System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting death information of residents of a facility, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate by a generation AI, a means for confirming death by a doctor online, a means for performing final confirmation and approval of the death certificate by the doctor, and a means for issuing the certificate and notifying a person concerned.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] Currently, when an elderly person residing in a facility dies, a doctor must travel to the facility to confirm the death and issue a death certificate, which requires a great deal of time and effort. This process places an additional burden on the patient, especially when the doctor travels from afar or is not the patient's primary physician, as time is required to understand the patient's medical history. Furthermore, family members and facility staff experience anxiety and stress while waiting for the doctor to arrive, resulting in overall inefficiency. To solve this issue, a system is needed that enables online confirmation of death and efficient issuance of death certificates. [Means for solving the problem]

[0005] To solve the above problems, we provide an online end-of-life support system that utilizes generative AI. This system includes the following means:

[0006] The system includes a means for entering death information for facility residents, a means for acquiring past medical history and information from the facility, a means for automatically generating death certificates using generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, and a means for issuing the death certificate and notifying relevant parties. This system reduces the travel burden on doctors and enables them to quickly and efficiently confirm deaths and issue death certificates even from remote locations. As a result, the burden on family members and facility staff is also reduced, and the overall process becomes more efficient.

[0007] "Institutional residents" refers to elderly or sick people who are admitted to a specific facility.

[0008] "Death information" refers to information regarding the time, cause, and circumstances of a resident's death.

[0009] "Medical history" refers to information including a resident's past medical records, medical history, and diagnoses.

[0010] "Generative AI" refers to a system that uses artificial intelligence technology to analyze input information and automatically generate a specified document.

[0011] A "death certificate" is an official document in which a doctor certifies a death and lists the cause and other details of the death.

[0012] "Online video calling" refers to a means of communication using video and audio in real time over the Internet.

[0013] "Electronic signature" refers to a technology that electronically authenticates the signer of a document and verifies the validity of the signature.

[0014] "Cloud storage" refers to an online storage service that allows you to store data over the Internet and access and share it remotely as needed.

[0015] "Relevant parties" refers to any person or organization that should receive information, including the family of the deceased resident, facility staff, and other necessary agency personnel. [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] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities. This system includes the following main functions to reduce the burden on doctors, families, and facilities and achieve efficient end-of-life care.

[0038] 1. A user (facility staff member) logs in to the system through a terminal.

[0039] Facility staff use a dedicated login system to access the system from a terminal by entering their username and password.

[0040] 2. The server retrieves resident information from the facility database.

[0041] The server searches the facility's database based on the name of the deceased resident and collects all of the resident's medical history and most recent nursing notes.

[0042] Example: The server retrieves the medical history of "Taro Takayama," including his past treatment records for high blood pressure, diabetes, and heart disease.

[0043] 3. Generative AI automatically generates death certificates

[0044] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[0045] Example: The AI ​​generates a death certificate for "Taro Takayama" and lists the cause of death as "natural death due to cardiac arrest."

[0046] 4. The user (doctor) confirms the death online

[0047] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who report the resident's condition in real time (stopping breathing, checking heart rate, etc.).

[0048] Example: Facility staff report to a doctor that Taro Takayama has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[0049] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0050] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs and approves the certificate.

[0051] Example: A doctor reviews the medical certificate for "Taro Takayama," confirms that there are no problems with the cause of death or the date and time, and then electronically signs and approves it.

[0052] 6. The server issues a death certificate and notifies the relevant parties.

[0053] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[0054] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Takayama Taro's family and the facility manager.

[0055] This system allows for quick confirmation of death even when a doctor is far away, shortening waiting times for family members and facility staff. It also eliminates the need for facilities to have a doctor on standby, which is expected to reduce costs. This online end-of-life support system, which utilizes generative AI, is a useful solution for achieving efficiency in many areas.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[0059] Step 2:

[0060] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[0061] Step 3:

[0062] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[0063] Step 4:

[0064] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[0065] Step 5:

[0066] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[0067] Step 6:

[0068] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[0069] Step 7:

[0070] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[0071] Step 8:

[0072] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[0073] Step 9:

[0074] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[0075] Step 10:

[0076] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[0077] Step 11:

[0078] The server sends notifications to the relevant parties, and sends death certificates to the family, facility administrators, and necessary authorities via email and cloud storage.

[0079] Example 1

[0080] 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."

[0081] Traditionally, end-of-life care for elderly people in facilities required on-site visits by doctors and manual paperwork, resulting in delays and a heavy workload. It was particularly difficult to respond quickly when doctors were far away or at night or on holidays. Additionally, creating and reviewing documents took a lot of time, placing an increased burden on families and facilities. To solve these problems, a system was needed that could efficiently and quickly implement the end-of-life care process.

[0082] 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.

[0083] In this invention, the server includes a means for logging in via a terminal, a means for retrieving resident information from a database, a means for automatically generating a death certificate using generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, and a means for issuing the death certificate and notifying relevant parties. This enables quick and efficient confirmation of death and creation, approval, and notification of the death certificate.

[0084] "Means for logging in via a terminal" refers to a means by which facility staff and other users use a dedicated login system, enter a user name and password for authentication, and access the system.

[0085] "Means for obtaining resident information from the database" refers to the means by which the server connects to the database system within the facility and searches for and obtains the medical history and nursing records of a deceased resident based on the name of that resident.

[0086] "Means for automatically generating death certificates using generative AI" refers to a means for automatically generating death certificates based on acquired medical history information using a generative AI model installed on a server.

[0087] "Online means for doctors to confirm death" refers to a means by which doctors can log in to the system online and check the condition of a deceased resident through a video call with facility staff.

[0088] The "means for the physician to finalize and approve the death certificate" refers to the means by which a physician reviews the death certificate generated within the system, makes any necessary corrections, and then electronically signs and approves it.

[0089] "Means for issuing a death certificate and notifying relevant parties" refers to the means by which the server issues an electronically signed death certificate as an official document and notifies the family, facility administrator, and necessary relevant organizations.

[0090] The present invention is a system that uses a generative AI model to support end-of-life care for residents in elderly care facilities. Specific embodiments of this system are described below.

[0091] Log in through the terminal

[0092] Users (facility staff) access the login system using a terminal PC installed within the facility. A web browser (e.g., Google Chrome or Mozilla Firefox) is installed on this terminal, and they reach the login page by accessing a specific URL (e.g., https: / / facility-login.example.com). By entering their username and password and clicking the login button, the server verifies the provided authentication information against the user database, and if authentication is successful, they are redirected to the dashboard screen.

[0093] Specific examples:

[0094] The user enters the username and password and clicks the login button.

[0095] The server checks the credentials in its database and allows the login.

[0096] Retrieving resident information from the database

[0097] The server accesses the database based on the name of the deceased resident provided by the user, and searches for and retrieves the corresponding resident's medical history and nursing records. A common RDBMS (e.g., MySQL or PostgreSQL) is used as the database system. The server executes SQL queries to extract the necessary data and collects information for generating a medical certificate.

[0098] Specific examples:

[0099] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Example Name'" to retrieve the required data.

[0100] Automated generation of death certificates using generative AI

[0101] A generative AI model (e.g., GPT-3, BERT) installed on the server automatically generates a death certificate based on the acquired medical history information. The certificate includes basic information about the resident, cause of death, date and time of death, etc. The prompt is entered as "Please automatically generate a death certificate based on the resident's name and medical history." The generative AI model then generates the appropriate information and creates the certificate.

[0102] Specific prompt examples:

[0103] "Please generate a death certificate for Taro Takayama."

[0104] "Automatically generate death certificates based on resident names and medical histories."

[0105] A doctor will certify death online

[0106] Doctors log in to the system online and check the condition of the deceased resident via video call with facility staff. These video calls are made using common video call software such as Zoom or Microsoft Teams. The facility staff use the camera to report to the doctor in real time if the resident stops breathing or has a low heart rate. The doctor then confirms death based on this information.

[0107] Specific examples:

[0108] Facility staff report a resident's cardiac arrest to a doctor via video call, who then confirms the report.

[0109] Final review and approval of the medical certificate by the doctor

[0110] The physician reviews the death certificate generated in the system, makes any necessary corrections, and then electronically signs and approves the certificate using electronic signature software (e.g., Adobe Sign), completing the official certificate.

[0111] Specific examples:

[0112] The doctor reviews the generated medical certificate and approves it with an electronic signature.

[0113] Issue a death certificate and notify the appropriate parties

[0114] The server issues a digitally signed death certificate in PDF format and notifies the family, facility administrators, and necessary related organizations via email (e.g., SMTP protocol) or cloud storage services (e.g., Google Drive, Dropbox).

[0115] Specific examples:

[0116] The server creates a medical certificate in PDF format and sends it to the family and facility administrator by email.

[0117] This system will enable fast and efficient end-of-life care in elderly care facilities, reducing the burden on doctors, facility staff, and family members. By utilizing a generative AI model, the process from automatically generating medical certificates to approval and notification will be integrated, streamlining the overall work.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] The user logs in through the terminal. Using the terminal's web browser, the user accesses a specific URL. They enter their username and password and click the login button. The server receives the authentication information and verifies it against the user information in the database. If authentication is successful, the user is redirected to the dashboard screen.

[0121] Input: Username, Password

[0122] Data processing: Check the entered authentication information against the database

[0123] Output: Login success or failure, dashboard screen

[0124] Specific behavior:

[0125] The user enters the username and password and clicks the login button.

[0126] The server checks the entered information against a database.

[0127] Step 2:

[0128] The server retrieves resident information from the facility's database. Using the name of the deceased resident provided by the user, it connects to the database and executes an SQL query to retrieve the medical history and nursing records for that resident.

[0129] Input: Resident's name

[0130] Data processing: Execute SQL queries and extract relevant data from the database

[0131] Output: Resident medical history data and nursing records

[0132] Specific behavior:

[0133] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Taro Takayama'".

[0134] Retrieve Takayama Taro's medical history and nursing records from the database.

[0135] Step 3:

[0136] The generative AI automatically generates a death certificate. Using a generative AI model installed on the server, the AI ​​generates a death certificate by inputting the acquired medical history data as prompts. The generated certificate includes the resident's basic information, cause of death, and date and time of death.

[0137] Input: Resident's medical history data, prompt statement

[0138] Data processing: Generative AI models are used to generate medical reports

[0139] Output: Generated death certificate

[0140] Specific behavior:

[0141] Enter "Please generate a death certificate for Taro Takayama" as the prompt text into the generation AI.

[0142] AI generates Taro Takayama's death certificate.

[0143] Step 4:

[0144] The user (doctor) confirms death online. The doctor logs into the system online and makes a video call with facility staff. The facility staff uses a camera to share the resident's condition in real time, reporting any cessation of breathing or confirmation of heart rate. The doctor confirms death based on this information.

[0145] Input:Video call information

[0146] Data processing: Real-time information sharing and confirmation

[0147] Output: Death confirmation success / failure

[0148] Specific behavior:

[0149] Facility staff use a video camera to show doctors that a resident's heart has stopped beating.

[0150] The doctor will check in via video call.

[0151] Step 5:

[0152] The user (doctor) performs the final review and approval of the death certificate. The doctor reviews the death certificate generated within the system and makes any necessary corrections. He then electronically signs and approves it.

[0153] Input: Generated death certificate

[0154] Data processing: Doctor's confirmation and correction, electronic signature

[0155] Output: Approved death certificate

[0156] Specific behavior:

[0157] The doctor reviews the medical certificate and approves it with an electronic signature.

[0158] Step 6:

[0159] The server issues the death certificate and notifies the relevant parties. The server creates a digitally signed death certificate in PDF format and sends it to the family, facility administrators, and relevant agencies via email or cloud storage.

[0160] Input: Approved Death Certificate

[0161] Data processing: Creating documents in PDF format and sending them to recipients

[0162] Output: Medical certificate sent

[0163] Specific behavior:

[0164] The server generates a medical certificate in PDF format.

[0165] A medical certificate will be sent via email to the family and facility manager.

[0166] (Application example 1)

[0167] 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."

[0168] Caring for elderly people in nursing homes places a significant burden on medical professionals and facility staff, requiring time and effort. In particular, efficient processes, such as confirming the death, preparing a death certificate, and contacting relevant parties, require rapid and accurate information sharing and management. It is also important to ensure that remote medical professionals can smoothly confirm the death online and approve the death certificate. It is necessary to resolve these issues and simplify and streamline the procedures for caring for elderly people at the end of their lives.

[0169] 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.

[0170] In this invention, the server includes a means for inputting death information of facility residents, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using generation AI, a means for a medical professional to confirm the death online, a means for the medical professional to perform final review and approval of the medical certificate, a means for issuing the medical certificate as an electronic document and notifying relevant parties, a means for facility staff to log in and access using a smartphone application, and a means for communicating with medical professionals in real time via online video calls. This reduces the burden on medical professionals and facility staff and enables fast and efficient end-of-life care.

[0171] "Institutional residents" are elderly people who live in residential facilities such as nursing homes and care facilities.

[0172] "Death information" refers to detailed data on the deaths of facility residents, including the date, time, place, and cause of death.

[0173] "Medical history" refers to information about the illnesses and treatment history that a facility resident has experienced.

[0174] "Generative AI" refers to artificial intelligence that automatically generates documents using machine learning and natural language processing techniques.

[0175] A "death certificate" is a medical document that officially identifies and records the death of a resident.

[0176] "Online video calling" is a communication method that allows you to simultaneously send and receive video and audio over the Internet.

[0177] "Healthcare workers" is a general term that refers to doctors, nurses, and other health-related professionals.

[0178] An "electronic document" is a document that is created electronically and is handled in the form of a PDF or email.

[0179] "Stakeholders" are people such as family members, facility administrators, and government agencies with whom death certificates and other important information should be shared.

[0180] "Facility staff" are staff who work within the facility and support the daily lives of residents.

[0181] A "smartphone application" is software that runs on a smartphone and provides a variety of functions.

[0182] "Logging in" is the act of a user entering authentication information to access a particular system or service.

[0183] "Access" is the act of operating or checking information or systems.

[0184] "Real-time communication" refers to a means of communication in which information is exchanged immediately without delay.

[0185] This invention is a system that efficiently supports the end-of-life care of elderly people residing in nursing homes. This system uses a smartphone application to allow facility staff and medical professionals to cooperate in confirming deaths and preparing medical certificates online.

[0186] Hardware and Software Configuration

[0187] server

[0188] The server has the following main functions:

[0189] Entering death information for facility residents

[0190] The server accepts death information entered by facility staff and stores it in a database.

[0191] Obtaining medical history and facility information

[0192] The server retrieves the resident's past medical history and most recent nursing notes from the facility's database.

[0193] Generative AI for generating death certificates

[0194] The server automatically generates a death certificate based on the acquired medical history information using generation AI, which uses natural language processing technology to generate the contents of the certificate.

[0195] Final review and approval of medical certificate

[0196] The server provides an interface for medical professionals to review the medical certificate via an online video call and approve it with an electronic signature.

[0197] Notification to relevant parties

[0198] The server generates a medical certificate in PDF format and sends it to the family or facility administrator via email.

[0199] Device (smartphone)

[0200] The smartphone application running on the device has the following features:

[0201] Login and Information Access

[0202] Facility staff can log into the system by entering a username and password to view and enter resident information.

[0203] Online video call for death confirmation

[0204] Facility staff will communicate with medical personnel via video call to confirm deaths in real time.

[0205] Data Flow and Processing

[0206] 1. Resident death information is entered into the system:

[0207] Facility staff enter information about resident deaths via a smartphone application.

[0208] 2. Resident medical history information is retrieved by the server:

[0209] The server searches the database based on the entered information and retrieves the resident's medical history and nursing records.

[0210] 3. The generative AI model generates the diagnosis:

[0211] The generation AI on the server uses natural language processing technology to generate a death certificate based on the acquired information.

[0212] 4. Confirming death via online video call:

[0213] Facility staff use the video calling function on their smartphones to communicate with medical professionals and confirm the death of a resident.

[0214] 5. Medical certificate verification and approval:

[0215] Medical professionals can review the generated medical certificate through a smartphone application, make any necessary corrections, and electronically sign it.

[0216] 6. Issuance of medical certificate and notification to relevant parties:

[0217] The server generates a medical certificate in PDF format after final confirmation and approval and sends it via email to the family or facility administrator.

[0218] Specific examples

[0219] For example, if "Taro Tanaka," a resident of a certain facility, dies, facility staff enter the date, time, and location of "Taro Tanaka's" death into a smartphone application. The server receives this information and retrieves records of "Taro Tanaka's" medical history, including past records of high blood pressure, diabetes, and heart disease. Based on this information, the generative AI model generates a medical certificate stating "natural death due to cardiac arrest." A medical professional then confirms the death via an online video call, checks the generated medical certificate, and approves it. Finally, the server generates the medical certificate in PDF format and sends it by email to Tanaka's family and the facility administrator.

[0220] Prompt Sentence Examples

[0221] "Taro Tanaka" has a medical history of "high blood pressure, diabetes, heart disease." Based on this, please generate the following death certificate:

[0222] basic information

[0223] Cause of death: Natural causes due to cardiac arrest

[0224] Date of Death: YYYY-MM-DD HH:MM

[0225] This will reduce the burden on facility staff and medical professionals, and create a system that enables fast and efficient end-of-life care.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] Facility staff log in to the system via a smartphone application.

[0229] Input: Username, Password

[0230] Output: Authentication token

[0231] Specific operation: The user name and password entered from the terminal are sent to the server, and the server verifies the authentication information. If authentication is successful, the server returns an authentication token to the terminal.

[0232] Step 2:

[0233] Facility staff enters information about the death of a resident.

[0234] Input: Resident's name, date and time of death, place of death

[0235] Output: Death information is saved in the server database.

[0236] Specific operation: Death information entered on the device is sent to the server, which then stores the information in a database.

[0237] Step 3:

[0238] The server retrieves the resident's medical history information.

[0239] Input: Resident's name

[0240] Output: Resident's past medical history information

[0241] Specific operation: The server searches the database and retrieves past medical history information for the specified resident.

[0242] Step 4:

[0243] Generative AI models automatically generate death certificates.

[0244] Input: Resident medical history and mortality information

[0245] Output: Death certificate contents

[0246] Specific operation: The generation AI on the server uses the acquired medical history information and death information to generate the contents of the death certificate.

[0247] Step 5:

[0248] Facility staff will contact medical personnel via online video call to confirm the death.

[0249] Input: Video call ID

[0250] Output: Medical personnel confirms death

[0251] Specific operation: Using the device's video call function, a real-time call is made with a medical professional to confirm the death. The medical professional then visually confirms the death and conveys approval to the facility staff.

[0252] Step 6:

[0253] A medical professional will provide final review and approval of the medical certificate.

[0254] Input: Generated death certificate

[0255] Output: Digitally signed medical certificate

[0256] Specific operation: The server displays the generated medical certificate to the medical professional, who checks the contents. If there are no problems, the medical professional electronically signs and approves it.

