System

Abstract

A system is provided.SOLUTION: A system, comprising: camera means for monitoring behavior of a dementia patient in real time; generation and AI means for analyzing a video from a monitoring camera; audio output means for speaking deterrence to the patient when an abnormal behavior is detected; and communication means for transmitting a notification to a family member when an abnormal behavior is detected; and conversation support means for performing a daily conversation with the patient using the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As the number of dementia patients increases, a significant burden is placed on family members, particularly those providing care in the home environment, to monitor and care for the patient's daily life. It is also difficult to properly manage abnormal behavior (such as wandering or falling) of patients, which can lead to an increase in accidents at home. Furthermore, patients are prone to social isolation and often fall into a mentally unstable state. A sustainable system is needed to address these issues. [Means for solving the problem]

[0005] In the present invention, these problems are solved by the following means.

[0006] The system includes a camera means for monitoring the behavior of dementia patients in real time, a generating AI means for analyzing images from the surveillance camera, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to family members when abnormal behavior is detected, a data linking means for linking behavioral data to medical professionals, a reminder means for managing the patient's daily life tasks, and a conversation support means for using generating AI to hold daily conversations with the patient. This system can reduce the burden on family members and improve the safety and mental health of dementia patients.

[0007] "Dementia patients" refer to elderly people and adults whose mental functions have declined and who find it difficult to carry out daily activities independently.

[0008] "Camera means" refers to a device or apparatus that captures images and transmits data in real time.

[0009] "Generative AI means" refers to a system that includes an artificial intelligence algorithm for analyzing acquired video data and detecting abnormal behavior.

[0010] "Audio output means" refers to a device that provides audio instructions or deterrent messages to the patient when the generating AI means detects abnormal behavior.

[0011] "Communication means" refers to the communication circuitry or interface used to send alerts to family members or caregivers when abnormal behavior is detected.

[0012] "Data linkage means" refers to a system for storing analyzed behavioral data and sharing it with medical professionals.

[0013] "Reminder measures" refer to functions that manage patients' daily life tasks (e.g., time to take medicine or go to the toilet) and notify them at set times.

[0014] "Conversational support tools" refer to dialogue interfaces that use generative AI to conduct everyday conversations with patients and prevent social isolation.

[0015] The "system" refers to a collection of technologies that include each of the above means and comprehensively support the lives of people with dementia. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The present invention provides a care support system using a generative AI model for families of dementia patients, thereby building a sustainable care environment. Specific embodiments for this purpose are described below.

[0038] Overall system overview

[0039] The user first uses the application to input the dementia patient's profile information, which includes the patient's name, age, medical history, and desired reminder settings, and then sends it to the server. The server then stores the patient's basic information in a database for future analysis and treatment.

[0040] The terminal (device with camera) is installed in the patient's living environment and serves to monitor daily behavior in real time. The camera captures video and sends it to a server. The server analyzes the received video data using a generation AI (G) and detects abnormal behavior in real time.

[0041] If anomalous behavior is detected, the server will:

[0042] 1. Send a voice output instruction to the terminal and play a deterrent message to the patient such as "Mr. / Ms. XX, where are you going?"

[0043] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0044] The server also periodically aggregates behavioral data and shares it with medical professionals to help optimize care plans, allowing them to propose appropriate care plans based on the patient's daily behavior.

[0045] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0046] The device also features a conversation support function using generative AI (C), which supports everyday conversations. Generative AI (C) generates appropriate questions and comments based on the patient's responses, helping to keep the conversation moving smoothly. This function not only prevents patients from becoming socially isolated, but is also expected to slow the progression of dementia.

[0047] Specific examples

[0048] One day, a user registers a patient's profile in the system. The user sets basic information about the patient, reminders for taking medication, toilet time, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0049] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generation AI (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior.

[0050] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0051] Conversational support is also provided throughout the day by a generative AI (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user opens the application and logs in. They then enter their dementia patient profile information, including their name, age, medical history, and reminder settings (such as medication times and toilet time). Once complete, the user sends this information to the server.

[0055] Step 2:

[0056] The server stores the received profile information in a database, which is used to analyze patient behavior and generate reminders.

[0057] Step 3:

[0058] The device monitors the patient's daily life in real time using a camera, which captures video data and transmits it to a server.

[0059] Step 4:

[0060] The server analyzes the transmitted video data using the generation AI (G), which then analyzes the video data and determines whether abnormal behavior (such as wandering or falling) has been detected.

[0061] Step 5:

[0062] If abnormal behavior is detected, the server takes the following actions: First, it instructs the device to send a voice message to deter the user. Second, it sends an alert to the user's (family member's) smartphone to notify them of the abnormal behavior.

[0063] Step 6:

[0064] The terminal plays a voice message according to instructions from the server, for example, asking the patient, "Mr. / Ms. X, where are you going?"

[0065] Step 7:

[0066] Users receive alerts on their smartphones to let them know when abnormal behavior has occurred and, if necessary, rush to the patient's location or step up monitoring remotely.

[0067] Step 8:

[0068] The server periodically aggregates the patient's daily behavior data and generates reports that are shared with medical professionals, who then provide advice to optimize the patient's care plan.

[0069] Step 9:

[0070] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0071] Step 10:

[0072] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0073] Step 11:

[0074] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0075] Step 12:

[0076] The Generative AI (C) saves conversation logs and analyzes the patient's mental state and interests, providing data to further improve the quality of the conversation.

[0077] This specific processing flow will realize a sustainable care support system that improves the safety and mental health of dementia patients.

[0078] Example 1

[0079] 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."

[0080] It is necessary to monitor the daily lives of dementia patients, quickly detect abnormal behavior, and take appropriate action, but conventional systems often respond slowly. Another problem is the lack of a system that can comprehensively accumulate patient behavior data, notify family members in real time, and manage daily life tasks and provide conversation support.

[0081] 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.

[0082] In this invention, the server includes an information input means, a monitoring means, and a video analysis means. This allows for real-time monitoring of the daily life of a dementia patient and for rapid detection and response of abnormal behavior. The server also includes an abnormal behavior prevention means, a notification means, a data aggregation means, a task management means, and a conversation support means. This allows for immediate transmission of a deterrent message and notification to family members when abnormal behavior occurs. Furthermore, the server accumulates behavioral data and provides it to medical professionals, manages daily life tasks, and sends periodic reminder notifications, thereby preventing social isolation and the progression of dementia, while building a sustainable care environment.

[0083] The "information input means" is a means by which a user inputs profile information of a dementia patient using an application and transmits the information to a server.

[0084] The "monitoring means" is a means for monitoring the patient's daily activities in real time using a camera-equipped device and collecting video data.

[0085] "Video analysis means" refers to a means for analyzing video data obtained from a surveillance means using a generative AI model to detect abnormal behavior.

[0086] The "abnormal behavior deterrent means" is a means for playing back an audible deterrent message to the patient when abnormal behavior is detected.

[0087] The "notification means" is a means for sending an alert from the server to the family member's smartphone when abnormal behavior occurs.

[0088] The "data collection means" is a means for periodically collecting collected behavioral data and providing it to medical professionals.

[0089] "Task management tools" are tools for managing the patient's daily tasks (e.g., time to take medicine, time to go to the toilet).

[0090] "Conversation support means" is a means of using generative AI to conduct everyday conversations with patients, generating questions and comments to help the conversation progress smoothly.

[0091] MODE FOR CARRYING OUT THE INVENTION

[0092] This invention is a system that supports the care of dementia patients. The system begins when a user uses an application to input the patient's profile information and sends it to a server. Specifically, the profile information includes the patient's name, age, medical history, reminder settings, etc. This information is stored in a database by the server and used for future analysis and responses.

[0093] The device is equipped with a camera and is installed in the patient's living environment. The device monitors daily activities in real time and sends the video data to a server. The server analyzes the received video data using a generative AI model (G), and if abnormal behavior (e.g., wandering or falling) is detected, various measures are taken.

[0094] First, the server sends a voice output instruction to the device, playing a deterrent message such as "Mr. / Ms. X, where are you going?". It also sends an alert to the user's smartphone to notify them of abnormal behavior. The server then periodically aggregates the behavioral data and provides it to medical professionals, who can then optimize the care plan and suggest appropriate care for the patient.

[0095] Furthermore, the server manages daily tasks (e.g., medication times, toilet times) and sends reminder information to the device at set times. The device then notifies the patient with a voice message, helping the patient remember to perform the necessary tasks.

[0096] A conversation support function using a generative AI model (C) is also provided. The device supports everyday conversations with patients, and the generative AI model (C) generates appropriate questions and comments based on the patient's responses. This is expected to prevent social isolation and slow the progression of dementia.

[0097] Specific examples

[0098] One day, a user registers a patient profile in the system, setting basic patient information, medication times, toilet time reminders, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0099] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generative AI model (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone to notify them of the abnormal behavior.

[0100] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0101] Conversational support is also provided throughout the day by a generative AI model (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0102] Example prompt sentence:

[0103] "Enter basic patient information, medication times, bathroom time reminders, etc."

[0104] "What message do you want the device to play if loitering is detected?"

[0105] "How do you guide a conversation with a patient using a generative AI conversation support system?"

[0106] This system allows families of dementia patients to monitor their daily lives with peace of mind and respond quickly to any abnormal behavior. It also provides conversation support to prevent patients from becoming socially isolated, contributing to the creation of a sustainable care environment.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The user uses the application to enter the patient's profile information. The profile information includes the patient's name, age, medical history, reminder settings, etc. After entering this information and pressing the submit button, the profile information is sent to the server. The entered information is saved as profile data on the server.

[0110] Step 2:

[0111] The server stores the received profile information in a database. A unique ID is assigned to each patient in this database, which will be used for future searches and analysis. The patient's name, age, medical history, and reminder settings are also saved here. This allows a series of patient data to be accumulated in the database.

[0112] Step 3:

[0113] A camera-equipped terminal is installed in the patient's living environment. The terminal operates constantly and monitors the patient's behavior in real time. The camera is positioned to capture images of the room and captures video data 24 hours a day. This captured video data is then sent from the terminal to a server.

[0114] Step 4:

[0115] The server analyzes the video data sent from the device. It uses a generative AI model (G) to analyze this video data in real time to detect abnormal behavior (e.g., wandering, falling). If abnormal behavior is detected through this analysis, the server proceeds to the next step in processing the information.

[0116] Step 5:

[0117] When abnormal behavior is detected, the server immediately sends a voice notification instruction to the device. Specifically, it sends an instruction to play a message such as "Mr. / Ms. X, where are you going?" Based on this instruction, the device conveys the message to the patient by voice. This directly deters the patient.

[0118] Step 6:

[0119] At the same time, the server sends an alert to the user's smartphone to notify them of the abnormal behavior. The alert contains detailed information about the type of abnormal behavior, the date and time it occurred, and the current situation. This allows the user to immediately understand the situation.

[0120] Step 7:

[0121] The server periodically aggregates the collected behavioral data and provides it to medical professionals. Data aggregation is performed at regular intervals, and the accumulated data is analyzed and compiled into a report. Medical professionals use this report to optimize care plans and propose appropriate care for patients.

[0122] Step 8:

[0123] The server manages reminders for daily tasks (e.g., time to take medicine, time to go to the toilet). When the reminder time approaches, the server sends the information to the device, which then notifies the patient by voice at the specified time, saying, "It's time to take your medicine at 2 o'clock."

[0124] Step 9:

[0125] When the device receives a reminder, it will notify you by voice, helping patients remember to perform set tasks (e.g., taking medicine, going to the toilet).

[0126] Step 10:

[0127] The device uses the generative AI model (C) to provide conversational support. For example, if a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the device will continue the conversation by saying, "That sounds good, let's go out." This conversational support helps prevent social isolation and maintain mental stability for patients.

[0128] (Application example 1)

[0129] 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."

[0130] Monitoring and caring for dementia patients places a significant burden on many families and is also an important issue for ensuring patient safety. Conventional systems often provide abnormal behavior detection, reminder functions, and daily conversation support separately, making integrated management difficult. Furthermore, it is difficult to accurately and quickly detect and respond to abnormal behavior in real time and notify family members, limiting the efficiency of care and improving the patient's quality of life.

[0131] 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.

[0132] In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generating AI means for analyzing video from the surveillance camera, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to family members when abnormal behavior is detected, a data linking means for linking behavioral data to medical professionals, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using the generating AI, and a processing means for executing a security service application. This makes it possible to ensure the safety of dementia patients in real time, reduce the burden on their families, and improve the quality of life of the patients.

[0133] "Real-time monitoring" means observing the behavior of dementia patients in real time and collecting information.

[0134] The "camera means" is a device that acquires visual information and transmits it to the server as digital data.

[0135] The "generative AI means" is an artificial intelligence technology that analyzes video data from the camera means and detects specific patterns or abnormal behavior.

[0136] The "audio output means" is a device that generates an audio message to alert the patient when abnormal behavior is detected.

[0137] "Communication means" refers to a system for sending notifications to family members or caregivers when abnormal behavior is detected.

[0138] "Data linking means" is a function that allows collected behavioral data to be shared with experts and medical institutions.

[0139] "Reminder measures" are functions that manage important tasks in a patient's daily life (e.g., taking medicine, toilet time, etc.) and provide timely notifications.

[0140] "Conversation support means" is a function that uses generative AI to assist with everyday conversations and promote mental stability and social interaction for dementia patients.

[0141] The "security service application" is an application that ensures the safety of dementia patients and provides warnings and notifications to caregivers and family members.

[0142] The "processing means" is a control device for the entire system that receives and analyzes data sent from various means and executes necessary actions.

[0143] This invention is a system for monitoring the behavior of dementia patients and ensuring their safety. The system includes a camera, a generation AI, a voice output, a communication device, a data linking device, a reminder device, a conversation support device, and a security service application. Each device is described in detail below.

[0144] Camera Means

[0145] The camera means is a device for monitoring the behavior of dementia patients in real time, and video data is continuously acquired and transmitted to the server.

[0146] Generation AI means

[0147] The server uses the acquired video data to run the generative AI model "G." This model has the ability to analyze and detect abnormal behavior in real time. The video data is first preprocessed, and after feature extraction, it is input into the AI ​​model.

[0148] Audio output means

[0149] When abnormal behavior is detected, the server sends a command to the voice output means to warn the patient. This message has a warning message such as "Mr. / Ms. XX, where are you going?"

[0150] communication means

[0151] If abnormal behavior is detected, the server will send a notification to family members or caregivers via communication means, allowing for a prompt response.

[0152] Data linking method

[0153] Behavioral data is collected periodically and shared with medical professionals and care managers, who use it to optimize care plans.

[0154] Reminder measures

[0155] The server manages the patient's daily tasks and generates reminder information, which notifies the patient of important tasks such as taking medicine or going to the toilet at appropriate times. The reminders are delivered to the patient via voice notification.

[0156] Conversation support tools

[0157] Using the generative AI model "C," the server provides everyday conversation support. For example, if a patient asks, "How are you feeling today?", the AI ​​model responds, "I feel good, let's go out." This function is expected to prevent patients from becoming socially isolated and maintain their mental stability.

[0158] Security Service Applications

[0159] The security service application integrates these methods and functions as a comprehensive system. It operates as a smartphone application and provides real-time notifications and management functions to family members and caregivers.

[0160] Specific examples

[0161] Specific examples of abnormal behavior detection

[0162] If the patient begins to wander around the room around 10:00 a.m., the camera will send the footage to the server. The server's generative AI model "G" will analyze the footage and detect abnormal behavior (wandering). The server will then immediately send an instruction to the audio output device to play the message "Mr. / Ms. X, where are you going?", and simultaneously send an alert to the family member's smartphone via the communication device.

[0163] Prompt Sentence Examples

[0164] "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0165] Examples of reminder functions

[0166] If a medicine-taking reminder is set for 2 p.m., the server generates reminder information at the set time and causes the voice output means to notify the user, "It's time to take your medicine at 2 p.m."

[0167] Prompt Sentence Examples

[0168] "I have a medication reminder at 2 PM. Please send me the reminder information."

[0169] Examples of conversation support functions

[0170] When the patient asks, "How are you feeling today?", the server responds with "Feeling good, let's go out" using generative AI model "C."

[0171] Prompt Sentence Examples

[0172] "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0173] As described above, this invention can ensure the safety of dementia patients, reduce the burden on their families and caregivers, and improve the quality of life of patients.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user enters patient profile information

[0177] Using a smartphone application, users input basic information such as the patient's name, age, medical history, medication times, toilet time, and other reminder information. This information is sent as input data to the server, which stores it in a database for future analysis and response.

[0178] Step 2:

[0179] Acquisition and transmission of video data by camera means

[0180] A camera installed on the terminal monitors the daily activities of dementia patients in real time and captures video data. This video data is sent as input to the server. The server receives this video data and inputs it into the generative AI model "G."

[0181] Step 3:

[0182] Analysis of abnormal behavior using generative AI model "G"

[0183] The server preprocesses the received video data and extracts features for detecting abnormal behavior. These features are input into the generative AI model "G," which then determines whether the behavior is abnormal. If abnormal behavior is detected as the output, the system proceeds to the next step.

[0184] Step 4:

[0185] Implementing a warning by audio output means

[0186] When abnormal behavior is detected, the server sends instructions to the voice output means. Specifically, it outputs data to play a voice message such as "Mr. / Ms. X, where are you going?", which is received by the terminal and used to alert the patient via voice.

[0187] Step 5:

[0188] Notifying family members via communication means

[0189] When abnormal behavior is detected, the server sends a notification to the family member's smartphone using a communication device. This notification includes the time when the abnormal behavior occurred and the details of the abnormal behavior. The server generates notification data, outputs it to the communication device, and sends it to the family member's smartphone.

[0190] Step 6:

[0191] Reminder generation and delivery

[0192] The server generates a reminder notification according to the schedule based on the input reminder information. At the specified time, the reminder information is sent to the voice output means, and the terminal notifies the patient by voice, "It's time to take your medicine at 2 o'clock."

