system

Abstract

Provide a system. A sensor means for recording user activity information; an analysis means for analyzing the recorded activity information; abnormal behavior detection means for detecting abnormal behavior based on the analysis results; a notification means for sending a notification to a medical institution and a family member regarding the detected abnormal behavior; A system including:

Description

[Technical Field]

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

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

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

[0004] The increasing number of dementia patients has become a serious problem in modern society. Early detection of the progression of dementia and provision of appropriate treatment and support require detailed monitoring of patients' daily behavioral patterns and effective detection of abnormal behavior and early symptoms. However, current technology lacks a comprehensive system to meet these needs. Specifically, there is a lack of a system that effectively coordinates a series of processes, including behavioral monitoring of dementia patients, real-time detection of abnormal behavior, analysis of early symptoms, and prompt notification to medical institutions and families. [Means for solving the problem]

[0005] To address the above-mentioned problems, the present invention provides a system including a sensor for recording a user's activity information, an analysis unit for analyzing the recorded activity information, an abnormal behavior detection unit for detecting abnormal behavior based on the analysis results, and a notification unit for sending notifications regarding the detected abnormal behavior to medical institutions and family members. Furthermore, the system includes a behavior pattern identification unit for identifying the user's behavior pattern from the analyzed activity information, an early symptom detection unit for detecting early symptoms of dementia based on the identified behavior pattern, and a report generation unit for generating a report regarding the detected early symptoms, thereby achieving early detection and treatment support for dementia patients. Furthermore, the system includes an input unit for inputting the user's medical examination information and a treatment plan generation unit for analyzing the input medical examination information and generating the next medical examination schedule and treatment plan, thereby strengthening collaboration with medical institutions. This makes it possible to comprehensively protect the safety and health of dementia patients.

[0006] "Users" refers to dementia patients, their families, and medical professionals who use the system.

[0007] "Activity information" refers to data related to the user's actions and behavior in daily life.

[0008] "Sensor means" refers to various sensors (e.g., motion sensors, cameras, microphones, etc.) for detecting and recording user activity information.

[0009] "Analysis means" refers to a computer algorithm for analyzing data and detecting abnormal behavior based on activity information obtained by the sensor means.

[0010] "Abnormal behavior" refers to behavior that deviates from a user's daily behavioral patterns and may indicate the progression of dementia or other risks.

[0011] "Abnormal behavior detection means" refers to an algorithm for identifying abnormal behavior from the acquired and analyzed activity information.

[0012] "Notification means" refers to a means for sending information about detected abnormal behavior to a medical institution or family member.

[0013] "Behavioral patterns" refer to a collection of data such as a series of actions and usage times in a user's daily life.

[0014] The "behavior pattern identification means" refers to a method for identifying a user's behavior pattern from the data obtained by the analysis means.

[0015] "Early symptoms" refers to signs and symptoms seen in the early stages of dementia.

[0016] "Early symptom detection means" refers to a system for identifying early symptoms from user behavior patterns.

[0017] "Report generation means" refers to means for automatically generating a report based on detected early symptoms and abnormal behavior.

[0018] "Input means" refers to an interface that allows a doctor to input medical information into the system.

[0019] "Consultation information" refers to information such as the user's health condition, diagnosis results, and prescribed medications that are recorded by a doctor during medical examination.

[0020] The "treatment plan generation means" refers to a means for analyzing input examination information and generating an optimal treatment plan and next examination schedule. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The following describes an embodiment of the present invention. First, the system of the present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, family members, and governments of the necessary information.

[0043] Monitoring robot

[0044] Sensor means

[0045] The monitoring robot is equipped with multiple sensors to collect information on the user's daily life activities. These sensors include a motion sensor, a camera, and a microphone, and capture data such as the user's walking pattern, conversations, and room movements. For example, if the user is cooking in the kitchen, the robot will record their movements with a camera and detect surrounding sounds with a microphone.

[0046] Data collection and transmission

[0047] The data acquired by the sensors is sent in real time to a processing unit inside the robot. The processing unit performs initial data processing to remove unnecessary noise and extract necessary features. After this processing is complete, the data is sent to a server via the Internet. For example, once a certain amount of data on a user's walking has been collected, it is sent to the server as a batch.

[0048] Data analysis

[0049] AI-based analysis methods

[0050] The server stores the user's activity information in a database based on a chronological order. The stored data is analyzed in real time by an AI engine, which compares it with past behavioral data to detect abnormal behavior. The server can also perform detailed analysis of abnormal behavior patterns to determine whether they are due to the progression of dementia or other factors.

[0051] Abnormal behavior notification

[0052] If abnormal behavior is detected based on the analysis results, the server will automatically send a notification to a medical institution or family. For example, if a user is walking around the room at an unusual time, the server will recognize this information as abnormal behavior and send an email notification to a medical institution.

[0053] Early Detection System

[0054] Identifying behavioral patterns and detecting early symptoms

[0055] The server analyzes data collected over a long period of time to identify the user's behavioral patterns. The behavioral pattern identification means determines what the user is doing at what time of day. As a result, if there is a sudden increase in behavioral fluctuations observed over a short period of time, for example, this can be detected as an early symptom.

[0056] Generate and send reports

[0057] If early symptoms are detected, the generative AI creates a detailed report based on the detection results, including the user's behavioral patterns, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[0058] Medical support

[0059] Entering and analyzing medical information

[0060] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[0061] Notification and follow-up

[0062] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[0063] Government response services

[0064] Citizen Support

[0065] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they require, their name, and address, and this information is sent directly to a server. The server then analyzes the information and automatically generates an appropriate support plan.

[0066] Collaboration support

[0067] The server seamlessly shares information with government offices using the integrated data, including the user's health status, support, progress, etc. Government officials can view and update this information through their devices and take necessary actions.

[0068] In this way, the system of the present invention can comprehensively support everything from monitoring dementia patients to early detection, medical support, and government response, thereby comprehensively protecting the safety and health of users.

[0069] The processing flow will be explained below.

[0070] Monitoring robot processing flow

[0071] Step 1:

[0072] The robot activates its built-in motion sensors, camera, and microphone to collect information about the user's activities.

[0073] Step 2:

[0074] The robot initially processes the collected data to remove noise and extract key features (e.g., walking patterns and sound changes).

[0075] Step 3:

[0076] The robot sends the processed data to the server in real time or in batches.

[0077] Data analysis process flow

[0078] Step 4:

[0079] The server stores the received data in a database and records it with a timestamp.

[0080] Step 5:

[0081] The server analyzes the received data in real time using an AI engine and begins analysis to detect abnormal behavior.

[0082] Step 6:

[0083] The server detects abnormal behavior (e.g., falls, wandering at unusual times, etc.) based on the analysis results.

[0084] Step 7:

[0085] The server generates alerts about detected abnormal behavior and notifies medical institutions and family members.

[0086] Early detection system processing flow

[0087] Step 8:

[0088] The server analyzes the data collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral patterns.

[0089] Step 9:

[0090] The server applies an anomaly detection algorithm to the identified behavioral patterns to detect early symptoms of dementia.

[0091] Step 10:

[0092] The server uses generative AI to automatically generate a detailed report based on early symptoms.

[0093] Step 11:

[0094] The server periodically transmits the generated reports to the medical institution or care manager.

[0095] Medical response support process flow

[0096] Step 12:

[0097] The terminal provides an interface (eg, a form) for the physician to enter consultation information.

[0098] Step 13:

[0099] The doctor enters the user's medical information (e.g., health status, diagnosis, and prescription medications).

[0100] Step 14:

[0101] The terminal transmits the input medical information to the server.

[0102] Step 15:

[0103] The server analyzes the consultation information and uses generation AI to automatically generate the next consultation schedule and treatment plan.

[0104] Step 16:

[0105] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[0106] Processing flow of administrative services

[0107] Step 17:

[0108] Users enter information (e.g., name, address, and details of support) to request support from the government through a dedicated app.

[0109] Step 18:

[0110] The terminal receives the input and sends it to the server.

[0111] Step 19:

[0112] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[0113] Step 20:

[0114] The server notifies the user of the generated support plan.

[0115] Step 21:

[0116] The server uses the integrated data to seamlessly share information with government offices.

[0117] Step 22:

[0118] The terminal provides an interface (e.g., a dashboard) for government employees to view and update the data they receive.

[0119] This is the specific processing flow of the system, which can comprehensively support dementia patient monitoring, early detection, medical support, and government response.

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] As the aging population continues, the increase in dementia patients has become a serious problem. Conventional systems lack consistent, comprehensive support for early detection of abnormal behavior in the daily lives of dementia patients and appropriate medical and administrative support. As a result, early intervention to slow the progression of dementia is difficult, and safety management and appropriate treatment are often delayed. Furthermore, some information is not shared appropriately in administrative responses, resulting in insufficient support. The present invention aims to solve these problems.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0124] In this invention, the server includes a data processing means for initial processing of recorded activity information and removing unnecessary noise, a data transmission means for transmitting the processed data to the server, and an analysis means for saving and analyzing the received data in chronological order. This enables the detection of abnormal user behavior in real time and immediate notification to appropriate medical institutions and family members. Furthermore, the analysis of long-term behavioral patterns makes it possible to detect early symptoms of dementia, enabling earlier intervention. Furthermore, by generating reports using generative AI, detailed analysis results can be provided to medical institutions and care managers, allowing appropriate treatment plans and policies to be quickly formulated. Centralized information sharing is also possible in administrative responses, providing comprehensive support.

[0125] "Sensor means" refers to devices used to record user activity information, including motion sensors, cameras, microphones, and the like.

[0126] "Data processing means" refers to a device or software capable of initial processing of recorded activity information and removing unnecessary noise.

[0127] "Data transmission means" refers to a device or function that transmits processed data to a server.

[0128] "Analysis means" refers to a device or system that stores received data in chronological order and analyzes the data using an AI engine.

[0129] The "abnormal behavior detection means" is a device or software that has the function of detecting abnormal behavior of a user based on analyzed data.

[0130] "Notification means" refers to a device or system that sends notifications to medical institutions and family members regarding detected abnormal behavior.

[0131] The "behavior pattern identification means" is a device or software that has the function of identifying the user's behavior pattern from the analyzed data.

[0132] An "early symptom detection means" is a device or software that has the function of detecting early symptoms of dementia based on identified behavioral patterns.

[0133] "Report generation means" means a device or software capable of generating a detailed report on detected early symptoms using generative AI.

[0134] "Report sending means" refers to a device or function that sends the generated report to a medical institution or care manager.

[0135] "Input means" refers to a device or interface for inputting a user's medical information.

[0136] The "treatment plan generation means" is a device or software that has the function of analyzing input examination information and generating the next examination schedule and treatment plan.

[0137] "Generative AI" refers to an artificial intelligence engine that automatically performs data analysis across the entire system and generates reports.

[0138] The present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, families, and governments of the necessary information. Specific embodiments are described below.

[0139] Hardware and software used

[0140] Monitoring robot: Equipped with motion sensors, cameras, and microphones to collect information on the user's daily life activities.

[0141] Server: A central device for processing and analyzing data, equipped with an AI engine.

[0142] Terminal: Provides an interface for doctors to input consultation information.

[0143] Generative AI model: Refers to an artificial intelligence engine for data analysis and report generation.

[0144] Examples of data collection

[0145] The monitoring robot collects activity information as the user goes about their daily life. For example, if the user is cooking in the kitchen, the camera records their movements and the microphone detects surrounding sounds. The motion sensor tracks the user's walking pattern in real time. This data is then filtered out by a processing unit inside the monitoring robot, and necessary features are extracted before being sent to a server via the Internet.

[0146] Specific examples of data analysis

[0147] The server stores the received activity information in a database based on a chronological order. The stored data is analyzed in real time using an AI engine and compared with past behavioral data to detect abnormal behavior. For example, if a user is walking around a room at an unusual time, that information will be recognized as abnormal behavior.

[0148] Abnormal behavior detection and notification

[0149] If abnormal behavior is detected based on the analysis results, the server automatically sends a notification to a medical institution or family. For example, the server may notify a medical institution of details of the abnormal behavior via email. The notification will include any changes in the user's behavioral patterns and various sensor data.

[0150] Identifying early symptoms

[0151] The server analyzes data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means can detect a sudden increase in behavioral fluctuations over a short period of time as an early symptom.

[0152] Report generation and delivery

[0153] If early symptoms are detected, the Generative AI creates a detailed report based on the detection results. This report includes the user's behavioral patterns, the frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers. Examples of prompt sentences that are used include the following:

[0154] Example prompt sentence:

[0155] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[0156] Generate treatment plans

[0157] The device provides an interface for doctors to input consultation information. After the consultation, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, and this information is sent to the server. The server uses an AI engine to generate the next appointment schedule and optimal treatment plan, and notifies the user.

[0158] Administrative response

[0159] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like, their name, and address, and this information is sent directly to a server. The server analyzes the information it receives and automatically generates an appropriate support plan. It is also possible to seamlessly share information with government offices using the integrated data.

[0160] In this way, this system provides comprehensive support for dementia patients, from monitoring to early detection, medical support, and government response, and can comprehensively protect the safety and health of users.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Step 1: Data collection

[0163] Subject: Monitoring robot

[0164] The monitoring robot uses motion sensors, cameras, and microphones to collect information about the user's daily activities. Specifically, the robot captures the user's movements with a camera and records their voice with a microphone. The motion sensors detect the user's position and movements in real time. This data is sent to the robot's internal processing unit.

[0165] Input: User's daily activities

[0166] Output: Sensor data sent to a processing unit

[0167] Step 2: Data processing

[0168] Subject: Monitoring robot

[0169] A processing unit inside the robot initially processes the collected data and removes unnecessary noise, for example by deleting unwanted frames from camera footage and filtering background noise from audio data. Data from motion sensors is extracted as movement patterns.

[0170] Input: Sensor data

[0171] Output: Data after denoising and feature extraction

[0172] Step 3: Send data

[0173] Subject: Monitoring robot

[0174] The processed data is sent to a server via the Internet. Specifically, once a certain amount of data has been collected, it is batch processed and sent to the server in encrypted form.

[0175] Input: Data after initial processing

[0176] Output: Data sent to the server

[0177] Step 4: Save Data

[0178] Subject: Server

[0179] The server stores the received data in a database based on time sequence, and the stored data is used for subsequent analysis.

[0180] Input: Data sent

[0181] Output: Data stored in the database

[0182] Step 5: Data analysis

[0183] Subject: Server

[0184] The server analyzes the stored data in real time using an AI engine. Specifically, it compares it with past behavioral data to detect abnormal behavior. For example, if behavior that differs from a normal walking pattern is observed, it is recognized as abnormal behavior.

[0185] Input: Data stored in a database

[0186] Output: Analysis results and abnormal behavior detection

[0187] Step 6: Abnormal Behavior Notification

[0188] Subject: Server

[0189] If abnormal behavior is detected, the server automatically sends a notification to a medical institution or family member via email or smartphone app, detailing the nature of the abnormal behavior.

[0190] Input: Analysis results

[0191] Output: Notification to medical institutions and family members

[0192] Step 7: Identify behavioral patterns

[0193] Subject: Server

[0194] The server analyzes the data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means models daily behavior during specific time periods.

[0195] Input: Long-term data

[0196] Output: User behavior patterns

[0197] Step 8: Early symptom detection

[0198] Subject: Server

[0199] The system detects early symptoms of dementia based on identified behavioral patterns. For example, a sudden increase in behavioral fluctuations over a short period of time can be recognized as an early symptom.

[0200] Input: Behavioral pattern

[0201] Output: Early symptom detection

[0202] Step 9: Generate reports

[0203] Subject: Server

[0204] Generative AI is used to create a detailed report of detected early symptoms, including behavioral patterns, frequency of abnormal behavior, and recommended next steps.

[0205] Input: Early symptom data

[0206] Output: Detailed report

[0207] Step 10: Submit report

[0208] Subject: Server

[0209] The generated reports are sent periodically to medical institutions and care managers, with appropriate information provided using prompts.

[0210] Input: Detailed report

[0211] Output: Send to medical institutions and care managers

[0212] Example prompt sentence:

[0213] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[0214] Step 11: Generate a treatment plan

[0215] Subject: Device

[0216] The doctor inputs the patient's medical information into the device, which is then sent to the server, where the AI ​​engine generates the next appointment schedule and appropriate treatment plan.

[0217] Input: Medical Information

[0218] Output: Treatment plan and policy

[0219] Step 12: Care Plan Notification

[0220] Subject: Server

[0221] The generated treatment plan and treatment policy are notified to the user via a smartphone app or email.

[0222] Input: Treatment plan and treatment policy

[0223] Output: User notification

[0224] Step 13: Government response

[0225] Subject: User

[0226] The user uses a dedicated app to send a support request to the government office. The information entered in the app is sent to the server.

[0227] Input: Support request details

[0228] Output: Send to server

[0229] Step 14: Data analysis for administrative responses

[0230] Subject: Server

[0231] The server analyzes the received information and automatically generates an appropriate support plan. The integrated data is then used to share information with government offices.

[0232] Input: Support request details

[0233] Output: Generated support plan and submission to government office

[0234] In this way, the present system makes it possible to comprehensively protect the safety and health of users through a series of processing steps.

[0235] (Application example 1)

[0236] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0237] The purpose of this invention is to provide a system that comprehensively monitors, detects early, and provides medical support for dementia patients. Specifically, the objective is to ensure the safety of dementia patients and support appropriate medical intervention by visually and audibly monitoring their daily lives, detecting and analyzing abnormal behavior in real time, and notifying medical institutions and families in a timely manner. Furthermore, efficient generation and notification of treatment plans is also an important objective.

[0238] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0239] In this invention, the server includes a sensing unit that records the user's activity information, an analysis unit that analyzes the recorded activity information, and an abnormal behavior detection unit that detects abnormal behavior based on the analysis results. This enables the server to quickly detect the user's abnormal behavior and promptly notify medical institutions and family members. Furthermore, by visually and audibly monitoring the user's daily life using the camera and microphone unit built into the smart glasses, sending data to the server via the preprocessing unit and communication unit, and notifying the analysis results via a smartphone app or email, more accurate monitoring and early detection are achieved. Furthermore, comprehensive medical support can be provided by proposing the next appointment and treatment plan based on the report generated using the generative AI model.

[0240] "Sensing means" refers to a device or sensor for recording user activity information.

[0241] "Analysis means" refers to equipment or software for processing the recorded activity information and extracting and analyzing the necessary information.

[0242] The "abnormal behavior detection means" refers to a device or program that has the function of identifying and detecting abnormal user behavior based on analyzed data.

[0243] "Notification means" refers to a method or system for reporting detected abnormal behavior or important information to a medical institution or family member.

[0244] "Camera and microphone means" refers to video and audio collection devices for visual and audio monitoring of the user's daily life.

[0245] "Pre-processing means" refers to a method or device that performs initial noise removal and feature extraction on the data obtained by the processing unit built into the smart glasses.

[0246] "Communication means" refers to the internet connection or wireless communication mechanism for transmitting the preprocessed data to the server.

[0247] "Report generator" refers to a device or program for automatically generating a detailed report based on detected early symptoms.

[0248] The "behavior pattern identification means" refers to an analysis device or program for identifying a behavior pattern from user activity information.

[0249] "Early symptom detection means" refers to a function or device for detecting early symptoms of dementia based on identified behavioral patterns.

[0250] "Input means" refers to a terminal or program that allows a medical professional to input the user's medical information.

[0251] The "treatment plan generating means" refers to a program or device that generates the next examination schedule and treatment plan based on the input examination information.

[0252] "Proposal method" refers to a system or program that suggests the next steps in medical care based on a report generated using a generative AI model.

[0253] A "prompt generation means" is a program that inputs appropriate prompt sentences into the AI ​​to improve the efficiency of data analysis.

[0254] The system of the present invention aims to comprehensively monitor dementia patients, detect their condition early, provide medical support, and respond to government requests. The system collects and analyzes information on the user's daily activities, detects abnormal behavior, and sends necessary notifications to ensure the patient's safety and support appropriate medical intervention. Specific embodiments of the present invention are described below.

[0255] System Configuration

[0256] The system includes a sensing means, an analysis means, an abnormal behavior detection means, a notification means, a camera and microphone means, a preprocessing means, a communication means, a report generation means, a behavioral pattern identification means, an early symptom detection means, an input means, a treatment plan generation means, a suggestion means, and a prompt generation means.

[0257] sensing means

[0258] The smart glasses are equipped with multiple sensors to monitor the user's daily life, including a camera, microphone, and motion sensors, and collect information on the user's activities in real time.

[0259] Analysis means

[0260] The data collected by the smart glasses undergoes initial processing in the built-in data processing unit, where noise is removed and necessary features are extracted. The pre-processed data is then sent to a server via a communication means.

[0261] Abnormal behavior detection means

[0262] The server stores the data sent via communication means in a database. The AI ​​engine analyzes the collected data in real time and compares it with past behavioral data to detect abnormal behavior.

[0263] Notification means

[0264] If abnormal behavior is detected, the server will notify medical institutions and family members via a smartphone app or email, urging them to take prompt action.

[0265] Camera and microphone means

[0266] The cameras and microphones built into the smart glasses collect detailed visual and audio data from users' daily lives, which is crucial for improving the accuracy of abnormal behavior detection.

[0267] Pretreatment means

[0268] The processing unit built into the smart glasses performs initial noise removal and feature extraction of the data, thereby improving the quality of the data sent to the server via the communication means.

[0269] communication means

[0270] The pre-processed data is then sent to a server over the internet, using wireless communication and an internet connection.

[0271] Report generation means

[0272] Based on the detected early symptoms, a detailed report is generated and sent periodically to the medical institution or care manager.