[0257] Step 7:

[0258] The server issues the medical certificate as an electronic document and notifies the relevant parties.

[0259] Input: Electronically signed medical certificate, contact information of the parties involved

[0260] Output: Notify relevant parties and send a PDF medical report

[0261] What happens: The server generates an approved medical certificate in PDF format and emails it to the appropriate parties, including family members and facility managers.

[0262] This will establish an efficient processing flow for the system that realizes the application example, enabling prompt and accurate end-of-life care for the elderly.

[0263] 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.

[0264] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities, and further combines it with an emotion engine that recognizes the user's emotions. This system includes the following main functions, reducing the burden on doctors, families, and facilities and realizing efficient, emotion-sensitive end-of-life care.

[0265] 1. A user (facility staff member) logs in to the system through a terminal.

[0266] Facility staff access the system by entering their username and password on a dedicated login screen.

[0267] 2. The server retrieves resident information from the facility database.

[0268] The server searches the facility database based on the entered resident's name and collects the individual's medical history, medical record information, and latest nursing records.

[0269] Example: The server retrieves the medical history of "Jiro Suzuki," including past treatment records for high blood pressure, diabetes, and heart disease.

[0270] 3. Generative AI automatically generates death certificates

[0271] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[0272] Example: A generation AI generates a death certificate for "Suzuki Jiro" and lists the cause of death as "natural death due to cardiac arrest."

[0273] 4. The user (doctor) confirms the death online

[0274] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who then report the resident's condition in real time.

[0275] Example: Facility staff report to a doctor that Suzuki Jiro has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[0276] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0277] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs the certificate to give final approval.

[0278] Example: A doctor checks the contents of Suzuki Jiro's medical certificate, confirms that there are no problems with the cause of death or the date and time, and approves it by electronically signing.

[0279] 6. The server issues a death certificate and notifies the relevant parties.

[0280] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[0281] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Suzuki Jiro's family and the facility manager.

[0282] 7. Use an emotion engine that recognizes user emotions

[0283] The emotion engine analyzes user input, voice, and facial expression data to recognize the user's emotional state (e.g., stress, anxiety, sadness, etc.).

[0284] Example: If a facility staff member is very sad, the emotion engine will detect this state and the system will provide support information on emotional care.

[0285] 8. Feedback emotion recognition results to generative AI

[0286] The emotion engine analyzes the user's emotional data and feeds the results back to the generation AI, which can then create comments and advice for the diagnosis that take emotions into consideration.

[0287] Example: The emotion engine detects tension among facility staff, and the generative AI adds a comment to the medical certificate such as, "Please pay special attention to caring for the bereaved family."

[0288] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, improving the efficiency of the overall process while also providing psychological support.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[0292] Step 2:

[0293] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[0294] Step 3:

[0295] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[0296] Step 4:

[0297] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[0298] Step 5:

[0299] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[0300] Step 6:

[0301] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[0302] Step 7:

[0303] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[0304] Step 8:

[0305] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[0306] Step 9:

[0307] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[0308] Step 10:

[0309] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[0310] Step 11:

[0311] The server sends notifications to the relevant parties. The server sends the death certificate to the family, facility administrators, and necessary related agencies via email and cloud storage.

[0312] Step 12:

[0313] The emotion engine recognizes the user's emotions. The emotion engine collects input, voice, and facial expression data from the device and analyzes the user's emotional state.

[0314] Step 13:

[0315] The emotion engine feeds the analysis results back to the generative AI, which analyzes the user's emotions and passes the results to the generative AI, which then adjusts the diagnosis and system response accordingly.

[0316] Step 14:

[0317] The emotion engine provides users with appropriate guidance and support information. For users who are feeling stressed or anxious, the emotion engine displays information such as relaxation methods and support contact information.

[0318] Example 2

[0319] 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."

[0320] In modern elderly care facilities, the series of procedures that must be carried out when a resident passes away requires a lot of time and effort, placing a heavy burden on those involved. It is also often difficult to confirm the cause of death and prepare a medical certificate in a timely manner. Furthermore, there is a lack of emotional support for those involved, placing a heavy mental burden on them. A system that can solve these issues is needed.

[0321] The identification processing 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 means for inputting death information of a facility resident, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using a generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, a means for issuing the death certificate and notifying relevant parties, a means for using an emotion engine that recognizes the user's emotions, and a means for feeding back the emotion recognition results to the generation AI. This not only streamlines procedures at the time of death and enables prompt and accurate responses, but also provides support that takes into consideration the emotional state of those involved.

[0322] "Institutional resident" refers to an individual who resides in a nursing home or care facility.

[0323] "Means for inputting death information" refers to an interface that allows a user to input the death status of a facility resident into the system using a terminal.

[0324] "Means for obtaining past medical history and information from the facility" refers to the function by which the server searches for and obtains the medical history and chart information of facility residents from the database.

[0325] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze data and perform a specific task (in this case, automatically generating death certificates).

[0326] "Means for automatically generating death certificates" refers to the process by which the generation AI automatically creates death certificates based on the data collected.

[0327] "Means for doctors to confirm death online" refers to a function that allows doctors to confirm the death status of facility residents in real time using a video call system.

[0328] "Means for final review and approval of the medical certificate by a physician" refers to the process by which a physician reviews the generated death certificate and, if necessary, amends and approves it.

[0329] "Means for issuing a death certificate and notifying relevant parties" refers to the function of the server to generate an official death certificate and notify family members, facility managers, and relevant organizations.

[0330] An "emotion engine that recognizes user emotions" refers to a system that analyzes a user's voice, facial expressions, and text data to identify their emotional state.

[0331] "Means for feeding back emotion recognition results to the generation AI" refers to the process in which the emotion engine sends the detected emotion data to the generation AI, and the generation AI adds or modifies the corresponding information based on the results.

[0332] This invention relates to a system that utilizes generative AI and an emotion engine to provide efficient and emotionally sensitive support for end-of-life care for elderly people residing in facilities.

[0333] The system uses the following major hardware and software:

[0334] Hardware:

[0335] Server: Used for database management, running generative AI, and analyzing the emotion engine.

[0336] Terminals: Windows PCs and tablets are used as interfaces for facility staff and doctors to operate the system.

[0337] software:

[0338] Database management system: Resident information is managed using database software such as MySQL.

[0339] Generative AI models: Use advanced generative AI, such as OpenAI GPT-4, to automatically generate death certificates.

[0340] Emotion engine: Analyzes the user's emotional state using emotion analysis software such as IBM Watson.

[0341] Video call system: Doctors can remotely certify death using video call applications such as Zoom.

[0342] The main functions of the system and the specific operations at each step are shown below.

[0343] 1. A user (facility staff member) logs in to the system through a terminal.

[0344] Facility staff enter their username and password into a dedicated login screen and send the authentication information to the server, which then compares it with the database for authentication and returns the authentication result, allowing the facility staff to access the system.

[0345] 2. The server retrieves resident information from the facility database.

[0346] The user enters the name of the resident they are searching for into the terminal and sends the data to the server, which searches the MySQL database to gather the resident's medical history, medical record information, and latest nursing notes. The collected information is returned to the terminal and displayed to the user.

[0347] 3. Generative AI automatically generates death certificates

[0348] The server inputs the collected resident information into a generative AI model to automatically generate a death certificate. The generated certificate includes the resident's basic information, cause of death, and date and time of death. The generated certificate is stored in a database and a preview is displayed on the device.

[0349] 4. The user (doctor) confirms the death online

[0350] Doctors log in to the system via video chat, and facility staff report the status of residents in real time. The doctors then confirm the death status of the residents and send the results to the server.

[0351] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0352] The doctor logs in to the terminal, checks the generated death certificate, makes any necessary corrections, and electronically signs it using an electronic signature tool for final approval.

[0353] 6. The server issues a death certificate and notifies the relevant parties.

[0354] The server issues the approved medical certificate as an official document in PDF format. The certificate is then sent to the family, facility administrator, and relevant organizations via email or cloud storage. A message confirming the completion of the transmission is displayed on the device.

[0355] 7. Use an emotion engine that recognizes user emotions

[0356] The device sends the user's input, voice, and facial expression data to the emotion engine, which analyzes the user's emotional state. The analysis results are returned to the server, and necessary support information is provided to the user based on the results.

[0357] 8. Feedback emotion recognition results to generative AI

[0358] The server inputs the emotion data obtained from the emotion engine into the generative AI model, which then adds emotion-sensitive comments and advice to the diagnosis. The updated diagnosis is saved in a database and displayed on the device.

[0359] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, streamlining the overall process and providing psychological support.

[0360] Example prompt sentence:

[0361] "Please create a prompt that embodies how the emotion engine analyzes the user's emotions in the process of creating a death certificate for an elderly person and feeds the results back to the generative AI."

[0362] As described above, the present invention realizes an effective system for comprehensively supporting the end-of-life care of facility residents.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] A user (facility staff member) logs into the system through a terminal.

[0366] Input: Username and Password.

[0367] Specific operation: Facility staff enter their username and password into a dedicated login screen. The terminal then sends the entered data to the server.

[0368] Data processing: The server checks the authentication information against a database.

[0369] Output: Authentication result (success or failure).

[0370] Specific operation: The server returns the authentication result to the terminal, and the terminal displays a login success message, allowing the user to access the system.

[0371] Step 2:

[0372] The server retrieves resident information from the facility's database.

[0373] Input: Resident's name.

[0374] Specific operation: The user enters the name of the resident they are searching for into the terminal, and the terminal sends the input data to the server.

[0375] Data processing: The server searches the database using a MySQL query.

[0376] Output: Medical history information, chart information, latest nursing records.

[0377] Specific operation: The server returns the acquired information to the terminal, which displays the information to the user.

[0378] Step 3:

[0379] Generative AI automatically generates death certificates.

[0380] Input: Collected resident information.

[0381] Specific operation: The server inputs the collected resident information into the generative AI model.

[0382] Data processing: Generative AI generates a death certificate based on the input data.

[0383] Output: The generated death certificate.

[0384] Specific operation: The server saves the generated medical certificate in the database. The server sends a preview of the medical certificate to the terminal and displays it to the user.

[0385] Step 4:

[0386] The user (doctor) certifies the death online.

[0387] Input: Real-time reporting from facility personnel.

[0388] Specific operations: A doctor logs in to the system via a video chat system. A facility staff member reports the resident's condition.

[0389] Data processing: Doctors review information via video call.

[0390] Output: Death confirmation result.

[0391] Specific operation: The doctor sends the death confirmation results to the server via the terminal.

[0392] Step 5:

[0393] The user (doctor) performs final review and approval of the medical certificate.

[0394] Input: The generated death certificate.

[0395] Specific operation: The doctor logs in to the terminal and checks the contents of the generated death certificate.

[0396] Data processing: The doctor will amend the medical certificate as necessary.

[0397] Output: Approved medical certificate.

[0398] Specific operation: The doctor electronically signs the medical certificate using the electronic signature tool. The server stores the signed medical certificate in the database.

[0399] Step 6:

[0400] The server issues a death certificate and notifies the relevant parties.

[0401] Input: Approved medical certificate.

[0402] Specific operation: The server issues the approved medical certificate as an official document.

[0403] Data processing: Generate medical certificate in PDF format.

[0404] Output: Death certificate in PDF format.

[0405] Specific operation: The server sends the medical certificate to the relevant parties via email or cloud storage. The device displays a message to the user that transmission is complete.

[0406] Step 7:

[0407] Uses an emotion engine that recognizes the user's emotions.

[0408] Input: User input, voice, and facial expression data.

[0409] Specific operation: The device sends the user's input, voice, and facial expression data to the emotion engine.

[0410] Data processing: The emotion engine analyzes the data and recognizes the user's emotional state.

[0411] Output: Emotion recognition result.

[0412] Specific operation: The emotion engine returns the results to the server. The device displays the results to the user and provides necessary support information.

[0413] Step 8:

[0414] The emotion recognition results are fed back to the generative AI.

[0415] Input: Emotion data.

[0416] Specific operation: The server obtains the emotion data obtained from the emotion engine.

[0417] Data processing: Emotional data is input into a generative AI model, which then adds emotionally sensitive comments and advice to the diagnosis report.

[0418] Output: Updated death certificate.

[0419] Specific operation: The server sends the updated medical certificate to the database and the terminal. The terminal displays the new medical certificate to the user.

[0420] (Application example 2)

[0421] 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."

[0422] In facilities with many elderly residents, a swift and accurate response is required when a resident dies, while at the same time reducing the burden on those involved and taking their emotions into consideration. Furthermore, customer service in retail stores requires an appropriate understanding of the emotional state of the elderly and flexible responses accordingly. Conventional systems have found it difficult to efficiently resolve these issues.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0424] In this invention, the server includes means for inputting death information of a facility resident, means for acquiring past medical history and information from the facility, means for automatically generating a death certificate using a generation AI, means for a doctor to confirm the death online, means for the doctor to perform final confirmation and approval of the death certificate, means for issuing the death certificate and notifying relevant parties, means for recognizing emotions when providing services for the elderly, and means for the generation AI to generate a response based on emotion recognition data. This enables a quick, accurate, and emotion-sensitive response when an elderly person dies, and also enables flexible emotional responses in retail stores for services for the elderly.

[0425] "Institutional residents" refers to elderly people or patients who are staying in nursing homes or medical facilities for an extended period of time.

[0426] "Death information" refers to basic information such as the date, time, place, and cause of death of a facility resident.

[0427] "Generative AI" refers to algorithms or systems that use artificial intelligence technology to automatically generate documents or information based on specific data or conditions.

[0428] A "death certificate" is an official document in which a doctor legally certifies and details the death.

[0429] "Online video calling" refers to a means of communicating in real time through audio and video using the Internet.

[0430] "Emotion recognition" refers to the technology of analyzing data such as voice, facial expressions, and behavior to determine a person's emotional state (e.g., sadness, joy, tension, etc.).

[0431] "Response generation" refers to the process of automatically generating appropriate responses or advice based on input data and circumstances.

[0432] "Medical history" refers to information that records details of a patient's past illnesses, their treatment history, and the medications they have used.

[0433] "Final review and approval" refers to the procedure in which experts and related parties finally review the accuracy of the contents of the generated documents and information and formally approve the contents.

[0434] This invention relates to a system that uses a generative AI and an emotion recognition engine to provide end-of-life care for the elderly and customer service support in retail stores. A specific embodiment of this system will be described in detail below.

[0435] Hardware and software used

[0436] Hardware: Smartphone (with camera), server

[0437] Software: OpenCV (for face detection and preprocessing), EmotionEngine (emotion recognition engine), ResponseGenerator (response generation AI)

[0438] Specific flow of data processing

[0439] The server first receives input from a smartphone. Facility staff enters information about the death of a facility resident using a smartphone. The server then retrieves information such as the resident's past medical history and the latest nursing records from the facility's database.

[0440] Based on the acquired information, the generation AI automatically generates a death certificate. At this time, the generation AI fills in basic information on the death certificate, such as the cause of death and the date and time of death. The generated certificate is then reviewed online by a doctor. The doctor confirms the death via video call and makes a final review and approval of the certificate. The finalized death certificate is issued by the server and notified to the relevant parties.

[0441] At the same time, in retail stores that provide services for the elderly, the Emotion Engine uses smartphone cameras to analyze the facial expressions of the elderly and determine their emotional state. Based on this, the Response Generator generates appropriate responses and advice.

[0442] Specific examples

[0443] A facility staff member enters information about Suzuki Jiro's death on a smartphone. The server retrieves Suzuki Jiro's past medical history (high blood pressure, diabetes, heart disease) from the facility's database and provides this information to the generation AI. Based on this, the generation AI automatically generates a death certificate for Suzuki Jiro, stating "natural death due to cardiac arrest." A doctor verifies the death online via video call and provides final confirmation and approval of the certificate's contents. The server then notifies the family and facility administrator of the official certificate.

[0444] In retail stores, the facial expressions of elderly people are captured with a smartphone camera and their emotional state is analyzed by the Emotion Engine. For example, if the customer is nervous, the Response Generator generates a response such as, "You seem nervous. Let us suggest a product that will have a relaxing effect."

[0445] Prompt Sentence Examples

[0446] The customer's emotional state is determined to be "tension." Please generate advice that can suggest products that have a relaxing effect for the elderly.

[0447] This system will enable a swift, accurate, and emotionally sensitive response when an elderly person dies, and will also enable flexible, emotionally sensitive services for the elderly in retail stores.

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] Users use their smartphones to input information about the deaths of facility residents. This information includes basic information such as the resident's name, date and time of death, location, and cause of death. This information is then sent to the server via the smartphone.

[0451] Step 2:

[0452] The server retrieves the resident's past medical history and nursing records from the facility's database. It searches for medical history data, treatment records, nursing records, etc. based on the specific resident's name and retrieves this information all at once. This prepares the data necessary for the generation AI.

[0453] Step 3:

[0454] The AI ​​on the server automatically generates a death certificate based on the acquired medical history information and the current death information entered by the user. The AI ​​analyzes the medical history data and creates a provisional death certificate that includes the cause of death, date and time of death, etc.

[0455] Step 4:

[0456] The user (doctor) confirms death via online video call. Using a smartphone or computer, the user connects with facility staff to check for breathing and cardiac arrest. The facility staff reports the resident's condition in real time, which the doctor confirms.

[0457] Step 5:

[0458] The doctor performs a final review and approval of the death certificate generated on the server. He logs into the online system, checks the provisional death certificate, corrects any deficiencies, and performs a final review. He then officially approves the certificate with an electronic signature.

[0459] Step 6:

[0460] The server issues a final, verified, and approved death certificate, notifies the relevant parties, creates a PDF of the certificate, emails it to the family and facility administrator, and shares it with any necessary authorities.

[0461] Step 7:

[0462] The user (store staff) uses a smartphone camera to analyze the facial expressions of elderly people who visit a retail store. The acquired video data is sent to a server.

[0463] Step 8:

[0464] The Emotion Engine on the server analyzes the received facial expression data and determines the emotional state of the elderly person. Specifically, it analyzes facial expression images and recognizes emotions such as "sadness," "happiness," and "tension."

[0465] Step 9:

[0466] The server's ResponseGenerator generates an optimal response based on the determined emotional state. For example, if the elderly person is nervous, the response generated will be "suggest products that have a relaxing effect."

[0467] Step 10:

[0468] The user (store staff) checks the generated response and responds appropriately to the elderly person, such as suggesting a relaxing herbal tea.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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."

[0485] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities. This system includes the following main functions to reduce the burden on doctors, families, and facilities and achieve efficient end-of-life care.

[0486] 1. A user (facility staff member) logs in to the system through a terminal.

[0487] Facility staff use a dedicated login system to access the system from a terminal by entering their username and password.

[0488] 2. The server retrieves resident information from the facility database.

[0489] The server searches the facility's database based on the name of the deceased resident and collects all of the resident's medical history and most recent nursing notes.

[0490] Example: The server retrieves the medical history of "Taro Takayama," including his past treatment records for high blood pressure, diabetes, and heart disease.