[0193] Step 7:

[0194] Linking behavioral data to healthcare professionals

[0195] The server periodically aggregates the collected behavioral data to share it with medical professionals. The aggregated data is sent to the medical professionals via the data sharing means. The server aggregates the behavioral data and outputs the data to the collaborating destination.

[0196] Step 8:

[0197] Conversation support using generative AI model "C"

[0198] The server uses the generative AI model "C" to provide everyday conversation support. When a patient asks a question by voice, the voice data is sent from the terminal to the server. The server inputs the data into the AI ​​model "C" and generates a response message. The generated response message is output to the voice output means, and the terminal plays it back.

[0199] Specific processing flow example

[0200] Example prompt: "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0201] Example prompt: "I have a medication reminder at 2 PM. Please send me the reminder information."

[0202] Example prompt: "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0203] Through the above processing steps, the server can ensure the safety of dementia patients and quickly provide necessary information to their families and caregivers.

[0204] 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.

[0205] The present invention provides a care support system for families of dementia patients that utilizes a generative AI model, and by adding emotion recognition technology, it realizes a sustainable care environment while also providing psychological care to the patient. Specific embodiments for this purpose are described below.

[0206] Overall system overview

[0207] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0208] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generation AI (G) to detect abnormal behavior. Furthermore, an emotion engine analyzes the patient's emotions from their facial expressions and voice.

[0209] If anomalous behavior is detected, the server will:

[0210] 1. Issue an instruction to the device to send a voice message to deter the user.

[0211] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0212] The emotion data analyzed by the emotion engine is used by the generative AI (C) to support conversations with patients. For example, this conversation can provide comforting responses when a patient feels anxious, preventing social isolation and reducing psychological burden.

[0213] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0214] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0215] Specific examples

[0216] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0217] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generation AI (G) detected abnormal behavior (wandering). Furthermore, the emotion engine analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0218] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0219] Conversation support is also provided by generative AI (C). For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0220] Data from the emotion engine and generative AI (G) is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as a report, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0221] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] The user opens the application and logs in. Then, they enter their dementia patient profile information, including their name, age, medical history, reminder settings (such as medication times or toilet time), and emotion engine settings. Once completed, the user sends this information to the server.

[0225] Step 2:

[0226] The server stores the received profile information in a database, which is used for patient behavior analysis, emotion analysis, and reminder generation.

[0227] Step 3:

[0228] The device (camera-equipped device) is installed in the patient's living environment and monitors their daily activities and facial expressions in real time. The camera captures video data and transmits it to a server.

[0229] Step 4:

[0230] The server analyzes the transmitted video data using the Generative AI (G) to detect abnormal behavior. The emotion engine also analyzes the patient's facial expressions and voice to determine emotions such as anxiety, sadness, and confusion.

[0231] Step 5:

[0232] If abnormal behavior is detected, the server takes the following action. First, it instructs the device to send a voice message to deter the user. For example, it could say, "Mr. / Ms. X, where are you going?" Next, it sends an alert of the abnormal behavior to the user's (family member's) smartphone.

[0233] Step 6:

[0234] If the emotion engine detects an emotion such as anxiety, the server generates an appropriate additional message and sends it to the device, such as "Please stay calm. Is there anything I can help you with?"

[0235] Step 7:

[0236] The device plays voice messages according to instructions from the server. First, it plays a deterrent message for abnormal behavior, followed by a message based on emotions.

[0237] Step 8:

[0238] Users receive alerts on their smartphones to identify abnormal behavior and the patient's emotional state, and can then rush to the patient's location or remotely monitor them if necessary.

[0239] Step 9:

[0240] The server periodically collects data on the patient's daily behavior and emotions and generates a report that is shared with medical professionals. Based on this report, medical professionals can optimize care plans that take into account both the patient's behavior and emotions.

[0241] Step 10:

[0242] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0243] Step 11:

[0244] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0245] Step 12:

[0246] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0247] Step 13:

[0248] The generative AI (C) generates appropriate responses based on the patient's emotional data and continues the conversation. For example, if the patient responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together."

[0249] Step 14:

[0250] The Generative AI (C) saves conversation logs and emotional data for analysis, and uses this information to understand the patient's mental state and interests, which it then reflects in the next conversation.

[0251] The specific processing flow described above will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0252] Example 2

[0253] 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."

[0254] Conventional care systems for dementia patients have difficulty monitoring and analyzing the patient's behavior and psychological state in real time and providing appropriate care. Furthermore, notification and data sharing with family members and medical professionals are insufficient, making effective care difficult. This increases the burden on caregivers and creates problems such as insufficient protection of the patient's safety and mental health.

[0255] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring camera, a voice output means for speaking to the patient to deter abnormal behavior when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data and emotional data to a medical professional, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using a generation AI, and an emotion recognition means for analyzing the patient's psychological state using emotion recognition technology. This makes it possible to monitor the behavior and emotions of a dementia patient in real time, take appropriate measures, and quickly provide information to family members and medical professionals.

[0256] The "camera means" is a photographing device for monitoring the behavior of dementia patients in real time.

[0257] The "generative AI means" is an artificial intelligence that analyzes footage from surveillance cameras and detects abnormal behavior.

[0258] The "audio output means" refers to a speaker or audio playback device that issues a warning to the patient when abnormal behavior is detected.

[0259] The "communication means" is a communication device for sending a notification to family members when abnormal behavior is detected.

[0260] The "data linking means" is a device or system with a data communication function for linking behavioral data and emotional data to medical professionals.

[0261] A "reminder means" is a device or system that manages a patient's daily tasks and has the function of notifying them of things like when to take their medicine or when to go to the toilet.

[0262] A "conversation support means" is a device or system with dialogue capabilities that uses generative AI to support everyday conversations with patients.

[0263] The "emotion recognition means" is a device or system that uses emotion recognition technology to analyze the psychological state of a patient and take appropriate action.

[0264] "Server" refers to a central processing unit that integrates, manages, and controls the above means, and transmits and receives information and analyzes data.

[0265] The present invention provides a care support system that utilizes generative AI models for families of dementia patients. By incorporating emotion recognition technology, this system realizes a sustainable care environment while also providing psychological care to patients. Specific embodiments of the present invention are described below.

[0266] Overall system overview

[0267] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0268] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes emotions from the patient's facial expressions and voice.

[0269] If anomalous behavior is detected, the server will:

[0270] 1. Issue an instruction to the device to send a voice message to deter the user.

[0271] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0272] The emotion data analyzed by the emotion engine is used by generative AI to support conversations with patients, for example, providing comforting responses when patients feel anxious, preventing social isolation and reducing psychological burdens.

[0273] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0274] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0275] Hardware and software used

[0276] Hardware: Camera-equipped devices (e.g., Raspberry Pi camera), smartphones, servers

[0277] Software: Applications, generative AI models (e.g., GPT-3), emotion recognition engines (e.g., Microsoft Azure Emotion API), database management systems (e.g., MySQL, PostgreSQL)

[0278] Specific examples

[0279] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0280] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generating AI detected abnormal behavior (wandering). The emotion engine then analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0281] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0282] Conversation support is also provided by generative AI. For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0283] Data from the emotion engine and generative AI is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as reports, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0284] Examples of prompt statements

[0285] Prompt example 1: Anomalous behavior detection

[0286] How do you talk to a patient if they are wandering around the room?

[0287] Example prompt 2: Emotional support

[0288] How do you respond when a patient says, "I feel lonely"?

[0289] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Step 1:

[0292] The user opens the application, enters the profile information of the dementia patient, and sends it to the server. Specifically, the user enters information such as name, age, medication time, toilet time, and emotion recognition engine settings into a form within the application, and presses the "Submit" button to upload the data. The entered data (patient profile information) is sent to the server and saved in a database.

[0293] Step 2:

[0294] The server stores the received profile information in a database and distributes the configuration information to the device. Specifically, the server stores the received information for each item in the database correctly, and then sends the configuration information to the device in JSON format. The input is the profile information sent by the user, and the output is storing it in the database and distributing the configuration information to the device.

[0295] Step 3:

[0296] Based on the received configuration information, the device activates the camera and microphone and begins real-time monitoring. Specifically, the device analyzes the received configuration and activates the Raspberry Pi camera and microphone devices. This causes the device to begin capturing video and audio data at 30 frames per second. The input is the configuration information sent to the device, and the output is the activation of the camera and microphone.

[0297] Step 4:

[0298] The device sends the captured video and audio data to the server. Specifically, the device uploads the video and audio data to the server in real time via Wi-Fi. The input is the video and audio data captured by the device, and the output is the data sent to the server.

[0299] Step 5:

[0300] The server analyzes the received video and audio data to detect abnormal behavior and emotions. Specifically, the server uses generative AI to analyze the video data and detect abnormal behavior (e.g., wandering). It also uses an emotion recognition engine to analyze the patient's emotions (e.g., anxiety, anger, happiness) from facial expressions and voice. The input is the video and audio data received from the device, and the output is the abnormal behavior detection results and emotional analysis results.

[0301] Step 6:

[0302] If abnormal behavior is detected, the server issues a voice message instruction to the device and sends an alert to the family. Specifically, the server sends a deterrent message to the device, such as "Mr. / Ms. X, where are you going?", which is played on the device. It also sends a notification containing abnormal behavior and emotional data to the user's smartphone. The input is the abnormal behavior detection result, and the output is a voice message instruction to the device and an alert notification to the user.

[0303] Step 7:

[0304] The server generates reminder information for daily life tasks and distributes it to the device. Specifically, the server generates a notification message such as "It's time to take your medicine at 2 PM" based on the set schedule (e.g., medicine is to be taken at 2 PM) and sends it to the device. The input is the set reminder schedule, and the output is the reminder notification sent to the device.

[0305] Step 8:

[0306] The generative AI supports everyday conversations with patients. Specifically, it uses emotional data analyzed by the emotion recognition engine to generate an appropriate response. For example, if a patient says, "I feel lonely," the generative AI responds, "Do you want to talk to someone? Let's talk together." The input is emotional data from the emotion recognition engine, and the output is the generated conversational response.

[0307] Step 9:

[0308] The server periodically aggregates the behavioral and emotional data and shares them with medical professionals as reports. Specifically, the server aggregates the data weekly, generates a PDF report, and emails it to medical professionals. The input is the saved behavioral and emotional data, and the output is the generated report.

[0309] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0310] (Application example 2)

[0311] 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."

[0312] Conventional care systems for dementia patients have problems such as a lack of real-time responses to abnormal behaviors and emotional care for patients. In addition, systems installed in physical stores have the problem of difficulty in properly monitoring the behavior and emotions of customers and providing necessary responses in real time.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring sensor, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data to a medical professional, a reminder means for managing the patient's daily tasks, a conversation support means for using a generation AI to conduct daily conversations with the patient, a behavior analysis means for monitoring the behavior and emotions of customers and instructing appropriate responses, and a profiling means for inputting and managing customer profile information. This enables rapid response to abnormal behavior of patients and customers and comprehensive support, including emotional care.

[0314] The "sensor means" is a device for monitoring the behavior of a subject in real time and generating video data.

[0315] The "generative AI means" is a model that uses artificial intelligence to analyze acquired video data and detect abnormal behavior and emotions.

[0316] The "audio output means" refers to a speaker or an audio playback device that issues a deterrent message to the subject when abnormal behavior is detected.

[0317] The "communication means" is a network communication device for sending a notification to family members and related parties when abnormal behavior is detected.

[0318] The "data linking means" is a device with a data transfer function that links collected behavioral data to medical professionals and provides appropriate care plans.

[0319] A "reminder tool" is a system that manages the patient's daily life tasks and notifies them at designated times.

[0320] The "conversation support tool" is a system that uses generative AI to support everyday conversations with the target person and provide psychological care.

[0321] The "behavior analysis means" is a data analysis device that monitors the behavior and emotions of customers and provides instructions on how to respond appropriately.

[0322] The "profiling method" is a database system for inputting and managing basic information and past behavioral history of customers.

[0323] This invention is a system that monitors the behavior and emotions of dementia patients and customers and responds appropriately. This system includes a sensor means, a generation AI means, a voice output means, a communication means, a data linkage means, a reminder means, a conversation support means, a behavior analysis means, and a profiling means.

[0324] Overall system overview

[0325] Users use the application to input profile information for dementia patients and store visitors, and send it to the server. This profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0326] The sensor means (a device with a camera) is installed in the patient's or visitor's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes the subject's emotions from their facial expressions and voice.

[0327] If anomalous behavior is detected, the server will:

[0328] 1. A voice message is sent by a voice output means to call out a deterrent.

[0329] 2. An alert is sent to the smartphones of relevant parties (patient's family members and store staff) via communication means to inform them of abnormal behavior.

[0330] The emotion data analyzed by the emotion engine is used by the generative AI to support conversations with the patient. For example, if a patient feels anxious, this conversation can provide a comforting response, preventing social isolation and reducing psychological burden. Appropriate words can also be used to provide a sense of security to customers.

[0331] The server also periodically collects behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0332] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing the user to perform the tasks regularly.

[0333] Program processing overview

[0334] The system operates using the following hardware and software:

[0335] Camera device: Monitors the subject's behavior and facial expressions in real time.

[0336] Server: Stores and analyzes data.

[0337] Generative AI model: Uses TensorFlow / Keras to analyze video data and perform emotion recognition.

[0338] Audio output device: Speaks out if abnormal behavior is detected.

[0339] Communication devices: Send notifications to family and friends.

[0340] Smartphone: Receive alerts and enter profile information.

[0341] Specifically, the data processing and calculation involves preprocessing video data acquired from a camera device and inputting it into an emotion recognition model and an abnormal behavior detection model. Based on the emotion recognition and abnormal behavior detection results, appropriate voice messages and notifications are generated.

[0342] Specific examples

[0343] One day, a user registers the profile information of a patient or visitor in the system. The user inputs basic personal information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the sensor means is activated and begins monitoring video and emotions.

[0344] At around 10:00 AM, the patient began walking around the room. The camera sends this video to the server, and the generating AI detects abnormal behavior (wandering). Furthermore, the emotion engine analyzes the patient's facial expression and expressions to determine whether they are anxious. The server immediately instructs the audio output device to play a deterrent message, such as "Mr. / Ms. X, where are you going?", and also sends emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert is sent to the family's smartphone via the communication device, informing them of the abnormal behavior and anxious feelings.

[0345] At 2:00 PM, the server sends a set medicine reminder to the sensor means, which then notifies the patient by voice, "It's time to take your 2:00 medicine," helping the patient to remember to take their medicine.

[0346] Conversation support is also provided by generative AI. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Do you want to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the customer's psychological state.

[0347] Prompt Sentence Examples

[0348] "Abnormal behavior has been detected. The customer currently appears anxious. Please take immediate action."

[0349] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and visitors, and reduces the burden on users.

[0350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0351] Step 1:

[0352] Users use the application to enter profile information about dementia patients and store visitors, and send it to the server. The profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is saved in the server's database. The input data is then processed, and the personal information is formatted into an appropriate format and stored in the database.

[0353] Step 2:

[0354] A camera installed on the terminal (sensor means) monitors the behavior and facial expressions of patients or visitors in real time and captures video data. The captured video data is then sent to a server. The input data is the camera video, which is then sent to the server and prepared for analysis.

[0355] Step 3:

[0356] The server preprocesses the received video data using a generative AI method. Specifically, it processes the video data by adjusting its image quality and removing noise, and then uses a generative AI model to detect abnormal behavior and analyze emotions. The input data is the preprocessed video data, and the output is a determination of whether or not there is abnormal behavior and the emotional state.

[0357] Step 4:

[0358] If abnormal behavior is detected, the server instructs the audio output means to send a voice message to deter the patient. Specifically, a message such as "Mr. / Ms. X, where are you going?" is played from the terminal to try to deter the patient. The input data is the abnormal behavior judgment result, and the output is the playback of a voice message.

[0359] Step 5:

[0360] If abnormal behavior is detected, the server sends an alert to the smartphone of the relevant person. The notification message includes the specific details of the abnormal behavior and the emotional state of the patient or visitor. The input data is the abnormal behavior and the results of emotional analysis, and the output is an alert notification.

[0361] Step 6:

[0362] Based on the emotional data analyzed by the emotion engine, the generative AI supports conversations with patients and customers. Specifically, the generative AI generates an emotional message such as "Is there something I can help you with?" and plays it back through a voice output means. The input data is the result of the emotion analysis, and the output is a response message according to the emotion.

[0363] Step 7:

[0364] The server uses a reminder means for managing daily tasks to generate reminder information based on the set schedule and sends a notification to the terminal. Specifically, it plays a notification message such as "It's time to take your medicine at 2 o'clock" by voice. The input data is the reminder schedule, and the output is the reminder notification.

[0365] Step 8:

[0366] The behavioral and emotional data are periodically aggregated and a report is generated to be shared with medical professionals. The server analyzes the data and forwards it to medical professionals. The input data are behavioral and emotional data, and the output is data in the form of a report.

[0367] Step 9:

[0368] Using conversation support tools, the generative AI will converse with patients and customers. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Would you like to talk to someone? Let's talk together." The input data is the conversation context, and the output is the dialogue response.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] [Second embodiment]

[0373] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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."

[0385] The present invention provides a care support system using a generative AI model for families of dementia patients, thereby building a sustainable care environment. Specific embodiments for this purpose are described below.

[0386] Overall system overview

[0387] The user first uses the application to input the dementia patient's profile information, which includes the patient's name, age, medical history, and desired reminder settings, and then sends it to the server. The server then stores the patient's basic information in a database for future analysis and treatment.

[0388] The terminal (device with camera) is installed in the patient's living environment and serves to monitor daily behavior in real time. The camera captures video and sends it to a server. The server analyzes the received video data using a generation AI (G) and detects abnormal behavior in real time.

[0389] If anomalous behavior is detected, the server will:

[0390] 1. Send a voice output instruction to the terminal and play a deterrent message to the patient such as "Mr. / Ms. XX, where are you going?"

[0391] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0392] The server also periodically aggregates behavioral data and shares it with medical professionals to help optimize care plans, allowing them to propose appropriate care plans based on the patient's daily behavior.

