication terminal.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] Means for obtaining location information,

[0884] A means of analyzing emotional states,

[0885] A means of learning past behavioral patterns,

[0886] Means for detecting abnormal behavior,

[0887] Means for acquiring voice and facial expression data,

[0888] A means of transmitting an alarm,

[0889] A means for analyzing changes in behavior and emotions and generating an alarm,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, wherein if the alarm is determined to be due to abnormal behavior or a significant change in emotion, the relevant parties are notified via communication means.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein the aforementioned hazardous area information is periodically updated from an external data communication network.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A functional means for acquiring position signals,

[0898] A functional means for learning the operation history,

[0899] A functional means for detecting abnormal operation,

[0900] A functional means for transmitting an alarm signal,

[0901] A functional means for collecting information on hazardous areas,

[0902] A functional means for generating an alert based on hazardous area information,

[0903] A functional means for analyzing voice and facial expression data to evaluate emotional state,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein if the alarm is determined to be an abnormal operation or a change in emotional state, the relevant parties are notified via a communication function.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the aforementioned hazardous area information is periodically updated from an external information network and analyzed in an integrated manner with sentiment analysis data. [Explanation of symbols]

[0909] 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 device means for acquiring location information, A device or means for learning past behavioral patterns, A device for detecting abnormal behavior, A device means for transmitting an alarm, A device and means for collecting information on dangerous areas, A device means for generating an alarm based on information about dangerous areas, A system that includes this.

2. The system according to claim 1, wherein if the aforementioned alarm is determined to be an abnormal behavior, the relevant parties are notified via a communication device.

3. The system according to claim 1, wherein the aforementioned hazardous area information is periodically updated from an external network.

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