[0273] Specific examples and prompts

[0274] It records detailed activity information such as the user's walking data, conversation content, and movement from room to room, and uses analytical methods to detect abnormal behavior. For example, it generates prompt sentences like the following and inputs them into the AI.

[0275] example:

[0276] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[0277] Based on the information collected and analyzed in this way, abnormal behavior can be detected and notified, supporting rapid medical response. This method can comprehensively realize everything from monitoring dementia patients to early detection and medical support.

[0278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0279] Step 1:

[0280] A user wears smart glasses while going about their daily life. The smart glasses' sensing means (camera, microphone, motion sensor) collect the user's activity information (walking patterns, conversations, room movements, etc.) in real time. The input is the user's behavioral data in their daily life, and the output is the collected raw data.

[0281] Step 2:

[0282] The collected raw data undergoes initial processing using the smart glasses' built-in pre-processing means. Specifically, noise removal and feature extraction are performed. The input is raw data, and the output is pre-processed data. This processing removes noise from the data and emphasizes the features required for analysis.

[0283] Step 3:

[0284] The pre-processed data is sent to the server via a communication means. The input is the pre-processed data, and the output is the data stored on the server. This communication can be via the internet or wirelessly.

[0285] Step 4:

[0286] The server stores the transmitted data in a database and analyzes it in real time. Using analytical tools, the collected data is compared with past behavioral data to detect abnormal behavior. The input is the user's activity information stored in the database, and the output is the analysis results.

[0287] Step 5:

[0288] When abnormal behavior is detected, the server uses a notification method to notify medical institutions and family members. The input is the analysis result and the output is a notification message. Notifications are sent via a smartphone app or email, encouraging a prompt response.

[0289] Step 6:

[0290] Based on the data collected over a long period of time, the server uses a behavioral pattern identification means and an early symptom detection means to identify the user's behavioral patterns and detect early symptoms.The input is the long-term data, and the output is a report of the behavioral patterns and early symptoms.

[0291] Step 7:

[0292] Based on the detected early symptoms, a report generator generates a detailed report. The input is the data of the early symptoms, and the output is a detailed report. This report is periodically sent to a medical institution or a care manager.

[0293] Step 8:

[0294] The user's medical examination information is sent to the server via the input means, and the next medical examination schedule and medical treatment plan are generated using the treatment plan generation means. The input is the medical examination information, and the output is a notification of the medical examination schedule and medical treatment plan.

[0295] Step 9:

[0296] Using a generative AI model, the next steps in medical care are suggested based on the generated report. A specific example involves generating prompts and inputting them into the AI. The input is a detailed report, and the output is specific medical suggestions.

[0297] example:

[0298] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[0299] Based on this prompt, the AI ​​will suggest the next step, creating a system that comprehensively monitors users, detects problems early, and provides medical support.

[0300] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0301] This section describes an embodiment of the present invention. This section describes a system that combines a system for monitoring dementia patients, early detection, medical support, and administrative response with an emotion engine that recognizes the user's emotions. This system collects and analyzes not only user activity information but also emotional information, enabling more accurate detection of abnormal behavior and identification of behavioral patterns.

[0302] Monitoring robot

[0303] Sensor Instruments and Emotion Engines

[0304] The monitoring robot is equipped with motion sensors, cameras, microphones, and sensors to recognize the user's emotions (e.g., facial expression recognition camera, voice analysis microphone). This allows it to collect emotional information not only from the user's actions while cooking in the kitchen, but also from their facial expressions and tone of voice.

[0305] Data collection and transmission

[0306] The activity and emotion information acquired by the sensors is initially processed by the processing unit inside the robot to remove noise. This data undergoes feature extraction processing and is then sent to the server in real time or in batches. For example, when a certain amount of data is collected showing that the user is feeling happy and cooking at the same time, that data is sent to the server as a batch.

[0307] Data analysis

[0308] AI and sentiment analysis tools

[0309] The server stores the received activity information and emotion information in a database, and records them with a timestamp. The stored data is analyzed in real time by the AI ​​engine. Emotional information recognized by the emotion engine is also analyzed simultaneously. For example, if the user is feeling depressed, the associated behavioral patterns are analyzed.

[0310] Detecting abnormal behavior and emotional anomalies

[0311] If abnormal behavior is detected based on the analysis results, the server also considers emotional information to further increase the likelihood of abnormality. For example, if a user walks around the room at an unusual time and looks anxious, the server will recognize this as high-risk abnormal behavior. This information is then notified to medical institutions and family members.

[0312] Early Detection System

[0313] Identifying behavioral patterns and detecting early symptoms

[0314] The server analyzes activity and emotional information collected over a long period of time to identify the user's behavioral patterns. By incorporating emotional fluctuations into the user's behavior, early symptom detection is more accurate. For example, if short-term behavioral fluctuations are linked to emotional fluctuations, this is considered an abnormality.

[0315] Generate and send reports

[0316] A detailed report based on the detected early symptoms is automatically generated by the generative AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[0317] Medical support

[0318] Entering and analyzing medical information

[0319] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[0320] Notification and follow-up

[0321] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[0322] Government response services

[0323] Citizen Support

[0324] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like to receive, their name, address, and emotional state, and this information is sent directly to a server. The server then analyzes the information and uses a generative AI to automatically generate an appropriate support plan.

[0325] Collaboration support

[0326] The server seamlessly shares information with administrative offices using integrated data, including activity information, emotional information, support details, and progress. Administrative staff can view and update this information through their devices and take necessary actions.

[0327] In this way, the system of the present invention can comprehensively monitor and analyze the user's activity information and emotional information, and effectively detect abnormal behavior and early symptoms, thereby comprehensively protecting the safety and health of dementia patients.

[0328] The processing flow will be explained below.

[0329] Monitoring robot processing flow

[0330] Step 1:

[0331] The robot activates its built-in motion sensors, camera, microphone, facial expression recognition camera, and voice analysis microphone to collect information on the user's activities and emotions.

[0332] Step 2:

[0333] The robot initially processes the collected activity and emotion information to remove noise and extract key features, such as walking patterns, facial expression changes, and tone of voice.

[0334] Step 3:

[0335] The robot sends the processed activity information and emotion information to the server in real time or in batches.

[0336] Data analysis process flow

[0337] Step 4:

[0338] The server stores the received activity information and emotion information in a database and records them with a timestamp.

[0339] Step 5:

[0340] The server analyzes the received activity information and emotion information in real time using an AI engine and emotion engine. For example, it analyzes the user's walking movements, facial expressions, and tone of voice.

[0341] Step 6:

[0342] The server detects abnormal behavior from the activity and emotion information based on the analysis results. If the user falls and simultaneously shows a fearful expression, it will recognize this as high-risk abnormal behavior.

[0343] Step 7:

[0344] The server notifies medical institutions and family members based on the detected abnormal behavior and emotional abnormalities, for example, by email notification or text message.

[0345] Early detection system processing flow

[0346] Step 8:

[0347] The server analyzes the activity information and emotion information collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral pattern, for example, analyzing the user's activity and emotion fluctuations over a week.

[0348] Step 9:

[0349] The server detects early symptoms from the identified behavioral patterns. For example, if the user frequently wanders around feeling anxious, this may be recognized as an early symptom.

[0350] Step 10:

[0351] The server uses generative AI to automatically generate a detailed report based on early symptoms, including behavioral patterns, emotional fluctuations, detected early symptoms, and recommended next steps.

[0352] Step 11:

[0353] The server periodically transmits the generated report to the medical institution or care manager, for example, by email as a weekly report.

[0354] Medical response support process flow

[0355] Step 12:

[0356] The terminal provides an interface for the doctor to input medical information and emotional information. For example, the medical examination form may include fields for inputting the user's facial expression and tone of voice.

[0357] Step 13:

[0358] The doctor inputs the user's medical information and emotional information.

[0359] Step 14:

[0360] The terminal transmits the input medical examination information and emotion information to the server.

[0361] Step 15:

[0362] The server analyzes the medical examination information and emotional information, and uses generative AI to automatically generate the next medical examination schedule and treatment plan.

[0363] Step 16:

[0364] The server notifies the user or medical institution of the generated medical plan and treatment policy, for example, via a smartphone app.

[0365] Processing flow of administrative services

[0366] Step 17:

[0367] Users enter information to request support from the government through a dedicated app, such as their name, address, and emotional state.

[0368] Step 18:

[0369] The terminal receives the input and sends it to the server.

[0370] Step 19:

[0371] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[0372] Step 20:

[0373] The server notifies the user of the generated support plan, for example by sending a push notification to a smartphone app.

[0374] Step 21:

[0375] The server seamlessly shares information with government agencies using the integrated activity and emotion information, for example by sending detailed data including the user's health status and emotional fluctuations.

[0376] Step 22:

[0377] The terminals provide an interface for government officials to view and update the data they receive, for example by checking the status on a dashboard and taking necessary actions.

[0378] This is the specific processing flow of the system, which enables comprehensive monitoring and analysis of user activity and emotional information, enabling the detection of abnormal behavior and early symptoms with high accuracy.

[0379] Example 2

[0380] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] Conventional systems for monitoring dementia patients, early detection, medical support, and government response only analyze user activity information, making it difficult to perform detailed analysis that includes emotional information. As a result, the accuracy of detecting abnormal behavior and early symptoms decreases, and appropriate intervention and support can be delayed. In addition, it is difficult to generate response policies and treatment plans that take emotional information into account, making it impossible to comprehensively understand the user's condition. This has led to challenges in effectively protecting the safety and health of dementia patients.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0383] In this invention, the server includes a sensor for recording the user's activity information and emotional information, a means for initial processing and feature extraction of the recorded activity information and emotional information, a means for transmitting the processed and feature-extracted data to the server, an AI and emotion analysis means for analyzing the received activity information and emotional information and detecting abnormal behavior and emotional abnormalities, and a notification means for transmitting notifications of detected abnormal behavior and emotional abnormalities to medical institutions and family members. This enables comprehensive monitoring of the activity information and emotional information of dementia patients, enabling more accurate detection of abnormal behavior and early symptoms. Furthermore, treatment guidelines and support plans that take emotional information into account can be generated, enabling a comprehensive understanding of the user's condition and appropriate responses.

[0384] "Sensor means" refers to a device for recording a user's activity information and emotional information, and includes a motion sensor, a camera, a microphone, a facial expression recognition camera, a voice analysis microphone, etc.

[0385] "Initial processing" refers to processing to remove noise from collected raw data and improve the quality of the data.

[0386] "Feature extraction" is the process of extracting necessary information from initially processed data and converting it into a form suitable for analysis.

[0387] The "means for transmitting to the server" is a mechanism for transmitting the data that has undergone initial processing and feature extraction to the server via the Internet or other network.

[0388] "AI and emotion analysis means" refers to artificial intelligence technology and emotion analysis algorithms for analyzing received activity information and emotion information and assessing a user's behavioral patterns and emotional state.

[0389] "Abnormal behavior" refers to behavior that deviates from a user's normal behavioral patterns and may indicate symptoms of dementia or other health issues.

[0390] "Emotional abnormalities" refer to emotional states that deviate from normal emotional patterns and may indicate psychological stress or changes in the user's health.

[0391] "Notification means" refers to a system for notifying medical institutions and family members of information about detected abnormal behavior and emotional abnormalities, and includes email, smartphone apps, telephone, etc.

[0392] This section describes an embodiment of the present invention. The purpose of this invention is to monitor dementia patients, detect them early, provide medical support, and respond to government requests. In addition, it includes an emotion engine that analyzes the user's emotions. This system collects and analyzes user activity information and emotion information, and achieves highly accurate detection of abnormal behavior and identification of behavioral patterns.

[0393] Monitoring robot

[0394] The monitoring robot is equipped with the following sensor means:

[0395] Motion sensor: Records user activity information in real time.

[0396] Camera: Visually records the user's actions.

[0397] Microphone: Collects user voice information.

[0398] Facial expression recognition camera: Analyzes the user's facial expressions and collects emotional information.

[0399] Voice analysis microphone: Analyzes the user's emotional state from the tone of their voice.

[0400] These sensors continuously record daily activities, such as when a user is cooking in the kitchen, and use facial recognition cameras and voice analysis microphones to collect emotional information, such as whether the user is enjoying themselves or feeling anxious.

[0401] Data collection and transmission

[0402] The monitoring robot initially processes activity and emotion information acquired by sensors in a processing unit inside the robot. After noise is removed, the data undergoes feature extraction processing and is then sent to the server. This allows high-quality data to be aggregated on the server. For example, if a user frequently smiles while cooking, that data is sent to the server at regular intervals as a batch process.

[0403] Data analysis

[0404] The server stores the received activity information and emotion information in a database. The stored data is recorded with a timestamp and analyzed in real time by an AI engine and emotion analysis engine. For example, if a user shows a depressed expression more frequently than usual, the data is analyzed and detected as a significant pattern.

[0405] Detecting abnormal behavior and emotional anomalies

[0406] The server detects abnormal behavior and emotional abnormalities based on the analysis results. For example, if a user walks around the room at an unusual time and looks anxious, this behavior will be analyzed as high-risk abnormal behavior. Detected abnormal behavior will be notified to medical institutions and family members.

[0407] Early Detection System

[0408] Activity and emotional information collected over a long period of time is stored on a server. The server uses this data to identify the user's behavioral and emotional patterns. Based on the identified patterns, early symptoms of dementia are analyzed. For example, if short-term behavioral fluctuations coincide with emotional fluctuations, this is deemed abnormal.

[0409] Generate and send reports

[0410] Based on the analyzed data, reports are automatically generated by the AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated reports are periodically sent to medical institutions and care managers.

[0411] Medical support

[0412] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, next appointment schedule, etc. This information is sent to a server, which analyzes it and generates a medical plan and treatment policy using generative AI.

[0413] Notification and follow-up

[0414] The generated medical plan and treatment policy are notified to the user and medical institution via a smartphone app or email. The user can check the next appointment schedule and follow-up instructions in the app.

[0415] Government response services

[0416] Users request dementia support from government offices through a dedicated app. The app has a form where users can enter information such as the type of support they would like, their name, address, and emotional state, and this information is sent to a server. The server analyzes this information and uses generative AI to automatically generate an appropriate support plan. Government staff can view this information on their devices and provide the necessary support.

[0417] Examples of concrete examples and prompts

[0418] As a specific example, if a 75-year-old user exhibits behavior that differs from their usual behavioral patterns and has an anxious expression on their face, this data will be analyzed as abnormal behavior and a medical institution will be notified.

[0419] Example prompt sentence:

[0420] "A 75-year-old user is walking around outside of their usual walking time and appears anxious. This is analyzed as abnormal behavior and a prompt is sent to notify a medical institution."

[0421] In this way, the system of the present invention comprehensively analyzes the user's activity information and emotional information, detects abnormal behavior and early symptoms with high accuracy, and enables appropriate medical and administrative responses.

[0422] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0423] Step 1:

[0424] Collecting user activity and emotion information

[0425] The user provides daily activity information and emotional information to the monitoring robot through sensors installed in the robot.

[0426] Input: User movements, facial expressions, and tone of voice

[0427] Output: Raw data (motion information, facial expression data, voice data)

[0428] Specific operation: The monitoring robot uses motion sensors, cameras, microphones, facial expression recognition cameras, and voice analysis microphones to record the user's movements, facial expressions, and voice information in real time. For example, when the user is cooking in the kitchen, the facial expression recognition camera will capture their smile, and the voice analysis microphone will record their happy tone of voice.

[0429] Step 2:

[0430] Initial data processing and feature extraction

[0431] The monitoring robot performs initial processing on the raw data to remove noise, then performs feature extraction to generate variables.

[0432] Input: Raw data (motion information, facial expression data, voice data)

[0433] Output: Feature-extracted data (filtered motion information, facial features, and audio features)

[0434] Specific operations: For example, a noise reduction filter is applied to voice data to extract features that indicate the speaker's emotional state. Also, features that measure the frequency and degree of smiling are generated from facial expression data.

[0435] Step 3:

[0436] Sending data to the server

[0437] The monitoring robot transmits the data that has undergone initial processing and feature extraction to the server.

[0438] Input: Feature-extracted data (filtered movement information, facial features, and audio features)

[0439] Output: Received data on the server side

[0440] Specific operation: Data is sent to the server in real time or in batches. For example, all collected data is sent together as a batch at the end of the day.

[0441] Step 4:

[0442] Data analysis using AI and sentiment analysis

[0443] The server stores the received data in a database and analyzes it using an AI engine and emotion analysis engine.

[0444] Input: Received data (filtered movement information, facial expression features, and voice features)

[0445] Output: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[0446] Specific operation: The AI ​​engine analyzes the user's behavioral patterns, and the emotion analysis engine analyzes the emotional fluctuation patterns. For example, if the user frequently shows a depressed expression, this will be output as the analysis result.

[0447] Step 5:

[0448] Detecting abnormal behavior and emotional anomalies

[0449] The server detects abnormal behavior and emotional abnormalities based on the analysis results.

[0450] Input: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[0451] Output: Abnormal behavior and emotional anomaly detection alerts

[0452] Specific behavior: For example, if a user walks around the room at an unusual time and looks anxious, this will trigger an alert as a high-risk abnormal behavior.

[0453] Step 6:

[0454] Generate and send reports

[0455] The server generates a report based on the analysis results and sends it to the medical institution or care manager.

[0456] Input: Abnormal behavior and emotional anomaly detection alerts

[0457] Output: Generated report, Send report

[0458] Specific operation: Using generative AI, a detailed report is automatically created. The report includes information on the user's activities, emotions, and frequency of abnormal behavior. The report is then sent via email or a dedicated app.

[0459] Step 7:

[0460] Entering and analyzing medical information

[0461] The terminal provides an interface for the doctor to input consultation information and transmits the information to the server.

[0462] Input: Medical information (health status, emotional state, prescription medications, next appointment)

[0463] Output: Analysis results (treatment plan, treatment policy)

[0464] Specific operation: A doctor inputs examination information into a device, and the server analyzes it and uses generative AI to generate a medical plan and treatment policy. For example, it creates a plan that suggests prescribing specific medications.

[0465] Step 8:

[0466] Notification and follow-up

[0467] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[0468] Input: Generated treatment plan and treatment policy

[0469] Output: Notification message (app notification, email notification)

[0470] Specific operation: The generated medical plan and treatment policy are notified to the user via smartphone app or email. The user checks the next appointment schedule and follow-up instructions in the app.

[0471] Step 9:

[0472] Providing administrative services

[0473] Users can request dementia support from government offices through a dedicated app.

[0474] Input: Support request information (support content, name, address, emotional state, etc.)

[0475] Output: Request to administrative office, generation of support plan

[0476] How it works: Users enter the necessary information into a dedicated app, and the server analyzes it and automatically generates an appropriate support plan. Government officials can view the information on their devices and provide the necessary support.

[0477] This enables the system to comprehensively analyze a user's activity and emotional information, detect abnormal behavior and early symptoms with high accuracy, and implement appropriate medical and administrative responses.

[0478] (Application example 2)

[0479] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0480] Conventional dementia patient monitoring systems only target user behavioral information, so they may miss signs of abnormal behavior due to emotional fluctuations. Furthermore, they lack a mechanism for detecting customer emotions in brick-and-mortar stores and recommending appropriate products and services, making them inadequate for improving customer satisfaction. Therefore, there is a need for a system that comprehensively analyzes user activity and emotional information to detect abnormal behavior, detect early dementia symptoms, and improve customer service in brick-and-mortar stores.

[0481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0482] In this invention, the server includes a sensor means for recording activity information and emotional information of the user, an analysis means for analyzing the recorded activity information and emotional information, an abnormal behavior detection means for detecting abnormal behavior and emotional abnormalities based on the analysis results, a notification means for sending a notification of the detected abnormal behavior and emotional abnormalities to a medical institution and family, and a recommended product display means for displaying recommended products based on the detected emotions. This enables consistent responses based on emotional and behavioral analysis, from preparing for dementia patients to customer service in physical stores.

[0483] "Sensor means" refers to a device for detecting and recording activity information and emotional information of a user.

[0484] "Analysis means" refers to a device or software that has the function of analyzing the recorded activity information and emotion information and extracting patterns and anomalies from the data.

[0485] The "abnormal behavior detection means" is a device or software for detecting abnormalities in the user's behavior or emotions that are different from normal based on the analysis results.

[0486] The "notification means" is a device or software for transmitting information about detected abnormal behavior and emotional abnormalities to a medical institution or family member.

[0487] The "recommended product display means" is a device or software for displaying appropriate products and services to the user based on the detected emotion.

[0488] The "behavioral pattern identification means" is a device or software having a function for identifying a consistent pattern of a user's behavior from the analyzed activity information and emotion information.

[0489] An "early symptom detection means" is a device or software for detecting early symptoms of dementia or the like based on identified behavioral patterns.

[0490] A "report generator" is a device or software for generating a detailed report regarding detected early symptoms.

[0491] The "input means" is a device or software that provides an interface for inputting the user's medical information.

[0492] The "treatment plan generation means" is a device or software that analyzes the input examination information and generates the next examination schedule and treatment plan.

[0493] A specific system for implementing this invention utilizes various sensors and AI technology to collect and analyze user activity and emotional information. Specifically, it is composed of the following elements:

[0494] 1. Sensor means

[0495] The sensor means records the user's activity information (movements, location, etc.) and emotional information (facial expressions, tone of voice, etc.). Specific examples include a motion sensor, a position tracking device, a facial expression recognition camera, and a voice analysis microphone. This makes it possible to record in detail everything from the user's smallest movements to changes in facial expressions and voice.