[0491] 3. Generative AI automatically generates death certificates

[0492] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[0493] Example: The AI ​​generates a death certificate for "Taro Takayama" and lists the cause of death as "natural death due to cardiac arrest."

[0494] 4. The user (doctor) confirms the death online

[0495] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who report the resident's condition in real time (stopping breathing, checking heart rate, etc.).

[0496] Example: Facility staff report to a doctor that Taro Takayama has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[0497] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0498] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs and approves the certificate.

[0499] Example: A doctor reviews the medical certificate for "Taro Takayama," confirms that there are no problems with the cause of death or the date and time, and then electronically signs and approves it.

[0500] 6. The server issues a death certificate and notifies the relevant parties.

[0501] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[0502] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Takayama Taro's family and the facility manager.

[0503] This system allows for quick confirmation of death even when a doctor is far away, shortening waiting times for family members and facility staff. It also eliminates the need for facilities to have a doctor on standby, which is expected to reduce costs. This online end-of-life support system, which utilizes generative AI, is a useful solution for achieving efficiency in many areas.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[0507] Step 2:

[0508] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[0509] Step 3:

[0510] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[0511] Step 4:

[0512] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[0513] Step 5:

[0514] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[0515] Step 6:

[0516] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[0517] Step 7:

[0518] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[0519] Step 8:

[0520] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[0521] Step 9:

[0522] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[0523] Step 10:

[0524] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[0525] Step 11:

[0526] The server sends notifications to the relevant parties, and sends death certificates to the family, facility administrators, and necessary authorities via email and cloud storage.

[0527] Example 1

[0528] 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."

[0529] Traditionally, end-of-life care for elderly people in facilities required on-site visits by doctors and manual paperwork, resulting in delays and a heavy workload. It was particularly difficult to respond quickly when doctors were far away or at night or on holidays. Additionally, creating and reviewing documents took a lot of time, placing an increased burden on families and facilities. To solve these problems, a system was needed that could efficiently and quickly implement the end-of-life care process.

[0530] 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.

[0531] In this invention, the server includes a means for logging in via a terminal, a means for retrieving resident information from a database, a means for automatically generating a death certificate using generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, and a means for issuing the death certificate and notifying relevant parties. This enables quick and efficient confirmation of death and creation, approval, and notification of the death certificate.

[0532] "Means for logging in via a terminal" refers to a means by which facility staff and other users use a dedicated login system, enter a user name and password for authentication, and access the system.

[0533] "Means for obtaining resident information from the database" refers to the means by which the server connects to the database system within the facility and searches for and obtains the medical history and nursing records of a deceased resident based on the name of that resident.

[0534] "Means for automatically generating death certificates using generative AI" refers to a means for automatically generating death certificates based on acquired medical history information using a generative AI model installed on a server.

[0535] "Online means for doctors to confirm death" refers to a means by which doctors can log in to the system online and check the condition of a deceased resident through a video call with facility staff.

[0536] The "means for the physician to finalize and approve the death certificate" refers to the means by which a physician reviews the death certificate generated within the system, makes any necessary corrections, and then electronically signs and approves it.

[0537] "Means for issuing a death certificate and notifying relevant parties" refers to the means by which the server issues an electronically signed death certificate as an official document and notifies the family, facility administrator, and necessary relevant organizations.

[0538] The present invention is a system that uses a generative AI model to support end-of-life care for residents in elderly care facilities. Specific embodiments of this system are described below.

[0539] Log in through the terminal

[0540] Users (facility staff) access the login system using a terminal PC installed within the facility. A web browser (e.g., Google Chrome or Mozilla Firefox) is installed on this terminal, and they reach the login page by accessing a specific URL (e.g., https: / / facility-login.example.com). By entering their username and password and clicking the login button, the server verifies the provided authentication information against the user database, and if authentication is successful, they are redirected to the dashboard screen.

[0541] Specific examples:

[0542] The user enters the username and password and clicks the login button.

[0543] The server checks the credentials in its database and allows the login.

[0544] Retrieving resident information from the database

[0545] The server accesses the database based on the name of the deceased resident provided by the user, and searches for and retrieves the corresponding resident's medical history and nursing records. A common RDBMS (e.g., MySQL or PostgreSQL) is used as the database system. The server executes SQL queries to extract the necessary data and collects information for generating a medical certificate.

[0546] Specific examples:

[0547] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Example Name'" to retrieve the required data.

[0548] Automated generation of death certificates using generative AI

[0549] A generative AI model (e.g., GPT-3, BERT) installed on the server automatically generates a death certificate based on the acquired medical history information. The certificate includes basic information about the resident, cause of death, date and time of death, etc. The prompt is entered as "Please automatically generate a death certificate based on the resident's name and medical history." The generative AI model then generates the appropriate information and creates the certificate.

[0550] Specific prompt examples:

[0551] "Please generate a death certificate for Taro Takayama."

[0552] "Automatically generate death certificates based on resident names and medical histories."

[0553] A doctor will certify death online

[0554] Doctors log in to the system online and check the condition of the deceased resident via video call with facility staff. These video calls are made using common video call software such as Zoom or Microsoft Teams. The facility staff use the camera to report to the doctor in real time if the resident stops breathing or has a low heart rate. The doctor then confirms death based on this information.

[0555] Specific examples:

[0556] Facility staff report a resident's cardiac arrest to a doctor via video call, who then confirms the report.

[0557] Final review and approval of the medical certificate by the doctor

[0558] The physician reviews the death certificate generated in the system, makes any necessary corrections, and then electronically signs and approves the certificate using electronic signature software (e.g., Adobe Sign), completing the official certificate.

[0559] Specific examples:

[0560] The doctor reviews the generated medical certificate and approves it with an electronic signature.

[0561] Issue a death certificate and notify the appropriate parties

[0562] The server issues a digitally signed death certificate in PDF format and notifies the family, facility administrators, and necessary related organizations via email (e.g., SMTP protocol) or cloud storage services (e.g., Google Drive, Dropbox).

[0563] Specific examples:

[0564] The server creates a medical certificate in PDF format and sends it to the family and facility administrator by email.

[0565] This system will enable fast and efficient end-of-life care in elderly care facilities, reducing the burden on doctors, facility staff, and family members. By utilizing a generative AI model, the process from automatically generating medical certificates to approval and notification will be integrated, streamlining the overall work.

[0566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0567] Step 1:

[0568] The user logs in through the terminal. Using the terminal's web browser, the user accesses a specific URL. They enter their username and password and click the login button. The server receives the authentication information and verifies it against the user information in the database. If authentication is successful, the user is redirected to the dashboard screen.

[0569] Input: Username, Password

[0570] Data processing: Check the entered authentication information against the database

[0571] Output: Login success or failure, dashboard screen

[0572] Specific behavior:

[0573] The user enters the username and password and clicks the login button.

[0574] The server checks the entered information against a database.

[0575] Step 2:

[0576] The server retrieves resident information from the facility's database. Using the name of the deceased resident provided by the user, it connects to the database and executes an SQL query to retrieve the medical history and nursing records for that resident.

[0577] Input: Resident's name

[0578] Data processing: Execute SQL queries and extract relevant data from the database

[0579] Output: Resident medical history data and nursing records

[0580] Specific behavior:

[0581] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Taro Takayama'".

[0582] Retrieve Takayama Taro's medical history and nursing records from the database.

[0583] Step 3:

[0584] The generative AI automatically generates a death certificate. Using a generative AI model installed on the server, the AI ​​generates a death certificate by inputting the acquired medical history data as prompts. The generated certificate includes the resident's basic information, cause of death, and date and time of death.

[0585] Input: Resident's medical history data, prompt statement

[0586] Data processing: Generative AI models are used to generate medical reports

[0587] Output: Generated death certificate

[0588] Specific behavior:

[0589] Enter "Please generate a death certificate for Taro Takayama" as the prompt text into the generation AI.

[0590] AI generates Taro Takayama's death certificate.

[0591] Step 4:

[0592] The user (doctor) confirms death online. The doctor logs into the system online and makes a video call with facility staff. The facility staff uses a camera to share the resident's condition in real time, reporting any cessation of breathing or confirmation of heart rate. The doctor confirms death based on this information.

[0593] Input:Video call information

[0594] Data processing: Real-time information sharing and confirmation

[0595] Output: Death confirmation success / failure

[0596] Specific behavior:

[0597] Facility staff use a video camera to show doctors that a resident's heart has stopped beating.

[0598] The doctor will check in via video call.

[0599] Step 5:

[0600] The user (doctor) performs the final review and approval of the death certificate. The doctor reviews the death certificate generated within the system and makes any necessary corrections. He then electronically signs and approves it.

[0601] Input: Generated death certificate

[0602] Data processing: Doctor's confirmation and correction, electronic signature

[0603] Output: Approved death certificate

[0604] Specific behavior:

[0605] The doctor reviews the medical certificate and approves it with an electronic signature.

[0606] Step 6:

[0607] The server issues the death certificate and notifies the relevant parties. The server creates a digitally signed death certificate in PDF format and sends it to the family, facility administrators, and relevant agencies via email or cloud storage.

[0608] Input: Approved Death Certificate

[0609] Data processing: Creating documents in PDF format and sending them to recipients

[0610] Output: Medical certificate sent

[0611] Specific behavior:

[0612] The server generates a medical certificate in PDF format.

[0613] A medical certificate will be sent via email to the family and facility manager.

[0614] (Application example 1)

[0615] 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."

[0616] Caring for elderly people in nursing homes places a significant burden on medical professionals and facility staff, requiring time and effort. In particular, efficient processes, such as confirming the death, preparing a death certificate, and contacting relevant parties, require rapid and accurate information sharing and management. It is also important to ensure that remote medical professionals can smoothly confirm the death online and approve the death certificate. It is necessary to resolve these issues and simplify and streamline the procedures for caring for elderly people at the end of their lives.

[0617] 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.

[0618] In this invention, the server includes a means for inputting death information of facility residents, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using generation AI, a means for a medical professional to confirm the death online, a means for the medical professional to perform final review and approval of the medical certificate, a means for issuing the medical certificate as an electronic document and notifying relevant parties, a means for facility staff to log in and access using a smartphone application, and a means for communicating with medical professionals in real time via online video calls. This reduces the burden on medical professionals and facility staff and enables fast and efficient end-of-life care.

[0619] "Institutional residents" are elderly people who live in residential facilities such as nursing homes and care facilities.

[0620] "Death information" refers to detailed data on the deaths of facility residents, including the date, time, place, and cause of death.

[0621] "Medical history" refers to information about the illnesses and treatment history that a facility resident has experienced.

[0622] "Generative AI" refers to artificial intelligence that automatically generates documents using machine learning and natural language processing techniques.

[0623] A "death certificate" is a medical document that officially identifies and records the death of a resident.

[0624] "Online video calling" is a communication method that allows you to simultaneously send and receive video and audio over the Internet.

[0625] "Healthcare workers" is a general term that refers to doctors, nurses, and other health-related professionals.

[0626] An "electronic document" is a document that is created electronically and is handled in the form of a PDF or email.

[0627] "Stakeholders" are people such as family members, facility administrators, and government agencies with whom death certificates and other important information should be shared.

[0628] "Facility staff" are staff who work within the facility and support the daily lives of residents.

[0629] A "smartphone application" is software that runs on a smartphone and provides a variety of functions.

[0630] "Logging in" is the act of a user entering authentication information to access a particular system or service.

[0631] "Access" is the act of operating or checking information or systems.

[0632] "Real-time communication" refers to a means of communication in which information is exchanged immediately without delay.

[0633] This invention is a system that efficiently supports the end-of-life care of elderly people residing in nursing homes. This system uses a smartphone application to allow facility staff and medical professionals to cooperate in confirming deaths and preparing medical certificates online.

[0634] Hardware and Software Configuration

[0635] server

[0636] The server has the following main functions:

[0637] Entering death information for facility residents

[0638] The server accepts death information entered by facility staff and stores it in a database.

[0639] Obtaining medical history and facility information

[0640] The server retrieves the resident's past medical history and most recent nursing notes from the facility's database.

[0641] Generative AI for generating death certificates

[0642] The server automatically generates a death certificate based on the acquired medical history information using generation AI, which uses natural language processing technology to generate the contents of the certificate.

[0643] Final review and approval of medical certificate

[0644] The server provides an interface for medical professionals to review the medical certificate via an online video call and approve it with an electronic signature.

[0645] Notification to relevant parties

[0646] The server generates a medical certificate in PDF format and sends it to the family or facility administrator via email.

[0647] Device (smartphone)

[0648] The smartphone application running on the device has the following features:

[0649] Login and Information Access

[0650] Facility staff can log into the system by entering a username and password to view and enter resident information.

[0651] Online video call for death confirmation

[0652] Facility staff will communicate with medical personnel via video call to confirm deaths in real time.

[0653] Data Flow and Processing

[0654] 1. Resident death information is entered into the system:

[0655] Facility staff enter information about resident deaths via a smartphone application.

[0656] 2. Resident medical history information is retrieved by the server:

[0657] The server searches the database based on the entered information and retrieves the resident's medical history and nursing records.

[0658] 3. The generative AI model generates the diagnosis:

[0659] The generation AI on the server uses natural language processing technology to generate a death certificate based on the acquired information.

[0660] 4. Confirming death via online video call:

[0661] Facility staff use the video calling function on their smartphones to communicate with medical professionals and confirm the death of a resident.

[0662] 5. Medical certificate verification and approval:

[0663] Medical professionals can review the generated medical certificate through a smartphone application, make any necessary corrections, and electronically sign it.

[0664] 6. Issuance of medical certificate and notification to relevant parties:

[0665] The server generates a medical certificate in PDF format after final confirmation and approval and sends it via email to the family or facility administrator.

[0666] Specific examples

[0667] For example, if "Taro Tanaka," a resident of a certain facility, dies, facility staff enter the date, time, and location of "Taro Tanaka's" death into a smartphone application. The server receives this information and retrieves records of "Taro Tanaka's" medical history, including past records of high blood pressure, diabetes, and heart disease. Based on this information, the generative AI model generates a medical certificate stating "natural death due to cardiac arrest." A medical professional then confirms the death via an online video call, checks the generated medical certificate, and approves it. Finally, the server generates the medical certificate in PDF format and sends it by email to Tanaka's family and the facility administrator.

[0668] Prompt Sentence Examples

[0669] "Taro Tanaka" has a medical history of "high blood pressure, diabetes, heart disease." Based on this, please generate the following death certificate:

[0670] basic information

[0671] Cause of death: Natural causes due to cardiac arrest

[0672] Date of Death: YYYY-MM-DD HH:MM

[0673] This will reduce the burden on facility staff and medical professionals, and create a system that enables fast and efficient end-of-life care.

[0674] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0675] Step 1:

[0676] Facility staff log in to the system via a smartphone application.

[0677] Input: Username, Password

[0678] Output: Authentication token

[0679] Specific operation: The user name and password entered from the terminal are sent to the server, and the server verifies the authentication information. If authentication is successful, the server returns an authentication token to the terminal.

[0680] Step 2:

[0681] Facility staff enters information about the death of a resident.

[0682] Input: Resident's name, date and time of death, place of death

[0683] Output: Death information is saved in the server database.

[0684] Specific operation: Death information entered on the device is sent to the server, which then stores the information in a database.

[0685] Step 3:

[0686] The server retrieves the resident's medical history information.

[0687] Input: Resident's name

[0688] Output: Resident's past medical history information

[0689] Specific operation: The server searches the database and retrieves past medical history information for the specified resident.

[0690] Step 4:

[0691] Generative AI models automatically generate death certificates.

[0692] Input: Resident medical history and mortality information

[0693] Output: Death certificate contents

[0694] Specific operation: The generation AI on the server uses the acquired medical history information and death information to generate the contents of the death certificate.

[0695] Step 5:

[0696] Facility staff will contact medical personnel via online video call to confirm the death.

[0697] Input: Video call ID

[0698] Output: Medical personnel confirms death

[0699] Specific operation: Using the device's video call function, a real-time call is made with a medical professional to confirm the death. The medical professional then visually confirms the death and conveys approval to the facility staff.

[0700] Step 6:

[0701] A medical professional will provide final review and approval of the medical certificate.

[0702] Input: Generated death certificate

[0703] Output: Digitally signed medical certificate

[0704] Specific operation: The server displays the generated medical certificate to the medical professional, who checks the contents. If there are no problems, the medical professional electronically signs and approves it.

[0705] Step 7:

[0706] The server issues the medical certificate as an electronic document and notifies the relevant parties.

[0707] Input: Electronically signed medical certificate, contact information of the parties involved

[0708] Output: Notify relevant parties and send a PDF medical report

[0709] What happens: The server generates an approved medical certificate in PDF format and emails it to the appropriate parties, including family members and facility managers.

[0710] This will establish an efficient processing flow for the system that realizes the application example, enabling prompt and accurate end-of-life care for the elderly.

[0711] 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.

[0712] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities, and further combines it with an emotion engine that recognizes the user's emotions. This system includes the following main functions, reducing the burden on doctors, families, and facilities and realizing efficient, emotion-sensitive end-of-life care.

[0713] 1. A user (facility staff member) logs in to the system through a terminal.

[0714] Facility staff access the system by entering their username and password on a dedicated login screen.

[0715] 2. The server retrieves resident information from the facility database.

[0716] The server searches the facility database based on the entered resident's name and collects the individual's medical history, medical record information, and latest nursing records.

[0717] Example: The server retrieves the medical history of "Jiro Suzuki," including past treatment records for high blood pressure, diabetes, and heart disease.

[0718] 3. Generative AI automatically generates death certificates

[0719] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[0720] Example: A generation AI generates a death certificate for "Suzuki Jiro" and lists the cause of death as "natural death due to cardiac arrest."

[0721] 4. The user (doctor) confirms the death online

[0722] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who then report the resident's condition in real time.

[0723] Example: Facility staff report to a doctor that Suzuki Jiro has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[0724] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0725] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs the certificate to give final approval.

[0726] Example: A doctor checks the contents of Suzuki Jiro's medical certificate, confirms that there are no problems with the cause of death or the date and time, and approves it by electronically signing.

[0727] 6. The server issues a death certificate and notifies the relevant parties.

[0728] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[0729] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Suzuki Jiro's family and the facility manager.

[0730] 7. Use an emotion engine that recognizes user emotions

[0731] The emotion engine analyzes user input, voice, and facial expression data to recognize the user's emotional state (e.g., stress, anxiety, sadness, etc.).

[0732] Example: If a facility staff member is very sad, the emotion engine will detect this state and the system will provide support information on emotional care.

[0733] 8. Feedback emotion recognition results to generative AI

[0734] The emotion engine analyzes the user's emotional data and feeds the results back to the generation AI, which can then create comments and advice for the diagnosis that take emotions into consideration.

[0735] Example: The emotion engine detects tension among facility staff, and the generative AI adds a comment to the medical certificate such as, "Please pay special attention to caring for the bereaved family."