[0393] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0394] The device also features a conversation support function using generative AI (C), which supports everyday conversations. Generative AI (C) generates appropriate questions and comments based on the patient's responses, helping to keep the conversation moving smoothly. This function not only prevents patients from becoming socially isolated, but is also expected to slow the progression of dementia.

[0395] Specific examples

[0396] One day, a user registers a patient's profile in the system. The user sets basic information about the patient, reminders for taking medication, toilet time, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0397] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generation AI (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior.

[0398] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0399] Conversational support is also provided throughout the day by a generative AI (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0400] The processing flow will be explained below.

[0401] Step 1:

[0402] The user opens the application and logs in. They then enter their dementia patient profile information, including their name, age, medical history, and reminder settings (such as medication times and toilet time). Once complete, the user sends this information to the server.

[0403] Step 2:

[0404] The server stores the received profile information in a database, which is used to analyze patient behavior and generate reminders.

[0405] Step 3:

[0406] The device monitors the patient's daily life in real time using a camera, which captures video data and transmits it to a server.

[0407] Step 4:

[0408] The server analyzes the transmitted video data using the generation AI (G), which then analyzes the video data and determines whether abnormal behavior (such as wandering or falling) has been detected.

[0409] Step 5:

[0410] If abnormal behavior is detected, the server takes the following actions: First, it instructs the device to send a voice message to deter the user. Second, it sends an alert to the user's (family member's) smartphone to notify them of the abnormal behavior.

[0411] Step 6:

[0412] The terminal plays a voice message according to instructions from the server, for example, asking the patient, "Mr. / Ms. X, where are you going?"

[0413] Step 7:

[0414] Users receive alerts on their smartphones to let them know when abnormal behavior has occurred and, if necessary, rush to the patient's location or step up monitoring remotely.

[0415] Step 8:

[0416] The server periodically aggregates the patient's daily behavior data and generates reports that are shared with medical professionals, who then provide advice to optimize the patient's care plan.

[0417] Step 9:

[0418] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0419] Step 10:

[0420] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0421] Step 11:

[0422] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0423] Step 12:

[0424] The Generative AI (C) saves conversation logs and analyzes the patient's mental state and interests, providing data to further improve the quality of the conversation.

[0425] This specific processing flow will realize a sustainable care support system that improves the safety and mental health of dementia patients.

[0426] Example 1

[0427] 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."

[0428] It is necessary to monitor the daily lives of dementia patients, quickly detect abnormal behavior, and take appropriate action, but conventional systems often respond slowly. Another problem is the lack of a system that can comprehensively accumulate patient behavior data, notify family members in real time, and manage daily life tasks and provide conversation support.

[0429] 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.

[0430] In this invention, the server includes an information input means, a monitoring means, and a video analysis means. This allows for real-time monitoring of the daily life of a dementia patient and for rapid detection and response of abnormal behavior. The server also includes an abnormal behavior prevention means, a notification means, a data aggregation means, a task management means, and a conversation support means. This allows for immediate transmission of a deterrent message and notification to family members when abnormal behavior occurs. Furthermore, the server accumulates behavioral data and provides it to medical professionals, manages daily life tasks, and sends periodic reminder notifications, thereby preventing social isolation and the progression of dementia, while building a sustainable care environment.

[0431] The "information input means" is a means by which a user inputs profile information of a dementia patient using an application and transmits the information to a server.

[0432] The "monitoring means" is a means for monitoring the patient's daily activities in real time using a camera-equipped device and collecting video data.

[0433] "Video analysis means" refers to a means for analyzing video data obtained from a surveillance means using a generative AI model to detect abnormal behavior.

[0434] The "abnormal behavior deterrent means" is a means for playing back an audible deterrent message to the patient when abnormal behavior is detected.

[0435] The "notification means" is a means for sending an alert from the server to the family member's smartphone when abnormal behavior occurs.

[0436] The "data collection means" is a means for periodically collecting collected behavioral data and providing it to medical professionals.

[0437] "Task management tools" are tools for managing the patient's daily tasks (e.g., time to take medicine, time to go to the toilet).

[0438] "Conversation support means" is a means of using generative AI to conduct everyday conversations with patients, generating questions and comments to help the conversation progress smoothly.

[0439] MODE FOR CARRYING OUT THE INVENTION

[0440] This invention is a system that supports the care of dementia patients. The system begins when a user uses an application to input the patient's profile information and sends it to a server. Specifically, the profile information includes the patient's name, age, medical history, reminder settings, etc. This information is stored in a database by the server and used for future analysis and responses.

[0441] The device is equipped with a camera and is installed in the patient's living environment. The device monitors daily activities in real time and sends the video data to a server. The server analyzes the received video data using a generative AI model (G), and if abnormal behavior (e.g., wandering or falling) is detected, various measures are taken.

[0442] First, the server sends a voice output instruction to the device, playing a deterrent message such as "Mr. / Ms. X, where are you going?". It also sends an alert to the user's smartphone to notify them of abnormal behavior. The server then periodically aggregates the behavioral data and provides it to medical professionals, who can then optimize the care plan and suggest appropriate care for the patient.

[0443] Furthermore, the server manages daily tasks (e.g., medication times, toilet times) and sends reminder information to the device at set times. The device then notifies the patient with a voice message, helping the patient remember to perform the necessary tasks.

[0444] A conversation support function using a generative AI model (C) is also provided. The device supports everyday conversations with patients, and the generative AI model (C) generates appropriate questions and comments based on the patient's responses. This is expected to prevent social isolation and slow the progression of dementia.

[0445] Specific examples

[0446] One day, a user registers a patient profile in the system, setting basic patient information, medication times, toilet time reminders, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0447] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generative AI model (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone to notify them of the abnormal behavior.

[0448] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0449] Conversational support is also provided throughout the day by a generative AI model (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0450] Example prompt sentence:

[0451] "Enter basic patient information, medication times, bathroom time reminders, etc."

[0452] "What message do you want the device to play if loitering is detected?"

[0453] "How do you guide a conversation with a patient using a generative AI conversation support system?"

[0454] This system allows families of dementia patients to monitor their daily lives with peace of mind and respond quickly to any abnormal behavior. It also provides conversation support to prevent patients from becoming socially isolated, contributing to the creation of a sustainable care environment.

[0455] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0456] Step 1:

[0457] The user uses the application to enter the patient's profile information. The profile information includes the patient's name, age, medical history, reminder settings, etc. After entering this information and pressing the submit button, the profile information is sent to the server. The entered information is saved as profile data on the server.

[0458] Step 2:

[0459] The server stores the received profile information in a database. A unique ID is assigned to each patient in this database, which will be used for future searches and analysis. The patient's name, age, medical history, and reminder settings are also saved here. This allows a series of patient data to be accumulated in the database.

[0460] Step 3:

[0461] A camera-equipped terminal is installed in the patient's living environment. The terminal operates constantly and monitors the patient's behavior in real time. The camera is positioned to capture images of the room and captures video data 24 hours a day. This captured video data is then sent from the terminal to a server.

[0462] Step 4:

[0463] The server analyzes the video data sent from the device. It uses a generative AI model (G) to analyze this video data in real time to detect abnormal behavior (e.g., wandering, falling). If abnormal behavior is detected through this analysis, the server proceeds to the next step in processing the information.

[0464] Step 5:

[0465] When abnormal behavior is detected, the server immediately sends a voice notification instruction to the device. Specifically, it sends an instruction to play a message such as "Mr. / Ms. X, where are you going?" Based on this instruction, the device conveys the message to the patient by voice. This directly deters the patient.

[0466] Step 6:

[0467] At the same time, the server sends an alert to the user's smartphone to notify them of the abnormal behavior. The alert contains detailed information about the type of abnormal behavior, the date and time it occurred, and the current situation. This allows the user to immediately understand the situation.

[0468] Step 7:

[0469] The server periodically aggregates the collected behavioral data and provides it to medical professionals. Data aggregation is performed at regular intervals, and the accumulated data is analyzed and compiled into a report. Medical professionals use this report to optimize care plans and propose appropriate care for patients.

[0470] Step 8:

[0471] The server manages reminders for daily tasks (e.g., time to take medicine, time to go to the toilet). When the reminder time approaches, the server sends the information to the device, which then notifies the patient by voice at the specified time, saying, "It's time to take your medicine at 2 o'clock."

[0472] Step 9:

[0473] When the device receives a reminder, it will notify you by voice, helping patients remember to perform set tasks (e.g., taking medicine, going to the toilet).

[0474] Step 10:

[0475] The device uses the generative AI model (C) to provide conversational support. For example, if a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the device will continue the conversation by saying, "That sounds good, let's go out." This conversational support helps prevent social isolation and maintain mental stability for patients.

[0476] (Application example 1)

[0477] 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."

[0478] Monitoring and caring for dementia patients places a significant burden on many families and is also an important issue for ensuring patient safety. Conventional systems often provide abnormal behavior detection, reminder functions, and daily conversation support separately, making integrated management difficult. Furthermore, it is difficult to accurately and quickly detect and respond to abnormal behavior in real time and notify family members, limiting the efficiency of care and improving the patient's quality of life.

[0479] 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.

[0480] In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generating AI means for analyzing video from the surveillance camera, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to family members when abnormal behavior is detected, a data linking means for linking behavioral data to medical professionals, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using the generating AI, and a processing means for executing a security service application. This makes it possible to ensure the safety of dementia patients in real time, reduce the burden on their families, and improve the quality of life of the patients.

[0481] "Real-time monitoring" means observing the behavior of dementia patients in real time and collecting information.

[0482] The "camera means" is a device that acquires visual information and transmits it to the server as digital data.

[0483] The "generative AI means" is an artificial intelligence technology that analyzes video data from the camera means and detects specific patterns or abnormal behavior.

[0484] The "audio output means" is a device that generates an audio message to alert the patient when abnormal behavior is detected.

[0485] "Communication means" refers to a system for sending notifications to family members or caregivers when abnormal behavior is detected.

[0486] "Data linking means" is a function that allows collected behavioral data to be shared with experts and medical institutions.

[0487] "Reminder measures" are functions that manage important tasks in a patient's daily life (e.g., taking medicine, toilet time, etc.) and provide timely notifications.

[0488] "Conversation support means" is a function that uses generative AI to assist with everyday conversations and promote mental stability and social interaction for dementia patients.

[0489] The "security service application" is an application that ensures the safety of dementia patients and provides warnings and notifications to caregivers and family members.

[0490] The "processing means" is a control device for the entire system that receives and analyzes data sent from various means and executes necessary actions.

[0491] This invention is a system for monitoring the behavior of dementia patients and ensuring their safety. The system includes a camera, a generation AI, a voice output, a communication device, a data linking device, a reminder device, a conversation support device, and a security service application. Each device is described in detail below.

[0492] Camera Means

[0493] The camera means is a device for monitoring the behavior of dementia patients in real time, and video data is continuously acquired and transmitted to the server.

[0494] Generation AI means

[0495] The server uses the acquired video data to run the generative AI model "G." This model has the ability to analyze and detect abnormal behavior in real time. The video data is first preprocessed, and after feature extraction, it is input into the AI ​​model.

[0496] Audio output means

[0497] When abnormal behavior is detected, the server sends a command to the voice output means to warn the patient. This message has a warning message such as "Mr. / Ms. XX, where are you going?"

[0498] communication means

[0499] If abnormal behavior is detected, the server will send a notification to family members or caregivers via communication means, allowing for a prompt response.

[0500] Data linking method

[0501] Behavioral data is collected periodically and shared with medical professionals and care managers, who use it to optimize care plans.

[0502] Reminder measures

[0503] The server manages the patient's daily tasks and generates reminder information, which notifies the patient of important tasks such as taking medicine or going to the toilet at appropriate times. The reminders are delivered to the patient via voice notification.

[0504] Conversation support tools

[0505] Using the generative AI model "C," the server provides everyday conversation support. For example, if a patient asks, "How are you feeling today?", the AI ​​model responds, "I feel good, let's go out." This function is expected to prevent patients from becoming socially isolated and maintain their mental stability.

[0506] Security Service Applications

[0507] The security service application integrates these methods and functions as a comprehensive system. It operates as a smartphone application and provides real-time notifications and management functions to family members and caregivers.

[0508] Specific examples

[0509] Specific examples of abnormal behavior detection

[0510] If the patient begins to wander around the room around 10:00 a.m., the camera will send the footage to the server. The server's generative AI model "G" will analyze the footage and detect abnormal behavior (wandering). The server will then immediately send an instruction to the audio output device to play the message "Mr. / Ms. X, where are you going?", and simultaneously send an alert to the family member's smartphone via the communication device.

[0511] Prompt Sentence Examples

[0512] "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0513] Examples of reminder functions

[0514] If a medicine-taking reminder is set for 2 p.m., the server generates reminder information at the set time and causes the voice output means to notify the user, "It's time to take your medicine at 2 p.m."

[0515] Prompt Sentence Examples

[0516] "I have a medication reminder at 2 PM. Please send me the reminder information."

[0517] Examples of conversation support functions

[0518] When the patient asks, "How are you feeling today?", the server responds with "Feeling good, let's go out" using generative AI model "C."

[0519] Prompt Sentence Examples

[0520] "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0521] As described above, this invention can ensure the safety of dementia patients, reduce the burden on their families and caregivers, and improve the quality of life of patients.

[0522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0523] Step 1:

[0524] The user enters patient profile information

[0525] Using a smartphone application, users input basic information such as the patient's name, age, medical history, medication times, toilet time, and other reminder information. This information is sent as input data to the server, which stores it in a database for future analysis and response.

[0526] Step 2:

[0527] Acquisition and transmission of video data by camera means

[0528] A camera installed on the terminal monitors the daily activities of dementia patients in real time and captures video data. This video data is sent as input to the server. The server receives this video data and inputs it into the generative AI model "G."

[0529] Step 3:

[0530] Analysis of abnormal behavior using generative AI model "G"

[0531] The server preprocesses the received video data and extracts features for detecting abnormal behavior. These features are input into the generative AI model "G," which then determines whether the behavior is abnormal. If abnormal behavior is detected as the output, the system proceeds to the next step.

[0532] Step 4:

[0533] Implementing a warning by audio output means

[0534] When abnormal behavior is detected, the server sends instructions to the voice output means. Specifically, it outputs data to play a voice message such as "Mr. / Ms. X, where are you going?", which is received by the terminal and used to alert the patient via voice.

[0535] Step 5:

[0536] Notifying family members via communication means

[0537] When abnormal behavior is detected, the server sends a notification to the family member's smartphone using a communication device. This notification includes the time when the abnormal behavior occurred and the details of the abnormal behavior. The server generates notification data, outputs it to the communication device, and sends it to the family member's smartphone.

[0538] Step 6:

[0539] Reminder generation and delivery

[0540] The server generates a reminder notification according to the schedule based on the input reminder information. At the specified time, the reminder information is sent to the voice output means, and the terminal notifies the patient by voice, "It's time to take your medicine at 2 o'clock."

[0541] Step 7:

[0542] Linking behavioral data to healthcare professionals

[0543] The server periodically aggregates the collected behavioral data to share it with medical professionals. The aggregated data is sent to the medical professionals via the data sharing means. The server aggregates the behavioral data and outputs the data to the collaborating destination.

[0544] Step 8:

[0545] Conversation support using generative AI model "C"

[0546] The server uses the generative AI model "C" to provide everyday conversation support. When a patient asks a question by voice, the voice data is sent from the terminal to the server. The server inputs the data into the AI ​​model "C" and generates a response message. The generated response message is output to the voice output means, and the terminal plays it back.

[0547] Specific processing flow example

[0548] Example prompt: "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0549] Example prompt: "I have a medication reminder at 2 PM. Please send me the reminder information."

[0550] Example prompt: "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0551] Through the above processing steps, the server can ensure the safety of dementia patients and quickly provide necessary information to their families and caregivers.

[0552] 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.

[0553] The present invention provides a care support system for families of dementia patients that utilizes a generative AI model, and by adding emotion recognition technology, it realizes a sustainable care environment while also providing psychological care to the patient. Specific embodiments for this purpose are described below.

[0554] Overall system overview

[0555] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0556] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generation AI (G) to detect abnormal behavior. Furthermore, an emotion engine analyzes the patient's emotions from their facial expressions and voice.

[0557] If anomalous behavior is detected, the server will:

[0558] 1. Issue an instruction to the device to send a voice message to deter the user.

[0559] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0560] The emotion data analyzed by the emotion engine is used by the generative AI (C) to support conversations with patients. For example, this conversation can provide comforting responses when a patient feels anxious, preventing social isolation and reducing psychological burden.

[0561] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0562] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0563] Specific examples

[0564] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0565] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generation AI (G) detected abnormal behavior (wandering). Furthermore, the emotion engine analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0566] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0567] Conversation support is also provided by generative AI (C). For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0568] Data from the emotion engine and generative AI (G) is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as a report, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0569] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] The user opens the application and logs in. Then, they enter their dementia patient profile information, including their name, age, medical history, reminder settings (such as medication times or toilet time), and emotion engine settings. Once completed, the user sends this information to the server.

[0573] Step 2:

[0574] The server stores the received profile information in a database, which is used for patient behavior analysis, emotion analysis, and reminder generation.

[0575] Step 3:

[0576] The device (camera-equipped device) is installed in the patient's living environment and monitors their daily activities and facial expressions in real time. The camera captures video data and transmits it to a server.

[0577] Step 4:

[0578] The server analyzes the transmitted video data using the Generative AI (G) to detect abnormal behavior. The emotion engine also analyzes the patient's facial expressions and voice to determine emotions such as anxiety, sadness, and confusion.

[0579] Step 5:

[0580] If abnormal behavior is detected, the server takes the following action. First, it instructs the device to send a voice message to deter the user. For example, it could say, "Mr. / Ms. X, where are you going?" Next, it sends an alert of the abnormal behavior to the user's (family member's) smartphone.

[0581] Step 6:

[0582] If the emotion engine detects an emotion such as anxiety, the server generates an appropriate additional message and sends it to the device, such as "Please stay calm. Is there anything I can help you with?"