[0496] 2. Analysis method

[0497] The server receives the recorded activity information and emotion information and analyzes it. An AI engine and emotion engine are used for the analysis. The AI ​​engine analyzes patterns in the activity information, and the emotion engine analyzes the user's emotions. This allows for highly accurate capture of fluctuations in the user's behavior and emotions.

[0498] 3. Abnormal Behavior Detection Method

[0499] Based on the analysis results, the server detects abnormal behavior and emotional abnormalities. The abnormal behavior detection means judges information when there is a deviation from normal behavior patterns or abnormal emotional fluctuations. This makes it possible to detect abnormal behavior of dementia patients or customer dissatisfaction in physical stores in real time.

[0500] 4. Means of notification

[0501] When the server detects abnormal behavior or emotional abnormalities, it immediately notifies medical institutions and family members via email, SMS, in-app notifications, etc. This allows the relevant parties to respond quickly.

[0502] 5. Recommended product display methods

[0503] In physical stores, recommended products are displayed to users based on the analyzed emotional information. For example, if a customer shows interest in a particular product and their emotional state is positive, the system can recommend that product and related products specifically. This allows for more personalized service to be provided to customers.

[0504] Natural language processing explanation

[0505] The overall processing of this system is carried out as follows: The sensor means collects various data about the user, which is then sent to the server. The server then analyzes the received data using an AI engine and an emotion engine. If an abnormality is detected, the information is notified and, if necessary, recommended products are displayed.

[0506] For example, if a customer is looking at products in the "cosmetics section" of a store with an excited (happy) expression, the server will analyze that information and recommend "Product A" or "Product B." At the same time, if anxious expressions or behavior are observed, the server will immediately notify the customer. In this way, the system can be adapted for a wide range of uses, from monitoring dementia patients to customer service in physical stores.

[0507] Prompt Sentence Examples

[0508] Here are some examples of prompts for generative AI models:

[0509] "We would like to build a system that recognizes the behavior and emotions of customers in a store and proposes appropriate measures in real time. For example, if a customer looks excited (happy) in the cosmetics section, we would like to recommend appropriate products to that customer, or if the customer looks unhappy near the cash register, we would like to send an alert to the staff. We would like you to generate a program that can handle this."

[0510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0511] Step 1:

[0512] The sensor means records the user's activity information and emotional information. This information includes movement data from the motion sensor, facial expression data from the facial expression recognition camera, and voice tone data from the voice analysis microphone. The input is the data sent in real time from each sensor, and the output is the data being sent to the server.

[0513] Step 2:

[0514] The server analyzes the received activity information and emotional information. The AI ​​engine analyzes the user's behavioral data, and the emotion engine analyzes the user's emotional data. Pattern recognition and emotional analysis are performed on the data received as input, and the respective analysis results are generated. The output is the analyzed data.

[0515] Step 3:

[0516] The server detects abnormal behavior and emotional anomalies based on the analysis results. Specifically, it compares the analysis results with normal behavioral and emotional patterns in the database to check for deviations. The input is the analyzed activity information and emotional information, and the output is the detection results of abnormal behavior and emotional anomalies.

[0517] Step 4:

[0518] If abnormal behavior or emotional abnormalities are detected, the server notifies medical institutions and family members via email, SMS, and in-app notifications. The input is the detected abnormal behavior and emotional abnormality data, and the output is the notification to be sent.

[0519] Step 5:

[0520] The server selects recommended products based on the emotional information analyzed in the physical store. Specifically, it automatically selects appropriate products and services based on the results of the emotion engine and the user's behavioral patterns. The input is the emotional analysis data, and the output is a list of recommended products.

[0521] Step 6:

[0522] The recommended product list is automatically displayed on the user's device. A push notification is sent from the server to the user's smartphone or tablet, allowing the user to check it immediately. The input is the recommended product list, and the output is the recommended product information displayed on the user's device.

[0523] The above steps enable comprehensive analysis of user activity information and emotional information, making it possible to detect abnormal behavior and respond to customers in physical stores.

[0524] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0525] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0526] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0527] [Second embodiment]

[0528] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0529] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0530] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0531] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0532] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0533] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0534] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0535] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0536] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0537] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0538] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0539] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0540] The following describes an embodiment of the present invention. First, the system of the present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, family members, and governments of the necessary information.

[0541] Monitoring robot

[0542] Sensor means

[0543] The monitoring robot is equipped with multiple sensors to collect information on the user's daily life activities. These sensors include a motion sensor, a camera, and a microphone, and capture data such as the user's walking pattern, conversations, and room movements. For example, if the user is cooking in the kitchen, the robot will record their movements with a camera and detect surrounding sounds with a microphone.

[0544] Data collection and transmission

[0545] The data acquired by the sensors is sent in real time to a processing unit inside the robot. The processing unit performs initial data processing to remove unnecessary noise and extract necessary features. After this processing is complete, the data is sent to a server via the Internet. For example, once a certain amount of data on a user's walking has been collected, it is sent to the server as a batch.

[0546] Data analysis

[0547] AI-based analysis methods

[0548] The server stores the user's activity information in a database based on a chronological order. The stored data is analyzed in real time by an AI engine, which compares it with past behavioral data to detect abnormal behavior. The server can also perform detailed analysis of abnormal behavior patterns to determine whether they are due to the progression of dementia or other factors.

[0549] Abnormal behavior notification

[0550] If abnormal behavior is detected based on the analysis results, the server will automatically send a notification to a medical institution or family. For example, if a user is walking around the room at an unusual time, the server will recognize this information as abnormal behavior and send an email notification to a medical institution.

[0551] Early Detection System

[0552] Identifying behavioral patterns and detecting early symptoms

[0553] The server analyzes data collected over a long period of time to identify the user's behavioral patterns. The behavioral pattern identification means determines what the user is doing at what time of day. As a result, if there is a sudden increase in behavioral fluctuations observed over a short period of time, for example, this can be detected as an early symptom.

[0554] Generate and send reports

[0555] If early symptoms are detected, the generative AI creates a detailed report based on the detection results, including the user's behavioral patterns, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[0556] Medical support

[0557] Entering and analyzing medical information

[0558] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[0559] Notification and follow-up

[0560] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[0561] Government response services

[0562] Citizen Support

[0563] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they require, their name, and address, and this information is sent directly to a server. The server then analyzes the information and automatically generates an appropriate support plan.

[0564] Collaboration support

[0565] The server seamlessly shares information with government offices using the integrated data, including the user's health status, support, progress, etc. Government officials can view and update this information through their devices and take necessary actions.

[0566] In this way, the system of the present invention can comprehensively support everything from monitoring dementia patients to early detection, medical support, and government response, thereby comprehensively protecting the safety and health of users.

[0567] The processing flow will be explained below.

[0568] Monitoring robot processing flow

[0569] Step 1:

[0570] The robot activates its built-in motion sensors, camera, and microphone to collect information about the user's activities.

[0571] Step 2:

[0572] The robot initially processes the collected data to remove noise and extract key features (e.g., walking patterns and sound changes).

[0573] Step 3:

[0574] The robot sends the processed data to the server in real time or in batches.

[0575] Data analysis process flow

[0576] Step 4:

[0577] The server stores the received data in a database and records it with a timestamp.

[0578] Step 5:

[0579] The server analyzes the received data in real time using an AI engine and begins analysis to detect abnormal behavior.

[0580] Step 6:

[0581] The server detects abnormal behavior (e.g., falls, wandering at unusual times, etc.) based on the analysis results.

[0582] Step 7:

[0583] The server generates alerts about detected abnormal behavior and notifies medical institutions and family members.

[0584] Early detection system processing flow

[0585] Step 8:

[0586] The server analyzes the data collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral patterns.

[0587] Step 9:

[0588] The server applies an anomaly detection algorithm to the identified behavioral patterns to detect early symptoms of dementia.

[0589] Step 10:

[0590] The server uses generative AI to automatically generate a detailed report based on early symptoms.

[0591] Step 11:

[0592] The server periodically transmits the generated reports to the medical institution or care manager.

[0593] Medical response support process flow

[0594] Step 12:

[0595] The terminal provides an interface (eg, a form) for the physician to enter consultation information.

[0596] Step 13:

[0597] The doctor enters the user's medical information (e.g., health status, diagnosis, and prescription medications).

[0598] Step 14:

[0599] The terminal transmits the input medical information to the server.

[0600] Step 15:

[0601] The server analyzes the consultation information and uses generation AI to automatically generate the next consultation schedule and treatment plan.

[0602] Step 16:

[0603] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[0604] Processing flow of administrative services

[0605] Step 17:

[0606] Users enter information (e.g., name, address, and details of support) to request support from the government through a dedicated app.

[0607] Step 18:

[0608] The terminal receives the input and sends it to the server.

[0609] Step 19:

[0610] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[0611] Step 20:

[0612] The server notifies the user of the generated support plan.

[0613] Step 21:

[0614] The server uses the integrated data to seamlessly share information with government offices.

[0615] Step 22:

[0616] The terminal provides an interface (e.g., a dashboard) for government employees to view and update the data they receive.

[0617] This is the specific processing flow of the system, which can comprehensively support dementia patient monitoring, early detection, medical support, and government response.

[0618] Example 1

[0619] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0620] As the aging population continues, the increase in dementia patients has become a serious problem. Conventional systems lack consistent, comprehensive support for early detection of abnormal behavior in the daily lives of dementia patients and appropriate medical and administrative support. As a result, early intervention to slow the progression of dementia is difficult, and safety management and appropriate treatment are often delayed. Furthermore, some information is not shared appropriately in administrative responses, resulting in insufficient support. The present invention aims to solve these problems.

[0621] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0622] In this invention, the server includes a data processing means for initial processing of recorded activity information and removing unnecessary noise, a data transmission means for transmitting the processed data to the server, and an analysis means for saving and analyzing the received data in chronological order. This enables the detection of abnormal user behavior in real time and immediate notification to appropriate medical institutions and family members. Furthermore, the analysis of long-term behavioral patterns makes it possible to detect early symptoms of dementia, enabling earlier intervention. Furthermore, by generating reports using generative AI, detailed analysis results can be provided to medical institutions and care managers, allowing appropriate treatment plans and policies to be quickly formulated. Centralized information sharing is also possible in administrative responses, providing comprehensive support.

[0623] "Sensor means" refers to devices used to record user activity information, including motion sensors, cameras, microphones, and the like.

[0624] "Data processing means" refers to a device or software capable of initial processing of recorded activity information and removing unnecessary noise.

[0625] "Data transmission means" refers to a device or function that transmits processed data to a server.

[0626] "Analysis means" refers to a device or system that stores received data in chronological order and analyzes the data using an AI engine.

[0627] The "abnormal behavior detection means" is a device or software that has the function of detecting abnormal behavior of a user based on analyzed data.

[0628] "Notification means" refers to a device or system that sends notifications to medical institutions and family members regarding detected abnormal behavior.

[0629] The "behavior pattern identification means" is a device or software that has the function of identifying the user's behavior pattern from the analyzed data.

[0630] An "early symptom detection means" is a device or software that has the function of detecting early symptoms of dementia based on identified behavioral patterns.

[0631] "Report generation means" means a device or software capable of generating a detailed report on detected early symptoms using generative AI.

[0632] "Report sending means" refers to a device or function that sends the generated report to a medical institution or care manager.

[0633] "Input means" refers to a device or interface for inputting a user's medical information.

[0634] The "treatment plan generation means" is a device or software that has the function of analyzing input examination information and generating the next examination schedule and treatment plan.

[0635] "Generative AI" refers to an artificial intelligence engine that automatically performs data analysis across the entire system and generates reports.

[0636] The present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, families, and governments of the necessary information. Specific embodiments are described below.

[0637] Hardware and software used

[0638] Monitoring robot: Equipped with motion sensors, cameras, and microphones to collect information on the user's daily life activities.

[0639] Server: A central device for processing and analyzing data, equipped with an AI engine.

[0640] Terminal: Provides an interface for doctors to input consultation information.

[0641] Generative AI model: Refers to an artificial intelligence engine for data analysis and report generation.

[0642] Examples of data collection

[0643] The monitoring robot collects activity information as the user goes about their daily life. For example, if the user is cooking in the kitchen, the camera records their movements and the microphone detects surrounding sounds. The motion sensor tracks the user's walking pattern in real time. This data is then filtered out by a processing unit inside the monitoring robot, and necessary features are extracted before being sent to a server via the Internet.

[0644] Specific examples of data analysis

[0645] The server stores the received activity information in a database based on a chronological order. The stored data is analyzed in real time using an AI engine and compared with past behavioral data to detect abnormal behavior. For example, if a user is walking around a room at an unusual time, that information will be recognized as abnormal behavior.

[0646] Abnormal behavior detection and notification

[0647] If abnormal behavior is detected based on the analysis results, the server automatically sends a notification to a medical institution or family. For example, the server may notify a medical institution of details of the abnormal behavior via email. The notification will include any changes in the user's behavioral patterns and various sensor data.

[0648] Identifying early symptoms

[0649] The server analyzes data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means can detect a sudden increase in behavioral fluctuations over a short period of time as an early symptom.

[0650] Report generation and delivery

[0651] If early symptoms are detected, the Generative AI creates a detailed report based on the detection results. This report includes the user's behavioral patterns, the frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers. Examples of prompt sentences that are used include the following:

[0652] Example prompt sentence:

[0653] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[0654] Generate treatment plans

[0655] The device provides an interface for doctors to input consultation information. After the consultation, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, and this information is sent to the server. The server uses an AI engine to generate the next appointment schedule and optimal treatment plan, and notifies the user.

[0656] Administrative response

[0657] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like, their name, and address, and this information is sent directly to a server. The server analyzes the information it receives and automatically generates an appropriate support plan. It is also possible to seamlessly share information with government offices using the integrated data.

[0658] In this way, this system provides comprehensive support for dementia patients, from monitoring to early detection, medical support, and government response, and can comprehensively protect the safety and health of users.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1: Data collection

[0661] Subject: Monitoring robot

[0662] The monitoring robot uses motion sensors, cameras, and microphones to collect information about the user's daily activities. Specifically, the robot captures the user's movements with a camera and records their voice with a microphone. The motion sensors detect the user's position and movements in real time. This data is sent to the robot's internal processing unit.

[0663] Input: User's daily activities

[0664] Output: Sensor data sent to a processing unit

[0665] Step 2: Data processing

[0666] Subject: Monitoring robot

[0667] A processing unit inside the robot initially processes the collected data and removes unnecessary noise, for example by deleting unwanted frames from camera footage and filtering background noise from audio data. Data from motion sensors is extracted as movement patterns.

[0668] Input: Sensor data

[0669] Output: Data after denoising and feature extraction

[0670] Step 3: Send data

[0671] Subject: Monitoring robot

[0672] The processed data is sent to a server via the Internet. Specifically, once a certain amount of data has been collected, it is batch processed and sent to the server in encrypted form.

[0673] Input: Data after initial processing

[0674] Output: Data sent to the server

[0675] Step 4: Save Data

[0676] Subject: Server

[0677] The server stores the received data in a database based on time sequence, and the stored data is used for subsequent analysis.

[0678] Input: Data sent

[0679] Output: Data stored in the database

[0680] Step 5: Data analysis

[0681] Subject: Server

[0682] The server analyzes the stored data in real time using an AI engine. Specifically, it compares it with past behavioral data to detect abnormal behavior. For example, if behavior that differs from a normal walking pattern is observed, it is recognized as abnormal behavior.

[0683] Input: Data stored in a database

[0684] Output: Analysis results and abnormal behavior detection

[0685] Step 6: Abnormal Behavior Notification

[0686] Subject: Server

[0687] If abnormal behavior is detected, the server automatically sends a notification to a medical institution or family member via email or smartphone app, detailing the nature of the abnormal behavior.

[0688] Input: Analysis results

[0689] Output: Notification to medical institutions and family members

[0690] Step 7: Identify behavioral patterns

[0691] Subject: Server

[0692] The server analyzes the data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means models daily behavior during specific time periods.

[0693] Input: Long-term data

[0694] Output: User behavior patterns

[0695] Step 8: Early symptom detection

[0696] Subject: Server

[0697] The system detects early symptoms of dementia based on identified behavioral patterns. For example, a sudden increase in behavioral fluctuations over a short period of time can be recognized as an early symptom.

[0698] Input: Behavioral pattern

[0699] Output: Early symptom detection

[0700] Step 9: Generate reports

[0701] Subject: Server

[0702] Generative AI is used to create a detailed report of detected early symptoms, including behavioral patterns, frequency of abnormal behavior, and recommended next steps.

[0703] Input: Early symptom data

[0704] Output: Detailed report

[0705] Step 10: Submit report

[0706] Subject: Server

[0707] The generated reports are sent periodically to medical institutions and care managers, with appropriate information provided using prompts.

[0708] Input: Detailed report

[0709] Output: Send to medical institutions and care managers

[0710] Example prompt sentence:

[0711] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[0712] Step 11: Generate a treatment plan

[0713] Subject: Device

[0714] The doctor inputs the patient's medical information into the device, which is then sent to the server, where the AI ​​engine generates the next appointment schedule and appropriate treatment plan.

[0715] Input: Medical Information

[0716] Output: Treatment plan and policy

[0717] Step 12: Care Plan Notification

[0718] Subject: Server

[0719] The generated treatment plan and treatment policy are notified to the user via a smartphone app or email.

[0720] Input: Treatment plan and treatment policy

[0721] Output: User notification

[0722] Step 13: Government response

[0723] Subject: User

[0724] The user uses a dedicated app to send a support request to the government office. The information entered in the app is sent to the server.

[0725] Input: Support request details

[0726] Output: Send to server

[0727] Step 14: Data analysis for administrative responses

[0728] Subject: Server

[0729] The server analyzes the received information and automatically generates an appropriate support plan. The integrated data is then used to share information with government offices.

[0730] Input: Support request details

[0731] Output: Generated support plan and submission to government office

[0732] In this way, the present system makes it possible to comprehensively protect the safety and health of users through a series of processing steps.

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0735] The purpose of this invention is to provide a system that comprehensively monitors, detects early, and provides medical support for dementia patients. Specifically, the objective is to ensure the safety of dementia patients and support appropriate medical intervention by visually and audibly monitoring their daily lives, detecting and analyzing abnormal behavior in real time, and notifying medical institutions and families in a timely manner. Furthermore, efficient generation and notification of treatment plans is also an important objective.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes a sensing unit that records the user's activity information, an analysis unit that analyzes the recorded activity information, and an abnormal behavior detection unit that detects abnormal behavior based on the analysis results. This enables the server to quickly detect the user's abnormal behavior and promptly notify medical institutions and family members. Furthermore, by visually and audibly monitoring the user's daily life using the camera and microphone unit built into the smart glasses, sending data to the server via the preprocessing unit and communication unit, and notifying the analysis results via a smartphone app or email, more accurate monitoring and early detection are achieved. Furthermore, comprehensive medical support can be provided by proposing the next appointment and treatment plan based on the report generated using the generative AI model.

[0738] "Sensing means" refers to a device or sensor for recording user activity information.

[0739] "Analysis means" refers to equipment or software for processing the recorded activity information and extracting and analyzing the necessary information.

[0740] The "abnormal behavior detection means" refers to a device or program that has the function of identifying and detecting abnormal user behavior based on analyzed data.

[0741] "Notification means" refers to a method or system for reporting detected abnormal behavior or important information to a medical institution or family member.

[0742] "Camera and microphone means" refers to video and audio collection devices for visual and audio monitoring of the user's daily life.

[0743] "Pre-processing means" refers to a method or device that performs initial noise removal and feature extraction on the data obtained by the processing unit built into the smart glasses.

[0744] "Communication means" refers to the internet connection or wireless communication mechanism for transmitting the preprocessed data to the server.

[0745] "Report generator" refers to a device or program for automatically generating a detailed report based on detected early symptoms.

[0746] The "behavior pattern identification means" refers to an analysis device or program for identifying a behavior pattern from user activity information.

[0747] "Early symptom detection means" refers to a function or device for detecting early symptoms of dementia based on identified behavioral patterns.

[0748] "Input means" refers to a terminal or program that allows a medical professional to input the user's medical information.

[0749] The "treatment plan generating means" refers to a program or device that generates the next examination schedule and treatment plan based on the input examination information.

[0750] "Proposal method" refers to a system or program that suggests the next steps in medical care based on a report generated using a generative AI model.

[0751] A "prompt generation means" is a program that inputs appropriate prompt sentences into the AI ​​to improve the efficiency of data analysis.

[0752] The system of the present invention aims to comprehensively monitor dementia patients, detect their condition early, provide medical support, and respond to government requests. The system collects and analyzes information on the user's daily activities, detects abnormal behavior, and sends necessary notifications to ensure the patient's safety and support appropriate medical intervention. Specific embodiments of the present invention are described below.

[0753] System Configuration

[0754] The system includes a sensing means, an analysis means, an abnormal behavior detection means, a notification means, a camera and microphone means, a preprocessing means, a communication means, a report generation means, a behavioral pattern identification means, an early symptom detection means, an input means, a treatment plan generation means, a suggestion means, and a prompt generation means.

[0755] sensing means

[0756] The smart glasses are equipped with multiple sensors to monitor the user's daily life, including a camera, microphone, and motion sensors, and collect information on the user's activities in real time.

[0757] Analysis means

[0758] The data collected by the smart glasses undergoes initial processing in the built-in data processing unit, where noise is removed and necessary features are extracted. The pre-processed data is then sent to a server via a communication means.

[0759] Abnormal behavior detection means

[0760] The server stores the data sent via communication means in a database. The AI ​​engine analyzes the collected data in real time and compares it with past behavioral data to detect abnormal behavior.