[0736] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, improving the efficiency of the overall process while also providing psychological support.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[0740] Step 2:

[0741] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[0742] Step 3:

[0743] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[0744] Step 4:

[0745] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[0746] Step 5:

[0747] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[0748] Step 6:

[0749] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[0750] Step 7:

[0751] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[0752] Step 8:

[0753] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[0754] Step 9:

[0755] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[0756] Step 10:

[0757] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[0758] Step 11:

[0759] The server sends notifications to the relevant parties. The server sends the death certificate to the family, facility administrators, and necessary related agencies via email and cloud storage.

[0760] Step 12:

[0761] The emotion engine recognizes the user's emotions. The emotion engine collects input, voice, and facial expression data from the device and analyzes the user's emotional state.

[0762] Step 13:

[0763] The emotion engine feeds the analysis results back to the generative AI, which analyzes the user's emotions and passes the results to the generative AI, which then adjusts the diagnosis and system response accordingly.

[0764] Step 14:

[0765] The emotion engine provides users with appropriate guidance and support information. For users who are feeling stressed or anxious, the emotion engine displays information such as relaxation methods and support contact information.

[0766] Example 2

[0767] 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."

[0768] In modern elderly care facilities, the series of procedures that must be carried out when a resident passes away requires a lot of time and effort, placing a heavy burden on those involved. It is also often difficult to confirm the cause of death and prepare a medical certificate in a timely manner. Furthermore, there is a lack of emotional support for those involved, placing a heavy mental burden on them. A system that can solve these issues is needed.

[0769] The identification processing 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 means for inputting death information of a facility resident, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using a generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, a means for issuing the death certificate and notifying relevant parties, a means for using an emotion engine that recognizes the user's emotions, and a means for feeding back the emotion recognition results to the generation AI. This not only streamlines procedures at the time of death and enables prompt and accurate responses, but also provides support that takes into consideration the emotional state of those involved.

[0770] "Institutional resident" refers to an individual who resides in a nursing home or care facility.

[0771] "Means for inputting death information" refers to an interface that allows a user to input the death status of a facility resident into the system using a terminal.

[0772] "Means for obtaining past medical history and information from the facility" refers to the function by which the server searches for and obtains the medical history and chart information of facility residents from the database.

[0773] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze data and perform a specific task (in this case, automatically generating death certificates).

[0774] "Means for automatically generating death certificates" refers to the process by which the generation AI automatically creates death certificates based on the data collected.

[0775] "Means for doctors to confirm death online" refers to a function that allows doctors to confirm the death status of facility residents in real time using a video call system.

[0776] "Means for final review and approval of the medical certificate by a physician" refers to the process by which a physician reviews the generated death certificate and, if necessary, amends and approves it.

[0777] "Means for issuing a death certificate and notifying relevant parties" refers to the function of the server to generate an official death certificate and notify family members, facility managers, and relevant organizations.

[0778] An "emotion engine that recognizes user emotions" refers to a system that analyzes a user's voice, facial expressions, and text data to identify their emotional state.

[0779] "Means for feeding back emotion recognition results to the generation AI" refers to the process in which the emotion engine sends the detected emotion data to the generation AI, and the generation AI adds or modifies the corresponding information based on the results.

[0780] This invention relates to a system that utilizes generative AI and an emotion engine to provide efficient and emotionally sensitive support for end-of-life care for elderly people residing in facilities.

[0781] The system uses the following major hardware and software:

[0782] Hardware:

[0783] Server: Used for database management, running generative AI, and analyzing the emotion engine.

[0784] Terminals: Windows PCs and tablets are used as interfaces for facility staff and doctors to operate the system.

[0785] software:

[0786] Database management system: Resident information is managed using database software such as MySQL.

[0787] Generative AI models: Use advanced generative AI, such as OpenAI GPT-4, to automatically generate death certificates.

[0788] Emotion engine: Analyzes the user's emotional state using emotion analysis software such as IBM Watson.

[0789] Video call system: Doctors can remotely certify death using video call applications such as Zoom.

[0790] The main functions of the system and the specific operations at each step are shown below.

[0791] 1. A user (facility staff member) logs in to the system through a terminal.

[0792] Facility staff enter their username and password into a dedicated login screen and send the authentication information to the server, which then compares it with the database for authentication and returns the authentication result, allowing the facility staff to access the system.

[0793] 2. The server retrieves resident information from the facility database.

[0794] The user enters the name of the resident they are searching for into the terminal and sends the data to the server, which searches the MySQL database to gather the resident's medical history, medical record information, and latest nursing notes. The collected information is returned to the terminal and displayed to the user.

[0795] 3. Generative AI automatically generates death certificates

[0796] The server inputs the collected resident information into a generative AI model to automatically generate a death certificate. The generated certificate includes the resident's basic information, cause of death, and date and time of death. The generated certificate is stored in a database and a preview is displayed on the device.

[0797] 4. The user (doctor) confirms the death online

[0798] Doctors log in to the system via video chat, and facility staff report the status of residents in real time. The doctors then confirm the death status of the residents and send the results to the server.

[0799] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0800] The doctor logs in to the terminal, checks the generated death certificate, makes any necessary corrections, and electronically signs it using an electronic signature tool for final approval.

[0801] 6. The server issues a death certificate and notifies the relevant parties.

[0802] The server issues the approved medical certificate as an official document in PDF format. The certificate is then sent to the family, facility administrator, and relevant organizations via email or cloud storage. A message confirming the completion of the transmission is displayed on the device.

[0803] 7. Use an emotion engine that recognizes user emotions

[0804] The device sends the user's input, voice, and facial expression data to the emotion engine, which analyzes the user's emotional state. The analysis results are returned to the server, and necessary support information is provided to the user based on the results.

[0805] 8. Feedback emotion recognition results to generative AI

[0806] The server inputs the emotion data obtained from the emotion engine into the generative AI model, which then adds emotion-sensitive comments and advice to the diagnosis. The updated diagnosis is saved in a database and displayed on the device.

[0807] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, streamlining the overall process and providing psychological support.

[0808] Example prompt sentence:

[0809] "Please create a prompt that embodies how the emotion engine analyzes the user's emotions in the process of creating a death certificate for an elderly person and feeds the results back to the generative AI."

[0810] As described above, the present invention realizes an effective system for comprehensively supporting the end-of-life care of facility residents.

[0811] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0812] Step 1:

[0813] A user (facility staff member) logs into the system through a terminal.

[0814] Input: Username and Password.

[0815] Specific operation: Facility staff enter their username and password into a dedicated login screen. The terminal then sends the entered data to the server.

[0816] Data processing: The server checks the authentication information against a database.

[0817] Output: Authentication result (success or failure).

[0818] Specific operation: The server returns the authentication result to the terminal, and the terminal displays a login success message, allowing the user to access the system.

[0819] Step 2:

[0820] The server retrieves resident information from the facility's database.

[0821] Input: Resident's name.

[0822] Specific operation: The user enters the name of the resident they are searching for into the terminal, and the terminal sends the input data to the server.

[0823] Data processing: The server searches the database using a MySQL query.

[0824] Output: Medical history information, chart information, latest nursing records.

[0825] Specific operation: The server returns the acquired information to the terminal, which displays the information to the user.

[0826] Step 3:

[0827] Generative AI automatically generates death certificates.

[0828] Input: Collected resident information.

[0829] Specific operation: The server inputs the collected resident information into the generative AI model.

[0830] Data processing: Generative AI generates a death certificate based on the input data.

[0831] Output: The generated death certificate.

[0832] Specific operation: The server saves the generated medical certificate in the database. The server sends a preview of the medical certificate to the terminal and displays it to the user.

[0833] Step 4:

[0834] The user (doctor) certifies the death online.

[0835] Input: Real-time reporting from facility personnel.

[0836] Specific operations: A doctor logs in to the system via a video chat system. A facility staff member reports the resident's condition.

[0837] Data processing: Doctors review information via video call.

[0838] Output: Death confirmation result.

[0839] Specific operation: The doctor sends the death confirmation results to the server via the terminal.

[0840] Step 5:

[0841] The user (doctor) performs final review and approval of the medical certificate.

[0842] Input: The generated death certificate.

[0843] Specific operation: The doctor logs in to the terminal and checks the contents of the generated death certificate.

[0844] Data processing: The doctor will amend the medical certificate as necessary.

[0845] Output: Approved medical certificate.

[0846] Specific operation: The doctor electronically signs the medical certificate using the electronic signature tool. The server stores the signed medical certificate in the database.

[0847] Step 6:

[0848] The server issues a death certificate and notifies the relevant parties.

[0849] Input: Approved medical certificate.

[0850] Specific operation: The server issues the approved medical certificate as an official document.

[0851] Data processing: Generate medical certificate in PDF format.

[0852] Output: Death certificate in PDF format.

[0853] Specific operation: The server sends the medical certificate to the relevant parties via email or cloud storage. The device displays a message to the user that transmission is complete.

[0854] Step 7:

[0855] Uses an emotion engine that recognizes the user's emotions.

[0856] Input: User input, voice, and facial expression data.

[0857] Specific operation: The device sends the user's input, voice, and facial expression data to the emotion engine.

[0858] Data processing: The emotion engine analyzes the data and recognizes the user's emotional state.

[0859] Output: Emotion recognition result.

[0860] Specific operation: The emotion engine returns the results to the server. The device displays the results to the user and provides necessary support information.

[0861] Step 8:

[0862] The emotion recognition results are fed back to the generative AI.

[0863] Input: Emotion data.

[0864] Specific operation: The server obtains the emotion data obtained from the emotion engine.

[0865] Data processing: Emotional data is input into a generative AI model, which then adds emotionally sensitive comments and advice to the diagnosis report.

[0866] Output: Updated death certificate.

[0867] Specific operation: The server sends the updated medical certificate to the database and the terminal. The terminal displays the new medical certificate to the user.

[0868] (Application example 2)

[0869] 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."

[0870] In facilities with many elderly residents, a swift and accurate response is required when a resident dies, while at the same time reducing the burden on those involved and taking their emotions into consideration. Furthermore, customer service in retail stores requires an appropriate understanding of the emotional state of the elderly and flexible responses accordingly. Conventional systems have found it difficult to efficiently resolve these issues.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0872] In this invention, the server includes means for inputting death information of a facility resident, means for acquiring past medical history and information from the facility, means for automatically generating a death certificate using a generation AI, means for a doctor to confirm the death online, means for the doctor to perform final confirmation and approval of the death certificate, means for issuing the death certificate and notifying relevant parties, means for recognizing emotions when providing services for the elderly, and means for the generation AI to generate a response based on emotion recognition data. This enables a quick, accurate, and emotion-sensitive response when an elderly person dies, and also enables flexible emotional responses in retail stores for services for the elderly.

[0873] "Institutional residents" refers to elderly people or patients who are staying in nursing homes or medical facilities for an extended period of time.

[0874] "Death information" refers to basic information such as the date, time, place, and cause of death of a facility resident.

[0875] "Generative AI" refers to algorithms or systems that use artificial intelligence technology to automatically generate documents or information based on specific data or conditions.

[0876] A "death certificate" is an official document in which a doctor legally certifies and details the death.

[0877] "Online video calling" refers to a means of communicating in real time through audio and video using the Internet.

[0878] "Emotion recognition" refers to the technology of analyzing data such as voice, facial expressions, and behavior to determine a person's emotional state (e.g., sadness, joy, tension, etc.).

[0879] "Response generation" refers to the process of automatically generating appropriate responses or advice based on input data and circumstances.

[0880] "Medical history" refers to information that records details of a patient's past illnesses, their treatment history, and the medications they have used.

[0881] "Final review and approval" refers to the procedure in which experts and related parties finally review the accuracy of the contents of the generated documents and information and formally approve the contents.

[0882] This invention relates to a system that uses a generative AI and an emotion recognition engine to provide end-of-life care for the elderly and customer service support in retail stores. A specific embodiment of this system will be described in detail below.

[0883] Hardware and software used

[0884] Hardware: Smartphone (with camera), server

[0885] Software: OpenCV (for face detection and preprocessing), EmotionEngine (emotion recognition engine), ResponseGenerator (response generation AI)

[0886] Specific flow of data processing

[0887] The server first receives input from a smartphone. Facility staff enters information about the death of a facility resident using a smartphone. The server then retrieves information such as the resident's past medical history and the latest nursing records from the facility's database.

[0888] Based on the acquired information, the generation AI automatically generates a death certificate. At this time, the generation AI fills in basic information on the death certificate, such as the cause of death and the date and time of death. The generated certificate is then reviewed online by a doctor. The doctor confirms the death via video call and makes a final review and approval of the certificate. The finalized death certificate is issued by the server and notified to the relevant parties.

[0889] At the same time, in retail stores that provide services for the elderly, the Emotion Engine uses smartphone cameras to analyze the facial expressions of the elderly and determine their emotional state. Based on this, the Response Generator generates appropriate responses and advice.

[0890] Specific examples

[0891] A facility staff member enters information about Suzuki Jiro's death on a smartphone. The server retrieves Suzuki Jiro's past medical history (high blood pressure, diabetes, heart disease) from the facility's database and provides this information to the generation AI. Based on this, the generation AI automatically generates a death certificate for Suzuki Jiro, stating "natural death due to cardiac arrest." A doctor verifies the death online via video call and provides final confirmation and approval of the certificate's contents. The server then notifies the family and facility administrator of the official certificate.

[0892] In retail stores, the facial expressions of elderly people are captured with a smartphone camera and their emotional state is analyzed by the Emotion Engine. For example, if the customer is nervous, the Response Generator generates a response such as, "You seem nervous. Let us suggest a product that will have a relaxing effect."

[0893] Prompt Sentence Examples

[0894] The customer's emotional state is determined to be "tension." Please generate advice that can suggest products that have a relaxing effect for the elderly.

[0895] This system will enable a swift, accurate, and emotionally sensitive response when an elderly person dies, and will also enable flexible, emotionally sensitive services for the elderly in retail stores.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Step 1:

[0898] Users use their smartphones to input information about the deaths of facility residents. This information includes basic information such as the resident's name, date and time of death, location, and cause of death. This information is then sent to the server via the smartphone.

[0899] Step 2:

[0900] The server retrieves the resident's past medical history and nursing records from the facility's database. It searches for medical history data, treatment records, nursing records, etc. based on the specific resident's name and retrieves this information all at once. This prepares the data necessary for the generation AI.

[0901] Step 3:

[0902] The AI ​​on the server automatically generates a death certificate based on the acquired medical history information and the current death information entered by the user. The AI ​​analyzes the medical history data and creates a provisional death certificate that includes the cause of death, date and time of death, etc.

[0903] Step 4:

[0904] The user (doctor) confirms death via online video call. Using a smartphone or computer, the user connects with facility staff to check for breathing and cardiac arrest. The facility staff reports the resident's condition in real time, which the doctor confirms.

[0905] Step 5:

[0906] The doctor performs a final review and approval of the death certificate generated on the server. He logs into the online system, checks the provisional death certificate, corrects any deficiencies, and performs a final review. He then officially approves the certificate with an electronic signature.

[0907] Step 6:

[0908] The server issues a final, verified, and approved death certificate, notifies the relevant parties, creates a PDF of the certificate, emails it to the family and facility administrator, and shares it with any necessary authorities.

[0909] Step 7:

[0910] The user (store staff) uses a smartphone camera to analyze the facial expressions of elderly people who visit a retail store. The acquired video data is sent to a server.

[0911] Step 8:

[0912] The Emotion Engine on the server analyzes the received facial expression data and determines the emotional state of the elderly person. Specifically, it analyzes facial expression images and recognizes emotions such as "sadness," "happiness," and "tension."

[0913] Step 9:

[0914] The server's ResponseGenerator generates an optimal response based on the determined emotional state. For example, if the elderly person is nervous, the response generated will be "suggest products that have a relaxing effect."

[0915] Step 10:

[0916] The user (store staff) checks the generated response and responds appropriately to the elderly person, such as suggesting a relaxing herbal tea.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] [Third embodiment]

[0921] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0922] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0923] 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).

[0924] 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.

[0925] 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.

[0926] 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).

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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."

[0933] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities. This system includes the following main functions to reduce the burden on doctors, families, and facilities and achieve efficient end-of-life care.

[0934] 1. A user (facility staff member) logs in to the system through a terminal.

[0935] Facility staff use a dedicated login system to access the system from a terminal by entering their username and password.

[0936] 2. The server retrieves resident information from the facility database.

[0937] The server searches the facility's database based on the name of the deceased resident and collects all of the resident's medical history and most recent nursing notes.

[0938] Example: The server retrieves the medical history of "Taro Takayama," including his past treatment records for high blood pressure, diabetes, and heart disease.

[0939] 3. Generative AI automatically generates death certificates

[0940] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[0941] Example: The AI ​​generates a death certificate for "Taro Takayama" and lists the cause of death as "natural death due to cardiac arrest."

[0942] 4. The user (doctor) confirms the death online

[0943] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who report the resident's condition in real time (stopping breathing, checking heart rate, etc.).

[0944] Example: Facility staff report to a doctor that Taro Takayama has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[0945] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[0946] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs and approves the certificate.

[0947] Example: A doctor reviews the medical certificate for "Taro Takayama," confirms that there are no problems with the cause of death or the date and time, and then electronically signs and approves it.

[0948] 6. The server issues a death certificate and notifies the relevant parties.

[0949] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[0950] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Takayama Taro's family and the facility manager.

[0951] This system allows for quick confirmation of death even when a doctor is far away, shortening waiting times for family members and facility staff. It also eliminates the need for facilities to have a doctor on standby, which is expected to reduce costs. This online end-of-life support system, which utilizes generative AI, is a useful solution for achieving efficiency in many areas.

[0952] The processing flow will be explained below.

[0953] Step 1:

[0954] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[0955] Step 2:

[0956] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[0957] Step 3:

[0958] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[0959] Step 4:

[0960] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[0961] Step 5:

[0962] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[0963] Step 6:

[0964] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[0965] Step 7:

[0966] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[0967] Step 8:

[0968] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[0969] Step 9:

[0970] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[0971] Step 10:

[0972] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[0973] Step 11:

[0974] The server sends notifications to the relevant parties, and sends death certificates to the family, facility administrators, and necessary authorities via email and cloud storage.

[0975] Example 1

[0976] 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."

[0977] Traditionally, end-of-life care for elderly people in facilities required on-site visits by doctors and manual paperwork, resulting in delays and a heavy workload. It was particularly difficult to respond quickly when doctors were far away or at night or on holidays. Additionally, creating and reviewing documents took a lot of time, placing an increased burden on families and facilities. To solve these problems, a system was needed that could efficiently and quickly implement the end-of-life care process.

[0978] 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.

[0979] In this invention, the server includes a means for logging in via a terminal, a means for retrieving resident information from a database, a means for automatically generating a death certificate using generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, and a means for issuing the death certificate and notifying relevant parties. This enables quick and efficient confirmation of death and creation, approval, and notification of the death certificate.

[0980] "Means for logging in via a terminal" refers to a means by which facility staff and other users use a dedicated login system, enter a user name and password for authentication, and access the system.