[0583] Step 7:

[0584] The device plays voice messages according to instructions from the server. First, it plays a deterrent message for abnormal behavior, followed by a message based on emotions.

[0585] Step 8:

[0586] Users receive alerts on their smartphones to identify abnormal behavior and the patient's emotional state, and can then rush to the patient's location or remotely monitor them if necessary.

[0587] Step 9:

[0588] The server periodically collects data on the patient's daily behavior and emotions and generates a report that is shared with medical professionals. Based on this report, medical professionals can optimize care plans that take into account both the patient's behavior and emotions.

[0589] Step 10:

[0590] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0591] Step 11:

[0592] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0593] Step 12:

[0594] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0595] Step 13:

[0596] The generative AI (C) generates appropriate responses based on the patient's emotional data and continues the conversation. For example, if the patient responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together."

[0597] Step 14:

[0598] The Generative AI (C) saves conversation logs and emotional data for analysis, and uses this information to understand the patient's mental state and interests, which it then reflects in the next conversation.

[0599] The specific processing flow described above will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0600] Example 2

[0601] 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."

[0602] Conventional care systems for dementia patients have difficulty monitoring and analyzing the patient's behavior and psychological state in real time and providing appropriate care. Furthermore, notification and data sharing with family members and medical professionals are insufficient, making effective care difficult. This increases the burden on caregivers and creates problems such as insufficient protection of the patient's safety and mental health.

[0603] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring camera, a voice output means for speaking to the patient to deter abnormal behavior when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data and emotional data to a medical professional, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using a generation AI, and an emotion recognition means for analyzing the patient's psychological state using emotion recognition technology. This makes it possible to monitor the behavior and emotions of a dementia patient in real time, take appropriate measures, and quickly provide information to family members and medical professionals.

[0604] The "camera means" is a photographing device for monitoring the behavior of dementia patients in real time.

[0605] The "generative AI means" is an artificial intelligence that analyzes footage from surveillance cameras and detects abnormal behavior.

[0606] The "audio output means" refers to a speaker or audio playback device that issues a warning to the patient when abnormal behavior is detected.

[0607] The "communication means" is a communication device for sending a notification to family members when abnormal behavior is detected.

[0608] The "data linking means" is a device or system with a data communication function for linking behavioral data and emotional data to medical professionals.

[0609] A "reminder means" is a device or system that manages a patient's daily tasks and has the function of notifying them of things like when to take their medicine or when to go to the toilet.

[0610] A "conversation support means" is a device or system with dialogue capabilities that uses generative AI to support everyday conversations with patients.

[0611] The "emotion recognition means" is a device or system that uses emotion recognition technology to analyze the psychological state of a patient and take appropriate action.

[0612] "Server" refers to a central processing unit that integrates, manages, and controls the above means, and transmits and receives information and analyzes data.

[0613] The present invention provides a care support system that utilizes generative AI models for families of dementia patients. By incorporating emotion recognition technology, this system realizes a sustainable care environment while also providing psychological care to patients. Specific embodiments of the present invention are described below.

[0614] Overall system overview

[0615] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0616] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes emotions from the patient's facial expressions and voice.

[0617] If anomalous behavior is detected, the server will:

[0618] 1. Issue an instruction to the device to send a voice message to deter the user.

[0619] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0620] The emotion data analyzed by the emotion engine is used by generative AI to support conversations with patients, for example, providing comforting responses when patients feel anxious, preventing social isolation and reducing psychological burdens.

[0621] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0622] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0623] Hardware and software used

[0624] Hardware: Camera-equipped devices (e.g., Raspberry Pi camera), smartphones, servers

[0625] Software: Applications, generative AI models (e.g., GPT-3), emotion recognition engines (e.g., Microsoft Azure Emotion API), database management systems (e.g., MySQL, PostgreSQL)

[0626] Specific examples

[0627] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0628] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generating AI detected abnormal behavior (wandering). The emotion engine then analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0629] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0630] Conversation support is also provided by generative AI. For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0631] Data from the emotion engine and generative AI is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as reports, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0632] Examples of prompt statements

[0633] Prompt example 1: Anomalous behavior detection

[0634] How do you talk to a patient if they are wandering around the room?

[0635] Example prompt 2: Emotional support

[0636] How do you respond when a patient says, "I feel lonely"?

[0637] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0638] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0639] Step 1:

[0640] The user opens the application, enters the profile information of the dementia patient, and sends it to the server. Specifically, the user enters information such as name, age, medication time, toilet time, and emotion recognition engine settings into a form within the application, and presses the "Submit" button to upload the data. The entered data (patient profile information) is sent to the server and saved in a database.

[0641] Step 2:

[0642] The server stores the received profile information in a database and distributes the configuration information to the device. Specifically, the server stores the received information for each item in the database correctly, and then sends the configuration information to the device in JSON format. The input is the profile information sent by the user, and the output is storing it in the database and distributing the configuration information to the device.

[0643] Step 3:

[0644] Based on the received configuration information, the device activates the camera and microphone and begins real-time monitoring. Specifically, the device analyzes the received configuration and activates the Raspberry Pi camera and microphone devices. This causes the device to begin capturing video and audio data at 30 frames per second. The input is the configuration information sent to the device, and the output is the activation of the camera and microphone.

[0645] Step 4:

[0646] The device sends the captured video and audio data to the server. Specifically, the device uploads the video and audio data to the server in real time via Wi-Fi. The input is the video and audio data captured by the device, and the output is the data sent to the server.

[0647] Step 5:

[0648] The server analyzes the received video and audio data to detect abnormal behavior and emotions. Specifically, the server uses generative AI to analyze the video data and detect abnormal behavior (e.g., wandering). It also uses an emotion recognition engine to analyze the patient's emotions (e.g., anxiety, anger, happiness) from facial expressions and voice. The input is the video and audio data received from the device, and the output is the abnormal behavior detection results and emotional analysis results.

[0649] Step 6:

[0650] If abnormal behavior is detected, the server issues a voice message instruction to the device and sends an alert to the family. Specifically, the server sends a deterrent message to the device, such as "Mr. / Ms. X, where are you going?", which is played on the device. It also sends a notification containing abnormal behavior and emotional data to the user's smartphone. The input is the abnormal behavior detection result, and the output is a voice message instruction to the device and an alert notification to the user.

[0651] Step 7:

[0652] The server generates reminder information for daily life tasks and distributes it to the device. Specifically, the server generates a notification message such as "It's time to take your medicine at 2 PM" based on the set schedule (e.g., medicine is to be taken at 2 PM) and sends it to the device. The input is the set reminder schedule, and the output is the reminder notification sent to the device.

[0653] Step 8:

[0654] The generative AI supports everyday conversations with patients. Specifically, it uses emotional data analyzed by the emotion recognition engine to generate an appropriate response. For example, if a patient says, "I feel lonely," the generative AI responds, "Do you want to talk to someone? Let's talk together." The input is emotional data from the emotion recognition engine, and the output is the generated conversational response.

[0655] Step 9:

[0656] The server periodically aggregates the behavioral and emotional data and shares them with medical professionals as reports. Specifically, the server aggregates the data weekly, generates a PDF report, and emails it to medical professionals. The input is the saved behavioral and emotional data, and the output is the generated report.

[0657] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0658] (Application example 2)

[0659] 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."

[0660] Conventional care systems for dementia patients have problems such as a lack of real-time responses to abnormal behaviors and emotional care for patients. In addition, systems installed in physical stores have the problem of difficulty in properly monitoring the behavior and emotions of customers and providing necessary responses in real time.

[0661] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring sensor, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data to a medical professional, a reminder means for managing the patient's daily tasks, a conversation support means for using a generation AI to conduct daily conversations with the patient, a behavior analysis means for monitoring the behavior and emotions of customers and instructing appropriate responses, and a profiling means for inputting and managing customer profile information. This enables rapid response to abnormal behavior of patients and customers and comprehensive support, including emotional care.

[0662] The "sensor means" is a device for monitoring the behavior of a subject in real time and generating video data.

[0663] The "generative AI means" is a model that uses artificial intelligence to analyze acquired video data and detect abnormal behavior and emotions.

[0664] The "audio output means" refers to a speaker or an audio playback device that issues a deterrent message to the subject when abnormal behavior is detected.

[0665] The "communication means" is a network communication device for sending a notification to family members and related parties when abnormal behavior is detected.

[0666] The "data linking means" is a device with a data transfer function that links collected behavioral data to medical professionals and provides appropriate care plans.

[0667] A "reminder tool" is a system that manages the patient's daily life tasks and notifies them at designated times.

[0668] The "conversation support tool" is a system that uses generative AI to support everyday conversations with the target person and provide psychological care.

[0669] The "behavior analysis means" is a data analysis device that monitors the behavior and emotions of customers and provides instructions on how to respond appropriately.

[0670] The "profiling method" is a database system for inputting and managing basic information and past behavioral history of customers.

[0671] This invention is a system that monitors the behavior and emotions of dementia patients and customers and responds appropriately. This system includes a sensor means, a generation AI means, a voice output means, a communication means, a data linkage means, a reminder means, a conversation support means, a behavior analysis means, and a profiling means.

[0672] Overall system overview

[0673] Users use the application to input profile information for dementia patients and store visitors, and send it to the server. This profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0674] The sensor means (a device with a camera) is installed in the patient's or visitor's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes the subject's emotions from their facial expressions and voice.

[0675] If anomalous behavior is detected, the server will:

[0676] 1. A voice message is sent by a voice output means to call out a deterrent.

[0677] 2. An alert is sent to the smartphones of relevant parties (patient's family members and store staff) via communication means to inform them of abnormal behavior.

[0678] The emotion data analyzed by the emotion engine is used by the generative AI to support conversations with the patient. For example, if a patient feels anxious, this conversation can provide a comforting response, preventing social isolation and reducing psychological burden. Appropriate words can also be used to provide a sense of security to customers.

[0679] The server also periodically collects behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0680] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing the user to perform the tasks regularly.

[0681] Program processing overview

[0682] The system operates using the following hardware and software:

[0683] Camera device: Monitors the subject's behavior and facial expressions in real time.

[0684] Server: Stores and analyzes data.

[0685] Generative AI model: Uses TensorFlow / Keras to analyze video data and perform emotion recognition.

[0686] Audio output device: Speaks out if abnormal behavior is detected.

[0687] Communication devices: Send notifications to family and friends.

[0688] Smartphone: Receive alerts and enter profile information.

[0689] Specifically, the data processing and calculation involves preprocessing video data acquired from a camera device and inputting it into an emotion recognition model and an abnormal behavior detection model. Based on the emotion recognition and abnormal behavior detection results, appropriate voice messages and notifications are generated.

[0690] Specific examples

[0691] One day, a user registers the profile information of a patient or visitor in the system. The user inputs basic personal information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the sensor means is activated and begins monitoring video and emotions.

[0692] At around 10:00 AM, the patient began walking around the room. The camera sends this video to the server, and the generating AI detects abnormal behavior (wandering). Furthermore, the emotion engine analyzes the patient's facial expression and expressions to determine whether they are anxious. The server immediately instructs the audio output device to play a deterrent message, such as "Mr. / Ms. X, where are you going?", and also sends emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert is sent to the family's smartphone via the communication device, informing them of the abnormal behavior and anxious feelings.

[0693] At 2:00 PM, the server sends a set medicine reminder to the sensor means, which then notifies the patient by voice, "It's time to take your 2:00 medicine," helping the patient to remember to take their medicine.

[0694] Conversation support is also provided by generative AI. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Do you want to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the customer's psychological state.

[0695] Prompt Sentence Examples

[0696] "Abnormal behavior has been detected. The customer currently appears anxious. Please take immediate action."

[0697] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and visitors, and reduces the burden on users.

[0698] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0699] Step 1:

[0700] Users use the application to enter profile information about dementia patients and store visitors, and send it to the server. The profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is saved in the server's database. The input data is then processed, and the personal information is formatted into an appropriate format and stored in the database.

[0701] Step 2:

[0702] A camera installed on the terminal (sensor means) monitors the behavior and facial expressions of patients or visitors in real time and captures video data. The captured video data is then sent to a server. The input data is the camera video, which is then sent to the server and prepared for analysis.

[0703] Step 3:

[0704] The server preprocesses the received video data using a generative AI method. Specifically, it processes the video data by adjusting its image quality and removing noise, and then uses a generative AI model to detect abnormal behavior and analyze emotions. The input data is the preprocessed video data, and the output is a determination of whether or not there is abnormal behavior and the emotional state.

[0705] Step 4:

[0706] If abnormal behavior is detected, the server instructs the audio output means to send a voice message to deter the patient. Specifically, a message such as "Mr. / Ms. X, where are you going?" is played from the terminal to try to deter the patient. The input data is the abnormal behavior judgment result, and the output is the playback of a voice message.

[0707] Step 5:

[0708] If abnormal behavior is detected, the server sends an alert to the smartphone of the relevant person. The notification message includes the specific details of the abnormal behavior and the emotional state of the patient or visitor. The input data is the abnormal behavior and the results of emotional analysis, and the output is an alert notification.

[0709] Step 6:

[0710] Based on the emotional data analyzed by the emotion engine, the generative AI supports conversations with patients and customers. Specifically, the generative AI generates an emotional message such as "Is there something I can help you with?" and plays it back through a voice output means. The input data is the result of the emotion analysis, and the output is a response message according to the emotion.

[0711] Step 7:

[0712] The server uses a reminder means for managing daily tasks to generate reminder information based on the set schedule and sends a notification to the terminal. Specifically, it plays a notification message such as "It's time to take your medicine at 2 o'clock" by voice. The input data is the reminder schedule, and the output is the reminder notification.

[0713] Step 8:

[0714] The behavioral and emotional data are periodically aggregated and a report is generated to be shared with medical professionals. The server analyzes the data and forwards it to medical professionals. The input data are behavioral and emotional data, and the output is data in the form of a report.

[0715] Step 9:

[0716] Using conversation support tools, the generative AI will converse with patients and customers. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Would you like to talk to someone? Let's talk together." The input data is the conversation context, and the output is the dialogue response.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] [Third embodiment]

[0721] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0722] 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.

[0723] 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).

[0724] 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.

[0725] 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.

[0726] 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).

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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."

[0733] The present invention provides a care support system using a generative AI model for families of dementia patients, thereby building a sustainable care environment. Specific embodiments for this purpose are described below.

[0734] Overall system overview

[0735] The user first uses the application to input the dementia patient's profile information, which includes the patient's name, age, medical history, and desired reminder settings, and then sends it to the server. The server then stores the patient's basic information in a database for future analysis and treatment.

[0736] The terminal (device with camera) is installed in the patient's living environment and serves to monitor daily behavior in real time. The camera captures video and sends it to a server. The server analyzes the received video data using a generation AI (G) and detects abnormal behavior in real time.

[0737] If anomalous behavior is detected, the server will:

[0738] 1. Send a voice output instruction to the terminal and play a deterrent message to the patient such as "Mr. / Ms. XX, where are you going?"

[0739] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0740] The server also periodically aggregates behavioral data and shares it with medical professionals to help optimize care plans, allowing them to propose appropriate care plans based on the patient's daily behavior.

[0741] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0742] The device also features a conversation support function using generative AI (C), which supports everyday conversations. Generative AI (C) generates appropriate questions and comments based on the patient's responses, helping to keep the conversation moving smoothly. This function not only prevents patients from becoming socially isolated, but is also expected to slow the progression of dementia.

[0743] Specific examples

[0744] One day, a user registers a patient's profile in the system. The user sets basic information about the patient, reminders for taking medication, toilet time, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0745] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generation AI (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior.

[0746] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0747] Conversational support is also provided throughout the day by a generative AI (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0748] The processing flow will be explained below.

[0749] Step 1:

[0750] The user opens the application and logs in. They then enter their dementia patient profile information, including their name, age, medical history, and reminder settings (such as medication times and toilet time). Once complete, the user sends this information to the server.

[0751] Step 2:

[0752] The server stores the received profile information in a database, which is used to analyze patient behavior and generate reminders.

[0753] Step 3:

[0754] The device monitors the patient's daily life in real time using a camera, which captures video data and transmits it to a server.

[0755] Step 4:

[0756] The server analyzes the transmitted video data using the generation AI (G), which then analyzes the video data and determines whether abnormal behavior (such as wandering or falling) has been detected.

[0757] Step 5:

[0758] If abnormal behavior is detected, the server takes the following actions: First, it instructs the device to send a voice message to deter the user. Second, it sends an alert to the user's (family member's) smartphone to notify them of the abnormal behavior.

[0759] Step 6:

[0760] The terminal plays a voice message according to instructions from the server, for example, asking the patient, "Mr. / Ms. X, where are you going?"

[0761] Step 7:

[0762] Users receive alerts on their smartphones to let them know when abnormal behavior has occurred and, if necessary, rush to the patient's location or step up monitoring remotely.

[0763] Step 8:

[0764] The server periodically aggregates the patient's daily behavior data and generates reports that are shared with medical professionals, who then provide advice to optimize the patient's care plan.

[0765] Step 9:

[0766] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0767] Step 10:

[0768] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0769] Step 11:

[0770] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0771] Step 12:

[0772] The Generative AI (C) saves conversation logs and analyzes the patient's mental state and interests, providing data to further improve the quality of the conversation.

[0773] This specific processing flow will realize a sustainable care support system that improves the safety and mental health of dementia patients.

[0774] Example 1

[0775] 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."

[0776] It is necessary to monitor the daily lives of dementia patients, quickly detect abnormal behavior, and take appropriate action, but conventional systems often respond slowly. Another problem is the lack of a system that can comprehensively accumulate patient behavior data, notify family members in real time, and manage daily life tasks and provide conversation support.

[0777] 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.

[0778] In this invention, the server includes an information input means, a monitoring means, and a video analysis means. This allows for real-time monitoring of the daily life of a dementia patient and for rapid detection and response of abnormal behavior. The server also includes an abnormal behavior prevention means, a notification means, a data aggregation means, a task management means, and a conversation support means. This allows for immediate transmission of a deterrent message and notification to family members when abnormal behavior occurs. Furthermore, the server accumulates behavioral data and provides it to medical professionals, manages daily life tasks, and sends periodic reminder notifications, thereby preventing social isolation and the progression of dementia, while building a sustainable care environment.