[0761] Notification means

[0762] If abnormal behavior is detected, the server will notify medical institutions and family members via a smartphone app or email, urging them to take prompt action.

[0763] Camera and microphone means

[0764] The cameras and microphones built into the smart glasses collect detailed visual and audio data from users' daily lives, which is crucial for improving the accuracy of abnormal behavior detection.

[0765] Pretreatment means

[0766] The processing unit built into the smart glasses performs initial noise removal and feature extraction of the data, thereby improving the quality of the data sent to the server via the communication means.

[0767] communication means

[0768] The pre-processed data is then sent to a server over the internet, using wireless communication and an internet connection.

[0769] Report generation means

[0770] Based on the detected early symptoms, a detailed report is generated and sent periodically to the medical institution or care manager.

[0771] Specific examples and prompts

[0772] It records detailed activity information such as the user's walking data, conversation content, and movement from room to room, and uses analytical methods to detect abnormal behavior. For example, it generates prompt sentences like the following and inputs them into the AI.

[0773] example:

[0774] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[0775] Based on the information collected and analyzed in this way, abnormal behavior can be detected and notified, supporting rapid medical response. This method can comprehensively realize everything from monitoring dementia patients to early detection and medical support.

[0776] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0777] Step 1:

[0778] A user wears smart glasses while going about their daily life. The smart glasses' sensing means (camera, microphone, motion sensor) collect the user's activity information (walking patterns, conversations, room movements, etc.) in real time. The input is the user's behavioral data in their daily life, and the output is the collected raw data.

[0779] Step 2:

[0780] The collected raw data undergoes initial processing using the smart glasses' built-in pre-processing means. Specifically, noise removal and feature extraction are performed. The input is raw data, and the output is pre-processed data. This processing removes noise from the data and emphasizes the features required for analysis.

[0781] Step 3:

[0782] The pre-processed data is sent to the server via a communication means. The input is the pre-processed data, and the output is the data stored on the server. This communication can be via the internet or wirelessly.

[0783] Step 4:

[0784] The server stores the transmitted data in a database and analyzes it in real time. Using analytical tools, the collected data is compared with past behavioral data to detect abnormal behavior. The input is the user's activity information stored in the database, and the output is the analysis results.

[0785] Step 5:

[0786] When abnormal behavior is detected, the server uses a notification method to notify medical institutions and family members. The input is the analysis result and the output is a notification message. Notifications are sent via a smartphone app or email, encouraging a prompt response.

[0787] Step 6:

[0788] Based on the data collected over a long period of time, the server uses a behavioral pattern identification means and an early symptom detection means to identify the user's behavioral patterns and detect early symptoms.The input is the long-term data, and the output is a report of the behavioral patterns and early symptoms.

[0789] Step 7:

[0790] Based on the detected early symptoms, a report generator generates a detailed report. The input is the data of the early symptoms, and the output is a detailed report. This report is periodically sent to a medical institution or a care manager.

[0791] Step 8:

[0792] The user's medical examination information is sent to the server via the input means, and the next medical examination schedule and medical treatment plan are generated using the treatment plan generation means. The input is the medical examination information, and the output is a notification of the medical examination schedule and medical treatment plan.

[0793] Step 9:

[0794] Using a generative AI model, the next steps in medical care are suggested based on the generated report. A specific example involves generating prompts and inputting them into the AI. The input is a detailed report, and the output is specific medical suggestions.

[0795] example:

[0796] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[0797] Based on this prompt, the AI ​​will suggest the next step, creating a system that comprehensively monitors users, detects problems early, and provides medical support.

[0798] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0799] This section describes an embodiment of the present invention. This section describes a system that combines a system for monitoring dementia patients, early detection, medical support, and administrative response with an emotion engine that recognizes the user's emotions. This system collects and analyzes not only user activity information but also emotional information, enabling more accurate detection of abnormal behavior and identification of behavioral patterns.

[0800] Monitoring robot

[0801] Sensor Instruments and Emotion Engines

[0802] The monitoring robot is equipped with motion sensors, cameras, microphones, and sensors to recognize the user's emotions (e.g., facial expression recognition camera, voice analysis microphone). This allows it to collect emotional information not only from the user's actions while cooking in the kitchen, but also from their facial expressions and tone of voice.

[0803] Data collection and transmission

[0804] The activity and emotion information acquired by the sensors is initially processed by the processing unit inside the robot to remove noise. This data undergoes feature extraction processing and is then sent to the server in real time or in batches. For example, when a certain amount of data is collected showing that the user is feeling happy and cooking at the same time, that data is sent to the server as a batch.

[0805] Data analysis

[0806] AI and sentiment analysis tools

[0807] The server stores the received activity information and emotion information in a database, and records them with a timestamp. The stored data is analyzed in real time by the AI ​​engine. Emotional information recognized by the emotion engine is also analyzed simultaneously. For example, if the user is feeling depressed, the associated behavioral patterns are analyzed.

[0808] Detecting abnormal behavior and emotional anomalies

[0809] If abnormal behavior is detected based on the analysis results, the server also considers emotional information to further increase the likelihood of abnormality. For example, if a user walks around the room at an unusual time and looks anxious, the server will recognize this as high-risk abnormal behavior. This information is then notified to medical institutions and family members.

[0810] Early Detection System

[0811] Identifying behavioral patterns and detecting early symptoms

[0812] The server analyzes activity and emotional information collected over a long period of time to identify the user's behavioral patterns. By incorporating emotional fluctuations into the user's behavior, early symptom detection is more accurate. For example, if short-term behavioral fluctuations are linked to emotional fluctuations, this is considered an abnormality.

[0813] Generate and send reports

[0814] A detailed report based on the detected early symptoms is automatically generated by the generative AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[0815] Medical support

[0816] Entering and analyzing medical information

[0817] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[0818] Notification and follow-up

[0819] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[0820] Government response services

[0821] Citizen Support

[0822] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like to receive, their name, address, and emotional state, and this information is sent directly to a server. The server then analyzes the information and uses a generative AI to automatically generate an appropriate support plan.

[0823] Collaboration support

[0824] The server seamlessly shares information with administrative offices using integrated data, including activity information, emotional information, support details, and progress. Administrative staff can view and update this information through their devices and take necessary actions.

[0825] In this way, the system of the present invention can comprehensively monitor and analyze the user's activity information and emotional information, and effectively detect abnormal behavior and early symptoms, thereby comprehensively protecting the safety and health of dementia patients.

[0826] The processing flow will be explained below.

[0827] Monitoring robot processing flow

[0828] Step 1:

[0829] The robot activates its built-in motion sensors, camera, microphone, facial expression recognition camera, and voice analysis microphone to collect information on the user's activities and emotions.

[0830] Step 2:

[0831] The robot initially processes the collected activity and emotion information to remove noise and extract key features, such as walking patterns, facial expression changes, and tone of voice.

[0832] Step 3:

[0833] The robot sends the processed activity information and emotion information to the server in real time or in batches.

[0834] Data analysis process flow

[0835] Step 4:

[0836] The server stores the received activity information and emotion information in a database and records them with a timestamp.

[0837] Step 5:

[0838] The server analyzes the received activity information and emotion information in real time using an AI engine and emotion engine. For example, it analyzes the user's walking movements, facial expressions, and tone of voice.

[0839] Step 6:

[0840] The server detects abnormal behavior from the activity and emotion information based on the analysis results. If the user falls and simultaneously shows a fearful expression, it will recognize this as high-risk abnormal behavior.

[0841] Step 7:

[0842] The server notifies medical institutions and family members based on the detected abnormal behavior and emotional abnormalities, for example, by email notification or text message.

[0843] Early detection system processing flow

[0844] Step 8:

[0845] The server analyzes the activity information and emotion information collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral pattern, for example, analyzing the user's activity and emotion fluctuations over a week.

[0846] Step 9:

[0847] The server detects early symptoms from the identified behavioral patterns. For example, if the user frequently wanders around feeling anxious, this may be recognized as an early symptom.

[0848] Step 10:

[0849] The server uses generative AI to automatically generate a detailed report based on early symptoms, including behavioral patterns, emotional fluctuations, detected early symptoms, and recommended next steps.

[0850] Step 11:

[0851] The server periodically transmits the generated report to the medical institution or care manager, for example, by email as a weekly report.

[0852] Medical response support process flow

[0853] Step 12:

[0854] The terminal provides an interface for the doctor to input medical information and emotional information. For example, the medical examination form may include fields for inputting the user's facial expression and tone of voice.

[0855] Step 13:

[0856] The doctor inputs the user's medical information and emotional information.

[0857] Step 14:

[0858] The terminal transmits the input medical examination information and emotion information to the server.

[0859] Step 15:

[0860] The server analyzes the medical examination information and emotional information, and uses generative AI to automatically generate the next medical examination schedule and treatment plan.

[0861] Step 16:

[0862] The server notifies the user or medical institution of the generated medical plan and treatment policy, for example, via a smartphone app.

[0863] Processing flow of administrative services

[0864] Step 17:

[0865] Users enter information to request support from the government through a dedicated app, such as their name, address, and emotional state.

[0866] Step 18:

[0867] The terminal receives the input and sends it to the server.

[0868] Step 19:

[0869] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[0870] Step 20:

[0871] The server notifies the user of the generated support plan, for example by sending a push notification to a smartphone app.

[0872] Step 21:

[0873] The server seamlessly shares information with government agencies using the integrated activity and emotion information, for example by sending detailed data including the user's health status and emotional fluctuations.

[0874] Step 22:

[0875] The terminals provide an interface for government officials to view and update the data they receive, for example by checking the status on a dashboard and taking necessary actions.

[0876] This is the specific processing flow of the system, which enables comprehensive monitoring and analysis of user activity and emotional information, enabling the detection of abnormal behavior and early symptoms with high accuracy.

[0877] Example 2

[0878] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0879] Conventional systems for monitoring dementia patients, early detection, medical support, and government response only analyze user activity information, making it difficult to perform detailed analysis that includes emotional information. As a result, the accuracy of detecting abnormal behavior and early symptoms decreases, and appropriate intervention and support can be delayed. In addition, it is difficult to generate response policies and treatment plans that take emotional information into account, making it impossible to comprehensively understand the user's condition. This has led to challenges in effectively protecting the safety and health of dementia patients.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0881] In this invention, the server includes a sensor for recording the user's activity information and emotional information, a means for initial processing and feature extraction of the recorded activity information and emotional information, a means for transmitting the processed and feature-extracted data to the server, an AI and emotion analysis means for analyzing the received activity information and emotional information and detecting abnormal behavior and emotional abnormalities, and a notification means for transmitting notifications of detected abnormal behavior and emotional abnormalities to medical institutions and family members. This enables comprehensive monitoring of the activity information and emotional information of dementia patients, enabling more accurate detection of abnormal behavior and early symptoms. Furthermore, treatment guidelines and support plans that take emotional information into account can be generated, enabling a comprehensive understanding of the user's condition and appropriate responses.

[0882] "Sensor means" refers to a device for recording a user's activity information and emotional information, and includes a motion sensor, a camera, a microphone, a facial expression recognition camera, a voice analysis microphone, etc.

[0883] "Initial processing" refers to processing to remove noise from collected raw data and improve the quality of the data.

[0884] "Feature extraction" is the process of extracting necessary information from initially processed data and converting it into a form suitable for analysis.

[0885] The "means for transmitting to the server" is a mechanism for transmitting the data that has undergone initial processing and feature extraction to the server via the Internet or other network.

[0886] "AI and emotion analysis means" refers to artificial intelligence technology and emotion analysis algorithms for analyzing received activity information and emotion information and assessing a user's behavioral patterns and emotional state.

[0887] "Abnormal behavior" refers to behavior that deviates from a user's normal behavioral patterns and may indicate symptoms of dementia or other health issues.

[0888] "Emotional abnormalities" refer to emotional states that deviate from normal emotional patterns and may indicate psychological stress or changes in the user's health.

[0889] "Notification means" refers to a system for notifying medical institutions and family members of information about detected abnormal behavior and emotional abnormalities, and includes email, smartphone apps, telephone, etc.

[0890] This section describes an embodiment of the present invention. The purpose of this invention is to monitor dementia patients, detect them early, provide medical support, and respond to government requests. In addition, it includes an emotion engine that analyzes the user's emotions. This system collects and analyzes user activity information and emotion information, and achieves highly accurate detection of abnormal behavior and identification of behavioral patterns.

[0891] Monitoring robot

[0892] The monitoring robot is equipped with the following sensor means:

[0893] Motion sensor: Records user activity information in real time.

[0894] Camera: Visually records the user's actions.

[0895] Microphone: Collects user voice information.

[0896] Facial expression recognition camera: Analyzes the user's facial expressions and collects emotional information.

[0897] Voice analysis microphone: Analyzes the user's emotional state from the tone of their voice.

[0898] These sensors continuously record daily activities, such as when a user is cooking in the kitchen, and use facial recognition cameras and voice analysis microphones to collect emotional information, such as whether the user is enjoying themselves or feeling anxious.

[0899] Data collection and transmission

[0900] The monitoring robot initially processes activity and emotion information acquired by sensors in a processing unit inside the robot. After noise is removed, the data undergoes feature extraction processing and is then sent to the server. This allows high-quality data to be aggregated on the server. For example, if a user frequently smiles while cooking, that data is sent to the server at regular intervals as a batch process.

[0901] Data analysis

[0902] The server stores the received activity information and emotion information in a database. The stored data is recorded with a timestamp and analyzed in real time by an AI engine and emotion analysis engine. For example, if a user shows a depressed expression more frequently than usual, the data is analyzed and detected as a significant pattern.

[0903] Detecting abnormal behavior and emotional anomalies

[0904] The server detects abnormal behavior and emotional abnormalities based on the analysis results. For example, if a user walks around the room at an unusual time and looks anxious, this behavior will be analyzed as high-risk abnormal behavior. Detected abnormal behavior will be notified to medical institutions and family members.

[0905] Early Detection System

[0906] Activity and emotional information collected over a long period of time is stored on a server. The server uses this data to identify the user's behavioral and emotional patterns. Based on the identified patterns, early symptoms of dementia are analyzed. For example, if short-term behavioral fluctuations coincide with emotional fluctuations, this is deemed abnormal.

[0907] Generate and send reports

[0908] Based on the analyzed data, reports are automatically generated by the AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated reports are periodically sent to medical institutions and care managers.

[0909] Medical support

[0910] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, next appointment schedule, etc. This information is sent to a server, which analyzes it and generates a medical plan and treatment policy using generative AI.

[0911] Notification and follow-up

[0912] The generated medical plan and treatment policy are notified to the user and medical institution via a smartphone app or email. The user can check the next appointment schedule and follow-up instructions in the app.

[0913] Government response services

[0914] Users request dementia support from government offices through a dedicated app. The app has a form where users can enter information such as the type of support they would like, their name, address, and emotional state, and this information is sent to a server. The server analyzes this information and uses generative AI to automatically generate an appropriate support plan. Government staff can view this information on their devices and provide the necessary support.

[0915] Examples of concrete examples and prompts

[0916] As a specific example, if a 75-year-old user exhibits behavior that differs from their usual behavioral patterns and has an anxious expression on their face, this data will be analyzed as abnormal behavior and a medical institution will be notified.

[0917] Example prompt sentence:

[0918] "A 75-year-old user is walking around outside of their usual walking time and appears anxious. This is analyzed as abnormal behavior and a prompt is sent to notify a medical institution."

[0919] In this way, the system of the present invention comprehensively analyzes the user's activity information and emotional information, detects abnormal behavior and early symptoms with high accuracy, and enables appropriate medical and administrative responses.

[0920] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0921] Step 1:

[0922] Collecting user activity and emotion information

[0923] The user provides daily activity information and emotional information to the monitoring robot through sensors installed in the robot.

[0924] Input: User movements, facial expressions, and tone of voice

[0925] Output: Raw data (motion information, facial expression data, voice data)

[0926] Specific operation: The monitoring robot uses motion sensors, cameras, microphones, facial expression recognition cameras, and voice analysis microphones to record the user's movements, facial expressions, and voice information in real time. For example, when the user is cooking in the kitchen, the facial expression recognition camera will capture their smile, and the voice analysis microphone will record their happy tone of voice.

[0927] Step 2:

[0928] Initial data processing and feature extraction

[0929] The monitoring robot performs initial processing on the raw data to remove noise, then performs feature extraction to generate variables.

[0930] Input: Raw data (motion information, facial expression data, voice data)

[0931] Output: Feature-extracted data (filtered motion information, facial features, and audio features)

[0932] Specific operations: For example, a noise reduction filter is applied to voice data to extract features that indicate the speaker's emotional state. Also, features that measure the frequency and degree of smiling are generated from facial expression data.

[0933] Step 3:

[0934] Sending data to the server

[0935] The monitoring robot transmits the data that has undergone initial processing and feature extraction to the server.

[0936] Input: Feature-extracted data (filtered movement information, facial features, and audio features)

[0937] Output: Received data on the server side

[0938] Specific operation: Data is sent to the server in real time or in batches. For example, all collected data is sent together as a batch at the end of the day.

[0939] Step 4:

[0940] Data analysis using AI and sentiment analysis

[0941] The server stores the received data in a database and analyzes it using an AI engine and emotion analysis engine.

[0942] Input: Received data (filtered movement information, facial expression features, and voice features)

[0943] Output: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[0944] Specific operation: The AI ​​engine analyzes the user's behavioral patterns, and the emotion analysis engine analyzes the emotional fluctuation patterns. For example, if the user frequently shows a depressed expression, this will be output as the analysis result.

[0945] Step 5:

[0946] Detecting abnormal behavior and emotional anomalies

[0947] The server detects abnormal behavior and emotional abnormalities based on the analysis results.

[0948] Input: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[0949] Output: Abnormal behavior and emotional anomaly detection alerts

[0950] Specific behavior: For example, if a user walks around the room at an unusual time and looks anxious, this will trigger an alert as a high-risk abnormal behavior.

[0951] Step 6:

[0952] Generate and send reports

[0953] The server generates a report based on the analysis results and sends it to the medical institution or care manager.

[0954] Input: Abnormal behavior and emotional anomaly detection alerts

[0955] Output: Generated report, Send report

[0956] Specific operation: Using generative AI, a detailed report is automatically created. The report includes information on the user's activities, emotions, and frequency of abnormal behavior. The report is then sent via email or a dedicated app.

[0957] Step 7:

[0958] Entering and analyzing medical information

[0959] The terminal provides an interface for the doctor to input consultation information and transmits the information to the server.

[0960] Input: Medical information (health status, emotional state, prescription medications, next appointment)

[0961] Output: Analysis results (treatment plan, treatment policy)

[0962] Specific operation: A doctor inputs examination information into a device, and the server analyzes it and uses generative AI to generate a medical plan and treatment policy. For example, it creates a plan that suggests prescribing specific medications.

[0963] Step 8:

[0964] Notification and follow-up

[0965] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[0966] Input: Generated treatment plan and treatment policy

[0967] Output: Notification message (app notification, email notification)

[0968] Specific operation: The generated medical plan and treatment policy are notified to the user via smartphone app or email. The user checks the next appointment schedule and follow-up instructions in the app.

[0969] Step 9:

[0970] Providing administrative services

[0971] Users can request dementia support from government offices through a dedicated app.

[0972] Input: Support request information (support content, name, address, emotional state, etc.)

[0973] Output: Request to administrative office, generation of support plan

[0974] How it works: Users enter the necessary information into a dedicated app, and the server analyzes it and automatically generates an appropriate support plan. Government officials can view the information on their devices and provide the necessary support.

[0975] This enables the system to comprehensively analyze a user's activity and emotional information, detect abnormal behavior and early symptoms with high accuracy, and implement appropriate medical and administrative responses.

[0976] (Application example 2)

[0977] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0978] Conventional dementia patient monitoring systems only target user behavioral information, so they may miss signs of abnormal behavior due to emotional fluctuations. Furthermore, they lack a mechanism for detecting customer emotions in brick-and-mortar stores and recommending appropriate products and services, making them inadequate for improving customer satisfaction. Therefore, there is a need for a system that comprehensively analyzes user activity and emotional information to detect abnormal behavior, detect early dementia symptoms, and improve customer service in brick-and-mortar stores.

[0979] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0980] In this invention, the server includes a sensor means for recording activity information and emotional information of the user, an analysis means for analyzing the recorded activity information and emotional information, an abnormal behavior detection means for detecting abnormal behavior and emotional abnormalities based on the analysis results, a notification means for sending a notification of the detected abnormal behavior and emotional abnormalities to a medical institution and family, and a recommended product display means for displaying recommended products based on the detected emotions. This enables consistent responses based on emotional and behavioral analysis, from preparing for dementia patients to customer service in physical stores.

[0981] "Sensor means" refers to a device for detecting and recording activity information and emotional information of a user.

[0982] "Analysis means" refers to a device or software that has the function of analyzing the recorded activity information and emotion information and extracting patterns and anomalies from the data.

[0983] The "abnormal behavior detection means" is a device or software for detecting abnormalities in the user's behavior or emotions that are different from normal based on the analysis results.

[0984] The "notification means" is a device or software for transmitting information about detected abnormal behavior and emotional abnormalities to a medical institution or family member.

[0985] The "recommended product display means" is a device or software for displaying appropriate products and services to the user based on the detected emotion.

[0986] The "behavioral pattern identification means" is a device or software having a function for identifying a consistent pattern of a user's behavior from the analyzed activity information and emotion information.

[0987] An "early symptom detection means" is a device or software for detecting early symptoms of dementia or the like based on identified behavioral patterns.

[0988] A "report generator" is a device or software for generating a detailed report regarding detected early symptoms.

[0989] The "input means" is a device or software that provides an interface for inputting the user's medical information.