[0981] "Means for obtaining resident information from the database" refers to the means by which the server connects to the database system within the facility and searches for and obtains the medical history and nursing records of a deceased resident based on the name of that resident.

[0982] "Means for automatically generating death certificates using generative AI" refers to a means for automatically generating death certificates based on acquired medical history information using a generative AI model installed on a server.

[0983] "Online means for doctors to confirm death" refers to a means by which doctors can log in to the system online and check the condition of a deceased resident through a video call with facility staff.

[0984] The "means for the physician to finalize and approve the death certificate" refers to the means by which a physician reviews the death certificate generated within the system, makes any necessary corrections, and then electronically signs and approves it.

[0985] "Means for issuing a death certificate and notifying relevant parties" refers to the means by which the server issues an electronically signed death certificate as an official document and notifies the family, facility administrator, and necessary relevant organizations.

[0986] The present invention is a system that uses a generative AI model to support end-of-life care for residents in elderly care facilities. Specific embodiments of this system are described below.

[0987] Log in through the terminal

[0988] Users (facility staff) access the login system using a terminal PC installed within the facility. A web browser (e.g., Google Chrome or Mozilla Firefox) is installed on this terminal, and they reach the login page by accessing a specific URL (e.g., https: / / facility-login.example.com). By entering their username and password and clicking the login button, the server verifies the provided authentication information against the user database, and if authentication is successful, they are redirected to the dashboard screen.

[0989] Specific examples:

[0990] The user enters the username and password and clicks the login button.

[0991] The server checks the credentials in its database and allows the login.

[0992] Retrieving resident information from the database

[0993] The server accesses the database based on the name of the deceased resident provided by the user, and searches for and retrieves the corresponding resident's medical history and nursing records. A common RDBMS (e.g., MySQL or PostgreSQL) is used as the database system. The server executes SQL queries to extract the necessary data and collects information for generating a medical certificate.

[0994] Specific examples:

[0995] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Example Name'" to retrieve the required data.

[0996] Automated generation of death certificates using generative AI

[0997] A generative AI model (e.g., GPT-3, BERT) installed on the server automatically generates a death certificate based on the acquired medical history information. The certificate includes basic information about the resident, cause of death, date and time of death, etc. The prompt is entered as "Please automatically generate a death certificate based on the resident's name and medical history." The generative AI model then generates the appropriate information and creates the certificate.

[0998] Specific prompt examples:

[0999] "Please generate a death certificate for Taro Takayama."

[1000] "Automatically generate death certificates based on resident names and medical histories."

[1001] A doctor will certify death online

[1002] Doctors log in to the system online and check the condition of the deceased resident via video call with facility staff. These video calls are made using common video call software such as Zoom or Microsoft Teams. The facility staff use the camera to report to the doctor in real time if the resident stops breathing or has a low heart rate. The doctor then confirms death based on this information.

[1003] Specific examples:

[1004] Facility staff report a resident's cardiac arrest to a doctor via video call, who then confirms the report.

[1005] Final review and approval of the medical certificate by the doctor

[1006] The physician reviews the death certificate generated in the system, makes any necessary corrections, and then electronically signs and approves the certificate using electronic signature software (e.g., Adobe Sign), completing the official certificate.

[1007] Specific examples:

[1008] The doctor reviews the generated medical certificate and approves it with an electronic signature.

[1009] Issue a death certificate and notify the appropriate parties

[1010] The server issues a digitally signed death certificate in PDF format and notifies the family, facility administrators, and necessary related organizations via email (e.g., SMTP protocol) or cloud storage services (e.g., Google Drive, Dropbox).

[1011] Specific examples:

[1012] The server creates a medical certificate in PDF format and sends it to the family and facility administrator by email.

[1013] This system will enable fast and efficient end-of-life care in elderly care facilities, reducing the burden on doctors, facility staff, and family members. By utilizing a generative AI model, the process from automatically generating medical certificates to approval and notification will be integrated, streamlining the overall work.

[1014] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1015] Step 1:

[1016] The user logs in through the terminal. Using the terminal's web browser, the user accesses a specific URL. They enter their username and password and click the login button. The server receives the authentication information and verifies it against the user information in the database. If authentication is successful, the user is redirected to the dashboard screen.

[1017] Input: Username, Password

[1018] Data processing: Check the entered authentication information against the database

[1019] Output: Login success or failure, dashboard screen

[1020] Specific behavior:

[1021] The user enters the username and password and clicks the login button.

[1022] The server checks the entered information against a database.

[1023] Step 2:

[1024] The server retrieves resident information from the facility's database. Using the name of the deceased resident provided by the user, it connects to the database and executes an SQL query to retrieve the medical history and nursing records for that resident.

[1025] Input: Resident's name

[1026] Data processing: Execute SQL queries and extract relevant data from the database

[1027] Output: Resident medical history data and nursing records

[1028] Specific behavior:

[1029] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Taro Takayama'".

[1030] Retrieve Takayama Taro's medical history and nursing records from the database.

[1031] Step 3:

[1032] The generative AI automatically generates a death certificate. Using a generative AI model installed on the server, the AI ​​generates a death certificate by inputting the acquired medical history data as prompts. The generated certificate includes the resident's basic information, cause of death, and date and time of death.

[1033] Input: Resident's medical history data, prompt statement

[1034] Data processing: Generative AI models are used to generate medical reports

[1035] Output: Generated death certificate

[1036] Specific behavior:

[1037] Enter "Please generate a death certificate for Taro Takayama" as the prompt text into the generation AI.

[1038] AI generates Taro Takayama's death certificate.

[1039] Step 4:

[1040] The user (doctor) confirms death online. The doctor logs into the system online and makes a video call with facility staff. The facility staff uses a camera to share the resident's condition in real time, reporting any cessation of breathing or confirmation of heart rate. The doctor confirms death based on this information.

[1041] Input:Video call information

[1042] Data processing: Real-time information sharing and confirmation

[1043] Output: Death confirmation success / failure

[1044] Specific behavior:

[1045] Facility staff use a video camera to show doctors that a resident's heart has stopped beating.

[1046] The doctor will check in via video call.

[1047] Step 5:

[1048] The user (doctor) performs the final review and approval of the death certificate. The doctor reviews the death certificate generated within the system and makes any necessary corrections. He then electronically signs and approves it.

[1049] Input: Generated death certificate

[1050] Data processing: Doctor's confirmation and correction, electronic signature

[1051] Output: Approved death certificate

[1052] Specific behavior:

[1053] The doctor reviews the medical certificate and approves it with an electronic signature.

[1054] Step 6:

[1055] The server issues the death certificate and notifies the relevant parties. The server creates a digitally signed death certificate in PDF format and sends it to the family, facility administrators, and relevant agencies via email or cloud storage.

[1056] Input: Approved Death Certificate

[1057] Data processing: Creating documents in PDF format and sending them to recipients

[1058] Output: Medical certificate sent

[1059] Specific behavior:

[1060] The server generates a medical certificate in PDF format.

[1061] A medical certificate will be sent via email to the family and facility manager.

[1062] (Application example 1)

[1063] 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."

[1064] Caring for elderly people in nursing homes places a significant burden on medical professionals and facility staff, requiring time and effort. In particular, efficient processes, such as confirming the death, preparing a death certificate, and contacting relevant parties, require rapid and accurate information sharing and management. It is also important to ensure that remote medical professionals can smoothly confirm the death online and approve the death certificate. It is necessary to resolve these issues and simplify and streamline the procedures for caring for elderly people at the end of their lives.

[1065] 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.

[1066] In this invention, the server includes a means for inputting death information of facility residents, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using generation AI, a means for a medical professional to confirm the death online, a means for the medical professional to perform final review and approval of the medical certificate, a means for issuing the medical certificate as an electronic document and notifying relevant parties, a means for facility staff to log in and access using a smartphone application, and a means for communicating with medical professionals in real time via online video calls. This reduces the burden on medical professionals and facility staff and enables fast and efficient end-of-life care.

[1067] "Institutional residents" are elderly people who live in residential facilities such as nursing homes and care facilities.

[1068] "Death information" refers to detailed data on the deaths of facility residents, including the date, time, place, and cause of death.

[1069] "Medical history" refers to information about the illnesses and treatment history that a facility resident has experienced.

[1070] "Generative AI" refers to artificial intelligence that automatically generates documents using machine learning and natural language processing techniques.

[1071] A "death certificate" is a medical document that officially identifies and records the death of a resident.

[1072] "Online video calling" is a communication method that allows you to simultaneously send and receive video and audio over the Internet.

[1073] "Healthcare workers" is a general term that refers to doctors, nurses, and other health-related professionals.

[1074] An "electronic document" is a document that is created electronically and is handled in the form of a PDF or email.

[1075] "Stakeholders" are people such as family members, facility administrators, and government agencies with whom death certificates and other important information should be shared.

[1076] "Facility staff" are staff who work within the facility and support the daily lives of residents.

[1077] A "smartphone application" is software that runs on a smartphone and provides a variety of functions.

[1078] "Logging in" is the act of a user entering authentication information to access a particular system or service.

[1079] "Access" is the act of operating or checking information or systems.

[1080] "Real-time communication" refers to a means of communication in which information is exchanged immediately without delay.

[1081] This invention is a system that efficiently supports the end-of-life care of elderly people residing in nursing homes. This system uses a smartphone application to allow facility staff and medical professionals to cooperate in confirming deaths and preparing medical certificates online.

[1082] Hardware and Software Configuration

[1083] server

[1084] The server has the following main functions:

[1085] Entering death information for facility residents

[1086] The server accepts death information entered by facility staff and stores it in a database.

[1087] Obtaining medical history and facility information

[1088] The server retrieves the resident's past medical history and most recent nursing notes from the facility's database.

[1089] Generative AI for generating death certificates

[1090] The server automatically generates a death certificate based on the acquired medical history information using generation AI, which uses natural language processing technology to generate the contents of the certificate.

[1091] Final review and approval of medical certificate

[1092] The server provides an interface for medical professionals to review the medical certificate via an online video call and approve it with an electronic signature.

[1093] Notification to relevant parties

[1094] The server generates a medical certificate in PDF format and sends it to the family or facility administrator via email.

[1095] Device (smartphone)

[1096] The smartphone application running on the device has the following features:

[1097] Login and Information Access

[1098] Facility staff can log into the system by entering a username and password to view and enter resident information.

[1099] Online video call for death confirmation

[1100] Facility staff will communicate with medical personnel via video call to confirm deaths in real time.

[1101] Data Flow and Processing

[1102] 1. Resident death information is entered into the system:

[1103] Facility staff enter information about resident deaths via a smartphone application.

[1104] 2. Resident medical history information is retrieved by the server:

[1105] The server searches the database based on the entered information and retrieves the resident's medical history and nursing records.

[1106] 3. The generative AI model generates the diagnosis:

[1107] The generation AI on the server uses natural language processing technology to generate a death certificate based on the acquired information.

[1108] 4. Confirming death via online video call:

[1109] Facility staff use the video calling function on their smartphones to communicate with medical professionals and confirm the death of a resident.

[1110] 5. Medical certificate verification and approval:

[1111] Medical professionals can review the generated medical certificate through a smartphone application, make any necessary corrections, and electronically sign it.

[1112] 6. Issuance of medical certificate and notification to relevant parties:

[1113] The server generates a medical certificate in PDF format after final confirmation and approval and sends it via email to the family or facility administrator.

[1114] Specific examples

[1115] For example, if "Taro Tanaka," a resident of a certain facility, dies, facility staff enter the date, time, and location of "Taro Tanaka's" death into a smartphone application. The server receives this information and retrieves records of "Taro Tanaka's" medical history, including past records of high blood pressure, diabetes, and heart disease. Based on this information, the generative AI model generates a medical certificate stating "natural death due to cardiac arrest." A medical professional then confirms the death via an online video call, checks the generated medical certificate, and approves it. Finally, the server generates the medical certificate in PDF format and sends it by email to Tanaka's family and the facility administrator.

[1116] Prompt Sentence Examples

[1117] "Taro Tanaka" has a medical history of "high blood pressure, diabetes, heart disease." Based on this, please generate the following death certificate:

[1118] basic information

[1119] Cause of death: Natural causes due to cardiac arrest

[1120] Date of Death: YYYY-MM-DD HH:MM

[1121] This will reduce the burden on facility staff and medical professionals, and create a system that enables fast and efficient end-of-life care.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Step 1:

[1124] Facility staff log in to the system via a smartphone application.

[1125] Input: Username, Password

[1126] Output: Authentication token

[1127] Specific operation: The user name and password entered from the terminal are sent to the server, and the server verifies the authentication information. If authentication is successful, the server returns an authentication token to the terminal.

[1128] Step 2:

[1129] Facility staff enters information about the death of a resident.

[1130] Input: Resident's name, date and time of death, place of death

[1131] Output: Death information is saved in the server database.

[1132] Specific operation: Death information entered on the device is sent to the server, which then stores the information in a database.

[1133] Step 3:

[1134] The server retrieves the resident's medical history information.

[1135] Input: Resident's name

[1136] Output: Resident's past medical history information

[1137] Specific operation: The server searches the database and retrieves past medical history information for the specified resident.

[1138] Step 4:

[1139] Generative AI models automatically generate death certificates.

[1140] Input: Resident medical history and mortality information

[1141] Output: Death certificate contents

[1142] Specific operation: The generation AI on the server uses the acquired medical history information and death information to generate the contents of the death certificate.

[1143] Step 5:

[1144] Facility staff will contact medical personnel via online video call to confirm the death.

[1145] Input: Video call ID

[1146] Output: Medical personnel confirms death

[1147] Specific operation: Using the device's video call function, a real-time call is made with a medical professional to confirm the death. The medical professional then visually confirms the death and conveys approval to the facility staff.

[1148] Step 6:

[1149] A medical professional will provide final review and approval of the medical certificate.

[1150] Input: Generated death certificate

[1151] Output: Digitally signed medical certificate

[1152] Specific operation: The server displays the generated medical certificate to the medical professional, who checks the contents. If there are no problems, the medical professional electronically signs and approves it.

[1153] Step 7:

[1154] The server issues the medical certificate as an electronic document and notifies the relevant parties.

[1155] Input: Electronically signed medical certificate, contact information of the parties involved

[1156] Output: Notify relevant parties and send a PDF medical report

[1157] What happens: The server generates an approved medical certificate in PDF format and emails it to the appropriate parties, including family members and facility managers.

[1158] This will establish an efficient processing flow for the system that realizes the application example, enabling prompt and accurate end-of-life care for the elderly.

[1159] 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.

[1160] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities, and further combines it with an emotion engine that recognizes the user's emotions. This system includes the following main functions, reducing the burden on doctors, families, and facilities and realizing efficient, emotion-sensitive end-of-life care.

[1161] 1. A user (facility staff member) logs in to the system through a terminal.

[1162] Facility staff access the system by entering their username and password on a dedicated login screen.

[1163] 2. The server retrieves resident information from the facility database.

[1164] The server searches the facility database based on the entered resident's name and collects the individual's medical history, medical record information, and latest nursing records.

[1165] Example: The server retrieves the medical history of "Jiro Suzuki," including past treatment records for high blood pressure, diabetes, and heart disease.

[1166] 3. Generative AI automatically generates death certificates

[1167] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[1168] Example: A generation AI generates a death certificate for "Suzuki Jiro" and lists the cause of death as "natural death due to cardiac arrest."

[1169] 4. The user (doctor) confirms the death online

[1170] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who then report the resident's condition in real time.

[1171] Example: Facility staff report to a doctor that Suzuki Jiro has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[1172] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[1173] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs the certificate to give final approval.

[1174] Example: A doctor checks the contents of Suzuki Jiro's medical certificate, confirms that there are no problems with the cause of death or the date and time, and approves it by electronically signing.

[1175] 6. The server issues a death certificate and notifies the relevant parties.

[1176] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[1177] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Suzuki Jiro's family and the facility manager.

[1178] 7. Use an emotion engine that recognizes user emotions

[1179] The emotion engine analyzes user input, voice, and facial expression data to recognize the user's emotional state (e.g., stress, anxiety, sadness, etc.).

[1180] Example: If a facility staff member is very sad, the emotion engine will detect this state and the system will provide support information on emotional care.

[1181] 8. Feedback emotion recognition results to generative AI

[1182] The emotion engine analyzes the user's emotional data and feeds the results back to the generation AI, which can then create comments and advice for the diagnosis that take emotions into consideration.

[1183] Example: The emotion engine detects tension among facility staff, and the generative AI adds a comment to the medical certificate such as, "Please pay special attention to caring for the bereaved family."

[1184] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, improving the efficiency of the overall process while also providing psychological support.

[1185] The processing flow will be explained below.

[1186] Step 1:

[1187] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[1188] Step 2:

[1189] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[1190] Step 3:

[1191] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[1192] Step 4:

[1193] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[1194] Step 5:

[1195] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[1196] Step 6:

[1197] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[1198] Step 7:

[1199] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[1200] Step 8:

[1201] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[1202] Step 9:

[1203] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[1204] Step 10:

[1205] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[1206] Step 11:

[1207] The server sends notifications to the relevant parties. The server sends the death certificate to the family, facility administrators, and necessary related agencies via email and cloud storage.

[1208] Step 12:

[1209] The emotion engine recognizes the user's emotions. The emotion engine collects input, voice, and facial expression data from the device and analyzes the user's emotional state.

[1210] Step 13:

[1211] The emotion engine feeds the analysis results back to the generative AI, which analyzes the user's emotions and passes the results to the generative AI, which then adjusts the diagnosis and system response accordingly.

[1212] Step 14:

[1213] The emotion engine provides users with appropriate guidance and support information. For users who are feeling stressed or anxious, the emotion engine displays information such as relaxation methods and support contact information.

[1214] Example 2

[1215] 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."

[1216] In modern elderly care facilities, the series of procedures that must be carried out when a resident passes away requires a lot of time and effort, placing a heavy burden on those involved. It is also often difficult to confirm the cause of death and prepare a medical certificate in a timely manner. Furthermore, there is a lack of emotional support for those involved, placing a heavy mental burden on them. A system that can solve these issues is needed.

[1217] The identification processing 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 means for inputting death information of a facility resident, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using a generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, a means for issuing the death certificate and notifying relevant parties, a means for using an emotion engine that recognizes the user's emotions, and a means for feeding back the emotion recognition results to the generation AI. This not only streamlines procedures at the time of death and enables prompt and accurate responses, but also provides support that takes into consideration the emotional state of those involved.

[1218] "Institutional resident" refers to an individual who resides in a nursing home or care facility.

[1219] "Means for inputting death information" refers to an interface that allows a user to input the death status of a facility resident into the system using a terminal.

[1220] "Means for obtaining past medical history and information from the facility" refers to the function by which the server searches for and obtains the medical history and chart information of facility residents from the database.

[1221] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze data and perform a specific task (in this case, automatically generating death certificates).

[1222] "Means for automatically generating death certificates" refers to the process by which the generation AI automatically creates death certificates based on the data collected.