[0779] The "information input means" is a means by which a user inputs profile information of a dementia patient using an application and transmits the information to a server.

[0780] The "monitoring means" is a means for monitoring the patient's daily activities in real time using a camera-equipped device and collecting video data.

[0781] "Video analysis means" refers to a means for analyzing video data obtained from a surveillance means using a generative AI model to detect abnormal behavior.

[0782] The "abnormal behavior deterrent means" is a means for playing back an audible deterrent message to the patient when abnormal behavior is detected.

[0783] The "notification means" is a means for sending an alert from the server to the family member's smartphone when abnormal behavior occurs.

[0784] The "data collection means" is a means for periodically collecting collected behavioral data and providing it to medical professionals.

[0785] "Task management tools" are tools for managing the patient's daily tasks (e.g., time to take medicine, time to go to the toilet).

[0786] "Conversation support means" is a means of using generative AI to conduct everyday conversations with patients, generating questions and comments to help the conversation progress smoothly.

[0787] MODE FOR CARRYING OUT THE INVENTION

[0788] This invention is a system that supports the care of dementia patients. The system begins when a user uses an application to input the patient's profile information and sends it to a server. Specifically, the profile information includes the patient's name, age, medical history, reminder settings, etc. This information is stored in a database by the server and used for future analysis and responses.

[0789] The device is equipped with a camera and is installed in the patient's living environment. The device monitors daily activities in real time and sends the video data to a server. The server analyzes the received video data using a generative AI model (G), and if abnormal behavior (e.g., wandering or falling) is detected, various measures are taken.

[0790] First, the server sends a voice output instruction to the device, playing a deterrent message such as "Mr. / Ms. X, where are you going?". It also sends an alert to the user's smartphone to notify them of abnormal behavior. The server then periodically aggregates the behavioral data and provides it to medical professionals, who can then optimize the care plan and suggest appropriate care for the patient.

[0791] Furthermore, the server manages daily tasks (e.g., medication times, toilet times) and sends reminder information to the device at set times. The device then notifies the patient with a voice message, helping the patient remember to perform the necessary tasks.

[0792] A conversation support function using a generative AI model (C) is also provided. The device supports everyday conversations with patients, and the generative AI model (C) generates appropriate questions and comments based on the patient's responses. This is expected to prevent social isolation and slow the progression of dementia.

[0793] Specific examples

[0794] One day, a user registers a patient profile in the system, setting basic patient information, medication times, toilet time reminders, etc. After the settings are made, the device is turned on and begins monitoring the video.

[0795] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generative AI model (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone to notify them of the abnormal behavior.

[0796] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0797] Conversational support is also provided throughout the day by a generative AI model (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[0798] Example prompt sentence:

[0799] "Enter basic patient information, medication times, bathroom time reminders, etc."

[0800] "What message do you want the device to play if loitering is detected?"

[0801] "How do you guide a conversation with a patient using a generative AI conversation support system?"

[0802] This system allows families of dementia patients to monitor their daily lives with peace of mind and respond quickly to any abnormal behavior. It also provides conversation support to prevent patients from becoming socially isolated, contributing to the creation of a sustainable care environment.

[0803] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0804] Step 1:

[0805] The user uses the application to enter the patient's profile information. The profile information includes the patient's name, age, medical history, reminder settings, etc. After entering this information and pressing the submit button, the profile information is sent to the server. The entered information is saved as profile data on the server.

[0806] Step 2:

[0807] The server stores the received profile information in a database. A unique ID is assigned to each patient in this database, which will be used for future searches and analysis. The patient's name, age, medical history, and reminder settings are also saved here. This allows a series of patient data to be accumulated in the database.

[0808] Step 3:

[0809] A camera-equipped terminal is installed in the patient's living environment. The terminal operates constantly and monitors the patient's behavior in real time. The camera is positioned to capture images of the room and captures video data 24 hours a day. This captured video data is then sent from the terminal to a server.

[0810] Step 4:

[0811] The server analyzes the video data sent from the device. It uses a generative AI model (G) to analyze this video data in real time to detect abnormal behavior (e.g., wandering, falling). If abnormal behavior is detected through this analysis, the server proceeds to the next step in processing the information.

[0812] Step 5:

[0813] When abnormal behavior is detected, the server immediately sends a voice notification instruction to the device. Specifically, it sends an instruction to play a message such as "Mr. / Ms. X, where are you going?" Based on this instruction, the device conveys the message to the patient by voice. This directly deters the patient.

[0814] Step 6:

[0815] At the same time, the server sends an alert to the user's smartphone to notify them of the abnormal behavior. The alert contains detailed information about the type of abnormal behavior, the date and time it occurred, and the current situation. This allows the user to immediately understand the situation.

[0816] Step 7:

[0817] The server periodically aggregates the collected behavioral data and provides it to medical professionals. Data aggregation is performed at regular intervals, and the accumulated data is analyzed and compiled into a report. Medical professionals use this report to optimize care plans and propose appropriate care for patients.

[0818] Step 8:

[0819] The server manages reminders for daily tasks (e.g., time to take medicine, time to go to the toilet). When the reminder time approaches, the server sends the information to the device, which then notifies the patient by voice at the specified time, saying, "It's time to take your medicine at 2 o'clock."

[0820] Step 9:

[0821] When the device receives a reminder, it will notify you by voice, helping patients remember to perform set tasks (e.g., taking medicine, going to the toilet).

[0822] Step 10:

[0823] The device uses the generative AI model (C) to provide conversational support. For example, if a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the device will continue the conversation by saying, "That sounds good, let's go out." This conversational support helps prevent social isolation and maintain mental stability for patients.

[0824] (Application example 1)

[0825] 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."

[0826] Monitoring and caring for dementia patients places a significant burden on many families and is also an important issue for ensuring patient safety. Conventional systems often provide abnormal behavior detection, reminder functions, and daily conversation support separately, making integrated management difficult. Furthermore, it is difficult to accurately and quickly detect and respond to abnormal behavior in real time and notify family members, limiting the efficiency of care and improving the patient's quality of life.

[0827] 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.

[0828] In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generating AI means for analyzing video from the surveillance camera, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to family members when abnormal behavior is detected, a data linking means for linking behavioral data to medical professionals, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using the generating AI, and a processing means for executing a security service application. This makes it possible to ensure the safety of dementia patients in real time, reduce the burden on their families, and improve the quality of life of the patients.

[0829] "Real-time monitoring" means observing the behavior of dementia patients in real time and collecting information.

[0830] The "camera means" is a device that acquires visual information and transmits it to the server as digital data.

[0831] The "generative AI means" is an artificial intelligence technology that analyzes video data from the camera means and detects specific patterns or abnormal behavior.

[0832] The "audio output means" is a device that generates an audio message to alert the patient when abnormal behavior is detected.

[0833] "Communication means" refers to a system for sending notifications to family members or caregivers when abnormal behavior is detected.

[0834] "Data linking means" is a function that allows collected behavioral data to be shared with experts and medical institutions.

[0835] "Reminder measures" are functions that manage important tasks in a patient's daily life (e.g., taking medicine, toilet time, etc.) and provide timely notifications.

[0836] "Conversation support means" is a function that uses generative AI to assist with everyday conversations and promote mental stability and social interaction for dementia patients.

[0837] The "security service application" is an application that ensures the safety of dementia patients and provides warnings and notifications to caregivers and family members.

[0838] The "processing means" is a control device for the entire system that receives and analyzes data sent from various means and executes necessary actions.

[0839] This invention is a system for monitoring the behavior of dementia patients and ensuring their safety. The system includes a camera, a generation AI, a voice output, a communication device, a data linking device, a reminder device, a conversation support device, and a security service application. Each device is described in detail below.

[0840] Camera Means

[0841] The camera means is a device for monitoring the behavior of dementia patients in real time, and video data is continuously acquired and transmitted to the server.

[0842] Generation AI means

[0843] The server uses the acquired video data to run the generative AI model "G." This model has the ability to analyze and detect abnormal behavior in real time. The video data is first preprocessed, and after feature extraction, it is input into the AI ​​model.

[0844] Audio output means

[0845] When abnormal behavior is detected, the server sends a command to the voice output means to warn the patient. This message has a warning message such as "Mr. / Ms. XX, where are you going?"

[0846] communication means

[0847] If abnormal behavior is detected, the server will send a notification to family members or caregivers via communication means, allowing for a prompt response.

[0848] Data linking method

[0849] Behavioral data is collected periodically and shared with medical professionals and care managers, who use it to optimize care plans.

[0850] Reminder measures

[0851] The server manages the patient's daily tasks and generates reminder information, which notifies the patient of important tasks such as taking medicine or going to the toilet at appropriate times. The reminders are delivered to the patient via voice notification.

[0852] Conversation support tools

[0853] Using the generative AI model "C," the server provides everyday conversation support. For example, if a patient asks, "How are you feeling today?", the AI ​​model responds, "I feel good, let's go out." This function is expected to prevent patients from becoming socially isolated and maintain their mental stability.

[0854] Security Service Applications

[0855] The security service application integrates these methods and functions as a comprehensive system. It operates as a smartphone application and provides real-time notifications and management functions to family members and caregivers.

[0856] Specific examples

[0857] Specific examples of abnormal behavior detection

[0858] If the patient begins to wander around the room around 10:00 a.m., the camera will send the footage to the server. The server's generative AI model "G" will analyze the footage and detect abnormal behavior (wandering). The server will then immediately send an instruction to the audio output device to play the message "Mr. / Ms. X, where are you going?", and simultaneously send an alert to the family member's smartphone via the communication device.

[0859] Prompt Sentence Examples

[0860] "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0861] Examples of reminder functions

[0862] If a medicine-taking reminder is set for 2 p.m., the server generates reminder information at the set time and causes the voice output means to notify the user, "It's time to take your medicine at 2 p.m."

[0863] Prompt Sentence Examples

[0864] "I have a medication reminder at 2 PM. Please send me the reminder information."

[0865] Examples of conversation support functions

[0866] When the patient asks, "How are you feeling today?", the server responds with "Feeling good, let's go out" using generative AI model "C."

[0867] Prompt Sentence Examples

[0868] "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0869] As described above, this invention can ensure the safety of dementia patients, reduce the burden on their families and caregivers, and improve the quality of life of patients.

[0870] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0871] Step 1:

[0872] The user enters patient profile information

[0873] Using a smartphone application, users input basic information such as the patient's name, age, medical history, medication times, toilet time, and other reminder information. This information is sent as input data to the server, which stores it in a database for future analysis and response.

[0874] Step 2:

[0875] Acquisition and transmission of video data by camera means

[0876] A camera installed on the terminal monitors the daily activities of dementia patients in real time and captures video data. This video data is sent as input to the server. The server receives this video data and inputs it into the generative AI model "G."

[0877] Step 3:

[0878] Analysis of abnormal behavior using generative AI model "G"

[0879] The server preprocesses the received video data and extracts features for detecting abnormal behavior. These features are input into the generative AI model "G," which then determines whether the behavior is abnormal. If abnormal behavior is detected as the output, the system proceeds to the next step.

[0880] Step 4:

[0881] Implementing a warning by audio output means

[0882] When abnormal behavior is detected, the server sends instructions to the voice output means. Specifically, it outputs data to play a voice message such as "Mr. / Ms. X, where are you going?", which is received by the terminal and used to alert the patient via voice.

[0883] Step 5:

[0884] Notifying family members via communication means

[0885] When abnormal behavior is detected, the server sends a notification to the family member's smartphone using a communication device. This notification includes the time when the abnormal behavior occurred and the details of the abnormal behavior. The server generates notification data, outputs it to the communication device, and sends it to the family member's smartphone.

[0886] Step 6:

[0887] Reminder generation and delivery

[0888] The server generates a reminder notification according to the schedule based on the input reminder information. At the specified time, the reminder information is sent to the voice output means, and the terminal notifies the patient by voice, "It's time to take your medicine at 2 o'clock."

[0889] Step 7:

[0890] Linking behavioral data to healthcare professionals

[0891] The server periodically aggregates the collected behavioral data to share it with medical professionals. The aggregated data is sent to the medical professionals via the data sharing means. The server aggregates the behavioral data and outputs the data to the collaborating destination.

[0892] Step 8:

[0893] Conversation support using generative AI model "C"

[0894] The server uses the generative AI model "C" to provide everyday conversation support. When a patient asks a question by voice, the voice data is sent from the terminal to the server. The server inputs the data into the AI ​​model "C" and generates a response message. The generated response message is output to the voice output means, and the terminal plays it back.

[0895] Specific processing flow example

[0896] Example prompt: "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[0897] Example prompt: "I have a medication reminder at 2 PM. Please send me the reminder information."

[0898] Example prompt: "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[0899] Through the above processing steps, the server can ensure the safety of dementia patients and quickly provide necessary information to their families and caregivers.

[0900] 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.

[0901] The present invention provides a care support system for families of dementia patients that utilizes a generative AI model, and by adding emotion recognition technology, it realizes a sustainable care environment while also providing psychological care to the patient. Specific embodiments for this purpose are described below.

[0902] Overall system overview

[0903] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0904] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generation AI (G) to detect abnormal behavior. Furthermore, an emotion engine analyzes the patient's emotions from their facial expressions and voice.

[0905] If anomalous behavior is detected, the server will:

[0906] 1. Issue an instruction to the device to send a voice message to deter the user.

[0907] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0908] The emotion data analyzed by the emotion engine is used by the generative AI (C) to support conversations with patients. For example, this conversation can provide comforting responses when a patient feels anxious, preventing social isolation and reducing psychological burden.

[0909] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0910] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0911] Specific examples

[0912] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0913] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generation AI (G) detected abnormal behavior (wandering). Furthermore, the emotion engine analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0914] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0915] Conversation support is also provided by generative AI (C). For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0916] Data from the emotion engine and generative AI (G) is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as a report, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0917] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0918] The processing flow will be explained below.

[0919] Step 1:

[0920] The user opens the application and logs in. Then, they enter their dementia patient profile information, including their name, age, medical history, reminder settings (such as medication times or toilet time), and emotion engine settings. Once completed, the user sends this information to the server.

[0921] Step 2:

[0922] The server stores the received profile information in a database, which is used for patient behavior analysis, emotion analysis, and reminder generation.

[0923] Step 3:

[0924] The device (camera-equipped device) is installed in the patient's living environment and monitors their daily activities and facial expressions in real time. The camera captures video data and transmits it to a server.

[0925] Step 4:

[0926] The server analyzes the transmitted video data using the Generative AI (G) to detect abnormal behavior. The emotion engine also analyzes the patient's facial expressions and voice to determine emotions such as anxiety, sadness, and confusion.

[0927] Step 5:

[0928] If abnormal behavior is detected, the server takes the following action. First, it instructs the device to send a voice message to deter the user. For example, it could say, "Mr. / Ms. X, where are you going?" Next, it sends an alert of the abnormal behavior to the user's (family member's) smartphone.

[0929] Step 6:

[0930] If the emotion engine detects an emotion such as anxiety, the server generates an appropriate additional message and sends it to the device, such as "Please stay calm. Is there anything I can help you with?"

[0931] Step 7:

[0932] The device plays voice messages according to instructions from the server. First, it plays a deterrent message for abnormal behavior, followed by a message based on emotions.

[0933] Step 8:

[0934] Users receive alerts on their smartphones to identify abnormal behavior and the patient's emotional state, and can then rush to the patient's location or remotely monitor them if necessary.

[0935] Step 9:

[0936] The server periodically collects data on the patient's daily behavior and emotions and generates a report that is shared with medical professionals. Based on this report, medical professionals can optimize care plans that take into account both the patient's behavior and emotions.

[0937] Step 10:

[0938] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[0939] Step 11:

[0940] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[0941] Step 12:

[0942] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[0943] Step 13:

[0944] The generative AI (C) generates appropriate responses based on the patient's emotional data and continues the conversation. For example, if the patient responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together."

[0945] Step 14:

[0946] The Generative AI (C) saves conversation logs and emotional data for analysis, and uses this information to understand the patient's mental state and interests, which it then reflects in the next conversation.

[0947] The specific processing flow described above will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0948] Example 2

[0949] 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."

[0950] Conventional care systems for dementia patients have difficulty monitoring and analyzing the patient's behavior and psychological state in real time and providing appropriate care. Furthermore, notification and data sharing with family members and medical professionals are insufficient, making effective care difficult. This increases the burden on caregivers and creates problems such as insufficient protection of the patient's safety and mental health.

[0951] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring camera, a voice output means for speaking to the patient to deter abnormal behavior when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data and emotional data to a medical professional, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using a generation AI, and an emotion recognition means for analyzing the patient's psychological state using emotion recognition technology. This makes it possible to monitor the behavior and emotions of a dementia patient in real time, take appropriate measures, and quickly provide information to family members and medical professionals.

[0952] The "camera means" is a photographing device for monitoring the behavior of dementia patients in real time.

[0953] The "generative AI means" is an artificial intelligence that analyzes footage from surveillance cameras and detects abnormal behavior.

[0954] The "audio output means" refers to a speaker or audio playback device that issues a warning to the patient when abnormal behavior is detected.

[0955] The "communication means" is a communication device for sending a notification to family members when abnormal behavior is detected.

[0956] The "data linking means" is a device or system with a data communication function for linking behavioral data and emotional data to medical professionals.

[0957] A "reminder means" is a device or system that manages a patient's daily tasks and has the function of notifying them of things like when to take their medicine or when to go to the toilet.

[0958] A "conversation support means" is a device or system with dialogue capabilities that uses generative AI to support everyday conversations with patients.

[0959] The "emotion recognition means" is a device or system that uses emotion recognition technology to analyze the psychological state of a patient and take appropriate action.

[0960] "Server" refers to a central processing unit that integrates, manages, and controls the above means, and transmits and receives information and analyzes data.

[0961] The present invention provides a care support system that utilizes generative AI models for families of dementia patients. By incorporating emotion recognition technology, this system realizes a sustainable care environment while also providing psychological care to patients. Specific embodiments of the present invention are described below.