[0990] The "treatment plan generation means" is a device or software that analyzes the input examination information and generates the next examination schedule and treatment plan.

[0991] A specific system for implementing this invention utilizes various sensors and AI technology to collect and analyze user activity and emotional information. Specifically, it is composed of the following elements:

[0992] 1. Sensor means

[0993] The sensor means records the user's activity information (movements, location, etc.) and emotional information (facial expressions, tone of voice, etc.). Specific examples include a motion sensor, a position tracking device, a facial expression recognition camera, and a voice analysis microphone. This makes it possible to record in detail everything from the user's smallest movements to changes in facial expressions and voice.

[0994] 2. Analysis method

[0995] The server receives the recorded activity information and emotion information and analyzes it. An AI engine and emotion engine are used for the analysis. The AI ​​engine analyzes patterns in the activity information, and the emotion engine analyzes the user's emotions. This allows for highly accurate capture of fluctuations in the user's behavior and emotions.

[0996] 3. Abnormal Behavior Detection Method

[0997] Based on the analysis results, the server detects abnormal behavior and emotional abnormalities. The abnormal behavior detection means judges information when there is a deviation from normal behavior patterns or abnormal emotional fluctuations. This makes it possible to detect abnormal behavior of dementia patients or customer dissatisfaction in physical stores in real time.

[0998] 4. Means of notification

[0999] When the server detects abnormal behavior or emotional abnormalities, it immediately notifies medical institutions and family members via email, SMS, in-app notifications, etc. This allows the relevant parties to respond quickly.

[1000] 5. Recommended product display methods

[1001] In physical stores, recommended products are displayed to users based on the analyzed emotional information. For example, if a customer shows interest in a particular product and their emotional state is positive, the system can recommend that product and related products specifically. This allows for more personalized service to be provided to customers.

[1002] Natural language processing explanation

[1003] The overall processing of this system is carried out as follows: The sensor means collects various data about the user, which is then sent to the server. The server then analyzes the received data using an AI engine and an emotion engine. If an abnormality is detected, the information is notified and, if necessary, recommended products are displayed.

[1004] For example, if a customer is looking at products in the "cosmetics section" of a store with an excited (happy) expression, the server will analyze that information and recommend "Product A" or "Product B." At the same time, if anxious expressions or behavior are observed, the server will immediately notify the customer. In this way, the system can be adapted for a wide range of uses, from monitoring dementia patients to customer service in physical stores.

[1005] Prompt Sentence Examples

[1006] Here are some examples of prompts for generative AI models:

[1007] "We would like to build a system that recognizes the behavior and emotions of customers in a store and proposes appropriate measures in real time. For example, if a customer looks excited (happy) in the cosmetics section, we would like to recommend appropriate products to that customer, or if the customer looks unhappy near the cash register, we would like to send an alert to the staff. We would like you to generate a program that can handle this."

[1008] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1009] Step 1:

[1010] The sensor means records the user's activity information and emotional information. This information includes movement data from the motion sensor, facial expression data from the facial expression recognition camera, and voice tone data from the voice analysis microphone. The input is the data sent in real time from each sensor, and the output is the data being sent to the server.

[1011] Step 2:

[1012] The server analyzes the received activity information and emotional information. The AI ​​engine analyzes the user's behavioral data, and the emotion engine analyzes the user's emotional data. Pattern recognition and emotional analysis are performed on the data received as input, and the respective analysis results are generated. The output is the analyzed data.

[1013] Step 3:

[1014] The server detects abnormal behavior and emotional anomalies based on the analysis results. Specifically, it compares the analysis results with normal behavioral and emotional patterns in the database to check for deviations. The input is the analyzed activity information and emotional information, and the output is the detection results of abnormal behavior and emotional anomalies.

[1015] Step 4:

[1016] If abnormal behavior or emotional abnormalities are detected, the server notifies medical institutions and family members via email, SMS, and in-app notifications. The input is the detected abnormal behavior and emotional abnormality data, and the output is the notification to be sent.

[1017] Step 5:

[1018] The server selects recommended products based on the emotional information analyzed in the physical store. Specifically, it automatically selects appropriate products and services based on the results of the emotion engine and the user's behavioral patterns. The input is the emotional analysis data, and the output is a list of recommended products.

[1019] Step 6:

[1020] The recommended product list is automatically displayed on the user's device. A push notification is sent from the server to the user's smartphone or tablet, allowing the user to check it immediately. The input is the recommended product list, and the output is the recommended product information displayed on the user's device.

[1021] The above steps enable comprehensive analysis of user activity information and emotional information, making it possible to detect abnormal behavior and respond to customers in physical stores.

[1022] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1023] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1024] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1025] [Third embodiment]

[1026] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1027] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1029] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1030] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1031] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1033] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1034] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1036] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1037] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1038] The following describes an embodiment of the present invention. First, the system of the present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, family members, and governments of the necessary information.

[1039] Monitoring robot

[1040] Sensor means

[1041] The monitoring robot is equipped with multiple sensors to collect information on the user's daily life activities. These sensors include a motion sensor, a camera, and a microphone, and capture data such as the user's walking pattern, conversations, and room movements. For example, if the user is cooking in the kitchen, the robot will record their movements with a camera and detect surrounding sounds with a microphone.

[1042] Data collection and transmission

[1043] The data acquired by the sensors is sent in real time to a processing unit inside the robot. The processing unit performs initial data processing to remove unnecessary noise and extract necessary features. After this processing is complete, the data is sent to a server via the Internet. For example, once a certain amount of data on a user's walking has been collected, it is sent to the server as a batch.

[1044] Data analysis

[1045] AI-based analysis methods

[1046] The server stores the user's activity information in a database based on a chronological order. The stored data is analyzed in real time by an AI engine, which compares it with past behavioral data to detect abnormal behavior. The server can also perform detailed analysis of abnormal behavior patterns to determine whether they are due to the progression of dementia or other factors.

[1047] Abnormal behavior notification

[1048] If abnormal behavior is detected based on the analysis results, the server will automatically send a notification to a medical institution or family. For example, if a user is walking around the room at an unusual time, the server will recognize this information as abnormal behavior and send an email notification to a medical institution.

[1049] Early Detection System

[1050] Identifying behavioral patterns and detecting early symptoms

[1051] The server analyzes data collected over a long period of time to identify the user's behavioral patterns. The behavioral pattern identification means determines what the user is doing at what time of day. As a result, if there is a sudden increase in behavioral fluctuations observed over a short period of time, for example, this can be detected as an early symptom.

[1052] Generate and send reports

[1053] If early symptoms are detected, the generative AI creates a detailed report based on the detection results, including the user's behavioral patterns, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[1054] Medical support

[1055] Entering and analyzing medical information

[1056] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[1057] Notification and follow-up

[1058] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[1059] Government response services

[1060] Citizen Support

[1061] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they require, their name, and address, and this information is sent directly to a server. The server then analyzes the information and automatically generates an appropriate support plan.

[1062] Collaboration support

[1063] The server seamlessly shares information with government offices using the integrated data, including the user's health status, support, progress, etc. Government officials can view and update this information through their devices and take necessary actions.

[1064] In this way, the system of the present invention can comprehensively support everything from monitoring dementia patients to early detection, medical support, and government response, thereby comprehensively protecting the safety and health of users.

[1065] The processing flow will be explained below.

[1066] Monitoring robot processing flow

[1067] Step 1:

[1068] The robot activates its built-in motion sensors, camera, and microphone to collect information about the user's activities.

[1069] Step 2:

[1070] The robot initially processes the collected data to remove noise and extract key features (e.g., walking patterns and sound changes).

[1071] Step 3:

[1072] The robot sends the processed data to the server in real time or in batches.

[1073] Data analysis process flow

[1074] Step 4:

[1075] The server stores the received data in a database and records it with a timestamp.

[1076] Step 5:

[1077] The server analyzes the received data in real time using an AI engine and begins analysis to detect abnormal behavior.

[1078] Step 6:

[1079] The server detects abnormal behavior (e.g., falls, wandering at unusual times, etc.) based on the analysis results.

[1080] Step 7:

[1081] The server generates alerts about detected abnormal behavior and notifies medical institutions and family members.

[1082] Early detection system processing flow

[1083] Step 8:

[1084] The server analyzes the data collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral patterns.

[1085] Step 9:

[1086] The server applies an anomaly detection algorithm to the identified behavioral patterns to detect early symptoms of dementia.

[1087] Step 10:

[1088] The server uses generative AI to automatically generate a detailed report based on early symptoms.

[1089] Step 11:

[1090] The server periodically transmits the generated reports to the medical institution or care manager.

[1091] Medical response support process flow

[1092] Step 12:

[1093] The terminal provides an interface (eg, a form) for the physician to enter consultation information.

[1094] Step 13:

[1095] The doctor enters the user's medical information (e.g., health status, diagnosis, and prescription medications).

[1096] Step 14:

[1097] The terminal transmits the input medical information to the server.

[1098] Step 15:

[1099] The server analyzes the consultation information and uses generation AI to automatically generate the next consultation schedule and treatment plan.

[1100] Step 16:

[1101] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[1102] Processing flow of administrative services

[1103] Step 17:

[1104] Users enter information (e.g., name, address, and details of support) to request support from the government through a dedicated app.

[1105] Step 18:

[1106] The terminal receives the input and sends it to the server.

[1107] Step 19:

[1108] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[1109] Step 20:

[1110] The server notifies the user of the generated support plan.

[1111] Step 21:

[1112] The server uses the integrated data to seamlessly share information with government offices.

[1113] Step 22:

[1114] The terminal provides an interface (e.g., a dashboard) for government employees to view and update the data they receive.

[1115] This is the specific processing flow of the system, which can comprehensively support dementia patient monitoring, early detection, medical support, and government response.

[1116] Example 1

[1117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1118] As the aging population continues, the increase in dementia patients has become a serious problem. Conventional systems lack consistent, comprehensive support for early detection of abnormal behavior in the daily lives of dementia patients and appropriate medical and administrative support. As a result, early intervention to slow the progression of dementia is difficult, and safety management and appropriate treatment are often delayed. Furthermore, some information is not shared appropriately in administrative responses, resulting in insufficient support. The present invention aims to solve these problems.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1120] In this invention, the server includes a data processing means for initial processing of recorded activity information and removing unnecessary noise, a data transmission means for transmitting the processed data to the server, and an analysis means for saving and analyzing the received data in chronological order. This enables the detection of abnormal user behavior in real time and immediate notification to appropriate medical institutions and family members. Furthermore, the analysis of long-term behavioral patterns makes it possible to detect early symptoms of dementia, enabling earlier intervention. Furthermore, by generating reports using generative AI, detailed analysis results can be provided to medical institutions and care managers, allowing appropriate treatment plans and policies to be quickly formulated. Centralized information sharing is also possible in administrative responses, providing comprehensive support.

[1121] "Sensor means" refers to devices used to record user activity information, including motion sensors, cameras, microphones, and the like.

[1122] "Data processing means" refers to a device or software capable of initial processing of recorded activity information and removing unnecessary noise.

[1123] "Data transmission means" refers to a device or function that transmits processed data to a server.

[1124] "Analysis means" refers to a device or system that stores received data in chronological order and analyzes the data using an AI engine.

[1125] The "abnormal behavior detection means" is a device or software that has the function of detecting abnormal behavior of a user based on analyzed data.

[1126] "Notification means" refers to a device or system that sends notifications to medical institutions and family members regarding detected abnormal behavior.

[1127] The "behavior pattern identification means" is a device or software that has the function of identifying the user's behavior pattern from the analyzed data.

[1128] An "early symptom detection means" is a device or software that has the function of detecting early symptoms of dementia based on identified behavioral patterns.

[1129] "Report generation means" means a device or software capable of generating a detailed report on detected early symptoms using generative AI.

[1130] "Report sending means" refers to a device or function that sends the generated report to a medical institution or care manager.

[1131] "Input means" refers to a device or interface for inputting a user's medical information.

[1132] The "treatment plan generation means" is a device or software that has the function of analyzing input examination information and generating the next examination schedule and treatment plan.

[1133] "Generative AI" refers to an artificial intelligence engine that automatically performs data analysis across the entire system and generates reports.

[1134] The present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, families, and governments of the necessary information. Specific embodiments are described below.

[1135] Hardware and software used

[1136] Monitoring robot: Equipped with motion sensors, cameras, and microphones to collect information on the user's daily life activities.

[1137] Server: A central device for processing and analyzing data, equipped with an AI engine.

[1138] Terminal: Provides an interface for doctors to input consultation information.

[1139] Generative AI model: Refers to an artificial intelligence engine for data analysis and report generation.

[1140] Examples of data collection

[1141] The monitoring robot collects activity information as the user goes about their daily life. For example, if the user is cooking in the kitchen, the camera records their movements and the microphone detects surrounding sounds. The motion sensor tracks the user's walking pattern in real time. This data is then filtered out by a processing unit inside the monitoring robot, and necessary features are extracted before being sent to a server via the Internet.

[1142] Specific examples of data analysis

[1143] The server stores the received activity information in a database based on a chronological order. The stored data is analyzed in real time using an AI engine and compared with past behavioral data to detect abnormal behavior. For example, if a user is walking around a room at an unusual time, that information will be recognized as abnormal behavior.

[1144] Abnormal behavior detection and notification

[1145] If abnormal behavior is detected based on the analysis results, the server automatically sends a notification to a medical institution or family. For example, the server may notify a medical institution of details of the abnormal behavior via email. The notification will include any changes in the user's behavioral patterns and various sensor data.

[1146] Identifying early symptoms

[1147] The server analyzes data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means can detect a sudden increase in behavioral fluctuations over a short period of time as an early symptom.

[1148] Report generation and delivery

[1149] If early symptoms are detected, the Generative AI creates a detailed report based on the detection results. This report includes the user's behavioral patterns, the frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers. Examples of prompt sentences that are used include the following:

[1150] Example prompt sentence:

[1151] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[1152] Generate treatment plans

[1153] The device provides an interface for doctors to input consultation information. After the consultation, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, and this information is sent to the server. The server uses an AI engine to generate the next appointment schedule and optimal treatment plan, and notifies the user.

[1154] Administrative response

[1155] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like, their name, and address, and this information is sent directly to a server. The server analyzes the information it receives and automatically generates an appropriate support plan. It is also possible to seamlessly share information with government offices using the integrated data.

[1156] In this way, this system provides comprehensive support for dementia patients, from monitoring to early detection, medical support, and government response, and can comprehensively protect the safety and health of users.

[1157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1158] Step 1: Data collection

[1159] Subject: Monitoring robot

[1160] The monitoring robot uses motion sensors, cameras, and microphones to collect information about the user's daily activities. Specifically, the robot captures the user's movements with a camera and records their voice with a microphone. The motion sensors detect the user's position and movements in real time. This data is sent to the robot's internal processing unit.

[1161] Input: User's daily activities

[1162] Output: Sensor data sent to a processing unit

[1163] Step 2: Data processing

[1164] Subject: Monitoring robot

[1165] A processing unit inside the robot initially processes the collected data and removes unnecessary noise, for example by deleting unwanted frames from camera footage and filtering background noise from audio data. Data from motion sensors is extracted as movement patterns.

[1166] Input: Sensor data

[1167] Output: Data after denoising and feature extraction

[1168] Step 3: Send data

[1169] Subject: Monitoring robot

[1170] The processed data is sent to a server via the Internet. Specifically, once a certain amount of data has been collected, it is batch processed and sent to the server in encrypted form.

[1171] Input: Data after initial processing

[1172] Output: Data sent to the server

[1173] Step 4: Save Data

[1174] Subject: Server

[1175] The server stores the received data in a database based on time sequence, and the stored data is used for subsequent analysis.

[1176] Input: Data sent

[1177] Output: Data stored in the database

[1178] Step 5: Data analysis

[1179] Subject: Server

[1180] The server analyzes the stored data in real time using an AI engine. Specifically, it compares it with past behavioral data to detect abnormal behavior. For example, if behavior that differs from a normal walking pattern is observed, it is recognized as abnormal behavior.

[1181] Input: Data stored in a database

[1182] Output: Analysis results and abnormal behavior detection

[1183] Step 6: Abnormal Behavior Notification

[1184] Subject: Server

[1185] If abnormal behavior is detected, the server automatically sends a notification to a medical institution or family member via email or smartphone app, detailing the nature of the abnormal behavior.

[1186] Input: Analysis results

[1187] Output: Notification to medical institutions and family members

[1188] Step 7: Identify behavioral patterns

[1189] Subject: Server

[1190] The server analyzes the data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means models daily behavior during specific time periods.

[1191] Input: Long-term data

[1192] Output: User behavior patterns

[1193] Step 8: Early symptom detection

[1194] Subject: Server

[1195] The system detects early symptoms of dementia based on identified behavioral patterns. For example, a sudden increase in behavioral fluctuations over a short period of time can be recognized as an early symptom.

[1196] Input: Behavioral pattern

[1197] Output: Early symptom detection

[1198] Step 9: Generate reports

[1199] Subject: Server

[1200] Generative AI is used to create a detailed report of detected early symptoms, including behavioral patterns, frequency of abnormal behavior, and recommended next steps.

[1201] Input: Early symptom data

[1202] Output: Detailed report

[1203] Step 10: Submit report

[1204] Subject: Server

[1205] The generated reports are sent periodically to medical institutions and care managers, with appropriate information provided using prompts.

[1206] Input: Detailed report

[1207] Output: Send to medical institutions and care managers

[1208] Example prompt sentence:

[1209] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[1210] Step 11: Generate a treatment plan

[1211] Subject: Device

[1212] The doctor inputs the patient's medical information into the device, which is then sent to the server, where the AI ​​engine generates the next appointment schedule and appropriate treatment plan.

[1213] Input: Medical Information

[1214] Output: Treatment plan and policy

[1215] Step 12: Care Plan Notification

[1216] Subject: Server

[1217] The generated treatment plan and treatment policy are notified to the user via a smartphone app or email.

[1218] Input: Treatment plan and treatment policy

[1219] Output: User notification

[1220] Step 13: Government response

[1221] Subject: User

[1222] The user uses a dedicated app to send a support request to the government office. The information entered in the app is sent to the server.

[1223] Input: Support request details

[1224] Output: Send to server

[1225] Step 14: Data analysis for administrative responses

[1226] Subject: Server

[1227] The server analyzes the received information and automatically generates an appropriate support plan. The integrated data is then used to share information with government offices.

[1228] Input: Support request details

[1229] Output: Generated support plan and submission to government office

[1230] In this way, the present system makes it possible to comprehensively protect the safety and health of users through a series of processing steps.

[1231] (Application example 1)

[1232] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1233] The purpose of this invention is to provide a system that comprehensively monitors, detects early, and provides medical support for dementia patients. Specifically, the objective is to ensure the safety of dementia patients and support appropriate medical intervention by visually and audibly monitoring their daily lives, detecting and analyzing abnormal behavior in real time, and notifying medical institutions and families in a timely manner. Furthermore, efficient generation and notification of treatment plans is also an important objective.

[1234] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1235] In this invention, the server includes a sensing unit that records the user's activity information, an analysis unit that analyzes the recorded activity information, and an abnormal behavior detection unit that detects abnormal behavior based on the analysis results. This enables the server to quickly detect the user's abnormal behavior and promptly notify medical institutions and family members. Furthermore, by visually and audibly monitoring the user's daily life using the camera and microphone unit built into the smart glasses, sending data to the server via the preprocessing unit and communication unit, and notifying the analysis results via a smartphone app or email, more accurate monitoring and early detection are achieved. Furthermore, comprehensive medical support can be provided by proposing the next appointment and treatment plan based on the report generated using the generative AI model.

[1236] "Sensing means" refers to a device or sensor for recording user activity information.

[1237] "Analysis means" refers to equipment or software for processing the recorded activity information and extracting and analyzing the necessary information.

[1238] The "abnormal behavior detection means" refers to a device or program that has the function of identifying and detecting abnormal user behavior based on analyzed data.

[1239] "Notification means" refers to a method or system for reporting detected abnormal behavior or important information to a medical institution or family member.

[1240] "Camera and microphone means" refers to video and audio collection devices for visual and audio monitoring of the user's daily life.

[1241] "Pre-processing means" refers to a method or device that performs initial noise removal and feature extraction on the data obtained by the processing unit built into the smart glasses.

[1242] "Communication means" refers to the internet connection or wireless communication mechanism for transmitting the preprocessed data to the server.

[1243] "Report generator" refers to a device or program for automatically generating a detailed report based on detected early symptoms.

[1244] The "behavior pattern identification means" refers to an analysis device or program for identifying a behavior pattern from user activity information.

[1245] "Early symptom detection means" refers to a function or device for detecting early symptoms of dementia based on identified behavioral patterns.

[1246] "Input means" refers to a terminal or program that allows a medical professional to input the user's medical information.

[1247] The "treatment plan generating means" refers to a program or device that generates the next examination schedule and treatment plan based on the input examination information.

[1248] "Proposal method" refers to a system or program that suggests the next steps in medical care based on a report generated using a generative AI model.

[1249] A "prompt generation means" is a program that inputs appropriate prompt sentences into the AI ​​to improve the efficiency of data analysis.

[1250] The system of the present invention aims to comprehensively monitor dementia patients, detect their condition early, provide medical support, and respond to government requests. The system collects and analyzes information on the user's daily activities, detects abnormal behavior, and sends necessary notifications to ensure the patient's safety and support appropriate medical intervention. Specific embodiments of the present invention are described below.

[1251] System Configuration

[1252] The system includes a sensing means, an analysis means, an abnormal behavior detection means, a notification means, a camera and microphone means, a preprocessing means, a communication means, a report generation means, a behavioral pattern identification means, an early symptom detection means, an input means, a treatment plan generation means, a suggestion means, and a prompt generation means.