[1223] "Means for doctors to confirm death online" refers to a function that allows doctors to confirm the death status of facility residents in real time using a video call system.

[1224] "Means for final review and approval of the medical certificate by a physician" refers to the process by which a physician reviews the generated death certificate and, if necessary, amends and approves it.

[1225] "Means for issuing a death certificate and notifying relevant parties" refers to the function of the server to generate an official death certificate and notify family members, facility managers, and relevant organizations.

[1226] An "emotion engine that recognizes user emotions" refers to a system that analyzes a user's voice, facial expressions, and text data to identify their emotional state.

[1227] "Means for feeding back emotion recognition results to the generation AI" refers to the process in which the emotion engine sends the detected emotion data to the generation AI, and the generation AI adds or modifies the corresponding information based on the results.

[1228] This invention relates to a system that utilizes generative AI and an emotion engine to provide efficient and emotionally sensitive support for end-of-life care for elderly people residing in facilities.

[1229] The system uses the following major hardware and software:

[1230] Hardware:

[1231] Server: Used for database management, running generative AI, and analyzing the emotion engine.

[1232] Terminals: Windows PCs and tablets are used as interfaces for facility staff and doctors to operate the system.

[1233] software:

[1234] Database management system: Resident information is managed using database software such as MySQL.

[1235] Generative AI models: Use advanced generative AI, such as OpenAI GPT-4, to automatically generate death certificates.

[1236] Emotion engine: Analyzes the user's emotional state using emotion analysis software such as IBM Watson.

[1237] Video call system: Doctors can remotely certify death using video call applications such as Zoom.

[1238] The main functions of the system and the specific operations at each step are shown below.

[1239] 1. A user (facility staff member) logs in to the system through a terminal.

[1240] Facility staff enter their username and password into a dedicated login screen and send the authentication information to the server, which then compares it with the database for authentication and returns the authentication result, allowing the facility staff to access the system.

[1241] 2. The server retrieves resident information from the facility database.

[1242] The user enters the name of the resident they are searching for into the terminal and sends the data to the server, which searches the MySQL database to gather the resident's medical history, medical record information, and latest nursing notes. The collected information is returned to the terminal and displayed to the user.

[1243] 3. Generative AI automatically generates death certificates

[1244] The server inputs the collected resident information into a generative AI model to automatically generate a death certificate. The generated certificate includes the resident's basic information, cause of death, and date and time of death. The generated certificate is stored in a database and a preview is displayed on the device.

[1245] 4. The user (doctor) confirms the death online

[1246] Doctors log in to the system via video chat, and facility staff report the status of residents in real time. The doctors then confirm the death status of the residents and send the results to the server.

[1247] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[1248] The doctor logs in to the terminal, checks the generated death certificate, makes any necessary corrections, and electronically signs it using an electronic signature tool for final approval.

[1249] 6. The server issues a death certificate and notifies the relevant parties.

[1250] The server issues the approved medical certificate as an official document in PDF format. The certificate is then sent to the family, facility administrator, and relevant organizations via email or cloud storage. A message confirming the completion of the transmission is displayed on the device.

[1251] 7. Use an emotion engine that recognizes user emotions

[1252] The device sends the user's input, voice, and facial expression data to the emotion engine, which analyzes the user's emotional state. The analysis results are returned to the server, and necessary support information is provided to the user based on the results.

[1253] 8. Feedback emotion recognition results to generative AI

[1254] The server inputs the emotion data obtained from the emotion engine into the generative AI model, which then adds emotion-sensitive comments and advice to the diagnosis. The updated diagnosis is saved in a database and displayed on the device.

[1255] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, streamlining the overall process and providing psychological support.

[1256] Example prompt sentence:

[1257] "Please create a prompt that embodies how the emotion engine analyzes the user's emotions in the process of creating a death certificate for an elderly person and feeds the results back to the generative AI."

[1258] As described above, the present invention realizes an effective system for comprehensively supporting the end-of-life care of facility residents.

[1259] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1260] Step 1:

[1261] A user (facility staff member) logs into the system through a terminal.

[1262] Input: Username and Password.

[1263] Specific operation: Facility staff enter their username and password into a dedicated login screen. The terminal then sends the entered data to the server.

[1264] Data processing: The server checks the authentication information against a database.

[1265] Output: Authentication result (success or failure).

[1266] Specific operation: The server returns the authentication result to the terminal, and the terminal displays a login success message, allowing the user to access the system.

[1267] Step 2:

[1268] The server retrieves resident information from the facility's database.

[1269] Input: Resident's name.

[1270] Specific operation: The user enters the name of the resident they are searching for into the terminal, and the terminal sends the input data to the server.

[1271] Data processing: The server searches the database using a MySQL query.

[1272] Output: Medical history information, chart information, latest nursing records.

[1273] Specific operation: The server returns the acquired information to the terminal, which displays the information to the user.

[1274] Step 3:

[1275] Generative AI automatically generates death certificates.

[1276] Input: Collected resident information.

[1277] Specific operation: The server inputs the collected resident information into the generative AI model.

[1278] Data processing: Generative AI generates a death certificate based on the input data.

[1279] Output: The generated death certificate.

[1280] Specific operation: The server saves the generated medical certificate in the database. The server sends a preview of the medical certificate to the terminal and displays it to the user.

[1281] Step 4:

[1282] The user (doctor) certifies the death online.

[1283] Input: Real-time reporting from facility personnel.

[1284] Specific operations: A doctor logs in to the system via a video chat system. A facility staff member reports the resident's condition.

[1285] Data processing: Doctors review information via video call.

[1286] Output: Death confirmation result.

[1287] Specific operation: The doctor sends the death confirmation results to the server via the terminal.

[1288] Step 5:

[1289] The user (doctor) performs final review and approval of the medical certificate.

[1290] Input: The generated death certificate.

[1291] Specific operation: The doctor logs in to the terminal and checks the contents of the generated death certificate.

[1292] Data processing: The doctor will amend the medical certificate as necessary.

[1293] Output: Approved medical certificate.

[1294] Specific operation: The doctor electronically signs the medical certificate using the electronic signature tool. The server stores the signed medical certificate in the database.

[1295] Step 6:

[1296] The server issues a death certificate and notifies the relevant parties.

[1297] Input: Approved medical certificate.

[1298] Specific operation: The server issues the approved medical certificate as an official document.

[1299] Data processing: Generate medical certificate in PDF format.

[1300] Output: Death certificate in PDF format.

[1301] Specific operation: The server sends the medical certificate to the relevant parties via email or cloud storage. The device displays a message to the user that transmission is complete.

[1302] Step 7:

[1303] Uses an emotion engine that recognizes the user's emotions.

[1304] Input: User input, voice, and facial expression data.

[1305] Specific operation: The device sends the user's input, voice, and facial expression data to the emotion engine.

[1306] Data processing: The emotion engine analyzes the data and recognizes the user's emotional state.

[1307] Output: Emotion recognition result.

[1308] Specific operation: The emotion engine returns the results to the server. The device displays the results to the user and provides necessary support information.

[1309] Step 8:

[1310] The emotion recognition results are fed back to the generative AI.

[1311] Input: Emotion data.

[1312] Specific operation: The server obtains the emotion data obtained from the emotion engine.

[1313] Data processing: Emotional data is input into a generative AI model, which then adds emotionally sensitive comments and advice to the diagnosis report.

[1314] Output: Updated death certificate.

[1315] Specific operation: The server sends the updated medical certificate to the database and the terminal. The terminal displays the new medical certificate to the user.

[1316] (Application example 2)

[1317] 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."

[1318] In facilities with many elderly residents, a swift and accurate response is required when a resident dies, while at the same time reducing the burden on those involved and taking their emotions into consideration. Furthermore, customer service in retail stores requires an appropriate understanding of the emotional state of the elderly and flexible responses accordingly. Conventional systems have found it difficult to efficiently resolve these issues.

[1319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1320] In this invention, the server includes means for inputting death information of a facility resident, means for acquiring past medical history and information from the facility, means for automatically generating a death certificate using a generation AI, means for a doctor to confirm the death online, means for the doctor to perform final confirmation and approval of the death certificate, means for issuing the death certificate and notifying relevant parties, means for recognizing emotions when providing services for the elderly, and means for the generation AI to generate a response based on emotion recognition data. This enables a quick, accurate, and emotion-sensitive response when an elderly person dies, and also enables flexible emotional responses in retail stores for services for the elderly.

[1321] "Institutional residents" refers to elderly people or patients who are staying in nursing homes or medical facilities for an extended period of time.

[1322] "Death information" refers to basic information such as the date, time, place, and cause of death of a facility resident.

[1323] "Generative AI" refers to algorithms or systems that use artificial intelligence technology to automatically generate documents or information based on specific data or conditions.

[1324] A "death certificate" is an official document in which a doctor legally certifies and details the death.

[1325] "Online video calling" refers to a means of communicating in real time through audio and video using the Internet.

[1326] "Emotion recognition" refers to the technology of analyzing data such as voice, facial expressions, and behavior to determine a person's emotional state (e.g., sadness, joy, tension, etc.).

[1327] "Response generation" refers to the process of automatically generating appropriate responses or advice based on input data and circumstances.

[1328] "Medical history" refers to information that records details of a patient's past illnesses, their treatment history, and the medications they have used.

[1329] "Final review and approval" refers to the procedure in which experts and related parties finally review the accuracy of the contents of the generated documents and information and formally approve the contents.

[1330] This invention relates to a system that uses a generative AI and an emotion recognition engine to provide end-of-life care for the elderly and customer service support in retail stores. A specific embodiment of this system will be described in detail below.

[1331] Hardware and software used

[1332] Hardware: Smartphone (with camera), server

[1333] Software: OpenCV (for face detection and preprocessing), EmotionEngine (emotion recognition engine), ResponseGenerator (response generation AI)

[1334] Specific flow of data processing

[1335] The server first receives input from a smartphone. Facility staff enters information about the death of a facility resident using a smartphone. The server then retrieves information such as the resident's past medical history and the latest nursing records from the facility's database.

[1336] Based on the acquired information, the generation AI automatically generates a death certificate. At this time, the generation AI fills in basic information on the death certificate, such as the cause of death and the date and time of death. The generated certificate is then reviewed online by a doctor. The doctor confirms the death via video call and makes a final review and approval of the certificate. The finalized death certificate is issued by the server and notified to the relevant parties.

[1337] At the same time, in retail stores that provide services for the elderly, the Emotion Engine uses smartphone cameras to analyze the facial expressions of the elderly and determine their emotional state. Based on this, the Response Generator generates appropriate responses and advice.

[1338] Specific examples

[1339] A facility staff member enters information about Suzuki Jiro's death on a smartphone. The server retrieves Suzuki Jiro's past medical history (high blood pressure, diabetes, heart disease) from the facility's database and provides this information to the generation AI. Based on this, the generation AI automatically generates a death certificate for Suzuki Jiro, stating "natural death due to cardiac arrest." A doctor verifies the death online via video call and provides final confirmation and approval of the certificate's contents. The server then notifies the family and facility administrator of the official certificate.

[1340] In retail stores, the facial expressions of elderly people are captured with a smartphone camera and their emotional state is analyzed by the Emotion Engine. For example, if the customer is nervous, the Response Generator generates a response such as, "You seem nervous. Let us suggest a product that will have a relaxing effect."

[1341] Prompt Sentence Examples

[1342] The customer's emotional state is determined to be "tension." Please generate advice that can suggest products that have a relaxing effect for the elderly.

[1343] This system will enable a swift, accurate, and emotionally sensitive response when an elderly person dies, and will also enable flexible, emotionally sensitive services for the elderly in retail stores.

[1344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1345] Step 1:

[1346] Users use their smartphones to input information about the deaths of facility residents. This information includes basic information such as the resident's name, date and time of death, location, and cause of death. This information is then sent to the server via the smartphone.

[1347] Step 2:

[1348] The server retrieves the resident's past medical history and nursing records from the facility's database. It searches for medical history data, treatment records, nursing records, etc. based on the specific resident's name and retrieves this information all at once. This prepares the data necessary for the generation AI.

[1349] Step 3:

[1350] The AI ​​on the server automatically generates a death certificate based on the acquired medical history information and the current death information entered by the user. The AI ​​analyzes the medical history data and creates a provisional death certificate that includes the cause of death, date and time of death, etc.

[1351] Step 4:

[1352] The user (doctor) confirms death via online video call. Using a smartphone or computer, the user connects with facility staff to check for breathing and cardiac arrest. The facility staff reports the resident's condition in real time, which the doctor confirms.

[1353] Step 5:

[1354] The doctor performs a final review and approval of the death certificate generated on the server. He logs into the online system, checks the provisional death certificate, corrects any deficiencies, and performs a final review. He then officially approves the certificate with an electronic signature.

[1355] Step 6:

[1356] The server issues a final, verified, and approved death certificate, notifies the relevant parties, creates a PDF of the certificate, emails it to the family and facility administrator, and shares it with any necessary authorities.

[1357] Step 7:

[1358] The user (store staff) uses a smartphone camera to analyze the facial expressions of elderly people who visit a retail store. The acquired video data is sent to a server.

[1359] Step 8:

[1360] The Emotion Engine on the server analyzes the received facial expression data and determines the emotional state of the elderly person. Specifically, it analyzes facial expression images and recognizes emotions such as "sadness," "happiness," and "tension."

[1361] Step 9:

[1362] The server's ResponseGenerator generates an optimal response based on the determined emotional state. For example, if the elderly person is nervous, the response generated will be "suggest products that have a relaxing effect."

[1363] Step 10:

[1364] The user (store staff) checks the generated response and responds appropriately to the elderly person, such as suggesting a relaxing herbal tea.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] [Fourth embodiment]

[1369] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1370] 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.

[1371] 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).

[1372] 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.

[1373] 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.

[1374] 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).

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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."

[1382] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities. This system includes the following main functions to reduce the burden on doctors, families, and facilities and achieve efficient end-of-life care.

[1383] 1. A user (facility staff member) logs in to the system through a terminal.

[1384] Facility staff use a dedicated login system to access the system from a terminal by entering their username and password.

[1385] 2. The server retrieves resident information from the facility database.

[1386] The server searches the facility's database based on the name of the deceased resident and collects all of the resident's medical history and most recent nursing notes.

[1387] Example: The server retrieves the medical history of "Taro Takayama," including his past treatment records for high blood pressure, diabetes, and heart disease.

[1388] 3. Generative AI automatically generates death certificates

[1389] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[1390] Example: The AI ​​generates a death certificate for "Taro Takayama" and lists the cause of death as "natural death due to cardiac arrest."

[1391] 4. The user (doctor) confirms the death online

[1392] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who report the resident's condition in real time (stopping breathing, checking heart rate, etc.).

[1393] Example: Facility staff report to a doctor that Taro Takayama has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[1394] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[1395] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs and approves the certificate.

[1396] Example: A doctor reviews the medical certificate for "Taro Takayama," confirms that there are no problems with the cause of death or the date and time, and then electronically signs and approves it.

[1397] 6. The server issues a death certificate and notifies the relevant parties.

[1398] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary relevant agencies via email or cloud storage.

[1399] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Takayama Taro's family and the facility manager.

[1400] This system allows for quick confirmation of death even when a doctor is far away, shortening waiting times for family members and facility staff. It also eliminates the need for facilities to have a doctor on standby, which is expected to reduce costs. This online end-of-life support system, which utilizes generative AI, is a useful solution for achieving efficiency in many areas.

[1401] The processing flow will be explained below.

[1402] Step 1:

[1403] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[1404] Step 2:

[1405] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[1406] Step 3:

[1407] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[1408] Step 4:

[1409] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[1410] Step 5:

[1411] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[1412] Step 6:

[1413] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to check online.

[1414] Step 7:

[1415] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[1416] Step 8:

[1417] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[1418] Step 9:

[1419] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[1420] Step 10:

[1421] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[1422] Step 11:

[1423] The server sends notifications to the relevant parties, and sends death certificates to the family, facility administrators, and necessary authorities via email and cloud storage.

[1424] Example 1

[1425] 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."

[1426] Traditionally, end-of-life care for elderly people in facilities required on-site visits by doctors and manual paperwork, resulting in delays and a heavy workload. It was particularly difficult to respond quickly when doctors were far away or at night or on holidays. Additionally, creating and reviewing documents took a lot of time, placing an increased burden on families and facilities. To solve these problems, a system was needed that could efficiently and quickly implement the end-of-life care process.

[1427] 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.

[1428] In this invention, the server includes a means for logging in via a terminal, a means for acquiring resident information from a database, a means for automatically generating a death certificate using generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, and a means for issuing the death certificate and notifying relevant parties. This enables quick and efficient confirmation of death and creation, approval, and notification of the death certificate.

[1429] "Means for logging in via a terminal" refers to a means by which facility staff and other users use a dedicated login system, enter a user name and password for authentication, and access the system.

[1430] The "means of obtaining resident information from the database" refers to the means by which the server connects to the database system within the facility and searches for and obtains the medical history and nursing records of a deceased resident based on the name of that resident.

[1431] "Means for automatically generating death certificates using generative AI" refers to a means for automatically generating death certificates based on acquired medical history information using a generative AI model installed on a server.

[1432] "Online means for doctors to confirm death" refers to a means by which doctors can log in to the system online and check the condition of a deceased resident through a video call with facility staff.

[1433] The "means for the physician to finalize and approve the death certificate" refers to the means by which a physician reviews the death certificate generated within the system, makes any necessary corrections, and then electronically signs and approves it.

[1434] "Means for issuing a death certificate and notifying relevant parties" refers to the means by which the server issues an electronically signed death certificate as an official document and notifies the family, facility administrator, and necessary relevant organizations.

[1435] The present invention is a system for supporting end-of-life care for residents in elderly care facilities using a generative AI model. Specific embodiments of this system are described below.

[1436] Log in through the terminal

[1437] Users (facility staff) access the login system using a terminal PC installed within the facility. A web browser (e.g., Google Chrome or Mozilla Firefox) is installed on this terminal, and they reach the login page by accessing a specific URL (e.g., https: / / facility-login.example.com). By entering their username and password and clicking the login button, the server verifies the provided authentication information against the user database, and if authentication is successful, they are redirected to the dashboard screen.

[1438] Specific examples:

[1439] The user enters the username and password and clicks the login button.

[1440] The server checks the credentials in its database and allows the login.

[1441] Retrieving resident information from the database

[1442] The server accesses the database based on the name of the deceased resident provided by the user, and searches for and retrieves the corresponding resident's medical history and nursing records. A common RDBMS (e.g., MySQL or PostgreSQL) is used as the database system. The server executes SQL queries to extract the necessary data and collects information for generating a medical certificate.

[1443] Specific examples:

[1444] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Example Name'" to retrieve the required data.

[1445] Automated generation of death certificates using generative AI

[1446] A generative AI model (e.g., GPT-3, BERT) installed on the server automatically generates a death certificate based on the acquired medical history information. The certificate includes basic information about the resident, cause of death, date and time of death, etc. The prompt is entered as "Please automatically generate a death certificate based on the resident's name and medical history." The generative AI model then generates the appropriate information and creates the certificate.