[0962] Overall system overview

[0963] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[0964] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes emotions from the patient's facial expressions and voice.

[0965] If anomalous behavior is detected, the server will:

[0966] 1. Issue an instruction to the device to send a voice message to deter the user.

[0967] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[0968] The emotion data analyzed by the emotion engine is used by generative AI to support conversations with patients, for example, providing comforting responses when patients feel anxious, preventing social isolation and reducing psychological burdens.

[0969] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[0970] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[0971] Hardware and software used

[0972] Hardware: Camera-equipped devices (e.g., Raspberry Pi camera), smartphones, servers

[0973] Software: Applications, generative AI models (e.g., GPT-3), emotion recognition engines (e.g., Microsoft Azure Emotion API), database management systems (e.g., MySQL, PostgreSQL)

[0974] Specific examples

[0975] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[0976] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generating AI detected abnormal behavior (wandering). The emotion engine then analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[0977] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[0978] Conversation support is also provided by generative AI. For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[0979] Data from the emotion engine and generative AI is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as reports, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[0980] Examples of prompt statements

[0981] Prompt example 1: Anomalous behavior detection

[0982] How do you talk to a patient if they are wandering around the room?

[0983] Example prompt 2: Emotional support

[0984] How do you respond when a patient says, "I feel lonely"?

[0985] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Step 1:

[0988] The user opens the application, enters the profile information of the dementia patient, and sends it to the server. Specifically, the user enters information such as name, age, medication time, toilet time, and emotion recognition engine settings into a form within the application, and presses the "Submit" button to upload the data. The entered data (patient profile information) is sent to the server and saved in a database.

[0989] Step 2:

[0990] The server stores the received profile information in a database and distributes the configuration information to the device. Specifically, the server stores the received information for each item in the database correctly, and then sends the configuration information to the device in JSON format. The input is the profile information sent by the user, and the output is storing it in the database and distributing the configuration information to the device.

[0991] Step 3:

[0992] Based on the received configuration information, the device activates the camera and microphone and begins real-time monitoring. Specifically, the device analyzes the received configuration and activates the Raspberry Pi camera and microphone devices. This causes the device to begin capturing video and audio data at 30 frames per second. The input is the configuration information sent to the device, and the output is the activation of the camera and microphone.

[0993] Step 4:

[0994] The device sends the captured video and audio data to the server. Specifically, the device uploads the video and audio data to the server in real time via Wi-Fi. The input is the video and audio data captured by the device, and the output is the data sent to the server.

[0995] Step 5:

[0996] The server analyzes the received video and audio data to detect abnormal behavior and emotions. Specifically, the server uses generative AI to analyze the video data and detect abnormal behavior (e.g., wandering). It also uses an emotion recognition engine to analyze the patient's emotions (e.g., anxiety, anger, happiness) from facial expressions and voice. The input is the video and audio data received from the device, and the output is the abnormal behavior detection results and emotional analysis results.

[0997] Step 6:

[0998] If abnormal behavior is detected, the server issues a voice message instruction to the device and sends an alert to the family. Specifically, the server sends a deterrent message to the device, such as "Mr. / Ms. X, where are you going?", which is played on the device. It also sends a notification containing abnormal behavior and emotional data to the user's smartphone. The input is the abnormal behavior detection result, and the output is a voice message instruction to the device and an alert notification to the user.

[0999] Step 7:

[1000] The server generates reminder information for daily life tasks and distributes it to the device. Specifically, the server generates a notification message such as "It's time to take your medicine at 2 PM" based on the set schedule (e.g., medicine is to be taken at 2 PM) and sends it to the device. The input is the set reminder schedule, and the output is the reminder notification sent to the device.

[1001] Step 8:

[1002] The generative AI supports everyday conversations with patients. Specifically, it uses emotional data analyzed by the emotion recognition engine to generate an appropriate response. For example, if a patient says, "I feel lonely," the generative AI responds, "Do you want to talk to someone? Let's talk together." The input is emotional data from the emotion recognition engine, and the output is the generated conversational response.

[1003] Step 9:

[1004] The server periodically aggregates the behavioral and emotional data and shares them with medical professionals as reports. Specifically, the server aggregates the data weekly, generates a PDF report, and emails it to medical professionals. The input is the saved behavioral and emotional data, and the output is the generated report.

[1005] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[1006] (Application example 2)

[1007] 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."

[1008] Conventional care systems for dementia patients have problems such as a lack of real-time responses to abnormal behaviors and emotional care for patients. In addition, systems installed in physical stores have the problem of difficulty in properly monitoring the behavior and emotions of customers and providing necessary responses in real time.

[1009] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring sensor, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data to a medical professional, a reminder means for managing the patient's daily tasks, a conversation support means for using a generation AI to conduct daily conversations with the patient, a behavior analysis means for monitoring the behavior and emotions of customers and instructing appropriate responses, and a profiling means for inputting and managing customer profile information. This enables rapid response to abnormal behavior of patients and customers and comprehensive support, including emotional care.

[1010] The "sensor means" is a device for monitoring the behavior of a subject in real time and generating video data.

[1011] The "generative AI means" is a model that uses artificial intelligence to analyze acquired video data and detect abnormal behavior and emotions.

[1012] The "audio output means" refers to a speaker or an audio playback device that issues a deterrent message to the subject when abnormal behavior is detected.

[1013] The "communication means" is a network communication device for sending a notification to family members and related parties when abnormal behavior is detected.

[1014] The "data linking means" is a device with a data transfer function that links collected behavioral data to medical professionals and provides appropriate care plans.

[1015] A "reminder tool" is a system that manages the patient's daily life tasks and notifies them at designated times.

[1016] The "conversation support tool" is a system that uses generative AI to support everyday conversations with the target person and provide psychological care.

[1017] The "behavior analysis means" is a data analysis device that monitors the behavior and emotions of customers and provides instructions on how to respond appropriately.

[1018] The "profiling method" is a database system for inputting and managing basic information and past behavioral history of customers.

[1019] This invention is a system that monitors the behavior and emotions of dementia patients and customers and responds appropriately. This system includes a sensor means, a generation AI means, a voice output means, a communication means, a data linkage means, a reminder means, a conversation support means, a behavior analysis means, and a profiling means.

[1020] Overall system overview

[1021] Users use the application to input profile information for dementia patients and store visitors, and send it to the server. This profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[1022] The sensor means (a device with a camera) is installed in the patient's or visitor's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes the subject's emotions from their facial expressions and voice.

[1023] If anomalous behavior is detected, the server will:

[1024] 1. A voice message is sent by a voice output means to call out a deterrent.

[1025] 2. An alert is sent to the smartphones of relevant parties (patient's family members and store staff) via communication means to inform them of abnormal behavior.

[1026] The emotion data analyzed by the emotion engine is used by the generative AI to support conversations with the patient. For example, if a patient feels anxious, this conversation can provide a comforting response, preventing social isolation and reducing psychological burden. Appropriate words can also be used to provide a sense of security to customers.

[1027] The server also periodically collects behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[1028] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing the user to perform the tasks regularly.

[1029] Program processing overview

[1030] The system operates using the following hardware and software:

[1031] Camera device: Monitors the subject's behavior and facial expressions in real time.

[1032] Server: Stores and analyzes data.

[1033] Generative AI model: Uses TensorFlow / Keras to analyze video data and perform emotion recognition.

[1034] Audio output device: Speaks out if abnormal behavior is detected.

[1035] Communication devices: Send notifications to family and friends.

[1036] Smartphone: Receive alerts and enter profile information.

[1037] Specifically, the data processing and calculation involves preprocessing video data acquired from a camera device and inputting it into an emotion recognition model and an abnormal behavior detection model. Based on the emotion recognition and abnormal behavior detection results, appropriate voice messages and notifications are generated.

[1038] Specific examples

[1039] One day, a user registers the profile information of a patient or visitor in the system. The user inputs basic personal information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the sensor means is activated and begins monitoring video and emotions.

[1040] At around 10:00 AM, the patient began walking around the room. The camera sends this video to the server, and the generating AI detects abnormal behavior (wandering). Furthermore, the emotion engine analyzes the patient's facial expression and expressions to determine whether they are anxious. The server immediately instructs the audio output device to play a deterrent message, such as "Mr. / Ms. X, where are you going?", and also sends emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert is sent to the family's smartphone via the communication device, informing them of the abnormal behavior and anxious feelings.

[1041] At 2:00 PM, the server sends a set medicine reminder to the sensor means, which then notifies the patient by voice, "It's time to take your 2:00 medicine," helping the patient to remember to take their medicine.

[1042] Conversation support is also provided by generative AI. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Do you want to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the customer's psychological state.

[1043] Prompt Sentence Examples

[1044] "Abnormal behavior has been detected. The customer currently appears anxious. Please take immediate action."

[1045] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and visitors, and reduces the burden on users.

[1046] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1047] Step 1:

[1048] Users use the application to enter profile information about dementia patients and store visitors, and send it to the server. The profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is saved in the server's database. The input data is then processed, and the personal information is formatted into an appropriate format and stored in the database.

[1049] Step 2:

[1050] A camera installed on the terminal (sensor means) monitors the behavior and facial expressions of patients or visitors in real time and captures video data. The captured video data is then sent to a server. The input data is the camera video, which is then sent to the server and prepared for analysis.

[1051] Step 3:

[1052] The server preprocesses the received video data using a generative AI method. Specifically, it processes the video data by adjusting its image quality and removing noise, and then uses a generative AI model to detect abnormal behavior and analyze emotions. The input data is the preprocessed video data, and the output is a determination of whether or not there is abnormal behavior and the emotional state.

[1053] Step 4:

[1054] If abnormal behavior is detected, the server instructs the audio output means to send a voice message to deter the patient. Specifically, a message such as "Mr. / Ms. X, where are you going?" is played from the terminal to try to deter the patient. The input data is the abnormal behavior judgment result, and the output is the playback of a voice message.

[1055] Step 5:

[1056] If abnormal behavior is detected, the server sends an alert to the smartphone of the relevant person. The notification message includes the specific details of the abnormal behavior and the emotional state of the patient or visitor. The input data is the abnormal behavior and the results of emotional analysis, and the output is an alert notification.

[1057] Step 6:

[1058] Based on the emotional data analyzed by the emotion engine, the generative AI supports conversations with patients and customers. Specifically, the generative AI generates an emotional message such as "Is there something I can help you with?" and plays it back through a voice output means. The input data is the result of the emotion analysis, and the output is a response message according to the emotion.

[1059] Step 7:

[1060] The server uses a reminder means for managing daily tasks to generate reminder information based on the set schedule and sends a notification to the terminal. Specifically, it plays a notification message such as "It's time to take your medicine at 2 o'clock" by voice. The input data is the reminder schedule, and the output is the reminder notification.

[1061] Step 8:

[1062] The behavioral and emotional data are periodically aggregated and a report is generated to be shared with medical professionals. The server analyzes the data and forwards it to medical professionals. The input data are behavioral and emotional data, and the output is data in the form of a report.

[1063] Step 9:

[1064] Using conversation support tools, the generative AI will converse with patients and customers. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Would you like to talk to someone? Let's talk together." The input data is the conversation context, and the output is the dialogue response.

[1065] 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.

[1066] 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.

[1067] 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.

[1068] [Fourth embodiment]

[1069] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1070] 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.

[1071] 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).

[1072] 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.

[1073] 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.

[1074] 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).

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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."

[1082] The present invention provides a care support system using a generative AI model for families of dementia patients, thereby building a sustainable care environment. Specific embodiments for this purpose are described below.

[1083] Overall system overview

[1084] The user first uses the application to input the dementia patient's profile information, which includes the patient's name, age, medical history, and desired reminder settings, and then sends it to the server. The server then stores the patient's basic information in a database for future analysis and treatment.

[1085] The terminal (device with camera) is installed in the patient's living environment and serves to monitor daily behavior in real time. The camera captures video and sends it to a server. The server analyzes the received video data using a generation AI (G) and detects abnormal behavior in real time.

[1086] If anomalous behavior is detected, the server will:

[1087] 1. Send a voice output instruction to the terminal and play a deterrent message to the patient such as "Mr. / Ms. XX, where are you going?"

[1088] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[1089] The server also periodically aggregates behavioral data and shares it with medical professionals to help optimize care plans, allowing them to propose appropriate care plans based on the patient's daily behavior.

[1090] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[1091] The device also features a conversation support function using generative AI (C), which supports everyday conversations. Generative AI (C) generates appropriate questions and comments based on the patient's responses, helping to keep the conversation moving smoothly. This function not only prevents patients from becoming socially isolated, but is also expected to slow the progression of dementia.

[1092] Specific examples

[1093] One day, a user registers a patient's profile in the system. The user sets basic information about the patient, reminders for taking medication, toilet time, etc. After the settings are made, the device is turned on and begins monitoring the video.

[1094] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generation AI (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior.

[1095] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[1096] Conversational support is also provided throughout the day by a generative AI (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The user opens the application and logs in. They then enter their dementia patient profile information, including their name, age, medical history, and reminder settings (such as medication times and toilet time). Once complete, the user sends this information to the server.

[1100] Step 2:

[1101] The server stores the received profile information in a database, which is used to analyze patient behavior and generate reminders.

[1102] Step 3:

[1103] The device monitors the patient's daily life in real time using a camera, which captures video data and transmits it to a server.

[1104] Step 4:

[1105] The server analyzes the transmitted video data using the generation AI (G), which then analyzes the video data and determines whether abnormal behavior (such as wandering or falling) has been detected.

[1106] Step 5:

[1107] If abnormal behavior is detected, the server takes the following actions: First, it instructs the device to send a voice message to deter the user. Second, it sends an alert to the user's (family member's) smartphone to notify them of the abnormal behavior.

[1108] Step 6:

[1109] The terminal plays a voice message according to instructions from the server, for example, asking the patient, "Mr. / Ms. X, where are you going?"

[1110] Step 7:

[1111] Users receive alerts on their smartphones to let them know when abnormal behavior has occurred and, if necessary, rush to the patient's location or step up monitoring remotely.

[1112] Step 8:

[1113] The server periodically aggregates the patient's daily behavior data and generates reports that are shared with medical professionals, who then provide advice to optimize the patient's care plan.

[1114] Step 9:

[1115] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[1116] Step 10:

[1117] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[1118] Step 11:

[1119] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[1120] Step 12:

[1121] The Generative AI (C) saves conversation logs and analyzes the patient's mental state and interests, providing data to further improve the quality of the conversation.

[1122] This specific processing flow will realize a sustainable care support system that improves the safety and mental health of dementia patients.

[1123] Example 1

[1124] 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."

[1125] It is necessary to monitor the daily lives of dementia patients, quickly detect abnormal behavior, and take appropriate action, but conventional systems often respond slowly. Another problem is the lack of a system that can comprehensively accumulate patient behavior data, notify family members in real time, and manage daily life tasks and provide conversation support.

[1126] 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.

[1127] In this invention, the server includes an information input means, a monitoring means, and a video analysis means. This allows for real-time monitoring of the daily life of a dementia patient and for rapid detection and response of abnormal behavior. The server also includes an abnormal behavior prevention means, a notification means, a data aggregation means, a task management means, and a conversation support means. This allows for immediate transmission of a deterrent message and notification to family members when abnormal behavior occurs. Furthermore, the server accumulates behavioral data and provides it to medical professionals, manages daily life tasks, and sends periodic reminder notifications, thereby preventing social isolation and the progression of dementia, while building a sustainable care environment.

[1128] The "information input means" is a means by which a user inputs profile information of a dementia patient using an application and transmits the information to a server.

[1129] The "monitoring means" is a means for monitoring the patient's daily activities in real time using a camera-equipped device and collecting video data.

[1130] "Video analysis means" refers to a means for analyzing video data obtained from a surveillance means using a generative AI model to detect abnormal behavior.

[1131] The "abnormal behavior deterrent means" is a means for playing back an audible deterrent message to the patient when abnormal behavior is detected.

[1132] The "notification means" is a means for sending an alert from the server to the family member's smartphone when abnormal behavior occurs.

[1133] The "data collection means" is a means for periodically collecting collected behavioral data and providing it to medical professionals.

[1134] "Task management tools" are tools for managing the patient's daily tasks (e.g., time to take medicine, time to go to the toilet).

[1135] "Conversation support means" is a means of using generative AI to conduct everyday conversations with patients, generating questions and comments to help the conversation progress smoothly.

[1136] MODE FOR CARRYING OUT THE INVENTION

[1137] This invention is a system that supports the care of dementia patients. The system begins when a user uses an application to input the patient's profile information and sends it to a server. Specifically, the profile information includes the patient's name, age, medical history, reminder settings, etc. This information is stored in a database by the server and used for future analysis and responses.

[1138] The device is equipped with a camera and is installed in the patient's living environment. The device monitors daily activities in real time and sends the video data to a server. The server analyzes the received video data using a generative AI model (G), and if abnormal behavior (e.g., wandering or falling) is detected, various measures are taken.

[1139] First, the server sends a voice output instruction to the device, playing a deterrent message such as "Mr. / Ms. X, where are you going?". It also sends an alert to the user's smartphone to notify them of abnormal behavior. The server then periodically aggregates the behavioral data and provides it to medical professionals, who can then optimize the care plan and suggest appropriate care for the patient.

[1140] Furthermore, the server manages daily tasks (e.g., medication times, toilet times) and sends reminder information to the device at set times. The device then notifies the patient with a voice message, helping the patient remember to perform the necessary tasks.

[1141] A conversation support function using a generative AI model (C) is also provided. The device supports everyday conversations with patients, and the generative AI model (C) generates appropriate questions and comments based on the patient's responses. This is expected to prevent social isolation and slow the progression of dementia.

[1142] Specific examples

[1143] One day, a user registers a patient profile in the system, setting basic patient information, medication times, toilet time reminders, etc. After the settings are made, the device is turned on and begins monitoring the video.