[1253] sensing means

[1254] The smart glasses are equipped with multiple sensors to monitor the user's daily life, including a camera, microphone, and motion sensors, and collect information on the user's activities in real time.

[1255] Analysis means

[1256] The data collected by the smart glasses undergoes initial processing in the built-in data processing unit, where noise is removed and necessary features are extracted. The pre-processed data is then sent to a server via a communication means.

[1257] Abnormal behavior detection means

[1258] The server stores the data sent via communication means in a database. The AI ​​engine analyzes the collected data in real time and compares it with past behavioral data to detect abnormal behavior.

[1259] Notification means

[1260] If abnormal behavior is detected, the server will notify medical institutions and family members via a smartphone app or email, urging them to take prompt action.

[1261] Camera and microphone means

[1262] The cameras and microphones built into the smart glasses collect detailed visual and audio data from users' daily lives, which is crucial for improving the accuracy of abnormal behavior detection.

[1263] Pretreatment means

[1264] The processing unit built into the smart glasses performs initial noise removal and feature extraction of the data, thereby improving the quality of the data sent to the server via the communication means.

[1265] communication means

[1266] The pre-processed data is then sent to a server over the internet, using wireless communication and an internet connection.

[1267] Report generation means

[1268] Based on the detected early symptoms, a detailed report is generated and sent periodically to the medical institution or care manager.

[1269] Specific examples and prompts

[1270] It records detailed activity information such as the user's walking data, conversation content, and movement from room to room, and uses analytical methods to detect abnormal behavior. For example, it generates prompt sentences like the following and inputs them into the AI.

[1271] example:

[1272] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[1273] Based on the information collected and analyzed in this way, abnormal behavior can be detected and notified, supporting rapid medical response. This method can comprehensively realize everything from monitoring dementia patients to early detection and medical support.

[1274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1275] Step 1:

[1276] A user wears smart glasses while going about their daily life. The smart glasses' sensing means (camera, microphone, motion sensor) collect the user's activity information (walking patterns, conversations, room movements, etc.) in real time. The input is the user's behavioral data in their daily life, and the output is the collected raw data.

[1277] Step 2:

[1278] The collected raw data undergoes initial processing using the smart glasses' built-in pre-processing means. Specifically, noise removal and feature extraction are performed. The input is raw data, and the output is pre-processed data. This processing removes noise from the data and emphasizes the features required for analysis.

[1279] Step 3:

[1280] The pre-processed data is sent to the server via a communication means. The input is the pre-processed data, and the output is the data stored on the server. This communication can be via the internet or wirelessly.

[1281] Step 4:

[1282] The server stores the transmitted data in a database and analyzes it in real time. Using analytical tools, the collected data is compared with past behavioral data to detect abnormal behavior. The input is the user's activity information stored in the database, and the output is the analysis results.

[1283] Step 5:

[1284] When abnormal behavior is detected, the server uses a notification method to notify medical institutions and family members. The input is the analysis result and the output is a notification message. Notifications are sent via a smartphone app or email, encouraging a prompt response.

[1285] Step 6:

[1286] Based on the data collected over a long period of time, the server uses a behavioral pattern identification means and an early symptom detection means to identify the user's behavioral patterns and detect early symptoms.The input is the long-term data, and the output is a report of the behavioral patterns and early symptoms.

[1287] Step 7:

[1288] Based on the detected early symptoms, a report generator generates a detailed report. The input is the data of the early symptoms, and the output is a detailed report. This report is periodically sent to a medical institution or a care manager.

[1289] Step 8:

[1290] The user's medical examination information is sent to the server via the input means, and the next medical examination schedule and medical treatment plan are generated using the treatment plan generation means. The input is the medical examination information, and the output is a notification of the medical examination schedule and medical treatment plan.

[1291] Step 9:

[1292] Using a generative AI model, the next steps in medical care are suggested based on the generated report. A specific example involves generating prompts and inputting them into the AI. The input is a detailed report, and the output is specific medical suggestions.

[1293] example:

[1294] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[1295] Based on this prompt, the AI ​​will suggest the next step, creating a system that comprehensively monitors users, detects problems early, and provides medical support.

[1296] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1297] This section describes an embodiment of the present invention. This section describes a system that combines a system for monitoring dementia patients, early detection, medical support, and administrative response with an emotion engine that recognizes the user's emotions. This system collects and analyzes not only user activity information but also emotional information, enabling more accurate detection of abnormal behavior and identification of behavioral patterns.

[1298] Monitoring robot

[1299] Sensor Instruments and Emotion Engines

[1300] The monitoring robot is equipped with motion sensors, cameras, microphones, and sensors to recognize the user's emotions (e.g., facial expression recognition camera, voice analysis microphone). This allows it to collect emotional information not only from the user's actions while cooking in the kitchen, but also from their facial expressions and tone of voice.

[1301] Data collection and transmission

[1302] The activity and emotion information acquired by the sensors is initially processed by the processing unit inside the robot to remove noise. This data undergoes feature extraction processing and is then sent to the server in real time or in batches. For example, when a certain amount of data is collected showing that the user is feeling happy and cooking at the same time, that data is sent to the server as a batch.

[1303] Data analysis

[1304] AI and sentiment analysis tools

[1305] The server stores the received activity information and emotion information in a database, and records them with a timestamp. The stored data is analyzed in real time by the AI ​​engine. Emotional information recognized by the emotion engine is also analyzed simultaneously. For example, if the user is feeling depressed, the associated behavioral patterns are analyzed.

[1306] Detecting abnormal behavior and emotional anomalies

[1307] If abnormal behavior is detected based on the analysis results, the server also considers emotional information to further increase the likelihood of abnormality. For example, if a user walks around the room at an unusual time and looks anxious, the server will recognize this as high-risk abnormal behavior. This information is then notified to medical institutions and family members.

[1308] Early Detection System

[1309] Identifying behavioral patterns and detecting early symptoms

[1310] The server analyzes activity and emotional information collected over a long period of time to identify the user's behavioral patterns. By incorporating emotional fluctuations into the user's behavior, early symptom detection is more accurate. For example, if short-term behavioral fluctuations are linked to emotional fluctuations, this is considered an abnormality.

[1311] Generate and send reports

[1312] A detailed report based on the detected early symptoms is automatically generated by the generative AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[1313] Medical support

[1314] Entering and analyzing medical information

[1315] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[1316] Notification and follow-up

[1317] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[1318] Government response services

[1319] Citizen Support

[1320] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like to receive, their name, address, and emotional state, and this information is sent directly to a server. The server then analyzes the information and uses a generative AI to automatically generate an appropriate support plan.

[1321] Collaboration support

[1322] The server seamlessly shares information with administrative offices using integrated data, including activity information, emotional information, support details, and progress. Administrative staff can view and update this information through their devices and take necessary actions.

[1323] In this way, the system of the present invention can comprehensively monitor and analyze the user's activity information and emotional information, and effectively detect abnormal behavior and early symptoms, thereby comprehensively protecting the safety and health of dementia patients.

[1324] The processing flow will be explained below.

[1325] Monitoring robot processing flow

[1326] Step 1:

[1327] The robot activates its built-in motion sensors, camera, microphone, facial expression recognition camera, and voice analysis microphone to collect information on the user's activities and emotions.

[1328] Step 2:

[1329] The robot initially processes the collected activity and emotion information to remove noise and extract key features, such as walking patterns, facial expression changes, and tone of voice.

[1330] Step 3:

[1331] The robot sends the processed activity information and emotion information to the server in real time or in batches.

[1332] Data analysis process flow

[1333] Step 4:

[1334] The server stores the received activity information and emotion information in a database and records them with a timestamp.

[1335] Step 5:

[1336] The server analyzes the received activity information and emotion information in real time using an AI engine and emotion engine. For example, it analyzes the user's walking movements, facial expressions, and tone of voice.

[1337] Step 6:

[1338] The server detects abnormal behavior from the activity and emotion information based on the analysis results. If the user falls and simultaneously shows a fearful expression, it will recognize this as high-risk abnormal behavior.

[1339] Step 7:

[1340] The server notifies medical institutions and family members based on the detected abnormal behavior and emotional abnormalities, for example, by email notification or text message.

[1341] Early detection system processing flow

[1342] Step 8:

[1343] The server analyzes the activity information and emotion information collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral pattern, for example, analyzing the user's activity and emotion fluctuations over a week.

[1344] Step 9:

[1345] The server detects early symptoms from the identified behavioral patterns. For example, if the user frequently wanders around feeling anxious, this may be recognized as an early symptom.

[1346] Step 10:

[1347] The server uses generative AI to automatically generate a detailed report based on early symptoms, including behavioral patterns, emotional fluctuations, detected early symptoms, and recommended next steps.

[1348] Step 11:

[1349] The server periodically transmits the generated report to the medical institution or care manager, for example, by email as a weekly report.

[1350] Medical response support process flow

[1351] Step 12:

[1352] The terminal provides an interface for the doctor to input medical information and emotional information. For example, the medical examination form may include fields for inputting the user's facial expression and tone of voice.

[1353] Step 13:

[1354] The doctor inputs the user's medical information and emotional information.

[1355] Step 14:

[1356] The terminal transmits the input medical examination information and emotion information to the server.

[1357] Step 15:

[1358] The server analyzes the medical examination information and emotional information, and uses generative AI to automatically generate the next medical examination schedule and treatment plan.

[1359] Step 16:

[1360] The server notifies the user or medical institution of the generated medical plan and treatment policy, for example, via a smartphone app.

[1361] Processing flow of administrative services

[1362] Step 17:

[1363] Users enter information to request support from the government through a dedicated app, such as their name, address, and emotional state.

[1364] Step 18:

[1365] The terminal receives the input and sends it to the server.

[1366] Step 19:

[1367] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[1368] Step 20:

[1369] The server notifies the user of the generated support plan, for example by sending a push notification to a smartphone app.

[1370] Step 21:

[1371] The server seamlessly shares information with government agencies using the integrated activity and emotion information, for example by sending detailed data including the user's health status and emotional fluctuations.

[1372] Step 22:

[1373] The terminals provide an interface for government officials to view and update the data they receive, for example by checking the status on a dashboard and taking necessary actions.

[1374] This is the specific processing flow of the system, which enables comprehensive monitoring and analysis of user activity and emotional information, enabling the detection of abnormal behavior and early symptoms with high accuracy.

[1375] Example 2

[1376] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1377] Conventional systems for monitoring dementia patients, early detection, medical support, and government response only analyze user activity information, making it difficult to perform detailed analysis that includes emotional information. As a result, the accuracy of detecting abnormal behavior and early symptoms decreases, and appropriate intervention and support can be delayed. In addition, it is difficult to generate response policies and treatment plans that take emotional information into account, making it impossible to comprehensively understand the user's condition. This has led to challenges in effectively protecting the safety and health of dementia patients.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1379] In this invention, the server includes a sensor for recording the user's activity information and emotional information, a means for initial processing and feature extraction of the recorded activity information and emotional information, a means for transmitting the processed and feature-extracted data to the server, an AI and emotion analysis means for analyzing the received activity information and emotional information and detecting abnormal behavior and emotional abnormalities, and a notification means for transmitting notifications of detected abnormal behavior and emotional abnormalities to medical institutions and family members. This enables comprehensive monitoring of the activity information and emotional information of dementia patients, enabling more accurate detection of abnormal behavior and early symptoms. Furthermore, treatment guidelines and support plans that take emotional information into account can be generated, enabling a comprehensive understanding of the user's condition and appropriate responses.

[1380] "Sensor means" refers to a device for recording a user's activity information and emotional information, and includes a motion sensor, a camera, a microphone, a facial expression recognition camera, a voice analysis microphone, etc.

[1381] "Initial processing" refers to processing to remove noise from collected raw data and improve the quality of the data.

[1382] "Feature extraction" is the process of extracting necessary information from initially processed data and converting it into a form suitable for analysis.

[1383] The "means for transmitting to the server" is a mechanism for transmitting the data that has undergone initial processing and feature extraction to the server via the Internet or other network.

[1384] "AI and emotion analysis means" refers to artificial intelligence technology and emotion analysis algorithms for analyzing received activity information and emotion information and assessing a user's behavioral patterns and emotional state.

[1385] "Abnormal behavior" refers to behavior that deviates from a user's normal behavioral patterns and may indicate symptoms of dementia or other health issues.

[1386] "Emotional abnormalities" refer to emotional states that deviate from normal emotional patterns and may indicate psychological stress or changes in the user's health.

[1387] "Notification means" refers to a system for notifying medical institutions and family members of information about detected abnormal behavior and emotional abnormalities, and includes email, smartphone apps, telephone, etc.

[1388] This section describes an embodiment of the present invention. The purpose of this invention is to monitor dementia patients, detect them early, provide medical support, and respond to government requests. In addition, it includes an emotion engine that analyzes the user's emotions. This system collects and analyzes user activity information and emotion information, and achieves highly accurate detection of abnormal behavior and identification of behavioral patterns.

[1389] Monitoring robot

[1390] The monitoring robot is equipped with the following sensor means:

[1391] Motion sensor: Records user activity information in real time.

[1392] Camera: Visually records the user's actions.

[1393] Microphone: Collects user voice information.

[1394] Facial expression recognition camera: Analyzes the user's facial expressions and collects emotional information.

[1395] Voice analysis microphone: Analyzes the user's emotional state from the tone of their voice.

[1396] These sensors continuously record daily activities, such as when a user is cooking in the kitchen, and use facial recognition cameras and voice analysis microphones to collect emotional information, such as whether the user is enjoying themselves or feeling anxious.

[1397] Data collection and transmission

[1398] The monitoring robot initially processes activity and emotion information acquired by sensors in a processing unit inside the robot. After noise is removed, the data undergoes feature extraction processing and is then sent to the server. This allows high-quality data to be aggregated on the server. For example, if a user frequently smiles while cooking, that data is sent to the server at regular intervals as a batch process.

[1399] Data analysis

[1400] The server stores the received activity information and emotion information in a database. The stored data is recorded with a timestamp and analyzed in real time by an AI engine and emotion analysis engine. For example, if a user shows a depressed expression more frequently than usual, the data is analyzed and detected as a significant pattern.

[1401] Detecting abnormal behavior and emotional anomalies

[1402] The server detects abnormal behavior and emotional abnormalities based on the analysis results. For example, if a user walks around the room at an unusual time and looks anxious, this behavior will be analyzed as high-risk abnormal behavior. Detected abnormal behavior will be notified to medical institutions and family members.

[1403] Early Detection System

[1404] Activity and emotional information collected over a long period of time is stored on a server. The server uses this data to identify the user's behavioral and emotional patterns. Based on the identified patterns, early symptoms of dementia are analyzed. For example, if short-term behavioral fluctuations coincide with emotional fluctuations, this is deemed abnormal.

[1405] Generate and send reports

[1406] Based on the analyzed data, reports are automatically generated by the AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated reports are periodically sent to medical institutions and care managers.

[1407] Medical support

[1408] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, next appointment schedule, etc. This information is sent to a server, which analyzes it and generates a medical plan and treatment policy using generative AI.

[1409] Notification and follow-up

[1410] The generated medical plan and treatment policy are notified to the user and medical institution via a smartphone app or email. The user can check the next appointment schedule and follow-up instructions in the app.

[1411] Government response services

[1412] Users request dementia support from government offices through a dedicated app. The app has a form where users can enter information such as the type of support they would like, their name, address, and emotional state, and this information is sent to a server. The server analyzes this information and uses generative AI to automatically generate an appropriate support plan. Government staff can view this information on their devices and provide the necessary support.

[1413] Examples of concrete examples and prompts

[1414] As a specific example, if a 75-year-old user exhibits behavior that differs from their usual behavioral patterns and has an anxious expression on their face, this data will be analyzed as abnormal behavior and a medical institution will be notified.

[1415] Example prompt sentence:

[1416] "A 75-year-old user is walking around outside of their usual walking time and appears anxious. This is analyzed as abnormal behavior and a prompt is sent to notify a medical institution."

[1417] In this way, the system of the present invention comprehensively analyzes the user's activity information and emotional information, detects abnormal behavior and early symptoms with high accuracy, and enables appropriate medical and administrative responses.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Step 1:

[1420] Collecting user activity and emotion information

[1421] The user provides daily activity information and emotional information to the monitoring robot through sensors installed in the robot.

[1422] Input: User movements, facial expressions, and tone of voice

[1423] Output: Raw data (motion information, facial expression data, voice data)

[1424] Specific operation: The monitoring robot uses motion sensors, cameras, microphones, facial expression recognition cameras, and voice analysis microphones to record the user's movements, facial expressions, and voice information in real time. For example, when the user is cooking in the kitchen, the facial expression recognition camera will capture their smile, and the voice analysis microphone will record their happy tone of voice.

[1425] Step 2:

[1426] Initial data processing and feature extraction

[1427] The monitoring robot performs initial processing on the raw data to remove noise, then performs feature extraction to generate variables.

[1428] Input: Raw data (motion information, facial expression data, voice data)

[1429] Output: Feature-extracted data (filtered motion information, facial features, and audio features)

[1430] Specific operations: For example, a noise reduction filter is applied to voice data to extract features that indicate the speaker's emotional state. Also, features that measure the frequency and degree of smiling are generated from facial expression data.

[1431] Step 3:

[1432] Sending data to the server

[1433] The monitoring robot transmits the data that has undergone initial processing and feature extraction to the server.

[1434] Input: Feature-extracted data (filtered movement information, facial features, and audio features)

[1435] Output: Received data on the server side

[1436] Specific operation: Data is sent to the server in real time or in batches. For example, all collected data is sent together as a batch at the end of the day.

[1437] Step 4:

[1438] Data analysis using AI and sentiment analysis

[1439] The server stores the received data in a database and analyzes it using an AI engine and emotion analysis engine.

[1440] Input: Received data (filtered movement information, facial expression features, and voice features)

[1441] Output: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[1442] Specific operation: The AI ​​engine analyzes the user's behavioral patterns, and the emotion analysis engine analyzes the emotional fluctuation patterns. For example, if the user frequently shows a depressed expression, this will be output as the analysis result.

[1443] Step 5:

[1444] Detecting abnormal behavior and emotional anomalies

[1445] The server detects abnormal behavior and emotional abnormalities based on the analysis results.

[1446] Input: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[1447] Output: Abnormal behavior and emotional anomaly detection alerts

[1448] Specific behavior: For example, if a user walks around the room at an unusual time and looks anxious, this will trigger an alert as a high-risk abnormal behavior.

[1449] Step 6:

[1450] Generate and send reports

[1451] The server generates a report based on the analysis results and sends it to the medical institution or care manager.

[1452] Input: Abnormal behavior and emotional anomaly detection alerts

[1453] Output: Generated report, Send report

[1454] Specific operation: Using generative AI, a detailed report is automatically created. The report includes information on the user's activities, emotions, and frequency of abnormal behavior. The report is then sent via email or a dedicated app.

[1455] Step 7:

[1456] Entering and analyzing medical information

[1457] The terminal provides an interface for the doctor to input consultation information and transmits the information to the server.

[1458] Input: Medical information (health status, emotional state, prescription medications, next appointment)

[1459] Output: Analysis results (treatment plan, treatment policy)

[1460] Specific operation: A doctor inputs examination information into a device, and the server analyzes it and uses generative AI to generate a medical plan and treatment policy. For example, it creates a plan that suggests prescribing specific medications.

[1461] Step 8:

[1462] Notification and follow-up

[1463] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[1464] Input: Generated treatment plan and treatment policy

[1465] Output: Notification message (app notification, email notification)

[1466] Specific operation: The generated medical plan and treatment policy are notified to the user via smartphone app or email. The user checks the next appointment schedule and follow-up instructions in the app.

[1467] Step 9:

[1468] Providing administrative services

[1469] Users can request dementia support from government offices through a dedicated app.

[1470] Input: Support request information (support content, name, address, emotional state, etc.)

[1471] Output: Request to administrative office, generation of support plan

[1472] How it works: Users enter the necessary information into a dedicated app, and the server analyzes it and automatically generates an appropriate support plan. Government officials can view the information on their devices and provide the necessary support.

[1473] This enables the system to comprehensively analyze a user's activity and emotional information, detect abnormal behavior and early symptoms with high accuracy, and implement appropriate medical and administrative responses.

[1474] (Application example 2)

[1475] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1476] Conventional dementia patient monitoring systems only target user behavioral information, so they may miss signs of abnormal behavior due to emotional fluctuations. Furthermore, they lack a mechanism for detecting customer emotions in brick-and-mortar stores and recommending appropriate products and services, making them inadequate for improving customer satisfaction. Therefore, there is a need for a system that comprehensively analyzes user activity and emotional information to detect abnormal behavior, detect early dementia symptoms, and improve customer service in brick-and-mortar stores.

[1477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1478] In this invention, the server includes a sensor means for recording activity information and emotional information of the user, an analysis means for analyzing the recorded activity information and emotional information, an abnormal behavior detection means for detecting abnormal behavior and emotional abnormalities based on the analysis results, a notification means for sending a notification of the detected abnormal behavior and emotional abnormalities to a medical institution and family, and a recommended product display means for displaying recommended products based on the detected emotions. This enables consistent responses based on emotional and behavioral analysis, from preparing for dementia patients to customer service in physical stores.

[1479] "Sensor means" refers to a device for detecting and recording activity information and emotional information of a user.

[1480] "Analysis means" refers to a device or software that has the function of analyzing the recorded activity information and emotion information and extracting patterns and anomalies from the data.

[1481] The "abnormal behavior detection means" is a device or software for detecting abnormalities in the user's behavior or emotions that are different from normal based on the analysis results.