[1447] Specific prompt examples:

[1448] "Please generate a death certificate for Taro Takayama."

[1449] "Automatically generate death certificates based on resident names and medical histories."

[1450] A doctor will certify death online

[1451] Doctors log in to the system online and check the condition of the deceased resident via video call with facility staff. These video calls are made using common video call software such as Zoom or Microsoft Teams. The facility staff use the camera to report to the doctor in real time if the resident stops breathing or has a low heart rate. The doctor then confirms death based on this information.

[1452] Specific examples:

[1453] Facility staff report a resident's cardiac arrest to a doctor via video call, who then confirms the report.

[1454] Final review and approval of the medical certificate by the doctor

[1455] The physician reviews the death certificate generated in the system, makes any necessary corrections, and then electronically signs and approves the certificate using electronic signature software (e.g., Adobe Sign), making it official.

[1456] Specific examples:

[1457] The doctor reviews the generated medical certificate and approves it with an electronic signature.

[1458] Issue a death certificate and notify the appropriate parties

[1459] The server issues a digitally signed death certificate in PDF format and notifies the family, facility administrators, and necessary related organizations via email (e.g., SMTP protocol) or cloud storage services (e.g., Google Drive, Dropbox).

[1460] Specific examples:

[1461] The server creates a medical certificate in PDF format and sends it to the family and facility administrator by email.

[1462] This system will enable fast and efficient end-of-life care in elderly care facilities, reducing the burden on doctors, facility staff, and family members. By utilizing a generative AI model, the process from automatically generating medical certificates to approval and notification will be integrated, streamlining the overall work.

[1463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1464] Step 1:

[1465] The user logs in through the terminal. Using the terminal's web browser, they access a specific URL. They enter their username and password and click the login button. The server receives the authentication information and authenticates it by checking it against the user information in the database. If authentication is successful, they are redirected to the dashboard screen.

[1466] Input: Username, Password

[1467] Data processing: Check the entered authentication information against the database

[1468] Output: Login success or failure, dashboard screen

[1469] Specific behavior:

[1470] The user enters the username and password and clicks the login button.

[1471] The server checks the entered information against a database.

[1472] Step 2:

[1473] The server retrieves resident information from the facility's database. Using the name of the deceased resident provided by the user, it connects to the database and executes an SQL query to retrieve the medical history and nursing records for that resident.

[1474] Input: Resident's name

[1475] Data processing: Execute SQL queries and extract relevant data from the database

[1476] Output: Resident medical history data and nursing records

[1477] Specific behavior:

[1478] The server executes the SQL query "SELECT FROM resident_records WHERE name = 'Taro Takayama'".

[1479] Retrieve Takayama Taro's medical history and nursing records from the database.

[1480] Step 3:

[1481] The generative AI automatically generates a death certificate. Using a generative AI model installed on the server, the AI ​​generates a death certificate by inputting the acquired medical history data as prompts. The generated certificate includes the resident's basic information, cause of death, and date and time of death.

[1482] Input: Resident's medical history data, prompt statement

[1483] Data processing: Generative AI models are used to generate medical reports

[1484] Output: Generated death certificate

[1485] Specific behavior:

[1486] Enter "Please generate a death certificate for Taro Takayama" as the prompt text into the generation AI.

[1487] AI generates Taro Takayama's death certificate.

[1488] Step 4:

[1489] The user (doctor) confirms death online. The doctor logs into the system online and makes a video call with facility staff. The facility staff uses a camera to share the resident's condition in real time, reporting any cessation of breathing or confirmation of heart rate. The doctor confirms death based on this information.

[1490] Input:Video call information

[1491] Data processing: Real-time information sharing and confirmation

[1492] Output: Death confirmation success / failure

[1493] Specific behavior:

[1494] Facility staff use a video camera to show doctors that a resident's heart has stopped beating.

[1495] The doctor will check in via video call.

[1496] Step 5:

[1497] The user (doctor) performs the final review and approval of the death certificate. The doctor reviews the death certificate generated within the system and makes any necessary corrections. He then electronically signs and approves it.

[1498] Input: Generated death certificate

[1499] Data processing: Doctor's confirmation and correction, electronic signature

[1500] Output: Approved death certificate

[1501] Specific behavior:

[1502] The doctor reviews the medical certificate and approves it with an electronic signature.

[1503] Step 6:

[1504] The server issues the death certificate and notifies the relevant parties. The server creates a digitally signed death certificate in PDF format and sends it to the family, facility administrators, and relevant agencies via email or cloud storage.

[1505] Input: Approved Death Certificate

[1506] Data processing: Creating documents in PDF format and sending them to recipients

[1507] Output: Medical certificate sent

[1508] Specific behavior:

[1509] The server generates a medical certificate in PDF format.

[1510] A medical certificate will be sent via email to the family and facility manager.

[1511] (Application example 1)

[1512] 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."

[1513] Caring for elderly people in nursing homes places a significant burden on medical professionals and facility staff, requiring time and effort. In particular, efficient processes, such as confirming the death, preparing a death certificate, and contacting relevant parties, require rapid and accurate information sharing and management. It is also important to ensure that remote medical professionals can smoothly confirm the death online and approve the death certificate. It is necessary to resolve these issues and simplify and streamline the procedures for caring for elderly people at the end of their lives.

[1514] 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.

[1515] In this invention, the server includes a means for inputting death information of facility residents, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using generation AI, a means for a medical professional to confirm the death online, a means for the medical professional to perform final review and approval of the medical certificate, a means for issuing the medical certificate as an electronic document and notifying relevant parties, a means for facility staff to log in and access using a smartphone application, and a means for communicating with medical professionals in real time via online video calls. This reduces the burden on medical professionals and facility staff and enables fast and efficient end-of-life care.

[1516] "Institutional residents" are elderly people who live in residential facilities such as nursing homes and care facilities.

[1517] "Death information" is detailed data on the deaths of facility residents, including the date, time, place, and cause of death.

[1518] "Medical history" refers to information about the illnesses and treatment history that a facility resident has experienced.

[1519] "Generative AI" refers to artificial intelligence that automatically generates documents using machine learning and natural language processing techniques.

[1520] A "death certificate" is a medical document that officially identifies and records the death of a resident.

[1521] "Online video calling" is a communication method that allows you to simultaneously send and receive video and audio over the Internet.

[1522] "Healthcare workers" is a general term that refers to doctors, nurses, and other health-related professionals.

[1523] An "electronic document" is a document that is created electronically and is handled in the form of a PDF or email.

[1524] "Stakeholders" are people such as family members, facility administrators, and government agencies with whom death certificates and other important information should be shared.

[1525] "Facility staff" are staff who work within the facility and support the daily lives of residents.

[1526] A "smartphone application" is software that runs on a smartphone and provides a variety of functions.

[1527] "Logging in" is the act of a user entering authentication information to access a particular system or service.

[1528] "Access" is the act of operating or checking information or systems.

[1529] "Real-time communication" refers to a means of communication in which information is exchanged immediately without delay.

[1530] This invention is a system that efficiently supports the end-of-life care of elderly people residing in nursing homes. This system uses a smartphone application to allow facility staff and medical professionals to cooperate in confirming deaths and preparing medical certificates online.

[1531] Hardware and Software Configuration

[1532] server

[1533] The server has the following main functions:

[1534] Entering death information for facility residents

[1535] The server accepts death information entered by facility staff and stores it in a database.

[1536] Obtaining medical history and facility information

[1537] The server retrieves the resident's past medical history and most recent nursing notes from the facility's database.

[1538] Generative AI for generating death certificates

[1539] The server automatically generates a death certificate based on the acquired medical history information using generation AI, which uses natural language processing technology to generate the contents of the certificate.

[1540] Final review and approval of medical certificate

[1541] The server provides an interface for medical professionals to review the medical certificate via an online video call and approve it with an electronic signature.

[1542] Notification to relevant parties

[1543] The server generates a medical certificate in PDF format and sends it to the family or facility administrator via email.

[1544] Device (smartphone)

[1545] The smartphone application running on the device has the following features:

[1546] Login and Information Access

[1547] Facility staff can log into the system by entering a username and password to view and enter resident information.

[1548] Online video call for death confirmation

[1549] Facility staff will communicate with medical personnel via video call to confirm deaths in real time.

[1550] Data Flow and Processing

[1551] 1. Resident death information is entered into the system:

[1552] Facility staff enter information about resident deaths via a smartphone application.

[1553] 2. Resident medical history information is retrieved by the server:

[1554] The server searches the database based on the entered information and retrieves the resident's medical history and nursing records.

[1555] 3. The generative AI model generates the diagnosis:

[1556] The generation AI on the server uses natural language processing technology to generate a death certificate based on the acquired information.

[1557] 4. Confirming death via online video call:

[1558] Facility staff use the video calling function on their smartphones to communicate with medical professionals and confirm the death of a resident.

[1559] 5. Medical certificate verification and approval:

[1560] Medical professionals can review the generated medical certificate through a smartphone application, make any necessary corrections, and electronically sign it.

[1561] 6. Issuance of medical certificate and notification to relevant parties:

[1562] The server generates a medical certificate in PDF format after final confirmation and approval and sends it via email to the family or facility administrator.

[1563] Specific examples

[1564] For example, if "Taro Tanaka," a resident of a certain facility, dies, facility staff enter the date, time, and location of "Taro Tanaka's" death into a smartphone application. The server receives this information and retrieves records of "Taro Tanaka's" medical history, including past records of high blood pressure, diabetes, and heart disease. Based on this information, the generative AI model generates a medical certificate stating "natural death due to cardiac arrest." A medical professional then confirms the death via an online video call, checks the generated medical certificate, and approves it. Finally, the server generates the medical certificate in PDF format and sends it by email to Tanaka's family and the facility administrator.

[1565] Prompt Sentence Examples

[1566] "Taro Tanaka" has a medical history of "high blood pressure, diabetes, heart disease." Based on this, please generate the following death certificate:

[1567] basic information

[1568] Cause of death: Natural causes due to cardiac arrest

[1569] Date of Death: YYYY-MM-DD HH:MM

[1570] This will reduce the burden on facility staff and medical professionals, and create a system that enables fast and efficient end-of-life care.

[1571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1572] Step 1:

[1573] Facility staff log in to the system via a smartphone application.

[1574] Input: Username, Password

[1575] Output: Authentication token

[1576] Specific operation: The user name and password entered from the terminal are sent to the server, and the server verifies the authentication information. If authentication is successful, the server returns an authentication token to the terminal.

[1577] Step 2:

[1578] Facility staff enters information about the death of a resident.

[1579] Input: Resident's name, date and time of death, place of death

[1580] Output: Death information is saved in the server database.

[1581] Specific operation: Death information entered on the device is sent to the server, which then stores the information in a database.

[1582] Step 3:

[1583] The server retrieves the resident's medical history information.

[1584] Input: Resident's name

[1585] Output: Resident's past medical history information

[1586] Specific operation: The server searches the database and retrieves past medical history information for the specified resident.

[1587] Step 4:

[1588] Generative AI models automatically generate death certificates.

[1589] Input: Resident medical history and mortality information

[1590] Output: Death certificate contents

[1591] Specific operation: The generation AI on the server uses the acquired medical history information and death information to generate the contents of the death certificate.

[1592] Step 5:

[1593] Facility staff will contact medical personnel via online video call to confirm the death.

[1594] Input: Video call ID

[1595] Output: Medical personnel confirms death

[1596] Specific operation: Using the device's video call function, a real-time call is made with a medical professional to confirm the death. The medical professional then visually confirms the death and conveys approval to the facility staff.

[1597] Step 6:

[1598] A medical professional will provide final review and approval of the medical certificate.

[1599] Input: Generated death certificate

[1600] Output: Digitally signed medical certificate

[1601] Specific operation: The server displays the generated medical certificate to the medical professional, who checks the contents. If there are no problems, the medical professional electronically signs and approves it.

[1602] Step 7:

[1603] The server issues the medical certificate as an electronic document and notifies the relevant parties.

[1604] Input: Electronically signed medical certificate, contact information of the parties involved

[1605] Output: Notify relevant parties and send a PDF medical report

[1606] What happens: The server generates an approved medical certificate in PDF format and emails it to the appropriate parties, including family members and facility managers.

[1607] This will establish an efficient processing flow for the system that realizes the application example, enabling prompt and accurate end-of-life care for the elderly.

[1608] 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.

[1609] This invention relates to a system that utilizes generative AI to support the end-of-life care of elderly people residing in facilities, and further combines it with an emotion engine that recognizes the user's emotions. This system includes the following main functions, reducing the burden on doctors, families, and facilities and realizing efficient, emotion-sensitive end-of-life care.

[1610] 1. A user (facility staff member) logs in to the system through a terminal.

[1611] Facility staff access the system by entering their username and password on a dedicated login screen.

[1612] 2. The server retrieves resident information from the facility database.

[1613] The server searches the facility database based on the entered resident's name and collects the individual's medical history, medical record information, and latest nursing records.

[1614] Example: The server retrieves the medical history of "Jiro Suzuki," including past treatment records for high blood pressure, diabetes, and heart disease.

[1615] 3. Generative AI automatically generates death certificates

[1616] The AI ​​on the server automatically generates a death certificate based on the medical history information it has acquired. The certificate includes basic information about the resident, the cause of death, and the date and time of death.

[1617] Example: A generation AI generates a death certificate for "Suzuki Jiro" and lists the cause of death as "natural death due to cardiac arrest."

[1618] 4. The user (doctor) confirms the death online

[1619] Doctors log in to the system online and check the condition of the deceased resident through a video call with facility staff, who then report the resident's condition in real time.

[1620] Example: Facility staff report to a doctor that Suzuki Jiro has stopped breathing and his heartbeat has been confirmed, and the doctor confirms this online.

[1621] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[1622] The doctor reviews the generated death certificate in the system, makes any necessary corrections, and then electronically signs the certificate to give final approval.

[1623] Example: A doctor checks the contents of Suzuki Jiro's medical certificate, confirms that there are no problems with the cause of death or the date and time, and approves it by electronically signing.

[1624] 6. The server issues a death certificate and notifies the relevant parties.

[1625] The server issues the approved medical certificate as an official document and sends it to the family, facility administrators, and necessary related organizations via email or cloud storage.

[1626] Example: The server creates an electronically signed medical certificate in PDF format and sends it by email to Mr. Suzuki Jiro's family and the facility manager.

[1627] 7. Use an emotion engine that recognizes user emotions

[1628] The emotion engine analyzes user input, voice, and facial expression data to recognize the user's emotional state (e.g., stress, anxiety, sadness, etc.).

[1629] Example: If a facility staff member is very sad, the emotion engine will detect this state and the system will provide support information on emotional care.

[1630] 8. Feedback emotion recognition results to generative AI

[1631] The emotion engine analyzes the user's emotional data and feeds the results back to the generation AI, which can then create comments and advice for the diagnosis that take emotions into consideration.

[1632] Example: The emotion engine detects tension among facility staff, and the generative AI adds a comment to the medical certificate such as, "Please pay special attention to caring for the bereaved family."

[1633] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, improving the efficiency of the overall process while also providing psychological support.

[1634] The processing flow will be explained below.

[1635] Step 1:

[1636] A user (facility staff member) logs in to the system through a terminal. The facility staff member enters a username and password on the login screen to perform authentication.

[1637] Step 2:

[1638] The user enters information about the death of a facility resident. The user opens an input form on the terminal and enters the resident's name, date and time of death, and details of the death.

[1639] Step 3:

[1640] The server retrieves the resident's information from the facility's database. Based on the entered name, the server searches the facility's database and retrieves the resident's medical history, chart information, and latest nursing notes.

[1641] Step 4:

[1642] The server passes the acquired information to the generation AI, which analyzes the acquired medical history and death circumstances data and organizes information such as the cause of death.

[1643] Step 5:

[1644] The AI ​​automatically generates a death certificate based on the analyzed data, entering the necessary information (resident information, cause of death, date and time of death, etc.).

[1645] Step 6:

[1646] The server stores the generated death certificate and notifies the doctor. The server temporarily stores the generated certificate and makes it available for the doctor to review online.

[1647] Step 7:

[1648] A user (doctor) logs in online and checks the death certificate. A doctor logs in to the system from a remote location and checks the contents of the generated death certificate.

[1649] Step 8:

[1650] The user (doctor) confirms death via video call. The doctor uses the system's video call function to check the resident's condition (stopped breathing, no heartbeat, etc.) with facility staff in real time.

[1651] Step 9:

[1652] The user (doctor) performs final review and approval of the death certificate. The doctor checks the generated certificate for any errors and enters any necessary corrections. After that, the doctor electronically signs the certificate, giving final approval to the contents of the certificate.

[1653] Step 10:

[1654] The server officially issues the death certificate. After the doctor approves it, the server saves it as an official certificate and prepares it for issuance.

[1655] Step 11:

[1656] The server sends notifications to the relevant parties. The server sends the death certificate to the family, facility administrators, and necessary related agencies via email and cloud storage.

[1657] Step 12:

[1658] The emotion engine recognizes the user's emotions. The emotion engine collects input, voice, and facial expression data from the device and analyzes the user's emotional state.

[1659] Step 13:

[1660] The emotion engine feeds the analysis results back to the generative AI, which analyzes the user's emotions and passes the results to the generative AI, which then adjusts the diagnosis and system response accordingly.

[1661] Step 14:

[1662] The emotion engine provides users with appropriate guidance and support information. For users who are feeling stressed or anxious, the emotion engine displays information such as relaxation methods and support contact information.

[1663] Example 2

[1664] 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."

[1665] In modern elderly care facilities, the series of procedures that must be carried out when a resident passes away requires a lot of time and effort, placing a heavy burden on those involved. It is also often difficult to confirm the cause of death and prepare a medical certificate in a timely manner. Furthermore, there is a lack of emotional support for those involved, placing a heavy mental burden on them. A system that can solve these issues is needed.

[1666] The identification processing 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 means for inputting death information of a facility resident, a means for acquiring past medical history and information from the facility, a means for automatically generating a death certificate using a generation AI, a means for a doctor to confirm the death online, a means for the doctor to perform final confirmation and approval of the death certificate, a means for issuing the death certificate and notifying relevant parties, a means for using an emotion engine that recognizes the user's emotions, and a means for feeding back the emotion recognition results to the generation AI. This not only streamlines procedures at the time of death and enables prompt and accurate responses, but also provides support that takes into consideration the emotional state of those involved.

[1667] "Institutional resident" refers to an individual who resides in a nursing home or care facility.

[1668] "Means for inputting death information" refers to an interface that allows a user to input the death status of a facility resident into the system using a terminal.

[1669] "Means for obtaining past medical history and information from the facility" refers to the function by which the server searches for and obtains the medical history and chart information of facility residents from the database.

[1670] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze data and perform a specific task (in this case, automatically generating death certificates).

[1671] "Means for automatically generating death certificates" refers to the process by which the generation AI automatically creates death certificates based on the data collected.