[1144] At around 10:00 AM, the patient began wandering around the room. The camera sent this video to the server, and the generative AI model (G) detected the abnormal behavior (wandering). The server immediately instructed the device to play a deterrent message: "Mr. / Ms. X, where are you going?" At the same time, an alert was sent to the user's smartphone to notify them of the abnormal behavior.

[1145] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[1146] Conversational support is also provided throughout the day by a generative AI model (C). When a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the AI ​​continues the conversation by saying, "That sounds good, let's go out." This allows patients to maintain a stable mental state through daily social contact.

[1147] Example prompt sentence:

[1148] "Enter basic patient information, medication times, bathroom time reminders, etc."

[1149] "What message do you want the device to play if loitering is detected?"

[1150] "How do you guide a conversation with a patient using a generative AI conversation support system?"

[1151] This system allows families of dementia patients to monitor their daily lives with peace of mind and respond quickly to any abnormal behavior. It also provides conversation support to prevent patients from becoming socially isolated, contributing to the creation of a sustainable care environment.

[1152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1153] Step 1:

[1154] The user uses the application to enter the patient's profile information. The profile information includes the patient's name, age, medical history, reminder settings, etc. After entering this information and pressing the submit button, the profile information is sent to the server. The entered information is saved as profile data on the server.

[1155] Step 2:

[1156] The server stores the received profile information in a database. A unique ID is assigned to each patient in this database, which will be used for future searches and analysis. The patient's name, age, medical history, and reminder settings are also saved here. This allows a series of patient data to be accumulated in the database.

[1157] Step 3:

[1158] A camera-equipped terminal is installed in the patient's living environment. The terminal operates constantly and monitors the patient's behavior in real time. The camera is positioned to capture images of the room and captures video data 24 hours a day. This captured video data is then sent from the terminal to a server.

[1159] Step 4:

[1160] The server analyzes the video data sent from the device. It uses a generative AI model (G) to analyze this video data in real time to detect abnormal behavior (e.g., wandering, falling). If abnormal behavior is detected through this analysis, the server proceeds to the next step in processing the information.

[1161] Step 5:

[1162] When abnormal behavior is detected, the server immediately sends a voice notification instruction to the device. Specifically, it sends an instruction to play a message such as "Mr. / Ms. X, where are you going?" Based on this instruction, the device conveys the message to the patient by voice. This directly deters the patient.

[1163] Step 6:

[1164] At the same time, the server sends an alert to the user's smartphone to notify them of the abnormal behavior. The alert contains detailed information about the type of abnormal behavior, the date and time it occurred, and the current situation. This allows the user to immediately understand the situation.

[1165] Step 7:

[1166] The server periodically aggregates the collected behavioral data and provides it to medical professionals. Data aggregation is performed at regular intervals, and the accumulated data is analyzed and compiled into a report. Medical professionals use this report to optimize care plans and propose appropriate care for patients.

[1167] Step 8:

[1168] The server manages reminders for daily tasks (e.g., time to take medicine, time to go to the toilet). When the reminder time approaches, the server sends the information to the device, which then notifies the patient by voice at the specified time, saying, "It's time to take your medicine at 2 o'clock."

[1169] Step 9:

[1170] When the device receives a reminder, it will notify you by voice, helping patients remember to perform set tasks (e.g., taking medicine, going to the toilet).

[1171] Step 10:

[1172] The device uses the generative AI model (C) to provide conversational support. For example, if a patient is asked, "How are you feeling today?" and responds, "I'd like to go for a walk," the device will continue the conversation by saying, "That sounds good, let's go out." This conversational support helps prevent social isolation and maintain mental stability for patients.

[1173] (Application example 1)

[1174] 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."

[1175] Monitoring and caring for dementia patients places a significant burden on many families and is also an important issue for ensuring patient safety. Conventional systems often provide abnormal behavior detection, reminder functions, and daily conversation support separately, making integrated management difficult. Furthermore, it is difficult to accurately and quickly detect and respond to abnormal behavior in real time and notify family members, limiting the efficiency of care and improving the patient's quality of life.

[1176] 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.

[1177] In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generating AI means for analyzing video from the surveillance camera, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to family members when abnormal behavior is detected, a data linking means for linking behavioral data to medical professionals, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using the generating AI, and a processing means for executing a security service application. This makes it possible to ensure the safety of dementia patients in real time, reduce the burden on their families, and improve the quality of life of the patients.

[1178] "Real-time monitoring" means observing the behavior of dementia patients in real time and collecting information.

[1179] The "camera means" is a device that acquires visual information and transmits it to the server as digital data.

[1180] The "generative AI means" is an artificial intelligence technology that analyzes video data from the camera means and detects specific patterns or abnormal behavior.

[1181] The "audio output means" is a device that generates an audio message to alert the patient when abnormal behavior is detected.

[1182] "Communication means" refers to a system for sending notifications to family members or caregivers when abnormal behavior is detected.

[1183] "Data linking means" is a function that allows collected behavioral data to be shared with experts and medical institutions.

[1184] "Reminder measures" are functions that manage important tasks in a patient's daily life (e.g., taking medicine, toilet time, etc.) and provide timely notifications.

[1185] "Conversation support means" is a function that uses generative AI to assist with everyday conversations and promote mental stability and social interaction for dementia patients.

[1186] The "security service application" is an application that ensures the safety of dementia patients and provides warnings and notifications to caregivers and family members.

[1187] The "processing means" is a control device for the entire system that receives and analyzes data sent from various means and executes necessary actions.

[1188] This invention is a system for monitoring the behavior of dementia patients and ensuring their safety. The system includes a camera, a generation AI, a voice output, a communication device, a data linking device, a reminder device, a conversation support device, and a security service application. Each device is described in detail below.

[1189] Camera Means

[1190] The camera means is a device for monitoring the behavior of dementia patients in real time, and video data is continuously acquired and transmitted to the server.

[1191] Generation AI means

[1192] The server uses the acquired video data to run the generative AI model "G." This model has the ability to analyze and detect abnormal behavior in real time. The video data is first preprocessed, and after feature extraction, it is input into the AI ​​model.

[1193] Audio output means

[1194] When abnormal behavior is detected, the server sends a command to the voice output means to warn the patient. This message has a warning message such as "Mr. / Ms. XX, where are you going?"

[1195] communication means

[1196] If abnormal behavior is detected, the server will send a notification to family members or caregivers via communication means, allowing for a prompt response.

[1197] Data linking method

[1198] Behavioral data is collected periodically and shared with medical professionals and care managers, who use it to optimize care plans.

[1199] Reminder measures

[1200] The server manages the patient's daily tasks and generates reminder information, which notifies the patient of important tasks such as taking medicine or going to the toilet at appropriate times. The reminders are delivered to the patient via voice notification.

[1201] Conversation support tools

[1202] Using the generative AI model "C," the server provides everyday conversation support. For example, if a patient asks, "How are you feeling today?", the AI ​​model responds, "I feel good, let's go out." This function is expected to prevent patients from becoming socially isolated and maintain their mental stability.

[1203] Security Service Applications

[1204] The security service application integrates these methods and functions as a comprehensive system. It operates as a smartphone application and provides real-time notifications and management functions to family members and caregivers.

[1205] Specific examples

[1206] Specific examples of abnormal behavior detection

[1207] If the patient begins to wander around the room around 10:00 a.m., the camera will send the footage to the server. The server's generative AI model "G" will analyze the footage and detect abnormal behavior (wandering). The server will then immediately send an instruction to the audio output device to play the message "Mr. / Ms. X, where are you going?", and simultaneously send an alert to the family member's smartphone via the communication device.

[1208] Prompt Sentence Examples

[1209] "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[1210] Examples of reminder functions

[1211] If a medicine-taking reminder is set for 2 p.m., the server generates reminder information at the set time and causes the voice output means to notify the user, "It's time to take your medicine at 2 p.m."

[1212] Prompt Sentence Examples

[1213] "I have a medication reminder at 2 PM. Please send me the reminder information."

[1214] Examples of conversation support functions

[1215] When the patient asks, "How are you feeling today?", the server responds with "Feeling good, let's go out" using generative AI model "C."

[1216] Prompt Sentence Examples

[1217] "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[1218] As described above, this invention can ensure the safety of dementia patients, reduce the burden on their families and caregivers, and improve the quality of life of patients.

[1219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1220] Step 1:

[1221] The user enters patient profile information

[1222] Using a smartphone application, users input basic information such as the patient's name, age, medical history, medication times, toilet time, and other reminder information. This information is sent as input data to the server, which stores it in a database for future analysis and response.

[1223] Step 2:

[1224] Acquisition and transmission of video data by camera means

[1225] A camera installed on the terminal monitors the daily activities of dementia patients in real time and captures video data. This video data is sent as input to the server. The server receives this video data and inputs it into the generative AI model "G."

[1226] Step 3:

[1227] Analysis of abnormal behavior using generative AI model "G"

[1228] The server preprocesses the received video data and extracts features for detecting abnormal behavior. These features are input into the generative AI model "G," which then determines whether the behavior is abnormal. If abnormal behavior is detected as the output, the system proceeds to the next step.

[1229] Step 4:

[1230] Implementing a warning by audio output means

[1231] When abnormal behavior is detected, the server sends instructions to the voice output means. Specifically, it outputs data to play a voice message such as "Mr. / Ms. X, where are you going?", which is received by the terminal and used to alert the patient via voice.

[1232] Step 5:

[1233] Notifying family members via communication means

[1234] When abnormal behavior is detected, the server sends a notification to the family member's smartphone using a communication device. This notification includes the time when the abnormal behavior occurred and the details of the abnormal behavior. The server generates notification data, outputs it to the communication device, and sends it to the family member's smartphone.

[1235] Step 6:

[1236] Reminder generation and delivery

[1237] The server generates a reminder notification according to the schedule based on the input reminder information. At the specified time, the reminder information is sent to the voice output means, and the terminal notifies the patient by voice, "It's time to take your medicine at 2 o'clock."

[1238] Step 7:

[1239] Linking behavioral data to healthcare professionals

[1240] The server periodically aggregates the collected behavioral data to share it with medical professionals. The aggregated data is sent to the medical professionals via the data sharing means. The server aggregates the behavioral data and outputs the data to the collaborating destination.

[1241] Step 8:

[1242] Conversation support using generative AI model "C"

[1243] The server uses the generative AI model "C" to provide everyday conversation support. When a patient asks a question by voice, the voice data is sent from the terminal to the server. The server inputs the data into the AI ​​model "C" and generates a response message. The generated response message is output to the voice output means, and the terminal plays it back.

[1244] Specific processing flow example

[1245] Example prompt: "Around 10:00 AM, the patient began wandering around the room. Abnormal behavior (wandering) has been detected. Please play a deterrent message."

[1246] Example prompt: "I have a medication reminder at 2 PM. Please send me the reminder information."

[1247] Example prompt: "The patient asks, 'How are you feeling today?' Generate an appropriate response."

[1248] Through the above processing steps, the server can ensure the safety of dementia patients and quickly provide necessary information to their families and caregivers.

[1249] 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.

[1250] The present invention provides a care support system for families of dementia patients that utilizes a generative AI model, and by adding emotion recognition technology, it realizes a sustainable care environment while also providing psychological care to the patient. Specific embodiments for this purpose are described below.

[1251] Overall system overview

[1252] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[1253] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generation AI (G) to detect abnormal behavior. Furthermore, an emotion engine analyzes the patient's emotions from their facial expressions and voice.

[1254] If anomalous behavior is detected, the server will:

[1255] 1. Issue an instruction to the device to send a voice message to deter the user.

[1256] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[1257] The emotion data analyzed by the emotion engine is used by the generative AI (C) to support conversations with patients. For example, this conversation can provide comforting responses when a patient feels anxious, preventing social isolation and reducing psychological burden.

[1258] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[1259] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[1260] Specific examples

[1261] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[1262] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generation AI (G) detected abnormal behavior (wandering). Furthermore, the emotion engine analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[1263] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[1264] Conversation support is also provided by generative AI (C). For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[1265] Data from the emotion engine and generative AI (G) is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as a report, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[1266] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] The user opens the application and logs in. Then, they enter their dementia patient profile information, including their name, age, medical history, reminder settings (such as medication times or toilet time), and emotion engine settings. Once completed, the user sends this information to the server.

[1270] Step 2:

[1271] The server stores the received profile information in a database, which is used for patient behavior analysis, emotion analysis, and reminder generation.

[1272] Step 3:

[1273] The device (camera-equipped device) is installed in the patient's living environment and monitors their daily activities and facial expressions in real time. The camera captures video data and transmits it to a server.

[1274] Step 4:

[1275] The server analyzes the transmitted video data using the Generative AI (G) to detect abnormal behavior. The emotion engine also analyzes the patient's facial expressions and voice to determine emotions such as anxiety, sadness, and confusion.

[1276] Step 5:

[1277] If abnormal behavior is detected, the server takes the following action. First, it instructs the device to send a voice message to deter the user. For example, it could say, "Mr. / Ms. X, where are you going?" Next, it sends an alert of the abnormal behavior to the user's (family member's) smartphone.

[1278] Step 6:

[1279] If the emotion engine detects an emotion such as anxiety, the server generates an appropriate additional message and sends it to the device, such as "Please stay calm. Is there anything I can help you with?"

[1280] Step 7:

[1281] The device plays voice messages according to instructions from the server. First, it plays a deterrent message for abnormal behavior, followed by a message based on emotions.

[1282] Step 8:

[1283] Users receive alerts on their smartphones to identify abnormal behavior and the patient's emotional state, and can then rush to the patient's location or remotely monitor them if necessary.

[1284] Step 9:

[1285] The server periodically collects data on the patient's daily behavior and emotions and generates a report that is shared with medical professionals. Based on this report, medical professionals can optimize care plans that take into account both the patient's behavior and emotions.

[1286] Step 10:

[1287] The server manages the task schedule based on the reminder settings, and sends the reminder information to the device when the set time arrives.

[1288] Step 11:

[1289] The device will then provide reminders to the patient, for example by playing a voice message saying, "It's time to take your 2 o'clock medicine."

[1290] Step 12:

[1291] The device supports everyday conversations with patients using a conversation support function powered by generative AI (C). For example, it might ask the patient, "How are you feeling today?" and continue the conversation based on the patient's response.

[1292] Step 13:

[1293] The generative AI (C) generates appropriate responses based on the patient's emotional data and continues the conversation. For example, if the patient responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together."

[1294] Step 14:

[1295] The Generative AI (C) saves conversation logs and emotional data for analysis, and uses this information to understand the patient's mental state and interests, which it then reflects in the next conversation.

[1296] The specific processing flow described above will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[1297] Example 2

[1298] 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."

[1299] Conventional care systems for dementia patients have difficulty monitoring and analyzing the patient's behavior and psychological state in real time and providing appropriate care. Furthermore, notification and data sharing with family members and medical professionals are insufficient, making effective care difficult. This increases the burden on caregivers and creates problems such as insufficient protection of the patient's safety and mental health.

[1300] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a camera means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring camera, a voice output means for speaking to the patient to deter abnormal behavior when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data and emotional data to a medical professional, a reminder means for managing the patient's daily life tasks, a conversation support means for conducting daily conversations with the patient using a generation AI, and an emotion recognition means for analyzing the patient's psychological state using emotion recognition technology. This makes it possible to monitor the behavior and emotions of a dementia patient in real time, take appropriate measures, and quickly provide information to family members and medical professionals.

[1301] The "camera means" is a photographing device for monitoring the behavior of dementia patients in real time.

[1302] The "generative AI means" is an artificial intelligence that analyzes footage from surveillance cameras and detects abnormal behavior.

[1303] The "audio output means" refers to a speaker or audio playback device that issues a warning to the patient when abnormal behavior is detected.

[1304] The "communication means" is a communication device for sending a notification to family members when abnormal behavior is detected.

[1305] The "data linking means" is a device or system with a data communication function for linking behavioral data and emotional data to medical professionals.

[1306] A "reminder means" is a device or system that manages a patient's daily tasks and has the function of notifying them of things like when to take their medicine or when to go to the toilet.

[1307] A "conversation support means" is a device or system with dialogue capabilities that uses generative AI to support everyday conversations with patients.

[1308] The "emotion recognition means" is a device or system that uses emotion recognition technology to analyze the psychological state of a patient and take appropriate action.

[1309] "Server" refers to a central processing unit that integrates, manages, and controls the above means, and transmits and receives information and analyzes data.

[1310] The present invention provides a care support system that utilizes generative AI models for families of dementia patients. By incorporating emotion recognition technology, this system realizes a sustainable care environment while also providing psychological care to patients. Specific embodiments of the present invention are described below.

[1311] Overall system overview

[1312] The user uses the application to input the profile information of the dementia patient and sends it to the server. This profile information includes the patient's basic information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[1313] The terminal (a device with a camera) is installed in the patient's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes emotions from the patient's facial expressions and voice.

[1314] If anomalous behavior is detected, the server will:

[1315] 1. Issue an instruction to the device to send a voice message to deter the user.

[1316] 2. An alert is sent to the user's (family member's) smartphone to notify them of abnormal behavior.

[1317] The emotion data analyzed by the emotion engine is used by generative AI to support conversations with patients, for example, providing comforting responses when patients feel anxious, preventing social isolation and reducing psychological burdens.

[1318] The server also periodically aggregates behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[1319] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing patients to perform tasks regularly.

[1320] Hardware and software used

[1321] Hardware: Camera-equipped devices (e.g., Raspberry Pi camera), smartphones, servers

[1322] Software: Applications, generative AI models (e.g., GPT-3), emotion recognition engines (e.g., Microsoft Azure Emotion API), database management systems (e.g., MySQL, PostgreSQL)

[1323] Specific examples

[1324] One day, a user registers a patient's profile in the system. The user enters the patient's basic information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the device starts up and begins monitoring video and emotions.

[1325] Around 10:00 AM, the patient began walking around the room. The camera sent this video to the server, and the generating AI detected abnormal behavior (wandering). The emotion engine then analyzed the patient's facial expression and expressions to determine whether they were anxious. The server immediately instructed the device to play a deterrent message, such as "Mr. / Ms. X, where are you going?" and also sent emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert was sent to the user's smartphone, informing them of the abnormal behavior and the patient's anxiety.