[1482] The "notification means" is a device or software for transmitting information about detected abnormal behavior and emotional abnormalities to a medical institution or family member.

[1483] The "recommended product display means" is a device or software for displaying appropriate products and services to the user based on the detected emotion.

[1484] The "behavioral pattern identification means" is a device or software having a function for identifying a consistent pattern of a user's behavior from the analyzed activity information and emotion information.

[1485] An "early symptom detection means" is a device or software for detecting early symptoms of dementia or the like based on identified behavioral patterns.

[1486] A "report generator" is a device or software for generating a detailed report regarding detected early symptoms.

[1487] The "input means" is a device or software that provides an interface for inputting the user's medical information.

[1488] The "treatment plan generation means" is a device or software that analyzes the input examination information and generates the next examination schedule and treatment plan.

[1489] A specific system for implementing this invention utilizes various sensors and AI technology to collect and analyze user activity and emotional information. Specifically, it is composed of the following elements:

[1490] 1. Sensor means

[1491] The sensor means records the user's activity information (movements, location, etc.) and emotional information (facial expressions, tone of voice, etc.). Specific examples include a motion sensor, a position tracking device, a facial expression recognition camera, and a voice analysis microphone. This makes it possible to record in detail everything from the user's smallest movements to changes in facial expressions and voice.

[1492] 2. Analysis method

[1493] The server receives the recorded activity information and emotion information and analyzes it. An AI engine and emotion engine are used for the analysis. The AI ​​engine analyzes patterns in the activity information, and the emotion engine analyzes the user's emotions. This allows for highly accurate capture of fluctuations in the user's behavior and emotions.

[1494] 3. Abnormal Behavior Detection Method

[1495] Based on the analysis results, the server detects abnormal behavior and emotional abnormalities. The abnormal behavior detection means judges information when there is a deviation from normal behavior patterns or abnormal emotional fluctuations. This makes it possible to detect abnormal behavior of dementia patients or customer dissatisfaction in physical stores in real time.

[1496] 4. Means of notification

[1497] When the server detects abnormal behavior or emotional abnormalities, it immediately notifies medical institutions and family members via email, SMS, in-app notifications, etc. This allows the relevant parties to respond quickly.

[1498] 5. Recommended product display methods

[1499] In physical stores, recommended products are displayed to users based on the analyzed emotional information. For example, if a customer shows interest in a particular product and their emotional state is positive, the system can recommend that product and related products specifically. This allows for more personalized service to be provided to customers.

[1500] Natural language processing explanation

[1501] The overall processing of this system is carried out as follows: The sensor means collects various data about the user, which is then sent to the server. The server then analyzes the received data using an AI engine and an emotion engine. If an abnormality is detected, the information is notified and, if necessary, recommended products are displayed.

[1502] For example, if a customer is looking at products in the "cosmetics section" of a store with an excited (happy) expression, the server will analyze that information and recommend "Product A" or "Product B." At the same time, if anxious expressions or behavior are observed, the server will immediately notify the customer. In this way, the system can be adapted for a wide range of uses, from monitoring dementia patients to customer service in physical stores.

[1503] Prompt Sentence Examples

[1504] Here are some examples of prompts for generative AI models:

[1505] "We would like to build a system that recognizes the behavior and emotions of customers in a store and proposes appropriate measures in real time. For example, if a customer looks excited (happy) in the cosmetics section, we would like to recommend appropriate products to that customer, or if the customer looks unhappy near the cash register, we would like to send an alert to the staff. We would like you to generate a program that can handle this."

[1506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1507] Step 1:

[1508] The sensor means records the user's activity information and emotional information. This information includes movement data from the motion sensor, facial expression data from the facial expression recognition camera, and voice tone data from the voice analysis microphone. The input is the data sent in real time from each sensor, and the output is the data being sent to the server.

[1509] Step 2:

[1510] The server analyzes the received activity information and emotional information. The AI ​​engine analyzes the user's behavioral data, and the emotion engine analyzes the user's emotional data. Pattern recognition and emotional analysis are performed on the data received as input, and the respective analysis results are generated. The output is the analyzed data.

[1511] Step 3:

[1512] The server detects abnormal behavior and emotional anomalies based on the analysis results. Specifically, it compares the analysis results with normal behavioral and emotional patterns in the database to check for deviations. The input is the analyzed activity information and emotional information, and the output is the detection results of abnormal behavior and emotional anomalies.

[1513] Step 4:

[1514] If abnormal behavior or emotional abnormalities are detected, the server notifies medical institutions and family members via email, SMS, and in-app notifications. The input is the detected abnormal behavior and emotional abnormality data, and the output is the notification to be sent.

[1515] Step 5:

[1516] The server selects recommended products based on the emotional information analyzed in the physical store. Specifically, it automatically selects appropriate products and services based on the results of the emotion engine and the user's behavioral patterns. The input is the emotional analysis data, and the output is a list of recommended products.

[1517] Step 6:

[1518] The recommended product list is automatically displayed on the user's device. A push notification is sent from the server to the user's smartphone or tablet, allowing the user to check it immediately. The input is the recommended product list, and the output is the recommended product information displayed on the user's device.

[1519] The above steps enable comprehensive analysis of user activity information and emotional information, making it possible to detect abnormal behavior and respond to customers in physical stores.

[1520] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1522] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1523] [Fourth embodiment]

[1524] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1525] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1526] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1527] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1528] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1529] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1531] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1532] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1533] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1534] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1535] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1536] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] The following describes an embodiment of the present invention. First, the system of the present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, family members, and governments of the necessary information.

[1538] Monitoring robot

[1539] Sensor means

[1540] The monitoring robot is equipped with multiple sensors to collect information on the user's daily life activities. These sensors include a motion sensor, a camera, and a microphone, and capture data such as the user's walking pattern, conversations, and room movements. For example, if the user is cooking in the kitchen, the robot will record their movements with a camera and detect surrounding sounds with a microphone.

[1541] Data collection and transmission

[1542] The data acquired by the sensors is sent in real time to a processing unit inside the robot. The processing unit performs initial data processing to remove unnecessary noise and extract necessary features. After this processing is complete, the data is sent to a server via the Internet. For example, once a certain amount of data on a user's walking has been collected, it is sent to the server as a batch.

[1543] Data analysis

[1544] AI-based analysis methods

[1545] The server stores the user's activity information in a database based on a chronological order. The stored data is analyzed in real time by an AI engine, which compares it with past behavioral data to detect abnormal behavior. The server can also perform detailed analysis of abnormal behavior patterns to determine whether they are due to the progression of dementia or other factors.

[1546] Abnormal behavior notification

[1547] If abnormal behavior is detected based on the analysis results, the server will automatically send a notification to a medical institution or family. For example, if a user is walking around the room at an unusual time, the server will recognize this information as abnormal behavior and send an email notification to a medical institution.

[1548] Early Detection System

[1549] Identifying behavioral patterns and detecting early symptoms

[1550] The server analyzes data collected over a long period of time to identify the user's behavioral patterns. The behavioral pattern identification means determines what the user is doing at what time of day. As a result, if there is a sudden increase in behavioral fluctuations observed over a short period of time, for example, this can be detected as an early symptom.

[1551] Generate and send reports

[1552] If early symptoms are detected, the generative AI creates a detailed report based on the detection results, including the user's behavioral patterns, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[1553] Medical support

[1554] Entering and analyzing medical information

[1555] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[1556] Notification and follow-up

[1557] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[1558] Government response services

[1559] Citizen Support

[1560] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they require, their name, and address, and this information is sent directly to a server. The server then analyzes the information and automatically generates an appropriate support plan.

[1561] Collaboration support

[1562] The server seamlessly shares information with government offices using the integrated data, including the user's health status, support, progress, etc. Government officials can view and update this information through their devices and take necessary actions.

[1563] In this way, the system of the present invention can comprehensively support everything from monitoring dementia patients to early detection, medical support, and government response, thereby comprehensively protecting the safety and health of users.

[1564] The processing flow will be explained below.

[1565] Monitoring robot processing flow

[1566] Step 1:

[1567] The robot activates its built-in motion sensors, camera, and microphone to collect information about the user's activities.

[1568] Step 2:

[1569] The robot initially processes the collected data to remove noise and extract key features (e.g., walking patterns and sound changes).

[1570] Step 3:

[1571] The robot sends the processed data to the server in real time or in batches.

[1572] Data analysis process flow

[1573] Step 4:

[1574] The server stores the received data in a database and records it with a timestamp.

[1575] Step 5:

[1576] The server analyzes the received data in real time using an AI engine and begins analysis to detect abnormal behavior.

[1577] Step 6:

[1578] The server detects abnormal behavior (e.g., falls, wandering at unusual times, etc.) based on the analysis results.

[1579] Step 7:

[1580] The server generates alerts about detected abnormal behavior and notifies medical institutions and family members.

[1581] Early detection system processing flow

[1582] Step 8:

[1583] The server analyzes the data collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral patterns.

[1584] Step 9:

[1585] The server applies an anomaly detection algorithm to the identified behavioral patterns to detect early symptoms of dementia.

[1586] Step 10:

[1587] The server uses generative AI to automatically generate a detailed report based on early symptoms.

[1588] Step 11:

[1589] The server periodically transmits the generated reports to the medical institution or care manager.

[1590] Medical response support process flow

[1591] Step 12:

[1592] The terminal provides an interface (eg, a form) for the physician to enter consultation information.

[1593] Step 13:

[1594] The doctor enters the user's medical information (e.g., health status, diagnosis, and prescription medications).

[1595] Step 14:

[1596] The terminal transmits the input medical information to the server.

[1597] Step 15:

[1598] The server analyzes the consultation information and uses generation AI to automatically generate the next consultation schedule and treatment plan.

[1599] Step 16:

[1600] The server notifies the user and medical institution of the generated medical plan and treatment policy.

[1601] Processing flow of administrative services

[1602] Step 17:

[1603] Users enter information (e.g., name, address, and details of support) to request support from the government through a dedicated app.

[1604] Step 18:

[1605] The terminal receives the input and sends it to the server.

[1606] Step 19:

[1607] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[1608] Step 20:

[1609] The server notifies the user of the generated support plan.

[1610] Step 21:

[1611] The server uses the integrated data to seamlessly share information with government offices.

[1612] Step 22:

[1613] The terminal provides an interface (e.g., a dashboard) for government employees to view and update the data they receive.

[1614] This is the specific processing flow of the system, which can comprehensively support dementia patient monitoring, early detection, medical support, and government response.

[1615] Example 1

[1616] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1617] As the aging population continues, the increase in dementia patients has become a serious problem. Conventional systems lack consistent, comprehensive support for early detection of abnormal behavior in the daily lives of dementia patients and appropriate medical and administrative support. As a result, early intervention to slow the progression of dementia is difficult, and safety management and appropriate treatment are often delayed. Furthermore, some information is not shared appropriately in administrative responses, resulting in insufficient support. The present invention aims to solve these problems.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1619] In this invention, the server includes a data processing means for initial processing of recorded activity information and removing unnecessary noise, a data transmission means for transmitting the processed data to the server, and an analysis means for saving and analyzing the received data in chronological order. This enables the detection of abnormal user behavior in real time and immediate notification to appropriate medical institutions and family members. Furthermore, the analysis of long-term behavioral patterns makes it possible to detect early symptoms of dementia, enabling earlier intervention. Furthermore, by generating reports using generative AI, detailed analysis results can be provided to medical institutions and care managers, allowing appropriate treatment plans and policies to be quickly formulated. Centralized information sharing is also possible in administrative responses, providing comprehensive support.

[1620] "Sensor means" refers to devices used to record user activity information, including motion sensors, cameras, microphones, and the like.

[1621] "Data processing means" refers to a device or software capable of initial processing of recorded activity information and removing unnecessary noise.

[1622] "Data transmission means" refers to a device or function that transmits processed data to a server.

[1623] "Analysis means" refers to a device or system that stores received data in chronological order and analyzes the data using an AI engine.

[1624] The "abnormal behavior detection means" is a device or software that has the function of detecting abnormal behavior of a user based on analyzed data.

[1625] "Notification means" refers to a device or system that sends notifications to medical institutions and family members regarding detected abnormal behavior.

[1626] The "behavior pattern identification means" is a device or software that has the function of identifying the user's behavior pattern from the analyzed data.

[1627] An "early symptom detection means" is a device or software that has the function of detecting early symptoms of dementia based on identified behavioral patterns.

[1628] "Report generation means" means a device or software capable of generating a detailed report on detected early symptoms using generative AI.

[1629] "Report sending means" refers to a device or function that sends the generated report to a medical institution or care manager.

[1630] "Input means" refers to a device or interface for inputting a user's medical information.

[1631] The "treatment plan generation means" is a device or software that has the function of analyzing input examination information and generating the next examination schedule and treatment plan.

[1632] "Generative AI" refers to an artificial intelligence engine that automatically performs data analysis across the entire system and generates reports.

[1633] The present invention is a comprehensive system that integrates dementia patient monitoring, early detection, medical support, and government response. The basic functions of this system are to collect and analyze user activity information, detect abnormal behavior, and notify medical institutions, families, and governments of the necessary information. Specific embodiments are described below.

[1634] Hardware and software used

[1635] Monitoring robot: Equipped with motion sensors, cameras, and microphones to collect information on the user's daily life activities.

[1636] Server: A central device for processing and analyzing data, equipped with an AI engine.

[1637] Terminal: Provides an interface for doctors to input consultation information.

[1638] Generative AI model: Refers to an artificial intelligence engine for data analysis and report generation.

[1639] Examples of data collection

[1640] The monitoring robot collects activity information as the user goes about their daily life. For example, if the user is cooking in the kitchen, the camera records their movements and the microphone detects surrounding sounds. The motion sensor tracks the user's walking pattern in real time. This data is then filtered out by a processing unit inside the monitoring robot, and necessary features are extracted before being sent to a server via the Internet.

[1641] Specific examples of data analysis

[1642] The server stores the received activity information in a database based on a chronological order. The stored data is analyzed in real time using an AI engine and compared with past behavioral data to detect abnormal behavior. For example, if a user is walking around a room at an unusual time, that information will be recognized as abnormal behavior.

[1643] Abnormal behavior detection and notification

[1644] If abnormal behavior is detected based on the analysis results, the server automatically sends a notification to a medical institution or family. For example, the server may notify a medical institution of details of the abnormal behavior via email. The notification will include any changes in the user's behavioral patterns and various sensor data.

[1645] Identifying early symptoms

[1646] The server analyzes data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means can detect a sudden increase in behavioral fluctuations over a short period of time as an early symptom.

[1647] Report generation and delivery

[1648] If early symptoms are detected, the Generative AI creates a detailed report based on the detection results. This report includes the user's behavioral patterns, the frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers. Examples of prompt sentences that are used include the following:

[1649] Example prompt sentence:

[1650] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[1651] Generate treatment plans

[1652] The device provides an interface for doctors to input consultation information. After the consultation, the doctor inputs the user's health condition, prescribed medications, and next appointment schedule, and this information is sent to the server. The server uses an AI engine to generate the next appointment schedule and optimal treatment plan, and notifies the user.

[1653] Administrative response

[1654] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like, their name, and address, and this information is sent directly to a server. The server analyzes the information it receives and automatically generates an appropriate support plan. It is also possible to seamlessly share information with government offices using the integrated data.

[1655] In this way, this system provides comprehensive support for dementia patients, from monitoring to early detection, medical support, and government response, and can comprehensively protect the safety and health of users.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657] Step 1: Data collection

[1658] Subject: Monitoring robot

[1659] The monitoring robot uses motion sensors, cameras, and microphones to collect information about the user's daily activities. Specifically, the robot captures the user's movements with a camera and records their voice with a microphone. The motion sensors detect the user's position and movements in real time. This data is sent to the robot's internal processing unit.

[1660] Input: User's daily activities

[1661] Output: Sensor data sent to a processing unit

[1662] Step 2: Data processing

[1663] Subject: Monitoring robot

[1664] A processing unit inside the robot initially processes the collected data and removes unnecessary noise, for example by deleting unwanted frames from camera footage and filtering background noise from audio data. Data from motion sensors is extracted as movement patterns.

[1665] Input: Sensor data

[1666] Output: Data after denoising and feature extraction

[1667] Step 3: Send data

[1668] Subject: Monitoring robot

[1669] The processed data is sent to a server via the Internet. Specifically, once a certain amount of data has been collected, it is batch processed and sent to the server in encrypted form.

[1670] Input: Data after initial processing

[1671] Output: Data sent to the server

[1672] Step 4: Save Data

[1673] Subject: Server

[1674] The server stores the received data in a database based on time sequence, and the stored data is used for subsequent analysis.

[1675] Input: Data sent

[1676] Output: Data stored in the database

[1677] Step 5: Data analysis

[1678] Subject: Server

[1679] The server analyzes the stored data in real time using an AI engine. Specifically, it compares it with past behavioral data to detect abnormal behavior. For example, if behavior that differs from a normal walking pattern is observed, it is recognized as abnormal behavior.

[1680] Input: Data stored in a database

[1681] Output: Analysis results and abnormal behavior detection

[1682] Step 6: Abnormal Behavior Notification

[1683] Subject: Server

[1684] If abnormal behavior is detected, the server automatically sends a notification to a medical institution or family member via email or smartphone app, detailing the nature of the abnormal behavior.

[1685] Input: Analysis results

[1686] Output: Notification to medical institutions and family members

[1687] Step 7: Identify behavioral patterns

[1688] Subject: Server

[1689] The server analyzes the data collected over a long period of time to identify user behavior patterns. The behavior pattern identification means models daily behavior during specific time periods.

[1690] Input: Long-term data

[1691] Output: User behavior patterns

[1692] Step 8: Early symptom detection

[1693] Subject: Server

[1694] The system detects early symptoms of dementia based on identified behavioral patterns. For example, a sudden increase in behavioral fluctuations over a short period of time can be recognized as an early symptom.

[1695] Input: Behavioral pattern

[1696] Output: Early symptom detection

[1697] Step 9: Generate reports

[1698] Subject: Server

[1699] Generative AI is used to create a detailed report of detected early symptoms, including behavioral patterns, frequency of abnormal behavior, and recommended next steps.

[1700] Input: Early symptom data

[1701] Output: Detailed report

[1702] Step 10: Submit report

[1703] Subject: Server

[1704] The generated reports are sent periodically to medical institutions and care managers, with appropriate information provided using prompts.

[1705] Input: Detailed report

[1706] Output: Send to medical institutions and care managers

[1707] Example prompt sentence:

[1708] "The monitoring robot's sensors have detected abnormal behavior by the user. Please create a detailed report and send notifications to medical institutions and family members. Please also include the type and frequency of abnormal behavior in the report."

[1709] Step 11: Generate a treatment plan

[1710] Subject: Device

[1711] The doctor inputs the patient's medical information into the device, which is then sent to the server, where the AI ​​engine generates the next appointment schedule and appropriate treatment plan.

[1712] Input: Medical Information

[1713] Output: Treatment plan and policy

[1714] Step 12: Care Plan Notification

[1715] Subject: Server

[1716] The generated treatment plan and treatment policy are notified to the user via a smartphone app or email.

[1717] Input: Treatment plan and treatment policy

[1718] Output: User notification

[1719] Step 13: Government response

[1720] Subject: User

[1721] The user uses a dedicated app to send a support request to the government office. The information entered in the app is sent to the server.

[1722] Input: Support request details

[1723] Output: Send to server

[1724] Step 14: Data analysis for administrative responses

[1725] Subject: Server

[1726] The server analyzes the received information and automatically generates an appropriate support plan. The integrated data is then used to share information with government offices.

[1727] Input: Support request details

[1728] Output: Generated support plan and submission to government office

[1729] In this way, the present system makes it possible to comprehensively protect the safety and health of users through a series of processing steps.

[1730] (Application example 1)

[1731] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1732] The purpose of this invention is to provide a system that comprehensively monitors, detects early, and provides medical support for dementia patients. Specifically, the objective is to ensure the safety of dementia patients and support appropriate medical intervention by visually and audibly monitoring their daily lives, detecting and analyzing abnormal behavior in real time, and notifying medical institutions and families in a timely manner. Furthermore, efficient generation and notification of treatment plans is also an important objective.

[1733] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1734] In this invention, the server includes a sensing unit that records the user's activity information, an analysis unit that analyzes the recorded activity information, and an abnormal behavior detection unit that detects abnormal behavior based on the analysis results. This enables the server to quickly detect the user's abnormal behavior and promptly notify medical institutions and family members. Furthermore, by visually and audibly monitoring the user's daily life using the camera and microphone unit built into the smart glasses, sending data to the server via the preprocessing unit and communication unit, and notifying the analysis results via a smartphone app or email, more accurate monitoring and early detection are achieved. Furthermore, comprehensive medical support can be provided by proposing the next appointment and treatment plan based on the report generated using the generative AI model.

[1735] "Sensing means" refers to a device or sensor for recording user activity information.

[1736] "Analysis means" refers to equipment or software for processing the recorded activity information and extracting and analyzing the necessary information.

[1737] The "abnormal behavior detection means" refers to a device or program that has the function of identifying and detecting abnormal user behavior based on analyzed data.

[1738] "Notification means" refers to a method or system for reporting detected abnormal behavior or important information to a medical institution or family member.

[1739] "Camera and microphone means" refers to video and audio collection devices for visual and audio monitoring of the user's daily life.

[1740] "Pre-processing means" refers to a method or device that performs initial noise removal and feature extraction on the data obtained by the processing unit built into the smart glasses.

[1741] "Communication means" refers to the internet connection or wireless communication mechanism for transmitting the preprocessed data to the server.