[1672] "Means for doctors to confirm death online" refers to a function that allows doctors to confirm the death status of facility residents in real time using a video call system.

[1673] "Means for final review and approval of the medical certificate by a physician" refers to the process by which a physician reviews the generated death certificate and, if necessary, amends and approves it.

[1674] "Means for issuing a death certificate and notifying relevant parties" refers to the function of the server to generate an official death certificate and notify family members, facility managers, and relevant organizations.

[1675] An "emotion engine that recognizes user emotions" refers to a system that analyzes a user's voice, facial expressions, and text data to identify their emotional state.

[1676] "Means for feeding back emotion recognition results to the generation AI" refers to the process in which the emotion engine sends the detected emotion data to the generation AI, and the generation AI adds or modifies the corresponding information based on the results.

[1677] This invention relates to a system that utilizes generative AI and an emotion engine to provide efficient and emotionally sensitive support for end-of-life care for elderly people residing in facilities.

[1678] The system uses the following major hardware and software:

[1679] Hardware:

[1680] Server: Used for database management, running generative AI, and analyzing the emotion engine.

[1681] Terminals: Windows PCs and tablets are used as interfaces for facility staff and doctors to operate the system.

[1682] software:

[1683] Database management system: Resident information is managed using database software such as MySQL.

[1684] Generative AI models: Use advanced generative AI, such as OpenAI GPT-4, to automatically generate death certificates.

[1685] Emotion engine: Analyzes the user's emotional state using emotion analysis software such as IBM Watson.

[1686] Video call system: Doctors can remotely certify death using video call applications such as Zoom.

[1687] The main functions of the system and the specific operations at each step are shown below.

[1688] 1. A user (facility staff member) logs in to the system through a terminal.

[1689] Facility staff enter their username and password into a dedicated login screen and send the authentication information to the server, which then compares it with the database for authentication and returns the authentication result, allowing the facility staff to access the system.

[1690] 2. The server retrieves resident information from the facility database.

[1691] The user enters the name of the resident they are searching for into the terminal and sends the data to the server, which searches the MySQL database to gather the resident's medical history, medical record information, and latest nursing notes. The collected information is returned to the terminal and displayed to the user.

[1692] 3. Generative AI automatically generates death certificates

[1693] The server inputs the collected resident information into a generative AI model to automatically generate a death certificate. The generated certificate includes the resident's basic information, cause of death, and date and time of death. The generated certificate is stored in a database and a preview is displayed on the device.

[1694] 4. The user (doctor) confirms the death online

[1695] Doctors log in to the system via video chat, and facility staff report the status of residents in real time. The doctors then confirm the death status of the residents and send the results to the server.

[1696] 5. The user (doctor) performs final confirmation and approval of the medical certificate

[1697] The doctor logs in to the terminal, checks the generated death certificate, makes any necessary corrections, and electronically signs it using an electronic signature tool for final approval.

[1698] 6. The server issues a death certificate and notifies the relevant parties.

[1699] The server issues the approved medical certificate as an official document in PDF format. The certificate is then sent to the family, facility administrator, and relevant organizations via email or cloud storage. A message confirming the completion of the transmission is displayed on the device.

[1700] 7. Use an emotion engine that recognizes user emotions

[1701] The device sends the user's input, voice, and facial expression data to the emotion engine, which analyzes the user's emotional state. The analysis results are returned to the server, and necessary support information is provided to the user based on the results.

[1702] 8. Feedback emotion recognition results to generative AI

[1703] The server inputs the emotion data obtained from the emotion engine into the generative AI model, which then adds emotion-sensitive comments and advice to the diagnosis. The updated diagnosis is saved in a database and displayed on the device.

[1704] This system allows doctors to quickly confirm death even from a distance, reducing waiting times for family members and facility staff. It also uses an emotion engine to provide support that takes into account the user's emotions, streamlining the overall process and providing psychological support.

[1705] Example prompt sentence:

[1706] "Please create a prompt that embodies how the emotion engine analyzes the user's emotions in the process of creating a death certificate for an elderly person and feeds the results back to the generative AI."

[1707] As described above, the present invention realizes an effective system for comprehensively supporting the end-of-life care of facility residents.

[1708] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1709] Step 1:

[1710] A user (facility staff member) logs into the system through a terminal.

[1711] Input: Username and Password.

[1712] Specific operation: Facility staff enter their username and password into a dedicated login screen. The terminal then sends the entered data to the server.

[1713] Data processing: The server checks the authentication information against a database.

[1714] Output: Authentication result (success or failure).

[1715] Specific operation: The server returns the authentication result to the terminal, and the terminal displays a login success message, allowing the user to access the system.

[1716] Step 2:

[1717] The server retrieves resident information from the facility's database.

[1718] Input: Resident's name.

[1719] Specific operation: The user enters the name of the resident they are searching for into the terminal, and the terminal sends the input data to the server.

[1720] Data processing: The server searches the database using a MySQL query.

[1721] Output: Medical history information, chart information, latest nursing records.

[1722] Specific operation: The server returns the acquired information to the terminal, which displays the information to the user.

[1723] Step 3:

[1724] Generative AI automatically generates death certificates.

[1725] Input: Collected resident information.

[1726] Specific operation: The server inputs the collected resident information into the generative AI model.

[1727] Data processing: Generative AI generates a death certificate based on the input data.

[1728] Output: The generated death certificate.

[1729] Specific operation: The server saves the generated medical certificate in the database. The server sends a preview of the medical certificate to the terminal and displays it to the user.

[1730] Step 4:

[1731] The user (doctor) certifies the death online.

[1732] Input: Real-time reporting from facility personnel.

[1733] Specific operations: A doctor logs in to the system via a video chat system. A facility staff member reports the resident's condition.

[1734] Data processing: Doctors review information via video call.

[1735] Output: Death confirmation result.

[1736] Specific operation: The doctor sends the death confirmation results to the server via the terminal.

[1737] Step 5:

[1738] The user (doctor) performs final review and approval of the medical certificate.

[1739] Input: The generated death certificate.

[1740] Specific operation: The doctor logs in to the terminal and checks the contents of the generated death certificate.

[1741] Data processing: The doctor will amend the medical certificate as necessary.

[1742] Output: Approved medical certificate.

[1743] Specific operation: The doctor electronically signs the medical certificate using the electronic signature tool. The server stores the signed medical certificate in the database.

[1744] Step 6:

[1745] The server issues a death certificate and notifies the relevant parties.

[1746] Input: Approved medical certificate.

[1747] Specific operation: The server issues the approved medical certificate as an official document.

[1748] Data processing: Generate medical certificate in PDF format.

[1749] Output: Death certificate in PDF format.

[1750] Specific operation: The server sends the medical certificate to the relevant parties via email or cloud storage. The device displays a message to the user that transmission is complete.

[1751] Step 7:

[1752] Uses an emotion engine that recognizes the user's emotions.

[1753] Input: User input, voice, and facial expression data.

[1754] Specific operation: The device sends the user's input, voice, and facial expression data to the emotion engine.

[1755] Data processing: The emotion engine analyzes the data and recognizes the user's emotional state.

[1756] Output: Emotion recognition result.

[1757] Specific operation: The emotion engine returns the results to the server. The device displays the results to the user and provides necessary support information.

[1758] Step 8:

[1759] The emotion recognition results are fed back to the generative AI.

[1760] Input: Emotion data.

[1761] Specific operation: The server obtains the emotion data obtained from the emotion engine.

[1762] Data processing: Emotional data is input into a generative AI model, which then adds emotionally sensitive comments and advice to the diagnosis report.

[1763] Output: Updated death certificate.

[1764] Specific operation: The server sends the updated medical certificate to the database and the terminal. The terminal displays the new medical certificate to the user.

[1765] (Application example 2)

[1766] 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."

[1767] In facilities with many elderly residents, a swift and accurate response is required when a resident dies, while at the same time reducing the burden on those involved and taking their emotions into consideration. Furthermore, customer service in retail stores requires an appropriate understanding of the emotional state of the elderly and flexible responses accordingly. Conventional systems have found it difficult to efficiently resolve these issues.

[1768] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1769] In this invention, the server includes means for inputting death information of a facility resident, means for acquiring past medical history and information from the facility, means for automatically generating a death certificate using a generation AI, means for a doctor to confirm the death online, means for the doctor to perform final confirmation and approval of the death certificate, means for issuing the death certificate and notifying relevant parties, means for recognizing emotions when providing services for the elderly, and means for the generation AI to generate a response based on emotion recognition data. This enables a quick, accurate, and emotion-sensitive response when an elderly person dies, and also enables flexible emotional responses in retail stores for services for the elderly.

[1770] "Institutional residents" refers to elderly people or patients who are staying in nursing homes or medical facilities for an extended period of time.

[1771] "Death information" refers to basic information such as the date, time, place, and cause of death of a facility resident.

[1772] "Generative AI" refers to algorithms or systems that use artificial intelligence technology to automatically generate documents or information based on specific data or conditions.

[1773] A "death certificate" is an official document in which a doctor legally certifies and details the death.

[1774] "Online video calling" refers to a means of communicating in real time through audio and video using the Internet.

[1775] "Emotion recognition" refers to the technology of analyzing data such as voice, facial expressions, and behavior to determine a person's emotional state (e.g., sadness, joy, tension, etc.).

[1776] "Response generation" refers to the process of automatically generating appropriate responses or advice based on input data and circumstances.

[1777] "Medical history" refers to information that records details of a patient's past illnesses, their treatment history, and the medications they have used.

[1778] "Final review and approval" refers to the procedure in which experts and related parties finally review the accuracy of the contents of the generated documents and information and formally approve the contents.

[1779] This invention relates to a system that uses a generative AI and an emotion recognition engine to provide end-of-life care for the elderly and customer service support in retail stores. A specific embodiment of this system will be described in detail below.

[1780] Hardware and software used

[1781] Hardware: Smartphone (with camera), server

[1782] Software: OpenCV (for face detection and preprocessing), EmotionEngine (emotion recognition engine), ResponseGenerator (response generation AI)

[1783] Specific flow of data processing

[1784] The server first receives input from a smartphone. Facility staff enters information about the death of a facility resident using a smartphone. The server then retrieves information such as the resident's past medical history and the latest nursing records from the facility's database.

[1785] Based on the acquired information, the generation AI automatically generates a death certificate. At this time, the generation AI fills in basic information on the death certificate, such as the cause of death and the date and time of death. The generated certificate is then reviewed online by a doctor. The doctor confirms the death via video call and makes a final review and approval of the certificate. The finalized death certificate is issued by the server and notified to the relevant parties.

[1786] At the same time, in retail stores that provide services for the elderly, the Emotion Engine uses smartphone cameras to analyze the facial expressions of the elderly and determine their emotional state. Based on this, the Response Generator generates appropriate responses and advice.

[1787] Specific examples

[1788] A facility staff member enters information about Suzuki Jiro's death on a smartphone. The server retrieves Suzuki Jiro's past medical history (high blood pressure, diabetes, heart disease) from the facility's database and provides this information to the generation AI. Based on this, the generation AI automatically generates a death certificate for Suzuki Jiro, stating "natural death due to cardiac arrest." A doctor verifies the death online via video call and provides final confirmation and approval of the certificate's contents. The server then notifies the family and facility administrator of the official certificate.

[1789] In retail stores, the facial expressions of elderly people are captured with a smartphone camera and their emotional state is analyzed by the Emotion Engine. For example, if the customer is nervous, the Response Generator generates a response such as, "You seem nervous. Let us suggest a product that will have a relaxing effect."

[1790] Prompt Sentence Examples

[1791] The customer's emotional state is determined to be "tension." Please generate advice that can suggest products that have a relaxing effect for the elderly.

[1792] This system will enable a swift, accurate, and emotionally sensitive response when an elderly person dies, and will also enable flexible, emotionally sensitive services for the elderly in retail stores.

[1793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1794] Step 1:

[1795] Users use their smartphones to input information about the deaths of facility residents. This information includes basic information such as the resident's name, date and time of death, location, and cause of death. This information is then sent to the server via the smartphone.

[1796] Step 2:

[1797] The server retrieves the resident's past medical history and nursing records from the facility's database. It searches for medical history data, treatment records, nursing records, etc. based on the specific resident's name and retrieves this information all at once. This prepares the data necessary for the generation AI.

[1798] Step 3:

[1799] The AI ​​on the server automatically generates a death certificate based on the acquired medical history information and the current death information entered by the user. The AI ​​analyzes the medical history data and creates a provisional death certificate that includes the cause of death, date and time of death, etc.

[1800] Step 4:

[1801] The user (doctor) confirms death via online video call. Using a smartphone or computer, the user connects with facility staff to check for breathing and cardiac arrest. The facility staff reports the resident's condition in real time, which the doctor confirms.

[1802] Step 5:

[1803] The doctor performs a final review and approval of the death certificate generated on the server. He logs into the online system, checks the provisional death certificate, corrects any deficiencies, and performs a final review. He then officially approves the certificate with an electronic signature.

[1804] Step 6:

[1805] The server issues a final, verified, and approved death certificate, notifies the relevant parties, creates a PDF of the certificate, emails it to the family and facility administrator, and shares it with any necessary authorities.

[1806] Step 7:

[1807] The user (store staff) uses a smartphone camera to analyze the facial expressions of elderly people who visit a retail store. The acquired video data is sent to a server.

[1808] Step 8:

[1809] The Emotion Engine on the server analyzes the received facial expression data and determines the emotional state of the elderly person. Specifically, it analyzes facial expression images and recognizes emotions such as "sadness," "happiness," and "tension."

[1810] Step 9:

[1811] The server's ResponseGenerator generates an optimal response based on the determined emotional state. For example, if the elderly person is nervous, the response generated will be "suggest products that have a relaxing effect."

[1812] Step 10:

[1813] The user (store staff) checks the generated response and responds appropriately to the elderly person, such as suggesting a relaxing herbal tea.

[1814] 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.

[1815] 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.

[1816] 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 robot 414.

[1817] 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.

[1818] FIG. 9 illustrates 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 behaviors 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.

[1819] 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.

[1820] 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).

[1821] 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.

[1822] 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."

[1823] 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.

[1824] 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).

[1825] 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.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] 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.

[1835] The following is further disclosed regarding the above embodiment.

[1836] (Claim 1)

[1837] a means for inputting death information of facility residents;

[1838] a means of obtaining past medical history and information from the facility;

[1839] A means to automatically generate death certificates using generative AI,

[1840] A means for doctors to certify death online,

[1841] A means for final review and approval of the medical certificate by a doctor;

[1842] A means of issuing a death certificate and notifying the relevant parties;

[1843] A system including:

[1844] (Claim 2)

[1845] The system of claim 1, further comprising means for generating a provisional death certificate based on the acquired medical history information and current circumstances using the generating AI.

[1846] (Claim 3)

[1847] 10. The system of claim 1, further comprising means for a physician to confirm death via an online video call.

[1848] (Claim 4)

[1849] 10. The system of claim 1, further comprising means for a physician to electronically sign and approve the medical report.

[1850] (Claim 5)

[1851] 10. The system of claim 1, further comprising means for transmitting the issued death certificate to an interested party via email or cloud storage.

[1852] "Example 1"

[1853] (Claim 1)

[1854] A means of logging in through a terminal;

[1855] a means for retrieving resident information from a database;

[1856] A means to automatically generate death certificates using generative AI,

[1857] A means for doctors to certify death online,

[1858] A means for final review and approval of the medical certificate by a doctor;

[1859] A means of issuing a death certificate and notifying the relevant parties;

[1860] A system including:

[1861] (Claim 2)

[1862] The system of claim 1, further comprising means for automatically generating a death certificate based on the acquired medical history information using the generation AI.

[1863] (Claim 3)

[1864] 10. The system of claim 1, further comprising means for a physician to confirm death online via video call.

[1865] "Application Example 1"

[1866] (Claim 1)

[1867] a means for inputting death information of facility residents;

[1868] a means of obtaining past medical history and information from the facility;

[1869] A means to automatically generate death certificates using generative AI,

[1870] A means for medical professionals to confirm death online,

[1871] A means for final review and approval of the medical certificate by a medical professional;

[1872] A means of issuing the medical certificate as an electronic document and notifying the relevant parties;

[1873] a means for facility personnel to log in and access the facility using a smartphone application;

[1874] A means of communicating with healthcare professionals in real time via online video calls; and

[1875] A system including:

[1876] (Claim 2)

[1877] The system of claim 1, further comprising means for generating a provisional death certificate based on the acquired medical history information and current circumstances using the generating AI.

[1878] (Claim 3)

[1879] 10. The system of claim 1, further comprising means for a medical professional to confirm death via an online video call.

[1880] "Example 2: Combining Emotion Engines"

[1881] (Claim 1)

[1882] a means for inputting death information of facility residents;

[1883] a means of obtaining past medical history and information from the facility;

[1884] A means to automatically generate death certificates using generative AI,

[1885] A means for doctors to certify death online,

[1886] A means for final review and approval of the medical certificate by a doctor;

[1887] A means of issuing a death certificate and notifying the relevant parties;

[1888] means for using an emotion engine to recognize the emotion of a user;

[1889] A means of feeding back emotion recognition results to the generation AI;

[1890] A system including:

[1891] (Claim 2)

[1892] The system of claim 1, further comprising means for generating a provisional death certificate based on the acquired medical history information and current circumstances using the generating AI.

[1893] (Claim 3)

[1894] 10. The system of claim 1, further comprising means for a physician to confirm death via an online video call.

[1895] "Application example 2 when combining emotion engines"

[1896] (Claim 1)

[1897] a means for inputting death information of facility residents;

[1898] a means of obtaining past medical history and information from the facility;

[1899] A means to automatically generate death certificates using generative AI,

[1900] A means for doctors to certify death online,

[1901] A means for final review and approval of the medical certificate by a doctor;

[1902] A means of issuing a death certificate and notifying the relevant parties;

[1903] and how to recognize emotions when providing services to older adults.

[1904] A means for the AI ​​to generate responses based on emotion recognition data;

[1905] A system including:

[1906] (Claim 2)

[1907] The system of claim 1, further comprising means for generating a provisional death certificate based on the acquired medical history information and current circumstances using the generating AI.

[1908] (Claim 3)

[1909] 10. The system of claim 1, further comprising means for a physician to confirm death via an online video call. [Explanation of symbols]

[1910] 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 means for inputting death information of facility residents; a means of obtaining past medical history and information from the facility; A means to automatically generate death certificates using generative AI, A means for doctors to certify death online, A means for final review and approval of the medical certificate by a doctor; A means of issuing a death certificate and notifying the relevant parties; A system including:

2. The system of claim 1 further comprising means for generating a provisional death certificate based on the acquired medical history information and the current situation using the generating AI.

3. The system of claim 1 , further comprising means for a physician to confirm death via an online video call.

4. 10. The system of claim 1, further comprising means for a physician to electronically sign and approve the medical report.

5. The system of claim 1 , further comprising means for transmitting the issued death certificate to interested parties via email or cloud storage.

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