[1326] At 2 p.m., the server sends a medication reminder to the device, which then notifies the patient by voice, "It's time to take your 2 o'clock medication," helping the patient remember to take their medication.

[1327] Conversation support is also provided by generative AI. For example, if a patient is asked, "How are you feeling today?" and responds, "I feel a little lonely," the AI ​​will respond, "Would you like to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the patient's psychological state.

[1328] Data from the emotion engine and generative AI is stored throughout the day, and the server periodically aggregates this data and provides it to medical professionals as reports, allowing them to understand the patient's behavioral patterns and emotional state and create more appropriate care plans.

[1329] Examples of prompt statements

[1330] Prompt example 1: Anomalous behavior detection

[1331] How do you talk to a patient if they are wandering around the room?

[1332] Example prompt 2: Emotional support

[1333] How do you respond when a patient says, "I feel lonely"?

[1334] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[1335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1336] Step 1:

[1337] The user opens the application, enters the profile information of the dementia patient, and sends it to the server. Specifically, the user enters information such as name, age, medication time, toilet time, and emotion recognition engine settings into a form within the application, and presses the "Submit" button to upload the data. The entered data (patient profile information) is sent to the server and saved in a database.

[1338] Step 2:

[1339] The server stores the received profile information in a database and distributes the configuration information to the device. Specifically, the server stores the received information for each item in the database correctly, and then sends the configuration information to the device in JSON format. The input is the profile information sent by the user, and the output is storing it in the database and distributing the configuration information to the device.

[1340] Step 3:

[1341] Based on the received configuration information, the device activates the camera and microphone and begins real-time monitoring. Specifically, the device analyzes the received configuration and activates the Raspberry Pi camera and microphone devices. This causes the device to begin capturing video and audio data at 30 frames per second. The input is the configuration information sent to the device, and the output is the activation of the camera and microphone.

[1342] Step 4:

[1343] The device sends the captured video and audio data to the server. Specifically, the device uploads the video and audio data to the server in real time via Wi-Fi. The input is the video and audio data captured by the device, and the output is the data sent to the server.

[1344] Step 5:

[1345] The server analyzes the received video and audio data to detect abnormal behavior and emotions. Specifically, the server uses generative AI to analyze the video data and detect abnormal behavior (e.g., wandering). It also uses an emotion recognition engine to analyze the patient's emotions (e.g., anxiety, anger, happiness) from facial expressions and voice. The input is the video and audio data received from the device, and the output is the abnormal behavior detection results and emotional analysis results.

[1346] Step 6:

[1347] If abnormal behavior is detected, the server issues a voice message instruction to the device and sends an alert to the family. Specifically, the server sends a deterrent message to the device, such as "Mr. / Ms. X, where are you going?", which is played on the device. It also sends a notification containing abnormal behavior and emotional data to the user's smartphone. The input is the abnormal behavior detection result, and the output is a voice message instruction to the device and an alert notification to the user.

[1348] Step 7:

[1349] The server generates reminder information for daily life tasks and distributes it to the device. Specifically, the server generates a notification message such as "It's time to take your medicine at 2 PM" based on the set schedule (e.g., medicine is to be taken at 2 PM) and sends it to the device. The input is the set reminder schedule, and the output is the reminder notification sent to the device.

[1350] Step 8:

[1351] The generative AI supports everyday conversations with patients. Specifically, it uses emotional data analyzed by the emotion recognition engine to generate an appropriate response. For example, if a patient says, "I feel lonely," the generative AI responds, "Do you want to talk to someone? Let's talk together." The input is emotional data from the emotion recognition engine, and the output is the generated conversational response.

[1352] Step 9:

[1353] The server periodically aggregates the behavioral and emotional data and shares them with medical professionals as reports. Specifically, the server aggregates the data weekly, generates a PDF report, and emails it to medical professionals. The input is the saved behavioral and emotional data, and the output is the generated report.

[1354] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and reduces the burden of caregiving on their families.

[1355] (Application example 2)

[1356] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1357] Conventional care systems for dementia patients have problems such as a lack of real-time responses to abnormal behaviors and emotional care for patients. In addition, systems installed in physical stores have the problem of difficulty in properly monitoring the behavior and emotions of customers and providing necessary responses in real time.

[1358] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a sensor means for monitoring the behavior of a dementia patient in real time, a generation AI means for analyzing video from the monitoring sensor, a voice output means for speaking to the patient to deter them when abnormal behavior is detected, a communication means for sending a notification to a family member when abnormal behavior is detected, a data linking means for linking behavioral data to a medical professional, a reminder means for managing the patient's daily tasks, a conversation support means for using a generation AI to conduct daily conversations with the patient, a behavior analysis means for monitoring the behavior and emotions of customers and instructing appropriate responses, and a profiling means for inputting and managing customer profile information. This enables rapid response to abnormal behavior of patients and customers and comprehensive support, including emotional care.

[1359] The "sensor means" is a device for monitoring the behavior of a subject in real time and generating video data.

[1360] The "generative AI means" is a model that uses artificial intelligence to analyze acquired video data and detect abnormal behavior and emotions.

[1361] The "audio output means" refers to a speaker or an audio playback device that issues a deterrent message to the subject when abnormal behavior is detected.

[1362] The "communication means" is a network communication device for sending a notification to family members and related parties when abnormal behavior is detected.

[1363] The "data linking means" is a device with a data transfer function that links collected behavioral data to medical professionals and provides appropriate care plans.

[1364] A "reminder tool" is a system that manages the patient's daily life tasks and notifies them at designated times.

[1365] The "conversation support tool" is a system that uses generative AI to support everyday conversations with the target person and provide psychological care.

[1366] The "behavior analysis means" is a data analysis device that monitors the behavior and emotions of customers and provides instructions on how to respond appropriately.

[1367] The "profiling method" is a database system for inputting and managing basic information and past behavioral history of customers.

[1368] This invention is a system that monitors the behavior and emotions of dementia patients and customers and responds appropriately. This system includes a sensor means, a generation AI means, a voice output means, a communication means, a data linkage means, a reminder means, a conversation support means, a behavior analysis means, and a profiling means.

[1369] Overall system overview

[1370] Users use the application to input profile information for dementia patients and store visitors, and send it to the server. This profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is then saved in the server's database.

[1371] The sensor means (a device with a camera) is installed in the patient's or visitor's living environment and monitors their daily behavior and facial expressions in real time. The camera captures the video and sends it to a server. The server then analyzes the received video data using a generative AI to detect abnormal behavior. Furthermore, an emotion engine analyzes the subject's emotions from their facial expressions and voice.

[1372] If anomalous behavior is detected, the server will:

[1373] 1. A voice message is sent by a voice output means to call out a deterrent.

[1374] 2. An alert is sent to the smartphones of relevant parties (patient's family members and store staff) via communication means to inform them of abnormal behavior.

[1375] The emotion data analyzed by the emotion engine is used by the generative AI to support conversations with the patient. For example, if a patient feels anxious, this conversation can provide a comforting response, preventing social isolation and reducing psychological burden. Appropriate words can also be used to provide a sense of security to customers.

[1376] The server also periodically collects behavioral and emotional data and shares it with medical professionals to help optimize care plans, allowing them to create care plans that take the patient's emotional state into account.

[1377] In addition, a reminder function is provided to manage daily tasks (e.g., time to take medicine, time to go to the toilet). The server generates reminder information based on a schedule and sends notifications to the device, allowing the user to perform the tasks regularly.

[1378] Program processing overview

[1379] The system operates using the following hardware and software:

[1380] Camera device: Monitors the subject's behavior and facial expressions in real time.

[1381] Server: Stores and analyzes data.

[1382] Generative AI model: Uses TensorFlow / Keras to analyze video data and perform emotion recognition.

[1383] Audio output device: Speaks out if abnormal behavior is detected.

[1384] Communication devices: Send notifications to family and friends.

[1385] Smartphone: Receive alerts and enter profile information.

[1386] Specifically, the data processing and calculation involves preprocessing video data acquired from a camera device and inputting it into an emotion recognition model and an abnormal behavior detection model. Based on the emotion recognition and abnormal behavior detection results, appropriate voice messages and notifications are generated.

[1387] Specific examples

[1388] One day, a user registers the profile information of a patient or visitor in the system. The user inputs basic personal information, medication times, toilet time, and emotion engine settings, and sends them to the server. After the settings are configured, the sensor means is activated and begins monitoring video and emotions.

[1389] At around 10:00 AM, the patient began walking around the room. The camera sends this video to the server, and the generating AI detects abnormal behavior (wandering). Furthermore, the emotion engine analyzes the patient's facial expression and expressions to determine whether they are anxious. The server immediately instructs the audio output device to play a deterrent message, such as "Mr. / Ms. X, where are you going?", and also sends emotion-based messages such as "Please stay calm. Is there something I can help you with?" At the same time, an alert is sent to the family's smartphone via the communication device, informing them of the abnormal behavior and anxious feelings.

[1390] At 2:00 PM, the server sends a set medicine reminder to the sensor means, which then notifies the patient by voice, "It's time to take your 2:00 medicine," helping the patient to remember to take their medicine.

[1391] Conversation support is also provided by generative AI. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Do you want to talk to someone? Let's talk together." By using the emotion engine in this way, appropriate conversations can be provided according to the customer's psychological state.

[1392] Prompt Sentence Examples

[1393] "Abnormal behavior has been detected. The customer currently appears anxious. Please take immediate action."

[1394] This specific processing flow will realize a sustainable system that improves the safety and mental health of dementia patients and visitors, and reduces the burden on users.

[1395] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1396] Step 1:

[1397] Users use the application to enter profile information about dementia patients and store visitors, and send it to the server. The profile information includes basic personal information, reminder settings, and emotion recognition engine settings. This input information is saved in the server's database. The input data is then processed, and the personal information is formatted into an appropriate format and stored in the database.

[1398] Step 2:

[1399] A camera installed on the terminal (sensor means) monitors the behavior and facial expressions of patients or visitors in real time and captures video data. The captured video data is then sent to a server. The input data is the camera video, which is then sent to the server and prepared for analysis.

[1400] Step 3:

[1401] The server preprocesses the received video data using a generative AI method. Specifically, it processes the video data by adjusting its image quality and removing noise, and then uses a generative AI model to detect abnormal behavior and analyze emotions. The input data is the preprocessed video data, and the output is a determination of whether or not there is abnormal behavior and the emotional state.

[1402] Step 4:

[1403] If abnormal behavior is detected, the server instructs the audio output means to send a voice message to deter the patient. Specifically, a message such as "Mr. / Ms. X, where are you going?" is played from the terminal to try to deter the patient. The input data is the abnormal behavior judgment result, and the output is the playback of a voice message.

[1404] Step 5:

[1405] If abnormal behavior is detected, the server sends an alert to the smartphone of the relevant person. The notification message includes the specific details of the abnormal behavior and the emotional state of the patient or visitor. The input data is the abnormal behavior and the results of emotional analysis, and the output is an alert notification.

[1406] Step 6:

[1407] Based on the emotional data analyzed by the emotion engine, the generative AI supports conversations with patients and customers. Specifically, the generative AI generates an emotional message such as "Is there something I can help you with?" and plays it back through a voice output means. The input data is the result of the emotion analysis, and the output is a response message according to the emotion.

[1408] Step 7:

[1409] The server uses a reminder means for managing daily tasks to generate reminder information based on the set schedule and sends a notification to the terminal. Specifically, it plays a notification message such as "It's time to take your medicine at 2 o'clock" by voice. The input data is the reminder schedule, and the output is the reminder notification.

[1410] Step 8:

[1411] The behavioral and emotional data are periodically aggregated and a report is generated to be shared with medical professionals. The server analyzes the data and forwards it to medical professionals. The input data are behavioral and emotional data, and the output is data in the form of a report.

[1412] Step 9:

[1413] Using conversation support tools, the generative AI will converse with patients and customers. For example, if a customer is asked, "How are you feeling today?" and responds, "I'm feeling a little lonely," the generative AI will respond, "Would you like to talk to someone? Let's talk together." The input data is the conversation context, and the output is the dialogue response.

[1414] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1415] 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.

[1416] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1417] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1418] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1419] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1420] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1421] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1422] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1423] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1424] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1425] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1426] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1427] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1428] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1429] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1430] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1431] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1432] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1433] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1434] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1435] The following is further disclosed regarding the above embodiment.

[1436] (Claim 1)

[1437] a camera means for monitoring the behavior of the dementia patient in real time;

[1438] A generative AI means for analyzing images from surveillance cameras;

[1439] a voice output means for giving a voice to the patient to restrain when abnormal behavior is detected;

[1440] a communication means for sending a notification to family members when abnormal behavior is detected;

[1441] a data linkage means for linking behavioral data to medical professionals;

[1442] a reminder means for managing the patient's daily living tasks;

[1443] A conversation support tool that uses generative AI to conduct everyday conversations with patients,

[1444] A system including:

[1445] (Claim 2)

[1446] 10. The system of claim 1, further comprising means for sending a real-time alert and a voice message to the patient and a means for sending a notification to family members when abnormal behavior is detected.

[1447] (Claim 3)

[1448] 10. The system of claim 1, further comprising a scheduling means for enabling daily living task management and reminder functions, and a communication means for transmitting reminder notifications to the patient.

[1449] "Example 1"

[1450] (Claim 1)

[1451] An information input means;

[1452] monitoring means;

[1453] Video analysis means;

[1454] Abnormal behavior deterrent means;

[1455] A notification means;

[1456] A data collection means;

[1457] A task management means;

[1458] A conversation support means;

[1459] A system including:

[1460] (Claim 2)

[1461] 10. The system of claim 1, further comprising means for sending an alert in real time upon detection of abnormal behavior, issuing a voice message, and sending a notification to family members.

[1462] (Claim 3)

[1463] 10. The system of claim 1, further comprising a scheduling means for enabling task management and reminder functions and a communication means for transmitting reminder notifications.

[1464] "Application Example 1"

[1465] (Claim 1)

[1466] a camera means for monitoring the behavior of the dementia patient in real time;

[1467] A generative AI means for analyzing images from surveillance cameras;

[1468] a voice output means for giving a voice to the patient to restrain when abnormal behavior is detected;

[1469] a communication means for sending a notification to family members when abnormal behavior is detected;

[1470] a data linkage means for linking behavioral data to medical professionals;

[1471] a reminder means for managing the patient's daily living tasks;

[1472] A conversation support tool that uses generative AI to conduct everyday conversations with patients,

[1473] processing means for executing security service applications;

[1474] A system including:

[1475] (Claim 2)

[1476] 10. The system of claim 1, further comprising means for sending a real-time alert and a voice message to the patient and a means for sending a notification to family members when abnormal behavior is detected.

[1477] (Claim 3)

[1478] 2. The system according to claim 1, further comprising a scheduling means for enabling daily living task management and reminder functions, and a communication means for transmitting reminder notifications to the patient.

[1479] (Claim 4)

[1480] The system of claim 1 has a security service function that uses a generation AI to detect abnormal behavior and, if abnormal behavior is detected, issues a warning to deter the behavior and notifies family members.

[1481] "Example 2: Combining Emotion Engines"

[1482] (Claim 1)

[1483] a camera means for monitoring the behavior of the dementia patient in real time;

[1484] A generative AI means for analyzing images from surveillance cameras;

[1485] a voice output means for giving a voice to the patient to restrain when abnormal behavior is detected;

[1486] a communication means for sending a notification to family members when abnormal behavior is detected;

[1487] a data linking means for linking the behavioral data and the emotional data to medical professionals;

[1488] a reminder means for managing the patient's daily living tasks;

[1489] A conversation support tool that uses generative AI to conduct everyday conversations with patients,

[1490] emotion recognition means for analyzing the psychological state of a patient using emotion recognition technology;

[1491] A system including:

[1492] (Claim 2)

[1493] 10. The system according to claim 1, further comprising: means for detecting abnormal behavior in real time and issuing a voice message; and means for communicating with family members to notify them.

[1494] (Claim 3)

[1495] 2. The system according to claim 1, further comprising a scheduling means for enabling daily living task management and reminder functions, and a communication means for transmitting reminder notifications to the patient.

[1496] "Application example 2 when combining emotion engines"

[1497] New Claims

[1498] (Claim 1)

[1499] a sensor means for monitoring the behavior of a dementia patient in real time;

[1500] A generating AI means for analyzing images from a monitoring sensor;

[1501] a voice output means for giving a voice to the patient to restrain when abnormal behavior is detected;

[1502] a communication means for sending a notification to family members when abnormal behavior is detected;

[1503] a data linkage means for linking behavioral data to medical professionals;

[1504] a reminder means for managing the patient's daily living tasks;

[1505] A conversation support tool that uses generative AI to conduct everyday conversations with patients,

[1506] A behavior analysis means for monitoring the behavior and emotions of customers and instructing appropriate responses;

[1507] A profiling means for inputting and managing customer profile information;

[1508] A system including:

[1509] (Claim 2)

[1510] 10. The system of claim 1, further comprising means for sending a real-time alert and a voice message to the subject when abnormal behavior is detected, and means for sending a notification to relevant parties.

[1511] (Claim 3)

[1512] 2. The system according to claim 1, further comprising a scheduling means for enabling daily life task management and reminder functions, and a communication means for transmitting reminder notifications to subjects. [Explanation of symbols]

[1513] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a camera means for monitoring the behavior of the dementia patient in real time; A generative AI means for analyzing images from surveillance cameras; a voice output means for giving a voice to the patient to restrain when abnormal behavior is detected; a communication means for sending a notification to family members when abnormal behavior is detected; a data linkage means for linking behavioral data to medical professionals; a reminder means for managing the patient's daily living tasks; A conversation support tool that uses generative AI to conduct everyday conversations with patients, A system including:

2. 10. The system of claim 1, further comprising means for sending a real-time alert and a voice message to the patient and a means for sending a notification to family members when abnormal behavior is detected.

3. 2. The system according to claim 1, further comprising a scheduling means for enabling daily living task management and reminder functions, and a communication means for transmitting reminder notifications to the patient.

Images