[1742] "Report generator" refers to a device or program for automatically generating a detailed report based on detected early symptoms.

[1743] The "behavior pattern identification means" refers to an analysis device or program for identifying a behavior pattern from user activity information.

[1744] "Early symptom detection means" refers to a function or device for detecting early symptoms of dementia based on identified behavioral patterns.

[1745] "Input means" refers to a terminal or program that allows a medical professional to input the user's medical information.

[1746] The "treatment plan generating means" refers to a program or device that generates the next examination schedule and treatment plan based on the input examination information.

[1747] "Proposal method" refers to a system or program that suggests the next steps in medical care based on a report generated using a generative AI model.

[1748] A "prompt generation means" is a program that inputs appropriate prompt sentences into the AI ​​to improve the efficiency of data analysis.

[1749] The system of the present invention aims to comprehensively monitor dementia patients, detect their condition early, provide medical support, and respond to government requests. The system collects and analyzes information on the user's daily activities, detects abnormal behavior, and sends necessary notifications to ensure the patient's safety and support appropriate medical intervention. Specific embodiments of the present invention are described below.

[1750] System Configuration

[1751] The system includes a sensing means, an analysis means, an abnormal behavior detection means, a notification means, a camera and microphone means, a preprocessing means, a communication means, a report generation means, a behavioral pattern identification means, an early symptom detection means, an input means, a treatment plan generation means, a suggestion means, and a prompt generation means.

[1752] sensing means

[1753] The smart glasses are equipped with multiple sensors to monitor the user's daily life, including a camera, microphone, and motion sensors, and collect information on the user's activities in real time.

[1754] Analysis means

[1755] The data collected by the smart glasses undergoes initial processing in the built-in data processing unit, where noise is removed and necessary features are extracted. The pre-processed data is then sent to a server via a communication means.

[1756] Abnormal behavior detection means

[1757] The server stores the data sent via communication means in a database. The AI ​​engine analyzes the collected data in real time and compares it with past behavioral data to detect abnormal behavior.

[1758] Notification means

[1759] If abnormal behavior is detected, the server will notify medical institutions and family members via a smartphone app or email, urging them to take prompt action.

[1760] Camera and microphone means

[1761] The cameras and microphones built into the smart glasses collect detailed visual and audio data from users' daily lives, which is crucial for improving the accuracy of abnormal behavior detection.

[1762] Pretreatment means

[1763] The processing unit built into the smart glasses performs initial noise removal and feature extraction of the data, thereby improving the quality of the data sent to the server via the communication means.

[1764] communication means

[1765] The pre-processed data is then sent to a server over the internet, using wireless communication and an internet connection.

[1766] Report generation means

[1767] Based on the detected early symptoms, a detailed report is generated and sent periodically to the medical institution or care manager.

[1768] Specific examples and prompts

[1769] It records detailed activity information such as the user's walking data, conversation content, and movement from room to room, and uses analytical methods to detect abnormal behavior. For example, it generates prompt sentences like the following and inputs them into the AI.

[1770] example:

[1771] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[1772] Based on the information collected and analyzed in this way, abnormal behavior can be detected and notified, supporting rapid medical response. This method can comprehensively realize everything from monitoring dementia patients to early detection and medical support.

[1773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1774] Step 1:

[1775] A user wears smart glasses while going about their daily life. The smart glasses' sensing means (camera, microphone, motion sensor) collect the user's activity information (walking patterns, conversations, room movements, etc.) in real time. The input is the user's behavioral data in their daily life, and the output is the collected raw data.

[1776] Step 2:

[1777] The collected raw data undergoes initial processing using the smart glasses' built-in pre-processing means. Specifically, noise removal and feature extraction are performed. The input is raw data, and the output is pre-processed data. This processing removes noise from the data and emphasizes the features required for analysis.

[1778] Step 3:

[1779] The pre-processed data is sent to the server via a communication means. The input is the pre-processed data, and the output is the data stored on the server. This communication can be via the internet or wirelessly.

[1780] Step 4:

[1781] The server stores the transmitted data in a database and analyzes it in real time. Using analytical tools, the collected data is compared with past behavioral data to detect abnormal behavior. The input is the user's activity information stored in the database, and the output is the analysis results.

[1782] Step 5:

[1783] When abnormal behavior is detected, the server uses a notification method to notify medical institutions and family members. The input is the analysis result and the output is a notification message. Notifications are sent via a smartphone app or email, encouraging a prompt response.

[1784] Step 6:

[1785] Based on the data collected over a long period of time, the server uses a behavioral pattern identification means and an early symptom detection means to identify the user's behavioral patterns and detect early symptoms.The input is the long-term data, and the output is a report of the behavioral patterns and early symptoms.

[1786] Step 7:

[1787] Based on the detected early symptoms, a report generator generates a detailed report. The input is the data of the early symptoms, and the output is a detailed report. This report is periodically sent to a medical institution or a care manager.

[1788] Step 8:

[1789] The user's medical examination information is sent to the server via the input means, and the next medical examination schedule and medical treatment plan are generated using the treatment plan generation means. The input is the medical examination information, and the output is a notification of the medical examination schedule and medical treatment plan.

[1790] Step 9:

[1791] Using a generative AI model, the next steps in medical care are suggested based on the generated report. A specific example involves generating prompts and inputting them into the AI. The input is a detailed report, and the output is specific medical suggestions.

[1792] example:

[1793] User's daily walking pattern: [Data] Conversation: [Data] Room movement: [Data]

[1794] Based on this prompt, the AI ​​will suggest the next step, creating a system that comprehensively monitors users, detects problems early, and provides medical support.

[1795] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1796] This section describes an embodiment of the present invention. This section describes a system that combines a system for monitoring dementia patients, early detection, medical support, and administrative response with an emotion engine that recognizes the user's emotions. This system collects and analyzes not only user activity information but also emotional information, enabling more accurate detection of abnormal behavior and identification of behavioral patterns.

[1797] Monitoring robot

[1798] Sensor Instruments and Emotion Engines

[1799] The monitoring robot is equipped with motion sensors, cameras, microphones, and sensors to recognize the user's emotions (e.g., facial expression recognition camera, voice analysis microphone). This allows it to collect emotional information not only from the user's actions while cooking in the kitchen, but also from their facial expressions and tone of voice.

[1800] Data collection and transmission

[1801] The activity and emotion information acquired by the sensors is initially processed by the processing unit inside the robot to remove noise. This data undergoes feature extraction processing and is then sent to the server in real time or in batches. For example, when a certain amount of data is collected showing that the user is feeling happy and cooking at the same time, that data is sent to the server as a batch.

[1802] Data analysis

[1803] AI and sentiment analysis tools

[1804] The server stores the received activity information and emotion information in a database, and records them with a timestamp. The stored data is analyzed in real time by the AI ​​engine. Emotional information recognized by the emotion engine is also analyzed simultaneously. For example, if the user is feeling depressed, the associated behavioral patterns are analyzed.

[1805] Detecting abnormal behavior and emotional anomalies

[1806] If abnormal behavior is detected based on the analysis results, the server also considers emotional information to further increase the likelihood of abnormality. For example, if a user walks around the room at an unusual time and looks anxious, the server will recognize this as high-risk abnormal behavior. This information is then notified to medical institutions and family members.

[1807] Early Detection System

[1808] Identifying behavioral patterns and detecting early symptoms

[1809] The server analyzes activity and emotional information collected over a long period of time to identify the user's behavioral patterns. By incorporating emotional fluctuations into the user's behavior, early symptom detection is more accurate. For example, if short-term behavioral fluctuations are linked to emotional fluctuations, this is considered an abnormality.

[1810] Generate and send reports

[1811] A detailed report based on the detected early symptoms is automatically generated by the generative AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated report is periodically sent from the server to medical institutions and care managers.

[1812] Medical support

[1813] Entering and analyzing medical information

[1814] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, and next appointment schedule, which are then sent to a server. The server analyzes the medical information and uses generative AI to generate an optimal medical plan and treatment policy.

[1815] Notification and follow-up

[1816] The generated medical plan and treatment policy are notified to the user and medical institution from the server. Through the notification method, the user can receive the next appointment schedule and follow-up treatment policy via a smartphone app or email.

[1817] Government response services

[1818] Citizen Support

[1819] Users use a dedicated app to request dementia support from government offices. The app has a form where users can enter information such as the type of support they would like to receive, their name, address, and emotional state, and this information is sent directly to a server. The server then analyzes the information and uses a generative AI to automatically generate an appropriate support plan.

[1820] Collaboration support

[1821] The server seamlessly shares information with administrative offices using integrated data, including activity information, emotional information, support details, and progress. Administrative staff can view and update this information through their devices and take necessary actions.

[1822] In this way, the system of the present invention can comprehensively monitor and analyze the user's activity information and emotional information, and effectively detect abnormal behavior and early symptoms, thereby comprehensively protecting the safety and health of dementia patients.

[1823] The processing flow will be explained below.

[1824] Monitoring robot processing flow

[1825] Step 1:

[1826] The robot activates its built-in motion sensors, camera, microphone, facial expression recognition camera, and voice analysis microphone to collect information on the user's activities and emotions.

[1827] Step 2:

[1828] The robot initially processes the collected activity and emotion information to remove noise and extract key features, such as walking patterns, facial expression changes, and tone of voice.

[1829] Step 3:

[1830] The robot sends the processed activity information and emotion information to the server in real time or in batches.

[1831] Data analysis process flow

[1832] Step 4:

[1833] The server stores the received activity information and emotion information in a database and records them with a timestamp.

[1834] Step 5:

[1835] The server analyzes the received activity information and emotion information in real time using an AI engine and emotion engine. For example, it analyzes the user's walking movements, facial expressions, and tone of voice.

[1836] Step 6:

[1837] The server detects abnormal behavior from the activity and emotion information based on the analysis results. If the user falls and simultaneously shows a fearful expression, it will recognize this as high-risk abnormal behavior.

[1838] Step 7:

[1839] The server notifies medical institutions and family members based on the detected abnormal behavior and emotional abnormalities, for example, by email notification or text message.

[1840] Early detection system processing flow

[1841] Step 8:

[1842] The server analyzes the activity information and emotion information collected over a long period of time using a behavioral pattern identification means to identify the user's behavioral pattern, for example, analyzing the user's activity and emotion fluctuations over a week.

[1843] Step 9:

[1844] The server detects early symptoms from the identified behavioral patterns. For example, if the user frequently wanders around feeling anxious, this may be recognized as an early symptom.

[1845] Step 10:

[1846] The server uses generative AI to automatically generate a detailed report based on early symptoms, including behavioral patterns, emotional fluctuations, detected early symptoms, and recommended next steps.

[1847] Step 11:

[1848] The server periodically transmits the generated report to the medical institution or care manager, for example, by email as a weekly report.

[1849] Medical response support process flow

[1850] Step 12:

[1851] The terminal provides an interface for the doctor to input medical information and emotional information. For example, the medical examination form may include fields for inputting the user's facial expression and tone of voice.

[1852] Step 13:

[1853] The doctor inputs the user's medical information and emotional information.

[1854] Step 14:

[1855] The terminal transmits the input medical examination information and emotion information to the server.

[1856] Step 15:

[1857] The server analyzes the medical examination information and emotional information, and uses generative AI to automatically generate the next medical examination schedule and treatment plan.

[1858] Step 16:

[1859] The server notifies the user or medical institution of the generated medical plan and treatment policy, for example, via a smartphone app.

[1860] Processing flow of administrative services

[1861] Step 17:

[1862] Users enter information to request support from the government through a dedicated app, such as their name, address, and emotional state.

[1863] Step 18:

[1864] The terminal receives the input and sends it to the server.

[1865] Step 19:

[1866] The server analyzes the received information and automatically generates an appropriate support plan using a generation AI.

[1867] Step 20:

[1868] The server notifies the user of the generated support plan, for example by sending a push notification to a smartphone app.

[1869] Step 21:

[1870] The server seamlessly shares information with government agencies using the integrated activity and emotion information, for example by sending detailed data including the user's health status and emotional fluctuations.

[1871] Step 22:

[1872] The terminals provide an interface for government officials to view and update the data they receive, for example by checking the status on a dashboard and taking necessary actions.

[1873] This is the specific processing flow of the system, which enables comprehensive monitoring and analysis of user activity and emotional information, enabling the detection of abnormal behavior and early symptoms with high accuracy.

[1874] Example 2

[1875] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1876] Conventional systems for monitoring dementia patients, early detection, medical support, and government response only analyze user activity information, making it difficult to perform detailed analysis that includes emotional information. As a result, the accuracy of detecting abnormal behavior and early symptoms decreases, and appropriate intervention and support can be delayed. In addition, it is difficult to generate response policies and treatment plans that take emotional information into account, making it impossible to comprehensively understand the user's condition. This has led to challenges in effectively protecting the safety and health of dementia patients.

[1877] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1878] In this invention, the server includes a sensor for recording the user's activity information and emotional information, a means for initial processing and feature extraction of the recorded activity information and emotional information, a means for transmitting the processed and feature-extracted data to the server, an AI and emotion analysis means for analyzing the received activity information and emotional information and detecting abnormal behavior and emotional abnormalities, and a notification means for transmitting notifications of detected abnormal behavior and emotional abnormalities to medical institutions and family members. This enables comprehensive monitoring of the activity information and emotional information of dementia patients, enabling more accurate detection of abnormal behavior and early symptoms. Furthermore, treatment guidelines and support plans that take emotional information into account can be generated, enabling a comprehensive understanding of the user's condition and appropriate responses.

[1879] "Sensor means" refers to a device for recording a user's activity information and emotional information, and includes a motion sensor, a camera, a microphone, a facial expression recognition camera, a voice analysis microphone, etc.

[1880] "Initial processing" refers to processing to remove noise from collected raw data and improve the quality of the data.

[1881] "Feature extraction" is the process of extracting necessary information from initially processed data and converting it into a form suitable for analysis.

[1882] The "means for transmitting to the server" is a mechanism for transmitting the data that has undergone initial processing and feature extraction to the server via the Internet or other network.

[1883] "AI and emotion analysis means" refers to artificial intelligence technology and emotion analysis algorithms for analyzing received activity information and emotion information and assessing a user's behavioral patterns and emotional state.

[1884] "Abnormal behavior" refers to behavior that deviates from a user's normal behavioral patterns and may indicate symptoms of dementia or other health issues.

[1885] "Emotional abnormalities" refer to emotional states that deviate from normal emotional patterns and may indicate psychological stress or changes in the user's health.

[1886] "Notification means" refers to a system for notifying medical institutions and family members of information about detected abnormal behavior and emotional abnormalities, and includes email, smartphone apps, telephone, etc.

[1887] This section describes an embodiment of the present invention. The purpose of this invention is to monitor dementia patients, detect them early, provide medical support, and respond to government requests. In addition, it includes an emotion engine that analyzes the user's emotions. This system collects and analyzes user activity information and emotion information, and achieves highly accurate detection of abnormal behavior and identification of behavioral patterns.

[1888] Monitoring robot

[1889] The monitoring robot is equipped with the following sensor means:

[1890] Motion sensor: Records user activity information in real time.

[1891] Camera: Visually records the user's actions.

[1892] Microphone: Collects user voice information.

[1893] Facial expression recognition camera: Analyzes the user's facial expressions and collects emotional information.

[1894] Voice analysis microphone: Analyzes the user's emotional state from the tone of their voice.

[1895] These sensors continuously record daily activities, such as when a user is cooking in the kitchen, and use facial recognition cameras and voice analysis microphones to collect emotional information, such as whether the user is enjoying themselves or feeling anxious.

[1896] Data collection and transmission

[1897] The monitoring robot initially processes activity and emotion information acquired by sensors in a processing unit inside the robot. After noise is removed, the data undergoes feature extraction processing and is then sent to the server. This allows high-quality data to be aggregated on the server. For example, if a user frequently smiles while cooking, that data is sent to the server at regular intervals as a batch process.

[1898] Data analysis

[1899] The server stores the received activity information and emotion information in a database. The stored data is recorded with a timestamp and analyzed in real time by an AI engine and emotion analysis engine. For example, if a user shows a depressed expression more frequently than usual, the data is analyzed and detected as a significant pattern.

[1900] Detecting abnormal behavior and emotional anomalies

[1901] The server detects abnormal behavior and emotional abnormalities based on the analysis results. For example, if a user walks around the room at an unusual time and looks anxious, this behavior will be analyzed as high-risk abnormal behavior. Detected abnormal behavior will be notified to medical institutions and family members.

[1902] Early Detection System

[1903] Activity and emotional information collected over a long period of time is stored on a server. The server uses this data to identify the user's behavioral and emotional patterns. Based on the identified patterns, early symptoms of dementia are analyzed. For example, if short-term behavioral fluctuations coincide with emotional fluctuations, this is deemed abnormal.

[1904] Generate and send reports

[1905] Based on the analyzed data, reports are automatically generated by the AI, including information on the user's activities, emotions, frequency of abnormal behavior, and recommended next steps. The generated reports are periodically sent to medical institutions and care managers.

[1906] Medical support

[1907] The device provides an interface for doctors to input medical information. After the examination, the doctor inputs the user's health condition, emotional state, prescribed medications, next appointment schedule, etc. This information is sent to a server, which analyzes it and generates a medical plan and treatment policy using generative AI.

[1908] Notification and follow-up

[1909] The generated medical plan and treatment policy are notified to the user and medical institution via a smartphone app or email. The user can check the next appointment schedule and follow-up instructions in the app.

[1910] Government response services

[1911] Users request dementia support from government offices through a dedicated app. The app has a form where users can enter information such as the type of support they would like, their name, address, and emotional state, and this information is sent to a server. The server analyzes this information and uses generative AI to automatically generate an appropriate support plan. Government staff can view this information on their devices and provide the necessary support.

[1912] Examples of concrete examples and prompts

[1913] As a specific example, if a 75-year-old user exhibits behavior that differs from their usual behavioral patterns and has an anxious expression on their face, this data will be analyzed as abnormal behavior and a medical institution will be notified.

[1914] Example prompt sentence:

[1915] "A 75-year-old user is walking around outside of their usual walking time and appears anxious. This is analyzed as abnormal behavior and a prompt is sent to notify a medical institution."

[1916] In this way, the system of the present invention comprehensively analyzes the user's activity information and emotional information, detects abnormal behavior and early symptoms with high accuracy, and enables appropriate medical and administrative responses.

[1917] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1918] Step 1:

[1919] Collecting user activity and emotion information

[1920] The user provides daily activity information and emotional information to the monitoring robot through sensors installed in the robot.

[1921] Input: User movements, facial expressions, and tone of voice

[1922] Output: Raw data (motion information, facial expression data, voice data)

[1923] Specific operation: The monitoring robot uses motion sensors, cameras, microphones, facial expression recognition cameras, and voice analysis microphones to record the user's movements, facial expressions, and voice information in real time. For example, when the user is cooking in the kitchen, the facial expression recognition camera will capture their smile, and the voice analysis microphone will record their happy tone of voice.

[1924] Step 2:

[1925] Initial data processing and feature extraction

[1926] The monitoring robot performs initial processing on the raw data to remove noise, then performs feature extraction to generate variables.

[1927] Input: Raw data (motion information, facial expression data, voice data)

[1928] Output: Feature-extracted data (filtered motion information, facial features, and audio features)

[1929] Specific operations: For example, a noise reduction filter is applied to voice data to extract features that indicate the speaker's emotional state. Also, features that measure the frequency and degree of smiling are generated from facial expression data.

[1930] Step 3:

[1931] Sending data to the server

[1932] The monitoring robot transmits the data that has undergone initial processing and feature extraction to the server.

[1933] Input: Feature-extracted data (filtered movement information, facial features, and audio features)

[1934] Output: Received data on the server side

[1935] Specific operation: Data is sent to the server in real time or in batches. For example, all collected data is sent together as a batch at the end of the day.

[1936] Step 4:

[1937] Data analysis using AI and sentiment analysis

[1938] The server stores the received data in a database and analyzes it using an AI engine and emotion analysis engine.

[1939] Input: Received data (filtered movement information, facial expression features, and voice features)

[1940] Output: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[1941] Specific operation: The AI ​​engine analyzes the user's behavioral patterns, and the emotion analysis engine analyzes the emotional fluctuation patterns. For example, if the user frequently shows a depressed expression, this will be output as the analysis result.

[1942] Step 5:

[1943] Detecting abnormal behavior and emotional anomalies

[1944] The server detects abnormal behavior and emotional abnormalities based on the analysis results.

[1945] Input: Analysis results (behavioral patterns, emotional patterns, abnormal behavior candidates)

[1946] Output: Abnormal behavior and emotional anomaly detection alerts

[1947] Specific behavior: For example, if a user walks around the room at an unusual time and looks anxious, this will trigger an alert as a high-risk abnormal behavior.

[1948] Step 6:

[1949] Generate and send reports

[1950] The server generates a report based on the analysis results and sends it to the medical institution or care manager.

[1951] Input:...

Claims

1. a sensor means for recording user activity information; an analysis means for analyzing the recorded activity information; abnormal behavior detection means for detecting abnormal behavior based on the analysis results; a notification means for sending a notification to a medical institution and a family member regarding the detected abnormal behavior; A system including:

2. a behavior pattern identification means for identifying a behavior pattern of the user from the analyzed activity information; an early symptom detection means for detecting early symptoms of dementia based on the identified behavioral patterns; report generation means for generating a report regarding the detected early symptoms; The system of claim 1 , comprising:

3. an input means for inputting the user's medical examination information; a treatment plan generating means for analyzing the input examination information and generating the next examination schedule and treatment plan; a notification means for notifying the generated consultation schedule and treatment plan; The system of claim 1 , comprising:

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